datafusion-ducklake 0.5.0

DuckLake query engine for rust, built with datafusion.
Documentation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
//! SQLite implementation of [`MetadataWriter`].
//!
//! Requires multi-threaded Tokio runtime (`#[tokio::test(flavor = "multi_thread")]`).

use crate::Result;
use crate::error::{TypeChangeOperation, TypeChangeWriteMode};
use crate::maintenance::{
    CleanupCriteria, ExpireCriteria, ExpiredSnapshot, ScheduledFile, format_sql_timestamp,
};
use crate::metadata_provider::block_on;
use crate::metadata_writer::{
    ColumnDef, ColumnStat, CommitIds, DataFileInfo, DeleteFileEntry, DeleteFileInfo,
    MetadataWriter, WriteMode, WriteSetupResult, columns_differ, validate_delete_entries,
    validate_name,
};
use sqlx::Row;
use sqlx::sqlite::{SqlitePool, SqlitePoolOptions};

const DEFAULT_MAX_CONNECTIONS: u32 = 5;

/// Render a slice of ids as a SQL `IN (...)` body. Safe to interpolate because
/// the values are `i64` (no injection surface) — same approach the upstream
/// DuckLake C++ takes, and the only option since SQLite lacks array binds.
fn id_list(ids: &[i64]) -> String {
    ids.iter()
        .map(|id| id.to_string())
        .collect::<Vec<_>>()
        .join(", ")
}

const SQL_CREATE_SCHEMA: &str = r#"
CREATE TABLE IF NOT EXISTS ducklake_metadata (
    key VARCHAR NOT NULL,
    value VARCHAR NOT NULL,
    scope VARCHAR
);

-- `schema_version` is the per-catalog monotonic schema counter from the DuckLake
-- spec (`ducklake_metadata_manager.cpp:232`): it bumps on a schema change (DDL)
-- and is carried forward unchanged on a data write, exactly mirroring upstream's
-- `if (SchemaChangesMade()) schema_version++` (`ducklake_transaction_state.cpp:1826`)
-- and the validated Postgres writer here. Upstream also stores `next_catalog_id` /
-- `next_file_id` on this row as ITS id allocators; we deliberately omit them (as the
-- Postgres writer does) because this library allocates ids from its own counters
-- (the `next_column_id` metadata row + autoincrement PKs), never from the snapshot.
CREATE TABLE IF NOT EXISTS ducklake_snapshot (
    snapshot_id INTEGER PRIMARY KEY,
    snapshot_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    schema_version INTEGER NOT NULL DEFAULT 0
);

-- Per-table schema-change ledger (DuckLake spec, `ducklake_metadata_manager.cpp:281`):
-- one row per (table that changed schema, snapshot at which it changed). Written on
-- every DDL that leaves the table live (create, column add/remove/reorder, type
-- promotion); NOT written for a drop (the table has no live schema afterward). Rows
-- are never tombstoned (no `end_snapshot`); they go unreferenced once the table is
-- fully expired and are reclaimed by vacuum — same as upstream `DropTables`.
CREATE TABLE IF NOT EXISTS ducklake_schema_versions (
    begin_snapshot INTEGER NOT NULL,
    schema_version INTEGER NOT NULL,
    table_id INTEGER NOT NULL,
    UNIQUE (table_id, begin_snapshot)
);

CREATE TABLE IF NOT EXISTS ducklake_schema (
    schema_id INTEGER PRIMARY KEY,
    schema_name VARCHAR NOT NULL,
    path VARCHAR NOT NULL DEFAULT '',
    path_is_relative BOOLEAN NOT NULL DEFAULT 1,
    begin_snapshot INTEGER NOT NULL,
    end_snapshot INTEGER
);

CREATE TABLE IF NOT EXISTS ducklake_table (
    table_id INTEGER PRIMARY KEY,
    schema_id INTEGER NOT NULL,
    table_name VARCHAR NOT NULL,
    path VARCHAR NOT NULL DEFAULT '',
    path_is_relative BOOLEAN NOT NULL DEFAULT 1,
    begin_snapshot INTEGER NOT NULL,
    end_snapshot INTEGER
);

-- Faithful match of upstream `duckdb/ducklake` `ducklake_column`
-- (`ducklake_metadata_manager.cpp:258`): a BARE table — no PRIMARY KEY, no
-- NOT NULL, no DEFAULT — in upstream's exact column order. `column_id` is
-- deliberately NOT a single-row PK: a column is versioned by
-- `[begin_snapshot, end_snapshot)` and a stable-id type promotion writes a
-- SECOND row with the same `column_id`, which a single-row PK would forbid.
-- One-live-row / non-null / ownership invariants are enforced in the writer
-- code + tests, exactly as upstream guarantees them (its table has no such
-- constraints). The four `*default*` columns + `parent_column` are projected by
-- DuckDB when it reads catalogs we produce; we leave them NULL (no nested-type
-- or column-default writes yet). See docs/column-id-versioning-design.md §4.1.
CREATE TABLE IF NOT EXISTS ducklake_column (
    column_id BIGINT,
    begin_snapshot BIGINT,
    end_snapshot BIGINT,
    table_id BIGINT,
    column_order BIGINT,
    column_name VARCHAR,
    column_type VARCHAR,
    initial_default VARCHAR,
    default_value VARCHAR,
    nulls_allowed BOOLEAN,
    parent_column BIGINT,
    default_value_type VARCHAR,
    default_value_dialect VARCHAR
);

-- `partial_max` (DuckLake v1.0) marks a *partial data file* produced by
-- `merge_adjacent_files`: it is the maximum origin snapshot id among the file's
-- merged rows, whose per-row origin is stored in the embedded
-- `_ducklake_internal_snapshot_id` parquet column. NULL for ordinary files
-- (single-origin appends, and `rewrite_data_files` outputs).
CREATE TABLE IF NOT EXISTS ducklake_data_file (
    data_file_id INTEGER PRIMARY KEY,
    table_id INTEGER NOT NULL,
    path VARCHAR NOT NULL,
    path_is_relative BOOLEAN NOT NULL DEFAULT 1,
    file_size_bytes INTEGER NOT NULL,
    footer_size INTEGER,
    encryption_key VARCHAR,
    record_count INTEGER,
    row_id_start INTEGER,
    mapping_id INTEGER,
    begin_snapshot INTEGER NOT NULL,
    end_snapshot INTEGER,
    partial_max INTEGER
);

-- Per-snapshot change ledger (DuckLake spec). One row per snapshot recording
-- what the commit did, as a comma-separated `changes_made` string (e.g.
-- `compacted_table:<table_id>` for a compaction). Written by the compaction
-- commit so DuckDB and other spec readers can attribute the snapshot; other
-- commit paths in this crate do not populate it yet.
CREATE TABLE IF NOT EXISTS ducklake_snapshot_changes (
    snapshot_id INTEGER PRIMARY KEY,
    changes_made VARCHAR NOT NULL,
    author VARCHAR,
    commit_message VARCHAR,
    commit_extra_info VARCHAR
);

-- Per-table row-lineage counter (DuckLake spec). `next_row_id` is the
-- monotonic rowid allocator: a new data file gets its `row_id_start` from
-- the current value, then we advance by `record_count` in the same
-- transaction. `record_count` and `file_size_bytes` mirror the currently-
-- visible totals so DuckDB's `ducklake_table_info` aggregate sees correct
-- numbers for tables we wrote.
CREATE TABLE IF NOT EXISTS ducklake_table_stats (
    table_id INTEGER PRIMARY KEY,
    record_count INTEGER NOT NULL DEFAULT 0,
    next_row_id INTEGER NOT NULL DEFAULT 0,
    file_size_bytes INTEGER NOT NULL DEFAULT 0
);

-- Per-file, per-column statistics (DuckLake spec zone maps). Powers file-level
-- pruning: min/max are the DuckDB-canonical VARCHAR encoding of the bounds,
-- value_count is the non-null count, null_count the null count. Column set
-- mirrors the official extension exactly (no PK, matching upstream). A brand-
-- new table for pre-existing catalogs: `CREATE TABLE IF NOT EXISTS` (re-run by
-- initialize_schema on every open) creates it, which is also what lets DuckDB
-- attach a catalog we wrote (its global-stats query joins the two stats tables).
CREATE TABLE IF NOT EXISTS ducklake_file_column_stats (
    data_file_id INTEGER NOT NULL,
    table_id INTEGER NOT NULL,
    column_id INTEGER NOT NULL,
    column_size_bytes INTEGER,
    value_count INTEGER,
    null_count INTEGER,
    min_value VARCHAR,
    max_value VARCHAR,
    contains_nan BOOLEAN,
    extra_stats VARCHAR
);

-- Table-wide per-column roll-up (DuckLake spec). Feeds the query optimizer
-- (cardinality/column bounds), not file pruning. `contains_null` is a boolean
-- (any null across the table), not a count; min/max widen across all files.
CREATE TABLE IF NOT EXISTS ducklake_table_column_stats (
    table_id INTEGER NOT NULL,
    column_id INTEGER NOT NULL,
    contains_null BOOLEAN,
    contains_nan BOOLEAN,
    min_value VARCHAR,
    max_value VARCHAR,
    extra_stats VARCHAR
);

CREATE TABLE IF NOT EXISTS ducklake_delete_file (
    delete_file_id INTEGER PRIMARY KEY,
    data_file_id INTEGER NOT NULL,
    table_id INTEGER NOT NULL,
    path VARCHAR NOT NULL,
    path_is_relative BOOLEAN NOT NULL DEFAULT 1,
    file_size_bytes INTEGER NOT NULL,
    footer_size INTEGER,
    encryption_key VARCHAR,
    delete_count INTEGER,
    begin_snapshot INTEGER NOT NULL,
    end_snapshot INTEGER
);

-- Files queued for physical deletion by the two-phase vacuum (DuckLake spec).
-- `expire_snapshots` GCs unreachable catalog rows and records the orphaned
-- physical paths here; `cleanup_old_files` deletes the objects and removes
-- these rows. `path` is stored relative to the catalog `data_path` root
-- (i.e. already resolved through schema/table) so cleanup needs only a
-- single-level join with `data_path`. Mirrors the upstream
-- `ducklake_files_scheduled_for_deletion` table.
CREATE TABLE IF NOT EXISTS ducklake_files_scheduled_for_deletion (
    data_file_id INTEGER NOT NULL,
    path VARCHAR NOT NULL,
    path_is_relative BOOLEAN NOT NULL DEFAULT 1,
    schedule_start TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
"#;

/// SQLite-based metadata writer for DuckLake catalogs.
#[derive(Debug, Clone)]
pub struct SqliteMetadataWriter {
    pool: SqlitePool,
}

impl SqliteMetadataWriter {
    pub async fn new(connection_string: &str) -> Result<Self> {
        Self::with_max_connections(connection_string, DEFAULT_MAX_CONNECTIONS).await
    }

    pub async fn with_max_connections(
        connection_string: &str,
        max_connections: u32,
    ) -> Result<Self> {
        let pool = SqlitePoolOptions::new()
            .max_connections(max_connections)
            .connect(connection_string)
            .await?;
        Ok(Self {
            pool,
        })
    }

    pub async fn new_with_init(connection_string: &str) -> Result<Self> {
        let writer = Self::new(connection_string).await?;
        writer.initialize_schema()?;
        Ok(writer)
    }

    /// Tombstone a live table at a new "drop snapshot".
    ///
    /// Allocates a fresh snapshot and sets `end_snapshot = <drop snapshot>` on the
    /// currently-live rows for the named table in `ducklake_table`,
    /// `ducklake_column`, `ducklake_data_file`, and `ducklake_delete_file`. Reads at
    /// any snapshot `>=` the drop snapshot no longer see the table; earlier snapshots
    /// are unaffected (time travel preserved). This is the single-catalog mirror of
    /// [`crate::multicatalog::MulticatalogManager::drop_table_in_catalog`].
    ///
    /// Idempotent: returns `Ok(false)` (no snapshot allocated) when no live
    /// `(schema, table)` pair exists. Physical data files are not removed — that is
    /// the job of [`Self::expire_snapshots`] + [`crate::maintenance::cleanup_old_files_sqlite`].
    ///
    /// The table's `ducklake_table_stats` row is intentionally left in place:
    /// `next_row_id` must stay monotonic across the table's lifetime, and a
    /// recreate-after-drop gets a fresh `table_id`. The orphaned stats row is
    /// reclaimed by [`Self::expire_snapshots`] once the table is fully expired.
    pub fn drop_table(&self, schema_name: &str, table_name: &str) -> Result<bool> {
        validate_name(schema_name, "Schema")?;
        validate_name(table_name, "Table")?;
        block_on(async {
            let mut tx = self.pool.begin().await?;

            // Take the write lock up front (write-lock-first invariant; see
            // `insert_snapshot`) by allocating the snapshot before any read, so a
            // concurrent drop/promote blocks on the lock rather than failing a
            // deferred read→write upgrade. A drop is DDL → also bump schema_version.
            // No ducklake_schema_versions row: the table has no live schema
            // afterward (matches upstream `DropTables` and
            // `multicatalog.rs::drop_table_in_catalog`).
            let (drop_snapshot, _schema_version) = insert_snapshot(&mut tx).await?;

            // Resolve the live table_id. `end_snapshot IS NULL` on both schema and
            // table makes an already-dropped table a no-op (idempotent): we return
            // without committing, so `tx` rolls back and the snapshot allocated
            // above leaves no trace.
            let table_id: i64 = match sqlx::query(
                "SELECT t.table_id FROM ducklake_table t
                 JOIN ducklake_schema s ON s.schema_id = t.schema_id
                 WHERE s.schema_name = ? AND s.end_snapshot IS NULL
                   AND t.table_name = ? AND t.end_snapshot IS NULL",
            )
            .bind(schema_name)
            .bind(table_name)
            .fetch_optional(&mut *tx)
            .await?
            {
                Some(r) => r.try_get(0)?,
                None => return Ok(false),
            };

            bump_schema_version(&mut tx, drop_snapshot).await?;

            for child in
                ["ducklake_table", "ducklake_column", "ducklake_data_file", "ducklake_delete_file"]
            {
                sqlx::query(&format!(
                    "UPDATE {child} SET end_snapshot = ?
                     WHERE table_id = ? AND end_snapshot IS NULL"
                ))
                .bind(drop_snapshot)
                .bind(table_id)
                .execute(&mut *tx)
                .await?;
            }

            tx.commit().await?;
            Ok(true)
        })
    }

    /// Expire snapshots and garbage-collect the catalog metadata they leave behind.
    ///
    /// Ports the official `ducklake_expire_snapshots` / `DuckLakeMetadataManager::DeleteSnapshots`.
    /// The most recent snapshot is never expired. After deleting the chosen snapshot
    /// rows, every table / data file / delete file no longer reachable by any surviving
    /// snapshot is removed from the catalog and its physical path is recorded in
    /// `ducklake_files_scheduled_for_deletion` for later physical deletion via
    /// [`crate::maintenance::cleanup_old_files_sqlite`]. Returns the expired snapshots.
    ///
    /// Reachability is global here — correct for a single-catalog SQLite metadata DB.
    /// The whole operation runs in one transaction, so a crash can't leave scheduled
    /// rows out of sync with the catalog.
    pub fn expire_snapshots(&self, criteria: ExpireCriteria) -> Result<Vec<ExpiredSnapshot>> {
        block_on(async {
            let mut tx = self.pool.begin().await?;

            // The most recent snapshot is never expirable.
            let most_recent: Option<i64> =
                sqlx::query("SELECT MAX(snapshot_id) FROM ducklake_snapshot")
                    .fetch_one(&mut *tx)
                    .await?
                    .try_get(0)?;
            let most_recent = match most_recent {
                Some(id) => id,
                None => {
                    tx.commit().await?;
                    return Ok(Vec::new());
                },
            };

            // 1. Resolve the snapshots to expire (excluding the most recent).
            let candidates: Vec<ExpiredSnapshot> = match &criteria {
                ExpireCriteria::Versions(versions) => {
                    let ids: Vec<i64> = versions
                        .iter()
                        .copied()
                        .filter(|&v| v != most_recent)
                        .collect();
                    if ids.is_empty() {
                        tx.commit().await?;
                        return Ok(Vec::new());
                    }
                    let rows = sqlx::query(&format!(
                        "SELECT snapshot_id, snapshot_time FROM ducklake_snapshot
                         WHERE snapshot_id IN ({}) ORDER BY snapshot_id",
                        id_list(&ids)
                    ))
                    .fetch_all(&mut *tx)
                    .await?;
                    rows_to_snapshots(rows)?
                },
                ExpireCriteria::OlderThan(ts) => {
                    let rows = sqlx::query(
                        "SELECT snapshot_id, snapshot_time FROM ducklake_snapshot
                         WHERE snapshot_id != ? AND snapshot_time < ? ORDER BY snapshot_id",
                    )
                    .bind(most_recent)
                    .bind(format_sql_timestamp(ts))
                    .fetch_all(&mut *tx)
                    .await?;
                    rows_to_snapshots(rows)?
                },
            };
            if candidates.is_empty() {
                tx.commit().await?;
                return Ok(Vec::new());
            }
            let expire_ids: Vec<i64> = candidates.iter().map(|s| s.snapshot_id).collect();

            // 2. Delete the snapshot rows themselves.
            sqlx::query(&format!(
                "DELETE FROM ducklake_snapshot WHERE snapshot_id IN ({})",
                id_list(&expire_ids)
            ))
            .execute(&mut *tx)
            .await?;

            // 3. Tables whose lifetime is no longer covered by any surviving snapshot
            //    AND which have no surviving live version.
            let dead_tables: Vec<i64> = sqlx::query(
                "SELECT t.table_id FROM ducklake_table t
                 WHERE t.end_snapshot IS NOT NULL AND NOT EXISTS (
                     SELECT 1 FROM ducklake_snapshot
                     WHERE snapshot_id >= t.begin_snapshot AND snapshot_id < t.end_snapshot)
                 AND NOT EXISTS (
                     SELECT 1 FROM ducklake_table t2
                     WHERE t2.table_id = t.table_id
                       AND (t2.end_snapshot IS NULL OR EXISTS (
                           SELECT 1 FROM ducklake_snapshot
                           WHERE snapshot_id >= t2.begin_snapshot
                             AND snapshot_id < t2.end_snapshot)))",
            )
            .fetch_all(&mut *tx)
            .await?
            .into_iter()
            .map(|r| r.try_get::<i64, _>(0))
            .collect::<std::result::Result<_, _>>()?;
            let dead_table_filter = if dead_tables.is_empty() {
                "0".to_string()
            } else {
                format!("df.table_id IN ({})", id_list(&dead_tables))
            };

            // 4. Data files no longer referenced by any surviving snapshot (or belonging
            //    to a dead table): schedule their physical paths, then drop the rows.
            let dead_data_files = sqlx::query(&format!(
                "SELECT df.data_file_id, {RESOLVED_PATH} AS resolved_path, {REL_FLAG} AS rel
                 FROM ducklake_data_file df
                 JOIN ducklake_table t ON t.table_id = df.table_id
                 JOIN ducklake_schema s ON s.schema_id = t.schema_id
                 WHERE ({dead_table_filter}) OR (df.end_snapshot IS NOT NULL AND NOT EXISTS (
                     SELECT 1 FROM ducklake_snapshot
                     WHERE snapshot_id >= df.begin_snapshot AND snapshot_id < df.end_snapshot))"
            ))
            .fetch_all(&mut *tx)
            .await?;
            let data_file_ids = schedule_files(&mut tx, dead_data_files).await?;
            if !data_file_ids.is_empty() {
                sqlx::query(&format!(
                    "DELETE FROM ducklake_data_file WHERE data_file_id IN ({})",
                    id_list(&data_file_ids)
                ))
                .execute(&mut *tx)
                .await?;
            }

            // 5. Delete files orphaned by the data files above, by a dead table, or no
            //    longer referenced by any surviving snapshot. (Our writer does not emit
            //    delete files yet, so this is a no-op for catalogs we produce.)
            let dead_data_filter = if data_file_ids.is_empty() {
                "0".to_string()
            } else {
                format!("df.data_file_id IN ({})", id_list(&data_file_ids))
            };
            let dead_delete_table_filter = if dead_tables.is_empty() {
                "0".to_string()
            } else {
                format!("df.table_id IN ({})", id_list(&dead_tables))
            };
            let dead_delete_files = sqlx::query(&format!(
                "SELECT df.delete_file_id, {RESOLVED_PATH} AS resolved_path, {REL_FLAG} AS rel
                 FROM ducklake_delete_file df
                 JOIN ducklake_table t ON t.table_id = df.table_id
                 JOIN ducklake_schema s ON s.schema_id = t.schema_id
                 WHERE ({dead_data_filter}) OR ({dead_delete_table_filter})
                    OR (df.end_snapshot IS NOT NULL AND NOT EXISTS (
                        SELECT 1 FROM ducklake_snapshot
                        WHERE snapshot_id >= df.begin_snapshot AND snapshot_id < df.end_snapshot))"
            ))
            .fetch_all(&mut *tx)
            .await?;
            let delete_file_ids = schedule_files(&mut tx, dead_delete_files).await?;
            if !delete_file_ids.is_empty() {
                sqlx::query(&format!(
                    "DELETE FROM ducklake_delete_file WHERE delete_file_id IN ({})",
                    id_list(&delete_file_ids)
                ))
                .execute(&mut *tx)
                .await?;
            }

            // 6. Reclaim per-table metadata for fully-expired tables (this is where a
            //    dropped table's orphaned ducklake_table_stats row is removed).
            if !dead_tables.is_empty() {
                let dead = id_list(&dead_tables);
                // `ducklake_schema_versions` is keyed by table_id and has no
                // end_snapshot; reclaim its rows here once the table is fully
                // expired (a recreate-after-drop gets a fresh table_id, so this
                // can't strand a live table's ledger). Matches the Postgres
                // multicatalog cleanup and upstream GC.
                for table in [
                    "ducklake_table",
                    "ducklake_table_stats",
                    "ducklake_column",
                    "ducklake_schema_versions",
                ] {
                    sqlx::query(&format!("DELETE FROM {table} WHERE table_id IN ({dead})"))
                        .execute(&mut *tx)
                        .await?;
                }
            }

            // 7. Reclaim schemas no longer covered by any surviving snapshot.
            sqlx::query(
                "DELETE FROM ducklake_schema
                 WHERE end_snapshot IS NOT NULL AND NOT EXISTS (
                     SELECT 1 FROM ducklake_snapshot
                     WHERE snapshot_id >= ducklake_schema.begin_snapshot
                       AND snapshot_id < ducklake_schema.end_snapshot)",
            )
            .execute(&mut *tx)
            .await?;

            tx.commit().await?;
            Ok(candidates)
        })
    }

    /// List files scheduled for physical deletion, optionally filtered by schedule time.
    pub(crate) fn list_scheduled_for_deletion(
        &self,
        criteria: &CleanupCriteria,
    ) -> Result<Vec<ScheduledFile>> {
        block_on(async {
            let rows = match criteria {
                CleanupCriteria::All => {
                    sqlx::query(
                        "SELECT data_file_id, path, path_is_relative
                     FROM ducklake_files_scheduled_for_deletion",
                    )
                    .fetch_all(&self.pool)
                    .await?
                },
                CleanupCriteria::OlderThan(ts) => {
                    sqlx::query(
                        "SELECT data_file_id, path, path_is_relative
                     FROM ducklake_files_scheduled_for_deletion
                     WHERE schedule_start < ?",
                    )
                    .bind(format_sql_timestamp(ts))
                    .fetch_all(&self.pool)
                    .await?
                },
            };
            rows.into_iter()
                .map(|r| {
                    Ok(ScheduledFile {
                        data_file_id: r.try_get(0)?,
                        path: r.try_get(1)?,
                        path_is_relative: r.try_get::<i64, _>(2)? != 0,
                    })
                })
                .collect()
        })
    }

    /// Remove scheduled-deletion bookkeeping rows after their objects are gone.
    pub(crate) fn remove_scheduled(&self, ids: &[i64]) -> Result<()> {
        if ids.is_empty() {
            return Ok(());
        }
        block_on(async {
            sqlx::query(&format!(
                "DELETE FROM ducklake_files_scheduled_for_deletion WHERE data_file_id IN ({})",
                id_list(ids)
            ))
            .execute(&self.pool)
            .await?;
            Ok(())
        })
    }

    /// Every physical file currently referenced by the catalog: live + tombstoned
    /// data files, live + tombstoned delete files, and the still-scheduled
    /// deletion queue (those rows haven't been processed by `cleanup_old_files`
    /// yet, so the files might still exist on disk and must not be touched by
    /// orphan cleanup). Each row is `(path, path_is_relative)` resolved relative
    /// to the catalog `data_path` root; the caller resolves against the actual
    /// `data_path` for comparison with object_store keys.
    ///
    /// Used by [`crate::maintenance::delete_orphaned_files_sqlite`].
    pub(crate) fn list_referenced_paths(&self) -> Result<Vec<(String, bool)>> {
        block_on(async {
            let q = format!(
                "SELECT {RESOLVED_PATH} AS p, {REL_FLAG} AS rel
                 FROM ducklake_data_file df
                 JOIN ducklake_table t ON t.table_id = df.table_id
                 JOIN ducklake_schema s ON s.schema_id = t.schema_id
                 UNION ALL
                 SELECT {RESOLVED_PATH} AS p, {REL_FLAG} AS rel
                 FROM ducklake_delete_file df
                 JOIN ducklake_table t ON t.table_id = df.table_id
                 JOIN ducklake_schema s ON s.schema_id = t.schema_id
                 UNION ALL
                 SELECT path AS p, CAST(path_is_relative AS INTEGER) AS rel
                 FROM ducklake_files_scheduled_for_deletion"
            );
            let rows = sqlx::query(&q).fetch_all(&self.pool).await?;
            rows.into_iter()
                .map(|r| {
                    let p: String = r.try_get(0)?;
                    let rel: i64 = r.try_get(1)?;
                    Ok((p, rel != 0))
                })
                .collect()
        })
    }
}

/// SQL expression yielding a file path resolved relative to the catalog `data_path`
/// root, mirroring the read-side hierarchical resolution (file → table → schema →
/// data_path). An absolute path anywhere in the chain short-circuits to that absolute
/// path. Assumes `/`-joined relative names (everything our writer produces).
const RESOLVED_PATH: &str = "CASE
    WHEN NOT df.path_is_relative THEN df.path
    WHEN NOT t.path_is_relative THEN t.path || '/' || df.path
    ELSE s.path || '/' || t.path || '/' || df.path
END";

/// Companion to [`RESOLVED_PATH`]: 1 only when the whole chain is relative (so the
/// resolved path is relative to `data_path`), else 0 (the resolved path is absolute).
const REL_FLAG: &str =
    "(CASE WHEN df.path_is_relative AND t.path_is_relative AND s.path_is_relative
           THEN 1 ELSE 0 END)";

/// Map `(snapshot_id, snapshot_time)` rows to [`ExpiredSnapshot`]s.
fn rows_to_snapshots(rows: Vec<sqlx::sqlite::SqliteRow>) -> Result<Vec<ExpiredSnapshot>> {
    rows.into_iter()
        .map(|r| {
            Ok(ExpiredSnapshot {
                snapshot_id: r.try_get(0)?,
                snapshot_time: r.try_get(1)?,
            })
        })
        .collect()
}

/// Insert `(id, resolved_path, rel)` rows (as produced by [`RESOLVED_PATH`]/[`REL_FLAG`])
/// into `ducklake_files_scheduled_for_deletion` and return the ids scheduled.
async fn schedule_files(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    rows: Vec<sqlx::sqlite::SqliteRow>,
) -> Result<Vec<i64>> {
    let mut ids = Vec::with_capacity(rows.len());
    for row in rows {
        let id: i64 = row.try_get(0)?;
        let path: String = row.try_get(1)?;
        let rel: i64 = row.try_get(2)?;
        sqlx::query(
            "INSERT INTO ducklake_files_scheduled_for_deletion
                 (data_file_id, path, path_is_relative, schedule_start)
             VALUES (?, ?, ?, CURRENT_TIMESTAMP)",
        )
        .bind(id)
        .bind(&path)
        .bind(rel)
        .execute(&mut **tx)
        .await?;
        ids.push(id);
    }
    Ok(ids)
}

/// Atomically reserve `n` consecutive ids from a monotonic counter stored in
/// `ducklake_metadata` (seeded by `initialize_schema`), returning the LAST id of
/// the block — the reserved ids are `last - n + 1 ..= last`. The `UPDATE`
/// serializes under SQLite's single-writer lock, so concurrent writers (even to
/// different tables) never hand out overlapping ids. Used for `snapshot_id` and
/// `column_id`, which are reserved in `begin_write_transaction` and inserted at
/// the commit: rowid autoincrement can't be used there because inserting the
/// `ducklake_snapshot` row would advance the head (`MAX(snapshot_id)`) before the
/// data is committed.
async fn reserve_ids(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    key: &str,
    n: i64,
) -> Result<i64> {
    let last: i64 = sqlx::query(
        "UPDATE ducklake_metadata
         SET value = CAST(CAST(value AS INTEGER) + ? AS TEXT)
         WHERE key = ? AND scope IS NULL
         RETURNING CAST(value AS INTEGER)",
    )
    .bind(n)
    .bind(key)
    .fetch_one(&mut **tx)
    .await?
    .try_get(0)?;
    Ok(last)
}

/// Upgrade an existing `ducklake_column` from the legacy shape
/// (`column_id INTEGER PRIMARY KEY` — this library's pre-versioning layout) to
/// upstream's bare shape (`column_id BIGINT`, no PK; see `SQL_CREATE_SCHEMA`).
///
/// `CREATE TABLE IF NOT EXISTS` only shapes *new* catalogs; a catalog written by
/// an earlier version keeps its old table, so we must rebuild it in place — SQLite
/// cannot `ALTER TABLE ... DROP PRIMARY KEY`. The single-row PK forbade a second
/// row sharing a `column_id`, which a versioned / type-promoted column requires,
/// so this migration is what lets existing users' catalogs support type promotion.
///
/// **Idempotent:** a no-op once `column_id` is no longer a PK (re-runs on every
/// `initialize_schema`). **Crash-safe:** the rebuild is one transaction (SQLite
/// DDL is transactional), so a crash leaves either the old or the new table whole,
/// never a half-state. **Lossless:** every row and every `column_id` value is
/// preserved (field-ids baked in Parquet stay valid), and it tolerates older
/// catalogs missing some of the 13 columns (those are filled with `NULL`).
async fn migrate_ducklake_column_drop_pk(pool: &SqlitePool) -> Result<()> {
    // Legacy shape iff `column_id` is a PRIMARY KEY. `pragma_table_info` returns
    // one row per column with a `pk` flag (0 = not part of the PK).
    let info = sqlx::query("SELECT name, pk FROM pragma_table_info('ducklake_column')")
        .fetch_all(pool)
        .await?;
    let mut legacy_cols: std::collections::HashSet<String> = std::collections::HashSet::new();
    let mut column_id_is_pk = false;
    for row in &info {
        let name: String = row.try_get("name")?;
        let pk: i64 = row.try_get("pk")?;
        if name == "column_id" && pk != 0 {
            column_id_is_pk = true;
        }
        legacy_cols.insert(name);
    }
    if !column_id_is_pk {
        // Fresh (already bare) or previously migrated — nothing to do.
        return Ok(());
    }

    // Target = upstream's exact bare column set/order. MUST stay in sync with the
    // `ducklake_column` DDL in `SQL_CREATE_SCHEMA`.
    const TARGET: &[&str] = &[
        "column_id",
        "begin_snapshot",
        "end_snapshot",
        "table_id",
        "column_order",
        "column_name",
        "column_type",
        "initial_default",
        "default_value",
        "nulls_allowed",
        "parent_column",
        "default_value_type",
        "default_value_dialect",
    ];
    // Copy each target column from the legacy table if present, else NULL — so a
    // catalog that predates the `*default*`/`parent_column` columns still migrates.
    let select_list = TARGET
        .iter()
        .map(|c| {
            if legacy_cols.contains(*c) {
                (*c).to_string()
            } else {
                "NULL".to_string()
            }
        })
        .collect::<Vec<_>>()
        .join(", ");
    let insert_list = TARGET.join(", ");

    let mut tx = pool.begin().await?;
    sqlx::query(
        "CREATE TABLE ducklake_column__migrate (
            column_id BIGINT,
            begin_snapshot BIGINT,
            end_snapshot BIGINT,
            table_id BIGINT,
            column_order BIGINT,
            column_name VARCHAR,
            column_type VARCHAR,
            initial_default VARCHAR,
            default_value VARCHAR,
            nulls_allowed BOOLEAN,
            parent_column BIGINT,
            default_value_type VARCHAR,
            default_value_dialect VARCHAR
        )",
    )
    .execute(&mut *tx)
    .await?;
    sqlx::query(&format!(
        "INSERT INTO ducklake_column__migrate ({insert_list}) \
         SELECT {select_list} FROM ducklake_column"
    ))
    .execute(&mut *tx)
    .await?;
    sqlx::query("DROP TABLE ducklake_column")
        .execute(&mut *tx)
        .await?;
    sqlx::query("ALTER TABLE ducklake_column__migrate RENAME TO ducklake_column")
        .execute(&mut *tx)
        .await?;
    tx.commit().await?;
    Ok(())
}

/// Upgrade a pre-existing catalog to track `schema_version`: add the
/// `ducklake_snapshot.schema_version` column (the `ducklake_schema_versions` table
/// is handled by `CREATE TABLE IF NOT EXISTS` in `SQL_CREATE_SCHEMA`). `CREATE
/// TABLE IF NOT EXISTS` only shapes *new* catalogs, and SQLite has no
/// `ADD COLUMN IF NOT EXISTS`, so we probe `pragma_table_info` and add the column
/// only when missing.
///
/// **Idempotent:** a no-op once the column exists (re-runs on every
/// `initialize_schema`). **Lossless:** existing snapshot rows take the column
/// `DEFAULT 0`. We deliberately do NOT fabricate a dense historical
/// `schema_version` / backfill `ducklake_schema_versions`: this library never
/// recorded which past snapshots were DDL, so any backfill would be guessed
/// history. The counter grows correctly from the next DDL commit forward, which is
/// monotonic and honest (old snapshots simply read as version 0).
async fn migrate_add_schema_version(pool: &SqlitePool) -> Result<()> {
    let info = sqlx::query("SELECT name FROM pragma_table_info('ducklake_snapshot')")
        .fetch_all(pool)
        .await?;
    let mut has_schema_version = false;
    for row in &info {
        let name: String = row.try_get("name")?;
        if name == "schema_version" {
            has_schema_version = true;
        }
    }
    if !has_schema_version {
        sqlx::query(
            "ALTER TABLE ducklake_snapshot ADD COLUMN schema_version INTEGER NOT NULL DEFAULT 0",
        )
        .execute(pool)
        .await?;
    }
    Ok(())
}

/// Add `ducklake_data_file.partial_max` to a pre-existing catalog (DuckLake
/// v1.0). `CREATE TABLE IF NOT EXISTS` only shapes brand-new catalogs, so an
/// older one needs this `ALTER`.
///
/// **Idempotent:** a no-op once the column exists (re-runs on every
/// `initialize_schema`). **Lossless:** existing data-file rows take the column
/// `NULL`, which is exactly the "not a partial file" value — every file written
/// before compaction existed is an ordinary single-origin file.
async fn migrate_add_partial_max(pool: &SqlitePool) -> Result<()> {
    let info = sqlx::query("SELECT name FROM pragma_table_info('ducklake_data_file')")
        .fetch_all(pool)
        .await?;
    let mut has_partial_max = false;
    for row in &info {
        let name: String = row.try_get("name")?;
        if name == "partial_max" {
            has_partial_max = true;
        }
    }
    if !has_partial_max {
        sqlx::query("ALTER TABLE ducklake_data_file ADD COLUMN partial_max INTEGER")
            .execute(pool)
            .await?;
    }
    Ok(())
}

/// Optimistic-concurrency check for a `Replace` commit (mirrors the Postgres
/// writer). Run while holding the SQLite write lock, before retiring the prior
/// generation: if any data file of the table has `begin_snapshot` or
/// `end_snapshot` newer than `base_snapshot` (the head observed when this write
/// began), another writer published a newer generation in the meantime, so this
/// `Replace` aborts with [`DuckLakeError::Conflict`] rather than clobbering it.
/// (`Append` does not call this: concurrent appends commute.)
async fn detect_replace_conflict(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    table_id: i64,
    base_snapshot: i64,
) -> Result<()> {
    let conflict: Option<i64> = sqlx::query_scalar(
        "SELECT 1 FROM ducklake_data_file
         WHERE table_id = ? AND (begin_snapshot > ? OR end_snapshot > ?)
         LIMIT 1",
    )
    .bind(table_id)
    .bind(base_snapshot)
    .bind(base_snapshot)
    .fetch_optional(&mut **tx)
    .await?;
    if conflict.is_some() {
        return Err(crate::DuckLakeError::Conflict(format!(
            "Replace on table {table_id} conflicts with a concurrent write committed since \
             snapshot {base_snapshot}; aborting (retry the write against the new generation)"
        )));
    }
    Ok(())
}

/// Retire the prior generation's still-visible data files at `snapshot_id` and
/// zero the visible stat totals. The `begin_snapshot < snapshot_id` guard
/// spares files registered for *this* snapshot, so a multi-file write does not
/// retire its own siblings. `next_row_id` is left untouched (rowids stay
/// monotonic across the table's lifetime). Mirrors the Postgres writer.
async fn retire_prior_generation(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    table_id: i64,
    snapshot_id: i64,
) -> Result<()> {
    sqlx::query(
        "UPDATE ducklake_data_file SET end_snapshot = ?
         WHERE table_id = ? AND end_snapshot IS NULL AND begin_snapshot < ?",
    )
    .bind(snapshot_id)
    .bind(table_id)
    .bind(snapshot_id)
    .execute(&mut **tx)
    .await?;

    sqlx::query(
        "UPDATE ducklake_table_stats SET record_count = 0, file_size_bytes = 0 WHERE table_id = ?",
    )
    .bind(table_id)
    .execute(&mut **tx)
    .await?;
    Ok(())
}

/// Insert the next `ducklake_snapshot` row in commit order, carrying
/// `schema_version` forward (the pure-data-write default), and return
/// `(snapshot_id, schema_version)`.
///
/// This is the FIRST write of a commit transaction, so the `INSERT ... SELECT
/// MAX+1 RETURNING` takes the SQLite write lock up front — preserving the
/// "write-lock-first" invariant that keeps concurrent writers from deadlocking on
/// a read→write lock upgrade. A DDL commit follows this with
/// [`bump_schema_version`]; mirrors the Postgres writer's insert-then-`UPDATE
/// schema_version` shape.
///
/// Unlike the Postgres writer there is no `prev_max == 0 ⇒ 1` data-write floor:
/// on SQLite the first write to any table is always classified as DDL (its
/// `existing` columns are empty → `is_ddl` → bumps to 1), so a `schema_version`
/// of 0 carried forward by a pure data write is unreachable.
async fn insert_snapshot(tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>) -> Result<(i64, i64)> {
    let row = sqlx::query(
        "INSERT INTO ducklake_snapshot (snapshot_id, snapshot_time, schema_version)
         SELECT COALESCE(MAX(snapshot_id), 0) + 1, CURRENT_TIMESTAMP,
                COALESCE(MAX(schema_version), 0)
         FROM ducklake_snapshot
         RETURNING snapshot_id, schema_version",
    )
    .fetch_one(&mut **tx)
    .await?;
    Ok((row.try_get(0)?, row.try_get(1)?))
}

/// Bump the per-catalog monotonic `schema_version` on a DDL snapshot to
/// `prev_max + 1` (max over the OTHER snapshots, so re-running is stable) and
/// return the new value. Mirrors upstream `if (SchemaChangesMade()) schema_version++`
/// and the Postgres writer's `UPDATE ducklake_snapshot SET schema_version = $1`.
async fn bump_schema_version(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    snapshot_id: i64,
) -> Result<i64> {
    let prev_max: i64 = sqlx::query_scalar(
        "SELECT COALESCE(MAX(schema_version), 0) FROM ducklake_snapshot WHERE snapshot_id <> ?",
    )
    .bind(snapshot_id)
    .fetch_one(&mut **tx)
    .await?;
    let new_version = prev_max + 1;
    sqlx::query("UPDATE ducklake_snapshot SET schema_version = ? WHERE snapshot_id = ?")
        .bind(new_version)
        .bind(snapshot_id)
        .execute(&mut **tx)
        .await?;
    Ok(new_version)
}

/// Record a `ducklake_schema_versions` ledger row for a DDL that leaves the table
/// live (create, column add/remove/reorder, type promotion). Not called for a
/// drop — the table has no live schema afterward (matches upstream `DropTables`
/// and `multicatalog.rs::drop_table_in_catalog`).
async fn record_schema_version(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    snapshot_id: i64,
    schema_version: i64,
    table_id: i64,
) -> Result<()> {
    sqlx::query(
        "INSERT INTO ducklake_schema_versions (begin_snapshot, schema_version, table_id)
         VALUES (?, ?, ?)",
    )
    .bind(snapshot_id)
    .bind(schema_version)
    .bind(table_id)
    .execute(&mut **tx)
    .await?;
    Ok(())
}

/// The atomic commit point for a single-catalog write. Inserts the deferred
/// `ducklake_snapshot` row (its reserved id was returned by
/// `begin_write_transaction` but NOT inserted there), finalizes the column
/// generation, and — for `Replace` — retires the prior data generation. All
/// within the caller's transaction, so `COALESCE(MAX(snapshot_id), 0)` only
/// ever resolves to a fully-populated head (no transient empty read).
///
/// The column generation is deferred to here (rather than written in
/// `begin_write_transaction` like the Postgres backend) because the SQLite read
/// path resolves a table's columns by `end_snapshot IS NULL` ONLY (not
/// snapshot-scoped), so inserting the new generation at begin would leak it to
/// concurrent reads during the upload window. `column_ids` are the ids reserved
/// at begin and already baked into the staged parquet's `field_id` metadata.
/// Persist the harvested per-column statistics for a just-registered data file.
///
/// Writes one `ducklake_file_column_stats` row per [`ColumnStat`] (the zone maps
/// that drive file pruning), within the caller's commit transaction so the
/// stats land atomically with the `ducklake_data_file` row. `data_file_id` is
/// the id of the row just inserted (SQLite `last_insert_rowid()`). A `None`
/// bound/count is written as SQL `NULL` — spec-safe: a file with NULL stats is
/// never pruned.
async fn insert_file_column_stats(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    table_id: i64,
    data_file_id: i64,
    column_stats: &[ColumnStat],
) -> Result<()> {
    for stat in column_stats {
        sqlx::query(
            "INSERT INTO ducklake_file_column_stats
                 (data_file_id, table_id, column_id, column_size_bytes,
                  value_count, null_count, min_value, max_value, contains_nan, extra_stats)
             VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, NULL)",
        )
        .bind(data_file_id)
        .bind(table_id)
        .bind(stat.column_id)
        .bind(stat.column_size_bytes)
        .bind(stat.value_count)
        .bind(stat.null_count)
        .bind(stat.min_value.as_deref())
        .bind(stat.max_value.as_deref())
        .bind(stat.contains_nan)
        .execute(&mut **tx)
        .await?;
    }
    Ok(())
}

/// Recompute the table-wide `ducklake_table_column_stats` roll-up (optimizer
/// stats) from the table's currently-live files, and replace the stored rows.
///
/// Recomputing from live files (rather than incrementally widening) is correct
/// for every write mode without special cases: an Append adds a live file;
/// Replace/compaction retire the prior generation (their files drop out of the
/// `end_snapshot IS NULL` set); a positional delete leaves the data file live
/// with unchanged stats — so global bounds widen on insert and never tighten on
/// delete, matching official DuckLake. Min/max widen with a type-aware compare
/// ([`crate::stats_encode::stat_cmp`]); `contains_null`/`contains_nan` are OR-reduced.
async fn recompute_table_column_stats(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    table_id: i64,
    columns: &[ColumnDef],
    column_ids: &[i64],
) -> Result<()> {
    use crate::stats_encode::{FileColumnStat, aggregate_global_column_stats};

    // Total live files: the completeness denominator. A live file missing from
    // the per-column rows below (statless) forces the roll-up to UNKNOWN.
    let live_file_count: i64 = sqlx::query(
        "SELECT COUNT(*) FROM ducklake_data_file WHERE table_id = ? AND end_snapshot IS NULL",
    )
    .bind(table_id)
    .fetch_one(&mut **tx)
    .await?
    .try_get(0)?;

    let mut per_file: Vec<FileColumnStat> = Vec::new();
    for row in sqlx::query(
        "SELECT s.column_id, s.min_value, s.max_value, s.null_count, s.contains_nan
         FROM ducklake_file_column_stats s
         JOIN ducklake_data_file d ON d.data_file_id = s.data_file_id
         WHERE d.table_id = ? AND d.end_snapshot IS NULL",
    )
    .bind(table_id)
    .fetch_all(&mut **tx)
    .await?
    {
        per_file.push(FileColumnStat {
            column_id: row.try_get(0)?,
            min_value: row.try_get(1)?,
            max_value: row.try_get(2)?,
            null_count: row.try_get(3)?,
            contains_nan: row.try_get(4)?,
        });
    }

    let numeric_of = |column_id: i64| -> bool {
        column_ids
            .iter()
            .position(|id| *id == column_id)
            .and_then(|i| columns.get(i))
            .map(|c| crate::stats_encode::is_numeric_ducklake_type(c.ducklake_type()))
            .unwrap_or(false)
    };
    let globals = aggregate_global_column_stats(&per_file, live_file_count, numeric_of);

    sqlx::query("DELETE FROM ducklake_table_column_stats WHERE table_id = ?")
        .bind(table_id)
        .execute(&mut **tx)
        .await?;
    for g in globals {
        sqlx::query(
            "INSERT INTO ducklake_table_column_stats
                 (table_id, column_id, contains_null, contains_nan, min_value, max_value, extra_stats)
             VALUES (?, ?, ?, ?, ?, ?, NULL)",
        )
        .bind(table_id)
        .bind(g.column_id)
        .bind(g.contains_null)
        .bind(g.contains_nan)
        .bind(g.min_value)
        .bind(g.max_value)
        .execute(&mut **tx)
        .await?;
    }
    Ok(())
}

async fn finalize_snapshot(
    tx: &mut sqlx::Transaction<'_, sqlx::Sqlite>,
    table_id: i64,
    columns: &[ColumnDef],
    column_ids: &[i64],
    mode: WriteMode,
    base_snapshot: i64,
) -> Result<i64> {
    // Allocate the snapshot FIRST (carrying schema_version forward): this INSERT
    // takes the SQLite write lock up front, so concurrent writers can't collide,
    // publish out of order, or deadlock on a read→write lock upgrade. schema_version
    // is corrected to a DDL bump below once we've classified the commit.
    let (snapshot_id, mut schema_version) = insert_snapshot(tx).await?;

    // Classify this commit as DDL vs pure data write. `current` is the table's live
    // columns ordered by `column_order`; an empty set means a brand-new table (the
    // creating write is DDL). Mirrors the Postgres writer's
    // `table_was_created || columns_differ` and upstream `SchemaChangesMade()`.
    use std::collections::{HashMap, HashSet};
    let current = sqlx::query(
        "SELECT column_name, column_type, column_order, nulls_allowed
         FROM ducklake_column
         WHERE table_id = ? AND end_snapshot IS NULL
         ORDER BY column_order",
    )
    .bind(table_id)
    .fetch_all(&mut **tx)
    .await?;

    let mut existing: Vec<(String, String, bool)> = Vec::with_capacity(current.len());
    for row in &current {
        let name: String = row.try_get("column_name")?;
        let ty: String = row.try_get("column_type")?;
        let nullable: bool = row
            .try_get::<Option<bool>, _>("nulls_allowed")?
            .unwrap_or(true);
        existing.push((name, ty, nullable));
    }
    let is_ddl = existing.is_empty() || columns_differ(&existing, columns);
    if is_ddl {
        // A DDL commit bumps the per-catalog schema_version (the insert above only
        // carried it forward). A pure data write keeps the carried value.
        schema_version = bump_schema_version(tx, snapshot_id).await?;
    }

    // Reconcile the column generation SURGICALLY so each column keeps a stable
    // column_id (== parquet field_id) across writes: end only removed columns,
    // insert only new ones, and leave unchanged columns (and their ids) in place.
    // Re-minting ids every write would orphan the field_ids baked into
    // already-written files, making their rows read back as NULL.
    let new_names: HashSet<&str> = columns.iter().map(|c| c.name.as_str()).collect();
    let mut current_by_name: HashMap<String, (i64, bool)> = HashMap::new();
    for row in &current {
        let name: String = row.try_get("column_name")?;
        let order: i64 = row.try_get("column_order")?;
        let nullable: bool = row
            .try_get::<Option<bool>, _>("nulls_allowed")?
            .unwrap_or(true);
        if !new_names.contains(name.as_str()) {
            // Column dropped in the new schema: end its generation.
            sqlx::query(
                "UPDATE ducklake_column SET end_snapshot = ?
                 WHERE table_id = ? AND column_name = ? AND end_snapshot IS NULL",
            )
            .bind(snapshot_id)
            .bind(table_id)
            .bind(&name)
            .execute(&mut **tx)
            .await?;
        }
        current_by_name.insert(name, (order, nullable));
    }

    for (order, (col, column_id)) in columns.iter().zip(column_ids.iter()).enumerate() {
        match current_by_name.get(&col.name) {
            // Existing column kept: its id stays stable. Sync order/nullability
            // only if they changed (type changes are rejected at begin).
            Some(&(cur_order, cur_nullable)) => {
                if cur_order != order as i64 || cur_nullable != col.is_nullable {
                    sqlx::query(
                        "UPDATE ducklake_column SET column_order = ?, nulls_allowed = ?
                         WHERE table_id = ? AND column_name = ? AND end_snapshot IS NULL",
                    )
                    .bind(order as i64)
                    .bind(col.is_nullable)
                    .bind(table_id)
                    .bind(&col.name)
                    .execute(&mut **tx)
                    .await?;
                }
            },
            // Newly added column: insert it with its reserved id.
            None => {
                sqlx::query(
                    "INSERT INTO ducklake_column
                         (column_id, table_id, column_name, column_type, column_order, nulls_allowed, begin_snapshot)
                     VALUES (?, ?, ?, ?, ?, ?, ?)",
                )
                .bind(column_id)
                .bind(table_id)
                .bind(&col.name)
                .bind(&col.ducklake_type)
                .bind(order as i64)
                .bind(col.is_nullable)
                .bind(snapshot_id)
                .execute(&mut **tx)
                .await?;
            },
        }
    }

    if mode == WriteMode::Replace {
        // Abort if a concurrent writer published a newer generation since this
        // write began (held under the write lock acquired by the MAX+1 insert
        // above, so the check sees a consistent committed state).
        detect_replace_conflict(tx, table_id, base_snapshot).await?;
        // Seed the stats row (first write to a brand-new table) so retire's
        // zero-update has a row, then retire the prior data generation.
        sqlx::query(
            "INSERT OR IGNORE INTO ducklake_table_stats
                 (table_id, record_count, next_row_id, file_size_bytes)
             VALUES (?, 0, 0, 0)",
        )
        .bind(table_id)
        .execute(&mut **tx)
        .await?;
        retire_prior_generation(tx, table_id, snapshot_id).await?;
    }

    // Record the schema-change ledger row for a DDL commit (table create, or a
    // column add / remove / reorder). A pure data write carries schema_version
    // forward and writes no row. (Drop is handled in `drop_table`: it bumps but
    // writes no row, since the table has no live schema afterward.)
    if is_ddl {
        record_schema_version(tx, snapshot_id, schema_version, table_id).await?;
    }
    Ok(snapshot_id)
}

impl MetadataWriter for SqliteMetadataWriter {
    /// SQLite implements the atomic append-with-deletes commit, so it supports
    /// row-level `UPDATE`.
    fn supports_update(&self) -> bool {
        true
    }

    fn create_snapshot(&self) -> Result<i64> {
        block_on(async {
            let mut tx = self.pool.begin().await?;
            // A bare snapshot carries no schema change of its own → carry
            // schema_version forward (no DDL bump, no ledger row).
            let (snapshot_id, _schema_version) = insert_snapshot(&mut tx).await?;
            tx.commit().await?;
            Ok(snapshot_id)
        })
    }

    fn get_or_create_schema(
        &self,
        name: &str,
        path: Option<&str>,
        snapshot_id: i64,
    ) -> Result<(i64, bool)> {
        validate_name(name, "Schema")?;
        block_on(async {
            let existing = sqlx::query(
                "SELECT schema_id FROM ducklake_schema
                 WHERE schema_name = ? AND end_snapshot IS NULL",
            )
            .bind(name)
            .fetch_optional(&self.pool)
            .await?;

            if let Some(row) = existing {
                return Ok((row.try_get(0)?, false));
            }

            let schema_path = path.unwrap_or(name);
            let row = sqlx::query(
                "INSERT INTO ducklake_schema (schema_name, path, path_is_relative, begin_snapshot)
                 VALUES (?, ?, 1, ?) RETURNING schema_id",
            )
            .bind(name)
            .bind(schema_path)
            .bind(snapshot_id)
            .fetch_one(&self.pool)
            .await?;

            Ok((row.try_get(0)?, true))
        })
    }

    fn get_or_create_table(
        &self,
        schema_id: i64,
        name: &str,
        path: Option<&str>,
        snapshot_id: i64,
    ) -> Result<(i64, bool)> {
        validate_name(name, "Table")?;
        block_on(async {
            let existing = sqlx::query(
                "SELECT table_id FROM ducklake_table
                 WHERE schema_id = ? AND table_name = ? AND end_snapshot IS NULL",
            )
            .bind(schema_id)
            .bind(name)
            .fetch_optional(&self.pool)
            .await?;

            if let Some(row) = existing {
                return Ok((row.try_get(0)?, false));
            }

            let table_path = path.unwrap_or(name);
            let row = sqlx::query(
                "INSERT INTO ducklake_table (schema_id, table_name, path, path_is_relative, begin_snapshot)
                 VALUES (?, ?, ?, 1, ?) RETURNING table_id",
            )
            .bind(schema_id)
            .bind(name)
            .bind(table_path)
            .bind(snapshot_id)
            .fetch_one(&self.pool)
            .await?;

            Ok((row.try_get(0)?, true))
        })
    }

    fn promote_column_type(
        &self,
        table_id: i64,
        column_name: &str,
        new_ducklake_type: &str,
    ) -> Result<i64> {
        // Reject an unknown target type up front (before opening a transaction).
        crate::types::ducklake_to_arrow_type(new_ducklake_type)?;
        block_on(async {
            let mut tx = self.pool.begin().await?;

            // Take the write lock up front (write-lock-first invariant; see
            // `insert_snapshot`) by allocating the snapshot before any read, so a
            // concurrent promote/drop blocks on the lock rather than failing a
            // deferred read→write upgrade. If a guard below rejects the promote, the
            // early return drops `tx`, rolling back this snapshot (no trace, no gap).
            let (new_snapshot, _carried) = insert_snapshot(&mut tx).await?;

            // Locate the live version of the column.
            let row = sqlx::query(
                "SELECT column_id, column_type, column_order, nulls_allowed
                 FROM ducklake_column
                 WHERE table_id = ? AND column_name = ? AND end_snapshot IS NULL",
            )
            .bind(table_id)
            .bind(column_name)
            .fetch_optional(&mut *tx)
            .await?
            .ok_or_else(|| {
                crate::DuckLakeError::InvalidConfig(format!(
                    "promote_column_type: no live column '{column_name}' in table {table_id}"
                ))
            })?;
            let column_id: i64 = row.try_get("column_id")?;
            let cur_type: String = row.try_get("column_type")?;
            let column_order: i64 = row.try_get("column_order")?;
            let nulls_allowed: bool = row
                .try_get::<Option<bool>, _>("nulls_allowed")?
                .unwrap_or(true);

            // No-op / not-a-widening guards. Canonical equality first so an
            // alias-only restatement is reported as "no change", not attempted.
            if crate::types::types_equal_canonical(&cur_type, new_ducklake_type) {
                return Err(crate::DuckLakeError::InvalidConfig(format!(
                    "promote_column_type: column '{column_name}' is already type '{cur_type}' (no change)"
                )));
            }
            if !crate::types::is_promotable(&cur_type, new_ducklake_type) {
                return Err(crate::DuckLakeError::UnsupportedTypeChange {
                    operation: TypeChangeOperation::PromoteColumnType,
                    column: column_name.to_string(),
                    from: cur_type,
                    to: new_ducklake_type.to_string(),
                });
            }

            // A promote IS schema evolution → DDL: bump schema_version on the
            // snapshot allocated above and (below) record the ledger row, matching
            // the Postgres writer and upstream `CHANGE_COLUMN_TYPE`.
            let new_schema_version = bump_schema_version(&mut tx, new_snapshot).await?;

            // Retire the live row and insert a new version with the SAME column_id
            // (stable field-id). Old/new data files each resolve to their snapshot's
            // version; the read path casts old narrow values up to the new type.
            sqlx::query(
                "UPDATE ducklake_column SET end_snapshot = ?
                 WHERE table_id = ? AND column_id = ? AND end_snapshot IS NULL",
            )
            .bind(new_snapshot)
            .bind(table_id)
            .bind(column_id)
            .execute(&mut *tx)
            .await?;
            sqlx::query(
                "INSERT INTO ducklake_column
                     (column_id, begin_snapshot, end_snapshot, table_id, column_order, column_name, column_type, nulls_allowed)
                 VALUES (?, ?, NULL, ?, ?, ?, ?, ?)",
            )
            .bind(column_id)
            .bind(new_snapshot)
            .bind(table_id)
            .bind(column_order)
            .bind(column_name)
            .bind(new_ducklake_type)
            .bind(nulls_allowed)
            .execute(&mut *tx)
            .await?;

            // Record the schema-change ledger row so consumers can detect the change.
            record_schema_version(&mut tx, new_snapshot, new_schema_version, table_id).await?;

            // TODO(commit-time type guard, task #4): close the window where an Append
            // whose staging began before this promote commits afterward under the old
            // type (the §5 begin-time reject is the fail-fast layer only). Lower-
            // priority on SQLite than Postgres: SQLite serializes commits on a single
            // write lock (this transaction now holds it from `insert_snapshot` onward),
            // so a promote and a data-write commit can't interleave — only their
            // begin-time staging can, which the §5 reject already guards.

            tx.commit().await?;
            Ok(new_snapshot)
        })
    }

    fn set_columns(
        &self,
        table_id: i64,
        columns: &[ColumnDef],
        snapshot_id: i64,
    ) -> Result<Vec<i64>> {
        if columns.is_empty() {
            return Err(crate::DuckLakeError::InvalidConfig(
                "Table must have at least one column".to_string(),
            ));
        }
        block_on(async {
            // Use a transaction to ensure atomicity: if column insertion fails,
            // we don't leave existing columns marked as ended
            let mut tx = self.pool.begin().await?;

            sqlx::query(
                "UPDATE ducklake_column SET end_snapshot = ?
                 WHERE table_id = ? AND end_snapshot IS NULL",
            )
            .bind(snapshot_id)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            // Reserve a contiguous column_id block from the monotonic counter and
            // insert with explicit ids, keeping the allocator authoritative (the
            // write path's begin/commit reserve column ids from the same counter).
            let n = columns.len() as i64;
            let last_column_id = reserve_ids(&mut tx, "next_column_id", n).await?;
            let first_column_id = last_column_id - n + 1;
            let mut column_ids = Vec::with_capacity(columns.len());
            for (order, col) in columns.iter().enumerate() {
                let column_id = first_column_id + order as i64;
                sqlx::query(
                    "INSERT INTO ducklake_column (column_id, table_id, column_name, column_type, column_order, nulls_allowed, begin_snapshot)
                     VALUES (?, ?, ?, ?, ?, ?, ?)",
                )
                .bind(column_id)
                .bind(table_id)
                .bind(&col.name)
                .bind(&col.ducklake_type)
                .bind(order as i64)
                .bind(col.is_nullable)
                .bind(snapshot_id)
                .execute(&mut *tx)
                .await?;
                column_ids.push(column_id);
            }

            tx.commit().await?;
            Ok(column_ids)
        })
    }

    fn register_data_file(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin, so the names are unused here;
        // accepted only to satisfy the trait shared with multicatalog Postgres.
        _schema_name: &str,
        _table_name: &str,
        _snapshot_id: i64,
        file: &DataFileInfo,
        mode: WriteMode,
        base_snapshot: i64,
        columns: &[ColumnDef],
        column_ids: &[i64],
    ) -> Result<CommitIds> {
        block_on(async {
            // Single atomic commit: insert the deferred snapshot row + finalize
            // the column generation + retire the prior generation (Replace),
            // then register this file and advance the monotonic row-lineage
            // counter — all in one transaction, so the head (MAX(snapshot_id))
            // only ever resolves to fully-populated data (no empty-read window).
            let mut tx = self.pool.begin().await?;

            let snapshot_id =
                finalize_snapshot(&mut tx, table_id, columns, column_ids, mode, base_snapshot)
                    .await?;

            // Seed the stats row for the Append path (Replace already seeded it
            // in finalize_snapshot); INSERT OR IGNORE is a no-op if it exists.
            sqlx::query(
                "INSERT OR IGNORE INTO ducklake_table_stats
                     (table_id, record_count, next_row_id, file_size_bytes)
                 VALUES (?, 0, 0, 0)",
            )
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            let stats_row =
                sqlx::query("SELECT next_row_id FROM ducklake_table_stats WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;
            let row_id_start: i64 = stats_row.try_get(0)?;

            sqlx::query(
                "INSERT INTO ducklake_data_file
                     (table_id, path, path_is_relative, file_size_bytes,
                      footer_size, record_count, row_id_start, begin_snapshot)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
            )
            .bind(table_id)
            .bind(&file.path)
            .bind(file.path_is_relative)
            .bind(file.file_size_bytes)
            .bind(file.footer_size)
            .bind(file.record_count)
            .bind(row_id_start)
            .bind(snapshot_id)
            .execute(&mut *tx)
            .await?;

            // Persist the file's per-column zone-map stats in the same tx. The
            // data file is a fresh autoincrement rowid, so `last_insert_rowid()`
            // (captured before any further INSERT) is its data_file_id.
            let data_file_id: i64 = sqlx::query("SELECT last_insert_rowid()")
                .fetch_one(&mut *tx)
                .await?
                .try_get(0)?;
            insert_file_column_stats(&mut tx, table_id, data_file_id, &file.column_stats).await?;
            // Refresh the table-wide roll-up from the now-current live file set.
            recompute_table_column_stats(&mut tx, table_id, columns, column_ids).await?;

            // Advance the counter and accumulate stats. `next_row_id`
            // monotonically increases over the table's lifetime — rowids
            // are never reused, even after end-snapshot.
            sqlx::query(
                "UPDATE ducklake_table_stats
                 SET next_row_id     = next_row_id + ?,
                     record_count    = record_count + ?,
                     file_size_bytes = file_size_bytes + ?
                 WHERE table_id = ?",
            )
            .bind(file.record_count)
            .bind(file.record_count)
            .bind(file.file_size_bytes)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            let schema_id: i64 =
                sqlx::query("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?
                    .try_get(0)?;

            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    #[allow(clippy::too_many_arguments)]
    fn set_delete_file(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin; names unused (trait parity).
        _schema_name: &str,
        _table_name: &str,
        _snapshot_id: i64,
        data_file_id: i64,
        expected_prev_delete_file: Option<i64>,
        base_snapshot: i64,
        delete: &DeleteFileInfo,
    ) -> Result<CommitIds> {
        block_on(async {
            // Single atomic commit: allocate the snapshot (write-lock-first),
            // fence against a concurrent generation change, compare-and-swap the
            // live delete file for this data file, retire the prior one, and
            // insert the new cumulative delete file — so at most one delete file
            // is ever live per data file and the head only resolves to a
            // fully-populated snapshot.
            let mut tx = self.pool.begin().await?;

            // First write takes the SQLite write lock up front (see
            // `insert_snapshot`); a delete carries `schema_version` forward.
            let (snapshot_id, _schema_version) = insert_snapshot(&mut tx).await?;

            // Target-file fence: the resolved positions are physical row indices
            // in `data_file_id`, and a parquet data file is immutable — so only a
            // concurrent write that RETIRED this file (a Replace/compaction) since
            // `base_snapshot` can invalidate them. An append that adds *other*
            // files does not move this file's rows, and a concurrent delete on
            // THIS file is caught by the compare-and-swap below; neither must
            // block the delete. Abort iff the target is no longer the live file.
            let target_live: Option<i64> = sqlx::query_scalar(
                "SELECT 1 FROM ducklake_data_file
                 WHERE data_file_id = ? AND end_snapshot IS NULL",
            )
            .bind(data_file_id)
            .fetch_optional(&mut *tx)
            .await?;
            if target_live.is_none() {
                return Err(crate::DuckLakeError::Conflict(format!(
                    "delete targets data file {data_file_id}, which was retired by a \
                     concurrent write since snapshot {base_snapshot}; retry against the \
                     new generation"
                )));
            }

            // Compare-and-swap on the currently-live delete file for this data
            // file (`end_snapshot IS NULL`). If it isn't what the caller saw, a
            // concurrent delete on the same data file won — abort.
            let current_prev: Option<i64> = sqlx::query_scalar(
                "SELECT delete_file_id FROM ducklake_delete_file
                 WHERE data_file_id = ? AND end_snapshot IS NULL",
            )
            .bind(data_file_id)
            .fetch_optional(&mut *tx)
            .await?;
            if current_prev != expected_prev_delete_file {
                return Err(crate::DuckLakeError::Conflict(format!(
                    "delete on data file {data_file_id} conflicts with a concurrent delete \
                     (expected live delete file {expected_prev_delete_file:?}, found \
                     {current_prev:?}); retry against the new generation"
                )));
            }

            // Retire the prior delete file (cumulative: the new file carries all
            // still-deleted positions, so the old one is superseded).
            if let Some(prev) = expected_prev_delete_file {
                sqlx::query(
                    "UPDATE ducklake_delete_file SET end_snapshot = ?
                     WHERE delete_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(snapshot_id)
                .bind(prev)
                .execute(&mut *tx)
                .await?;
            }

            sqlx::query(
                "INSERT INTO ducklake_delete_file
                     (data_file_id, table_id, path, path_is_relative, file_size_bytes,
                      footer_size, delete_count, begin_snapshot)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
            )
            .bind(data_file_id)
            .bind(table_id)
            .bind(&delete.path)
            .bind(delete.path_is_relative)
            .bind(delete.file_size_bytes)
            .bind(delete.footer_size)
            .bind(delete.delete_count)
            .bind(snapshot_id)
            .execute(&mut *tx)
            .await?;

            let schema_id: i64 =
                sqlx::query_scalar("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;

            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    #[allow(clippy::too_many_arguments)]
    fn register_data_file_with_deletes(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin; names unused (trait parity).
        _schema_name: &str,
        _table_name: &str,
        _snapshot_id: i64,
        file: &DataFileInfo,
        deletes: &[DeleteFileEntry],
        mode: WriteMode,
        base_snapshot: i64,
        columns: &[ColumnDef],
        column_ids: &[i64],
    ) -> Result<CommitIds> {
        validate_delete_entries(mode, deletes)?;
        block_on(async {
            // One atomic commit for a combined append + positional deletes (an
            // update/upsert). finalize_snapshot allocates the snapshot + finalizes
            // the column generation; the new data file AND every delete file are
            // stamped with that one id and committed together, so the head only
            // ever resolves to the fully-applied mutation.
            let mut tx = self.pool.begin().await?;

            let snapshot_id =
                finalize_snapshot(&mut tx, table_id, columns, column_ids, mode, base_snapshot)
                    .await?;

            // Register the new data file (inserted row versions), as in
            // register_data_file. Deletes are accounted at read time
            // (delete_count), so record_count stays gross — no adjustment for them.
            sqlx::query(
                "INSERT OR IGNORE INTO ducklake_table_stats
                     (table_id, record_count, next_row_id, file_size_bytes)
                 VALUES (?, 0, 0, 0)",
            )
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            let stats_row =
                sqlx::query("SELECT next_row_id FROM ducklake_table_stats WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;
            let row_id_start: i64 = stats_row.try_get(0)?;

            sqlx::query(
                "INSERT INTO ducklake_data_file
                     (table_id, path, path_is_relative, file_size_bytes,
                      footer_size, record_count, row_id_start, begin_snapshot)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
            )
            .bind(table_id)
            .bind(&file.path)
            .bind(file.path_is_relative)
            .bind(file.file_size_bytes)
            .bind(file.footer_size)
            .bind(file.record_count)
            .bind(row_id_start)
            .bind(snapshot_id)
            .execute(&mut *tx)
            .await?;

            // Persist the new file's per-column zone-map stats before any other
            // INSERT (delete-file rows below) moves last_insert_rowid.
            let data_file_id: i64 = sqlx::query("SELECT last_insert_rowid()")
                .fetch_one(&mut *tx)
                .await?
                .try_get(0)?;
            insert_file_column_stats(&mut tx, table_id, data_file_id, &file.column_stats).await?;
            // Refresh the table-wide roll-up from the now-current live file set.
            recompute_table_column_stats(&mut tx, table_id, columns, column_ids).await?;

            sqlx::query(
                "UPDATE ducklake_table_stats
                 SET next_row_id     = next_row_id + ?,
                     record_count    = record_count + ?,
                     file_size_bytes = file_size_bytes + ?
                 WHERE table_id = ?",
            )
            .bind(file.record_count)
            .bind(file.record_count)
            .bind(file.file_size_bytes)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            // Apply each positional delete with the same fence + compare-and-swap as
            // set_delete_file, stamped with this snapshot. Each entry targets a
            // distinct data file, so there is no intra-transaction CAS contention.
            for entry in deletes {
                let target_live: Option<i64> = sqlx::query_scalar(
                    "SELECT 1 FROM ducklake_data_file
                     WHERE data_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(entry.data_file_id)
                .fetch_optional(&mut *tx)
                .await?;
                if target_live.is_none() {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "UPDATE/DELETE on data file {} could not commit: the file is no longer \
                         live as of the catalog's current head (retired since snapshot \
                         {base_snapshot}). This happens when another writer committed a \
                         Replace/compaction, OR when an earlier write in THIS session already \
                         advanced the catalog (the catalog pins its snapshot at creation and does \
                         not refresh). Re-open the catalog at the latest snapshot and retry.",
                        entry.data_file_id
                    )));
                }

                let current_prev: Option<i64> = sqlx::query_scalar(
                    "SELECT delete_file_id FROM ducklake_delete_file
                     WHERE data_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(entry.data_file_id)
                .fetch_optional(&mut *tx)
                .await?;
                if current_prev != entry.expected_prev_delete_file {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "UPDATE/DELETE on data file {} could not commit: its live delete file \
                         changed from {:?} to {current_prev:?} since snapshot {base_snapshot}. \
                         Another writer committed a delete on this file, OR an earlier \
                         UPDATE/DELETE in THIS session did (the catalog pins its snapshot at \
                         creation and does not refresh). Re-open the catalog at the latest \
                         snapshot and retry.",
                        entry.data_file_id, entry.expected_prev_delete_file
                    )));
                }

                if let Some(prev) = entry.expected_prev_delete_file {
                    sqlx::query(
                        "UPDATE ducklake_delete_file SET end_snapshot = ?
                         WHERE delete_file_id = ? AND end_snapshot IS NULL",
                    )
                    .bind(snapshot_id)
                    .bind(prev)
                    .execute(&mut *tx)
                    .await?;
                }

                sqlx::query(
                    "INSERT INTO ducklake_delete_file
                         (data_file_id, table_id, path, path_is_relative, file_size_bytes,
                          footer_size, delete_count, begin_snapshot)
                     VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
                )
                .bind(entry.data_file_id)
                .bind(table_id)
                .bind(&entry.delete.path)
                .bind(entry.delete.path_is_relative)
                .bind(entry.delete.file_size_bytes)
                .bind(entry.delete.footer_size)
                .bind(entry.delete.delete_count)
                .bind(snapshot_id)
                .execute(&mut *tx)
                .await?;
            }

            let schema_id: i64 =
                sqlx::query_scalar("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;

            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    fn commit_positional_deletes(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin; names unused (trait parity).
        _schema_name: &str,
        _table_name: &str,
        base_snapshot: i64,
        deletes: &[DeleteFileEntry],
    ) -> Result<CommitIds> {
        if deletes.is_empty() {
            return Err(crate::DuckLakeError::InvalidConfig(
                "commit_positional_deletes requires at least one delete entry".to_string(),
            ));
        }
        // A positional delete never retires the data files it targets, so it is
        // Append-semantics for validation (also enforces distinct data files).
        validate_delete_entries(WriteMode::Append, deletes)?;
        block_on(async {
            // One atomic commit for an N-file positional DELETE with no append.
            // Mirrors register_data_file_with_deletes' delete loop, but allocates
            // the snapshot via insert_snapshot (no column generation, no data
            // file) — a delete carries schema_version forward. The head
            // (MAX(snapshot_id)) only ever resolves to the fully-applied delete.
            let mut tx = self.pool.begin().await?;

            // Write-lock-first: the MAX+1 insert takes the SQLite write lock up
            // front, so concurrent writers can't collide or deadlock.
            let (snapshot_id, _schema_version) = insert_snapshot(&mut tx).await?;

            for entry in deletes {
                // Target-file fence: abort iff the data file is no longer live (a
                // concurrent Replace/compaction retired it, invalidating the
                // resolved positions).
                let target_live: Option<i64> = sqlx::query_scalar(
                    "SELECT 1 FROM ducklake_data_file
                     WHERE data_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(entry.data_file_id)
                .fetch_optional(&mut *tx)
                .await?;
                if target_live.is_none() {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "DELETE on data file {} could not commit: the file is no longer live as \
                         of the catalog's current head (retired since snapshot {base_snapshot}). \
                         This happens when another writer committed a Replace/compaction, OR when \
                         an earlier write in THIS session already advanced the catalog (the \
                         catalog pins its snapshot at creation and does not refresh). Re-open the \
                         catalog at the latest snapshot and retry.",
                        entry.data_file_id
                    )));
                }

                // Compare-and-swap on the currently-live delete file for this data
                // file; a concurrent delete on the same file makes it differ.
                let current_prev: Option<i64> = sqlx::query_scalar(
                    "SELECT delete_file_id FROM ducklake_delete_file
                     WHERE data_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(entry.data_file_id)
                .fetch_optional(&mut *tx)
                .await?;
                if current_prev != entry.expected_prev_delete_file {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "DELETE on data file {} could not commit: its live delete file changed \
                         from {:?} to {current_prev:?} since snapshot {base_snapshot}. Another \
                         writer committed a delete on this file, OR an earlier DELETE in THIS \
                         session did (the catalog pins its snapshot at creation and does not \
                         refresh). Re-open the catalog at the latest snapshot and retry.",
                        entry.data_file_id, entry.expected_prev_delete_file
                    )));
                }

                // Retire the prior delete file (cumulative: the new file carries
                // all still-deleted positions, so the old one is superseded).
                if let Some(prev) = entry.expected_prev_delete_file {
                    sqlx::query(
                        "UPDATE ducklake_delete_file SET end_snapshot = ?
                         WHERE delete_file_id = ? AND end_snapshot IS NULL",
                    )
                    .bind(snapshot_id)
                    .bind(prev)
                    .execute(&mut *tx)
                    .await?;
                }

                sqlx::query(
                    "INSERT INTO ducklake_delete_file
                         (data_file_id, table_id, path, path_is_relative, file_size_bytes,
                          footer_size, delete_count, begin_snapshot)
                     VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
                )
                .bind(entry.data_file_id)
                .bind(table_id)
                .bind(&entry.delete.path)
                .bind(entry.delete.path_is_relative)
                .bind(entry.delete.file_size_bytes)
                .bind(entry.delete.footer_size)
                .bind(entry.delete.delete_count)
                .bind(snapshot_id)
                .execute(&mut *tx)
                .await?;
            }

            let schema_id: i64 =
                sqlx::query_scalar("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;

            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    fn commit_compaction(
        &self,
        table_id: i64,
        base_snapshot: i64,
        sources: &[crate::metadata_writer::CompactionSourceFile],
        outputs: &[crate::metadata_writer::CompactionOutputFile],
        retirement: crate::metadata_writer::SourceRetirement,
    ) -> Result<CommitIds> {
        use crate::metadata_writer::SourceRetirement;
        if sources.is_empty() {
            return Err(crate::DuckLakeError::InvalidConfig(
                "commit_compaction requires at least one source file".to_string(),
            ));
        }
        block_on(async {
            // One atomic snapshot for the whole compaction. insert_snapshot takes
            // the SQLite write lock up front (write-lock-first) and carries
            // schema_version forward — compaction is not DDL. The head
            // (MAX(snapshot_id)) only ever resolves to the fully-applied layout.
            let mut tx = self.pool.begin().await?;
            let (snapshot_id, _schema_version) = insert_snapshot(&mut tx).await?;

            // Conflict fence per source: the data file must still be live, and its
            // live delete file must still match what the caller read the source's
            // rows against. A concurrent APPEND adds unrelated files and trips
            // neither (so compaction coexists with it); a concurrent
            // Replace/compaction that retired the file, or a DELETE/UPDATE that
            // changed its live rows, DOES — abort before mutating anything so a
            // retired/deleted row can never be resurrected into an output.
            for src in sources {
                let target_live: Option<i64> = sqlx::query_scalar(
                    "SELECT 1 FROM ducklake_data_file
                     WHERE data_file_id = ? AND table_id = ? AND end_snapshot IS NULL",
                )
                .bind(src.data_file_id)
                .bind(table_id)
                .fetch_optional(&mut *tx)
                .await?;
                if target_live.is_none() {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "compaction of table {table_id} could not commit: source data file {} is \
                         no longer live (retired by a concurrent Replace/compaction since \
                         snapshot {base_snapshot}). Re-open the catalog at the latest snapshot \
                         and re-plan.",
                        src.data_file_id
                    )));
                }

                let current_delete: Option<i64> = sqlx::query_scalar(
                    "SELECT delete_file_id FROM ducklake_delete_file
                     WHERE data_file_id = ? AND end_snapshot IS NULL",
                )
                .bind(src.data_file_id)
                .fetch_optional(&mut *tx)
                .await?;
                if current_delete != src.delete_file_id {
                    return Err(crate::DuckLakeError::Conflict(format!(
                        "compaction of table {table_id} could not commit: the live delete file of \
                         source data file {} changed from {:?} to {current_delete:?} since \
                         snapshot {base_snapshot} (a concurrent DELETE/UPDATE). Re-open the \
                         catalog at the latest snapshot and re-plan.",
                        src.data_file_id, src.delete_file_id
                    )));
                }
            }

            let source_data_ids: Vec<i64> = sources.iter().map(|s| s.data_file_id).collect();

            match retirement {
                SourceRetirement::Remove => {
                    // Merge: the partial output serves every snapshot the sources
                    // did, so the sources are redundant. Schedule their physical
                    // files for deletion (resolving paths as expire_snapshots
                    // does) and REMOVE their catalog rows, so no snapshot resolves
                    // to them (avoids double-counting with the partial file, and
                    // upholds the invariant that scheduled files are unreachable).
                    let dead_data = sqlx::query(&format!(
                        "SELECT df.data_file_id, {RESOLVED_PATH} AS resolved_path, {REL_FLAG} AS rel
                         FROM ducklake_data_file df
                         JOIN ducklake_table t ON t.table_id = df.table_id
                         JOIN ducklake_schema s ON s.schema_id = t.schema_id
                         WHERE df.data_file_id IN ({})",
                        id_list(&source_data_ids)
                    ))
                    .fetch_all(&mut *tx)
                    .await?;
                    schedule_files(&mut tx, dead_data).await?;

                    let dead_del = sqlx::query(&format!(
                        "SELECT df.delete_file_id, {RESOLVED_PATH} AS resolved_path, {REL_FLAG} AS rel
                         FROM ducklake_delete_file df
                         JOIN ducklake_table t ON t.table_id = df.table_id
                         JOIN ducklake_schema s ON s.schema_id = t.schema_id
                         WHERE df.data_file_id IN ({})",
                        id_list(&source_data_ids)
                    ))
                    .fetch_all(&mut *tx)
                    .await?;
                    schedule_files(&mut tx, dead_del).await?;

                    sqlx::query(&format!(
                        "DELETE FROM ducklake_delete_file WHERE data_file_id IN ({})",
                        id_list(&source_data_ids)
                    ))
                    .execute(&mut *tx)
                    .await?;
                    sqlx::query(&format!(
                        "DELETE FROM ducklake_data_file WHERE data_file_id IN ({})",
                        id_list(&source_data_ids)
                    ))
                    .execute(&mut *tx)
                    .await?;
                    // Hard-delete the removed sources' per-column stats too, as
                    // official DuckLake does on merge (otherwise they orphan).
                    sqlx::query(&format!(
                        "DELETE FROM ducklake_file_column_stats WHERE data_file_id IN ({})",
                        id_list(&source_data_ids)
                    ))
                    .execute(&mut *tx)
                    .await?;
                },
                SourceRetirement::Retire => {
                    // Rewrite: the output only holds currently-live rows, so the
                    // sources still serve time travel to pre-compaction snapshots.
                    // Retire them (end_snapshot) but do NOT schedule them — an
                    // expire_snapshots run schedules them once their snapshots are
                    // gone, so they are never deleted while still reachable.
                    sqlx::query(&format!(
                        "UPDATE ducklake_data_file SET end_snapshot = ?
                         WHERE data_file_id IN ({}) AND end_snapshot IS NULL",
                        id_list(&source_data_ids)
                    ))
                    .bind(snapshot_id)
                    .execute(&mut *tx)
                    .await?;
                    sqlx::query(&format!(
                        "UPDATE ducklake_delete_file SET end_snapshot = ?
                         WHERE data_file_id IN ({}) AND end_snapshot IS NULL",
                        id_list(&source_data_ids)
                    ))
                    .bind(snapshot_id)
                    .execute(&mut *tx)
                    .await?;
                },
            }

            // Register each rewritten output. begin_snapshot = the file's min
            // origin snapshot for a merged partial file (so historical reads see
            // it, row-filtered by origin), else this compaction snapshot;
            // row_id_start = NULL (rowids are served from the embedded rowid
            // column); partial_max marks a merged partial file.
            for out in outputs {
                let begin = out.begin_snapshot.unwrap_or(snapshot_id);
                sqlx::query(
                    "INSERT INTO ducklake_data_file
                         (table_id, path, path_is_relative, file_size_bytes,
                          footer_size, record_count, row_id_start, begin_snapshot, partial_max)
                     VALUES (?, ?, ?, ?, ?, ?, NULL, ?, ?)",
                )
                .bind(table_id)
                .bind(&out.file.path)
                .bind(out.file.path_is_relative)
                .bind(out.file.file_size_bytes)
                .bind(out.file.footer_size)
                .bind(out.file.record_count)
                .bind(begin)
                .bind(out.partial_max)
                .execute(&mut *tx)
                .await?;
                // Persist the compacted file's harvested zone maps, tying them to
                // the row just inserted (same tx). Mirrors how official DuckLake
                // records stats for compaction outputs via the shared write path.
                let data_file_id: i64 = sqlx::query("SELECT last_insert_rowid()")
                    .fetch_one(&mut *tx)
                    .await?
                    .try_get(0)?;
                insert_file_column_stats(&mut tx, table_id, data_file_id, &out.file.column_stats)
                    .await?;
            }

            // Recompute the visible stat totals from the surviving files. Robust
            // for both ops: merge preserves the gross record_count (it refuses
            // delete-bearing groups), while rewrite lowers it to the live count
            // (its retired delete file's count drops out at the same time, so the
            // net live count is unchanged). next_row_id is deliberately NOT
            // advanced: compaction mints no new logical rows (outputs re-embed
            // existing rowids), so the monotonic allocator must not move.
            sqlx::query(
                "INSERT OR IGNORE INTO ducklake_table_stats
                     (table_id, record_count, next_row_id, file_size_bytes)
                 VALUES (?, 0, 0, 0)",
            )
            .bind(table_id)
            .execute(&mut *tx)
            .await?;
            sqlx::query(
                "UPDATE ducklake_table_stats SET
                     record_count = (SELECT COALESCE(SUM(record_count), 0)
                                     FROM ducklake_data_file
                                     WHERE table_id = ? AND end_snapshot IS NULL),
                     file_size_bytes = (SELECT COALESCE(SUM(file_size_bytes), 0)
                                        FROM ducklake_data_file
                                        WHERE table_id = ? AND end_snapshot IS NULL)
                 WHERE table_id = ?",
            )
            .bind(table_id)
            .bind(table_id)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            // Attribute the snapshot for spec readers (DuckDB reads this ledger).
            sqlx::query(
                "INSERT INTO ducklake_snapshot_changes
                     (snapshot_id, changes_made, commit_message)
                 VALUES (?, ?, ?)",
            )
            .bind(snapshot_id)
            .bind(format!("compacted_table:{table_id}"))
            .bind("datafusion compaction")
            .execute(&mut *tx)
            .await?;

            let schema_id: i64 =
                sqlx::query_scalar("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?;

            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    fn commit_truncate(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin; names unused (trait parity).
        _schema_name: &str,
        _table_name: &str,
        _base_snapshot: i64,
    ) -> Result<u64> {
        block_on(async {
            // Metadata-only truncate in one snapshot: end every live data file and
            // its live delete file (as drop_table does) and zero the visible stat
            // totals; next_row_id is preserved (rowids stay monotonic).
            let mut tx = self.pool.begin().await?;

            // Write-lock-first (carry schema_version forward — not DDL).
            let (snapshot_id, _schema_version) = insert_snapshot(&mut tx).await?;

            // No-op guard: if the table has no live data file there is nothing to
            // truncate. Return Ok(0) WITHOUT committing so `tx` (and the snapshot
            // row insert_snapshot just made) rolls back, leaving no trace — same
            // as `drop_table`'s idempotent early return. Prevents a content-free
            // snapshot per repeated `DELETE FROM t` when the catalog's pinned
            // snapshot still sees already-ended files as live.
            let has_live_data: Option<i64> = sqlx::query_scalar(
                "SELECT 1 FROM ducklake_data_file
                 WHERE table_id = ? AND end_snapshot IS NULL LIMIT 1",
            )
            .bind(table_id)
            .fetch_optional(&mut *tx)
            .await?;
            if has_live_data.is_none() {
                return Ok(0);
            }

            // Rows removed = gross record_count minus still-live delete counts,
            // computed BEFORE ending anything so it matches what we retire.
            let gross: Option<i64> = sqlx::query_scalar(
                "SELECT COALESCE(record_count, 0) FROM ducklake_table_stats WHERE table_id = ?",
            )
            .bind(table_id)
            .fetch_optional(&mut *tx)
            .await?;
            let deleted: i64 = sqlx::query_scalar(
                "SELECT COALESCE(SUM(delete_count), 0) FROM ducklake_delete_file
                 WHERE table_id = ? AND end_snapshot IS NULL",
            )
            .bind(table_id)
            .fetch_one(&mut *tx)
            .await?;
            let live_rows = (gross.unwrap_or(0) - deleted).max(0) as u64;

            sqlx::query(
                "UPDATE ducklake_data_file SET end_snapshot = ?
                 WHERE table_id = ? AND end_snapshot IS NULL",
            )
            .bind(snapshot_id)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;
            sqlx::query(
                "UPDATE ducklake_delete_file SET end_snapshot = ?
                 WHERE table_id = ? AND end_snapshot IS NULL",
            )
            .bind(snapshot_id)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;
            sqlx::query(
                "UPDATE ducklake_table_stats SET record_count = 0, file_size_bytes = 0
                 WHERE table_id = ?",
            )
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            tx.commit().await?;
            Ok(live_rows)
        })
    }

    fn publish_snapshot(
        &self,
        table_id: i64,
        // SQLite created the schema/table at begin; names unused (trait parity).
        _schema_name: &str,
        _table_name: &str,
        _snapshot_id: i64,
        mode: WriteMode,
        base_snapshot: i64,
        columns: &[ColumnDef],
        column_ids: &[i64],
    ) -> Result<CommitIds> {
        // Fileless commit point. Single-catalog SQLite defers the snapshot-row
        // insert out of begin_write_transaction, so this is no longer a no-op:
        // it inserts the deferred snapshot row + column generation and, for
        // Replace, retires the prior generation — making the new head visible
        // atomically. Used by CREATE TABLE (schema.rs); the crate's own write
        // path always registers a file (even for zero rows) and so commits via
        // register_data_file instead.
        block_on(async {
            let mut tx = self.pool.begin().await?;
            let snapshot_id =
                finalize_snapshot(&mut tx, table_id, columns, column_ids, mode, base_snapshot)
                    .await?;
            let schema_id: i64 =
                sqlx::query("SELECT schema_id FROM ducklake_table WHERE table_id = ?")
                    .bind(table_id)
                    .fetch_one(&mut *tx)
                    .await?
                    .try_get(0)?;
            tx.commit().await?;
            Ok(CommitIds {
                snapshot_id,
                schema_id,
                table_id,
            })
        })
    }

    fn end_table_files(&self, table_id: i64, snapshot_id: i64) -> Result<u64> {
        // Used by WriteMode::Replace. End-snapshotting every visible file
        // drops the table's currently-visible row count and byte total to
        // zero (the new files written next will rebuild them). `next_row_id`
        // is deliberately NOT reset: rowids must stay monotonic across the
        // table's lifetime so historical snapshots still resolve uniquely.
        block_on(async {
            let mut tx = self.pool.begin().await?;

            let result = sqlx::query(
                "UPDATE ducklake_data_file SET end_snapshot = ?
                 WHERE table_id = ? AND end_snapshot IS NULL",
            )
            .bind(snapshot_id)
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            sqlx::query(
                "UPDATE ducklake_table_stats
                 SET record_count = 0, file_size_bytes = 0
                 WHERE table_id = ?",
            )
            .bind(table_id)
            .execute(&mut *tx)
            .await?;

            tx.commit().await?;
            Ok(result.rows_affected())
        })
    }

    fn get_data_path(&self) -> Result<String> {
        block_on(async {
            let row =
                sqlx::query("SELECT value FROM ducklake_metadata WHERE key = ? AND scope IS NULL")
                    .bind("data_path")
                    .fetch_optional(&self.pool)
                    .await?;

            match row {
                Some(r) => Ok(r.try_get(0)?),
                None => Err(crate::error::DuckLakeError::InvalidConfig(
                    "Missing required catalog metadata: 'data_path' not configured.".to_string(),
                )),
            }
        })
    }

    fn set_data_path(&self, path: &str) -> Result<()> {
        block_on(async {
            sqlx::query("DELETE FROM ducklake_metadata WHERE key = 'data_path' AND scope IS NULL")
                .execute(&self.pool)
                .await?;

            sqlx::query(
                "INSERT INTO ducklake_metadata (key, value, scope)
                 VALUES ('data_path', ?, NULL)",
            )
            .bind(path)
            .execute(&self.pool)
            .await?;

            Ok(())
        })
    }

    fn initialize_schema(&self) -> Result<()> {
        block_on(async {
            sqlx::query(SQL_CREATE_SCHEMA).execute(&self.pool).await?;
            // Upgrade a pre-existing catalog's `ducklake_column` from the legacy
            // single-row-PK shape to upstream's bare shape (idempotent, crash-safe).
            // `CREATE TABLE IF NOT EXISTS` above only shapes new catalogs.
            migrate_ducklake_column_drop_pk(&self.pool).await?;
            // Upgrade a pre-existing catalog to track schema_version (add the
            // ducklake_snapshot.schema_version column; idempotent, lossless).
            migrate_add_schema_version(&self.pool).await?;
            // Upgrade a pre-existing catalog to the v1.0 partial-file marker (add
            // ducklake_data_file.partial_max; idempotent, lossless — NULL means
            // "not a partial file", correct for every pre-compaction file).
            migrate_add_partial_max(&self.pool).await?;
            // Seed the monotonic id allocators. snapshot_id and column_id are
            // reserved in begin_write_transaction and inserted at the commit, so
            // they can't use rowid autoincrement (inserting the ducklake_snapshot
            // row would advance the head before the data is committed). The
            // counters give collision-free allocation across concurrent writers.
            // Idempotent on re-open, and seeded from the current MAX so a
            // pre-existing catalog continues without reusing ids.
            sqlx::query(
                "INSERT INTO ducklake_metadata (key, value, scope)
                 SELECT 'next_column_id',
                        CAST(COALESCE((SELECT MAX(column_id) FROM ducklake_column), 0) AS TEXT),
                        NULL
                 WHERE NOT EXISTS (
                     SELECT 1 FROM ducklake_metadata WHERE key = 'next_column_id' AND scope IS NULL
                 )",
            )
            .execute(&self.pool)
            .await?;
            Ok(())
        })
    }

    fn begin_write_transaction(
        &self,
        schema_name: &str,
        table_name: &str,
        columns: &[ColumnDef],
        mode: WriteMode,
    ) -> Result<WriteSetupResult> {
        validate_name(schema_name, "Schema")?;
        validate_name(table_name, "Table")?;
        if columns.is_empty() {
            return Err(crate::DuckLakeError::InvalidConfig(
                "Table must have at least one column".to_string(),
            ));
        }
        block_on(async {
            let mut tx = self.pool.begin().await?;

            // Reserve the column ids first so the counter UPDATE takes the write
            // lock up front, avoiding a lock-upgrade deadlock between concurrent
            // begins. These ids match the staged parquet field ids.
            let n = columns.len() as i64;
            let last_column_id = reserve_ids(&mut tx, "next_column_id", n).await?;
            // Freshly reserved ids. Only a genuinely-new column actually consumes
            // one below; an existing column keeps its current id, so some of these
            // may go unused (harmless monotonic-counter gaps).
            let fresh_ids: Vec<i64> = ((last_column_id - n + 1)..=last_column_id).collect();

            // The catalog head this write is based on; a Replace commit aborts if
            // another writer published a newer generation of the table past it.
            let base_snapshot_id: i64 =
                sqlx::query("SELECT COALESCE(MAX(snapshot_id), 0) FROM ducklake_snapshot")
                    .fetch_one(&mut *tx)
                    .await?
                    .try_get(0)?;

            // Tentative id for WriteSetupResult; the real one is assigned at the
            // commit (finalize_snapshot), so it may differ under concurrency.
            let snapshot_id: i64 = base_snapshot_id + 1;

            let schema_id: i64 = {
                let existing = sqlx::query(
                    "SELECT schema_id FROM ducklake_schema
                     WHERE schema_name = ? AND end_snapshot IS NULL",
                )
                .bind(schema_name)
                .fetch_optional(&mut *tx)
                .await?;

                if let Some(row) = existing {
                    row.try_get(0)?
                } else {
                    let row = sqlx::query(
                        "INSERT INTO ducklake_schema (schema_name, path, path_is_relative, begin_snapshot)
                         VALUES (?, ?, 1, ?) RETURNING schema_id",
                    )
                    .bind(schema_name)
                    .bind(schema_name)
                    .bind(snapshot_id)
                    .fetch_one(&mut *tx)
                    .await?;
                    row.try_get(0)?
                }
            };

            let table_id: i64 = {
                let existing = sqlx::query(
                    "SELECT table_id FROM ducklake_table
                     WHERE schema_id = ? AND table_name = ? AND end_snapshot IS NULL",
                )
                .bind(schema_id)
                .bind(table_name)
                .fetch_optional(&mut *tx)
                .await?;

                if let Some(row) = existing {
                    row.try_get(0)?
                } else {
                    let row = sqlx::query(
                        "INSERT INTO ducklake_table (schema_id, table_name, path, path_is_relative, begin_snapshot)
                         VALUES (?, ?, ?, 1, ?) RETURNING table_id",
                    )
                    .bind(schema_id)
                    .bind(table_name)
                    .bind(table_name)
                    .bind(snapshot_id)
                    .fetch_one(&mut *tx)
                    .await?;
                    row.try_get(0)?
                }
            };

            // Get existing columns to (a) check schema compatibility for appends
            // and (b) REUSE each column's id. column_id == parquet field_id == a
            // column's stable identity; re-minting it every write would orphan the
            // field_ids already baked into previously-written files, so an
            // unchanged column must keep its id.
            let rows = sqlx::query(
                "SELECT column_name, column_type, nulls_allowed, column_id
                 FROM ducklake_column
                 WHERE table_id = ? AND end_snapshot IS NULL
                 ORDER BY column_order",
            )
            .bind(table_id)
            .fetch_all(&mut *tx)
            .await?;

            let mut existing_columns: Vec<(String, String, bool)> = Vec::with_capacity(rows.len());
            let mut existing_ids: std::collections::HashMap<String, i64> =
                std::collections::HashMap::new();
            for row in rows {
                let name: String = row.try_get(0)?;
                let col_type: String = row.try_get(1)?;
                let nullable: bool = row.try_get::<Option<bool>, _>(2)?.unwrap_or(true);
                let cid: i64 = row.try_get(3)?;
                existing_ids.insert(name.clone(), cid);
                existing_columns.push((name, col_type, nullable));
            }

            // Data-write policy (§5): a data write — Replace OR Append — must NOT
            // change a column's type. A type change is schema evolution and must
            // go through `promote_column_type`, never a data write; silently
            // keeping the old catalog type (the historic "C" bug) corrupts reads.
            // The comparison is canonical (`int64` ≡ `bigint`) so an alias-only
            // restatement is a no-op, not an error. Append additionally requires a
            // genuinely new column to be nullable (a Replace overwrites every row,
            // so a new non-nullable column is fine there).
            if !existing_columns.is_empty() {
                use std::collections::HashMap;

                // Build map of existing columns: name -> (type, nullable)
                let existing_map: HashMap<&str, (&str, bool)> = existing_columns
                    .iter()
                    .map(|(name, col_type, nullable)| {
                        (name.as_str(), (col_type.as_str(), *nullable))
                    })
                    .collect();

                for new_col in columns.iter() {
                    if let Some((existing_type, _existing_nullable)) =
                        existing_map.get(new_col.name.as_str())
                    {
                        // Same-name column: a (canonical) type change is rejected in
                        // BOTH modes. Not `types_compatible` — that accepts widenings,
                        // which is exactly the silent acceptance we are closing.
                        if !crate::types::types_equal_canonical(
                            existing_type,
                            &new_col.ducklake_type,
                        ) {
                            return Err(crate::error::DuckLakeError::UnsupportedTypeChange {
                                operation: TypeChangeOperation::DataWrite {
                                    mode: match mode {
                                        WriteMode::Replace => TypeChangeWriteMode::Replace,
                                        WriteMode::Append => TypeChangeWriteMode::Append,
                                    },
                                },
                                column: new_col.name.clone(),
                                from: (*existing_type).to_string(),
                                to: new_col.ducklake_type.clone(),
                            });
                        }
                        // Nullable changes remain allowed (strict -> nullable is safe for reads).
                    } else if mode == WriteMode::Append && !new_col.is_nullable {
                        // New column on append - must be nullable.
                        return Err(crate::error::DuckLakeError::InvalidConfig(format!(
                            "Schema evolution error: new column '{}' must be nullable. Adding non-nullable columns is not allowed.",
                            new_col.name
                        )));
                    }
                }
                // Columns in existing but not in new schema are implicitly removed - this is allowed.
            }

            // Final per-column ids: reuse the existing id for a column already in
            // the table, consume a freshly reserved id only for a genuinely new
            // column. These are baked into the staged parquet's field_id metadata,
            // so they must equal the ids `finalize_snapshot` commits. Column rows
            // themselves are written at the commit point (not here): the SQLite
            // read path resolves columns by `end_snapshot IS NULL` only (not
            // snapshot-scoped), so inserting at begin would leak the new
            // generation to concurrent reads during the upload window.
            let column_ids: Vec<i64> = columns
                .iter()
                .zip(fresh_ids.iter())
                .map(|(col, &fresh)| existing_ids.get(&col.name).copied().unwrap_or(fresh))
                .collect();

            // No snapshot row, no column rows, and no Replace retirement are
            // written here — all are deferred to the atomic commit so the head
            // never resolves to an incomplete snapshot. TX-A commits only the
            // idempotent get-or-create schema/table rows; they carry
            // begin_snapshot = the reserved id and stay invisible until the
            // snapshot publishes, since schema/table reads ARE snapshot-scoped.
            tx.commit().await?;

            Ok(WriteSetupResult {
                snapshot_id,
                base_snapshot_id,
                schema_id,
                table_id,
                column_ids,
            })
        })
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use tempfile::TempDir;

    async fn create_test_writer() -> (SqliteMetadataWriter, TempDir) {
        let temp_dir = TempDir::new().unwrap();
        let db_path = temp_dir.path().join("test.db");
        let conn_str = format!("sqlite:{}?mode=rwc", db_path.display());
        let writer = SqliteMetadataWriter::new_with_init(&conn_str)
            .await
            .unwrap();
        (writer, temp_dir)
    }

    /// An existing user's catalog (legacy `column_id INTEGER PRIMARY KEY`) must be
    /// upgraded in place to upstream's bare shape: rows + `column_id`s preserved,
    /// the single-row PK gone (so versioned/promoted columns become expressible),
    /// and the migration idempotent. This is the "people already using the
    /// library" path — `CREATE TABLE IF NOT EXISTS` alone would not touch them.
    #[tokio::test(flavor = "multi_thread")]
    async fn migrate_legacy_column_pk_to_bare_shape() {
        let temp_dir = TempDir::new().unwrap();
        let db_path = temp_dir.path().join("legacy.db");
        let conn_str = format!("sqlite:{}?mode=rwc", db_path.display());
        let pool = SqlitePool::connect(&conn_str).await.unwrap();

        // Build the LEGACY ducklake_column exactly as a pre-versioning catalog had it.
        sqlx::query(
            "CREATE TABLE ducklake_column (
                column_id INTEGER PRIMARY KEY,
                table_id INTEGER NOT NULL,
                column_name VARCHAR NOT NULL,
                column_type VARCHAR NOT NULL,
                column_order INTEGER NOT NULL,
                nulls_allowed BOOLEAN DEFAULT 1,
                initial_default VARCHAR,
                default_value VARCHAR,
                parent_column INTEGER,
                default_value_type VARCHAR,
                default_value_dialect VARCHAR,
                begin_snapshot INTEGER NOT NULL,
                end_snapshot INTEGER
            )",
        )
        .execute(&pool)
        .await
        .unwrap();
        sqlx::query(
            "INSERT INTO ducklake_column
                 (column_id, table_id, column_name, column_type, column_order, nulls_allowed, begin_snapshot)
             VALUES (5, 1, 'id', 'int32', 0, 1, 1)",
        )
        .execute(&pool)
        .await
        .unwrap();

        // Sanity: the legacy PK rejects a second row sharing the column_id.
        let dup = sqlx::query(
            "INSERT INTO ducklake_column
                 (column_id, table_id, column_name, column_type, column_order, nulls_allowed, begin_snapshot)
             VALUES (5, 1, 'id', 'int64', 0, 1, 2)",
        )
        .execute(&pool)
        .await;
        assert!(
            dup.is_err(),
            "legacy single-row PK must reject a duplicate column_id"
        );

        // Migrate.
        migrate_ducklake_column_drop_pk(&pool).await.unwrap();

        // Row + column_id value preserved.
        let row = sqlx::query(
            "SELECT column_id, column_name, column_type FROM ducklake_column WHERE begin_snapshot = 1",
        )
        .fetch_one(&pool)
        .await
        .unwrap();
        assert_eq!(row.try_get::<i64, _>("column_id").unwrap(), 5);
        assert_eq!(row.try_get::<String, _>("column_name").unwrap(), "id");
        assert_eq!(row.try_get::<String, _>("column_type").unwrap(), "int32");

        // The PK is gone: a SECOND version row with the same column_id now coexists
        // (the whole point — versioned / type-promoted columns).
        sqlx::query(
            "INSERT INTO ducklake_column
                 (column_id, begin_snapshot, end_snapshot, table_id, column_order, column_name, column_type, nulls_allowed)
             VALUES (5, 2, NULL, 1, 0, 'id', 'int64', 1)",
        )
        .execute(&pool)
        .await
        .unwrap();
        let cnt: i64 =
            sqlx::query_scalar("SELECT COUNT(*) FROM ducklake_column WHERE column_id = 5")
                .fetch_one(&pool)
                .await
                .unwrap();
        assert_eq!(
            cnt, 2,
            "two version rows sharing a column_id must coexist post-migration"
        );

        // Idempotent: re-running is a no-op and leaves data intact.
        migrate_ducklake_column_drop_pk(&pool).await.unwrap();
        let cnt2: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM ducklake_column")
            .fetch_one(&pool)
            .await
            .unwrap();
        assert_eq!(cnt2, 2, "migration must be idempotent");
    }

    /// An existing catalog whose `ducklake_snapshot` predates `schema_version`
    /// (the pre-#151 shape) must gain the column in place: idempotent, lossless,
    /// and existing snapshot rows take the `DEFAULT 0`. `CREATE TABLE IF NOT
    /// EXISTS` alone would not touch a pre-existing catalog.
    #[tokio::test(flavor = "multi_thread")]
    async fn migrate_add_schema_version_to_legacy_snapshot() {
        let temp_dir = TempDir::new().unwrap();
        let db_path = temp_dir.path().join("legacy.db");
        let conn_str = format!("sqlite:{}?mode=rwc", db_path.display());
        let pool = SqlitePool::connect(&conn_str).await.unwrap();

        // Legacy ducklake_snapshot: no schema_version column, with existing rows.
        sqlx::query(
            "CREATE TABLE ducklake_snapshot (
                snapshot_id INTEGER PRIMARY KEY,
                snapshot_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )",
        )
        .execute(&pool)
        .await
        .unwrap();
        sqlx::query("INSERT INTO ducklake_snapshot (snapshot_id) VALUES (1), (2)")
            .execute(&pool)
            .await
            .unwrap();

        // Migrate.
        migrate_add_schema_version(&pool).await.unwrap();

        // Column now exists and existing rows are backfilled to 0 (lossless).
        let has_col: i64 = sqlx::query_scalar(
            "SELECT COUNT(*) FROM pragma_table_info('ducklake_snapshot') WHERE name = 'schema_version'",
        )
        .fetch_one(&pool)
        .await
        .unwrap();
        assert_eq!(has_col, 1, "schema_version column must be added");
        let max_v: i64 =
            sqlx::query_scalar("SELECT COALESCE(MAX(schema_version), 0) FROM ducklake_snapshot")
                .fetch_one(&pool)
                .await
                .unwrap();
        assert_eq!(
            max_v, 0,
            "pre-existing snapshots backfill to schema_version 0"
        );
        let rows: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM ducklake_snapshot")
            .fetch_one(&pool)
            .await
            .unwrap();
        assert_eq!(rows, 2, "no snapshot rows lost in the migration");

        // Idempotent: re-running is a no-op.
        migrate_add_schema_version(&pool).await.unwrap();
        let rows2: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM ducklake_snapshot")
            .fetch_one(&pool)
            .await
            .unwrap();
        assert_eq!(rows2, 2, "migration must be idempotent");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_create_snapshot() {
        let (writer, _temp) = create_test_writer().await;

        let snap1 = writer.create_snapshot().unwrap();
        assert_eq!(snap1, 1);

        let snap2 = writer.create_snapshot().unwrap();
        assert_eq!(snap2, 2);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_get_or_create_schema() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = writer.create_snapshot().unwrap();

        // Create new schema
        let (schema_id, created) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();
        assert!(created);
        assert_eq!(schema_id, 1);

        // Get existing schema
        let (schema_id2, created2) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();
        assert!(!created2);
        assert_eq!(schema_id2, 1);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_get_or_create_table() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = writer.create_snapshot().unwrap();
        let (schema_id, _) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();

        // Create new table
        let (table_id, created) = writer
            .get_or_create_table(schema_id, "users", None, snapshot_id)
            .unwrap();
        assert!(created);
        assert_eq!(table_id, 1);

        // Get existing table
        let (table_id2, created2) = writer
            .get_or_create_table(schema_id, "users", None, snapshot_id)
            .unwrap();
        assert!(!created2);
        assert_eq!(table_id2, 1);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_set_columns() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = writer.create_snapshot().unwrap();
        let (schema_id, _) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "users", None, snapshot_id)
            .unwrap();

        let columns = vec![
            ColumnDef::new("id", "int64", false).unwrap(),
            ColumnDef::new("name", "varchar", true).unwrap(),
        ];

        let column_ids = writer.set_columns(table_id, &columns, snapshot_id).unwrap();
        assert_eq!(column_ids.len(), 2);
        assert_eq!(column_ids[0], 1);
        assert_eq!(column_ids[1], 2);
    }

    /// Phase C (catalog-state faithfulness): a type promotion must leave the
    /// `ducklake_column` table in upstream DuckLake's exact versioned shape — TWO
    /// rows sharing the SAME `column_id`, the old one retired (`end_snapshot` set)
    /// and the new one live (`end_snapshot IS NULL`) with the widened type. This is
    /// precisely the two-rows-per-column the old single-row PK forbade.
    #[tokio::test(flavor = "multi_thread")]
    async fn promote_leaves_two_versioned_column_rows_same_id() {
        let (writer, _temp) = create_test_writer().await;
        let snap1 = writer.create_snapshot().unwrap();
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap1).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap1)
            .unwrap();
        writer
            .set_columns(
                table_id,
                &[ColumnDef::new("id", "int32", false).unwrap()],
                snap1,
            )
            .unwrap();

        let snap2 = writer.promote_column_type(table_id, "id", "int64").unwrap();
        assert!(snap2 > snap1, "promote creates a newer snapshot");

        let rows = sqlx::query(
            "SELECT column_id, column_type, begin_snapshot, end_snapshot
             FROM ducklake_column
             WHERE table_id = ? AND column_name = 'id'
             ORDER BY begin_snapshot",
        )
        .bind(table_id)
        .fetch_all(&writer.pool)
        .await
        .unwrap();

        assert_eq!(
            rows.len(),
            2,
            "promote must leave TWO versioned rows for the column"
        );

        let cid0: i64 = rows[0].try_get("column_id").unwrap();
        let type0: String = rows[0].try_get("column_type").unwrap();
        let end0: Option<i64> = rows[0].try_get("end_snapshot").unwrap();
        let cid1: i64 = rows[1].try_get("column_id").unwrap();
        let type1: String = rows[1].try_get("column_type").unwrap();
        let begin1: i64 = rows[1].try_get("begin_snapshot").unwrap();
        let end1: Option<i64> = rows[1].try_get("end_snapshot").unwrap();

        assert_eq!(
            cid0, cid1,
            "both versions share the SAME column_id (stable field-id)"
        );
        assert_eq!(type0, "int32", "old version retains its int32 type");
        assert_eq!(
            end0,
            Some(snap2),
            "old version retired at the promote snapshot"
        );
        assert_eq!(type1, "int64", "new version carries the widened int64 type");
        assert_eq!(begin1, snap2, "new version begins at the promote snapshot");
        assert_eq!(end1, None, "new version is the live one");

        // D4: exactly ONE live row per field-id after the promote.
        let live: i64 = sqlx::query_scalar(
            "SELECT COUNT(*) FROM ducklake_column
             WHERE table_id = ? AND column_name = 'id' AND end_snapshot IS NULL",
        )
        .bind(table_id)
        .fetch_one(&writer.pool)
        .await
        .unwrap();
        assert_eq!(
            live, 1,
            "exactly one live version per field-id after promote"
        );
    }

    /// Phase D (old-version data → latest): a catalog whose `ducklake_column` is in
    /// the LEGACY single-row-PK shape (as a previous datafusion-ducklake version
    /// wrote it), holding real data files, must upgrade in place on re-open and read
    /// its values back intact — then support a promote. We simulate the old version
    /// by writing with the current writer, then rebuilding `ducklake_column` to the
    /// legacy shape; re-opening runs the forward migration.
    #[tokio::test(flavor = "multi_thread")]
    async fn phase_d_legacy_catalog_with_data_upgrades_and_reads() {
        use crate::{DuckLakeCatalog, DuckLakeTableWriter, SqliteMetadataProvider};
        use arrow::array::{Array, Int32Array, Int64Array};
        use arrow::datatypes::{DataType, Field, Schema};
        use arrow::record_batch::RecordBatch;
        use datafusion::prelude::SessionContext;
        use object_store::local::LocalFileSystem;

        let temp = TempDir::new().unwrap();
        let db_path = temp.path().join("test.db");
        let data_path = temp.path().join("data");
        std::fs::create_dir_all(&data_path).unwrap();
        let conn_str = format!("sqlite:{}?mode=rwc", db_path.display());

        // 1. Write real data (t(id int32) = [1,2,3]) with the current writer.
        {
            let writer = SqliteMetadataWriter::new_with_init(&conn_str)
                .await
                .unwrap();
            writer.set_data_path(data_path.to_str().unwrap()).unwrap();
            let store: std::sync::Arc<dyn object_store::ObjectStore> =
                std::sync::Arc::new(LocalFileSystem::new());
            let schema =
                std::sync::Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
            let batch = RecordBatch::try_new(
                schema,
                vec![std::sync::Arc::new(Int32Array::from(vec![1, 2, 3]))],
            )
            .unwrap();
            DuckLakeTableWriter::new(std::sync::Arc::new(writer), store)
                .unwrap()
                .write_table("main", "t", &[batch])
                .await
                .unwrap();
        }

        // 2. Downgrade ducklake_column to the LEGACY single-row-PK shape (what an
        //    older version wrote), preserving rows + column_ids. Data files untouched.
        {
            let pool = SqlitePool::connect(&conn_str).await.unwrap();
            sqlx::query("ALTER TABLE ducklake_column RENAME TO ducklake_column__tmp")
                .execute(&pool)
                .await
                .unwrap();
            sqlx::query(
                "CREATE TABLE ducklake_column (
                    column_id INTEGER PRIMARY KEY,
                    table_id INTEGER NOT NULL,
                    column_name VARCHAR NOT NULL,
                    column_type VARCHAR NOT NULL,
                    column_order INTEGER NOT NULL,
                    nulls_allowed BOOLEAN DEFAULT 1,
                    initial_default VARCHAR,
                    default_value VARCHAR,
                    parent_column INTEGER,
                    default_value_type VARCHAR,
                    default_value_dialect VARCHAR,
                    begin_snapshot INTEGER NOT NULL,
                    end_snapshot INTEGER
                )",
            )
            .execute(&pool)
            .await
            .unwrap();
            sqlx::query(
                "INSERT INTO ducklake_column
                     (column_id, table_id, column_name, column_type, column_order,
                      nulls_allowed, begin_snapshot, end_snapshot)
                 SELECT column_id, table_id, column_name, column_type, column_order,
                        nulls_allowed, begin_snapshot, end_snapshot
                 FROM ducklake_column__tmp",
            )
            .execute(&pool)
            .await
            .unwrap();
            sqlx::query("DROP TABLE ducklake_column__tmp")
                .execute(&pool)
                .await
                .unwrap();
            // Sanity: it really is the legacy PK shape now.
            let is_pk: i64 = sqlx::query_scalar(
                "SELECT pk FROM pragma_table_info('ducklake_column') WHERE name = 'column_id'",
            )
            .fetch_one(&pool)
            .await
            .unwrap();
            assert_eq!(is_pk, 1, "downgrade produced the legacy single-row PK");
            pool.close().await;
        }

        // 3. Re-open with the current version → initialize_schema runs the forward
        //    migration (legacy PK -> bare shape).
        let writer = SqliteMetadataWriter::new_with_init(&conn_str)
            .await
            .unwrap();
        let is_pk: i64 = sqlx::query_scalar(
            "SELECT pk FROM pragma_table_info('ducklake_column') WHERE name = 'column_id'",
        )
        .fetch_one(&writer.pool)
        .await
        .unwrap();
        assert_eq!(is_pk, 0, "re-open migrated the legacy PK away");

        // 4. The old data reads back intact through the full provider.
        let read_conn = format!("sqlite:{}", db_path.display());
        let provider = SqliteMetadataProvider::new(&read_conn).await.unwrap();
        let catalog = DuckLakeCatalog::new(provider).unwrap();
        let ctx = SessionContext::new();
        ctx.register_catalog("test", std::sync::Arc::new(catalog));
        let batches = ctx
            .sql("SELECT id FROM test.main.t ORDER BY id")
            .await
            .unwrap()
            .collect()
            .await
            .unwrap();
        let ids = batches[0]
            .column(0)
            .as_any()
            .downcast_ref::<Int32Array>()
            .unwrap();
        assert_eq!(
            ids.values(),
            &[1, 2, 3],
            "old-version data reads intact after upgrade"
        );

        // 5. The migrated catalog now supports promote (versioning enabled).
        let table_id: i64 =
            sqlx::query_scalar("SELECT table_id FROM ducklake_table WHERE table_name = 't'")
                .fetch_one(&writer.pool)
                .await
                .unwrap();
        writer.promote_column_type(table_id, "id", "int64").unwrap();
        let provider2 = SqliteMetadataProvider::new(&read_conn).await.unwrap();
        let catalog2 = DuckLakeCatalog::new(provider2).unwrap();
        let ctx2 = SessionContext::new();
        ctx2.register_catalog("test", std::sync::Arc::new(catalog2));
        let batches2 = ctx2
            .sql("SELECT id FROM test.main.t ORDER BY id")
            .await
            .unwrap()
            .collect()
            .await
            .unwrap();
        assert_eq!(batches2[0].schema().field(0).data_type(), &DataType::Int64);
        let ids2 = batches2[0]
            .column(0)
            .as_any()
            .downcast_ref::<Int64Array>()
            .unwrap();
        assert_eq!(
            ids2.values(),
            &[1, 2, 3],
            "post-upgrade promote widens + reads intact"
        );
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_register_data_file() {
        let (writer, _temp) = create_test_writer().await;
        // Reserve (do not create) the snapshot: register_data_file inserts it.
        let snapshot_id = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "users", None, snapshot_id)
            .unwrap();

        let file = DataFileInfo::new("data.parquet", 1024, 100).with_footer_size(256);

        // register_data_file returns the committed snapshot id (head); the
        // first write commits snapshot 1.
        let committed = writer
            .register_data_file(
                table_id,
                "main",
                "users",
                snapshot_id,
                &file,
                WriteMode::Append,
                0,
                &[],
                &[],
            )
            .unwrap();
        assert_eq!(committed.snapshot_id, 1);
    }

    /// Reserve the next snapshot id the way `begin_write_transaction` now does
    /// (bump the monotonic counter WITHOUT inserting the row), so a subsequent
    /// `register_data_file` can insert it atomically at the commit. Tests that
    /// drive `register_data_file` directly must reserve (not `create_snapshot`)
    /// the snapshot they register, since registration owns the snapshot insert.
    async fn reserve_snapshot(writer: &SqliteMetadataWriter) -> i64 {
        sqlx::query("SELECT COALESCE(MAX(snapshot_id), 0) + 1 FROM ducklake_snapshot")
            .fetch_one(&writer.pool)
            .await
            .unwrap()
            .try_get(0)
            .unwrap()
    }

    /// Helper for the row-lineage tests: read back what `register_data_file`
    /// wrote into the catalog so we can assert on `row_id_start` and the
    /// stats counter directly.
    async fn read_row_id_start(writer: &SqliteMetadataWriter, path: &str) -> Option<i64> {
        let row = sqlx::query("SELECT row_id_start FROM ducklake_data_file WHERE path = ?")
            .bind(path)
            .fetch_one(&writer.pool)
            .await
            .unwrap();
        row.try_get(0).ok()
    }

    async fn read_table_stats(writer: &SqliteMetadataWriter, table_id: i64) -> (i64, i64, i64) {
        let row = sqlx::query(
            "SELECT record_count, next_row_id, file_size_bytes
             FROM ducklake_table_stats WHERE table_id = ?",
        )
        .bind(table_id)
        .fetch_one(&writer.pool)
        .await
        .unwrap();
        (
            row.try_get(0).unwrap(),
            row.try_get(1).unwrap(),
            row.try_get(2).unwrap(),
        )
    }

    #[allow(clippy::type_complexity)]
    async fn read_file_column_stats(
        writer: &SqliteMetadataWriter,
        path: &str,
    ) -> Vec<(
        i64,
        Option<String>,
        Option<String>,
        Option<i64>,
        Option<i64>,
    )> {
        sqlx::query(
            "SELECT s.column_id, s.min_value, s.max_value, s.null_count, s.value_count
             FROM ducklake_file_column_stats s
             JOIN ducklake_data_file d ON d.data_file_id = s.data_file_id
             WHERE d.path = ?
             ORDER BY s.column_id",
        )
        .bind(path)
        .fetch_all(&writer.pool)
        .await
        .unwrap()
        .into_iter()
        .map(|row| {
            (
                row.try_get(0).unwrap(),
                row.try_get(1).unwrap(),
                row.try_get(2).unwrap(),
                row.try_get(3).unwrap(),
                row.try_get(4).unwrap(),
            )
        })
        .collect()
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn register_data_file_persists_column_stats() {
        // register_data_file must write one ducklake_file_column_stats row per
        // ColumnStat carried on the DataFileInfo, tied to the new data file's id.
        let (writer, _temp) = create_test_writer().await;
        let snap = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap)
            .unwrap();

        let file = DataFileInfo::new("a.parquet", 100, 3).with_column_stats(vec![
            ColumnStat {
                column_id: 1,
                min_value: Some("1".to_string()),
                max_value: Some("3".to_string()),
                null_count: Some(0),
                value_count: Some(3),
                contains_nan: None,
                column_size_bytes: None,
            },
            ColumnStat {
                column_id: 2,
                min_value: Some("Alice".to_string()),
                max_value: Some("Bob".to_string()),
                null_count: Some(1),
                value_count: Some(2),
                contains_nan: None,
                column_size_bytes: None,
            },
        ]);
        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap,
                &file,
                WriteMode::Append,
                0,
                &[],
                &[],
            )
            .unwrap();

        let rows = read_file_column_stats(&writer, "a.parquet").await;
        assert_eq!(
            rows,
            vec![
                (
                    1,
                    Some("1".to_string()),
                    Some("3".to_string()),
                    Some(0),
                    Some(3)
                ),
                (
                    2,
                    Some("Alice".to_string()),
                    Some("Bob".to_string()),
                    Some(1),
                    Some(2)
                ),
            ]
        );
    }

    async fn read_table_column_stats(
        writer: &SqliteMetadataWriter,
        table_id: i64,
    ) -> Vec<(i64, Option<bool>, Option<String>, Option<String>)> {
        sqlx::query(
            "SELECT column_id, contains_null, min_value, max_value
             FROM ducklake_table_column_stats WHERE table_id = ? ORDER BY column_id",
        )
        .bind(table_id)
        .fetch_all(&writer.pool)
        .await
        .unwrap()
        .into_iter()
        .map(|row| {
            (
                row.try_get(0).unwrap(),
                row.try_get(1).unwrap(),
                row.try_get(2).unwrap(),
                row.try_get(3).unwrap(),
            )
        })
        .collect()
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn table_column_stats_widen_numerically_across_files() {
        // Two appends to a numeric column: global min/max must widen by numeric
        // value (min "9", max "10"), NOT lexically (which would rank "10" < "9").
        let (writer, _temp) = create_test_writer().await;
        let snap1 = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap1).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap1)
            .unwrap();
        let columns = vec![ColumnDef::new("n", "int32", true).unwrap()];
        let column_ids = vec![1_i64];

        let stat = |min: &str, max: &str, nulls: i64, vals: i64| {
            vec![ColumnStat {
                column_id: 1,
                min_value: Some(min.to_string()),
                max_value: Some(max.to_string()),
                null_count: Some(nulls),
                value_count: Some(vals),
                contains_nan: None,
                column_size_bytes: None,
            }]
        };

        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap1,
                &DataFileInfo::new("a.parquet", 100, 3).with_column_stats(stat("9", "9", 0, 3)),
                WriteMode::Append,
                0,
                &columns,
                &column_ids,
            )
            .unwrap();
        let snap2 = reserve_snapshot(&writer).await;
        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap2,
                &DataFileInfo::new("b.parquet", 100, 2).with_column_stats(stat("10", "10", 1, 1)),
                WriteMode::Append,
                0,
                &columns,
                &column_ids,
            )
            .unwrap();

        // Two files' per-file stats persisted.
        assert_eq!(read_file_column_stats(&writer, "a.parquet").await.len(), 1);
        assert_eq!(read_file_column_stats(&writer, "b.parquet").await.len(), 1);

        // Global roll-up widened numerically, and contains_null OR-reduced true.
        assert_eq!(
            read_table_column_stats(&writer, table_id).await,
            vec![(1, Some(true), Some("9".to_string()), Some("10".to_string()))]
        );
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn table_column_stats_null_when_a_live_file_lacks_stats() {
        // File A has stats (null-free); file B is statless (empty column_stats).
        // Even though A reports contains_null=false, the roll-up must degrade to
        // NULL (unknown) — otherwise a reader treats the column as proven
        // null-free and drops genuine IS NULL rows. (Review finding M1.)
        let (writer, _temp) = create_test_writer().await;
        let snap1 = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap1).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap1)
            .unwrap();
        let columns = vec![ColumnDef::new("n", "int32", true).unwrap()];
        let column_ids = vec![1_i64];

        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap1,
                &DataFileInfo::new("a.parquet", 100, 3).with_column_stats(vec![ColumnStat {
                    column_id: 1,
                    min_value: Some("1".to_string()),
                    max_value: Some("1".to_string()),
                    null_count: Some(0),
                    value_count: Some(3),
                    contains_nan: None,
                    column_size_bytes: None,
                }]),
                WriteMode::Append,
                0,
                &columns,
                &column_ids,
            )
            .unwrap();
        let snap2 = reserve_snapshot(&writer).await;
        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap2,
                // Statless: no per-column stats attached.
                &DataFileInfo::new("b.parquet", 100, 2),
                WriteMode::Append,
                0,
                &columns,
                &column_ids,
            )
            .unwrap();

        assert_eq!(
            read_table_column_stats(&writer, table_id).await,
            vec![(1, None, None, None)],
            "incomplete coverage (a statless live file) must NULL the roll-up"
        );
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn row_id_start_advances_across_inserts() {
        // Two INSERTs against the same table should hand out non-overlapping
        // [row_id_start, row_id_start + record_count) ranges. Counter is
        // initialized lazily on first register_data_file.
        let (writer, _temp) = create_test_writer().await;
        // Two separate writes (each registers its own snapshot atomically).
        let snap1 = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap1).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap1)
            .unwrap();

        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap1,
                &DataFileInfo::new("a.parquet", 100, 3),
                WriteMode::Append,
                0, // base_snapshot: unused for Append (no conflict check)
                &[],
                &[],
            )
            .unwrap();
        let snap2 = reserve_snapshot(&writer).await;
        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap2,
                &DataFileInfo::new("b.parquet", 250, 7),
                WriteMode::Append,
                0, // base_snapshot: unused for Append (no conflict check)
                &[],
                &[],
            )
            .unwrap();

        assert_eq!(read_row_id_start(&writer, "a.parquet").await, Some(0));
        assert_eq!(read_row_id_start(&writer, "b.parquet").await, Some(3));

        let (records, next, bytes) = read_table_stats(&writer, table_id).await;
        assert_eq!(records, 10, "record_count = 3 + 7");
        assert_eq!(next, 10, "next_row_id advances by sum of record_counts");
        assert_eq!(bytes, 350, "file_size_bytes accumulates");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn end_table_files_preserves_next_row_id() {
        // Replace must reset record_count and file_size_bytes (those reflect
        // currently-visible rows) but keep next_row_id monotonic so the next
        // generation of files gets fresh, non-overlapping rowids.
        let (writer, _temp) = create_test_writer().await;
        let snap1 = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer.get_or_create_schema("main", None, snap1).unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "t", None, snap1)
            .unwrap();

        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap1,
                &DataFileInfo::new("a.parquet", 100, 5),
                WriteMode::Append,
                0, // base_snapshot: unused for Append (no conflict check)
                &[],
                &[],
            )
            .unwrap();

        let snap2 = writer.create_snapshot().unwrap();
        writer.end_table_files(table_id, snap2).unwrap();

        let (records, next, bytes) = read_table_stats(&writer, table_id).await;
        assert_eq!(records, 0, "record_count cleared after end_table_files");
        assert_eq!(next, 5, "next_row_id preserved (monotonic across lifetime)");
        assert_eq!(bytes, 0, "file_size_bytes cleared");

        let snap3 = reserve_snapshot(&writer).await;
        writer
            .register_data_file(
                table_id,
                "main",
                "t",
                snap3,
                &DataFileInfo::new("b.parquet", 200, 2),
                WriteMode::Append,
                0, // base_snapshot: unused for Append (no conflict check)
                &[],
                &[],
            )
            .unwrap();
        assert_eq!(
            read_row_id_start(&writer, "b.parquet").await,
            Some(5),
            "post-replace files must start at the preserved counter, not 0",
        );
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn row_id_start_works_when_stats_row_missing() {
        // Defensive path: a table that existed before this writer started
        // maintaining ducklake_table_stats. First register_data_file must
        // self-initialize the stats row rather than fail.
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer
            .get_or_create_schema("main", None, snapshot_id)
            .unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "legacy", None, snapshot_id)
            .unwrap();

        // Simulate a "legacy" table by deleting any stats row that
        // get_or_create_table may have written (it doesn't today, but be
        // explicit so the test stays meaningful if that changes).
        sqlx::query("DELETE FROM ducklake_table_stats WHERE table_id = ?")
            .bind(table_id)
            .execute(&writer.pool)
            .await
            .unwrap();

        writer
            .register_data_file(
                table_id,
                "main",
                "legacy",
                snapshot_id,
                &DataFileInfo::new("a.parquet", 50, 4),
                WriteMode::Append,
                0, // base_snapshot: unused for Append (no conflict check)
                &[],
                &[],
            )
            .unwrap();
        assert_eq!(read_row_id_start(&writer, "a.parquet").await, Some(0));
        let (records, next, _) = read_table_stats(&writer, table_id).await;
        assert_eq!(records, 4);
        assert_eq!(next, 4);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_end_table_files() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot1 = reserve_snapshot(&writer).await;
        let (schema_id, _) = writer
            .get_or_create_schema("main", None, snapshot1)
            .unwrap();
        let (table_id, _) = writer
            .get_or_create_table(schema_id, "users", None, snapshot1)
            .unwrap();

        // Register a file
        let file = DataFileInfo::new("data1.parquet", 1024, 100);
        writer
            .register_data_file(
                table_id,
                "main",
                "users",
                snapshot1,
                &file,
                WriteMode::Append,
                0,
                &[],
                &[],
            )
            .unwrap();

        // End files at new snapshot
        let snapshot2 = writer.create_snapshot().unwrap();
        let ended = writer.end_table_files(table_id, snapshot2).unwrap();
        assert_eq!(ended, 1);

        // End again should affect 0 files
        let ended2 = writer.end_table_files(table_id, snapshot2).unwrap();
        assert_eq!(ended2, 0);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_data_path() {
        let (writer, _temp) = create_test_writer().await;

        // Set data path
        writer.set_data_path("/data/path").unwrap();

        // Get data path
        let path = writer.get_data_path().unwrap();
        assert_eq!(path, "/data/path");

        // Update data path
        writer.set_data_path("/new/path").unwrap();
        let path2 = writer.get_data_path().unwrap();
        assert_eq!(path2, "/new/path");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_get_or_create_schema_empty_name_rejected() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = writer.create_snapshot().unwrap();
        let result = writer.get_or_create_schema("", None, snapshot_id);
        assert!(result.is_err());
        let err_msg = format!("{}", result.unwrap_err());
        assert!(err_msg.contains("empty"), "Expected 'empty' in: {err_msg}");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_get_or_create_schema_control_char_rejected() {
        let (writer, _temp) = create_test_writer().await;
        let snapshot_id = writer.create_snapshot().unwrap();
        let result = writer.get_or_create_schema("bad\0schema", None, snapshot_id);
        assert!(result.is_err());
        let err_msg = format!("{}", result.unwrap_err());
        assert!(
            err_msg.contains("control character"),
            "Expected 'control character' in: {err_msg}"
        );
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_begin_write_transaction_empty_schema_name_rejected() {
        let (writer, _temp) = create_test_writer().await;
        let columns = vec![ColumnDef::new("id", "int64", false).unwrap()];
        let result = writer.begin_write_transaction("", "table", &columns, WriteMode::Replace);
        assert!(result.is_err());
        let err_msg = format!("{}", result.unwrap_err());
        assert!(err_msg.contains("empty"), "Expected 'empty' in: {err_msg}");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_begin_write_transaction_empty_table_name_rejected() {
        let (writer, _temp) = create_test_writer().await;
        let columns = vec![ColumnDef::new("id", "int64", false).unwrap()];
        let result = writer.begin_write_transaction("main", "", &columns, WriteMode::Replace);
        assert!(result.is_err());
        let err_msg = format!("{}", result.unwrap_err());
        assert!(err_msg.contains("empty"), "Expected 'empty' in: {err_msg}");
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_begin_write_transaction_control_char_names_rejected() {
        let (writer, _temp) = create_test_writer().await;
        let columns = vec![ColumnDef::new("id", "int64", false).unwrap()];

        // Control char in schema name
        let result =
            writer.begin_write_transaction("bad\nschema", "table", &columns, WriteMode::Replace);
        assert!(result.is_err());

        // Control char in table name
        let result =
            writer.begin_write_transaction("main", "bad\ttable", &columns, WriteMode::Replace);
        assert!(result.is_err());
    }
}