datafusion_python/common/
data_type.rs

1// Licensed to the Apache Software Foundation (ASF) under one
2// or more contributor license agreements.  See the NOTICE file
3// distributed with this work for additional information
4// regarding copyright ownership.  The ASF licenses this file
5// to you under the Apache License, Version 2.0 (the
6// "License"); you may not use this file except in compliance
7// with the License.  You may obtain a copy of the License at
8//
9//   http://www.apache.org/licenses/LICENSE-2.0
10//
11// Unless required by applicable law or agreed to in writing,
12// software distributed under the License is distributed on an
13// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14// KIND, either express or implied.  See the License for the
15// specific language governing permissions and limitations
16// under the License.
17
18use datafusion::arrow::array::Array;
19use datafusion::arrow::datatypes::{DataType, IntervalUnit, TimeUnit};
20use datafusion::common::{DataFusionError, ScalarValue};
21use datafusion::logical_expr::sqlparser::ast::NullTreatment as DFNullTreatment;
22use pyo3::{exceptions::PyValueError, prelude::*};
23
24use crate::errors::py_datafusion_err;
25
26#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd)]
27pub struct PyScalarValue(pub ScalarValue);
28
29impl From<ScalarValue> for PyScalarValue {
30    fn from(value: ScalarValue) -> Self {
31        Self(value)
32    }
33}
34impl From<PyScalarValue> for ScalarValue {
35    fn from(value: PyScalarValue) -> Self {
36        value.0
37    }
38}
39
40#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
41#[pyclass(eq, eq_int, name = "RexType", module = "datafusion.common")]
42pub enum RexType {
43    Alias,
44    Literal,
45    Call,
46    Reference,
47    ScalarSubquery,
48    Other,
49}
50
51/// These bindings are tying together several disparate systems.
52/// You have SQL types for the SQL strings and RDBMS systems itself.
53/// Rust types for the DataFusion code
54/// Arrow types which represents the underlying arrow format
55/// Python types which represent the type in Python
56/// It is important to keep all of those types in a single
57/// and manageable location. Therefore this structure exists
58/// to map those types and provide a simple place for developers
59/// to map types from one system to another.
60#[derive(Debug, Clone)]
61#[pyclass(name = "DataTypeMap", module = "datafusion.common", subclass)]
62pub struct DataTypeMap {
63    #[pyo3(get, set)]
64    pub arrow_type: PyDataType,
65    #[pyo3(get, set)]
66    pub python_type: PythonType,
67    #[pyo3(get, set)]
68    pub sql_type: SqlType,
69}
70
71impl DataTypeMap {
72    fn new(arrow_type: DataType, python_type: PythonType, sql_type: SqlType) -> Self {
73        DataTypeMap {
74            arrow_type: PyDataType {
75                data_type: arrow_type,
76            },
77            python_type,
78            sql_type,
79        }
80    }
81
82    pub fn map_from_arrow_type(arrow_type: &DataType) -> Result<DataTypeMap, PyErr> {
83        match arrow_type {
84            DataType::Null => Ok(DataTypeMap::new(
85                DataType::Null,
86                PythonType::None,
87                SqlType::NULL,
88            )),
89            DataType::Boolean => Ok(DataTypeMap::new(
90                DataType::Boolean,
91                PythonType::Bool,
92                SqlType::BOOLEAN,
93            )),
94            DataType::Int8 => Ok(DataTypeMap::new(
95                DataType::Int8,
96                PythonType::Int,
97                SqlType::TINYINT,
98            )),
99            DataType::Int16 => Ok(DataTypeMap::new(
100                DataType::Int16,
101                PythonType::Int,
102                SqlType::SMALLINT,
103            )),
104            DataType::Int32 => Ok(DataTypeMap::new(
105                DataType::Int32,
106                PythonType::Int,
107                SqlType::INTEGER,
108            )),
109            DataType::Int64 => Ok(DataTypeMap::new(
110                DataType::Int64,
111                PythonType::Int,
112                SqlType::BIGINT,
113            )),
114            DataType::UInt8 => Ok(DataTypeMap::new(
115                DataType::UInt8,
116                PythonType::Int,
117                SqlType::TINYINT,
118            )),
119            DataType::UInt16 => Ok(DataTypeMap::new(
120                DataType::UInt16,
121                PythonType::Int,
122                SqlType::SMALLINT,
123            )),
124            DataType::UInt32 => Ok(DataTypeMap::new(
125                DataType::UInt32,
126                PythonType::Int,
127                SqlType::INTEGER,
128            )),
129            DataType::UInt64 => Ok(DataTypeMap::new(
130                DataType::UInt64,
131                PythonType::Int,
132                SqlType::BIGINT,
133            )),
134            DataType::Float16 => Ok(DataTypeMap::new(
135                DataType::Float16,
136                PythonType::Float,
137                SqlType::FLOAT,
138            )),
139            DataType::Float32 => Ok(DataTypeMap::new(
140                DataType::Float32,
141                PythonType::Float,
142                SqlType::FLOAT,
143            )),
144            DataType::Float64 => Ok(DataTypeMap::new(
145                DataType::Float64,
146                PythonType::Float,
147                SqlType::FLOAT,
148            )),
149            DataType::Timestamp(unit, tz) => Ok(DataTypeMap::new(
150                DataType::Timestamp(*unit, tz.clone()),
151                PythonType::Datetime,
152                SqlType::DATE,
153            )),
154            DataType::Date32 => Ok(DataTypeMap::new(
155                DataType::Date32,
156                PythonType::Datetime,
157                SqlType::DATE,
158            )),
159            DataType::Date64 => Ok(DataTypeMap::new(
160                DataType::Date64,
161                PythonType::Datetime,
162                SqlType::DATE,
163            )),
164            DataType::Time32(unit) => Ok(DataTypeMap::new(
165                DataType::Time32(*unit),
166                PythonType::Datetime,
167                SqlType::DATE,
168            )),
169            DataType::Time64(unit) => Ok(DataTypeMap::new(
170                DataType::Time64(*unit),
171                PythonType::Datetime,
172                SqlType::DATE,
173            )),
174            DataType::Duration(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
175                format!("{arrow_type:?}"),
176            ))),
177            DataType::Interval(interval_unit) => Ok(DataTypeMap::new(
178                DataType::Interval(*interval_unit),
179                PythonType::Datetime,
180                match interval_unit {
181                    IntervalUnit::DayTime => SqlType::INTERVAL_DAY,
182                    IntervalUnit::MonthDayNano => SqlType::INTERVAL_MONTH,
183                    IntervalUnit::YearMonth => SqlType::INTERVAL_YEAR_MONTH,
184                },
185            )),
186            DataType::Binary => Ok(DataTypeMap::new(
187                DataType::Binary,
188                PythonType::Bytes,
189                SqlType::BINARY,
190            )),
191            DataType::FixedSizeBinary(_) => Err(py_datafusion_err(
192                DataFusionError::NotImplemented(format!("{arrow_type:?}")),
193            )),
194            DataType::LargeBinary => Ok(DataTypeMap::new(
195                DataType::LargeBinary,
196                PythonType::Bytes,
197                SqlType::BINARY,
198            )),
199            DataType::Utf8 => Ok(DataTypeMap::new(
200                DataType::Utf8,
201                PythonType::Str,
202                SqlType::VARCHAR,
203            )),
204            DataType::LargeUtf8 => Ok(DataTypeMap::new(
205                DataType::LargeUtf8,
206                PythonType::Str,
207                SqlType::VARCHAR,
208            )),
209            DataType::List(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
210                "{arrow_type:?}"
211            )))),
212            DataType::FixedSizeList(_, _) => Err(py_datafusion_err(
213                DataFusionError::NotImplemented(format!("{arrow_type:?}")),
214            )),
215            DataType::LargeList(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
216                format!("{arrow_type:?}"),
217            ))),
218            DataType::Struct(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
219                format!("{arrow_type:?}"),
220            ))),
221            DataType::Union(_, _) => Err(py_datafusion_err(DataFusionError::NotImplemented(
222                format!("{arrow_type:?}"),
223            ))),
224            DataType::Dictionary(_, _) => Err(py_datafusion_err(DataFusionError::NotImplemented(
225                format!("{arrow_type:?}"),
226            ))),
227            DataType::Decimal128(precision, scale) => Ok(DataTypeMap::new(
228                DataType::Decimal128(*precision, *scale),
229                PythonType::Float,
230                SqlType::DECIMAL,
231            )),
232            DataType::Decimal256(precision, scale) => Ok(DataTypeMap::new(
233                DataType::Decimal256(*precision, *scale),
234                PythonType::Float,
235                SqlType::DECIMAL,
236            )),
237            DataType::Map(_, _) => Err(py_datafusion_err(DataFusionError::NotImplemented(
238                format!("{arrow_type:?}"),
239            ))),
240            DataType::RunEndEncoded(_, _) => Err(py_datafusion_err(
241                DataFusionError::NotImplemented(format!("{arrow_type:?}")),
242            )),
243            DataType::BinaryView => Err(py_datafusion_err(DataFusionError::NotImplemented(
244                format!("{arrow_type:?}"),
245            ))),
246            DataType::Utf8View => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
247                "{arrow_type:?}"
248            )))),
249            DataType::ListView(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
250                format!("{arrow_type:?}"),
251            ))),
252            DataType::LargeListView(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
253                format!("{arrow_type:?}"),
254            ))),
255        }
256    }
257
258    /// Generate the `DataTypeMap` from a `ScalarValue` instance
259    pub fn map_from_scalar_value(scalar_val: &ScalarValue) -> Result<DataTypeMap, PyErr> {
260        DataTypeMap::map_from_arrow_type(&DataTypeMap::map_from_scalar_to_arrow(scalar_val)?)
261    }
262
263    /// Maps a `ScalarValue` to an Arrow `DataType`
264    pub fn map_from_scalar_to_arrow(scalar_val: &ScalarValue) -> Result<DataType, PyErr> {
265        match scalar_val {
266            ScalarValue::Boolean(_) => Ok(DataType::Boolean),
267            ScalarValue::Float16(_) => Ok(DataType::Float16),
268            ScalarValue::Float32(_) => Ok(DataType::Float32),
269            ScalarValue::Float64(_) => Ok(DataType::Float64),
270            ScalarValue::Decimal128(_, precision, scale) => {
271                Ok(DataType::Decimal128(*precision, *scale))
272            }
273            ScalarValue::Decimal256(_, precision, scale) => {
274                Ok(DataType::Decimal256(*precision, *scale))
275            }
276            ScalarValue::Dictionary(data_type, scalar_type) => {
277                // Call this function again to map the dictionary scalar_value to an Arrow type
278                Ok(DataType::Dictionary(
279                    Box::new(*data_type.clone()),
280                    Box::new(DataTypeMap::map_from_scalar_to_arrow(scalar_type)?),
281                ))
282            }
283            ScalarValue::Int8(_) => Ok(DataType::Int8),
284            ScalarValue::Int16(_) => Ok(DataType::Int16),
285            ScalarValue::Int32(_) => Ok(DataType::Int32),
286            ScalarValue::Int64(_) => Ok(DataType::Int64),
287            ScalarValue::UInt8(_) => Ok(DataType::UInt8),
288            ScalarValue::UInt16(_) => Ok(DataType::UInt16),
289            ScalarValue::UInt32(_) => Ok(DataType::UInt32),
290            ScalarValue::UInt64(_) => Ok(DataType::UInt64),
291            ScalarValue::Utf8(_) => Ok(DataType::Utf8),
292            ScalarValue::LargeUtf8(_) => Ok(DataType::LargeUtf8),
293            ScalarValue::Binary(_) => Ok(DataType::Binary),
294            ScalarValue::LargeBinary(_) => Ok(DataType::LargeBinary),
295            ScalarValue::Date32(_) => Ok(DataType::Date32),
296            ScalarValue::Date64(_) => Ok(DataType::Date64),
297            ScalarValue::Time32Second(_) => Ok(DataType::Time32(TimeUnit::Second)),
298            ScalarValue::Time32Millisecond(_) => Ok(DataType::Time32(TimeUnit::Millisecond)),
299            ScalarValue::Time64Microsecond(_) => Ok(DataType::Time64(TimeUnit::Microsecond)),
300            ScalarValue::Time64Nanosecond(_) => Ok(DataType::Time64(TimeUnit::Nanosecond)),
301            ScalarValue::Null => Ok(DataType::Null),
302            ScalarValue::TimestampSecond(_, tz) => {
303                Ok(DataType::Timestamp(TimeUnit::Second, tz.to_owned()))
304            }
305            ScalarValue::TimestampMillisecond(_, tz) => {
306                Ok(DataType::Timestamp(TimeUnit::Millisecond, tz.to_owned()))
307            }
308            ScalarValue::TimestampMicrosecond(_, tz) => {
309                Ok(DataType::Timestamp(TimeUnit::Microsecond, tz.to_owned()))
310            }
311            ScalarValue::TimestampNanosecond(_, tz) => {
312                Ok(DataType::Timestamp(TimeUnit::Nanosecond, tz.to_owned()))
313            }
314            ScalarValue::IntervalYearMonth(..) => Ok(DataType::Interval(IntervalUnit::YearMonth)),
315            ScalarValue::IntervalDayTime(..) => Ok(DataType::Interval(IntervalUnit::DayTime)),
316            ScalarValue::IntervalMonthDayNano(..) => {
317                Ok(DataType::Interval(IntervalUnit::MonthDayNano))
318            }
319            ScalarValue::List(arr) => Ok(arr.data_type().to_owned()),
320            ScalarValue::Struct(_fields) => Err(py_datafusion_err(
321                DataFusionError::NotImplemented("ScalarValue::Struct".to_string()),
322            )),
323            ScalarValue::FixedSizeBinary(size, _) => Ok(DataType::FixedSizeBinary(*size)),
324            ScalarValue::FixedSizeList(_array_ref) => {
325                // The FieldRef was removed from ScalarValue::FixedSizeList in
326                // https://github.com/apache/arrow-datafusion/pull/8221, so we can no
327                // longer convert back to a DataType here
328                Err(py_datafusion_err(DataFusionError::NotImplemented(
329                    "ScalarValue::FixedSizeList".to_string(),
330                )))
331            }
332            ScalarValue::LargeList(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
333                "ScalarValue::LargeList".to_string(),
334            ))),
335            ScalarValue::DurationSecond(_) => Ok(DataType::Duration(TimeUnit::Second)),
336            ScalarValue::DurationMillisecond(_) => Ok(DataType::Duration(TimeUnit::Millisecond)),
337            ScalarValue::DurationMicrosecond(_) => Ok(DataType::Duration(TimeUnit::Microsecond)),
338            ScalarValue::DurationNanosecond(_) => Ok(DataType::Duration(TimeUnit::Nanosecond)),
339            ScalarValue::Union(_, _, _) => Err(py_datafusion_err(DataFusionError::NotImplemented(
340                "ScalarValue::LargeList".to_string(),
341            ))),
342            ScalarValue::Utf8View(_) => Ok(DataType::Utf8View),
343            ScalarValue::BinaryView(_) => Ok(DataType::BinaryView),
344            ScalarValue::Map(_) => Err(py_datafusion_err(DataFusionError::NotImplemented(
345                "ScalarValue::Map".to_string(),
346            ))),
347        }
348    }
349}
350
351#[pymethods]
352impl DataTypeMap {
353    #[new]
354    pub fn py_new(arrow_type: PyDataType, python_type: PythonType, sql_type: SqlType) -> Self {
355        DataTypeMap {
356            arrow_type,
357            python_type,
358            sql_type,
359        }
360    }
361
362    #[staticmethod]
363    #[pyo3(name = "from_parquet_type_str")]
364    /// When using pyarrow.parquet.read_metadata().schema.column(x).physical_type you are presented
365    /// with a String type for schema rather than an object type. Here we make a best effort
366    /// to convert that to a physical type.
367    pub fn py_map_from_parquet_type_str(parquet_str_type: String) -> PyResult<DataTypeMap> {
368        let arrow_dtype = match parquet_str_type.to_lowercase().as_str() {
369            "boolean" => Ok(DataType::Boolean),
370            "int32" => Ok(DataType::Int32),
371            "int64" => Ok(DataType::Int64),
372            "int96" => {
373                // Int96 is an old parquet datatype that is now deprecated. We convert to nanosecond timestamp
374                Ok(DataType::Timestamp(TimeUnit::Nanosecond, None))
375            }
376            "float" => Ok(DataType::Float32),
377            "double" => Ok(DataType::Float64),
378            "byte_array" => Ok(DataType::Utf8),
379            _ => Err(PyValueError::new_err(format!(
380                "Unable to determine Arrow Data Type from Parquet String type: {parquet_str_type:?}"
381            ))),
382        };
383        DataTypeMap::map_from_arrow_type(&arrow_dtype?)
384    }
385
386    #[staticmethod]
387    #[pyo3(name = "arrow")]
388    pub fn py_map_from_arrow_type(arrow_type: &PyDataType) -> PyResult<DataTypeMap> {
389        DataTypeMap::map_from_arrow_type(&arrow_type.data_type)
390    }
391
392    #[staticmethod]
393    #[pyo3(name = "arrow_str")]
394    pub fn py_map_from_arrow_type_str(arrow_type_str: String) -> PyResult<DataTypeMap> {
395        let data_type = PyDataType::py_map_from_arrow_type_str(arrow_type_str);
396        DataTypeMap::map_from_arrow_type(&data_type?.data_type)
397    }
398
399    #[staticmethod]
400    #[pyo3(name = "sql")]
401    pub fn py_map_from_sql_type(sql_type: &SqlType) -> PyResult<DataTypeMap> {
402        match sql_type {
403            SqlType::ANY => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
404                "{sql_type:?}"
405            )))),
406            SqlType::ARRAY => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
407                "{sql_type:?}"
408            )))),
409            SqlType::BIGINT => Ok(DataTypeMap::new(
410                DataType::Int64,
411                PythonType::Int,
412                SqlType::BIGINT,
413            )),
414            SqlType::BINARY => Ok(DataTypeMap::new(
415                DataType::Binary,
416                PythonType::Bytes,
417                SqlType::BINARY,
418            )),
419            SqlType::BOOLEAN => Ok(DataTypeMap::new(
420                DataType::Boolean,
421                PythonType::Bool,
422                SqlType::BOOLEAN,
423            )),
424            SqlType::CHAR => Ok(DataTypeMap::new(
425                DataType::UInt8,
426                PythonType::Int,
427                SqlType::CHAR,
428            )),
429            SqlType::COLUMN_LIST => Err(py_datafusion_err(DataFusionError::NotImplemented(
430                format!("{sql_type:?}"),
431            ))),
432            SqlType::CURSOR => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
433                "{sql_type:?}"
434            )))),
435            SqlType::DATE => Ok(DataTypeMap::new(
436                DataType::Date64,
437                PythonType::Datetime,
438                SqlType::DATE,
439            )),
440            SqlType::DECIMAL => Ok(DataTypeMap::new(
441                DataType::Decimal128(1, 1),
442                PythonType::Float,
443                SqlType::DECIMAL,
444            )),
445            SqlType::DISTINCT => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
446                "{sql_type:?}"
447            )))),
448            SqlType::DOUBLE => Ok(DataTypeMap::new(
449                DataType::Decimal256(1, 1),
450                PythonType::Float,
451                SqlType::DOUBLE,
452            )),
453            SqlType::DYNAMIC_STAR => Err(py_datafusion_err(DataFusionError::NotImplemented(
454                format!("{sql_type:?}"),
455            ))),
456            SqlType::FLOAT => Ok(DataTypeMap::new(
457                DataType::Decimal128(1, 1),
458                PythonType::Float,
459                SqlType::FLOAT,
460            )),
461            SqlType::GEOMETRY => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
462                "{sql_type:?}"
463            )))),
464            SqlType::INTEGER => Ok(DataTypeMap::new(
465                DataType::Int8,
466                PythonType::Int,
467                SqlType::INTEGER,
468            )),
469            SqlType::INTERVAL => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
470                "{sql_type:?}"
471            )))),
472            SqlType::INTERVAL_DAY => Err(py_datafusion_err(DataFusionError::NotImplemented(
473                format!("{sql_type:?}"),
474            ))),
475            SqlType::INTERVAL_DAY_HOUR => Err(py_datafusion_err(DataFusionError::NotImplemented(
476                format!("{sql_type:?}"),
477            ))),
478            SqlType::INTERVAL_DAY_MINUTE => Err(py_datafusion_err(
479                DataFusionError::NotImplemented(format!("{sql_type:?}")),
480            )),
481            SqlType::INTERVAL_DAY_SECOND => Err(py_datafusion_err(
482                DataFusionError::NotImplemented(format!("{sql_type:?}")),
483            )),
484            SqlType::INTERVAL_HOUR => Err(py_datafusion_err(DataFusionError::NotImplemented(
485                format!("{sql_type:?}"),
486            ))),
487            SqlType::INTERVAL_HOUR_MINUTE => Err(py_datafusion_err(
488                DataFusionError::NotImplemented(format!("{sql_type:?}")),
489            )),
490            SqlType::INTERVAL_HOUR_SECOND => Err(py_datafusion_err(
491                DataFusionError::NotImplemented(format!("{sql_type:?}")),
492            )),
493            SqlType::INTERVAL_MINUTE => Err(py_datafusion_err(DataFusionError::NotImplemented(
494                format!("{sql_type:?}"),
495            ))),
496            SqlType::INTERVAL_MINUTE_SECOND => Err(py_datafusion_err(
497                DataFusionError::NotImplemented(format!("{sql_type:?}")),
498            )),
499            SqlType::INTERVAL_MONTH => Err(py_datafusion_err(DataFusionError::NotImplemented(
500                format!("{sql_type:?}"),
501            ))),
502            SqlType::INTERVAL_SECOND => Err(py_datafusion_err(DataFusionError::NotImplemented(
503                format!("{sql_type:?}"),
504            ))),
505            SqlType::INTERVAL_YEAR => Err(py_datafusion_err(DataFusionError::NotImplemented(
506                format!("{sql_type:?}"),
507            ))),
508            SqlType::INTERVAL_YEAR_MONTH => Err(py_datafusion_err(
509                DataFusionError::NotImplemented(format!("{sql_type:?}")),
510            )),
511            SqlType::MAP => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
512                "{sql_type:?}"
513            )))),
514            SqlType::MULTISET => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
515                "{sql_type:?}"
516            )))),
517            SqlType::NULL => Ok(DataTypeMap::new(
518                DataType::Null,
519                PythonType::None,
520                SqlType::NULL,
521            )),
522            SqlType::OTHER => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
523                "{sql_type:?}"
524            )))),
525            SqlType::REAL => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
526                "{sql_type:?}"
527            )))),
528            SqlType::ROW => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
529                "{sql_type:?}"
530            )))),
531            SqlType::SARG => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
532                "{sql_type:?}"
533            )))),
534            SqlType::SMALLINT => Ok(DataTypeMap::new(
535                DataType::Int16,
536                PythonType::Int,
537                SqlType::SMALLINT,
538            )),
539            SqlType::STRUCTURED => Err(py_datafusion_err(DataFusionError::NotImplemented(
540                format!("{sql_type:?}"),
541            ))),
542            SqlType::SYMBOL => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
543                "{sql_type:?}"
544            )))),
545            SqlType::TIME => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
546                "{sql_type:?}"
547            )))),
548            SqlType::TIME_WITH_LOCAL_TIME_ZONE => Err(py_datafusion_err(
549                DataFusionError::NotImplemented(format!("{sql_type:?}")),
550            )),
551            SqlType::TIMESTAMP => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
552                "{sql_type:?}"
553            )))),
554            SqlType::TIMESTAMP_WITH_LOCAL_TIME_ZONE => Err(py_datafusion_err(
555                DataFusionError::NotImplemented(format!("{sql_type:?}")),
556            )),
557            SqlType::TINYINT => Ok(DataTypeMap::new(
558                DataType::Int8,
559                PythonType::Int,
560                SqlType::TINYINT,
561            )),
562            SqlType::UNKNOWN => Err(py_datafusion_err(DataFusionError::NotImplemented(format!(
563                "{sql_type:?}"
564            )))),
565            SqlType::VARBINARY => Ok(DataTypeMap::new(
566                DataType::LargeBinary,
567                PythonType::Bytes,
568                SqlType::VARBINARY,
569            )),
570            SqlType::VARCHAR => Ok(DataTypeMap::new(
571                DataType::Utf8,
572                PythonType::Str,
573                SqlType::VARCHAR,
574            )),
575        }
576    }
577
578    /// Unfortunately PyO3 does not allow for us to expose the DataType as an enum since
579    /// we cannot directly annotae the Enum instance of dependency code. Therefore, here
580    /// we provide an enum to mimic it.
581    #[pyo3(name = "friendly_arrow_type_name")]
582    pub fn friendly_arrow_type_name(&self) -> PyResult<&str> {
583        Ok(match &self.arrow_type.data_type {
584            DataType::Null => "Null",
585            DataType::Boolean => "Boolean",
586            DataType::Int8 => "Int8",
587            DataType::Int16 => "Int16",
588            DataType::Int32 => "Int32",
589            DataType::Int64 => "Int64",
590            DataType::UInt8 => "UInt8",
591            DataType::UInt16 => "UInt16",
592            DataType::UInt32 => "UInt32",
593            DataType::UInt64 => "UInt64",
594            DataType::Float16 => "Float16",
595            DataType::Float32 => "Float32",
596            DataType::Float64 => "Float64",
597            DataType::Timestamp(_, _) => "Timestamp",
598            DataType::Date32 => "Date32",
599            DataType::Date64 => "Date64",
600            DataType::Time32(_) => "Time32",
601            DataType::Time64(_) => "Time64",
602            DataType::Duration(_) => "Duration",
603            DataType::Interval(_) => "Interval",
604            DataType::Binary => "Binary",
605            DataType::FixedSizeBinary(_) => "FixedSizeBinary",
606            DataType::LargeBinary => "LargeBinary",
607            DataType::Utf8 => "Utf8",
608            DataType::LargeUtf8 => "LargeUtf8",
609            DataType::List(_) => "List",
610            DataType::FixedSizeList(_, _) => "FixedSizeList",
611            DataType::LargeList(_) => "LargeList",
612            DataType::Struct(_) => "Struct",
613            DataType::Union(_, _) => "Union",
614            DataType::Dictionary(_, _) => "Dictionary",
615            DataType::Decimal128(_, _) => "Decimal128",
616            DataType::Decimal256(_, _) => "Decimal256",
617            DataType::Map(_, _) => "Map",
618            DataType::RunEndEncoded(_, _) => "RunEndEncoded",
619            DataType::BinaryView => "BinaryView",
620            DataType::Utf8View => "Utf8View",
621            DataType::ListView(_) => "ListView",
622            DataType::LargeListView(_) => "LargeListView",
623        })
624    }
625}
626
627/// PyO3 requires that objects passed between Rust and Python implement the trait `PyClass`
628/// Since `DataType` exists in another package we cannot make that happen here so we wrap
629/// `DataType` as `PyDataType` This exists solely to satisfy those constraints.
630#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
631#[pyclass(name = "DataType", module = "datafusion.common")]
632pub struct PyDataType {
633    pub data_type: DataType,
634}
635
636impl PyDataType {
637    /// There are situations when obtaining dtypes on the Python side where the Arrow type
638    /// is presented as a String rather than an actual DataType. This function is used to
639    /// convert that String to a DataType for the Python side to use.
640    pub fn py_map_from_arrow_type_str(arrow_str_type: String) -> PyResult<PyDataType> {
641        // Certain string types contain "metadata" that should be trimmed here. Ex: "datetime64[ns, Europe/Berlin]"
642        let arrow_str_type = match arrow_str_type.find('[') {
643            Some(index) => arrow_str_type[0..index].to_string(),
644            None => arrow_str_type, // Return early if ',' is not found.
645        };
646
647        let arrow_dtype = match arrow_str_type.to_lowercase().as_str() {
648            "bool" => Ok(DataType::Boolean),
649            "boolean" => Ok(DataType::Boolean),
650            "uint8" => Ok(DataType::UInt8),
651            "uint16" => Ok(DataType::UInt16),
652            "uint32" => Ok(DataType::UInt32),
653            "uint64" => Ok(DataType::UInt64),
654            "int8" => Ok(DataType::Int8),
655            "int16" => Ok(DataType::Int16),
656            "int32" => Ok(DataType::Int32),
657            "int64" => Ok(DataType::Int64),
658            "float" => Ok(DataType::Float32),
659            "double" => Ok(DataType::Float64),
660            "float16" => Ok(DataType::Float16),
661            "float32" => Ok(DataType::Float32),
662            "float64" => Ok(DataType::Float64),
663            "datetime64" => Ok(DataType::Date64),
664            "object" => Ok(DataType::Utf8),
665            _ => Err(PyValueError::new_err(format!(
666                "Unable to determine Arrow Data Type from Arrow String type: {arrow_str_type:?}"
667            ))),
668        };
669        Ok(PyDataType {
670            data_type: arrow_dtype?,
671        })
672    }
673}
674
675impl From<PyDataType> for DataType {
676    fn from(data_type: PyDataType) -> DataType {
677        data_type.data_type
678    }
679}
680
681impl From<DataType> for PyDataType {
682    fn from(data_type: DataType) -> PyDataType {
683        PyDataType { data_type }
684    }
685}
686
687/// Represents the possible Python types that can be mapped to the SQL types
688#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
689#[pyclass(eq, eq_int, name = "PythonType", module = "datafusion.common")]
690pub enum PythonType {
691    Array,
692    Bool,
693    Bytes,
694    Datetime,
695    Float,
696    Int,
697    List,
698    None,
699    Object,
700    Str,
701}
702
703/// Represents the types that are possible for DataFusion to parse
704/// from a SQL query. Aka "SqlType" and are valid values for
705/// ANSI SQL
706#[allow(non_camel_case_types)]
707#[allow(clippy::upper_case_acronyms)]
708#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
709#[pyclass(eq, eq_int, name = "SqlType", module = "datafusion.common")]
710pub enum SqlType {
711    ANY,
712    ARRAY,
713    BIGINT,
714    BINARY,
715    BOOLEAN,
716    CHAR,
717    COLUMN_LIST,
718    CURSOR,
719    DATE,
720    DECIMAL,
721    DISTINCT,
722    DOUBLE,
723    DYNAMIC_STAR,
724    FLOAT,
725    GEOMETRY,
726    INTEGER,
727    INTERVAL,
728    INTERVAL_DAY,
729    INTERVAL_DAY_HOUR,
730    INTERVAL_DAY_MINUTE,
731    INTERVAL_DAY_SECOND,
732    INTERVAL_HOUR,
733    INTERVAL_HOUR_MINUTE,
734    INTERVAL_HOUR_SECOND,
735    INTERVAL_MINUTE,
736    INTERVAL_MINUTE_SECOND,
737    INTERVAL_MONTH,
738    INTERVAL_SECOND,
739    INTERVAL_YEAR,
740    INTERVAL_YEAR_MONTH,
741    MAP,
742    MULTISET,
743    NULL,
744    OTHER,
745    REAL,
746    ROW,
747    SARG,
748    SMALLINT,
749    STRUCTURED,
750    SYMBOL,
751    TIME,
752    TIME_WITH_LOCAL_TIME_ZONE,
753    TIMESTAMP,
754    TIMESTAMP_WITH_LOCAL_TIME_ZONE,
755    TINYINT,
756    UNKNOWN,
757    VARBINARY,
758    VARCHAR,
759}
760
761/// Specifies Ignore / Respect NULL within window functions.
762/// For example
763/// `FIRST_VALUE(column2) IGNORE NULLS OVER (PARTITION BY column1)`
764#[allow(non_camel_case_types)]
765#[allow(clippy::upper_case_acronyms)]
766#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
767#[pyclass(eq, eq_int, name = "NullTreatment", module = "datafusion.common")]
768pub enum NullTreatment {
769    IGNORE_NULLS,
770    RESPECT_NULLS,
771}
772
773impl From<NullTreatment> for DFNullTreatment {
774    fn from(null_treatment: NullTreatment) -> DFNullTreatment {
775        match null_treatment {
776            NullTreatment::IGNORE_NULLS => DFNullTreatment::IgnoreNulls,
777            NullTreatment::RESPECT_NULLS => DFNullTreatment::RespectNulls,
778        }
779    }
780}
781
782impl From<DFNullTreatment> for NullTreatment {
783    fn from(null_treatment: DFNullTreatment) -> NullTreatment {
784        match null_treatment {
785            DFNullTreatment::IgnoreNulls => NullTreatment::IGNORE_NULLS,
786            DFNullTreatment::RespectNulls => NullTreatment::RESPECT_NULLS,
787        }
788    }
789}
datafusion_python/common/data_type.rs

datafusion_python/common/
data_type.rs
