velesdb-core 1.7.2

//! CRUD operations for Collection (upsert, get, delete).
//!
//! Quantization caching helpers and secondary-index update helpers are in `crud_helpers.rs`.

use crate::collection::types::Collection;
use crate::error::{Error, Result};
use crate::index::VectorIndex;
use crate::point::Point;
use crate::quantization::{BinaryQuantizedVector, PQVector, QuantizedVector, StorageMode};
use crate::storage::{LogPayloadStorage, PayloadStorage, VectorStorage};
use crate::validation::validate_dimension_match;

use parking_lot::RwLockWriteGuard;
use std::collections::{BTreeMap, HashMap};

struct QuantizationGuards<'a> {
    sq8: Option<RwLockWriteGuard<'a, HashMap<u64, QuantizedVector>>>,
    binary: Option<RwLockWriteGuard<'a, HashMap<u64, BinaryQuantizedVector>>>,
    pq: Option<RwLockWriteGuard<'a, HashMap<u64, PQVector>>>,
}

impl<'a> QuantizationGuards<'a> {
    fn acquire(collection: &'a Collection, mode: StorageMode) -> Self {
        Self {
            sq8: matches!(mode, StorageMode::SQ8).then(|| collection.sq8_cache.write()),
            binary: matches!(mode, StorageMode::Binary).then(|| collection.binary_cache.write()),
            pq: matches!(mode, StorageMode::ProductQuantization)
                .then(|| collection.pq_cache.write()),
        }
    }
}

impl Collection {
    /// Inserts or updates points in the collection.
    ///
    /// Accepts any iterator of points (Vec, slice, array, etc.)
    ///
    /// # Errors
    ///
    /// Returns an error if any point has a mismatched dimension, or if
    /// attempting to insert vectors into a metadata-only collection.
    pub fn upsert(&self, points: impl IntoIterator<Item = Point>) -> Result<()> {
        let points: Vec<Point> = points.into_iter().collect();
        let config = self.config.read();
        let dimension = config.dimension;
        let storage_mode = config.storage_mode;

        if config.metadata_only {
            for point in &points {
                if !point.vector.is_empty() {
                    return Err(Error::VectorNotAllowed(config.name.clone()));
                }
            }
            drop(config);
            return self.upsert_metadata(points);
        }
        drop(config);

        for point in &points {
            validate_dimension_match(dimension, point.dimension())?;
        }

        let sparse_batch = self.upsert_storage_and_index(&points, storage_mode)?;

        self.apply_sparse_batch_upsert(&sparse_batch)?;
        self.invalidate_caches_and_bump_generation();
        Ok(())
    }

    /// Stores vectors, payloads, and indexes for a batch of points.
    ///
    /// Three-phase pipeline to minimize lock contention and I/O:
    /// 1. Batch storage: `store_batch()` for vectors + payloads (1 fsync each)
    /// 2. Per-point updates: secondary indexes, quantization, text, sparse
    /// 3. Batch HNSW insert via `bulk_index_or_defer()`
    ///
    /// # Crash Recovery
    ///
    /// A crash between Phase 1 and Phase 3 leaves vectors durably stored but
    /// absent from the HNSW index. On the next `Collection::open()`, gap
    /// detection compares storage IDs against HNSW mappings and re-indexes
    /// any missing vectors. The recovery window is bounded by one batch.
    ///
    /// Returns buffered sparse vectors for deferred insertion.
    fn upsert_storage_and_index(
        &self,
        points: &[Point],
        storage_mode: StorageMode,
    ) -> Result<Vec<(u64, BTreeMap<String, crate::index::sparse::SparseVector>)>> {
        // Phase 1: Batch storage under write locks (1 fsync per storage)
        let old_payloads = self.batch_store_all(points)?;

        // Phase 2: Per-point updates (no storage locks held)
        let sparse_batch = self.per_point_updates(points, &old_payloads, storage_mode);

        // Phase 3: Batch HNSW insert
        let vector_refs: Vec<(u64, &[f32])> =
            points.iter().map(|p| (p.id, p.vector.as_slice())).collect();
        self.bulk_index_or_defer(vector_refs);

        Ok(sparse_batch)
    }

    /// Phase 1: Batch-stores vectors and payloads with minimal lock scope.
    ///
    /// Pre-collects old payloads (needed for secondary index updates),
    /// then writes all vectors and payloads in single batch calls (1 fsync each).
    ///
    /// Deduplicates intra-batch duplicate IDs using last-writer-wins semantics:
    /// only the final occurrence per ID is written to the WAL, avoiding wasteful
    /// intermediate entries that would bloat the log and slow replay.
    ///
    /// After this method returns, vectors and payloads are durable on disk.
    /// A crash before Phase 3 (HNSW insertion) is recovered by gap detection
    /// on the next `Collection::open()`.
    ///
    /// Returns the old payloads for Phase 2.
    fn batch_store_all(&self, points: &[Point]) -> Result<Vec<Option<serde_json::Value>>> {
        let mut payload_storage = self.payload_storage.write();

        // Pre-collect old payloads, retrieving from storage only on the first
        // occurrence of each ID. Subsequent occurrences get None because Phase 2
        // (`per_point_updates`) uses `seen_payloads` to track intra-batch state.
        let old_payloads = Self::collect_old_payloads(points, &payload_storage);

        Self::write_deduped_payloads(points, &mut payload_storage)?;
        payload_storage.flush()?;
        drop(payload_storage);

        self.write_deduped_vectors(points)?;

        Ok(old_payloads)
    }

    /// Retrieves pre-batch payloads, querying storage only once per unique ID.
    ///
    /// For intra-batch duplicates, only the first occurrence needs the pre-batch
    /// value; subsequent occurrences are handled by `seen_payloads` in Phase 2.
    fn collect_old_payloads(
        points: &[Point],
        storage: &LogPayloadStorage,
    ) -> Vec<Option<serde_json::Value>> {
        let mut seen = std::collections::HashSet::new();
        points
            .iter()
            .map(|p| {
                if seen.insert(p.id) {
                    // First occurrence — retrieve pre-batch payload from storage
                    storage.retrieve(p.id).ok().flatten()
                } else {
                    None // Duplicate — Phase 2 uses seen_payloads instead
                }
            })
            .collect()
    }

    /// Writes only the last payload per ID to the WAL, then deletes IDs whose
    /// final occurrence has `payload=None`.
    fn write_deduped_payloads(points: &[Point], storage: &mut LogPayloadStorage) -> Result<()> {
        // Build last-writer-wins map: id -> (has_payload, index_of_last_occurrence)
        let mut last_idx: HashMap<u64, usize> = HashMap::new();
        for (i, p) in points.iter().enumerate() {
            last_idx.insert(p.id, i);
        }

        // Only write the final payload per ID (skip intermediate duplicates)
        let deduped: Vec<(u64, &serde_json::Value)> = points
            .iter()
            .enumerate()
            .filter(|&(i, p)| last_idx.get(&p.id) == Some(&i) && p.payload.is_some())
            .filter_map(|(_, p)| p.payload.as_ref().map(|pl| (p.id, pl)))
            .collect();
        storage.store_batch(&deduped)?;

        // Delete IDs whose final occurrence has payload=None
        for (i, p) in points.iter().enumerate() {
            if last_idx.get(&p.id) == Some(&i) && p.payload.is_none() {
                let _ = storage.delete(p.id);
            }
        }
        Ok(())
    }

    /// Writes only the last vector per ID to vector storage.
    fn write_deduped_vectors(&self, points: &[Point]) -> Result<()> {
        let mut last_idx: HashMap<u64, usize> = HashMap::new();
        for (i, p) in points.iter().enumerate() {
            last_idx.insert(p.id, i);
        }

        let deduped: Vec<(u64, &[f32])> = points
            .iter()
            .enumerate()
            .filter(|&(i, p)| last_idx.get(&p.id) == Some(&i))
            .map(|(_, p)| (p.id, p.vector.as_slice()))
            .collect();

        let mut vector_storage = self.vector_storage.write();
        vector_storage.store_batch(&deduped)?;
        let point_count = vector_storage.len();
        vector_storage.flush()?;
        drop(vector_storage);

        self.config.write().point_count = point_count;
        Ok(())
    }

    /// Phase 2: Per-point updates that don't need storage write locks.
    ///
    /// Tracks the effective "old payload" per ID to handle within-batch
    /// duplicates correctly: when id=5 appears twice, the second occurrence
    /// sees the first occurrence's payload as its "old" (not the pre-batch
    /// original), ensuring secondary indexes stay consistent.
    fn per_point_updates(
        &self,
        points: &[Point],
        old_payloads: &[Option<serde_json::Value>],
        storage_mode: StorageMode,
    ) -> Vec<(u64, BTreeMap<String, crate::index::sparse::SparseVector>)> {
        let mut quant_guards = QuantizationGuards::acquire(self, storage_mode);
        let mut sparse_batch = Vec::new();
        // Track effective old payload per ID for within-batch duplicate handling.
        // When id=5 appears twice, the second occurrence uses the first's payload
        // as "old" — not the pre-batch original — so secondary indexes stay correct.
        //
        // Uses `Option<Option<&Value>>`: outer Option = "seen this ID?",
        // inner Option = "had a payload?". This distinguishes "seen with None"
        // from "not seen" — `.flatten()` would collapse both to None.
        let mut seen_payloads: HashMap<u64, Option<&serde_json::Value>> = HashMap::new();

        for (point, pre_batch_old) in points.iter().zip(old_payloads) {
            let effective_old: Option<&serde_json::Value> =
                if let Some(&inner) = seen_payloads.get(&point.id) {
                    // ID was seen earlier in this batch — use that point's payload as "old"
                    inner
                } else {
                    // First occurrence — use the pre-batch original
                    pre_batch_old.as_ref()
                };

            let (sq8, binary, pq) = (
                quant_guards.sq8.as_deref_mut(),
                quant_guards.binary.as_deref_mut(),
                quant_guards.pq.as_deref_mut(),
            );
            self.cache_quantized_vector(point, storage_mode, sq8, binary, pq);

            self.update_secondary_indexes_on_upsert(
                point.id,
                effective_old,
                point.payload.as_ref(),
            );
            Self::update_text_index(&self.text_index, point);
            Self::collect_sparse_vectors(point, &mut sparse_batch);

            // Record this point's payload ref — zero-cost for the common case (no clone)
            seen_payloads.insert(point.id, point.payload.as_ref());
        }

        sparse_batch
    }

    fn collect_sparse_vectors(
        point: &Point,
        sparse_batch: &mut Vec<(u64, BTreeMap<String, crate::index::sparse::SparseVector>)>,
    ) {
        if let Some(sv_map) = &point.sparse_vectors {
            if !sv_map.is_empty() {
                sparse_batch.push((point.id, sv_map.clone()));
            }
        }
    }

    /// Updates the BM25 text index for a single point.
    fn update_text_index(text_index: &crate::index::Bm25Index, point: &Point) {
        if let Some(payload) = &point.payload {
            let text = Self::extract_text_from_payload(payload);
            if !text.is_empty() {
                text_index.add_document(point.id, &text);
            }
        } else {
            text_index.remove_document(point.id);
        }
    }

    /// Applies buffered sparse vector upserts with WAL-before-apply semantics.
    fn apply_sparse_batch_upsert(
        &self,
        sparse_batch: &[(u64, BTreeMap<String, crate::index::sparse::SparseVector>)],
    ) -> Result<()> {
        if sparse_batch.is_empty() {
            return Ok(());
        }
        #[cfg(feature = "persistence")]
        {
            for (point_id, sv_map) in sparse_batch {
                for (name, sv) in sv_map {
                    let wal_path =
                        crate::index::sparse::persistence::wal_path_for_name(&self.path, name);
                    crate::index::sparse::persistence::wal_append_upsert(&wal_path, *point_id, sv)?;
                }
            }
        }
        let mut indexes = self.sparse_indexes.write();
        for (point_id, sv_map) in sparse_batch {
            for (name, sv) in sv_map {
                let idx = indexes.entry(name.clone()).or_default();
                idx.insert(*point_id, sv);
            }
        }
        Ok(())
    }

    /// Invalidates stats cache and bumps write generation.
    fn invalidate_caches_and_bump_generation(&self) {
        *self.cached_stats.lock() = None;
        self.write_generation
            .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
    }

    /// Drains the deferred indexer and batch-inserts into HNSW.
    ///
    /// Filters out IDs that have been deleted from vector storage since they
    /// were buffered, preventing ghost vectors from being re-inserted into
    /// HNSW after a concurrent delete.
    ///
    /// Logs a warning if fewer vectors were inserted than expected, which
    /// indicates a partial failure (e.g., duplicate IDs filtered out,
    /// ghost-vector filtering, or graph insertion error). The drained
    /// vectors are not retried.
    #[cfg(feature = "persistence")]
    fn merge_deferred_batch(&self, di: &crate::collection::streaming::DeferredIndexer) {
        let drained = di.swap_and_drain();
        if drained.is_empty() {
            return;
        }
        // Filter out vectors deleted from storage during the buffer's
        // lifetime to prevent ghost re-insertion into HNSW.
        let storage = self.vector_storage.read();
        let valid: Vec<(u64, &[f32])> = drained
            .iter()
            .filter(|(id, _)| storage.retrieve(*id).ok().flatten().is_some())
            .map(|(id, v)| (*id, v.as_slice()))
            .collect();
        drop(storage); // Release read lock before batch insert
        let expected = valid.len();
        if valid.is_empty() {
            return;
        }
        let inserted = self.index.insert_batch_parallel(valid);
        if inserted < expected {
            tracing::warn!("merge_deferred_batch: inserted {inserted}/{expected} vectors");
        }
    }

    /// Inserts or updates metadata-only points (no vectors).
    ///
    /// This method is for metadata-only collections. Points should have
    /// empty vectors and only contain payload data.
    ///
    /// # Errors
    ///
    /// Returns an error if storage operations fail.
    pub fn upsert_metadata(&self, points: impl IntoIterator<Item = Point>) -> Result<()> {
        let points: Vec<Point> = points.into_iter().collect();

        let mut payload_storage = self.payload_storage.write();

        for point in &points {
            let old_payload = payload_storage.retrieve(point.id).ok().flatten();
            if let Some(payload) = &point.payload {
                payload_storage.store(point.id, payload)?;
            } else {
                let _ = payload_storage.delete(point.id);
            }
            Self::update_text_index(&self.text_index, point);
            self.update_secondary_indexes_on_upsert(
                point.id,
                old_payload.as_ref(),
                point.payload.as_ref(),
            );
        }

        // LOCK ORDER: flush while payload_storage(3) still held, then drop before acquiring config(1).
        let point_count = payload_storage.ids().len();
        payload_storage.flush()?;
        drop(payload_storage);

        // config(1) only — all higher-numbered locks released above.
        self.config.write().point_count = point_count;
        self.invalidate_caches_and_bump_generation();
        Ok(())
    }

    /// Bulk insert optimized for high-throughput import.
    ///
    /// # Performance
    ///
    /// This method is optimized for bulk loading:
    /// - Uses parallel HNSW insertion (rayon)
    /// - Single flush at the end (not per-point)
    /// - No HNSW index save (deferred for performance)
    /// - ~15x faster than previous sequential approach on large batches (5000+)
    /// - Benchmark: 25-30 Kvec/s on 768D vectors
    ///
    /// # Errors
    ///
    /// Returns an error if any point has a mismatched dimension.
    pub fn upsert_bulk(&self, points: &[Point]) -> Result<usize> {
        if points.is_empty() {
            return Ok(0);
        }

        let dimension = self.config.read().dimension;
        for point in points {
            validate_dimension_match(dimension, point.dimension())?;
        }

        let vector_refs: Vec<(u64, &[f32])> =
            points.iter().map(|p| (p.id, p.vector.as_slice())).collect();
        let sparse_batch = Self::collect_sparse_batch(points);

        self.bulk_store_vectors(&vector_refs)?;
        self.bulk_store_payloads(points)?;

        let inserted = self.bulk_index_or_defer(vector_refs);
        self.config.write().point_count = self.vector_storage.read().len();

        self.apply_sparse_batch_bulk(&sparse_batch)?;
        self.invalidate_caches_and_bump_generation();

        Ok(inserted)
    }

    /// Batch-inserts into HNSW or defers into the deferred indexer.
    ///
    /// Returns the number of vectors processed (whether indexed directly
    /// or deferred for later merge).
    ///
    /// Since v1.7.2, both `upsert()` and `upsert_bulk()` route through this
    /// method. The direct path calls `insert_batch_parallel` (rayon), which
    /// yields non-deterministic HNSW graph topology across runs. Search
    /// correctness and recall are unaffected.
    ///
    /// Invariant: `self.deferred_indexer` is `Some` only when enabled
    /// (`build_deferred_indexer` filters on `cfg.enabled`), so no
    /// redundant `is_enabled()` check is needed here.
    fn bulk_index_or_defer(&self, vector_refs: Vec<(u64, &[f32])>) -> usize {
        #[cfg(feature = "persistence")]
        if let Some(ref di) = self.deferred_indexer {
            di.extend(vector_refs.iter().map(|(id, v)| (*id, v.to_vec())));
            if di.should_merge() {
                self.merge_deferred_batch(di);
            }
            return vector_refs.len();
        }
        let inserted = self.index.insert_batch_parallel(vector_refs);
        self.index.set_searching_mode();
        inserted
    }

    /// Collects sparse vectors grouped by index name for batch insert.
    fn collect_sparse_batch(
        points: &[Point],
    ) -> BTreeMap<String, Vec<(u64, crate::index::sparse::SparseVector)>> {
        let mut batch: BTreeMap<String, Vec<(u64, crate::index::sparse::SparseVector)>> =
            BTreeMap::new();
        for point in points {
            if let Some(sv_map) = &point.sparse_vectors {
                for (name, sv) in sv_map {
                    batch
                        .entry(name.clone())
                        .or_default()
                        .push((point.id, sv.clone()));
                }
            }
        }
        batch
    }

    /// Stores vectors in bulk via batch WAL + mmap write.
    fn bulk_store_vectors(&self, vectors: &[(u64, &[f32])]) -> Result<()> {
        let mut storage = self.vector_storage.write();
        storage.store_batch(vectors)?;
        storage.flush()?;
        Ok(())
    }

    /// Stores payloads and updates BM25 text index in bulk.
    ///
    /// Uses `LogPayloadStorage::store_batch()` for a single WAL sync instead
    /// of per-point fsync, improving bulk insert throughput by 10-50x.
    fn bulk_store_payloads(&self, points: &[Point]) -> Result<()> {
        let entries: Vec<(u64, &serde_json::Value)> = points
            .iter()
            .filter_map(|p| p.payload.as_ref().map(|pl| (p.id, pl)))
            .collect();

        self.payload_storage.write().store_batch(&entries)?;

        for point in points {
            Self::update_text_index(&self.text_index, point);
        }

        Ok(())
    }

    /// Applies sparse batch with WAL-before-apply for bulk insert.
    fn apply_sparse_batch_bulk(
        &self,
        sparse_batch: &BTreeMap<String, Vec<(u64, crate::index::sparse::SparseVector)>>,
    ) -> Result<()> {
        if sparse_batch.is_empty() {
            return Ok(());
        }
        #[cfg(feature = "persistence")]
        {
            for (name, docs) in sparse_batch {
                let wal_path =
                    crate::index::sparse::persistence::wal_path_for_name(&self.path, name);
                for (point_id, sv) in docs {
                    crate::index::sparse::persistence::wal_append_upsert(&wal_path, *point_id, sv)?;
                }
            }
        }
        let mut indexes = self.sparse_indexes.write();
        for (name, docs) in sparse_batch {
            let idx = indexes.entry(name.clone()).or_default();
            idx.insert_batch_chunk(docs);
        }
        Ok(())
    }

    /// Retrieves points by their IDs.
    #[must_use]
    pub fn get(&self, ids: &[u64]) -> Vec<Option<Point>> {
        let config = self.config.read();
        let is_metadata_only = config.metadata_only;
        drop(config);

        let payload_storage = self.payload_storage.read();

        if is_metadata_only {
            // For metadata-only collections, only retrieve payload
            ids.iter()
                .map(|&id| {
                    let payload = payload_storage.retrieve(id).ok().flatten()?;
                    Some(Point {
                        id,
                        vector: Vec::new(),
                        payload: Some(payload),
                        sparse_vectors: None,
                    })
                })
                .collect()
        } else {
            // For vector collections, retrieve both vector and payload
            let vector_storage = self.vector_storage.read();
            ids.iter()
                .map(|&id| {
                    let vector = vector_storage.retrieve(id).ok().flatten()?;
                    let payload = payload_storage.retrieve(id).ok().flatten();
                    Some(Point {
                        id,
                        vector,
                        payload,
                        sparse_vectors: None,
                    })
                })
                .collect()
        }
    }

    /// Deletes points by their IDs.
    ///
    /// # Errors
    ///
    /// Returns an error if storage operations fail.
    pub fn delete(&self, ids: &[u64]) -> Result<()> {
        if self.config.read().metadata_only {
            self.delete_metadata_only(ids)?;
        } else {
            self.delete_vector_points(ids)?;
        }
        self.invalidate_caches_and_bump_generation();
        Ok(())
    }

    /// Deletes metadata-only points.
    fn delete_metadata_only(&self, ids: &[u64]) -> Result<()> {
        let mut payload_storage = self.payload_storage.write();
        for &id in ids {
            let old_payload = payload_storage.retrieve(id).ok().flatten();
            payload_storage.delete(id)?;
            self.text_index.remove_document(id);
            self.update_secondary_indexes_on_delete(id, old_payload.as_ref());
        }
        let point_count = payload_storage.ids().len();
        drop(payload_storage);
        self.config.write().point_count = point_count;
        Ok(())
    }

    /// Deletes vector points from all stores (vector, payload, index, caches, sparse, delta).
    fn delete_vector_points(&self, ids: &[u64]) -> Result<()> {
        let mut payload_storage = self.payload_storage.write();
        let mut vector_storage = self.vector_storage.write();
        let mut sq8_cache = self.sq8_cache.write();
        let mut binary_cache = self.binary_cache.write();
        let mut pq_cache = self.pq_cache.write();

        for &id in ids {
            let old_payload = payload_storage.retrieve(id).ok().flatten();
            vector_storage.delete(id)?;
            payload_storage.delete(id)?;
            self.index.remove(id);
            sq8_cache.remove(&id);
            binary_cache.remove(&id);
            pq_cache.remove(&id);
            self.text_index.remove_document(id);
            self.update_secondary_indexes_on_delete(id, old_payload.as_ref());
        }

        let point_count = vector_storage.len();
        drop(vector_storage);
        drop(payload_storage);
        drop(sq8_cache);
        drop(binary_cache);
        drop(pq_cache);
        self.config.write().point_count = point_count;

        self.delete_from_sparse_indexes(ids)?;

        // Lock order: delta_buffer(10) acquired after sparse_indexes(9) released.
        #[cfg(feature = "persistence")]
        for &id in ids {
            self.delta_buffer.remove(id);
        }

        // Lock order: deferred_indexer(11) acquired after delta_buffer(10).
        #[cfg(feature = "persistence")]
        if let Some(ref di) = self.deferred_indexer {
            for &id in ids {
                di.remove(id);
            }
        }

        Ok(())
    }

    /// Deletes IDs from sparse indexes with WAL-before-apply.
    fn delete_from_sparse_indexes(&self, ids: &[u64]) -> Result<()> {
        #[cfg(feature = "persistence")]
        {
            let indexes = self.sparse_indexes.read();
            for (name, _) in indexes.iter() {
                let wal_path =
                    crate::index::sparse::persistence::wal_path_for_name(&self.path, name);
                for &id in ids {
                    crate::index::sparse::persistence::wal_append_delete(&wal_path, id)?;
                }
            }
        }
        let indexes = self.sparse_indexes.read();
        for idx in indexes.values() {
            for &id in ids {
                idx.delete(id);
            }
        }
        Ok(())
    }

    /// Returns the number of points stored in the collection.
    ///
    /// This reflects the **storage count** (vectors written to disk), not the
    /// number of points currently indexed in the HNSW graph. During a batch
    /// upsert or when deferred indexing is active, `len()` may temporarily
    /// exceed the HNSW-indexed count until the deferred merge completes.
    ///
    /// Perf: Uses cached `point_count` from config instead of acquiring storage lock.
    #[must_use]
    pub fn len(&self) -> usize {
        self.config.read().point_count
    }

    /// Returns true if the collection is empty.
    ///
    /// Uses the same cached `point_count` as [`len()`](Self::len), reflecting
    /// the storage count rather than the HNSW-indexed count.
    #[must_use]
    pub fn is_empty(&self) -> bool {
        self.config.read().point_count == 0
    }

    /// Returns all point IDs in the collection.
    #[must_use]
    pub fn all_ids(&self) -> Vec<u64> {
        self.payload_storage.read().ids()
    }
}