---
title: "Explain a query plan"
sidebarTitle: "Query Plans"
description: "Read the plan LanceDB resolved for a query, and measure it with analyze."
icon: "map"
---

`explain_plan` prints the plan LanceDB resolved for a query without executing
it. It answers structural questions -- did the filter push into the scan, is the
vector index being used, which columns are actually read -- and costs nothing to
run, so it is the first thing to reach for when a query behaves unexpectedly.

To measure a query rather than read it, use
[`analyze_plan`](/search/explain), which executes it and annotates every
operator with runtime metrics.

<Note>
The SQL equivalent is [`EXPLAIN`](/sql/dql/explain).
</Note>

## What it shows {#inspect-a-query-plan}

Reveals the logical query plan before execution, helping you identify potential issues with query structure and index usage. This tool is useful for:

- Verifying query optimization strategies
- Validating index selection
- Understanding query execution order
- Detecting missing indices

Metadata filters are prefiltered by default, which usually shows the filter pushed into the
`LanceScan` or index scan. If you set `prefilter=False`, expect a separate `FilterExec` after
search instead; that can be useful for some expensive filters, but it changes both latency and
the number of rows available after filtering.

## Reading the Execution Plan {#reading-the-execution-plan}

To demonstrate query performance analysis, we'll use a table containing 1.2M rows sampled from the [Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia). Initially, the table has no indices, allowing us to observe the impact of optimization.

Let's examine a vector search query that:

- Filters rows where `identifier` is between 0 and 1,000,000
- Returns the top 100 matches
- Projects specific columns: `chunk_index`, `title`, and `identifier`

<CodeGroup>
```python Python icon="python"
# explain_plan
query_explain_plan = (
    table.search(query_embed)
    .where("identifier > 0 AND identifier < 1000000")
    .select(["chunk_index", "title", "identifier"])
    .limit(100)
    .explain_plan(True)
)
```

```typescript TypeScript icon="square-js"
// explain_plan
const explainPlan = await table
    .search(queryEmbed)
    .where("identifier > 0 AND identifier < 1000000")
    .select(["chunk_index", "title", "identifier"])
    .limit(100)
    .explainPlan(true);
```
</CodeGroup>

### Execution Plan Components {#execution-plan-components}

The execution plan reveals the sequence of operations performed to execute your query. Let's examine each component:

```
ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance]
  RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title"
    CoalesceBatchesExec: target_batch_size=1024
      GlobalLimitExec: skip=0, fetch=100
        FilterExec: _distance@3 IS NOT NULL
          SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false]
            KNNVectorDistance: metric=l2
              FilterExec: identifier@1 > 0 AND identifier@1 < 1000000
                LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false
```

#### 1\. Base Layer (LanceScan) {#1-base-layer-lancescan}

- Initial data scan loading only specified columns to minimize I/O
- Unordered scan enabling parallel processing

```
LanceScan: 
- projection=[vector, identifier]
- row_id=true, row_addr=false, ordered=false
```

#### 2\. First Filter {#2-first-filter}

- Apply requested filter on `identifier` column
- Reduces the number of vectors that need KNN computation

```
FilterExec: identifier@1 > 0 AND identifier@1 < 1000000
```

#### 3\. Vector Search {#3-vector-search}

- Computes L2 (Euclidean) distances between query vector and all vectors that passed the filter

```
KNNVectorDistance: metric=l2
```

#### 4\. Results Processing {#4-results-processing}

- Filters out null distance results
- Sorts by distance and takes top 100 results
- Processes in batches of 1024 for optimal memory usage

```
SortExec: TopK(fetch=100)
- expr=[_distance@3 ASC NULLS LAST]
- preserve_partitioning=[false]
FilterExec: _distance@3 IS NOT NULL
GlobalLimitExec: skip=0, fetch=100
CoalesceBatchesExec: target_batch_size=1024
```

#### 5\. Data Retrieval {#5-data-retrieval}

- `RemoteTake` is a key component of Lance's I/O cache
- Handles efficient data retrieval from remote storage locations
- Fetches specific rows and columns needed for the final output
- Optimizes network bandwidth by only retrieving required data

```
RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title"
```

#### 6\. Final Output {#6-final-output}

- Returns only requested columns and maintains column ordering

```python
ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance]
```

This plan demonstrates a basic search without index optimizations: it performs a full scan and filter before vector search.

## Non-vector queries {#analyzing-non-vector-queries}

`explain_plan` is not vector-specific -- it is available on every query builder,
including FTS and hybrid. The most common reason to read the plan for a
non-vector query is to confirm whether your `where` clause pushed into the scan
(good) or ran as a separate `FilterExec` on top of the search results (often
slower, and a hint that the filter column needs a scalar index).

### FTS queries {#fts-queries}

<CodeGroup>
```python Python icon="python"
plan = (
    table.search("puppy", query_type="fts")
    .where("category = 'animals'", prefilter=True)
    .limit(10)
    .explain_plan(True)
)
print(plan)
```

```typescript TypeScript icon="square-js"
const plan = await table
    .query()
    .fullTextSearch("puppy")
    .where("category = 'animals'")
    .limit(10)
    .explainPlan(true);
```
</CodeGroup>

In an indexed FTS plan you should see a `MatchQuery` (or other FTS execution node) reading from the inverted index, with the metadata filter pushed down. If the plan shows a `LanceScan` followed by `FilterExec` over the entire text column, the FTS index either isn't covering the column or the filter isn't using a scalar index — both worth investigating.

### Hybrid queries {#hybrid-queries}

For hybrid queries, `explain_plan` returns the reranker label followed by the vector and FTS sub-plans, indented for readability:

<CodeGroup>
```python Python icon="python"
plan = (
    table.search("flower moon", query_type="hybrid")
    .where("category = 'film'", prefilter=True)
    .limit(10)
    .explain_plan(True)
)
print(plan)
# RRFReranker(...)
#   <vector sub-plan>
#   <FTS sub-plan>
```
</CodeGroup>

Reading the plan tells you the shape. `analyze_plan` executes both sub-queries
and labels them `Vector Search Plan:` and `FTS Search Plan:`, which is how you
see whether the filter pushed into both halves uniformly and which half
dominates latency.

`analyze_plan` executes the query and returns the physical plan annotated with
runtime metrics -- elapsed time, rows produced, bytes read, index comparisons.
Where [`explain_plan`](#inspect-a-query-plan) tells you what a query intends to do,
this tells you what it cost.

<Note>
The SQL equivalent is [`EXPLAIN ANALYZE`](/sql/dql/explain).
</Note>

## What it measures {#analyze-a-query-plan}

Executes the query and provides detailed runtime metrics, including:

- Operation duration (`_elapsed_compute_`)
- Data processing statistics (`_output_rows_`, `_bytes_read_`)
- Index effectiveness (`_index_comparisons_`, `_indices_loaded_`)
- Resource utilization (`_iops_`, `_requests_`)

## Performance Analysis {#performance-analysis}

Let's use `analyze_plan` to run the query and analyze the query performance, which will help us identify potential bottlenecks:

<CodeGroup>
```python Python icon="python"
# analyze_plan
query_analyze_plan = (
    table.search(query_embed)
    .where("identifier > 0 AND identifier < 1000000")
    .select(["chunk_index", "title", "identifier"])
    .limit(100)
    .analyze_plan()
)
```

```typescript TypeScript icon="square-js"
// analyze_plan
const analyzePlan = await table
    .search(queryEmbed)
    .where("identifier > 0 AND identifier < 1000000")
    .select(["chunk_index", "title", "identifier"])
    .limit(100)
    .analyzePlan();
```
</CodeGroup>

### Performance Metrics Analysis {#performance-metrics-analysis}

```
ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance], metrics=[output_rows=100, elapsed_compute=1.424µs]
    RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title", metrics=[output_rows=100, elapsed_compute=175.53097ms, output_batches=1, remote_takes=100]
      CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=100, elapsed_compute=2.748µs]
        GlobalLimitExec: skip=0, fetch=100, metrics=[output_rows=100, elapsed_compute=1.819µs]
          FilterExec: _distance@3 IS NOT NULL, metrics=[output_rows=100, elapsed_compute=10.275µs]
            SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false], metrics=[output_rows=100, elapsed_compute=39.259451ms, row_replacements=546]
              KNNVectorDistance: metric=l2, metrics=[output_rows=1099508, elapsed_compute=56.783526ms, output_batches=1076]
                FilterExec: identifier@1 > 0 AND identifier@1 < 1000000, metrics=[output_rows=1099508, elapsed_compute=17.136819ms]
                  LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1200000, elapsed_compute=21.348178ms, bytes_read=1852931072, iops=78, requests=78]
```

#### 1\. Data Loading (LanceScan) {#1-data-loading-lancescan}

- Scanned 1,200,000 rows from the LanceDB table
- Read 1.86GB of data in 78 I/O operations
- Only loaded necessary columns (`vector` and `identifier`)
- Unordered scan for parallel processing

#### 2\. Filtering and Search {#2-filtering-and-search}

- Applied prefilter condition (`identifier > 0 AND identifier < 1000000`)
- Reduced dataset from 1.2M to 1,099,508 rows
- KNN search used L2 (Euclidean) distance metric
- Vector comparisons processed in 1076 batches

#### 3\. Results Processing {#3-results-processing}

- KNN results sorted by distance (TopK with fetch=100)
- Null distances filtered out
- Batches coalesced to target size of 1024 rows
- Additional columns fetched for final results
- Remote take operation for 100 results
- Final projection of required columns

### Distributed metrics on remote tables {#distributed-metrics-on-remote-tables}

<Badge color="red">Enterprise</Badge>

When you call `analyze_plan` against a LanceDB Enterprise table, the query runs across a pool of workers. By default the returned plan aggregates each operator's metrics into a single value, matching the local single-node output. Pass a `distributed_metrics` mode when you need to see how work was split across workers:

| Mode          | What it shows                                                                       | When to use it                                                                                          |
| :------------ | :---------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------ |
| `aggregate`   | One summary value per operator (default). Same shape as local `analyze_plan`.       | Everyday performance checks and comparisons against a local plan.                                       |
| `per_worker`  | Metrics reported separately for each worker that participated in the query.         | Diagnosing stragglers or uneven work distribution across the cluster.                                   |
| `full`        | Both the aggregate summary and the per-worker breakdown.                            | Deep investigations where you need the totals *and* the per-worker detail in a single output.           |

The parameter only affects remote plans — local queries always return the aggregate output regardless of what you pass.

<CodeGroup>
```python Python icon="python"
# Show a per-worker breakdown for a remote query
plan = (
    table.search(query_embed)
    .where("identifier > 0 AND identifier < 1000000")
    .limit(100)
    .analyze_plan(distributed_metrics="per_worker")
)
print(plan)
```

```typescript TypeScript icon="square-js"
// Show a per-worker breakdown for a remote query
const plan = await table
    .search(queryEmbed)
    .where("identifier > 0 AND identifier < 1000000")
    .limit(100)
    .analyzePlan("per_worker");
```
</CodeGroup>

<Note>
Use `per_worker` or `full` sparingly — the plan output grows with the size of your worker pool. Stick with the default `aggregate` mode for routine tuning.
</Note>

### Key Observations {#key-observations}

- Vector search is the primary bottleneck (1,099,508 vector comparisons)
- Significant I/O overhead (1.86GB data read)
- Full table scan due to lack of indices
- Substantial optimization potential through proper index implementation

## Optimized Query Execution {#optimized-query-execution}

After creating vector and scalar indices, the execution plan shows:

```
ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance]
  RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title"
    CoalesceBatchesExec: target_batch_size=1024
      GlobalLimitExec: skip=0, fetch=100
        SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false]
          ANNSubIndex: name=vector_idx, k=100, deltas=1
            ANNIvfPartition: uuid=83916fd5-fc45-4977-bad9-1f0737539bb9, nprobes=20, deltas=1
            ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000)
```

### Optimized Plan Analysis {#optimized-plan-analysis}

#### 1\. Scalar Index Query {#1-scalar-index-query}

```
ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000)
metrics=[
    output_rows=2
    index_comparisons=2,301,824
    indices_loaded=2
    output_batches=1
    parts_loaded=562
    elapsed_compute=86.979354ms
]
```

- Range filter using scalar index
- Only 2 index files and 562 scalar index parts loaded
- 2.3M index comparisons for matches

#### 2\. Vector Search {#2-vector-search}

```
ANNSubIndex: name=vector_idx, k=100, deltas=1
metrics=[
    output_rows=2,000
    index_comparisons=25,893
    indices_loaded=0
    output_batches=20
    parts_loaded=20
    elapsed_compute=111.849043ms
]
```

- IVF index with 20 probes
- Only 20 index parts loaded
- 25,893 vector comparisons
- 2,000 matching vectors

#### 3\. Results Processing {#3-results-processing-2}

```
SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false]
GlobalLimitExec: skip=0, fetch=100
CoalesceBatchesExec: target_batch_size=1024
```

- Sorts by distance
- Limits to top 100 results
- Batches into groups of 1024

#### 4\. Data Fetching {#4-data-fetching}

```
RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title"
metrics=[output_rows=100, elapsed_compute=113.491859ms, output_batches=1, remote_takes=100]
```

- Single output batch
- One remote take per row

#### 5\. Final Projection {#5-final-projection}

```
ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance]
```

- Returns specified columns: chunk_index, title, identifier, and distance

### Performance Improvements {#performance-improvements}

#### 1\. Initial Data Access {#1-initial-data-access}

```
ScalarIndexQuery metrics:
- indices_loaded=2
- parts_loaded=562
- output_batches=1
```

- Before: Full table scan of 1.2M rows, 1.86GB data
- After: Only 2 indices and 562 scalar index parts loaded
- Benefit: Eliminated table scans for prefilter

#### 2\. Vector Search Efficiency {#2-vector-search-efficiency}

```
ANNSubIndex:
- index_comparisons=25,893
- indices_loaded=0
- parts_loaded=20
- output_batches=20
```

- Before: L2 calculations on 1,099,508 vectors
- After: 
  - 99.8% reduction in vector comparisons
  - Decreased output batches from 1,076 to 20

#### 3\. Data Retrieval Optimization {#3-data-retrieval-optimization}

```
RemoteTake:
- remote_takes=100
- output_batches=1
```

- RemoteTake operation remains consistent

## Performance Optimization Guide {#performance-optimization-guide}

### 1\. Index Implementation {#1-index-implementation}

#### When to Create Indices {#when-to-create-indices}

- Columns used in WHERE clauses
- Vector columns for similarity searches
- Join columns used in `merge_insert`

#### Index Type Selection {#index-type-selection}

| Data Type   | Recommended Index  | Use Case                                 |
| ----------- | ------------------ | ---------------------------------------- |
| Vector      | IVF_PQ/IVF_HNSW_SQ/IVF_HNSW_FLAT | Approximate nearest neighbor search      |
| Scalar      | B-Tree             | Range queries and sorting                |
| Categorical | Bitmap             | Multi-value filters and set operations   |
| `List`      | Label_list         | Multi-label classification and filtering |

<Card title="Index Coverage Monitoring" icon="lightbulb">
Use `table.index_stats()` to monitor index coverage.
A well-optimized table should have `num_unindexed_rows ~ 0`.
</Card>

### 2\. Query Plan Optimization {#2-query-plan-optimization}

#### Common Patterns and Fixes {#common-patterns-and-fixes}

| Plan Pattern                               | Optimization                                 |
| ------------------------------------------ | -------------------------------------------- |
| LanceScan with high _bytes_read_ or _iops_ | Add missing index                            |
|                                            | Use `select()` to limit returned columns     |
|                                            | Check whether the dataset has been compacted |
| Multiple sequential filters                | Reorder filter conditions                    |

<Note>
Regularly analyze your query plans to identify and address performance
bottlenecks. The `analyze_plan` output provides detailed metrics to guide
optimization efforts.
</Note>

### 3\. Getting Started with Optimization {#3-getting-started-with-optimization}

For vector search performance:

- Create ANN index on your vector column(s) as described in the [index guide](/indexing/vector-index/)
- If you often filter by metadata, create [scalar indices](/indexing/scalar-index/) on those columns