Skip to content

This documentation site is deprecated. Please visit our new documentation site at lancedb.com/docs for the latest information.

@lancedb/lancedb • Docs

@lancedb/lancedb / QueryBase

Class: QueryBase<NativeQueryType>

Common methods supported by all query types

See

Extended by

Type Parameters

• NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery

Implements

  • AsyncIterable<RecordBatch>

Properties

inner

protected inner: NativeQueryType | Promise<NativeQueryType>;

Methods

analyzePlan()

analyzePlan(): Promise<string>

Executes the query and returns the physical query plan annotated with runtime metrics.

This is useful for debugging and performance analysis, as it shows how the query was executed and includes metrics such as elapsed time, rows processed, and I/O statistics.

Returns

Promise<string>

A query execution plan with runtime metrics for each step.

Example

import * as lancedb from "@lancedb/lancedb"

const db = await lancedb.connect("./.lancedb");
const table = await db.createTable("my_table", [
  { vector: [1.1, 0.9], id: "1" },
]);

const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();

Example output (with runtime metrics inlined):
AnalyzeExec verbose=true, metrics=[]
 ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]

execute()

protected execute(options?): RecordBatchIterator

Execute the query and return the results as an

Parameters

Returns

RecordBatchIterator

See

  • AsyncIterator of
  • RecordBatch.

By default, LanceDb will use many threads to calculate results and, when the result set is large, multiple batches will be processed at one time. This readahead is limited however and backpressure will be applied if this stream is consumed slowly (this constrains the maximum memory used by a single query)

explainPlan()

explainPlan(verbose): Promise<string>

Generates an explanation of the query execution plan.

Parameters

  • verbose: boolean = false If true, provides a more detailed explanation. Defaults to false.

Returns

Promise<string>

A Promise that resolves to a string containing the query execution plan explanation.

Example

import * as lancedb from "@lancedb/lancedb"
const db = await lancedb.connect("./.lancedb");
const table = await db.createTable("my_table", [
  { vector: [1.1, 0.9], id: "1" },
]);
const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();

outputSchema()

outputSchema(): Promise<Schema<any>>

Returns the schema of the output that will be returned by this query.

This can be used to inspect the types and names of the columns that will be returned by the query before executing it.

Returns

Promise<Schema<any>>

An Arrow Schema describing the output columns.

select()

select(columns): this

Return only the specified columns.

By default a query will return all columns from the table. However, this can have a very significant impact on latency. LanceDb stores data in a columnar fashion. This means we can finely tune our I/O to select exactly the columns we need.

As a best practice you should always limit queries to the columns that you need. If you pass in an array of column names then only those columns will be returned.

You can also use this method to create new "dynamic" columns based on your existing columns. For example, you may not care about "a" or "b" but instead simply want "a + b". This is often seen in the SELECT clause of an SQL query (e.g. SELECT a+b FROM my_table).

To create dynamic columns you can pass in a Map. A column will be returned for each entry in the map. The key provides the name of the column. The value is an SQL string used to specify how the column is calculated.

For example, an SQL query might state SELECT a + b AS combined, c. The equivalent input to this method would be:

Parameters

  • columns: string | string[] | Record<string, string> | Map<string, string>

Returns

this

Example

new Map([["combined", "a + b"], ["c", "c"]])

Columns will always be returned in the order given, even if that order is different than
the order used when adding the data.

Note that you can pass in a `Record<string, string>` (e.g. an object literal). This method
uses `Object.entries` which should preserve the insertion order of the object.  However,
object insertion order is easy to get wrong and `Map` is more foolproof.

toArray()

toArray(options?): Promise<any[]>

Collect the results as an array of objects.

Parameters

Returns

Promise<any[]>

toArrow()

toArrow(options?): Promise<Table<any>>

Collect the results as an Arrow

Parameters

Returns

Promise<Table<any>>

See

ArrowTable.

withRowId()

withRowId(): this

Whether to return the row id in the results.

This column can be used to match results between different queries. For example, to match results from a full text search and a vector search in order to perform hybrid search.

Returns

this