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| content_embeddings_potion_base_8M_256
sequencelengths 256
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|---|---|---|---|---|---|---|---|
Extensions | Spatial | Scalar Functions | Example | ```sql
SELECT ST_AsText(
ST_Union(
ST_GeomFromText('POINT(1 2)'),
ST_GeomFromText('POINT(3 4)')
)
);
----
MULTIPOINT (1 2, 3 4)
```
----
#### ST_Within {#docs:extensions:spatial:functions::st_within} | 76 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
BOOLEAN ST_Within (col0 POINT_2D, col1 POLYGON_2D)
BOOLEAN ST_Within (col0 GEOMETRY, col1 GEOMETRY)
``` | 39 | [
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Extensions | Spatial | Scalar Functions | Description | Returns true if geom1 is "within" geom2
----
#### ST_X {#docs:extensions:spatial:functions::st_x} | 31 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_X (col0 POINT_2D)
DOUBLE ST_X (col0 GEOMETRY)
``` | 24 | [
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Extensions | Spatial | Scalar Functions | Description | Returns the X value of a point geometry, or NULL if not a point or empty
----
#### ST_XMax {#docs:extensions:spatial:functions::st_xmax} | 39 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_XMax (col0 BOX_2D)
FLOAT ST_XMax (col0 BOX_2DF)
DOUBLE ST_XMax (col0 POINT_2D)
DOUBLE ST_XMax (col0 LINESTRING_2D)
DOUBLE ST_XMax (col0 POLYGON_2D)
DOUBLE ST_XMax (col0 GEOMETRY)
``` | 76 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the maximum X value of a geometry
----
#### ST_XMin {#docs:extensions:spatial:functions::st_xmin} | 30 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_XMin (col0 BOX_2D)
FLOAT ST_XMin (col0 BOX_2DF)
DOUBLE ST_XMin (col0 POINT_2D)
DOUBLE ST_XMin (col0 LINESTRING_2D)
DOUBLE ST_XMin (col0 POLYGON_2D)
DOUBLE ST_XMin (col0 GEOMETRY)
``` | 76 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the minimum X value of a geometry
----
#### ST_Y {#docs:extensions:spatial:functions::st_y} | 28 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_Y (col0 POINT_2D)
DOUBLE ST_Y (col0 GEOMETRY)
``` | 24 | [
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Extensions | Spatial | Scalar Functions | Description | Returns the Y value of a point geometry, or NULL if not a point or empty
----
#### ST_YMax {#docs:extensions:spatial:functions::st_ymax} | 39 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_YMax (col0 BOX_2D)
FLOAT ST_YMax (col0 BOX_2DF)
DOUBLE ST_YMax (col0 POINT_2D)
DOUBLE ST_YMax (col0 LINESTRING_2D)
DOUBLE ST_YMax (col0 POLYGON_2D)
DOUBLE ST_YMax (col0 GEOMETRY)
``` | 76 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the maximum Y value of a geometry
----
#### ST_YMin {#docs:extensions:spatial:functions::st_ymin} | 30 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_YMin (col0 BOX_2D)
FLOAT ST_YMin (col0 BOX_2DF)
DOUBLE ST_YMin (col0 POINT_2D)
DOUBLE ST_YMin (col0 LINESTRING_2D)
DOUBLE ST_YMin (col0 POLYGON_2D)
DOUBLE ST_YMin (col0 GEOMETRY)
``` | 76 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the minimum Y value of a geometry
----
#### ST_Z {#docs:extensions:spatial:functions::st_z} | 28 | [
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Extensions | Spatial | Scalar Functions | Signature | ```sql
DOUBLE ST_Z (col0 GEOMETRY)
``` | 13 | [
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Extensions | Spatial | Scalar Functions | Description | Returns the Z value of a point geometry, or NULL if not a point or empty
----
#### ST_ZMFlag {#docs:extensions:spatial:functions::st_zmflag} | 41 | [
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] |
Extensions | Spatial | Scalar Functions | Signatures | ```sql
UTINYINT ST_ZMFlag (col0 GEOMETRY)
UTINYINT ST_ZMFlag (col0 WKB_BLOB)
``` | 32 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns a flag indicating the presence of Z and M values in the input geometry.
0 = No Z or M values
1 = M values only
2 = Z values only
3 = Z and M values | 42 | [
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Extensions | Spatial | Scalar Functions | Example | ```sql
-- ZMFlag for a 2D geometry
SELECT ST_ZMFlag(ST_GeomFromText('POINT(1 1)'));
----
0
-- ZMFlag for a 3DZ geometry
SELECT ST_ZMFlag(ST_GeomFromText('POINT Z(1 1 1)'));
----
2
-- ZMFlag for a 3DM geometry
SELECT ST_ZMFlag(ST_GeomFromText('POINT M(1 1 1)'));
----
1
-- ZMFlag for a 4D geometry
SELECT ST_ZMFlag(ST_GeomFromText('POINT ZM(1 1 1 1)'));
----
3
```
----
#### ST_ZMax {#docs:extensions:spatial:functions::st_zmax} | 171 | [
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Extensions | Spatial | Scalar Functions | Signature | ```sql
DOUBLE ST_ZMax (col0 GEOMETRY)
``` | 14 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the maximum Z value of a geometry
----
#### ST_ZMin {#docs:extensions:spatial:functions::st_zmin} | 30 | [
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] |
Extensions | Spatial | Scalar Functions | Signature | ```sql
DOUBLE ST_ZMin (col0 GEOMETRY)
``` | 14 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Returns the minimum Z value of a geometry
---- | 10 | [
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Extensions | Spatial | Aggregate Functions | #### ST_Envelope_Agg {#docs:extensions:spatial:functions::st_envelope_agg} | 22 | [
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|
Extensions | Spatial | Aggregate Functions | Signature | ```sql
GEOMETRY ST_Envelope_Agg (col0 GEOMETRY)
``` | 17 | [
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Extensions | Spatial | Aggregate Functions | Description | Alias for [ST_Extent_Agg](#::st_extent_agg).
Computes the minimal-bounding-box polygon containing the set of input geometries. | 32 | [
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Extensions | Spatial | Aggregate Functions | Example | ```sql
SELECT ST_Extent_Agg(geom) FROM UNNEST([ST_Point(1,1), ST_Point(5,5)]) AS _(geom);
-- POLYGON ((1 1, 1 5, 5 5, 5 1, 1 1))
```
----
#### ST_Extent_Agg {#docs:extensions:spatial:functions::st_extent_agg} | 88 | [
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Extensions | Spatial | Aggregate Functions | Signature | ```sql
GEOMETRY ST_Extent_Agg (col0 GEOMETRY)
``` | 17 | [
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Extensions | Spatial | Aggregate Functions | Description | Computes the minimal-bounding-box polygon containing the set of input geometries | 15 | [
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Extensions | Spatial | Aggregate Functions | Example | ```sql
SELECT ST_Extent_Agg(geom) FROM UNNEST([ST_Point(1,1), ST_Point(5,5)]) AS _(geom);
-- POLYGON ((1 1, 1 5, 5 5, 5 1, 1 1))
```
----
#### ST_Intersection_Agg {#docs:extensions:spatial:functions::st_intersection_agg} | 88 | [
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Extensions | Spatial | Aggregate Functions | Signature | ```sql
GEOMETRY ST_Intersection_Agg (col0 GEOMETRY)
``` | 17 | [
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] |
Extensions | Spatial | Aggregate Functions | Description | Computes the intersection of a set of geometries
----
#### ST_Union_Agg {#docs:extensions:spatial:functions::st_union_agg} | 34 | [
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Extensions | Spatial | Aggregate Functions | Signature | ```sql
GEOMETRY ST_Union_Agg (col0 GEOMETRY)
``` | 17 | [
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Extensions | Spatial | Aggregate Functions | Description | Computes the union of a set of input geometries
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Extensions | Spatial | Table Functions | #### ST_Drivers {#docs:extensions:spatial:functions::st_drivers} | 20 | [
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|
Extensions | Spatial | Table Functions | Signature | ```sql
ST_Drivers ()
``` | 9 | [
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] |
Extensions | Spatial | Table Functions | Description | Returns the list of supported GDAL drivers and file formats
Note that far from all of these drivers have been tested properly, and some may require additional options to be passed to work as expected. If you run into any issues please first consult the [consult the GDAL docs](https://gdal.org/drivers/vector/index.html). | 66 | [
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Extensions | Spatial | Table Functions | Example | ```sql
SELECT * FROM ST_Drivers();
```
----
#### ST_Read {#docs:extensions:spatial:functions::st_read} | 32 | [
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Extensions | Spatial | Table Functions | Signature | ```sql
ST_Read (col0 VARCHAR, keep_wkb BOOLEAN, max_batch_size INTEGER, sequential_layer_scan BOOLEAN, layer VARCHAR, sibling_files VARCHAR[], spatial_filter WKB_BLOB, spatial_filter_box BOX_2D, allowed_drivers VARCHAR[], open_options VARCHAR[])
``` | 57 | [
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Extensions | Spatial | Table Functions | Description | Read and import a variety of geospatial file formats using the GDAL library.
The `ST_Read` table function is based on the [GDAL](https://gdal.org/index.html) translator library and enables reading spatial data from a variety of geospatial vector file formats as if they were DuckDB tables.
> See [ST_Drivers](#::st_drivers) for a list of supported file formats and drivers.
Except for the `path` parameter, all parameters are optional.
| Parameter | Type | Description |
| ----------------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | `VARCHAR` | The path to the file to read. Mandatory |
| `sequential_layer_scan` | `BOOLEAN` | If set to true, the table function will scan through all layers sequentially and return the first layer that matches the given layer name. This is required for some drivers to work properly, e.g., the OSM driver. |
| `spatial_filter` | `WKB_BLOB` | If set to a WKB blob, the table function will only return rows that intersect with the given WKB geometry. Some drivers may support efficient spatial filtering natively, in which case it will be pushed down. Otherwise the filtering is done by GDAL which may be much slower. |
| `open_options` | `VARCHAR[]` | A list of key-value pairs that are passed to the GDAL driver to control the opening of the file. E.g., the GeoJSON driver supports a FLATTEN_NESTED_ATTRIBUTES=YES option to flatten nested attributes. |
| `layer` | `VARCHAR` | The name of the layer to read from the file. If NULL, the first layer is returned. Can also be a layer index (starting at 0). |
| `allowed_drivers` | `VARCHAR[]` | A list of GDAL driver names that are allowed to be used to open the file. If empty, all drivers are allowed. |
| `sibling_files` | `VARCHAR[]` | A list of sibling files that are required to open the file. E.g., the ESRI Shapefile driver requires a .shx file to be present. Although most of the time these can be discovered automatically. |
| `spatial_filter_box` | `BOX_2D` | If set to a BOX_2D, the table function will only return rows that intersect with the given bounding box. Similar to spatial_filter. |
| `keep_wkb` | `BOOLEAN` | If set, the table function will return geometries in a wkb_geometry column with the type WKB_BLOB (which can be cast to BLOB) instead of GEOMETRY. This is useful if you want to use DuckDB with more exotic geometry subtypes that DuckDB spatial doesnt support representing in the GEOMETRY type yet. |
Note that GDAL is single-threaded, so this table function will not be able to make full use of parallelism.
By using `ST_Read`, the spatial extension also provides “replacement scans” for common geospatial file formats, allowing you to query files of these formats as if they were tables directly.
```sql
SELECT * FROM './path/to/some/shapefile/dataset.shp';
```
In practice this is just syntax-sugar for calling ST_Read, so there is no difference in performance. If you want to pass additional options, you should use the ST_Read table function directly.
The following formats are currently recognized by their file extension:
| Format | Extension |
| -------------- | --------- |
| ESRI ShapeFile | `.shp` |
| GeoPackage | `.gpkg` |
| FlatGeoBuf | `.fgb` | | 811 | [
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Extensions | Spatial | Table Functions | Example | ```sql
-- Read a Shapefile
SELECT * FROM ST_Read('some/file/path/filename.shp');
-- Read a GeoJSON file
CREATE TABLE my_geojson_table AS SELECT * FROM ST_Read('some/file/path/filename.json');
```
----
#### ST_ReadOSM {#docs:extensions:spatial:functions::st_readosm} | 75 | [
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Extensions | Spatial | Table Functions | Signature | ```sql
ST_ReadOSM (col0 VARCHAR)
``` | 13 | [
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Extensions | Spatial | Table Functions | Description | The `ST_ReadOsm()` table function enables reading compressed OpenStreetMap data directly from a `.osm.pbf file.`
This function uses multithreading and zero-copy protobuf parsing which makes it a lot faster than using the `ST_Read()` OSM driver, however it only outputs the raw OSM data (Nodes, Ways, Relations), without constructing any geometries. For simple node entities (like PoI's) you can trivially construct POINT geometries, but it is also possible to construct LINESTRING and POLYGON geometries by manually joining refs and nodes together in SQL, although with available memory usually being a limiting factor.
The `ST_ReadOSM()` function also provides a "replacement scan" to enable reading from a file directly as if it were a table. This is just syntax sugar for calling `ST_ReadOSM()` though. Example:
```sql
SELECT * FROM 'tmp/data/germany.osm.pbf' LIMIT 5;
``` | 199 | [
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Extensions | Spatial | Table Functions | Example | ```sql
SELECT *
FROM ST_ReadOSM('tmp/data/germany.osm.pbf')
WHERE tags['highway'] != []
LIMIT 5;
----
┌──────────────────────┬────────┬──────────────────────┬─────────┬────────────────────┬────────────┬───────────┬────────────────────────┐
│ kind │ id │ tags │ refs │ lat │ lon │ ref_roles │ ref_types │
│ enum('node', 'way'… │ int64 │ map(varchar, varch… │ int64[] │ double │ double │ varchar[] │ enum('node', 'way', … │
├──────────────────────┼────────┼──────────────────────┼─────────┼────────────────────┼────────────┼───────────┼────────────────────────┤
│ node │ 122351 │ {bicycle=yes, butt… │ │ 53.5492951 │ 9.977553 │ │ │
│ node │ 122397 │ {crossing=no, high… │ │ 53.520990100000006 │ 10.0156924 │ │ │
│ node │ 122493 │ {TMC:cid_58:tabcd_… │ │ 53.129614600000004 │ 8.1970173 │ │ │
│ node │ 123566 │ {highway=traffic_s… │ │ 54.617268200000005 │ 8.9718171 │ │ │
│ node │ 125801 │ {TMC:cid_58:tabcd_… │ │ 53.070685000000005 │ 8.7819939 │ │ │
└──────────────────────┴────────┴──────────────────────┴─────────┴────────────────────┴────────────┴───────────┴────────────────────────┘
```
----
#### ST_Read_Meta {#docs:extensions:spatial:functions::st_read_meta} | 476 | [
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Extensions | Spatial | Table Functions | Signature | ```sql
ST_Read_Meta (col0 VARCHAR)
ST_Read_Meta (col0 VARCHAR[])
``` | 20 | [
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Extensions | Spatial | Table Functions | Description | Read the metadata from a variety of geospatial file formats using the GDAL library.
The `ST_Read_Meta` table function accompanies the `ST_Read` table function, but instead of reading the contents of a file, this function scans the metadata instead.
Since the data model of the underlying GDAL library is quite flexible, most of the interesting metadata is within the returned `layers` column, which is a somewhat complex nested structure of DuckDB `STRUCT` and `LIST` types. | 100 | [
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Extensions | Spatial | Table Functions | Example | ```sql
-- Find the coordinate reference system authority name and code for the first layers first geometry column in the file
SELECT
layers[1].geometry_fields[1].crs.auth_name as name,
layers[1].geometry_fields[1].crs.auth_code as code
FROM st_read_meta('../../tmp/data/amsterdam_roads.fgb');
``` | 73 | [
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Extensions | Spatial | R-Tree Indexes | As of DuckDB v1.1.0 the [`spatial` extension](#docs:extensions:spatial:overview) provides basic support for spatial indexing through the R-tree extension index type. | 41 | [
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|
Extensions | Spatial | Why Should I Use an R-Tree Index? | When working with geospatial datasets, it is very common that you want to filter rows based on their spatial relationship with a specific region of interest. Unfortunately, even though DuckDB's vectorized execution engine is pretty fast, this sort of operation does not scale very well to large datasets as it always requires a full table scan to check every row in the table. However, by indexing a table with an R-tree, it is possible to accelerate these types of queries significantly. | 95 | [
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Extensions | Spatial | How Do R-Tree Indexes Work? | An R-tree is a balanced tree data structure that stores the approximate _minimum bounding rectangle_ of each geometry (and the internal ID of the corresponding row) in the leaf nodes, and the bounding rectangle enclosing all of the child nodes in each internal node.
> The _minimum bounding rectangle_ (MBR) of a geometry is the smallest rectangle that completely encloses the geometry. Usually when we talk about the bounding rectangle of a geometry (or the bounding "box" in the context of 2D geometry), we mean the minimum bounding rectangle. Additionally, we tend to assume that bounding boxes/rectangles are _axis-aligned,_ i.e., the rectangle is **not** rotated - the sides are always parallel to the coordinate axes. The MBR of a point is the point itself.
By traversing the R-tree from top to bottom, it is possible to very quickly search a R-tree-indexed table for only those rows where the indexed geometry column intersect a specific region of interest, as you can skip searching entire sub-trees if the bounding rectangles of their parent nodes don't intersect the query region at all. Once the leaf nodes are reached, only the specific rows whose geometries intersect the query region have to be fetched from disk, and the often much more expensive exact spatial predicate check (and any other filters) only have to be executed for these rows. | 277 | [
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|
Extensions | Spatial | What Are the Limitations of R-Tree Indexes in DuckDB? | Before you get started using the R-tree index, there are some limitations to be aware of:
- The R-tree index is only supported for the `GEOMETRY` data type.
- The R-tree index will only be used to perform "index scans" when the table is filtered (using a `WHERE` clause) with one of the following spatial predicate functions (as they all imply intersection): `ST_Equals`, `ST_Intersects`, `ST_Touches`, `ST_Crosses`, `ST_Within`, `ST_Contains`, `ST_Overlaps`, `ST_Covers`, `ST_CoveredBy`, `ST_ContainsProperly`.
- One of the arguments to the spatial predicate function must be a “constant” (i.e., a expression whose result is known at query planning time). This is because the query planner needs to know the bounding box of the query region _before_ the query itself is executed in order to use the R-tree index scan.
In the future we want to enable R-tree indexes to be used to accelerate additional predicate functions and more complex queries such a spatial joins. | 229 | [
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|
Extensions | Spatial | How To Use R-Tree Indexes in DuckDB | To create an R-tree index, simply use the `CREATE INDEX` statement with the `USING RTREE` clause, passing the geometry column to index within the parentheses. For example:
```sql
-- Create a table with a geometry column
CREATE TABLE my_table (geom GEOMETRY);
-- Create an R-tree index on the geometry column
CREATE INDEX my_idx ON my_table USING RTREE (geom);
```
You can also pass in additional options when creating an R-tree index using the `WITH` clause to control the behavior of the R-tree index. For example, to specify the maximum number of entries per node in the R-tree, you can use the `max_node_capacity` option:
```sql
CREATE INDEX my_idx ON my_table USING RTREE (geom) WITH (max_node_capacity = 16);
```
The impact tweaking these options will have on performance is highly dependent on the system setup DuckDB is running on, the spatial distribution of the dataset, and the query patterns of your specific workload. The defaults should be good enough, but you if you want to experiment with different parameters, see the [full list of options here](#::options). | 240 | [
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|
Extensions | Spatial | Example | Here is an example that shows how to create an R-tree index on a geometry column and where we can see that the `RTREE_INDEX_SCAN` operator is used when the table is filtered with a spatial predicate:
```sql
INSTALL spatial;
LOAD spatial;
-- Create a table with 10_000_000 random points
CREATE TABLE t1 AS SELECT point::GEOMETRY AS geom
FROM st_generatepoints({min_x: 0, min_y: 0, max_x: 100, max_y: 100}::BOX_2D, 10_000, 1337);
-- Create an index on the table.
CREATE INDEX my_idx ON t1 USING RTREE (geom);
-- Perform a query with a "spatial predicate" on the indexed geometry column
-- Note how the second argument in this case, the ST_MakeEnvelope call is a "constant"
SELECT count(*) FROM t1 WHERE ST_Within(geom, ST_MakeEnvelope(45, 45, 65, 65));
```
```text
390
```
We can check for ourselves that an R-tree index scan is used by using the `EXPLAIN` statement:
```sql
EXPLAIN SELECT count(*) FROM t1 WHERE ST_Within(geom, ST_MakeEnvelope(45, 45, 65, 65));
```
```text
┌───────────────────────────┐
│ UNGROUPED_AGGREGATE │
│ ──────────────────── │
│ Aggregates: │
│ count_star() │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ FILTER │
│ ──────────────────── │
│ ST_Within(geom, '...') │
│ │
│ ~2000 Rows │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ RTREE_INDEX_SCAN │
│ ──────────────────── │
│ t1 (RTREE INDEX SCAN : │
│ my_idx) │
│ │
│ Projections: geom │
│ │
│ ~10000 Rows │
└───────────────────────────┘
``` | 507 | [
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|
Extensions | Spatial | Performance Considerations | #### Bulk Loading & Maintenance {#docs:extensions:spatial:r-tree_indexes::bulk-loading--maintenance}
Creating R-trees on top of an already populated table is much faster than first creating the index and then inserting the data. This is because the R-tree will have to periodically rebalance itself and perform a somewhat costly splitting operation when a node reaches max capacity after an insert, potentially causing additional splits to cascade up the tree. However, when the R-tree index is created on an already populated table, a special bottom up "bulk loading algorithm" (Sort-Tile-Recursive) is used, which divides all entries into an already balanced tree as the total number of required nodes can be computed from the beginning.
Additionally, using the bulk loading algorithm tends to create a R-tree with a better structure (less overlap between bounding boxes), which usually leads to better query performance. If you find that the performance of querying the R-tree starts to deteriorate after a large number of of updates or deletions, dropping and re-creating the index might produce a higher quality R-tree.
#### Memory Usage {#docs:extensions:spatial:r-tree_indexes::memory-usage}
Like DuckDB's built in ART-index, all the associated buffers containing the R-tree will be lazily loaded from disk (when running DuckDB in disk-backed mode), but they are currently never unloaded unless the index is dropped. This means that if you end up scanning the entire index, the entire index will be loaded into memory and stay there for the duration of the database connection. However, all memory used by the R-tree index (even during bulk-loading) is tracked by DuckDB, and will count towards the memory limit set by the `memory_limit` configuration parameter.
#### Tuning {#docs:extensions:spatial:r-tree_indexes::tuning}
Depending on you specific workload, you might want to experiment with the `max_node_capacity` and `min_node_capacity` options to change the structure of the R-tree and how it responds to insertions and deletions, see the [full list of options here](#::options). In general, a tree with a higher total number of nodes (i.e., a lower `max_node_capacity`) _may_ result in a more granular structure that enables more aggressive pruning of sub-trees during query execution, but it will also require more memory to store the tree itself and be more punishing when querying larger regions as more internal nodes will have to be traversed. | 510 | [
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|
Extensions | Spatial | Options | The following options can be passed to the `WITH` clause when creating an R-tree index: (e.g., `CREATE INDEX my_idx ON my_table USING RTREE (geom) WITH (⟨option⟩ = ⟨value⟩);`)
| Option | Description | Default |
|----------------------|-----------------------------------------------------------------------------------------------|---------|
| `max_node_capacity` | The maximum number of entries per node in the R-tree. | `128` |
| `min_node_capacity` | The minimum number of entries per node in the R-tree. | `0.4 * max_node_capacity` |
*Should a node fall under the minimum number of entries after a deletion, the node will be dissolved and all the entries reinserted from the top of the tree. This is a common operation in R-tree implementations to prevent the tree from becoming too unbalanced. | 181 | [
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|
Extensions | Spatial | R-Tree Table Functions | The `rtree_index_dump(VARCHAR)` table function can be used to return all the nodes within an R-tree index which might come on handy when debugging, profiling or otherwise just inspecting the structure of the index. The function takes the name of the R-tree index as an argument and returns a table with the following columns:
| Column Name | Type | Description |
|-------------|------|-------------|
| `level` | `INTEGER` | The level of the node in the R-tree. The root node has level 0. |
| `bounds` | `BOX_2DF` | The bounding box of the node. |
| `row_id` | `ROW_TYPE` | If this is a leaf node, the `rowid` of the row in the table, otherwise `NULL`. |
Example:
```sql
-- Create a table with 64 random points
CREATE TABLE t1 AS SELECT point::GEOMETRY AS geom
FROM st_generatepoints({min_x: 0, min_y: 0, max_x: 100, max_y: 100}::BOX_2D, 64, 1337);
-- Create an R-tree index on the geometry column (with a low max_node_capacity for demonstration purposes)
CREATE INDEX my_idx ON t1 USING RTREE (geom) WITH (max_node_capacity = 4);
-- Inspect the R-tree index. Notice how the area of the bounding boxes of the branch nodes
-- decreases as we go deeper into the tree.
SELECT
level,
bounds::GEOMETRY AS geom,
CASE WHEN row_id IS NULL THEN st_area(geom) ELSE NULL END AS area,
row_id,
CASE WHEN row_id IS NULL THEN 'branch' ELSE 'leaf' END AS kind
FROM rtree_index_dump('my_idx')
ORDER BY area DESC;
```
```text
┌───────┬──────────────────────────────┬────────────────────┬────────┬─────────┐
│ level │ geom │ area │ row_id │ kind │
│ int32 │ geometry │ double │ int64 │ varchar │
├───────┼──────────────────────────────┼────────────────────┼────────┼─────────┤
│ 0 │ POLYGON ((2.17285037040710… │ 3286.396482226409 │ │ branch │
│ 0 │ POLYGON ((6.00962591171264… │ 3193.725100864862 │ │ branch │
│ 0 │ POLYGON ((0.74995160102844… │ 3099.921458393704 │ │ branch │
│ 0 │ POLYGON ((14.6168870925903… │ 2322.2760491675654 │ │ branch │
│ 1 │ POLYGON ((2.17285037040710… │ 604.1520104388514 │ │ branch │
│ 1 │ POLYGON ((26.6022186279296… │ 569.1665467030252 │ │ branch │
│ 1 │ POLYGON ((35.7942314147949… │ 435.24662436250037 │ │ branch │
│ 1 │ POLYGON ((62.2643051147460… │ 396.39027683023596 │ │ branch │
│ 1 │ POLYGON ((59.5225715637207… │ 386.09153403820187 │ │ branch │
│ 1 │ POLYGON ((82.3060836791992… │ 369.15115640929434 │ │ branch │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ 2 │ POLYGON ((20.5411434173584… │ │ 35 │ leaf │
│ 2 │ POLYGON ((14.6168870925903… │ │ 36 │ leaf │
│ 2 │ POLYGON ((43.7271652221679… │ │ 39 │ leaf │
│ 2 │ POLYGON ((53.4629211425781… │ │ 44 │ leaf │
│ 2 │ POLYGON ((26.6022186279296… │ │ 62 │ leaf │
│ 2 │ POLYGON ((53.1732063293457… │ │ 63 │ leaf │
│ 2 │ POLYGON ((78.1427154541015… │ │ 10 │ leaf │
│ 2 │ POLYGON ((75.1728591918945… │ │ 15 │ leaf │
│ 2 │ POLYGON ((62.2643051147460… │ │ 42 │ leaf │
│ 2 │ POLYGON ((80.5032577514648… │ │ 49 │ leaf │
├───────┴──────────────────────────────┴────────────────────┴────────┴─────────┤
│ 84 rows (20 shown) 5 columns │
└──────────────────────────────────────────────────────────────────────────────┘
``` | 1,209 | [
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|
Extensions | Spatial | GDAL Integration | The spatial extension integrates the [GDAL](https://gdal.org/en/latest/) translator library to read and write spatial data from a variety of geospatial vector file formats. See the documentation for the [`st_read` table function](#docs:extensions:spatial:functions::st_read) for how to make use of this in practice.
In order to spare users from having to setup and install additional dependencies on their system, the spatial extension bundles its own copy of the GDAL library. This also means that spatial's version of GDAL may not be the latest version available or provide support for all of the file formats that a system-wide GDAL installation otherwise would. Refer to the section on the [`st_drivers` table function](#docs:extensions:spatial:functions::st_drivers) to inspect which GDAL drivers are currently available. | 176 | [
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|
Extensions | Spatial | GDAL Based `COPY` Function | The spatial extension does not only enable _importing_ geospatial file formats (through the `ST_Read` function), it also enables _exporting_ DuckDB tables to different geospatial vector formats through a GDAL based `COPY` function.
For example, to export a table to a GeoJSON file, with generated bounding boxes, you can use the following query:
```sql
COPY ⟨table⟩ TO 'some/file/path/filename.geojson'
WITH (FORMAT GDAL, DRIVER 'GeoJSON', LAYER_CREATION_OPTIONS 'WRITE_BBOX=YES');
```
Available options:
* `FORMAT`: is the only required option and must be set to `GDAL` to use the GDAL based copy function.
* `DRIVER`: is the GDAL driver to use for the export. Use `ST_Drivers()` to list the names of all available drivers.
* `LAYER_CREATION_OPTIONS`: list of options to pass to the GDAL driver. See the GDAL docs for the driver you are using for a list of available options.
* `SRS`: Set a spatial reference system as metadata to use for the export. This can be a WKT string, an EPSG code or a proj-string, basically anything you would normally be able to pass to GDAL. Note that this will **not** perform any reprojection of the input geometry, it just sets the metadata if the target driver supports it. | 296 | [
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||
Extensions | SQLite Extension | Installing and Loading | The `sqlite` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL sqlite;
LOAD sqlite;
``` | 59 | [
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|
Extensions | SQLite Extension | Usage | To make a SQLite file accessible to DuckDB, use the `ATTACH` statement with the `SQLITE` or `SQLITE_SCANNER` type. Attached SQLite databases support both read and write operations.
For example, to attach to the [`sakila.db` file](https://github.com/duckdb/sqlite_scanner/raw/main/data/db/sakila.db), run:
```sql
ATTACH 'sakila.db' (TYPE SQLITE);
USE sakila;
```
The tables in the file can be read as if they were normal DuckDB tables, but the underlying data is read directly from the SQLite tables in the file at query time.
```sql
SHOW TABLES;
```
<div class="narrow_table monospace_table"></div>
| name |
|------------------------|
| actor |
| address |
| category |
| city |
| country |
| customer |
| customer_list |
| film |
| film_actor |
| film_category |
| film_list |
| film_text |
| inventory |
| language |
| payment |
| rental |
| sales_by_film_category |
| sales_by_store |
| staff |
| staff_list |
| store |
You can query the tables using SQL, e.g., using the example queries from [`sakila-examples.sql`](https://github.com/duckdb/sqlite_scanner/blob/main/data/sql/sakila-examples.sql):
```sql
SELECT
cat.name AS category_name,
sum(ifnull(pay.amount, 0)) AS revenue
FROM category cat
LEFT JOIN film_category flm_cat
ON cat.category_id = flm_cat.category_id
LEFT JOIN film fil
ON flm_cat.film_id = fil.film_id
LEFT JOIN inventory inv
ON fil.film_id = inv.film_id
LEFT JOIN rental ren
ON inv.inventory_id = ren.inventory_id
LEFT JOIN payment pay
ON ren.rental_id = pay.rental_id
GROUP BY cat.name
ORDER BY revenue DESC
LIMIT 5;
``` | 440 | [
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|
Extensions | SQLite Extension | Data Types | SQLite is a [weakly typed database system](https://www.sqlite.org/datatype3.html). As such, when storing data in a SQLite table, types are not enforced. The following is valid SQL in SQLite:
```sql
CREATE TABLE numbers (i INTEGER);
INSERT INTO numbers VALUES ('hello');
```
DuckDB is a strongly typed database system, as such, it requires all columns to have defined types and the system rigorously checks data for correctness.
When querying SQLite, DuckDB must deduce a specific column type mapping. DuckDB follows SQLite's [type affinity rules](https://www.sqlite.org/datatype3.html#type_affinity) with a few extensions.
1. If the declared type contains the string `INT` then it is translated into the type `BIGINT`
2. If the declared type of the column contains any of the strings `CHAR`, `CLOB`, or `TEXT` then it is translated into `VARCHAR`.
3. If the declared type for a column contains the string `BLOB` or if no type is specified then it is translated into `BLOB`.
4. If the declared type for a column contains any of the strings `REAL`, `FLOA`, `DOUB`, `DEC` or `NUM` then it is translated into `DOUBLE`.
5. If the declared type is `DATE`, then it is translated into `DATE`.
6. If the declared type contains the string `TIME`, then it is translated into `TIMESTAMP`.
7. If none of the above apply, then it is translated into `VARCHAR`.
As DuckDB enforces the corresponding columns to contain only correctly typed values, we cannot load the string “hello” into a column of type `BIGINT`. As such, an error is thrown when reading from the “numbers” table above:
```console
Error: Mismatch Type Error: Invalid type in column "i": column was declared as integer, found "hello" of type "text" instead.
```
This error can be avoided by setting the `sqlite_all_varchar` option:
```sql
SET GLOBAL sqlite_all_varchar = true;
```
When set, this option overrides the type conversion rules described above, and instead always converts the SQLite columns into a `VARCHAR` column. Note that this setting must be set *before* `sqlite_attach` is called. | 487 | [
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|
Extensions | SQLite Extension | Opening SQLite Databases Directly | SQLite databases can also be opened directly and can be used transparently instead of a DuckDB database file. In any client, when connecting, a path to a SQLite database file can be provided and the SQLite database will be opened instead.
For example, with the shell, a SQLite database can be opened as follows:
```bash
duckdb sakila.db
```
```sql
SELECT first_name
FROM actor
LIMIT 3;
```
| first_name |
|------------|
| PENELOPE |
| NICK |
| ED | | 115 | [
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|
Extensions | SQLite Extension | Writing Data to SQLite | In addition to reading data from SQLite, the extension also allows you to create new SQLite database files, create tables, ingest data into SQLite and make other modifications to SQLite database files using standard SQL queries.
This allows you to use DuckDB to, for example, export data that is stored in a SQLite database to Parquet, or read data from a Parquet file into SQLite.
Below is a brief example of how to create a new SQLite database and load data into it.
```sql
ATTACH 'new_sqlite_database.db' AS sqlite_db (TYPE SQLITE);
CREATE TABLE sqlite_db.tbl (id INTEGER, name VARCHAR);
INSERT INTO sqlite_db.tbl VALUES (42, 'DuckDB');
```
The resulting SQLite database can then be read into from SQLite.
```bash
sqlite3 new_sqlite_database.db
```
```sql
SQLite version 3.39.5 2022-10-14 20:58:05
sqlite> SELECT * FROM tbl;
```
```text
id name
-- ------
42 DuckDB
```
Many operations on SQLite tables are supported. All these operations directly modify the SQLite database, and the result of subsequent operations can then be read using SQLite. | 253 | [
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|
Extensions | SQLite Extension | Concurrency | DuckDB can read or modify a SQLite database while DuckDB or SQLite reads or modifies the same database from a different thread or a separate process. More than one thread or process can read the SQLite database at the same time, but only a single thread or process can write to the database at one time. Database locking is handled by the SQLite library, not DuckDB. Within the same process, SQLite uses mutexes. When accessed from different processes, SQLite uses file system locks. The locking mechanisms also depend on SQLite configuration, like WAL mode. Refer to the [SQLite documentation on locking](https://www.sqlite.org/lockingv3.html) for more information.
> **Warning. ** Linking multiple copies of the SQLite library into the same application can lead to application errors. See [sqlite_scanner Issue #82](https://github.com/duckdb/sqlite_scanner/issues/82) for more information. | 187 | [
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-0.0017703507328405976,
-0.005103739444166422,
0.014520001597702503,
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] |
|
Extensions | SQLite Extension | Supported Operations | Below is a list of supported operations.
#### `CREATE TABLE` {#docs:extensions:sqlite::create-table}
```sql
CREATE TABLE sqlite_db.tbl (id INTEGER, name VARCHAR);
```
#### `INSERT INTO` {#docs:extensions:sqlite::insert-into}
```sql
INSERT INTO sqlite_db.tbl VALUES (42, 'DuckDB');
```
#### `SELECT` {#docs:extensions:sqlite::select}
```sql
SELECT * FROM sqlite_db.tbl;
```
| id | name |
|---:|--------|
| 42 | DuckDB |
#### `COPY` {#docs:extensions:sqlite::copy}
```sql
COPY sqlite_db.tbl TO 'data.parquet';
COPY sqlite_db.tbl FROM 'data.parquet';
```
#### `UPDATE` {#docs:extensions:sqlite::update}
```sql
UPDATE sqlite_db.tbl SET name = 'Woohoo' WHERE id = 42;
```
#### `DELETE` {#docs:extensions:sqlite::delete}
```sql
DELETE FROM sqlite_db.tbl WHERE id = 42;
```
#### `ALTER TABLE` {#docs:extensions:sqlite::alter-table}
```sql
ALTER TABLE sqlite_db.tbl ADD COLUMN k INTEGER;
```
#### `DROP TABLE` {#docs:extensions:sqlite::drop-table}
```sql
DROP TABLE sqlite_db.tbl;
```
#### `CREATE VIEW` {#docs:extensions:sqlite::create-view}
```sql
CREATE VIEW sqlite_db.v1 AS SELECT 42;
```
#### Transactions {#docs:extensions:sqlite::transactions}
```sql
CREATE TABLE sqlite_db.tmp (i INTEGER);
```
```sql
BEGIN;
INSERT INTO sqlite_db.tmp VALUES (42);
SELECT * FROM sqlite_db.tmp;
```
| i |
|---:|
| 42 |
```sql
ROLLBACK;
SELECT * FROM sqlite_db.tmp;
```
| i |
|--:|
| |
> **Deprecated. ** The old `sqlite_attach` function is deprecated. It is recommended to switch over to the new [`ATTACH` syntax](#docs:sql:statements:attach). | 458 | [
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|
Extensions | Substrait Extension | The main goal of the `substrait` extension is to support both production and consumption of [Substrait](https://substrait.io/) query plans in DuckDB.
This extension is mainly exposed via 3 different APIs – the SQL API, the Python API, and the R API.
Here we depict how to consume and produce Substrait query plans in each API.
> The Substrait integration is currently experimental. Support is currently only available on request.
> If you have not asked for permission to ask for support, [contact us prior to opening an issue](https://duckdblabs.com/contact/).
> If you open an issue without doing so, we will close it without further review. | 145 | [
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||
Extensions | Substrait Extension | Installing and Loading | The Substrait extension is an autoloadable extensions, meaning that it will be loaded at runtime whenever one of the substrait functions is called. To explicitly install and load the released version of the Substrait extension, you can also use the following SQL commands.
```sql
INSTALL substrait;
LOAD substrait;
``` | 66 | [
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|
Extensions | Substrait Extension | SQL | In the SQL API, users can generate Substrait plans (into a BLOB or a JSON) and consume Substrait plans.
#### BLOB Generation {#docs:extensions:substrait::blob-generation}
To generate a Substrait BLOB the `get_substrait(sql)` function must be called with a valid SQL select query.
```sql
CREATE TABLE crossfit (exercise TEXT, difficulty_level INTEGER);
INSERT INTO crossfit VALUES ('Push Ups', 3), ('Pull Ups', 5), ('Push Jerk', 7), ('Bar Muscle Up', 10);
```
```sql
.mode line
CALL get_substrait('SELECT count(exercise) AS exercise FROM crossfit WHERE difficulty_level <= 5');
```
```text
Plan BLOB = \x12\x09\x1A\x07\x10\x01\x1A\x03lte\x12\x11\x1A\x0F\x10\x02\x1A\x0Bis_not_null\x12\x09\x1A\x07\x10\x03\x1A\x03and\x12\x0B\x1A\x09\x10\x04\x1A\x05count\x1A\xC8\x01\x12\xC5\x01\x0A\xB8\x01:\xB5\x01\x12\xA8\x01\x22\xA5\x01\x12\x94\x01\x0A\x91\x01\x12/\x0A\x08exercise\x0A\x10difficulty_level\x12\x11\x0A\x07\xB2\x01\x04\x08\x0D\x18\x01\x0A\x04*\x02\x10\x01\x18\x02\x1AJ\x1AH\x08\x03\x1A\x04\x0A\x02\x10\x01\x22\x22\x1A \x1A\x1E\x08\x01\x1A\x04*\x02\x10\x01\x22\x0C\x1A\x0A\x12\x08\x0A\x04\x12\x02\x08\x01\x22\x00\x22\x06\x1A\x04\x0A\x02(\x05\x22\x1A\x1A\x18\x1A\x16\x08\x02\x1A\x04*\x02\x10\x01\x22\x0C\x1A\x0A\x12\x08\x0A\x04\x12\x02\x08\x01\x22\x00\x22\x06\x0A\x02\x0A\x00\x10\x01:\x0A\x0A\x08crossfit\x1A\x00\x22\x0A\x0A\x08\x08\x04*\x04:\x02\x10\x01\x1A\x08\x12\x06\x0A\x02\x12\x00\x22\x00\x12\x08exercise2\x0A\x10\x18*\x06DuckDB
```
#### JSON Generation {#docs:extensions:substrait::json-generation}
To generate a JSON representing the Substrait plan the `get_substrait_json(sql)` function must be called with a valid SQL select query.
```sql
CALL get_substrait_json('SELECT count(exercise) AS exercise FROM crossfit WHERE difficulty_level <= 5');
```
```json
Json = {"extensions":[{"extensionFunction":{"functionAnchor":1,"name":"lte"}},{"extensionFunction":{"functionAnchor":2,"name":"is_not_null"}},{"extensionFunction":{"functionAnchor":3,"name":"and"}},{"extensionFunction":{"functionAnchor":4,"name":"count"}}],"relations":[{"root":{"input":{"project":{"input":{"aggregate":{"input":{"read":{"baseSchema":{"names":["exercise","difficulty_level"],"struct":{"types":[{"varchar":{"length":13,"nullability":"NULLABILITY_NULLABLE"}},{"i32":{"nullability":"NULLABILITY_NULLABLE"}}],"nullability":"NULLABILITY_REQUIRED"}},"filter":{"scalarFunction":{"functionReference":3,"outputType":{"bool":{"nullability":"NULLABILITY_NULLABLE"}},"arguments":[{"value":{"scalarFunction":{"functionReference":1,"outputType":{"i32":{"nullability":"NULLABILITY_NULLABLE"}},"arguments":[{"value":{"selection":{"directReference":{"structField":{"field":1}},"rootReference":{}}}},{"value":{"literal":{"i32":5}}}]}}},{"value":{"scalarFunction":{"functionReference":2,"outputType":{"i32":{"nullability":"NULLABILITY_NULLABLE"}},"arguments":[{"value":{"selection":{"directReference":{"structField":{"field":1}},"rootReference":{}}}}]}}}]}},"projection":{"select":{"structItems":[{}]},"maintainSingularStruct":true},"namedTable":{"names":["crossfit"]}}},"groupings":[{}],"measures":[{"measure":{"functionReference":4,"outputType":{"i64":{"nullability":"NULLABILITY_NULLABLE"}}}}]}},"expressions":[{"selection":{"directReference":{"structField":{}},"rootReference":{}}}]}},"names":["exercise"]}}],"version":{"minorNumber":24,"producer":"DuckDB"}}
```
#### BLOB Consumption {#docs:extensions:substrait::blob-consumption}
To consume a Substrait BLOB the `from_substrait(blob)` function must be called with a valid Substrait BLOB plan.
```sql
CALL from_substrait('\x12\x09\x1A\x07\x10\x01\x1A\x03lte\x12\x11\x1A\x0F\x10\x02\x1A\x0Bis_not_null\x12\x09\x1A\x07\x10\x03\x1A\x03and\x12\x0B\x1A\x09\x10\x04\x1A\x05count\x1A\xC8\x01\x12\xC5\x01\x0A\xB8\x01:\xB5\x01\x12\xA8\x01\x22\xA5\x01\x12\x94\x01\x0A\x91\x01\x12/\x0A\x08exercise\x0A\x10difficulty_level\x12\x11\x0A\x07\xB2\x01\x04\x08\x0D\x18\x01\x0A\x04*\x02\x10\x01\x18\x02\x1AJ\x1AH\x08\x03\x1A\x04\x0A\x02\x10\x01\x22\x22\x1A \x1A\x1E\x08\x01\x1A\x04*\x02\x10\x01\x22\x0C\x1A\x0A\x12\x08\x0A\x04\x12\x02\x08\x01\x22\x00\x22\x06\x1A\x04\x0A\x02(\x05\x22\x1A\x1A\x18\x1A\x16\x08\x02\x1A\x04*\x02\x10\x01\x22\x0C\x1A\x0A\x12\x08\x0A\x04\x12\x02\x08\x01\x22\x00\x22\x06\x0A\x02\x0A\x00\x10\x01:\x0A\x0A\x08crossfit\x1A\x00\x22\x0A\x0A\x08\x08\x04*\x04:\x02\x10\x01\x1A\x08\x12\x06\x0A\x02\x12\x00\x22\x00\x12\x08exercise2\x0A\x10\x18*\x06DuckDB'::BLOB);
```
```text
exercise = 2
``` | 1,599 | [
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|
Extensions | Substrait Extension | Python | Substrait extension is autoloadable, but if you prefer to do so explicitly, you can use the relevant Python syntax within a connection:
```python
import duckdb
con = duckdb.connect()
con.install_extension("substrait")
con.load_extension("substrait")
```
#### BLOB Generation {#docs:extensions:substrait::blob-generation}
To generate a Substrait BLOB the `get_substrait(sql)` function must be called, from a connection, with a valid SQL select query.
```python
con.execute(query = "CREATE TABLE crossfit (exercise TEXT, difficulty_level INTEGER)")
con.execute(query = "INSERT INTO crossfit VALUES ('Push Ups', 3), ('Pull Ups', 5), ('Push Jerk', 7), ('Bar Muscle Up', 10)")
proto_bytes = con.get_substrait(query="SELECT count(exercise) AS exercise FROM crossfit WHERE difficulty_level <= 5").fetchone()[0]
```
#### JSON Generation {#docs:extensions:substrait::json-generation}
To generate a JSON representing the Substrait plan the `get_substrait_json(sql)` function, from a connection, must be called with a valid SQL select query.
```python
json = con.get_substrait_json("SELECT count(exercise) AS exercise FROM crossfit WHERE difficulty_level <= 5").fetchone()[0]
```
#### BLOB Consumption {#docs:extensions:substrait::blob-consumption}
To consume a Substrait BLOB the `from_substrait(blob)` function must be called, from the connection, with a valid Substrait BLOB plan.
```python
query_result = con.from_substrait(proto=proto_bytes)
``` | 356 | [
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|
Extensions | Substrait Extension | R | By default the extension will be autoloaded on first use. To explicitly install and load this extension in R, use the following commands:
```r
library("duckdb")
con <- dbConnect(duckdb::duckdb())
dbExecute(con, "INSTALL substrait")
dbExecute(con, "LOAD substrait")
```
#### BLOB Generation {#docs:extensions:substrait::blob-generation}
To generate a Substrait BLOB the `duckdb_get_substrait(con, sql)` function must be called, with a connection and a valid SQL select query.
```r
dbExecute(con, "CREATE TABLE crossfit (exercise TEXT, difficulty_level INTEGER)")
dbExecute(con, "INSERT INTO crossfit VALUES ('Push Ups', 3), ('Pull Ups', 5), ('Push Jerk', 7), ('Bar Muscle Up', 10)")
proto_bytes <- duckdb::duckdb_get_substrait(con, "SELECT * FROM crossfit LIMIT 5")
```
#### JSON Generation {#docs:extensions:substrait::json-generation}
To generate a JSON representing the Substrait plan `duckdb_get_substrait_json(con, sql)` function, with a connection and a valid SQL select query.
```r
json <- duckdb::duckdb_get_substrait_json(con, "SELECT count(exercise) AS exercise FROM crossfit WHERE difficulty_level <= 5")
```
#### BLOB Consumption {#docs:extensions:substrait::blob-consumption}
To consume a Substrait BLOB the `duckdb_prepare_substrait(con, blob)` function must be called, with a connection and a valid Substrait BLOB plan.
```r
result <- duckdb::duckdb_prepare_substrait(con, proto_bytes)
df <- dbFetch(result)
``` | 372 | [
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|
Extensions | TPC-DS Extension | The `tpcds` extension implements the data generator and queries for the [TPC-DS benchmark](https://www.tpc.org/tpcds/). | 34 | [
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||
Extensions | TPC-DS Extension | Installing and Loading | The `tpcds` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL tpcds;
LOAD tpcds;
``` | 65 | [
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|
Extensions | TPC-DS Extension | Usage | To generate data for scale factor 1, use:
```sql
CALL dsdgen(sf = 1);
```
To run a query, e.g., query 8, use:
```sql
PRAGMA tpcds(8);
```
| s_store_name | sum(ss_net_profit) |
|--------------|-------------------:|
| able | -10354620.18 |
| ation | -10576395.52 |
| bar | -10625236.01 |
| ese | -10076698.16 |
| ought | -10994052.78 | | 133 | [
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|
Extensions | TPC-DS Extension | Generating the Schema | It's possible to generate the schema of TPC-DS without any data by setting the scale factor to 0:
```sql
CALL dsdgen(sf = 0);
``` | 38 | [
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|
Extensions | TPC-DS Extension | Limitations | The `tpchds(⟨query_id⟩)` function runs a fixed TPC-DS query with pre-defined bind parameters (a.k.a. substitution parameters).
It is not possible to change the query parameters using the `tpcds` extension. | 52 | [
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|
Extensions | TPC-H Extension | The `tpch` extension implements the data generator and queries for the [TPC-H benchmark](https://www.tpc.org/tpch/). | 31 | [
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||
Extensions | TPC-H Extension | Installing and Loading | The `tpch` extension is shipped by default in some DuckDB builds, otherwise it will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use.
If you would like to install and load it manually, run:
```sql
INSTALL tpch;
LOAD tpch;
``` | 69 | [
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|
Extensions | TPC-H Extension | Usage | #### Generating Data {#docs:extensions:tpch::generating-data}
To generate data for scale factor 1, use:
```sql
CALL dbgen(sf = 1);
```
Calling `dbgen` does not clean up existing TPC-H tables.
To clean up existing tables, use `DROP TABLE` before running `dbgen`:
```sql
DROP TABLE IF EXISTS customer;
DROP TABLE IF EXISTS lineitem;
DROP TABLE IF EXISTS nation;
DROP TABLE IF EXISTS orders;
DROP TABLE IF EXISTS part;
DROP TABLE IF EXISTS partsupp;
DROP TABLE IF EXISTS region;
DROP TABLE IF EXISTS supplier;
```
#### Running a Query {#docs:extensions:tpch::running-a-query}
To run a query, e.g., query 4, use:
```sql
PRAGMA tpch(4);
```
| o_orderpriority | order_count |
|-----------------|------------:|
| 1-URGENT | 10594 |
| 2-HIGH | 10476 |
| 3-MEDIUM | 10410 |
| 4-NOT SPECIFIED | 10556 |
| 5-LOW | 10487 |
#### Listing Queries {#docs:extensions:tpch::listing-queries}
To list all 22 queries, run:
```sql
FROM tpch_queries();
```
This function returns a table with columns `query_nr` and `query`.
#### Listing Expected Answers {#docs:extensions:tpch::listing-expected-answers}
To produced the expected results for all queries on scale factors 0.01, 0.1, and 1, run:
```sql
FROM tpch_answers();
```
This function returns a table with columns `query_nr`, `scale_factor`, and `answer`. | 384 | [
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|
Extensions | TPC-H Extension | Generating the Schema | It's possible to generate the schema of TPC-H without any data by setting the scale factor to 0:
```sql
CALL dbgen(sf = 0);
``` | 36 | [
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|
Extensions | TPC-H Extension | Data Generator Parameters | The data generator function `dbgen` has the following parameters:
<div class="narrow_table"></div>
| Name | Type | Description |
|--|--|------------|
| `catalog` | `VARCHAR` | Target catalog |
| `children` | `UINTEGER` | Number of partitions |
| `overwrite` | `BOOLEAN` | (Not used) |
| `sf` | `DOUBLE` | Scale factor |
| `step` | `UINTEGER` | Defines the partition to be generated, indexed from 0 to `children` - 1. Must be defined when the `children` arguments is defined |
| `suffix` | `VARCHAR` | Append the `suffix` to table names | | 165 | [
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|
Extensions | TPC-H Extension | Resource Usage of the Data Generator | Generating TPC-H data sets for large scale factors takes a significant amount of time.
Additionally, when the generation is done in a single step, it requires a large amount of memory.
The following table gives an estimate on the resources required to produce DuckDB database files containing the generated TPC-H data set using 128 threads.
| Scale factor | Database size | Data generation time | Generator's memory usage |
| -----------: | ------------: | -------------------: | -----------------------: |
| 100 | 26 GB | 17 minutes | 71 GB |
| 300 | 78 GB | 51 minutes | 211 GB |
| 1000 | 265 GB | 2h 53 minutes | 647 GB |
| 3000 | 796 GB | 8h 30 minutes | 1799 GB |
The numbers shown above were achieved by running the `dbgen` function in a single step, for example:
```sql
CALL dbgen(sf = 300);
```
If you have a limited amount of memory available, you can run the `dbgen` function in steps.
For example, you may generate SF300 in 10 steps:
```sql
CALL dbgen(sf = 300, children = 10, step = 0);
CALL dbgen(sf = 300, children = 10, step = 1);
...
CALL dbgen(sf = 300, children = 10, step = 9);
``` | 319 | [
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|
Extensions | TPC-H Extension | Limitation | The `tpch(⟨query_id⟩)` function runs a fixed TPC-H query with pre-defined bind parameters (a.k.a. substitution parameters). It is not possible to change the query parameters using the `tpch` extension. To run the queries with the parameters prescribed by the TPC-H benchmark, use a TPC-H framework implementation. | 72 | [
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|
Extensions | Vector Similarity Search Extension | The `vss` extension is an experimental extension for DuckDB that adds indexing support to accelerate vector similarity search queries using DuckDB's new fixed-size `ARRAY` type.
See the [announcement blog post](https://duckdb.org/2024/05/03/vector-similarity-search-vss) and the [“What's New in the Vector Similarity Search Extension?” post](https://duckdb.org/2024/10/23/whats-new-in-the-vss-extension). | 102 | [
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||
Extensions | Vector Similarity Search Extension | Usage | To create a new HNSW (Hierarchical Navigable Small Worlds) index on a table with an `ARRAY` column, use the `CREATE INDEX` statement with the `USING HNSW` clause. For example:
```sql
CREATE TABLE my_vector_table (vec FLOAT[3]);
INSERT INTO my_vector_table SELECT array_value(a, b, c) FROM range(1, 10) ra(a), range(1, 10) rb(b), range(1, 10) rc(c);
CREATE INDEX my_hnsw_index ON my_vector_table USING HNSW (vec);
```
The index will then be used to accelerate queries that use a `ORDER BY` clause evaluating one of the supported distance metric functions against the indexed columns and a constant vector, followed by a `LIMIT` clause. For example:
```sql
SELECT * FROM my_vector_table ORDER BY array_distance(vec, [1, 2, 3]::FLOAT[3]) LIMIT 3;
```
Additionally, the overloaded `min_by(col, arg, n)` can also be accelerated with the `HNSW` index if the `arg` argument is a matching distance metric function. This can be used to do quick one-shot nearest neighbor searches. For example, to get the top 3 rows with the closest vectors to `[1, 2, 3]`:
```sql
SELECT min_by(my_vector_table, array_distance(vec, [1, 2, 3]::FLOAT[3]), 3) AS result FROM my_vector_table;
---- [{'vec': [1.0, 2.0, 3.0]}, {'vec': [1.0, 2.0, 4.0]}, {'vec': [2.0, 2.0, 3.0]}]
```
Note how we pass the table name as the first argument to `min_by` to return a struct containing the entire matched row.
We can verify that the index is being used by checking the `EXPLAIN` output and looking for the `HNSW_INDEX_SCAN` node in the plan:
```sql
EXPLAIN SELECT * FROM my_vector_table ORDER BY array_distance(vec, [1, 2, 3]::FLOAT[3]) LIMIT 3;
```
```text
┌───────────────────────────┐
│ PROJECTION │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ #0 │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ PROJECTION │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ vec │
│array_distance(vec, [1.0, 2│
│ .0, 3.0]) │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ HNSW_INDEX_SCAN │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ t1 (HNSW INDEX SCAN : │
│ my_idx) │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ vec │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ EC: 3 │
└───────────────────────────┘
```
By default the HNSW index will be created using the euclidean distance `l2sq` (L2-norm squared) metric, matching DuckDBs `array_distance` function, but other distance metrics can be used by specifying the `metric` option during index creation. For example:
```sql
CREATE INDEX my_hnsw_cosine_index
ON my_vector_table
USING HNSW (vec)
WITH (metric = 'cosine');
```
The following table shows the supported distance metrics and their corresponding DuckDB functions
| Metric | Function | Description |
| -------- | ------------------------- | ------------------ |
| `l2sq` | `array_distance` | Euclidean distance |
| `cosine` | `array_cosine_distance` | Cosine similarity distance |
| `ip` | `array_negative_inner_product` | Negative inner product |
Note that while each `HNSW` index only applies to a single column you can create multiple `HNSW` indexes on the same table each individually indexing a different column. Additionally, you can also create multiple `HNSW` indexes to the same column, each supporting a different distance metric. | 1,043 | [
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|
Extensions | Vector Similarity Search Extension | Index Options | Besides the `metric` option, the `HNSW` index creation statement also supports the following options to control the hyperparameters of the index construction and search process:
| Option | Default | Description |
|-------|--:|----------------------------|
| `ef_construction` | 128 | The number of candidate vertices to consider during the construction of the index. A higher value will result in a more accurate index, but will also increase the time it takes to build the index. |
| `ef_search` | 64 | The number of candidate vertices to consider during the search phase of the index. A higher value will result in a more accurate index, but will also increase the time it takes to perform a search. |
| `M` | 16 | The maximum number of neighbors to keep for each vertex in the graph. A higher value will result in a more accurate index, but will also increase the time it takes to build the index. |
| `M0` | 2 * `M` | The base connectivity, or the number of neighbors to keep for each vertex in the zero-th level of the graph. A higher value will result in a more accurate index, but will also increase the time it takes to build the index. |
Additionally, you can also override the `ef_search` parameter set at index construction time by setting the `SET hnsw_ef_search = ⟨int⟩` configuration option at runtime. This can be useful if you want to trade search performance for accuracy or vice-versa on a per-connection basis. You can also unset the override by calling `RESET hnsw_ef_search`. | 340 | [
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|
Extensions | Vector Similarity Search Extension | Persistence | Due to some known issues related to peristence of custom extension indexes, the `HNSW` index can only be created on tables in in-memory databases by default, unless the `SET hnsw_enable_experimental_persistence = ⟨bool⟩` configuration option is set to `true`.
The reasoning for locking this feature behind an experimental flag is that “WAL” recovery is not yet properly implemented for custom indexes, meaning that if a crash occurs or the database is shut down unexpectedly while there are uncommitted changes to a `HNSW`-indexed table, you can end up with **data loss or corruption of the index**.
If you enable this option and experience an unexpected shutdown, you can try to recover the index by first starting DuckDB separately, loading the `vss` extension and then `ATTACH`ing the database file, which ensures that the `HNSW` index functionality is available during WAL-playback, allowing DuckDB's recovery process to proceed without issues. But we still recommend that you do not use this feature in production environments.
With the `hnsw_enable_experimental_persistence` option enabled, the index will be persisted into the DuckDB database file (if you run DuckDB with a disk-backed database file), which means that after a database restart, the index can be loaded back into memory from disk instead of having to be re-created. With that in mind, there are no incremental updates to persistent index storage, so every time DuckDB performs a checkpoint the entire index will be serialized to disk and overwrite itself. Similarly, after a restart of the database, the index will be deserialized back into main memory in its entirety. Although this will be deferred until you first access the table associated with the index. Depending on how large the index is, the deserialization process may take some time, but it should still be faster than simply dropping and re-creating the index. | 390 | [
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|
Extensions | Vector Similarity Search Extension | Inserts, Updates, Deletes and Re-Compaction | The HNSW index does support inserting, updating and deleting rows from the table after index creation. However, there are two things to keep in mind:
* It's faster to create the index after the table has been populated with data as the initial bulk load can make better use of parallelism on large tables.
* Deletes are not immediately reflected in the index, but are instead “marked” as deleted, which can cause the index to grow stale over time and negatively impact query quality and performance.
To remedy the last point, you can call the `PRAGMA hnsw_compact_index('⟨index name⟩')` pragma function to trigger a re-compaction of the index pruning deleted items, or re-create the index after a significant number of updates. | 155 | [
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|
Extensions | Vector Similarity Search Extension | Bonus: Vector Similarity Search Joins | The `vss` extension also provides a couple of table macros to simplify matching multiple vectors against eachother, so called "fuzzy joins". These are:
* `vss_join(left_table, right_table, left_col, right_col, k, metric := 'l2sq')`
* `vss_match(right_table", left_col, right_col, k, metric := 'l2sq')`
These **do not** currently make use of the `HNSW` index but are provided as convenience utility functions for users who are ok with performing brute-force vector similarity searches without having to write out the join logic themselves. In the future these might become targets for index-based optimizations as well.
These functions can be used as follows:
```sql
CREATE TABLE haystack (id int, vec FLOAT[3]);
CREATE TABLE needle (search_vec FLOAT[3]);
INSERT INTO haystack SELECT row_number() OVER (), array_value(a,b,c)
FROM range(1, 10) ra(a), range(1, 10) rb(b), range(1, 10) rc(c);
INSERT INTO needle VALUES ([5, 5, 5]), ([1, 1, 1]);
SELECT * FROM vss_join(needle, haystack, search_vec, vec, 3) AS res;
```
```text
┌───────┬─────────────────────────────────┬─────────────────────────────────────┐
│ score │ left_tbl │ right_tbl │
│ float │ struct(search_vec float[3]) │ struct(id integer, vec float[3]) │
├───────┼─────────────────────────────────┼─────────────────────────────────────┤
│ 0.0 │ {'search_vec': [5.0, 5.0, 5.0]} │ {'id': 365, 'vec': [5.0, 5.0, 5.0]} │
│ 1.0 │ {'search_vec': [5.0, 5.0, 5.0]} │ {'id': 364, 'vec': [5.0, 4.0, 5.0]} │
│ 1.0 │ {'search_vec': [5.0, 5.0, 5.0]} │ {'id': 356, 'vec': [4.0, 5.0, 5.0]} │
│ 0.0 │ {'search_vec': [1.0, 1.0, 1.0]} │ {'id': 1, 'vec': [1.0, 1.0, 1.0]} │
│ 1.0 │ {'search_vec': [1.0, 1.0, 1.0]} │ {'id': 10, 'vec': [2.0, 1.0, 1.0]} │
│ 1.0 │ {'search_vec': [1.0, 1.0, 1.0]} │ {'id': 2, 'vec': [1.0, 2.0, 1.0]} │
└───────┴─────────────────────────────────┴─────────────────────────────────────┘
```
```sql
-- Alternatively, we can use the vss_match macro as a "lateral join"
-- to get the matches already grouped by the left table.
-- Note that this requires us to specify the left table first, and then
-- the vss_match macro which references the search column from the left
-- table (in this case, `search_vec`).
SELECT * FROM needle, vss_match(haystack, search_vec, vec, 3) AS res;
```
```text
┌─────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ search_vec │ matches │
│ float[3] │ struct(score float, "row" struct(id integer, vec float[3]))[] │
├─────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [5.0, 5.0, 5.0] │ [{'score': 0.0, 'row': {'id': 365, 'vec': [5.0, 5.0, 5.0]}}, {'score': 1.0, 'row': {'id': 364, 'vec': [5.0, 4.0, 5.0]}}, {'score': 1.0, 'row': {'id': 356, 'vec': [4.0, 5.0, 5.0]}}] │
│ [1.0, 1.0, 1.0] │ [{'score': 0.0, 'row': {'id': 1, 'vec': [1.0, 1.0, 1.0]}}, {'score': 1.0, 'row': {'id': 10, 'vec': [2.0, 1.0, 1.0]}}, {'score': 1.0, 'row': {'id': 2, 'vec': [1.0, 2.0, 1.0]}}] │
└─────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
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|
Extensions | Vector Similarity Search Extension | Limitations | * Only vectors consisting of `FLOAT`s (32-bit, single precision) are supported at the moment.
* The index itself is not buffer managed and must be able to fit into RAM memory.
* The size of the index in memory does not count towards DuckDB's `memory_limit` configuration parameter.
* `HNSW` indexes can only be created on tables in in-memory databases, unless the `SET hnsw_enable_experimental_persistence = ⟨bool⟩` configuration option is set to `true`, see [Persistence](#::persistence) for more information.
* The vector join table macros (` vss_join` and `vss_match`) do not require or make use of the `HNSW` index. | 148 | [
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|
Guides | Guides | The guides section contains compact how-to guides that are focused on achieving a single goal. For an API references and examples, see the rest of the documentation.
Note that there are many tools using DuckDB, which are not covered in the official guides. To find a list of these tools, check out the [Awesome DuckDB repository](https://github.com/davidgasquez/awesome-duckdb).
> **Tip. ** For a short introductory tutorial, check out the [“Analyzing Railway Traffic in the Netherlands”](https://duckdb.org/2024/05/31/analyzing-railway-traffic-in-the-netherlands) tutorial. | 136 | [
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||
Guides | Guides | Data Import and Export | * [Data import overview](#docs:guides:file_formats:overview)
#### CSV Files {#docs:guides:overview::csv-files}
* [How to load a CSV file into a table](#docs:guides:file_formats:csv_import)
* [How to export a table to a CSV file](#docs:guides:file_formats:csv_export)
#### Parquet Files {#docs:guides:overview::parquet-files}
* [How to load a Parquet file into a table](#docs:guides:file_formats:parquet_import)
* [How to export a table to a Parquet file](#docs:guides:file_formats:parquet_export)
* [How to run a query directly on a Parquet file](#docs:guides:file_formats:query_parquet)
#### HTTP(S), S3 and GCP {#docs:guides:overview::https-s3-and-gcp}
* [How to load a Parquet file directly from HTTP(S)](#docs:guides:network_cloud_storage:http_import)
* [How to load a Parquet file directly from S3](#docs:guides:network_cloud_storage:s3_import)
* [How to export a Parquet file to S3](#docs:guides:network_cloud_storage:s3_export)
* [How to load a Parquet file from S3 Express One](#docs:guides:network_cloud_storage:s3_express_one)
* [How to load a Parquet file directly from GCS](#docs:guides:network_cloud_storage:gcs_import)
* [How to load a Parquet file directly from Cloudflare R2](#docs:guides:network_cloud_storage:cloudflare_r2_import)
* [How to load an Iceberg table directly from S3](#docs:guides:network_cloud_storage:s3_iceberg_import)
#### JSON Files {#docs:guides:overview::json-files}
* [How to load a JSON file into a table](#docs:guides:file_formats:json_import)
* [How to export a table to a JSON file](#docs:guides:file_formats:json_export)
#### Excel Files with the Spatial Extension {#docs:guides:overview::excel-files-with-the-spatial-extension}
* [How to load an Excel file into a table](#docs:guides:file_formats:excel_import)
* [How to export a table to an Excel file](#docs:guides:file_formats:excel_export)
#### Querying Other Database Systems {#docs:guides:overview::querying-other-database-systems}
* [How to directly query a MySQL database](#docs:guides:database_integration:mysql)
* [How to directly query a PostgreSQL database](#docs:guides:database_integration:postgres)
* [How to directly query a SQLite database](#docs:guides:database_integration:sqlite)
#### Directly Reading Files {#docs:guides:overview::directly-reading-files}
* [How to directly read a binary file](#docs:guides:file_formats:read_file::read_blob)
* [How to directly read a text file](#docs:guides:file_formats:read_file::read_text) | 703 | [
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|
Guides | Guides | Performance | * [My workload is slow (troubleshooting guide)](#docs:guides:performance:my_workload_is_slow)
* [How to design the schema for optimal performance](#docs:guides:performance:schema)
* [What is the ideal hardware environment for DuckDB](#docs:guides:performance:environment)
* [What performance implications do Parquet files and (compressed) CSV files have](#docs:guides:performance:file_formats)
* [How to tune workloads](#docs:guides:performance:how_to_tune_workloads)
* [Benchmarks](#docs:guides:performance:benchmarks) | 137 | [
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|
Guides | Guides | Meta Queries | * [How to list all tables](#docs:guides:meta:list_tables)
* [How to view the schema of the result of a query](#docs:guides:meta:describe)
* [How to quickly get a feel for a dataset using summarize](#docs:guides:meta:summarize)
* [How to view the query plan of a query](#docs:guides:meta:explain)
* [How to profile a query](#docs:guides:meta:explain_analyze) | 110 | [
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|
Guides | Guides | ODBC | * [How to set up an ODBC application (and more!)]({% link docs/guides/odbc/general.md %}) | 29 | [
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] |
|
Guides | Guides | Python Client | * [How to install the Python client](#docs:guides:python:install)
* [How to execute SQL queries](#docs:guides:python:execute_sql)
* [How to easily query DuckDB in Jupyter Notebooks](#docs:guides:python:jupyter)
* [How to use Multiple Python Threads with DuckDB](#docs:guides:python:multiple_threads)
* [How to use fsspec filesystems with DuckDB](#docs:guides:python:filesystems)
#### Pandas {#docs:guides:overview::pandas}
* [How to execute SQL on a Pandas DataFrame](#docs:guides:python:sql_on_pandas)
* [How to create a table from a Pandas DataFrame](#docs:guides:python:import_pandas)
* [How to export data to a Pandas DataFrame](#docs:guides:python:export_pandas)
#### Apache Arrow {#docs:guides:overview::apache-arrow}
* [How to execute SQL on Apache Arrow](#docs:guides:python:sql_on_arrow)
* [How to create a DuckDB table from Apache Arrow](#docs:guides:python:import_arrow)
* [How to export data to Apache Arrow](#docs:guides:python:export_arrow)
#### Relational API {#docs:guides:overview::relational-api}
* [How to query Pandas DataFrames with the Relational API](#docs:guides:python:relational_api_pandas)
#### Python Library Integrations {#docs:guides:overview::python-library-integrations}
* [How to use Ibis to query DuckDB with or without SQL](#docs:guides:python:ibis)
* [How to use DuckDB with Polars DataFrames via Apache Arrow](#docs:guides:python:polars) | 409 | [
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|
Guides | Guides | SQL Features | * [Friendly SQL](#docs:sql:dialect:friendly_sql)
* [As-of join](#docs:guides:sql_features:asof_join)
* [Full-text search](#docs:guides:sql_features:full_text_search)
* [`query` and `query_table` functions](#docs:guides:sql_features:query_and_query_table_functions) | 80 | [
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|
Guides | Guides | SQL Editors and IDEs | * [How to set up the DBeaver SQL IDE](#docs:guides:sql_editors:dbeaver) | 26 | [
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