Work with vector embeddings using Cloud SQL for MySQL

MySQL | PostgreSQL | SQL Server

This page details how you can interact with Cloud SQL to build applications that use vector embeddings.

Cloud SQL for MySQL supports the storage of vector embeddings. You can then create vector search indexes and perform similarity searches on these vector embeddings along with the rest of the data that you store in Cloud SQL.

Vector embedding storage

You can use Cloud SQL for MySQL to store vector embeddings by creating a vector embedding column in a table. The special vector embedding column maps to the VARBINARY data type. Like other relational data in the table, you can access vector embeddings in the table with existing transactional guarantees. A table that has a vector embedding column is a regular InnoDB table and is therefore compliant with atomicity, consistency, isolation, and durability (ACID) properties. ACID properties deviate only for vector search index lookups.

You can create up to one vector embedding column in a table and one vector search index per table. Each vector embedding stored in the same column must have exactly the same dimensions that you specified when you defined the column. A vector embedding has an upper limit of 16,000 dimensions. If you have enough storage and memory available, then you can have separate tables with different vector embedding columns and vector search indexes on the same instance.

While there's no hard limit to the number of vector embeddings that you can store in a table, vector search indexes require memory. For this reason, we recommend that you store no more than 10 million vector embeddings in a table.

Replication works the same way for the vector embedding column as it does for other MySQL InnoDB columns.

Similarity search

Cloud SQL supports similarity search using both K-nearest neighbor (KNN) and approximate nearest neighbor (ANN) search queries. You can use both types of vector searches in your Cloud SQL instances. You can create a vector search index for ANN searches.

K-nearest neighbor (KNN) search

Cloud SQL supports querying using KNN vector search, also referred to as exact nearest neighbor search. Performing a KNN vector search provides perfect recall. You can perform KNN searches without having to create a vector search index. KNN search is based on performing a table scan algorithm.

For KNN search, Cloud SQL also supports the following vector distance search functions:

Cosine
Dot product
L2 squared distance

For more information about using vector search distance functions, see Query the distance of a vector embedding.

Approximate nearest neighbor (ANN) search

Cloud SQL supports creating and querying ANN searches through the creation of vector search indexes. An ANN vector search index lets you optimize for fast performance instead of perfect recall. For ANN search, Cloud SQL supports the following index types:

BRUTE_FORCE: the default vector search index type for a base table that has fewer than 10,000 rows. This type is best suited for searches within a smaller subset of an original dataset. The memory used by the index is equal to the size of the dataset. This index type isn't persisted to disk.
TREE_SQ: the default vector search index type for a base table that has 10,000 or more rows. This type uses the least amount of memory or approximately 25% of the size of the dataset. TREE_SQ indexes are persisted to disk.
TREE_AH: a vector search index type that provides an asymmetric hashing search type algorithm. As implemented in Cloud SQL, this index type isn't optimized for memory footprint and isn't persisted.

Update vector search indexes

Cloud SQL for MySQL updates vector search indexes in real time. Any transaction that performs Data Manipulation Language (DML) operations on the base table also propagates changes to the associated vector search indexes. The changes in a vector search index are visible immediately to all other transactions, which means an isolation level of READ_UNCOMMITTED.

If you roll back a transaction, then the corresponding rollback changes also occur in the vector search index.

Replication of vector search indexes

Cloud SQL for MySQL replicates vector search indexes to all read replicas. Replication filters and the replication of vector search indexes to cascading replicas aren't supported.

Configure an instance to support vector embeddings

This section describes how to configure your Cloud SQL instance to support the storage, indexing, and querying of vector embeddings.

Both Cloud SQL Enterprise edition and Cloud SQL Enterprise Plus edition instances support vector embeddings.

Before you begin

Your instance must be running Cloud SQL for MySQL MySQL version 8.0.36.R20240401.03_00 or later.
Your instance must have sufficient disk space and memory to allocate memory for the total number of vector embeddings on the instance.

Turn on support for vector embeddings

To turn on support for vector embeddings, you must configure MySQL database flags.

gcloud sql instances patch INSTANCE_NAME \
  --database-flags=FLAGS

Replace INSTANCE_NAME with the name of the instance on which you want to enable vector embedding support.

In FLAGS, configure the following MySQL flags on your instance:

cloudsql_vector: set this flag to on to enable vector embedding storage and search support. You can create new vector embedding columns and vector search indexes on the instance.
cloudsql_vector_max_mem_size: optional. Specify the maximum memory allocation in bytes for all vector search indexes on the instance. If you don't specify this flag, then the default memory allocation is 1 GB, which is the minimum memory allocation. For more information about how to calculate the amount to specify, see Configure the memory allocation for vector search indexes.

This dedicated memory comes from the memory allocated to your innodb_buffer_pool_size. Your available buffer pool is reduced by the same amount. The maximum allowed value for this flag is 50% of your total innodb_buffer_pool_size.

If you specify a value that's greater than 50% of your total innodb_buffer_pool_size, then Cloud SQL reduces the effective value to 50% of the available size and logs a warning message for the instance.

After you configure the flags, your command might look similar to the following:

gcloud sql instances patch my-instance \
  --database-flags=cloudsql_vector=on,cloudsql_vector_max_mem_size=4294967296

The flags to configure vector embeddings support in Cloud SQL for MySQL are static flags. After you update the instance with the flags, your instance restarts automatically in order for the configuration changes to take effect.

For more information about how to configure database flags for MySQL, see Configure database flags.

Turn off support for vector embeddings

To turn off support for vector embeddings, set the cloudsql_vector flag to off.

For example:

gcloud sql instances patch INSTANCE_NAME \
  --database-flags=cloudsql_vector=off

Replace INSTANCE_NAME with the name of the instance on which you're turning off vector embedding support.

Setting cloudsql_vector to off prevents you from creating new vector embedding columns and vector search indexes. After you configure this static flag, the instance restarts automatically for the configuration change to take effect.

After the restart of the instance, Cloud SQL for MySQL does the following:

Removes all persisted TREE_SQ vector search indexes from the persistent disk.
Keeps the data dictionary table entries for the vector search indexes that have been built. However, Cloud SQL for MySQL doesn't rebuild the indexes and any search queries to these indexes return an error.
Continues to store the vector embeddings in the base tables. The vector embeddings remain accessible.

If you later re-enable the cloudsql_vector flag for the instance, then Cloud SQL attempts to rebuild the indexes while the instance restarts based on the entries in the data dictionary table.

Configure the memory allocation for vector search indexes

Cloud SQL builds and maintains vector search indexes in memory. The TREE_SQ index type persists on a clean shutdown and reloads after the instance restarts. During runtime, all vector search indexes need to stay in memory.

To make sure that Cloud SQL has enough memory available to keep all vector search indexes in memory, configure the Cloud SQL instance with a cloudsql_vector_max_mem_size database flag. cloudsql_vector_max_mem_size governs how much memory the Cloud SQL instance dedicates for vector search indexes. When you configure the value for the flag, keep the following in mind:

The default and minimum value is 1 GB. The upper limit is 50% of the buffer pool size.
After you set this flag, your instance automatically restarts for the configuration change to take effect.
If your instance has used up all its configured memory, then you can't create or alter any vector search indexes.

To update the memory allocated for vector search indexes on the instance, change the value of the cloudsql_vector_max_mem_size flag.

gcloud sql instances patch INSTANCE_NAME \
  --database-flags= cloudsql_vector_max_mem_size=NEW_MEMORY_VALUE

Replace the following:

INSTANCE_NAME: the name of the instance on which you are changing the memory allocation.
NEW_MEMORY_VALUE: the updated memory allocation, in bytes, for your vector search indexes

This change restarts your instance automatically so that the change can take effect.

Calculate required memory

The amount of memory that an index requires depends on the index type, the number of vector embeddings, and the dimensionality of the embeddings. There are two memory requirements to consider:

Build time memory: the memory required during the build of the index
Index memory: the memory that the index occupies after the index is built

For a given index, its dataset size is the memory needed to read all the vector embeddings in memory. Given that each dimension is represented by a float which uses 4 bytes of memory, you can determine the dataset_size as follows:

dataset_size = <num_embeddings> * (4 * <dimensions>)

For example, if you have 1 million embeddings of 768 dimensions, your dataset_size is 3 GB.

Based on the previous example, the memory requirements for the different index types are as follows:

Index type	Build time memory	Index memory
`TREE_SQ`	4 GB	1 GB
`TREE_AH`	3.5 GB	3.5 GB
`BRUTE_FORCE`	3 GB	3 GB

If you're using TREE_SQ vector search indexes, then you must also factor in the memory required for persistence at runtime. To the total amount of memory in your configuration, add the amount of index memory used by the largest active TREE_SQ vector search index.

Whenever the base table where the vector embeddings are stored undergoes DML operations, the vector search index is updated in real time. These updates change the memory footprint of the index, which can shrink or expand depending on the DML operation. You can monitor the memory footprint of an index by querying the information_schema.innodb_vector_indexes table. For information about monitoring the size of your vector search index, see Monitor vector search indexes.

Read replica configuration

If the instance meets the maintenance version and flag enablement criteria, then Cloud SQL fully supports vector embeddings on a read replica.

If you create a replica from a primary instance that has vector embedding support enabled, then the read replica inherits the vector embedding support settings from the primary instance. You must enable vector embedding support individually on already existing read replica instances.

In terms of impact to replication lag, creating and maintaining of vector search indexes operates in the same way as regular MySQL indexes.

Vector search indexes aren't supported on cascading replicas.

Example: A simple ANN vector search index and query

The following example walkthrough provides steps to create an ANN-based vector search index and query in Cloud SQL.

Generate vector embeddings. You can create vector embeddings manually or use a text embedding API of your choice. For an example that uses Vertex AI, see Generate vector embeddings based on row data.

Create a table in Cloud SQL for MySQL that contains a vector embedding column with three dimensions.

CREATE TABLE books (
id   INTEGER PRIMARY KEY AUTO_INCREMENT,
title VARCHAR(60),
embedding VECTOR(3) USING VARBINARY
);

Insert a vector embedding into the column.

INSERT INTO books VALUES (
1,
'book title',
 string_to_vector('[1,2,3]')
);

Commit the changes.
```
commit;
```

Create the vector search index. If you're creating a TREE_SQ or a TREE_AH index, then your table must have at least 1,000 rows.

CALL mysql.create_vector_index('vectorIndex',
                               'dbname.books',
                               'embedding',
                               'index_type=BRUTE_FORCE, distance_measure=L2_SQUARED'
                               );

Get the nearest neighbors.

SELECT title FROM books
WHERE
NEAREST(embedding) TO (string_to_vector('[1,2,3]'));

Generate vector embeddings based on row data

You can generate a vector embedding for a given row's data by using a text embedding API such as Vertex AI or OpenAI. You can use any text embedding API with Cloud SQL vector embeddings. However, you must use the same text embedding API for query string vector generation. You can't combine different APIs for source data and query vectorization.

For example, you can generate a vector embedding from Vertex AI:

from vertexai.language_models import TextEmbeddingModel

def text_embedding() -> list:
    """Text embedding with a Large Language Model."""
    model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
    embeddings = model.get_embeddings(["What is life?"])
    for embedding in embeddings:
        vector = embedding.values
        print(f"Length of Embedding Vector: {len(vector)}")
    return vector

if __name__ == "__main__":
    text_embedding()

Store vector embeddings

This section provides examples statements for storing vector embeddings in Cloud SQL.

Create a new table with a vector embedding column

CREATE TABLE books (
  id INTEGER PRIMARY KEY AUTO_INCREMENT,
  title VARCHAR(60),
  embedding VECTOR(3) USING VARBINARY
  );

Add a vector embedding column to an existing table

ALTER TABLE books
ADD COLUMN embedding
VECTOR(3) USING VARBINARY;

Insert a vector embedding

INSERT INTO books (
  title,
  embedding
  ) VALUES (
    'book title',
    string_to_vector('[1,2,3]')
);

Insert multiple vector embeddings

INSERT INTO books (
  title,
  embedding
  ) VALUES (
    'book title',
    string_to_vector('[1,2,3]')),
     ('book title', string_to_vector('[4,5,6]')
);

Upsert a vector embedding

INSERT INTO books (
  id,
  title,
  embedding
  ) VALUES (
    1,
    'book title',
     string_to_vector('[1,2,3]')
     )
ON DUPLICATE KEY UPDATE embedding = string_to_vector('[1,2,3]');

Update a vector embedding

UPDATE books
SET embedding = string_to_vector('[1,2,3]')
WHERE id = 1;

Delete a vector embedding

DELETE FROM books
WHERE embedding = string_to_vector('[1,2,3]');

Work with vector search indexes

By default, you can perform the exact nearest neighbor search, which provides perfect recall. You can also add an index to use ANN search, which trades some recall for speed. Unlike typical indexes, after you add an approximate index, you see different results for queries.

Recommendations

This section provides best practices for working with vector search indexes. Every workload is different, and you might need to adjust accordingly.

Before you create a vector search index, you must load data into the table. Your base table must have at least 1,000 rows. These requirements apply only to the TREE_SQ and TREE_AH search index types. If you have more data points available, then you'll have better partitioning and training of the index.
Monitor the memory usage of indexes. If the instance runs out of memory, then you can't create or build any indexes. For existing indexes, after reaching the threshold, Cloud SQL writes warnings to the MySQL error log periodically. You can view memory usage in the information_schema.innodb_vector_indexes table.
If the underlying base table has undergone major DML changes, then rebuild the vector search indexes. To get the initial size of the index at build time and the current size of the index, query the information_schema.innodb_vector_indexes table.
Generally, it's acceptable to leave the number of partitions to be computed internally. If you have a use case where you want to specify the number of partitions, then you must have at least 100 data points per partition.

Read-only base table during vector search index operations

For the duration of all three vector search index operations—create, alter, and drop— the base table is put into a read-only mode. During these operations, no DMLs are allowed on the base table.

Persistence, shutdown, and impact on maintenance

Only vector search indexes that use the TREE_SQ type persist to disk on a clean shutdown of an instance shutdown. Vector search indexes that use the TREE_AH and BRUTE_FORCE types are in-memory only.

After a clean shutdown of an instance, Cloud SQL reloads vector search indexes as the instance restarts. However, after a crash or an unclean shutdown, Cloud SQL must rebuild the vector search indexes. For example, any time that your instance undergoes a crash and recovery from backup and restore, point-in-time recovery (PITR), or high-availability (HA) failover, Cloud SQL rebuilds your vector search indexes. For these events, the following occurs:

The rebuild happen in the background automatically.
During the rebuild, the base table is in read-only mode.
If the automatic rebuild can't get a lock on the table within a specific timeout period, then the rebuild fails. You might need to rebuild the index manually instead.

The time required for an index rebuild might increase the time required for a shutdown, which might also increase the required maintenance and update time for an instance.

Create a vector search index

The statement to create a vector search index uses the following syntax:

CALL mysql.create_vector_index('INDEX_NAME',
                                'DB_NAME.TABLE_NAME',
                                'COLUMN_NAME',
                                'PARAMETERS'
                              );

For example:

CALL mysql.create_vector_index('vectorIndex',
                                'db.books',
                                'embedding',
                                'index_type=TREE_SQ, distance_measure=l2_squared'
                               );

The index name that you specify must be unique within the database.

Vector search index parameters

The create search index (and alter search index) functions support multiple parameters that you can specify with comma-separated key-value pairs. All search index create function parameters are optional. If you specify an empty string or NULL, then the default parameter values are configured for the index.

distance_measure: the supported values are: L2_SQUARED, COSINE, and DOT_PRODUCT. L2_SQUARED is the default.
num_neighbors: the number of neighbors to return by default during ANN queries. You can also override this parameter when performing the search query. The default is 10.
index_type: specifies the type of index to be built. Valid values are: BRUTE_FORCE, TREE_SQ, and TREE_AH.
- BRUTE_FORCE is the default for a table that has fewer than 10,000 rows
- TREE_SQ is the default for a table that has 10,000 or more rows
To specify the TREE_AH or TREE_SQ index type, the size of your base table must be greater than 1,000 rows.
num_parititions: specifies how many K-means clusters to build. This parameter is only allowed if you have configured an index_type. This option isn't applicable to BRUTE_FORCE. If you specify the TREE_SQ or TREE_AH index type, then the size of your base table must be greater than or equal to num_partitions * 100.

Alter a vector search index

CALL mysql.alter_vector_index('DB_NAME.INDEX_NAME', 'PARAMETERS');

The alter_vector_index function is used explicitly to rebuild a vector search index. To use this function, the index must already exist. You might want to rebuild an index for the following use cases:

You want to rebuild the index with different options. For example, you might want to use a different index type or different distance measure.
You want to rebuild the index because the base table has undergone major DML changes. For example, you need to retrain the vector search index based on current data in the base table.

All parameters for rebuilding the index are identical to the ones available for creating the index and are also optional. If you specify an empty string or NULL when you rebuild the index, then the index is rebuilt based on the parameters specified at index creation time. If no parameters are provided at index creation time, then the default parameter values are used.

The existing vector search index is available during the alter operation. You can still perform search queries against the index.

Drop a vector search index

You can't perform a DDL operation on a table that has a vector search index. Before performing the DDL operation on the table, you must drop the vector search index.

CALL mysql.drop_vector_index('DB_NAME.INDEX_NAME');

Query vector embeddings

This section provides examples for the different ways that you can query vector embeddings.

View the vector embeddings

SELECT vector_to_string(embedding) FROM books;

Get the exact neighbor search to a vector embedding

SELECT id,cosine_distance(embedding,
   string_to_vector('[1,2,3]')) dist
FROM books
ORDER BY dist
LIMIT 10;

Get the approximate neighbor search to a vector embedding

SELECT title FROM books
WHERE
NEAREST(embedding) TO (string_to_vector('[1,2,3]'), 'num_neighbors=10');

Performing an ANN search supports two parameters. Both are optional.

num_partitions: specify the number of partitions to probe for an ANN vector search. If you don't specify the number of partitions, then the search uses a value generated based on the size of the table, number of partitions in the vector search index, and other factors.
num_neighbors: specify the number of neighbors to return. This value overrides the value set at create vector search index time.

Filter vector embeddings

Use extra columns as predicates to fine tune the filtering of your vector embedding query results. For example, if you add a printyear column, then you can add a specific year value as a filter to your query.

SELECT title FROM books
WHERE
NEAREST(embedding) TO (string_to_vector('[1,2,3]'))
AND printyear > 1991;

Query the distance of a vector embedding

This section provides examples of vector distance functions that are available for KNN search.

Get the Cosine distance

SELECT cosine_distance(embedding, string_to_vector('[3,1,2]'))
AS distance FROM books WHERE id=10;

Get the Dot Product distance

SELECT dot_product(embedding, string_to_vector('[3,1,2]'))
AS distance FROM books WHERE id=10;

Get the L2 Squared distance

SELECT l2_squared_distance(embedding, string_to_vector('[3,1,2]'))
AS distance FROM books WHERE id=10;

Get rows within a certain distance

SELECT * FROM books
WHERE l2_squared_distance(embedding, string_to_vector('[1,2,3]')) < 10;

You can combine with ORDER BY and LIMIT

SELECT id, vector_to_string(embedding),
       l2_squared_distance(embedding, string_to_vector('[1,2,3]')) dist
FROM books ORDER BY dist LIMIT 10;

Monitor vector search indexes

To get real-time information about all the vector search indexes in the instance, use the information_schema.innodb_vector_indexes table.

To view the table, run the following command:

SELECT * FROM information_schema.innodb_vector_indexes;

Sample output might look like the following:

*************************** 1. row ***************************
       INDEX_NAME: test.t4_index
       TABLE_NAME: test.t4_bf
       INDEX_TYPE: BRUTE_FORCE
     DIST_MEASURE: SquaredL2Distance
           STATUS: Ready
            STATE: INDEX_READY_TO_USE
       PARTITIONS: 0
SEARCH_PARTITIONS: 0
     INITIAL_SIZE: 40000
     CURRENT_SIZE: 40000
          QUERIES: 0
        MUTATIONS: 0
     INDEX_MEMORY: 160000
   DATASET_MEMORY: 0

In the information_schema.innodb_vector_indexes table, you can view the following:

The options that are potentially generated. In other words, num_partitions or the number of partitions to probe for a query.
The STATE and STATUS columns tell you the current state of the index. During the build phase, the status column gives information about how far the vector search index is in the build phase.
The INITIAL_SIZE column provides the table size during index creation. You can compare this size with CURRENT_SIZE to get an idea on how much the index has changed since its creation due to DMLs on the base table.
The QUERIES and MUTATIONS columns provide you with real-time insights into how busy the index is.
The INDEX_MEMORY and DATASET_MEMORY columns provide information about memory consumption of the index. INDEX_MEMORY

indicates how much memory is consumed by the index and DATASET_MEMORY indicates how much additional memory is consumed during build time.

To get a list of the search vector indexes created on the instance, you can view the mysql.vector_indexes data dictionary table.