Feature Spanner supports automatic parameterization of SQL query literals to improve query performance, reduce latency, and lower CPU costs. Spanner converts literal values hardcoded in CRUD-style queries, such as primary key lookups, index lookups, and primary key joins, into query parameters, allowing execution plans to be cached and reused to reduce latency and CPU costs. For more information,…
Feature Spanner queues are generally available (GA). Spanner queues provide transactional messaging to help you manage asynchronous work. The feature pairs this capability with the scalability and reliability of Spanner, letting you build event-driven applications. For more information, see the Spanner queues overview.
Feature Spanner Graph supports the following enhancements for CREATE PROPERTY GRAPH: Semantic options for labels and properties: You can configure OPTIONS (description and synonyms) on labels and properties to provide context and improve discoverability for natural language querying interfaces. Element key uniqueness validation option: You can configure the validate_element_key_uniqueness option i…
Feature Spanner supports the following PostgreSQL-dialect database operators: ILIKE (~~*) NOT ILIKE (!~~*) Spanner supports the following PostgreSQL-dialect database functions: pg.ilike pg.not_ilike For more information, see Pattern matching operators and Functions.
Feature Spanner supports using the TABLESAMPLE operator in PostgreSQL-dialect databases to select a random sample of a dataset. For more information, see TABLESAMPLE operator.
Feature Spanner supports creating secondary indexes based on scalar expressions that apply to one or more table columns. Expression indexes eliminate the need to create and maintain a dedicated generated column in your table for indexing purposes. For more information, see Create an index based on a scalar expression.
Feature For DML statements, Spanner now enforces the 80,000 mutation limit (including indexes) per statement rather than cumulatively across the transaction. This allows a transaction to execute multiple DML statements that collectively exceed the limit, as long as each individual statement remains under it. For the Mutation API, the 80,000 limit applies to all mutations in the commit. All transac…
Feature Dynamic channel pooling (DCP) for gRPC channels in the Spanner Go and Java client libraries is generally available (GA). DCP prevents performance issues from under-provisioned or over-provisioned channels and reduces configuration overhead. DCP is disabled by default. For more information, see Configure the number of sessions and gRPC channels in the pools.
Feature Spanner supports creating tables without defining primary keys. When you create a table without a primary key, Spanner creates a hidden rowid column that serves as the primary key. For more information, see Create a table without defining a primary key.
Feature You can use Gemini in Spanner Studio to fix errors in your SQL queries. When you run a query that contains an error, you can click Fix to view a line-by-line comparison of your query and a recommended correction, along with an explanation of the change. This feature is available in Preview.
Feature Spanner supports direct connectivity. When enabled, your application traffic is routed directly to Spanner servers, bypassing the Google Front End (GFE) servers. This can reduce your overall latency. Direct connectivity is generally available (GA).
Feature You can publish a Spanner conversational analytics agent in Gemini Enterprise. This feature is in Preview.
Feature Spanner Graph supports a suite of graph algorithms covering use cases such as fraud detection, entity resolution, and recommendations. You can invoke graph algorithms as built-in function calls in Spanner Graph queries. You can save your output to Cloud Storage or Spanner. This feature is available in Preview.
Feature You can optimize Spanner Graph query performance by using factorized execution. If a graph pattern traversal generates duplicate intermediate results, add the @{factorize_mode} hint to the pattern traversal or at the query level to optimize the query. For more information, see Use factorized execution to optimize queries.
Feature Spanner Studio supports query parameters, which let you bind literal values to a templated SQL statement at query time in the Google Cloud console. For more information, see query parameters.
Feature Spanner supports the FACTORIZED_MODE join hint. You can control which sides of a join to factorize (deduplicate) before executing the join by using the FACTORIZE_LEFT, FACTORIZE_RIGHT, or FACTORIZE_BOTH hint values. For more information, see Factorized mode. Announcement The Spanner change streams default retention period has been increased from 1 day to 7 days. This change affects both ne…
Feature Spanner vector index and approximate nearest neighbor (ANN) distance functions are generally available (GA) for PostgreSQL databases.
Feature Spanner full-text search supports custom dictionaries to create custom synonym mappings. You can use custom dictionaries with the SEARCH, SCORE, and SNIPPET functions.
Feature The Spanner remote MCP server is generally available (GA). The Spanner remote MCP server lets you interact with Spanner instances from LLMs, AI applications, and AI-enabled development platforms. Feature You can use the Database Insights remote MCP server to analyze Spanner's performance and system metrics. This feature is generally available (GA). Feature You can now use Spanner Studio to…
Feature Repeatable read isolation is generally available. You can use it to reduce latency and transaction failure rates for workloads that have many reads contending with fewer writes. For more information, see Repeatable read isolation. Feature Spanner supports Gemini Cloud Assist investigation capabilities. You can create, run, and edit Gemini Cloud Assist investigations only if you have a Prem…
Feature Spanner supports importing data from Cloud SQL for MySQL. This feature lets you migrate the schema and perform a one-time bulk data load to evaluate Spanner for your use case. For more information, see Import from Cloud SQL to Spanner. This feature is generally available.
Feature The QueryData tool lets you to query the data in your database using conversational language and build data agents. For more information, see QueryData tool overview. This feature is available in (Preview). The preview release increases the accuracy of SQL generation with value search queries which match values and their context within a database. Value search queries trigger automatically…
Feature Spanner offers conversational analytics, which lets users query their operational data using natural language. This feature is powered by the Conversational Analytics API, which can help you translate complex human dialog into precise database queries to provide actionable insights. This feature is in Preview. For more information, see Conversational analytics for Spanner overview. Feature…
Feature The Spark Spanner connector supports writing a Spark Dataframe to a Spanner table using the Spark data source API. For more information, see Use the Spark Spanner connector.
Feature Spanner now offers AI functions, as a part of machine learning functions, that help you perform semantic operations using Large Language Models (LLMs) in SQL to classify, evaluate, and rank your data: AI.CLASSIFY: Classify a natural language input into user-defined categories. AI.IF: Evaluate a condition described in natural language. AI.SCORE: Rate natural language input and assign it a s…
Feature Spanner supports the optimistic concurrency control mode. Optimistic concurrency control is suitable for transactional workloads with low read-write contention. It assumes that conflicts in transactions are rare. Reads and queries within a read-write transaction proceed without acquiring locks. For more information, see Concurrency control.
Feature You can use continuous queries to stream BigQuery data to Spanner in real time. This feature is generally available (GA).
Feature Spanner supports the ON CONFLICT clause for GoogleSQL INSERT statements to handle unique constraint violations. For more information, see ON CONFLICT DO NOTHING and ON CONFLICT DO UPDATE.
Feature Spanner supports using the pending commit timestamp function as a default value and an ON UPDATE expression for column definitions. For more information, see the GoogleSQL ON UPDATE clause and the PostgreSQL ON UPDATE clause.
Feature Spanner provides JSON string versions of the following statistics columns for PostgreSQL-dialect databases: TOTAL_LATENCY_DISTRIBUTION_JSON_STRING in Transaction statistics OPERATIONS_BY_TABLE_JSON_STRING in Transaction statistics LATENCY_DISTRIBUTION_JSON_STRING in Query statistics SAMPLE_LOCK_REQUESTS_JSON_STRING in Lock statistics You can use these columns to retrieve statistics in JSON…