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.

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.

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

You can create BigQuery non-incremental materialized views over Spanner data to improve query performance by periodically caching results. This feature is generally available (GA).

Feature

You can use Cloud resource connections with EXPORT DATA statements to reverse ETL (extract, transform, load) BigQuery data to Spanner. This feature is generally available (GA).

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.

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 score.

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.

You can use continuous queries to stream BigQuery data to Spanner in real time. This feature is generally available (GA).

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.

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.

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 format from the SPANNER_SYS tables.

Spanner supports the following:

• Monitor the usage of Enterprise edition and Enterprise Plus edition features in your Spanner instance.
• Downgrade the instance's edition using the Google Cloud console.
• Verify edition compatibility when restoring from a backup. If the database uses higher-tier features, you must restore the backup to a destination instance that uses the same or a higher-tier edition.
• Set custom edition constraints within your organization policies, limiting the edition of your instance that your users can create.

Feature

Spanner supports the following managed autoscaler features:

• Autoscaling based on total CPU utilization target. When an instance's total CPU utilization exceeds or falls below the target that you have set, Spanner adds or removes compute capacity.
• Autoscaling for instance partitions (in Preview).

For more information, see Managed autoscaler.

Spanner provides UNSPLITTABLE_REASONS in hotspot insights and split statistics views to detect when splits can't be split further. Unsplittable reasons can help you identify schema anti-patterns that require intervention. For more information, see Find hotspots in your database and Split statistics.

This feature is generally available (GA).

Control of MCP use with organization policies is deprecated. After March 17, 2026, organization policies that use the gcp.managed.allowedMCPServices constraint won't work, and you can control MCP use with IAM deny policies.

For more information about controlling MCP use, see Control MCP use with IAM.

Change

After March 17, 2026, when you enable Spanner, the Spanner MCP server is automatically enabled.

Announcement

New best practices are available for securing generative AI agents using Model Context Protocol (MCP) with Google Cloud databases. This guide covers key security measures like least privilege, native database controls, and secure agent design to help you build safer AI applications. For more information, see Best practices for securing agent interactions with Model Context Protocol.

This feature is in Preview.

You can use the Spanner remote MCP server to interact with Spanner instances and databases from agentic AI applications such as Gemini CLI, agent mode in Gemini Code Assist, or Claude.ai.

This feature is in Preview.

You can right-click a node in a Spanner Graph query visualization to access options like expanding or collapsing adjacent nodes, highlighting or hiding nodes, and viewing only a node's neighbors.

For more information, see Work with visualizations.

You can create and host remote functions in Cloud Run and call them from Spanner queries using the GoogleSQL dialect. This feature is in Preview.

Spanner supports the UUID data type for both GoogleSQL and PostgreSQL-dialect databases. This data type stores universally unique identifiers (UUIDs) as 128-bit values.

You can use the GoogleSQL NEW_UUID() function or the PostgreSQL gen_random_uuid() to function to create UUID values.

For more information, see Use a universally unique identifier (UUID).

Spanner supports the following compression functions:

• ZSTD_COMPRESS
• ZSTD_DECOMPRESS_TO_BYTES
• ZSTD_DECOMPRESS_TO_STRING

These functions use the Zstandard (Zstd) lossless data compression algorithm to compress and decompress STRING or BYTES values. For more information, see Compression functions.

Columnar engine for Spanner is now in Public Preview. Columnar engine is a storage technique used with analytical queries to speed up scans up to 200 times faster on live operational data without affecting transaction workloads. In databases or tables enabled with columnar engine, this release:

• Supports the ability to execute columnar queries automatically and perform faster columnar scans using vectorized execution.
• Provides a new major compaction API to accelerate the conversion of non-columnar data into columnar data.

For more information, see the Columnar engine for Spanner overview.

You can create Spanner regional instance configurations in Bangkok, Thailand (asia-southeast3). For more information, see Google Cloud locations and Spanner pricing.

Several updates have been made to full-text search:

• Named schemas support full-text search.
• Spanner search indexes can accelerate pattern matching expressions such as LIKE, STARTS_WITH, and ENDS_WITH for pattern matching, and REGEXP_CONTAINS for regular expression matching.
• TOKENIZE_FULLTEXT has an argument for removing diacritics. SEARCH and SCORE use this if the data was tokenized with this option.
• TOKENIZE_SUBSTRING supports emojis.

You can use SQL views to create a graph. For requirements, considerations, and the benefits of using SQL views to create a graph, see Overview of graphs created from SQL views. To learn how to create a graph from views, see Create a property graph from SQL views.

The GoogleSQL function ELEMENT_DEFINITION_NAME is available. ELEMENT_DEFINITION_NAME returns the name of the graph element table underlying a graph element.

You can build data agents that interact with the data in your database using conversational language. Use these data agents as tools to empower your applications. For more information, see Data agents overview. This feature is available in Preview, and access to it requires a sign-up.

Spanner supports the PostgreSQL generate_series() function. You can use this function to create a sequence of numbers. For more information, see Set returning functions.

Spanner Data Boost includes a the Data Boost concurrent requests in milli-operations per second per region quota, which applies fine-grained control over how multiple concurrent requests for your project share Data Boost resources. Instead of counting 1 request against 1 unit of quota under the existing concurrency quota regime, Data Boost splits a request at a granularity of 1/1000, allowing for a greater number of concurrent requests to make progress. For more information, see Quotas and limits.

Feature

Spanner Graph supports using the ANY CHEAPEST path search prefix in a query to return the path with the lowest total compute cost. For more information, see Path search prefix in the Spanner Graph Language reference.

Spanner supports the following new columns in the SPANNER_SYS oldest active queries table:

• CLIENT_IP_ADDRESS
• API_CLIENT_HEADER
• USER_AGENT_HEADER
• SERVER_REGION
• PRIORITY
• TRANSACTION_TYPE

You can also view these columns in the Spanner query insights page on the Google Cloud console. For more information, see Monitor active queries.

String values in Spanner Studio query results are enclosed in double quotes, providing a visual cue to differentiate string values from other data types. This enhancement is for display purposes only and doesn't affect how data is exported or accessed.

Query optimizer version 8 is the default version for Spanner.

The GoogleSQL function IS_FIRST is available for graph queries. IS_FIRST returns true if a row is in the first k rows (1-based) within a window. You can use IS_FIRST in graph queries to limit traversed edges to improve query performance. You can also use IS_FIRST to sample intermediate nodes to optimize multi-hop queries.