2026 Performance improvements¶

Important

Performance improvements often target specific query patterns or workloads. These improvements might or might not have a material impact on a specific workload.

The following performance improvements were introduced in 2026:


Released	Description	Impact
April 2026	Improved runtime pruning for expressions with TIMESTAMP_TZ data types. Snowflake now prunes micro-partitions more effectively for timestamp-based filter predicates.	Improves performance for time-series queries with timestamp filters by skipping significantly more irrelevant micro-partitions, reducing I/O and execution time.
April 2026	Continued improvements to skew handling in hash joins, further reducing processing bottlenecks from unevenly distributed data.	Improves execution time for join queries with data skew by dynamically adjusting redistribution based on warehouse configuration.
April 2026	Performance Explorer now applies granular access control, aligning visibility with your privileges on warehouses, databases, and Snowflake database roles.	More users can access Performance Explorer to identify and troubleshoot slow queries without requiring ACCOUNTADMIN privileges.
April 2026	Dynamic table refresh boundaries with `DYNAMIC_TABLE_REFRESH_BOUNDARY()`. Decouples upstream and downstream dynamic table refreshes so each pipeline refreshes independently.	Avoids unnecessary cascaded refreshes, reducing compute cost and refresh latency for multi-stage dynamic table pipelines.
March 2026	Task graph overlap policy. Tasks support `ALLOW_CHILD_OVERLAP` and `ALLOW_ALL_OVERLAP` policies that permit concurrent graph runs.	Increases throughput for task-based data pipelines by allowing parallel execution instead of serial-only scheduling.
March 2026	Enhanced parallel scanning for queries accelerated by the Query Acceleration Service (QAS).	Improves execution time for QAS-accelerated queries by enabling more parallel I/O during scan operations.
March 2026	Cortex Search updates (General availability): Multi-index search and search service selection for agents, reducing latency and cost by querying one service instead of all.	Lower search latency and cost for Cortex Search-based applications and agents.
March 2026	Dynamic table `SCHEDULER` attribute (General availability). Set `SCHEDULER = DISABLE` for manual-only refresh; manual refreshes don’t cascade through the pipeline.	Reduces unnecessary refresh compute when external orchestrators (such as dbt) control refresh timing.
March 2026	MIN_BY and MAX_BY functions are supported with dynamic table incremental refresh (General availability).	More dynamic table pipelines can stay on incremental refresh instead of falling back to less efficient full refresh paths.
March 2026	Dynamically adjusts network message sizes based on the execution plan to optimize data transfer between processing nodes.	Improves execution time for interactive and latency-sensitive workloads by reducing network overhead. Particularly benefits short-running queries and high-concurrency scenarios.
March 2026	Improved scanset construction to reduce lock contention during parallel query execution.	Improves execution time for scan-heavy queries, especially on larger warehouses with high concurrency. Reduces CPU overhead during parallel scan coordination.
March 2026	Identifies opportunities to push aggregations earlier in the query plan when common table expressions (CTEs) are present.	Improves execution time for complex queries with CTEs by reducing the volume of data processed in later stages of the query plan.
March 2026	Improved extraction pushdown through view columns, enabling more efficient scan and metadata usage for subcolumns accessed through views.	Improves execution time for queries that access subcolumns through views.
February 2026	Cortex Code CLI query optimizer skill (General availability). Use natural language to get AI-driven query optimization recommendations.	Helps users identify and fix slow queries by providing optimization suggestions through the Cortex Code command-line interface.
February 2026	Performance improvements to the file pruner, reducing per-file pruning overhead for queries scanning many files.	Faster pruning decisions during compilation and execution, especially for queries that scan tables with many micro-partitions.
February 2026	Improved range-based micro-partition pruning for more query patterns.	Reduces both compilation and execution time for queries with range predicates by skipping more irrelevant micro-partitions.
February 2026	More efficient aggregation processing when data fits on a single server node, avoiding unnecessary distributed processing overhead.	Improves performance for aggregation queries where the data volume doesn’t require distributed computation.
January 2026	AI_FILTER (General availability). Includes query-engine optimization that routes qualifying AI_FILTER patterns through optimized execution paths.	Up to 2-10x speedup and approximately 60% lower token usage on suitable queries.
January 2026	AI_AGG and AI_SUMMARIZE_AGG (General availability). Set-based AI aggregation functions that process groups natively instead of row-by-row AI_COMPLETE calls.	Up to approximately 2x throughput for large aggregation workloads compared to equivalent row-wise AI_COMPLETE patterns.
January 2026	Search optimization: Support for structured data types. Search optimization can improve performance of point lookup and substring queries on ARRAY, OBJECT, and MAP columns on standard and Apache Iceberg™ tables.	Improves query performance for queries that filter on structured data columns.
January 2026	Improved query performance with Snowflake Optima Metadata, which continuously analyzes your workload patterns and creates metadata to optimize pruning of unused micro-partitions. Snowflake Optima is only available on Snowflake generation 2 standard warehouses (Gen2).	Improves performance of queries by creating metadata for more efficient pruning.
January 2026	Improved pruning for join queries with inequality predicates. For example, the following join query uses the `>` operator in an inequality predicate: SELECT * FROM employees e, managers m WHERE e.employee_id = m.employee_id AND e.salary > m.salary AND m.level = 'M5'; For this query, Snowflake prunes micro-partitions from the `employees` table where all salaries are below the lowest `M5` manager salary.	Improves the performance of join queries that have inequality predicates.
January 2026	Faster JSON parsing for PARSE_JSON operations.	Improves execution time for queries that parse JSON data. Queries with heavy JSON processing may see significant speedups.
January 2026	Improved compilation performance for queries with deeply nested CASE expressions by keeping them in a simplified form throughout the compilation process.	Reduces compilation time for queries with large CASE expressions, especially those with many branches.