2024 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 2024.

Released	Description	Impact
December 2024	Improved sharing of common or similar parts of a query.	Reduces query execution time for queries with multiple WITH clauses.
December 2024	Improved scaling of document pre-processing and inference in Document AI.	Decreases processing time of documents.
November 2024	Top-k pruning for queries that contain aggregate functions.	Expands top-k pruning to include queries that contain aggregate functions.
October 2024	Improved performance for queries that have equivalent (or similar) subqueries or sub-expressions.	Reduces query execution time by eliminating duplicate parts of a query plan.
October 2024	Improved handling of skew.	Reduces query execution time by automatically detecting and resolving skew in the build side of joins.
October 2024	Search Optimization Update: Support for join queries. (General Availability)	Improves the performance of join queries that have a small number of distinct values on the build side of the join.
October 2024	Improved metadata replication.	Reduces the time spent in the SECONDARY_UPLOADING_INVENTORY, PRIMARY_UPLOADING_METADATA, and SECONDARY_DOWNLOADING_METADATA phases of a replication refresh by optimizing serverless compute allocation. This improvement targets refreshes with larger metadata sizes.
September 2024	Improved cloning operations through parallelization.	Reduces the time it takes to clone objects, especially for databases and schemas with extensive metadata.
September 2024	Improved replication refreshes through parallelization.	Reduces the overall refresh time when replicating large volumes of data.
August 2024	Improved performance for LIMIT queries.	Reduces compilation and execution time for queries that use a LIMIT clause to return `n` rows from a table. This optimization shrinks the partitions that are scanned to cover only the first `n` rows.
July 2024	Improved table column synchronization for replication.	Reduces the time spent in the SECONDARY_DOWNLOADING_METADATA phase of a refresh operation.
July 2024	Improved warehouse utilization for queries that scan only a small amount of micro-partitions when compared to the compute resources that are available to the virtual warehouse.	Faster execution for queries with expensive operations when scanning data from a small number of micro-partitions, which is common in BI and dashboard use cases.
July 2024	Improved query processing that: Pushes down LIMIT clauses into aggregation nodes that do not contain any aggregations besides the ANY_VALUE function. Eliminates redundant grouping keys when PRIMARY KEY or UNIQUE constraints are enforced by validation, or when the RELY constraint property is used.	Faster execution for some queries with LIMIT clauses and GROUP BY statements.
June 2024	Improved single instruction, multiple data (SIMD) processing.	Reduces query execution time and improves scan performance for queries that access columns that contain NULL values. Provides better scan performance by decoding numbers more efficiently when reading data from remote storage.
May 2024	Improved efficiency of Automatic Clustering.	Reduces the cost of Automatic Clustering because it works more efficiently.
May 2024	Improved object replication.	Reduces the time spent in the SECONDARY_UPLOADING_INVENTORY and SECONDARY_DOWNLOADING_METADATA phases of a refresh operation by optimizing the synchronization of some objects and the authorization mechanism for replication operations.
May 2024	Reduced the latency for loading most Parquet files by up to 50% when the file format option, USE_VECTORIZED_SCANNER, is set to `TRUE`.	The vectorized scanner is well suited for the columnar format of a Parquet (https://parquet.apache.org/docs/file-format/) file and reduces the ingestion latency by downloading only relevant sections of the Parquet file into memory, such as the subset of selected columns.
May 2024	Improved evaluation of aggregations so they are made at more intermediate join trees.	Reduces query execution time for complex queries with aggregations by reducing the amount of data that needs to be processed at the earliest point possible.
May 2024	Improved query execution times for queries that spend a significant amount of time communicating across virtual warehouse nodes.	Increases throughput between compute resources in a warehouse. Each warehouse is a cluster of compute resources.
May 2024	Improved top-k pruning for LIMIT and ORDER BY queries.	Reduces execution time for top-k queries due to fewer scanned files and file header reads. Expands existing top-k improvements to include STRING/BINARY support in ORDER BY columns. Further increases pruning efficiency by sorting the scan set in order of largest/smallest files with respect to the value domain.
May 2024	Improved join order decisions by calculating selectivity estimates with more granularity.	Reduces compilation time and query execution time by calculating selectivity estimates at the micro-partition level.
May 2024	Faster loading time for Python.	Improves performance for Streamlit in Snowflake apps (including Streamlit apps within a Snowflake Native App), Python worksheets, Python UDFs, and stored procedures in Python.
April 2024	Reduced lock/mutex contention.	Reduces query execution times by improving scan performance in a variety of scenarios such as highly concurrent queries running on a warehouse.
April 2024	Improved broadcast join decisions.	Reduces query execution time and improves memory management by optimizing broadcast joins in scenarios like right-deep join trees.
April 2024	Faster query results in Snowsight.	Reduces the time it takes for query results to appear when run in Snowsight. Improvements are most noticeable for queries that return result sets larger than 10,000 rows.
March 2024	Improved metadata replication.	Reduces the time spent in the PRIMARY_UPLOADING_METADATA, SECONDARY_DOWNLOADING_METADATA, and SECONDARY_UPLOADING_INVENTORY phases for metadata.
March 2024	Improved query performance as a result of more accurately calculating selectivity estimates in order to optimize the order of joins.	Reduces execution time when there are mismatches between partition metadata and actual cardinality from join filters.
March 2024	Improved performance for loading JSON files.	Results in lower ingestion latency of up to 25% for many JSON loading scenarios.
February 2024	Improved object replication.	Reduces the time spent in the PRIMARY_UPLOADING_METADATA, SECONDARY_DOWNLOADING_METADATA, and SECONDARY_UPLOADING_INVENTORY phases of a refresh operation by optimizing portions of the snapshot operation and the way some objects are added to the replication inventory.
February 2024	Support for the `upper` and `lower` collation specifications added to some functions.	Ability to set the `upper` and `lower` collation specifications for some functions. The `upper` and `lower` collation specifications perform better than the `ci` specification for some use cases. The `upper` and `lower` collation specifications are now supported for the following functions: CHARINDEX, CONTAINS, ENDSWITH, POSITION, SPLIT, SPLIT_PART, and STARTSWITH. For more information, see Differences between ci and upper / lower.
January 2024	Improved execution time for LIMIT 0 queries.	Reduces execution time for queries that use a count of `0` with LIMIT, which is often used by applications to return column headings and data types for query results.
January 2024	General Availability of larger warehouses (5X-LARGE and 6X-LARGE) in Microsoft Azure regions, excluding Azure Government regions.	Ability to use larger compute resources for memory-intensive queries compared to smaller warehouses.