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|[Vector search](/vector-search/vector-search-overview.md)| E | N | N | N | N | N | N | N | N | N |
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|[Prepared statement cache](/sql-prepared-plan-cache.md)| Y | Y | Y | Y | Y | Y | Y | Y | E | E |
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|[Non-prepared statement cache](/sql-non-prepared-plan-cache.md)| Y | Y | Y | E | N | N | N | N | N | N |
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|[Instance-level execution plan cache](/system-variables.md#tidb_enable_instance_plan_cache-new-in-v840)| E | N | N | N | N | N | N | N | N | N |
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|[SQL binding](/sql-plan-management.md#sql-binding)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[Cross-database binding](/sql-plan-management.md#cross-database-binding)| Y | Y | N | N | N | N | N | N | N | N |
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|[Create bindings according to historical execution plans](/sql-plan-management.md#create-a-binding-according-to-a-historical-execution-plan)| Y | Y | Y | Y | E | N | N | N | N | N |
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|[Coprocessor cache](/coprocessor-cache.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[Stale Read](/stale-read.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[Follower reads](/follower-read.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[Read historical data (tidb_snapshot)](/read-historical-data.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[Optimizer hints](/optimizer-hints.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[MPP execution engine](/explain-mpp.md)| Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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|[MPP execution engine - compression exchange](/explain-mpp.md#mpp-version-and-exchange-data-compression)| Y | Y | Y | Y | N | N | N | N | N | N |
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|[TiFlash Pipeline Model](/tiflash/tiflash-pipeline-model.md)| Y | Y | Y | N | N | N | N | N | N | N |
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|[TiFlash replica selection strategy](/system-variables.md#tiflash_replica_read-new-in-v730)| Y | Y | Y | N | N | N | N | N | N | N |
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|[Index Merge](/explain-index-merge.md)| Y | Y | Y | Y | Y | Y | Y | E | E | E |
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|[Placement Rules in SQL](/placement-rules-in-sql.md)| Y | Y | Y | Y | Y | Y | E | E | N | N |
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|[Cascades Planner](/system-variables.md#tidb_enable_cascades_planner)| E | E | E | E | E | E | E | E | E | E |
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|[Runtime Filter](/runtime-filter.md)| Y | Y | Y | N | N | N | N | N | N | N |
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>>>>>>> 90b3b375f3 (*: add a vector-search directory and update links (#19893))
Copy file name to clipboardExpand all lines: br/backup-and-restore-overview.md
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| Global temporary tables || Make sure that you are using v5.3.0 or a later version of BR to back up and restore data. Otherwise, an error occurs in the definition of the backed global temporary tables. |
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| TiDB Lightning Physical Import|| If the upstream database uses the physical import mode of TiDB Lightning, data cannot be backed up in log backup. It is recommended to perform a full backup after the data import. For more information, see [When the upstream database imports data using TiDB Lightning in the physical import mode, the log backup feature becomes unavailable. Why?](/faq/backup-and-restore-faq.md#when-the-upstream-database-imports-data-using-tidb-lightning-in-the-physical-import-mode-the-log-backup-feature-becomes-unavailable-why).|
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| TiCDC || BR v8.2.0 and later: if the target cluster to be restored has a changefeed and the changefeed [CheckpointTS](/ticdc/ticdc-architecture.md#checkpointts) is earlier than the BackupTS, BR does not perform the restoration. BR versions before v8.2.0: if the target cluster to be restored has any active TiCDC changefeeds, BR does not perform the restoration. |
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| Vector search || Make sure that you are using v8.4.0 or a later version of BR to back up and restore data. Restoring tables with [vector data types](/vector-search-data-types.md) to TiDB clusters earlier than v8.4.0 is not supported. |
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| Vector search || Make sure that you are using v8.4.0 or a later version of BR to back up and restore data. Restoring tables with [vector data types](/vector-search/vector-search-data-types.md) to TiDB clusters earlier than v8.4.0 is not supported. |
Vector search is a search method based on data semantics, which provides more relevant search results. As one of the core functions of AI and large language models (LLMs), vector search can be used in various scenarios such as Retrieval-Augmented Generation (RAG), semantic search, and recommendation systems.
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Starting from v8.4.0, TiDB supports [vector data types](/vector-search-data-types.md) and [vector search indexes](/vector-search-index.md), offering powerful vector search capabilities. TiDB vector data types support up to 16,383 dimensions and support various [distance functions](/vector-search-functions-and-operators.md#vector-functions), including L2 distance (Euclidean distance), cosine distance, negative inner product, and L1 distance (Manhattan distance).
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Starting from v8.4.0, TiDB supports [vector data types](/vector-search/vector-search-data-types.md) and [vector search indexes](/vector-search/vector-search-index.md), offering powerful vector search capabilities. TiDB vector data types support up to 16,383 dimensions and support various [distance functions](/vector-search/vector-search-functions-and-operators.md#vector-functions), including L2 distance (Euclidean distance), cosine distance, negative inner product, and L1 distance (Manhattan distance).
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To start vector search, you only need to create a table with vector data types, insert vector data, and then perform a query of vector data. You can also perform mixed queries of vector data and traditional relational data.
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To enhance the performance of vector search, you can create and use [vector search indexes](/vector-search-index.md). Note that TiDB vector search indexes rely on TiFlash. Before using vector search indexes, make sure that TiFlash nodes are deployed in your TiDB cluster.
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To enhance the performance of vector search, you can create and use [vector search indexes](/vector-search/vector-search-index.md). Note that TiDB vector search indexes rely on TiFlash. Before using vector search indexes, make sure that TiFlash nodes are deployed in your TiDB cluster.
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For more information, see [documentation](/vector-search-overview.md).
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For more information, see [documentation](/vector-search/vector-search-overview.md).
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