Important

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TiDB Experimental Features

This document introduces the experimental features of TiDB in different versions. It is NOT recommended to use these features in the production environment.

Performance

Randomly sample about 10000 rows of data to quickly build statistics (Introduced in v3.0)

Stability

Improve the stability of the optimizer's choice of indexes (Introduced in v5.0)
- Extend the statistics feature by collecting the multi-column order dependency information.
- Refactor the statistics module, including deleting the TopN value from CMSKetch and the histogram, and adding NDV information for histogram buckets of each table index. For details, see descriptions about Statistics - tidb_analyze_version = 2.

Scheduling

Cascading Placement Rules feature. It is a replica rule system that guides PD to generate corresponding schedules for different types of data. By combining different scheduling rules, you can finely control the attributes of any continuous data range, such as the number of replicas, the storage location, the host type, whether to participate in Raft election, and whether to act as the Raft leader. See Cascading Placement Rules for details. (Introduced in v4.0)
Elastic scheduling feature. It enables the TiDB cluster to dynamically scale out and in on Kubernetes based on real-time workloads, which effectively reduces the stress during your application's peak hours and saves overheads. See Enable TidbCluster Auto-scaling for details. (Introduced in v4.0)

SQL

List Partition (Introduced in v5.0)
List COLUMNS Partition (Introduced in v5.0)
Dynamic Pruning Mode for Partitioned Tables. (Introduced in v5.1)
The expression index feature. The expression index is also called the function-based index. When you create an index, the index fields do not have to be a specific column but can be an expression calculated from one or more columns. This feature is useful for quickly accessing the calculation-based tables. See Expression index for details. (Introduced in v4.0)
Generated Columns.
User-Defined Variables.
JSON data type and JSON functions.
Prepare Plan cache. (Introduced in v4.0)
Using ALTER TABLE to modify multiple columns or indexes (Introduced in v5.0.0)
Cascades Planner: a cascades framework-based top-down query optimizer (Introduced in v3.0)
Table Lock (Introduced in v4.0.0)

Configuration management

Persistently store configuration parameters in PD, and support dynamically modifying configuration items. (Introduced in v4.0)

Integrate TiCDC with Kafka Connect (Confluent Platform) (Introduced in v5.0)
The cyclic replication feature of TiCDC (Introduced in v5.0)

Storage

Backup and restoration

Back up Raw KV.

Garbage collection

Green GC.