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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

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

Configuration management

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

Data sharing and subscription

Storage

Backup and restoration

Garbage collection

Diagnostics

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