This roadmap brings you what's coming in the 1-year future, so you can see the new features or improvements in advance, follow the progress, learn about the key milestones on the way, and give feedback as the development work goes on. In the course of development, this roadmap is subject to change based on user needs and feedback. If you have a feature request or want to prioritize a feature, please file an issue on GitHub.
Safe harbor statement:
Any unreleased features discussed or referenced in our documents, roadmaps, blogs, websites, press releases, or public statements that are not currently available ("unreleased features") are subject to change at our discretion and may not be delivered as planned or at all. Customers acknowledge that purchase decisions are solely based on features and functions that are currently available, and that PingCAP is not obliged to deliver aforementioned unreleased features as part of the contractual agreement unless otherwise stated.
| Scenario | Feature | Description |
|---|---|---|
| Support JSON | Support JSON function. | In business scenarios that require flexible schema definitions (such as SaaS, Web3, and gaming), the application can use JSON to store information for ODS, transaction indicators, commodities, game characters, and props. |
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Provide query acceleration for specific field indexes in JSON scenarios. | |
| Flashback | Support cluster-level flashback. | In game rollback scenarios, the flashback can be used to achieve a fast rollback of the current cluster. This solves the common problems in the gaming industry such as version errors and bugs. |
TiFlash result write-back (supports INSERT INTO SELECT) |
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These features combined enable a way to materialize intermediate results. The analysis results can be easily reused, which reduces unnecessary ad-hoc queries, improves the performance of BI and other applications (by pulling results directly) and reduces system load (by avoiding duplicated computation), thereby improving the overall data pipeline efficiency and reducing costs. It will make TiFlash an online service. |
| Time to live (TTL) | Support automatically deleting expired table data based on custom rules. | This feature enables automatic data cleanup in limited data archiving scenarios. |
| Multi-value Index | Support array index. | Array is one of the commonly used data types in JSON scenarios. For inclusive queries in arrays, multi-value indexes can efficiently improve the query speed. |
| TiFlash kernel optimization |
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Improve the basic computing capability of TiFlash, and optimize the performance and reliability of the underlying algorithms of the columnar storage and MPP engine. |
| TiDB proxy | Implement automatic load balancing so that upgrading a cluster or modifying configurations does not affect the application. After scaling out or scaling in the cluster, the application can automatically rebalance the connection without reconnecting. | In scenarios such as upgrades and configuration changes, TiDB proxy is more business-friendly. |
| PB-level scalability | Support huge region size. | Scenarios with fast business growth and a large amount of data |
| Distributed DDL parallel framework | Implement a distributed parallel DDL execution framework, so that DDL tasks executed by only one TiDB Owner node can be coordinated and executed by all TiDB nodes in the cluster. Improve the execution speed of DDL tasks and cluster resource utilization. | By converting the execution of DDL tasks to distributed mode, this feature accelerates the execution speed of DDL tasks and improves the utilization of computing resources in the entire cluster. At present, DDL tasks that need to improve the speed include large table indexing and lossy column type modification tasks. |
| Non-prepared Plan Cache | Support plan cache for general SQL statements in a session to save cache resources, improve the hit rate of general execution plans, and improve SQL performance. | Non-prepared plan cache. Improve real-time and throughputs of OLTP in general scenarios, save PoC time, and increase PoC win rate. |
| SQL blocklist | Support a rule-based SQL blocklist mechanism. | In multi-service aggregation scenarios, provide SQL management and control capabilities, and improve cluster stability by prohibiting high-resource-consuming SQL statements. |
| Resource management | Provide a basic resource management and control framework to effectively control the resource squeeze of background tasks on front-end tasks (user operations), and improve cluster stability. | Refine resource management in the multi-service aggregation scenario. |
| Prepared Plan Cache | Support in-session subquery, expression index, and prepared plan cache for Partition. | Expand the usage scenarios of plan cache. |
| PB-level scalability | Support dynamic region size adjustment (heterogeneous). | For scenarios with fast business growth and a large amount of data. |
| Instance plan cache | Support cross-session plan cache, save cache resources, improve the hit rate of general execution plans, and improve SQL performance. | In general scenarios, reuse execution plans to improve memory utilization and to achieve higher throughputs. |
| Scenario | Feature | Description |
|---|---|---|
| SQL tuning for HTAP workloads |
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| Scenario | Feature | Description |
|---|---|---|
| Backup and restore | EBS snapshot-based backup and restore | Support backup and restore based on AWS EBS or GCP persistent disk snapshots. |
| Point-in-time recovery (PITR) | Table-level and database-level PITR. | BR supports table-level or database-level PITR. |
| Data replication to downstream systems via TiCDC | Reduce TiCDC replication latency in planned offline scenarios. | When TiKV, TiDB, PD, or TiCDC nodes are offline in a planned maintenance window, the replication latency of TiCDC can be reduced to less than 10 seconds. |
| Support replicating data to object storage such as S3. | TiCDC supports replicating data changes to common object storage services. | |
| Data migration | TiDB Lightning supports table-level and partition-level incremental data import. | TiDB Lightning provides comprehensive table-level and partition-level data import capabilities. |
| Scenario | Feature | Description |
|---|---|---|
| ShangMi (SM) algorithms | Encryption-at-rest (TiKV and TiFlash) supports the SM4 algorithm. | Supports encrypting data stored in TiKV and TiFlash based on the SM4 algorithm. |
| TiDB authentication supports the SM3 algorithm. | Provide a user authentication plugin based on the SM3 algorithm, which encrypts the password using the SM3 algorithm. | |
| Log redaction |
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Redact sensitive information in execution plans and various logs to enhance the security of user data. |
| Password complexity check | A strong password is required. | To improve security, empty passwords and weak passwords are not allowed. The required password length is not less than 8. The password must contain an uppercase letter, a lowercase letter, a number, and a character. |
| Password expiration | TiDB provides password expiration management and requires users to change passwords regularly. | Reduce the security risk of password cracking or leakage caused by using the same password for a long time. |
| Password policy management | TiDB provides a password reuse mechanism and brute-force cracking prevention capabilities. | TiDB supports password policy management to protect password security. |
| Column-level access control | TiDB supports column-level privilege management. | TiDB already supports cluster-level, database-level, and table-level privilege management. On top of that, TiDB will support column-level privilege management to meet the principle of least privilege and provide fine-grained data access control. |
| Audit logging capability refactor | Support configurable audit log policies, configurable audit filters (filter by objects, users, and operation types), and visual access to audit logs. | Improve the completeness and usability of the audit log feature. |