This guide describes how to explore and use the features of TiDB Hybrid Transactional and Analytical Processing (HTAP).
TiDB HTAP can handle the massive data that increases rapidly, reduce the cost of DevOps, and be deployed in either self-hosted or cloud environments easily, which brings the value of data assets in real time.
The following are the typical use cases of HTAP:
When using TiDB for real-time Online Analytical Processing (OLAP) in hybrid load scenarios, you only need to provide an entry point of TiDB to your data. TiDB automatically selects different processing engines based on the specific business.
Real-time stream processing
When using TiDB in real-time stream processing scenarios, TiDB ensures that all the data flowed in constantly can be queried in real time. At the same time, TiDB also can handle highly concurrent data workloads and Business Intelligence (BI) queries.
When using TiDB as a data hub, TiDB can meet specific business needs by seamlessly connecting the data for the application and the data warehouse.
For more information about use cases of TiDB HTAP, see blogs about HTAP on the PingCAP website.
In TiDB, a row-based storage engine TiKV for Online Transactional Processing (OLTP) and a columnar storage engine TiFlash for Online Analytical Processing (OLAP) co-exist, replicate data automatically, and keep strong consistency.
For more information about the architecture, see architecture of TiDB HTAP.
Before exploring the features of TiDB HTAP, you need to deploy TiDB and the corresponding storage engines according to the data volume. If the data volume is large (for example, 100 T), it is recommended to use TiFlash Massively Parallel Processing (MPP) as the primary solution and TiSpark as the supplementary solution.
If you have deployed a TiDB cluster with no TiFlash node, add the TiFlash nodes in the current TiDB cluster. For detailed information, see Scale out a TiFlash cluster.
When deciding how to choose the number of TiFlash nodes, consider the following scenarios:
- If your use case requires OLTP with small-scale analytical processing and Ad-Hoc queries, deploy one or several TiFlash nodes. They can dramatically increase the speed of analytic queries.
- If the OLTP throughput does not cause significant pressure to I/O usage rate of the TiFlash nodes, each TiFlash node uses more resources for computation, and thus the TiFlash cluster can have near-linear scalability. The number of TiFlash nodes should be tuned based on expected performance and response time.
- If the OLTP throughput is relatively high (for example, the write or update throughput is higher than 10 million lines/hours), due to the limited write capacity of network and physical disks, the I/O between TiKV and TiFlash becomes a bottleneck and is also prone to read and write hotspots. In this case, the number of TiFlash nodes has a complex non-linear relationship with the computation volume of analytical processing, so you need to tune the number of TiFlash nodes based on the actual status of the system.
- If your data needs to be analyzed with Spark, deploy TiSpark. For specific process, see TiSpark User Guide.
After TiFlash is deployed, TiKV does not replicate data to TiFlash automatically. You need to manually specify which tables need to be replicated to TiFlash. After that, TiDB creates the corresponding TiFlash replicas.
- If there is no data in the TiDB Cluster, migrate the data to TiDB first. For detailed information, see data migration.
- If the TiDB cluster already has the replicated data from upstream, after TiFlash is deployed, data replication does not automatically begin. You need to manually specify the tables to be replicated to TiFlash. For detailed information, see Use TiFlash.
With TiDB, you can simply enter SQL statements for query or write requests. For the tables with TiFlash replicas, TiDB uses the front-end optimizer to automatically choose the optimal execution plan.
When using TiDB, you can monitor the TiDB cluster status and performance metrics in either of the following ways:
- TiDB Dashboard: you can see the overall running status of the TiDB cluster, analyse distribution and trends of read and write traffic, and learn the detailed execution information of slow queries.
- Monitoring system (Prometheus & Grafana): you can see the monitoring parameters of TiDB cluster-related componants including PD, TiDB, TiKV, TiFlash,TiCDC, and Node_exporter.
If any issue occurs during using TiDB, refer to the following documents:
- Analyze slow queries
- Identify expensive queries
- Troubleshoot hotspot issues
- TiDB cluster troubleshooting guide
- Troubleshoot a TiFlash Cluster