tolerance levels, you can configure the geographic location and number of replicas as needed. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios With the fast growth of 5G, Internet of Things, and artificial intelligence, the data analysis. This solution 33 has multiple disadvantages such as high storage costs and poor real-time performance. TiDB introduces the TiFlash columnar storage engine in v4.0, which combines with the

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

tolerance levels, you can configure the geographic location and number of replicas as needed. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios With the fast growth of 5G, Internet of Things, and artificial intelligence, the data analysis. This solution 32 has multiple disadvantages such as high storage costs and poor real-time performance. TiDB introduces the TiFlash columnar storage engine in v4.0, which combines with the

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing

geographic location and number of replicas as needed to meet different disaster tolerance levels. • Real-time HTAP TiDB provides two storage engines: TiKV, a row-based storage engine, and TiFlash, a columnar maximum of 1,000 concurrencies, and the maximum cluster capacity is at the PB (petabytes) level. • Real-time HTAP scenarios TiDB is ideal for scenarios with massive data and high concurrency that require With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing