ShardingSphere‐JDBC adopts decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java; ShardingSphere‐Proxy provides static entry and all languages support increase the system capacity by removing database connection restrictions. It is also applicable to OLTP operation, which usually has sharding keys and can be routed to a single shard. So it is a wise choice circumstance, two requests waiting for each other will not happen, so there is no need for locking. Most OLTP operations use sharding keys to route to the only data node, which will make the system in a totally

ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static entry and all languages support ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static entry and all languages support brought more complex situations to the database. Apache ShardingSphere wants to solve massive data OLTP problem first and complete relevant OLAP support problem little by little. SQL SQL Supporting Status

ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static entry and all languages support ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static entry and all languages support brought more complex situations to the database. Apache ShardingSphere wants to solve massive data OLTP problem first and complete relevant OLAP support problem little by little. 4.3. Sharding 37 Apache

methods reported on the following pages. About the Benchmark: TPC‐C and tpcc‐mysql TPC‐C is an OLTP industry standard benchmark developed and maintained by the Transaction Processing Performance Council processing orders. It is a mixture of read‐only and update‐intensive transactions that represent complex OLTP application activities. The benchmark implements five types of transactions (new order, payment, box” code, downloaded from the Percona GitHub site. 2. Hardware Configurations In the past, OLTP systems were frequently bottlenecked by IO, meaning CPUs were constantly waiting on HDDs to respond

· · · · · · · · · · · · · · · · · · · · · · · · · · 1771 11.1.8 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1796 11.1.9 TiFlash Performance Analysis and Tuning · · · · · · · · · · · · · · · · · · · · · · · · · · · 6726 17.11.3 Online Transaction Processing (OLTP) · · · · · · · · · · · · · · · · · · · · · · · · · · · 6726 17.11.4 Out of Memory (OOM)· · · · · avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is

· · · · · · · · · · · · · · · · · · · · · · · · · · 1305 11.1.3 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1328 11.1.4 TiFlash Performance Analysis and Tuning avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is basic knowledge about TiKV, a row- based storage engine for TiDB Online Transactional Processing (OLTP), and TiFlash, a columnar storage engine for TiDB Online Analytical Processing (OLAP). • Storage

· · · · · · · · · · · · · · · · · · · · · · · · · · 1473 11.1.8 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1498 11.1.9 TiFlash Performance Analysis and Tuning avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is by caching more �→ execution plans in memory, decreasing overall SQL execution time. It �→ improves OLTP performance and throughput while providing better �→ control over memory usage and enhancing database

· · · · · · · · · · · · · · · · · · · · · · · · · · 1291 11.1.3 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1314 11.1.4 TiFlash Performance Analysis and Tuning avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is basic knowledge about TiKV, a row- based storage engine for TiDB Online Transactional Processing (OLTP), and TiFlash, a columnar storage engine for TiDB Online Analytical Processing (OLAP). • Storage

· · · · · · · · · · · · · · · · · · · · · · · · · · 1287 11.1.3 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1310 11.1.4 TiFlash Performance Analysis and Tuning avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is introduces non-prepared plan cache as an experimental feature to improve the load capacity of concurrent OLTP. In v7.1.0, TiDB enhances this feature and supports caching more SQL statements. To improve memory

· · · · · · · · · · · · · · · · · · · · · · · · · · 1076 11.1.3 Performance Tuning Practices for OLTP Scenarios· · · · · · · · · · · · · · · · · · 1098 11.2 Configuration Tuning· · · · · · · · · · · avail- ability. The goal of TiDB is to provide users with a one-stop database solution that covers OLTP (Online Transactional Processing), OLAP (Online Analytical Processing), and HTAP services. TiDB is the PB level. The traditional solution is to process online trans- actional applications using an OLTP database and use an ETL (Extract, Transform, Load) tool to replicate the data into an OLAP database