TPCC-Mysql Benchmark Tool: Achieving Less Randomness with Multi-Schema Support

The second change I proposed is replacing fully random text fields with generated text, something similar to what is used in the TPC-H benchmark. The problem with fully random strings is that they take a majority of the space in

We are in the process of running other benchmark tools, such as tpcc-mysql, and you can also run any benchmark tool that supports MySQL. Feel free to publish benchmark scores or file bug reports if you uncover anything interesting.

tpcc-mysql benchmark tool: less random with multi-schema support

Download: https://9riopermulmu.blogspot.com/?wc=2vEwAO

Optimizing a database is an important activity for new and existing application workloads. You need to take cost, operations, performance, security, and reliability into consideration. Conducting benchmark tests help with these considerations. With Amazon Aurora PostgreSQL-Compatible Edition, you can run multiple benchmark tests with different transaction characteristics matching your data access patterns. In this post, we provide a solution to automate and scale benchmark tests. The solution supports running multiple workloads utilizing multiple client instances, which gives you the ability to create realistic benchmarks.

The default built-in transaction script (also invoked with -b tpcb-like) issues seven commands per transaction over randomly chosen aid, tid, bid and delta. The scenario is inspired by the TPC-B benchmark, but is not actually TPC-B, hence the name.

HammerDB is a leading open source relational database load testing and benchmarking tool used by many database professionals to stress and benchmark the most popular relational databases both commercial and open source. HammerDB supports testing of both MySQL and PostgreSQL with workloads based on industry standard specifications and is hosted by the industry standard body the TPC (Transaction Processing Performance Council). HammerDB implements both OLTP and OLAP workloads, in this paper we focus on the OLTP workload called TPROC-C. TPROC-C means "Transaction Processing Benchmark derived from the TPC "C" specification" and is the OLTP workload implemented in HammerDB derived from the OLTP TPC-C specification with modifications to make running HammerDB considerably more straightforward and cost-effective than adhering strictly to the specification whilst still delivering valuable insights into relational database performance. Such a workload lowers the barrier to entry for database benchmarking to make comparison of database performance reliable and predictable yet also widely available.

An index, created using one or more columns of a database table, provides the basis for both rapid random lookups and efficient access of ordered records when querying by those columns. However, it is not easy to identify the comprehensive list of indexes at the time of table creation because speeding up queries is essentially an ever-changing business requirement. YugabyteDB 2.2 supports online build of indexes that are created on non-empty tables thus ensuring that the data for the new indexes is backfilled for the existing rows, without any downtime on the cluster. This support includes simple and unique indexes for YCQL as well as simple indexes for YSQL. Unique indexes for YSQL are a work in progress.

To measure CockroachDB performance, we used the brianfrankcooper/YCSB 0.17.0 benchmark with PostgreNoSQL binding and CockroachDB v20.1.6 YCSB port to the Go programming language that offers better support for this database. For ScyllaDB 4.2.0, we used the brianfrankcooper/YCSB 0.18.0 with a ScyllaDB-native binding and a Token Aware balancing policy.

SQLite 3.36.0.3, configured to use WAL and withsynchronous=NORMAL was tested in aseparate, less reliable run. A rough estimate is that SQLite performs approximately 2-5x worse in the simple benchmarks,which perform simple work in the database, resulting in a low work-per-transaction ratio. SQLite becomes competitive asthe complexity of the database interactions increases. The results seemed to vary drastically across machine, and morereliable results should be obtained. Benchmark on your production hardware.

The following is a short HOWTO about deployment and use of Benchmark-kit (BMK-kit). The main idea of this kit is to simplify your life in running various MySQL benchmark workloads with less blood and minimal potential errors.

db_STRESS generates OLTP workload to stress database as much as possible. Performance level of workload is measured mainly in TPS (transactions per second), but you have also a freedom to have more or less queries per "transaction", that's why any result should be present with its scenario context to make sense (same for QPS). During any given transaction we are first randomly choosing OBJECT reference and then performing READ or READ and WRITE operations : 2ff7e9595c