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Using DBGen to Generate TPC-H Test Data

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In this post I describe the Data Base Generator (DBGen) utility in terms of what it is, how to install it, and how to use it.   This post assumes a Linux operating system.   There are different build procedures for DBGen on Windows, but the concepts carry over. DBGen is used to create a TPC-H schema (i.e., it provides the CREATE TABLE statements) and to generate the data.   DBGen is used in official TPC-H benchmarking, but it can also be used in informal TPC-H-like benchmarking.   The schema and data is compatible with most 3 rd party benchmarking tools like HammerDB, so even if you plan to run a TPC-H-like test using HammerDB you may still use DBGen to create the schema and data. The  CREATE TABLE   statements are ANSI standard SQL and can be run in any DBMS without editing.   There are no SQL statements for indexes or foreign key constraints provided by DBGen since those types of objects are not required.   The data is created as delimited flat files, with one file for each of
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Oracle Database Smart Flash Cache (DSFC)

The Oracle Database Smart Flash Cache solution, or simply DSFC, accelerates primarily OLTP workloads by caching frequently accessed blocks in a hybrid memory area that spans from the Oracle database buffer cache to one or more flash memory storage devices.  The feature is recommended for any Oracle database stored entirely on disk, which is well known for higher wait times and lower throughput than flash. This feature is only available on Oracle branded operating systems including Oracle Linux and Solaris.   It does not require the Oracle branded kernel known as UEK.   If you are not using an Oracle operating system, then consider moving specific tablespaces to a flash storage device. Physically, the DSFC is just a file.   The file can span one or more flash storage devices or partitions.   This spanning of devices is inherent to DSFC, which acts like software defined RAID 0.   You may use logical volume management or Oracle ASM disk groups to do the same thing, but that's over
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Understanding TPC and TPC-like Database Benchmarks

This post is Part 1 in a series about running benchmarks to measure the performance of relational databases.  This post provides a very brief introduction to the popular industry standard benchmarks TPC-C, E, H, and DS and the software programs you can use to run a variation of each benchmark. The discussion here is generic and can be applied to any relational database like Oracle, SQL Server, MySQL, and PostgreSQL.   The TPC has many benchmarks for non-relational databases not discussed here.   The discussion here is also intended for informal benchmarking without publication.   Why Benchmark? Benchmark tools can answer a variety of questions and help predict success or failure.   Consider the following examples of why organizations perform internal benchmarking of database systems: Predict change impact .   Organizations run benchmarks to see how a system change impacts performance.   Run a benchmark to establish a baseline, make a system or database change, and then re-run t
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