![]() ![]() ![]() There are many to choose from, but few are well-established, and even fewer are (somewhat) representative of real-world workloads. It starts with the choice of workload and the benchmarking tool. ![]() Why it’s hard to benchmark database performanceīenchmarking databases, especially at large scale, is challenging-and comparative benchmarks are even harder. In this blog post, you’ll see why benchmarking databases is so hard, why the TPC council’s HammerDB is an awesome benchmarking tool, and why Citus is fast. You can find the full GigaOM benchmark report at: GigaOM: Transaction Processing & Price-Performance Testing. Azure Cosmos DB for PostgreSQL (with Citus distributed tables).GigaOM compared the transaction performance and price-performance of these popular managed services of distributed PostgreSQL, using the HammerDB benchmark software: We therefore asked GigaOM to run performance benchmarks comparing Azure Cosmos DB for PostgreSQL to other distributed implementations. That all sounds good in theory, but to see whether this holds up in practice, you need benchmark numbers. As such, Citus inherits the performance characteristics of a single PostgreSQL server but applies them at scale. It also leans entirely on PostgreSQL for storage, indexing, low-level query planning and execution, and various performance features. The unique thing about Citus, the technology powering Azure Cosmos DB for PostgreSQL, is that it is fully implemented as an open-source extension to PostgreSQL. Others (like us) have built a distributed database on top of PostgreSQL itself.įor the Citus database team, distributed PostgreSQL is primarily about achieving high performance at scale. Several distributed database vendors have added support for the PostgreSQL protocol as a convenient way to gain access to the PostgreSQL ecosystem. Distributed PostgreSQL has become a hot topic. ![]()
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