Monthly Archives: May 2019

Improving OLAP Workload Performance for PostgreSQL with ClickHouse Database

Every database management system is not optimized for every workload. Database systems are designed for specific loads, and thereby give better performance for that workload. Similarly, some kinds of queries work better on some database systems and worse on others. This is the era of specialization, where a product is designed for a specific requirement or a specific set of requirements. We cannot achieve everything performance-wise from a single database system. PostgreSQL is one of the finest object databases and performs really well in OLTP types of workloads, but I have observed that its performance is not as good as some other database systems for OLAP workloads. ClickHouse is one of the examples that outperforms PostgreSQL for OLAP.
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Benchmark ClickHouse Database and clickhousedb_fdw

In this research, I wanted to see what kind of performance improvements could be gained by using a ClickHouse data source rather than PostgreSQL. Assuming that I would see performance advantages with using ClickHouse, would those advantages be retained if I access ClickHouse from within postgres using a foreign data wrapper (FDW)? The FDW in question is clickhousedb_fdw – an open source project from Percona!
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