ClickHouse vs. Elasticsearch: The Billion-Row Matchup
This blog examines the performance of ClickHouse vs. Elasticsearch for workloads commonly present in large-scale data analytics and observability use cases – count(*) aggregations over billions of table rows. It shows that ClickHouse vastly outperforms Elasticsearch for running aggregation queries over large data volumes. Specifically:
For these above-mentioned reasons, we increasingly see users migrating from Elasticsearch to ClickHouse, with customers highlighting:
“ClickHouse helped us to scale from millions to billions of rows monthly.” “After the switch, we saw a 100x improvement on average read latencies” The Guild
In this post, we will compare storage sizes and count(*) aggregation query performance for a typical data analytics scenario. To keep the scope suitable for one blog, we will compare the single-node performance of running count(*) aggregation queries in isolation over large data sets.
The rest of this blog first motivates why we focussed on benchmarking count(*) aggregations. We then describe the benchmark setup and explain our count(*) aggregation performance test queries and benchmark methodology. Finally, we will present the benchmark results.
