At small scale any log store works; at scale the invoice decides, not the feature list. The four dominant logging backends make fundamentally different storage bets that fix their cost-per-GB and what queries are even fast. A deep dive on the index-everything camp versus the cheap-storage camp, and the ingest discipline that beats all of them.
Clickhouse
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Modern Logging Architecture: Loki, Splunk, Elasticsearch, ClickHouse, and the Cost-Per-GB That Decides Everything -
ClickHouse for Analytics Workloads ClickHouse columnar storage, the MergeTree engine family, primary key vs sorting key, materialized views for pre-aggregation, query profiling with EXPLAIN, replication with ClickHouse Keeper, distributed tables for sharding, and when to choose ClickHouse over PostgreSQL.
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ClickHouse for Observability: Logs, Metrics, and Traces at Scale How ClickHouse's columnar storage architecture makes it the ideal backend for logs, metrics, and traces at scale — replacing Elasticsearch with a fraction of the resources.