Log Analytics
Log analytics is the practice of collecting machine-generated logs, storing them, and querying them to debug issues, monitor system health, and detect anomalies. It is a core component of observability alongside metrics and traces.
Turning raw logs into actionable data
Every application, service, and infrastructure component generates logs. Log analytics platforms ingest those logs at high volume, structure them where possible, and make them searchable. When something breaks, engineers query the logs to understand what happened, when, and why.
The traditional tool for log analytics has been Elasticsearch, built on inverted indexes designed for full-text search. That works well for finding specific log entries, but log analytics increasingly involves analytical queries: counting errors by service over time, computing error rates, correlating logs with metrics. For those queries, columnar storage is a better fit.
The log analytics market is shifting. Elasticsearch recently added a columnar mode. Purpose-built log databases like Loki and Quickwit have emerged. And analytical databases like Arc are being used as log backends where query performance and storage cost matter.
How Arc handles Log Analytics
Arc stores logs in columnar Parquet, which makes analytical queries over log data fast and storage efficient. On ClickBench, Arc is 6 to 9x faster than Elasticsearch on analytical queries and 45x faster on ingestion. For teams that need to aggregate and analyze logs, not just search them, Arc offers a purpose-built alternative.
Related terms
Arc is a SQL-native time-series database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.