Profiles / BigQuery
BigQuery
Google's serverless warehouse: no clusters to size, just tables and SQL. It stores the ClickBench data compactly and scales without thought, but every query carries a fixed overhead of a few hundred milliseconds, which shows up in interactive workloads and puts its relative score near 5× ClickHouse.
Easiest to start, hardest to predict the billProprietary
Strengths
- +No infrastructure to size or tune.
- +Most compact storage reported in the managed group (8.8 GB).
- +Deep integration with the rest of Google Cloud.
Weaknesses
- −Roughly 370 ms even for a full COUNT(*), which ClickHouse answers in about 2 ms.
- −On-demand pricing per byte scanned makes the cost of a busy dashboard hard to forecast.
- −Slowest bulk load in the managed group except Redshift (777 s).
- −Google Cloud only.
Capabilities
| Real-time ingestion | Storage Write API streaming, priced separately |
|---|---|
| Columnar storage | Capacitor format |
| Open source | Proprietary |
| Managed cloud | Google Cloud only |
| Query speed at scale | 4.89 managed |
| High concurrency | Not tested; slot and quota limits apply |
| Cost model | Per TiB scanned, or reserved slots |
BigQuery vs ClickHouse
Against ClickHouse Cloud, BigQuery scores 4.89 on hot runs to ClickHouse’s 1.06, and stores the data in 8.8 GB to ClickHouse’s 10.3 GB. For batch reporting and ad-hoc analysis inside Google Cloud, where per-query latency is not critical, BigQuery is a reasonable choice; for real-time analytics, we recommend ClickHouse.