Load tests ClickHouse over its HTTP interface: a ping, a trivial SELECT 1, a scan of ten million generated rows, a GROUP BY on generated data and a system.metrics read. It separates connection and parsing overhead from heavy analytical work, so you can see where concurrency starts to hurt.
It holds 19 ready-made jobs: 5 single scenarios and an enterprise test plan of 14 stages to run in order, 12 of them with pass/fail targets (SLOs). Each job is a plain request pattern you can change before running.
Scenarios include ping, SELECT 1, scan 10 million generated rows, GROUP BY on generated data and system.metrics.
How to load test ClickHouse
Open the template in BLASTA.
Set url to point at your own ClickHouse system, ideally a staging copy.
Pick a job and choose the rate and duration.
Start the run and watch requests per second, latency percentiles and errors live; the result is saved to your history.
Enterprise plan, step 1 of 10. One request a second for 30 seconds. Run this first, every time: it proves the address, credentials and headers are right and that the environment is up before any real load is applied. Gate: zero errors. Reference request: SELECT 1.
Step 2 of 10. About a fifth of normal traffic for 5 minutes: the uncontended latency of this request. Every later result is judged against it, so record p50 and p95. Gate: at most 0.5% errors and the default latency targets. Reference request: SELECT 1.
Step 3 of 10. Normal busy-hour traffic for 10 minutes. The rate is the reference job's rate: raise it to your measured production peak-hour rate. This is the run that proves (or breaks) your SLO. Gate: at most 1% errors, p95 and p99 inside the targets. Reference request: SELECT 1.
Step 4 of 10. Twice the average for 10 minutes: the busiest hour of the year plus headroom. Latency may rise, but must stay in SLO; if it does not, you have no headroom. Gate: at most 2% errors, latency targets doubled. Reference request: SELECT 1.
Step 5 of 10. Ramps to four times average over 10 minutes, then holds for 2. Finds where it degrades and HOW: gracefully (latency rises, errors stay low) or badly (errors, timeouts, crashes, restarts). Observation only, no gate. Reference request: SELECT 1.
Step 6 of 10. Reaches ten times average within 10 seconds and holds for 2 minutes: a campaign email, a news link, a failover. Checks autoscaling, queue limits and load shedding. Gate: at most 5% errors, because shedding load is acceptable and crashing is not. Reference request: SELECT 1.
Step 7 of 10. Run IMMEDIATELY after the spike, at average load for 5 minutes. Latency and errors must return to the baseline from step 2. If they do not, something is stuck: queues, connection pools, GC, an autoscaler cool-down. Gate: same as average load. Reference request: SELECT 1.
Step 8 of 10. One hour of steady load. Finds leaks and slow decay in memory, connections, file descriptors, disk, log volume and cache churn. Watch the resource graphs: any line that climbs and never flattens is a finding. Gate: at most 0.5% errors. Reference request: SELECT 1.
Step 9 of 10. Ramps to twenty times average over 20 minutes. Stop it when errors pass about 5%: the rate at that moment is your ceiling, and ceiling divided by peak is your capacity margin. Use a production-like environment, never production. Reference request: SELECT 1.
Step 10 of 10. Average load for 15 minutes. About 5 minutes in, cause the event you are testing: kill a pod or node, fail over the database, roll out a new version, drain a zone. Errors in the window are your real availability loss. Gate: at most 1% errors overall; read the time series for how long the dip lasted. Reference request: SELECT 1.
Sends Connection: close, so every request pays for a new TCP and TLS handshake: the cost for clients that do not reuse connections (scripts, some mobile SDKs, health checkers). Shows load balancer and TLS termination limits. Reference request: SELECT 1.
Same request identified as a search crawler. Tests WAF and bot-management rules and whether crawlers get cached or origin responses. Crawlers can easily be a third of all traffic. Reference request: SELECT 1.
Adds a 4 KB Cookie header, like a logged-in user with many tracking cookies. Proxies and servers reject headers around 8 KB, so this shows how close you are to that limit. Reference request: SELECT 1.
A fleet of synthetic monitors checking the URL with HEAD requests every 30 seconds from 20 locations, plus load balancer probes. Cheap each, constant in total. Reference request: SELECT 1.
Frequently asked questions
What does the ClickHouse load test cover?
The ClickHouse template has 19 jobs: 5 single scenarios and an enterprise test plan of 14 stages (smoke, baseline, load, stress, spike, soak, breakpoint and failover window). Scenarios include ping, SELECT 1, scan 10 million generated rows and GROUP BY on generated data. 12 of them have pass/fail targets (SLOs), so a run can be judged against limits you set.
How do I load test ClickHouse with BLASTA?
Open the template in BLASTA and set url, then pick a job and start it. Results stream live: requests per second, latency percentiles and errors, and the run is kept in your history. To run from the command line, use blasta preset new clickhouse with your address.
Is it safe to run the ClickHouse load test against production?
All 19 jobs in this template are read-only: they request pages or data and do not change anything. Even so, a load test can slow a live system down, so start with a low rate and prefer a staging copy. Only test systems you own or have permission to test.
Set up a target above and press Start test. Charts and numbers appear here as the test runs.
Load generator resources
CPU used by BLASTA % of capacity
RAM used by BLASTA
Requests per second
Latency p95 lower is better
Response time
Status codes
Errors
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