ClickHouse Cost Optimization
Cut your ClickHouse bill from inside the cluster. OhChimp finds weak codecs, costly queries, merge pressure, and idle replicas, verified on your real bill.
What OhChimp optimizes for ClickHouse
Some of the cost levers OhChimp checks for ClickHouse. Each one becomes a reviewable plan you approve before anything changes.
Fix weak column codecs
Columns that compress under 2x burn disk and slow scans. OhChimp reads system.columns to find large columns (over 1 MB uncompressed) with a compression ratio below 2x, then recommends a codec change (DoubleDelta for timestamps, ZSTD for strings) so you store the same data for less.
Find your most expensive queries
A handful of SELECTs usually drive most of your read bytes, memory, and runtime. OhChimp ranks the top SELECTs from the last 7 days in system.query_log by bytes read, then surfaces the ones whose cost justifies a rewrite or an index change.
Relieve merge pressure
Tables carrying more than 50 active parts sit under constant merge load, which eats CPU and I/O. OhChimp counts active parts from system.parts and in-flight merges from system.merges, then flags the tables to fix with a partition-key or merge-setting change.
Coarsen over-partitioned tables
Tables with more than 1000 partitions slow startup and pile load onto ZooKeeper. OhChimp groups system.parts by partition to find the worst offenders, then recommends a coarser partition key so the cluster runs leaner.
Reclaim idle and lagging replicas
Replicas delayed past 60 seconds point to under-provisioned nodes, and spare replicas you do not need are pure cost. OhChimp reads system.replicas for absolute_delay and the total and active replica counts, then flags nodes to resize or retire.
Right-size memory and thread settings
An unlimited max_memory_usage invites OOM restarts, and a max_threads above 16 pushes you toward bigger, pricier nodes. OhChimp audits these settings in system.settings and recommends limits that match your real load.
ClickHouse cost optimization FAQ
How does OhChimp connect to ClickHouse?
OhChimp connects with a read-only user over TLS (default secure port 8443). It reads the server version to confirm the connection, then runs probe queries against system tables with a 30-second execution cap. The user only needs read access to system tables.
Does OhChimp need write access or see my data?
No. OhChimp uses a read-only user and reads metadata, table and column sizes, and aggregated query stats from ClickHouse system tables. It never stores your application rows, secrets, or workload contents.
What ClickHouse costs does OhChimp actually reduce?
Waste inside the engine that a node bill never shows: columns with weak compression codecs, expensive SELECT queries, tables under merge pressure, over-partitioned tables, idle or lagging replicas, and memory or thread settings that force bigger nodes than your load needs.
Who applies the changes, and can they be rolled back?
You do. Each fix is a reviewable plan with a confidence score, a risk level, and rollback steps. Nothing changes until you click apply, and OhChimp ships the matching code and infrastructure changes together. You are always the one who presses the button.
How are ClickHouse savings verified, and how does pricing work?
Against your real bill. A plan is marked VERIFIED only after 7 or more days, a drop of at least 10%, and 3 consecutive positive checks. Until then it stays flagged "not implemented." Pricing is a flat monthly fee with no cut of your savings, plus a year-one ROI guarantee: a full refund of subscription fees if OhChimp does not pay for itself in your first 12 months on a paid plan.
Related integrations
Teams running ClickHouse usually run these too. OhChimp finds the waste in each and proves it on the bill.
Neo4j
Instance sizing, indexes, and storage
Cassandra
Node sizing, replication, and storage
Memcached
Cache hit rate and memory sizing