Run customer-operated ClickHouse on isolated dedicated servers sized for inserts, concurrent queries, background merges, replication, and recovery, so headroom and saturation signals stay attributable.
Match CPU and RAM to scan volume, query concurrency, inserts, joins, compression, and background merges.
Choose SSD or NVMe layouts from measured latency, throughput, capacity, merge demand, and replica catch-up.
Cover client results, distributed queries, replication, maintenance, recovery, and replica catch-up in bandwidth sizing.
Your team owns ClickHouse and Keeper installation, tuning, security, updates, monitoring, backups, and recovery.
A ClickHouse warehouse runs continuous inserts, concurrent scans, aggregations, joins, and background merges against the same resource pool. Retention, replicas, maintenance, and recovery add pressure that steady-state sizing misses. Capacity planning should expose which resource closes first before re-sharding becomes an emergency engineering project.
Melbicom supplies isolated hardware with configurable compute, memory, local storage, bandwidth choices, and regional placement. This bare metal server hosting model makes query, merge, replication, and replica catch-up pressure attributable to known nodes, while ready-to-deploy configurations give data teams a practical path for adding measured capacity.
Melbicom keeps the boundary explicit through dedicated capacity and 24/7 infrastructure support; your team owns ClickHouse and ClickHouse Keeper installation, topology, tuning, security, updates, observability, backups, restores, and releases. Test production-like demand, scale vertically with headroom, and change replicas or shards only when workload evidence justifies the work.
Test concurrency, scan volume, query latency, memory pressure, and result traffic under representative product load.
Absorb sustained and burst inserts while preserving CPU, storage, and merge headroom for analytical queries.
Query high-cardinality logs, metrics, and traces while tracking memory pressure, disk queues, and merge backlog.
Track tenant concurrency, scan patterns, result sizes, cache assumptions, and query latency under representative load.
Test joins, aggregations, and wide scans on production-like data to expose spills, queueing, and tail latency.
Reserve compute, storage, and network headroom for maintenance, replica catch-up, failures, and recovery testing.
Model compressed, replicated, and retained data before headroom closes and re-sharding demands urgent work.
Place ClickHouse Keeper around failure tolerance and disk-latency needs while your team operates topology and health.
