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ClickHouse Optimization & Query Tuning Services
Boost ClickHouse performance with expert query tuning, table engine optimization, and cluster-level improvements.
Mafiree’s ClickHouse consultants analyze slow queries, fix partitioning or primary key issues, optimize MergeTree tables, and fine-tune cluster configuration so your analytical workloads run at exceptional speed — across self-hosted clusters, ClickHouse Cloud, or hybrid deployments.
Why ClickHouse Optimization Matters
Even though ClickHouse is built for lightning-fast analytics, performance can degrade when schema design, partitions, or queries are inefficient.
Poorly structured MergeTree tables, oversized partitions, missing primary keys, or unoptimized aggregations can lead to:
- High merge load
- Slow queries on large datasets
- Disk I/O spikes
- Replication delays
- Cluster instability under heavy workloads
At Mafiree, our specialists uncover hidden performance issues across compute, storage, table engines, and distributed architectures. Whether you're using ClickHouse locally, in Kubernetes, or in ClickHouse Cloud, we design optimization strategies that reduce latency, improve throughput, and stabilize your data pipelines.
Why Choose Mafiree for ClickHouse Performance Tuning
- Deep analysis of slow queries, aggregations, joins, and group-bys
- Expert tuning of MergeTree engines, partitions, primary keys, and projections
- Optimization of distributed queries and multi-shard architectures
- Cloud-native tuning for ClickHouse Cloud, AWS, Azure, and GCP
- 24/7 monitoring, alerting, and continuous improvement for large clusters
Key ClickHouse Optimization Services
Query & Aggregation Optimization
- Identify slow aggregations, JOINs, or heavy group-by operations
- Rewrite queries using projections, pre-aggregations, or optimized functions
- Analyze EXPLAIN, query_log, and trace_log to reduce CPU, memory, and disk usage
Table Engine, Partitioning & Schema Design
- Redesign MergeTree tables with correct primary keys and sorting orders
- Fix inefficient partitioning strategies to reduce unnecessary scanning
- Add projections, materialized views, or column codecs for faster analytics
Materialized Views & Refreshable Materialized Views Optimization
ClickHouse’s materialized views can massively speed up analytics but only when designed and tuned correctly. We optimize both classic materialized views and refreshable materialized views (RMVs) for peak efficiency.
Key Enhancements We Provide
- Design high-performance MV structures for heavy aggregations
- Reduce storage overhead by optimizing target engines (AggregatingMergeTree, SummingMergeTree, MergeTree)
- Ensure correct use of TTL, ORDER BY, and PARTITION BY in MV targets
- Tune refreshable MVs to avoid heavy rebuilds
- Optimize refresh windows using incremental updates
- Analyze refresh performance for RMVs and adjust logic for large datasets
- Prevent duplicate data with correct ON CLUSTER and POPULATE usage
What This Solves
- Slow dashboards due to repeated heavy aggregations
- High CPU spikes during MV rebuilds
- Inefficient full refresh cycles
- Performance issues caused by misconfigured MV targets
- Overlaps or inconsistencies in MV data when ingesting high-volume events
Server & Configuration Tuning
- Tune background merges, memory allocations, and max threads
- Optimize compression codecs based on workload (ZSTD, LZ4, etc.)
- Resolve performance issues tied to merges, disk I/O, and IO scheduling
Sharding & Distributed Query Optimization
- Balance shards and replicate tables to eliminate bottlenecks
- Optimize distributed queries for parallel execution
- Tune replication settings to eliminate delays
Cloud & Hybrid Optimization
- Optimize ClickHouse Cloud for performance and cost
- Tune autoscaling, storage, and backup strategies
- Solve latency issues in hybrid or multi-region setups
Our Proven Optimization Process
- Performance Audit – Analyze logs, query patterns, table structures, and system metrics
- Bottleneck Analysis – Identify inefficiencies in queries, partitions, engines, merges, or config
- Optimization Execution – Apply changes: rewrite queries, redesign partitions, tune nodes
- Testing & Validation – Benchmark improvements before and after optimization
- Ongoing Monitoring – Use dashboards & alerts to maintain long-term performance
Common ClickHouse Performance Challenges We Solve
- Slow aggregations on large MergeTree tables
- Heavy disk I/O or merge backlog
- Unbalanced shards or slow distributed queries
- Replication lag during high ingestion
- Wrong partition keys or sorting orders
- Memory spikes or instability during high-concurrency loads
- Materialized view rebuild performance issues
- Inefficient refresh logic in refreshable materialized views
