Query Optimization

Query Optimization

JOIN Strategies

• join_algorithm setting: hash (default, in-memory), partial_merge (spills to disk for large right table), auto (lets ClickHouse decide)
• JOIN ... USING avoids repeated column names vs ON for same-name columns
• Filter both sides before joining to reduce intermediate data
• GLOBAL JOIN broadcasts the right table to all shards for distributed queries

EXPLAIN Analysis

• EXPLAIN PLAN — logical plan, shows projection/pushdown transformations
• EXPLAIN PIPELINE — physical execution with parallelism info and port counts
• EXPLAIN INDEXES — which indexes fire, granules selected vs total
• Look for: full table scans, missing index usage, excessive granule reads

Index Usage

• Skip index types with use-cases:
  • minmax — range queries on numeric/date columns
  • set(N) — equality on low-cardinality columns, stores N unique values per granule
  • bloom_filter — equality on high-cardinality strings
  • tokenbf_v1 — tokenized text search (logs, URLs)
• Check effectiveness via ProfileEvents['SelectedRows'] vs result size

Query Profiling

• ProfileEvents map counters: SelectedRows, MergedRows, FileOpen, SeekCount
• normalized_query_hash to group parameterized query variants
• system.query_log columns: query_duration_ms, memory_usage, read_bytes

Optimizer Settings

• enable_optimizer = 1 — activates ClickHouse's new cost-based query optimizer (v22.6+)
• max_threads — controls query parallelism; higher = faster but more memory; lower for concurrent workloads
• prefer_localhost_replica = 1 — avoids network round-trip by reading from local replica on distributed queries
• system.query_plan (v23.6+) — persisted query plans for analysis across runs