Optimizing Complex Queries in ClickHouse: Multi‑Stage Execution, Exchange Management, and Performance Enhancements

Project Background ClickHouse has become a mainstream open‑source OLAP engine, but its two‑stage execution model can cause bottlenecks for large, complex queries, especially when data volumes grow and query patterns become more demanding.

Technical Solution ByteHouse redesigns the execution flow into multiple independent Stages connected by an ExchangeManager , similar to Presto or Impala. The design goals include modular interfaces, plug‑in architecture, and clear separation of data exchange from computation.

Key Terminology

ExchangeNode – represents a data‑exchange point in the query plan.

PlanSegment – the executable fragment for a single Stage.

ExchangeManager – handles data transfer between Stages.

SegmentScheduler – dispatches PlanSegments to workers.

InterpreterPlanSegment – runs a PlanSegment on a worker.

Execution Flow

Coordinator inserts Exchange nodes into the logical plan and splits it into PlanSegments.

SegmentScheduler sends each PlanSegment to appropriate workers.

Workers execute their segments, reading data locally or from upstream exchanges.

ExchangeManager pushes results downstream, applying back‑pressure when needed.

Coordinator aggregates final results and returns them to the client.

Plan Splitting Example A two‑table join is divided into four Stages, each handling a specific part of the computation and data movement.

Segment Scheduler Strategies

Dependency scheduling – respects Stage DAG dependencies, similar to topological sorting.

AllAtOnce – schedules all Stages simultaneously after global analysis.

Runtime Filter A join‑side filter (min‑max or bloom filter) is built on the right table and pushed to the left table to prune unnecessary rows before the join, dramatically reducing data shuffling for star‑schema queries.

Diagnostics and Optimization Extensive metrics (stage latency, I/O, operator profiling) and back‑pressure monitoring help locate bottlenecks. Additional optimizations include shuffle‑join, broadcast‑join, colocate‑join, connection reuse, RDMA, and compression.

Performance Results

Complex aggregation reduced from 8.514 s to 2.198 s by parallelizing the second‑stage aggregation.

Large‑table hash join time dropped from 17.210 s to 1.749 s using shuffle‑based data distribution.

Five‑table join execution time improved from 8.583 s to 4.464 s with multi‑Stage execution.

Future Outlook Ongoing work focuses on further improving Stage execution and Exchange performance, enhancing metrics and intelligent diagnostics, and extending optimizer rules (CBO, RBO) to make complex query writing seamless for users.

ByteHouse is now available for trial; users can scan the QR code or visit the official website to experience the advanced complex‑query capabilities.