How to Analyze Execution Plans in ClickHouse

How do I view and understand execution plans in ClickHouse?

EXPLAIN variants show ClickHouse’s execution plan so you can spot bottlenecks and optimize queries.

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Description

What does EXPLAIN do in ClickHouse?

EXPLAIN returns the logical or physical execution plan for a query, revealing how ClickHouse will read, filter, join, and aggregate data before it runs the statement.

Which EXPLAIN modes are available?

Use EXPLAIN AST (abstract syntax), EXPLAIN SYNTAX (rewritten SQL), EXPLAIN PLAN (logical plan), and EXPLAIN PIPELINE (execution pipeline with threads).

How do I view a logical plan quickly?

Prefix your query with EXPLAIN PLAN.ClickHouse prints each step—table scan, filtering, aggregation—with estimated rows and cost.

Example

EXPLAIN PLAN SELECT customer_id, sum(total_amount)FROM Orders GROUP BY customer_id;

When should I use EXPLAIN PIPELINE?

Run EXPLAIN PIPELINE to see threaded stages and buffer sizes. This helps identify CPU-bound joins or mis-balanced parallelism on large ecommerce tables.

How do system tables help?

Query system.query_log, system.part_log, and system.processes to cross-check actual run times, parts read, and memory versus EXPLAIN estimates.

Best practices for plan analysis

Compare EXPLAIN PLAN and EXPLAIN PIPELINE.Watch for full table scans on Orders or OrderItems when a composite primary key or skip index could help. Test effects of SET max_threads and SET optimize_predicate_expression.

How to speed up slow aggregates?

If EXPLAIN shows a single-thread aggregation on Orders, add GROUP BY keys matching the primary key, enable GROUP BY IN ORDER, or create a materialized aggregate table.

Common pitfalls to avoid

Relying only on EXPLAIN AST hides parallelism details.Ignoring the rows column in EXPLAIN PLAN may lead to wrong memory sizing.

Need a GUI?

Tools like Galaxy display EXPLAIN output side-by-side with the query, highlight expensive steps, and let you share optimized versions with teammates instantly.

Why How to Analyze Execution Plans in ClickHouse is important

How to Analyze Execution Plans in ClickHouse Example Usage


EXPLAIN PIPELINE SELECT c.name, sum(oi.quantity * p.price) AS lifetime_value
FROM Customers c
JOIN Orders o       ON o.customer_id = c.id
JOIN OrderItems oi  ON oi.order_id   = o.id
JOIN Products p     ON p.id          = oi.product_id
GROUP BY c.name
ORDER BY lifetime_value DESC
LIMIT 10;

How to Analyze Execution Plans in ClickHouse Syntax


EXPLAIN [AST | SYNTAX | PLAN | PIPELINE] [SETTINGS setting_name=value, ...] <SQL_query>

-- Ecommerce examples
EXPLAIN PLAN SELECT * FROM Customers WHERE id = 42;
EXPLAIN PIPELINE SELECT customer_id, sum(total_amount) FROM Orders GROUP BY customer_id;
EXPLAIN SYNTAX SELECT oi.order_id, p.name FROM OrderItems oi JOIN Products p ON p.id = oi.product_id;

Common Mistakes

Mistake: Running only EXPLAIN AST. Why: It omits parallel stages, so you miss CPU hotspots. Fix: Use EXPLAIN PIPELINE or PLAN for full insight.
Mistake: Ignoring SETTINGS. Why: Planner may choose different paths under default limits. Fix: Re-run EXPLAIN with realistic SETTINGS (e.g., max_threads, max_memory_usage) before optimising.