TIMEOUT_EXCEEDED ClickHouse error

This error occurs when a query takes longer than the configured timeout limit. It's common with complex queries or large datasets that exceed performance thresholds.

The TIMEOUT_EXCEEDED error in ClickHouse (and Tinybird) happens when a query execution time exceeds the configured timeout limit. This typically occurs with complex queries, large datasets, or operations that require more time than allowed by the system configuration.

What causes this error

You'll typically see it when:

  • Complex queries with multiple JOINs or subqueries
  • Large datasets that exceed memory or processing limits
  • Queries with insufficient indexes or optimization
  • System resource constraints (CPU, memory, I/O)
  • Network latency issues in distributed queries
  • Queries that trigger complex aggregations or transformations

Timeout errors often indicate that your query needs optimization rather than just increasing the timeout limit.

Example errors

Fails: complex query with multiple JOINs
SELECT
    u.id,
    u.name,
    COUNT(e.id) as event_count,
    AVG(e.value) as avg_value
FROM users u
JOIN events e ON u.id = e.user_id
JOIN sessions s ON u.id = s.user_id
JOIN metrics m ON s.id = m.session_id
WHERE e.timestamp >= '2024-01-01'
GROUP BY u.id, u.name
HAVING event_count > 100
ORDER BY avg_value DESC
Fails: large dataset without proper filtering
SELECT * FROM events
WHERE timestamp >= '2020-01-01'
ORDER BY timestamp DESC
Fails: complex aggregation without optimization
SELECT
    user_id,
    COUNT(*) as total_events,
    COUNT(DISTINCT event_type) as unique_events,
    AVG(value) as avg_value,
    STDDEV(value) as std_value
FROM events
GROUP BY user_id
HAVING total_events > 1000

How to fix it

Optimize query structure

Break down complex queries into simpler parts:

Fix: simplified query
-- First, get the user list
WITH user_events AS (
    SELECT user_id, COUNT(*) as event_count
    FROM events
    WHERE timestamp >= '2024-01-01'
    GROUP BY user_id
    HAVING event_count > 100
)
-- Then, get detailed information
SELECT
    u.id,
    u.name,
    ue.event_count
FROM users u
JOIN user_events ue ON u.id = ue.user_id
ORDER BY ue.event_count DESC

Add proper filtering

Use WHERE clauses to limit data early:

Fix: add time filtering
SELECT * FROM events
WHERE timestamp >= '2024-01-01'
  AND timestamp < '2024-02-01'
ORDER BY timestamp DESC

Use appropriate indexes

Ensure your tables have proper indexes:

Create optimized table
CREATE TABLE events (
    id UInt64,
    user_id UInt64,
    event_type String,
    timestamp DateTime,
    value Float64
) ENGINE = MergeTree()
ORDER BY (timestamp, user_id)
PARTITION BY toYYYYMM(timestamp)

Limit result sets

Use LIMIT clauses to prevent large results:

Fix: add LIMIT clause
SELECT user_id, event_type, timestamp
FROM events
WHERE timestamp >= '2024-01-01'
ORDER BY timestamp DESC
LIMIT 1000

Common patterns and solutions

Complex JOIN optimization

Break down complex JOINs:

Optimized JOIN approach
-- Step 1: Get filtered base data
WITH base_events AS (
    SELECT user_id, event_type, timestamp
    FROM events
    WHERE timestamp >= '2024-01-01'
)
-- Step 2: Join with user data
SELECT
    u.id,
    u.name,
    be.event_type,
    be.timestamp
FROM users u
JOIN base_events be ON u.id = be.user_id
LIMIT 1000

Aggregation optimization

Optimize aggregations with pre-filtering:

Optimized aggregation
SELECT
    user_id,
    COUNT(*) as total_events,
    COUNT(DISTINCT event_type) as unique_events
FROM events
WHERE timestamp >= '2024-01-01'
  AND user_id IN (
    SELECT DISTINCT user_id
    FROM events
    WHERE timestamp >= '2024-01-01'
    GROUP BY user_id
    HAVING COUNT(*) > 100
  )
GROUP BY user_id

Subquery optimization

Replace complex subqueries with CTEs:

Using CTEs instead of subqueries
WITH user_stats AS (
    SELECT user_id, COUNT(*) as event_count
    FROM events
    WHERE timestamp >= '2024-01-01'
    GROUP BY user_id
),
top_users AS (
    SELECT user_id
    FROM user_stats
    WHERE event_count > 100
)
SELECT
    u.id,
    u.name,
    us.event_count
FROM users u
JOIN user_stats us ON u.id = us.user_id
JOIN top_users tu ON u.id = tu.user_id

Tinybird-specific notes

In Tinybird, timeout errors often occur when:

  • Creating complex Materialized Views
  • Processing large amounts of data in Pipes
  • Running queries against very large datasets
  • Using complex transformations in API endpoints

To debug in Tinybird:

  1. Use the Query Builder to test query performance
  2. Check the query execution plan
  3. Monitor resource usage in the UI
  4. Consider breaking complex operations into smaller Pipes

Use Tinybird's query profiling to identify bottlenecks before they cause timeouts.

Best practices

Query design

  • Start with simple queries and add complexity gradually
  • Use appropriate WHERE clauses to filter data early
  • Avoid SELECT * on large tables
  • Use LIMIT clauses for exploratory queries

Performance optimization

  • Create proper indexes on frequently queried columns
  • Use partitioning for time-series data
  • Consider materialized views for complex aggregations
  • Monitor query execution plans

Resource management

  • Set appropriate timeout limits for different query types
  • Monitor system resource usage
  • Use connection pooling for multiple queries
  • Implement retry logic with exponential backoff

Configuration options

Adjusting timeout settings

Check current timeout settings
SELECT name, value
FROM system.settings
WHERE name LIKE '%timeout%'

Setting query timeout

Set query timeout
SET max_execution_time = 300; -- 5 minutes

See also

Updated