---
title: Performance issues
meta:
  description: Common issues and solutions for performance problems in ClickHouse and Tinybird.
---

# Performance issues

Common issues and solutions for performance problems in ClickHouse and Tinybird.

## Overview

This section covers troubleshooting for performance-related issues that don't necessarily cause errors but impact query speed and resource usage.

## Performance categories

### Slow queries

Common causes of slow query performance:

- **Missing indexes** - Queries scanning entire tables
- **Large result sets** - Queries returning too much data
- **Inefficient WHERE clauses** - Non-selective filters
- **Complex aggregations** - Heavy operations on large datasets

[View slow queries troubleshooting →](/sql-reference/performance/slow-queries)

### Too many rows or bytes

Issues with query result limits:

- **Large result sets** - Queries exceeding memory limits
- **Missing WHERE clauses** - Queries without proper filters
- **Memory management** - Streaming and chunked processing
- **Configuration limits** - Adjusting query limits

[View too many rows or bytes troubleshooting →](/sql-reference/performance/too-many-rows-or-bytes)

### Merge aggregations

Performance issues with merge operations:

- **Inefficient merge operations** - Large dataset merging
- **Memory pressure** - Merge operations consuming too much memory
- **Pre-aggregation strategies** - Optimizing merge performance
- **Materialized views** - Using views for common aggregations

[View merge aggregations troubleshooting →](/sql-reference/performance/merge-aggregations)

### Joins

Performance issues with join operations:

- **Large table joins** - Joining tables without optimization
- **Missing join conditions** - Cartesian products
- **Join order optimization** - Efficient join ordering
- **Memory pressure** - Joins consuming too much memory

[View join performance troubleshooting →](/sql-reference/performance/joins)

### Nullable columns

Performance impact of nullable columns:

- **Performance impact** - Nullable columns being slower
- **Index considerations** - Nullable columns in indexes
- **Memory overhead** - Storage considerations
- **Storage optimization** - Handling nullable data efficiently

[View nullable columns troubleshooting →](/sql-reference/performance/nullable-columns)

## Common patterns

### Query optimization

General strategies for improving performance:

1. **Use indexes** - Add indexes for frequently filtered columns
2. **Limit results** - Always use LIMIT for large datasets
3. **Pre-aggregate** - Do heavy aggregations in data sources
4. **Use selective filters** - Filter early and often
5. **Monitor query plans** - Use EXPLAIN to understand execution

### Memory management

Strategies for managing memory usage:

1. **Use streaming** - For large datasets, use streaming queries
2. **Process in chunks** - Break down large operations
3. **Monitor memory usage** - Track memory consumption
4. **Optimize data types** - Use appropriate column types
5. **Use materialized views** - For frequently accessed data

## Best practices

1. **Profile queries** - Use EXPLAIN and monitoring tools
2. **Test with realistic data** - Use production-like datasets
3. **Monitor resource usage** - Track CPU, memory, and I/O
4. **Optimize incrementally** - Make small improvements
5. **Document performance patterns** - Keep track of what works

## Related documentation

- [Query optimization](/forward/work-with-data/optimize)
- [Performance monitoring](/forward/monitoring)
- [Common error patterns](/forward/dev-reference/common-error-patterns)
- [ClickHouse errors](/sql-reference/clickhouse-errors)