Join performance troubleshooting

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

Common issues

Large table joins

Issue: Joining large tables without optimization

Solution: Use selective filters and proper indexes

-- Add filters before join
SELECT * FROM large_table l
JOIN small_table s ON l.id = s.id
WHERE l.date >= today() - 7

Missing join conditions

Issue: Cartesian products from missing join conditions

Solution: Always specify join conditions

-- Always specify join conditions
SELECT * FROM table1 t1
JOIN table2 t2 ON t1.id = t2.id

Performance optimization

Join order optimization

Issue: Inefficient join order causing performance issues

Solution: Join smaller tables first

-- Join smaller table first
SELECT * FROM small_table s
JOIN large_table l ON s.id = l.id

Using appropriate join types

Issue: Using wrong join type for the use case

Solution: Choose appropriate join type

-- Use LEFT JOIN when you need all records from left table
SELECT * FROM users u
LEFT JOIN events e ON u.id = e.user_id

-- Use INNER JOIN for matching records only
SELECT * FROM users u
INNER JOIN events e ON u.id = e.user_id

Memory management

Memory pressure during joins

Issue: Joins consuming too much memory

Solution: Use streaming and proper limits

-- Use streaming for large joins
SELECT * FROM large_table l
JOIN small_table s ON l.id = s.id
SETTINGS max_block_size = 10000

Chunked join processing

Issue: Processing entire datasets in single join

Solution: Process joins in chunks

-- Process joins by date ranges
SELECT * FROM events e
JOIN users u ON e.user_id = u.id
WHERE e.date BETWEEN '2023-01-01' AND '2023-01-07'

Best practices

  1. Filter before joining - Apply WHERE clauses before joins
  2. Use appropriate indexes - Index join columns
  3. Choose right join type - Use INNER, LEFT, RIGHT based on needs
  4. Join smaller tables first - Optimize join order
  5. Use streaming for large joins - Prevent memory issues