Merge aggregations troubleshooting

Common issues and solutions for merge aggregation performance in ClickHouse and Tinybird.

Common issues

Inefficient merge operations

Issue: Merging large datasets without proper optimization

Solution: Use appropriate merge strategies

-- Use optimized merge for large datasets
SELECT 
  user_id,
  sumMerge(value_sum) as total_value,
  countMerge(value_count) as total_count
FROM aggregated_events 
GROUP BY user_id

Memory pressure during merges

Issue: Merge operations consuming too much memory

Solution: Use streaming and chunked processing

-- Process merges in chunks
SELECT * FROM aggregated_events 
WHERE date BETWEEN '2023-01-01' AND '2023-01-07'
SETTINGS max_block_size = 10000

Performance optimization

Pre-aggregation strategies

Issue: Merging raw data instead of pre-aggregated data

Solution: Pre-aggregate in data sources

-- Pre-aggregate in data source
SELECT 
  user_id,
  date,
  sum(value) as daily_value,
  count() as daily_count
FROM events 
GROUP BY user_id, date

Using materialized views

Issue: Complex merge operations on large datasets

Solution: Use materialized views for common aggregations

-- Create materialized view for common aggregations
CREATE MATERIALIZED VIEW daily_stats
ENGINE = SummingMergeTree()
ORDER BY (user_id, date)
AS SELECT 
  user_id,
  toDate(timestamp) as date,
  sum(value) as total_value,
  count() as event_count
FROM events 
GROUP BY user_id, toDate(timestamp)

Merge patterns

Handling duplicates

Issue: Duplicate data in merge operations

Solution: Use appropriate merge engines

-- Use ReplacingMergeTree for deduplication
CREATE TABLE events_deduped
ENGINE = ReplacingMergeTree()
ORDER BY (user_id, timestamp)
AS SELECT * FROM events

Optimizing merge order

Issue: Inefficient merge order causing performance issues

Solution: Order by most selective columns first

-- Optimize merge order
SELECT * FROM events 
ORDER BY user_id, date, timestamp

Best practices

  1. Pre-aggregate data - Do heavy aggregations in data sources
  2. Use appropriate engines - Choose merge engines based on use case
  3. Optimize merge order - Order by most selective columns first
  4. Use materialized views - For frequently accessed aggregations
  5. Monitor merge performance - Track merge operation metrics