Too many rows or bytes troubleshooting

Common issues and solutions for TOO_MANY_ROWS_OR_BYTES errors in ClickHouse and Tinybird.

Common causes

Large result sets

Issue: Queries returning more data than allowed

Solution: Use LIMIT and proper filtering

-- Add limits to prevent large results
SELECT * FROM events 
WHERE date >= today() - 7 
LIMIT 10000

Missing WHERE clauses

Issue: Queries without proper filters

Solution: Add selective filters

-- Add specific filters
SELECT * FROM events 
WHERE user_id = 123 AND date >= '2023-01-01'

Memory management

Streaming queries

Issue: Large datasets consuming too much memory

Solution: Use streaming and smaller block sizes

-- Use streaming for large datasets
SELECT * FROM events 
WHERE date >= today() - 30
SETTINGS max_block_size = 10000

Chunked processing

Issue: Processing entire datasets at once

Solution: Process data in chunks

-- Process by date ranges
SELECT * FROM events 
WHERE date BETWEEN '2023-01-01' AND '2023-01-07'

Query optimization

Using aggregations

Issue: Returning raw data instead of summaries

Solution: Use aggregations to reduce result size

-- Aggregate instead of returning raw data
SELECT 
  user_id,
  count() as event_count,
  sum(value) as total_value
FROM events 
GROUP BY user_id

Column selection

Issue: Selecting unnecessary columns

Solution: Select only needed columns

-- Select specific columns only
SELECT user_id, event_type, timestamp 
FROM events 
WHERE date = today()

Configuration solutions

Adjusting limits

Issue: Default limits too restrictive

Solution: Configure appropriate limits

-- Set higher limits when needed
SET max_result_rows = 1000000
SET max_result_bytes = 1000000000

Using sampling

Issue: Need to analyze large datasets

Solution: Use sampling for analysis

-- Use sampling for large datasets
SELECT * FROM events SAMPLE 0.1
WHERE date >= today() - 30

Best practices

  1. Always use LIMIT - Prevent unexpectedly large results
  2. Add selective filters - Filter data early in queries
  3. Use aggregations - Summarize data instead of returning raw rows
  4. Select specific columns - Avoid SELECT * on large tables
  5. Use streaming - For large datasets, use streaming queries