name: clickhouse-cost-tuning description: "Optimize ClickHouse Cloud costs \u2014 compute scaling, storage tiering,
\ compression,\nand query efficiency for lower bills.\nUse when analyzing ClickHouse
\ Cloud bills, reducing storage costs,\nor optimizing compute utilization.\nTrigger:
\ "clickhouse cost", "clickhouse billing", "reduce clickhouse spend",\n"
clickhouse pricing", "clickhouse expensive", "clickhouse storage cost".\n" allowed-tools: Read, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• database
• analytics
• clickhouse
• olap compatibility: Designed for Claude Code

ClickHouse Cost Tuning

Overview

Reduce ClickHouse Cloud costs through storage optimization, compression tuning, TTL policies, compute scaling, and query efficiency improvements.

Prerequisites

• ClickHouse Cloud account with billing access
• Understanding of current data volumes and query patterns

Instructions

Step 1: Understand ClickHouse Cloud Pricing

Key insight: ClickHouse bills on compressed storage, and ClickHouse compresses extremely well (often 10-20x). Your cost driver is usually compute, not storage.

Step 2: Analyze Storage Usage

-- Storage cost breakdown by table
SELECT
    database,
    table,
    formatReadableSize(sum(bytes_on_disk)) AS compressed_size,
    formatReadableSize(sum(data_uncompressed_bytes)) AS raw_size,
    round(sum(data_uncompressed_bytes) / sum(bytes_on_disk), 1) AS compression_ratio,
    sum(rows) AS total_rows,
    count() AS parts
FROM system.parts
WHERE active
GROUP BY database, table
ORDER BY sum(bytes_on_disk) DESC;

-- Storage by column (find bloated columns)
SELECT
    table,
    column,
    type,
    formatReadableSize(sum(column_data_compressed_bytes)) AS compressed,
    formatReadableSize(sum(column_data_uncompressed_bytes)) AS raw,
    round(sum(column_data_uncompressed_bytes) / sum(column_data_compressed_bytes), 1) AS ratio
FROM system.parts_columns
WHERE active AND database = 'analytics'
GROUP BY table, column, type
ORDER BY sum(column_data_compressed_bytes) DESC
LIMIT 30;

Step 3: Improve Compression

-- Check current codec per column
SELECT name, type, compression_codec
FROM system.columns
WHERE database = 'analytics' AND table = 'events';

-- Apply better codecs to large columns
ALTER TABLE analytics.events
    MODIFY COLUMN properties String CODEC(ZSTD(3));  -- JSON blobs

ALTER TABLE analytics.events
    MODIFY COLUMN created_at DateTime CODEC(DoubleDelta, ZSTD);  -- Timestamps

ALTER TABLE analytics.events
    MODIFY COLUMN user_id UInt64 CODEC(Delta, ZSTD);  -- Sequential IDs

-- Verify improvement after next merge
OPTIMIZE TABLE analytics.events FINAL;

-- Check new compression ratio
SELECT
    column,
    formatReadableSize(sum(column_data_compressed_bytes)) AS compressed,
    round(sum(column_data_uncompressed_bytes) / sum(column_data_compressed_bytes), 1) AS ratio
FROM system.parts_columns
WHERE active AND database = 'analytics' AND table = 'events'
GROUP BY column ORDER BY sum(column_data_compressed_bytes) DESC;

Step 4: TTL for Data Lifecycle

-- Expire old data automatically (reduces storage)
ALTER TABLE analytics.events
    MODIFY TTL created_at + INTERVAL 90 DAY;

-- Move old data to cheaper storage tier (ClickHouse Cloud)
ALTER TABLE analytics.events
    MODIFY TTL
        created_at + INTERVAL 30 DAY TO VOLUME 'hot',
        created_at + INTERVAL 90 DAY TO VOLUME 'cold',
        created_at + INTERVAL 365 DAY DELETE;

-- Drop entire partitions manually (fastest way to delete bulk data)
ALTER TABLE analytics.events
    DROP PARTITION '202401';   -- Drops January 2024

-- Check TTL status
SELECT database, table, result_ttl_expression
FROM system.tables
WHERE database = 'analytics';

Step 5: Compute Cost Reduction

-- ClickHouse Cloud: Scale compute dynamically
-- Configure in Cloud Console:
-- - Auto-scaling: min 2 / max 8 replicas
-- - Idle timeout: 5 minutes (auto-suspend when no queries)
-- - Use "Development" tier for staging environments

-- Reduce per-query compute consumption
SET max_threads = 4;                  -- Use fewer cores per query
SET max_memory_usage = 5000000000;    -- 5GB cap per query

-- Server-side async inserts (reduces insert compute)
SET async_insert = 1;
SET async_insert_max_data_size = 10000000;  -- Flush at 10MB
SET async_insert_busy_timeout_ms = 5000;    -- or every 5 seconds

Step 6: Query Efficiency = Lower Costs

-- Find the most expensive queries (by data scanned)
SELECT
    normalized_query_hash,
    count() AS executions,
    formatReadableSize(sum(read_bytes)) AS total_read,
    round(avg(query_duration_ms)) AS avg_ms,
    any(substring(query, 1, 200)) AS sample
FROM system.query_log
WHERE type = 'QueryFinish'
  AND event_time >= now() - INTERVAL 7 DAY
GROUP BY normalized_query_hash
ORDER BY sum(read_bytes) DESC
LIMIT 20;

-- Use materialized views to avoid repeated full scans
-- Instead of: SELECT count() FROM events WHERE date = today()
-- Pre-compute:
-- CREATE MATERIALIZED VIEW daily_counts_mv TO daily_counts AS
--   SELECT toDate(created_at) AS date, count() AS cnt FROM events GROUP BY date;
-- Then: SELECT cnt FROM daily_counts WHERE date = today()

-- Use PREWHERE to read less data
SELECT user_id, properties FROM analytics.events
PREWHERE event_type = 'purchase'    -- Filter first, read fewer columns
WHERE created_at >= today() - 7;

Step 7: Monitor Costs

// Track query costs in your application
async function queryWithCostTracking<T>(
  client: ReturnType<typeof import('@clickhouse/client').createClient>,
  sql: string,
): Promise<{ rows: T[]; cost: { readRows: number; readBytes: number; durationMs: number } }> {
  const start = Date.now();
  const rs = await client.query({ query: sql, format: 'JSONEachRow' });
  const rows = await rs.json<T>();
  const durationMs = Date.now() - start;

  // Log for cost analysis
  console.log({
    query: sql.slice(0, 100),
    readRows: rs.response_headers['x-clickhouse-summary']
      ? JSON.parse(rs.response_headers['x-clickhouse-summary']).read_rows
      : 'unknown',
    durationMs,
  });

  return { rows, cost: { readRows: 0, readBytes: 0, durationMs } };
}

Cost Optimization Checklist

• Compression codecs applied to large columns (ZSTD, Delta, DoubleDelta)
• TTL configured for data expiration
• Auto-scaling and idle suspension enabled (Cloud)
• Development/staging on smaller tiers
• Materialized views for dashboard queries
• max_threads limited for non-critical queries
• async_insert enabled for high-frequency small inserts
• Monthly cost review with system.query_log analysis

Error Handling

Resources

• ClickHouse Cloud Pricing
• Data Compression
• TTL for Data Management

Next Steps

For architecture patterns, see clickhouse-reference-architecture.