Snowflake Performance Tuning

Overview

Optimize Snowflake query performance using clustering keys, materialized views, result caching, query profiling, and warehouse tuning.

Prerequisites

• Access to

  SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY

• Understanding of micro-partitions and pruning
• Role with

  MONITOR

  privilege on warehouses

Instructions

Step 1: Identify Slow Queries

-- Top 20 slowest queries in last 24 hours
SELECT query_id, query_text, total_elapsed_time / 1000 AS seconds,
       bytes_scanned / 1e9 AS gb_scanned,
       partitions_scanned, partitions_total,
       ROUND(partitions_scanned / NULLIF(partitions_total, 0) * 100, 1) AS pct_scanned,
       warehouse_name, warehouse_size
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE execution_status = 'SUCCESS'
  AND start_time >= DATEADD(hours, -24, CURRENT_TIMESTAMP())
  AND query_type = 'SELECT'
ORDER BY total_elapsed_time DESC
LIMIT 20;

-- Queries scanning too many partitions (poor pruning)
SELECT query_id, query_text,
       partitions_scanned, partitions_total,
       bytes_scanned / 1e9 AS gb_scanned
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE partitions_scanned > partitions_total * 0.5
  AND partitions_total > 100
  AND start_time >= DATEADD(hours, -24, CURRENT_TIMESTAMP())
ORDER BY partitions_scanned DESC
LIMIT 10;

Step 2: Add Clustering Keys

-- Clustering improves pruning for large tables (> 1TB)
-- Choose columns used in WHERE and JOIN clauses

-- Cluster by date (most common filter)
ALTER TABLE orders CLUSTER BY (order_date);

-- Multi-column clustering
ALTER TABLE events CLUSTER BY (event_date, event_type);

-- Check clustering depth (lower = better)
SELECT SYSTEM$CLUSTERING_INFORMATION('orders', '(order_date)');

-- Monitor automatic reclustering
SELECT table_name, num_rows, bytes,
       SYSTEM$CLUSTERING_DEPTH('orders') AS clustering_depth
FROM INFORMATION_SCHEMA.TABLES
WHERE table_name = 'ORDERS';

Step 3: Use Materialized Views

-- Pre-compute expensive aggregations
CREATE OR REPLACE MATERIALIZED VIEW daily_revenue_mv
  CLUSTER BY (metric_date)
AS
  SELECT
    DATE_TRUNC('day', order_date) AS metric_date,
    COUNT(*) AS order_count,
    SUM(amount) AS total_revenue,
    AVG(amount) AS avg_order_value
  FROM orders
  GROUP BY DATE_TRUNC('day', order_date);

-- Query the MV instead of base table — automatic rewrite may also apply
SELECT * FROM daily_revenue_mv
WHERE metric_date >= DATEADD(days, -30, CURRENT_DATE());

-- Check MV freshness
SELECT name, is_secure, text, refresh_on
FROM INFORMATION_SCHEMA.MATERIALIZED_VIEWS
WHERE name = 'DAILY_REVENUE_MV';

Step 4: Leverage Result Caching

-- Result cache is ON by default — same query returns instantly
-- Cache is valid for 24 hours if underlying data hasn't changed

-- Check if a query used cache
SELECT query_id, query_text,
       CASE WHEN bytes_scanned = 0 AND rows_produced > 0
            THEN 'CACHE HIT' ELSE 'CACHE MISS' END AS cache_status,
       total_elapsed_time
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE start_time >= DATEADD(hours, -1, CURRENT_TIMESTAMP())
ORDER BY start_time DESC;

-- Disable cache for benchmarking
ALTER SESSION SET USE_CACHED_RESULT = FALSE;
-- Re-enable
ALTER SESSION SET USE_CACHED_RESULT = TRUE;

Step 5: Optimize Common Query Patterns

-- ANTI-PATTERN: SELECT * on wide tables
-- SELECT * FROM events;  -- Scans all columns

-- BETTER: Select only needed columns
SELECT event_id, event_type, event_date FROM events WHERE event_date = CURRENT_DATE();

-- ANTI-PATTERN: Cartesian joins
-- SELECT * FROM a, b WHERE a.id = b.id;

-- BETTER: Explicit JOIN with filter pushdown
SELECT a.id, a.name, b.amount
FROM customers a
INNER JOIN orders b ON a.id = b.customer_id
WHERE b.order_date >= '2026-01-01';

-- ANTI-PATTERN: LIKE with leading wildcard
-- WHERE name LIKE '%smith%'  -- Full scan

-- BETTER: Use search optimization service for LIKE queries
ALTER TABLE customers ADD SEARCH OPTIMIZATION ON EQUALITY(name), SUBSTRING(name);

-- ANTI-PATTERN: Many small queries in a loop
-- for row in rows: execute(f"INSERT INTO t VALUES ({row})")

-- BETTER: Batch inserts
INSERT INTO target_table
SELECT * FROM source_table WHERE condition;

Step 6: Query Profile Analysis

-- Use EXPLAIN to see execution plan
EXPLAIN SELECT * FROM orders WHERE order_date = CURRENT_DATE();

-- Get query profile data programmatically
SELECT *
FROM TABLE(GET_QUERY_OPERATOR_STATS('<query_id>'));

-- Key metrics to watch:
-- TableScan: partitions_scanned vs partitions_total
-- Filter: if filter is AFTER scan, consider clustering
-- Sort: high spilling_to_remote_storage = needs bigger warehouse
-- Join: broadcast vs hash, skew detection

Step 7: Warehouse Tuning

-- Match warehouse size to workload
-- Small: simple queries, < 100GB scans
-- Medium: moderate joins, 100GB-1TB
-- Large: complex analytics, > 1TB scans

-- Scale up for single-query performance
ALTER WAREHOUSE ANALYTICS_WH SET WAREHOUSE_SIZE = 'LARGE';

-- Scale out for concurrent queries (multi-cluster)
ALTER WAREHOUSE ANALYTICS_WH SET
  MIN_CLUSTER_COUNT = 1
  MAX_CLUSTER_COUNT = 4
  SCALING_POLICY = 'STANDARD';

-- Monitor warehouse efficiency
SELECT warehouse_name,
       SUM(credits_used) AS total_credits,
       COUNT(DISTINCT query_id) AS total_queries,
       SUM(credits_used) / NULLIF(COUNT(DISTINCT query_id), 0) AS credits_per_query
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY q
JOIN SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY w
  ON q.warehouse_name = w.warehouse_name
  AND DATE_TRUNC('hour', q.start_time) = w.start_time
WHERE q.start_time >= DATEADD(days, -7, CURRENT_TIMESTAMP())
GROUP BY warehouse_name
ORDER BY total_credits DESC;

Performance Benchmarks

Error Handling

USE_CACHED_RESULT = TRUE

Resources

• Query Performance
• Clustering Keys
• Materialized Views
• Search Optimization

Next Steps

For cost optimization, see

snowflake-cost-tuning