Snowflake Advanced Troubleshooting

Step 2: Lock and Transaction Contention

-- Find blocked/waiting queries
SELECT query_id, query_text, blocked_query_id,
       DATEDIFF('second', start_time, CURRENT_TIMESTAMP()) AS wait_seconds,
       user_name, warehouse_name
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE execution_status = 'BLOCKED'
  AND start_time >= DATEADD(minutes, -30, CURRENT_TIMESTAMP());

-- Find long-running transactions (holding locks)
SELECT *
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE execution_status = 'RUNNING'
  AND DATEDIFF('minute', start_time, CURRENT_TIMESTAMP()) > 30
ORDER BY start_time;

-- Kill a blocking query
SELECT SYSTEM$CANCEL_QUERY('<blocking_query_id>');

-- Common lock scenarios:
-- DDL on table blocks DML (ALTER TABLE blocks INSERT)
-- Concurrent MERGE on same table → serialization
-- Long COPY INTO blocks other COPY INTO on same table

Step 3: Metadata Operation Analysis

-- Cloud services credit spike (metadata-heavy operations)
SELECT DATE_TRUNC('hour', start_time) AS hour,
       SUM(credits_used_cloud_services) AS cloud_credits,
       COUNT(*) AS query_count,
       SUM(credits_used_cloud_services) / NULLIF(COUNT(*), 0) AS credits_per_query
FROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
WHERE start_time >= DATEADD(hours, -24, CURRENT_TIMESTAMP())
  AND credits_used_cloud_services > 0
GROUP BY hour
ORDER BY cloud_credits DESC;

-- Excessive metadata queries (SHOW, DESCRIBE, INFORMATION_SCHEMA)
SELECT query_type, COUNT(*) AS count,
       AVG(total_elapsed_time) AS avg_ms
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE start_time >= DATEADD(hours, -24, CURRENT_TIMESTAMP())
  AND query_type IN ('SHOW', 'DESCRIBE', 'GET_DDL')
GROUP BY query_type
ORDER BY count DESC;
-- If very high, consider caching INFORMATION_SCHEMA results in your app

Step 4: Partition Pruning Analysis

-- Queries scanning too many partitions (candidate for clustering)
SELECT query_id,
       SUBSTR(query_text, 1, 200) AS query_preview,
       partitions_scanned,
       partitions_total,
       ROUND(partitions_scanned * 100.0 / NULLIF(partitions_total, 0), 1) AS scan_pct,
       bytes_scanned / 1e9 AS gb_scanned,
       total_elapsed_time / 1000 AS seconds
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE partitions_total > 100
  AND partitions_scanned > partitions_total * 0.5
  AND start_time >= DATEADD(hours, -24, CURRENT_TIMESTAMP())
  AND query_type = 'SELECT'
ORDER BY partitions_scanned DESC
LIMIT 10;

-- Check if table would benefit from clustering
SELECT SYSTEM$CLUSTERING_INFORMATION('my_db.my_schema.orders', '(order_date)');
-- Look at average_depth: > 5 means clustering would help
-- Look at partition_depth_histogram: should be concentrated at depth 1-2

Step 5: Network and Connectivity Debugging

# DNS resolution test
dig +short $(echo $SNOWFLAKE_ACCOUNT).snowflakecomputing.com

# TLS handshake test
openssl s_client -connect ${SNOWFLAKE_ACCOUNT}.snowflakecomputing.com:443 \
  -servername ${SNOWFLAKE_ACCOUNT}.snowflakecomputing.com < /dev/null 2>/dev/null \
  | openssl x509 -noout -subject -dates

# Latency test
curl -o /dev/null -s -w "DNS: %{time_namelookup}s\nConnect: %{time_connect}s\nTLS: %{time_appconnect}s\nTotal: %{time_total}s\n" \
  "https://${SNOWFLAKE_ACCOUNT}.snowflakecomputing.com/"

# Check if OCSP responder is reachable (Snowflake uses OCSP for cert validation)
curl -s -o /dev/null -w "%{http_code}" "http://ocsp.snowflakecomputing.com/ocsp"

Step 6: Driver-Level Debugging

// Enable debug logging in Node.js driver
import snowflake from 'snowflake-sdk';

snowflake.configure({
  logLevel: 'TRACE',  // DEBUG, INFO, WARN, ERROR, TRACE
});

// Log all SQL statements with timing
const originalExecute = connection.execute.bind(connection);
connection.execute = function(opts: any) {
  const start = Date.now();
  const sql = opts.sqlText;
  console.log(`[SF] Executing: ${sql.substring(0, 200)}`);

  const originalComplete = opts.complete;
  opts.complete = (err: any, stmt: any, rows: any) => {
    const duration = Date.now() - start;
    if (err) {
      console.error(`[SF] FAILED (${duration}ms): ${err.code} ${err.message}`);
    } else {
      console.log(`[SF] OK (${duration}ms): ${rows?.length || 0} rows`);
    }
    originalComplete(err, stmt, rows);
  };

  return originalExecute(opts);
};

# Enable debug logging in Python connector
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger('snowflake.connector')
logger.setLevel(logging.DEBUG)

# Connection with debug parameters
conn = snowflake.connector.connect(
    # ...
    login_timeout=30,
    network_timeout=60,
    socket_timeout=60,
)

Step 7: Systematic Isolation Checklist

1. Can you connect at all? → SELECT 1;
2. Can you reach the right context? → SELECT CURRENT_DATABASE(), CURRENT_ROLE();
3. Does the object exist? → SHOW TABLES LIKE 'xxx';
4. Do you have permissions? → SHOW GRANTS TO ROLE xxx;
5. Is the warehouse available? → SHOW WAREHOUSES LIKE 'xxx';
6. Does a simple query work? → SELECT COUNT(*) FROM table;
7. Does the specific query work on smaller data? → Add LIMIT 100
8. Is there spilling? → Check bytes_spilled_to_remote_storage
9. Is there partition skew? → Check partition scan ratio
10. Is there contention? → Check blocked queries

Error Handling

Resources

• Query Profile
• Monitoring Warehouse Load
• GET_QUERY_OPERATOR_STATS

Next Steps

For load testing, see snowflake-load-scale.