Clickhouse Cloud Connection

ClickHouse Cloud Connection

Validate ClickHouse Cloud connectivity and troubleshoot connection issues for gapless-crypto-clickhouse service.

Purpose

Test connection to ClickHouse Cloud service using clickhouse-connect library with credentials from Doppler. This skill guides the workflow of:

1. Loading credentials from Doppler environment
2. Configuring clickhouse-connect client (secure=True for Cloud)
3. Testing connection with diagnostic queries
4. Validating service accessibility and performance

When to Use

Use this skill when:

• Connection validation: Verifying ClickHouse Cloud service is accessible
• Troubleshooting: Diagnosing connection errors or authentication failures
• Environment verification: Confirming Doppler credentials are correctly configured
• Post-deployment testing: Validating service after setup or credential rotation

Triggers: User mentions "test connection", "connection failed", "ClickHouse unreachable", "validate credentials"

Prerequisites

Required Environment Variables (loaded from Doppler aws-credentials/prd):

• CLICKHOUSE_HOST: Service hostname (e.g., ebmf8f35lu.us-west-2.aws.clickhouse.cloud)
• CLICKHOUSE_PORT: HTTPS port (8443)
• CLICKHOUSE_USER: Database user (default)
• CLICKHOUSE_PASSWORD: Database password

Required Library:

# Install clickhouse-connect (if not already installed)
uv pip install clickhouse-connect

Workflow

Step 1: Load Credentials from Doppler

# Run Python with Doppler environment
doppler run --project aws-credentials --config prd -- python test_connection.py

Environment Loading: Doppler injects all CLICKHOUSE_* secrets as environment variables

Step 2: Configure clickhouse-connect Client

import os
import clickhouse_connect

# Load from environment (set by Doppler)
client = clickhouse_connect.get_client(
    host=os.getenv("CLICKHOUSE_HOST"),
    port=int(os.getenv("CLICKHOUSE_PORT", "8443")),
    username=os.getenv("CLICKHOUSE_USER", "default"),
    password=os.getenv("CLICKHOUSE_PASSWORD"),
    secure=True  # CRITICAL: Required for ClickHouse Cloud
)

Key Configuration:

• secure=True: Enforces TLS/SSL for ClickHouse Cloud (HTTPS endpoint)
• Port 8443: HTTPS protocol (not 8123 for HTTP)
• Environment fallbacks: Defaults for port and user

Step 3: Test Connection with Version Query

# Query ClickHouse version and current user
result = client.query("SELECT version() as version, currentUser() as user")
version = result.result_rows[0][0]
current_user = result.result_rows[0][1]

print(f"✅ Connection successful!")
print(f"   ClickHouse version: {version}")
print(f"   User: {current_user}")

Expected Output:

✅ Connection successful!
   ClickHouse version: 25.8.1.8702
   User: default

Step 4: Validate Service Accessibility

# Test query: Count tables in system database
result = client.query("SELECT count() FROM system.tables")
table_count = result.result_rows[0][0]
print(f"   Tables visible: {table_count}")

Success Criteria: Query executes without errors, returns numeric count

Step 5: Test Data Query Performance (Optional)

# Test query: Fetch first row from gapless_crypto.klines (if exists)
try:
    result = client.query("SELECT * FROM gapless_crypto.klines LIMIT 1")
    print(f"   Data accessible: ✅ (gapless_crypto.klines)")
except Exception as e:
    print(f"   Data accessible: ⚠️ (table not yet created)")

Example Connection Test Script

See: references/connection-test.py for complete executable example

Quick Test (one-liner):

doppler run --project aws-credentials --config prd -- python -c "
import os, clickhouse_connect
client = clickhouse_connect.get_client(
    host=os.getenv('CLICKHOUSE_HOST'),
    port=int(os.getenv('CLICKHOUSE_PORT')),
    username=os.getenv('CLICKHOUSE_USER'),
    password=os.getenv('CLICKHOUSE_PASSWORD'),
    secure=True
)
print('✅ Connected:', client.query('SELECT version()').result_rows[0][0])
"

Success Criteria

• ✅ Connection established without authentication errors
• ✅ Version query returns ClickHouse version 25.8+
• ✅ User query confirms default user
• ✅ Table count query executes successfully
• ✅ Service response time <1 second (idle service may take 5-10s to resume)

Troubleshooting

Issue: "Connection refused" or "Timeout"

• Check: Verify CLICKHOUSE_HOST is correct (should be *.aws.clickhouse.cloud)
• Check: Port is 8443 (HTTPS), not 8123 (HTTP)
• Check: Service state is running (may be paused due to idle scaling)
• Action: Check ClickHouse Cloud console for service status

Issue: "Authentication failed"

• Check: Verify CLICKHOUSE_PASSWORD in Doppler matches console password
• Action: Reset password in ClickHouse Cloud console → Settings → Reset Password
• Verify: Store new password in Doppler and 1Password

Issue: "SSL/TLS error"

• Check: secure=True parameter is set in get_client()
• Verify: ClickHouse Cloud requires TLS, cannot connect without secure=True

Issue: "Query slow (>10 seconds)"

• Check: Service may be resuming from idle state (first query after 15min idle)
• Expected: Subsequent queries should be fast (<1s)
• Action: Wait for service to fully resume, retry query

Issue: "Table not found (gapless_crypto.klines)"

• Status: Table not yet created (expected for new service)
• Action: Run schema migration from local Docker to ClickHouse Cloud

Connection Parameters Reference

Service Details

• Service ID: a3163f31-21f4-4e22-844e-ef3fbc26ace2
• Organization: "TE's Organization" (2404d339-6921-4f1c-bf80-b07d5e23b91a)
• Region: us-west-2 (AWS)
• Idle Scaling: Enabled (15 minutes)
• Expected latency: <100ms (active), 5-10s (resuming from idle)

References

• Connection Test Script: references/connection-test.py
• clickhouse-connect Documentation: https://clickhouse.com/docs/en/integrations/python
• ClickHouse Cloud Console: https://clickhouse.cloud/services/a3163f31-21f4-4e22-844e-ef3fbc26ace2
• Doppler Dashboard: https://dashboard.doppler.com/workplace/13e9e4203ede563b1d37/projects/aws-credentials

Next Steps

After successful connection validation:

1. Schema Migration: Import schema from local Docker to ClickHouse Cloud
2. Data Ingestion: Configure gapless-crypto-clickhouse package to use Cloud endpoints
3. Production Testing: Validate data collection and query performance