Redash

Connect Parseable to Redash for SQL-based querying and dashboard creation.

Overview

Integrate Parseable with Redash to:

• SQL Queries - Write and save SQL queries against log data
• Visualizations - Create charts from query results
• Dashboards - Combine visualizations into dashboards
• Alerts - Set up query-based alerts

Integration Options

Option 1: Custom Query Runner

You can create a custom Redash query runner that uses Parseable's HTTP API. This requires modifying your Redash installation.

Option 2: Export to Supported Database

Export data from Parseable to a Redash-supported database (PostgreSQL, MySQL, etc.):

import requests
import pandas as pd
from sqlalchemy import create_engine

# Query Parseable
response = requests.post(
    "http://your-parseable-host:8000/api/v1/query",
    auth=("username", "password"),
    json={
        "query": "SELECT * FROM \"application-logs\" WHERE p_timestamp > NOW() - INTERVAL '24 hours'",
        "startTime": "2024-01-01T00:00:00Z",
        "endTime": "2024-01-02T00:00:00Z"
    }
)

# Load into PostgreSQL for Redash
df = pd.DataFrame(response.json())
engine = create_engine('postgresql://user:pass@localhost/analytics')
df.to_sql('parseable_logs', engine, if_exists='replace', index=False)

Option 3: Use Apache Superset

For real-time connectivity to Parseable, we recommend using Apache Superset which has native Parseable support via the sqlalchemy-parseable driver and offers similar functionality to Redash.

Example Parseable Queries

These SQL queries can be used with Parseable's Query API:

Error Count by Hour:

SELECT 
  date_trunc('hour', p_timestamp) as hour,
  COUNT(*) as error_count
FROM "application-logs"
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '24 hours'
GROUP BY hour
ORDER BY hour;

Top Error Messages:

SELECT 
  message,
  COUNT(*) as count
FROM "application-logs"
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY message
ORDER BY count DESC
LIMIT 10;

Best Practices

1. Schedule Data Syncs - Automate exports from Parseable to your analytics database
2. Use Incremental Loads - Only export new data since the last sync
3. Filter at Source - Apply time range filters in Parseable queries to reduce data volume
4. Consider Native Options - For real-time dashboards, use Apache Superset or Parseable's built-in dashboards

Next Steps

• Create dashboards in Parseable's built-in UI
• Set up alerts for monitoring
• Explore Apache Superset for native integration