Apache Superset

Connect Parseable to Apache Superset for powerful data exploration, visualization, and dashboarding.

Overview

Integrate Parseable with Apache Superset to:

• Data Exploration - Explore log data with an intuitive interface
• Rich Visualizations - Create charts, graphs, and maps
• Interactive Dashboards - Build real-time monitoring dashboards
• SQL Lab - Run ad-hoc queries on your log data

Prerequisites

• Apache Superset instance
• Parseable instance with data
• Python 3.11.6 or higher

Installation

Install Parseable SQLAlchemy Driver

Install the Parseable connector for Apache Superset:

# Create and activate a virtual environment (recommended)
python -m venv superset-env
source superset-env/bin/activate  # On Windows: superset-env\Scripts\activate

# Install Apache Superset
pip install apache-superset

# Install Parseable SQLAlchemy driver
pip install sqlalchemy-parseable

# Initialize Superset
superset db upgrade
superset fab create-admin
superset init

# Run Superset
superset run -p 8088 --with-threads --reload --debugger

Connection Setup

Add Database Connection

1. Log in to Apache Superset at http://localhost:8088
2. Go to Data → Databases → + Database
3. Select Other as the database type
4. Use the following SQLAlchemy URI format:

parseable://username:password@host:port/dataset_name

Example Connection String

parseable://admin:admin@demo.parseable.com:443/ingress-nginx

For local Parseable instance:

parseable://admin:admin@localhost:8000/application-logs

Creating Visualizations

Add Dataset

1. Go to Data → Datasets
2. Click + Dataset
3. Select your Parseable database
4. Choose a dataset as the table
5. Click Add

Create Chart

1. Go to Charts → + Chart
2. Select your dataset
3. Choose a visualization type:
   • Time-series for log trends
   • Bar Chart for categorical data
   • Table for detailed views
   • Big Number for KPIs

Example: Error Rate Over Time

1. Create a new chart with Time-series visualization
2. Configure:
   • Time Column: p_timestamp
   • Metric: COUNT(*)
   • Filter: level = 'error'
   • Time Grain: hour

Building Dashboards

Create Dashboard

1. Go to Dashboards → + Dashboard
2. Add charts by dragging from the chart list
3. Arrange and resize as needed
4. Add filters for interactivity
5. Save and publish

Dashboard Filters

Add cross-filtering to your dashboard:

1. Edit dashboard
2. Click Filter icon
3. Add filter components:
   • Time range filter
   • Stream selector
   • Log level filter

SQL Lab Queries

Use SQL Lab for ad-hoc analysis:

-- Error count by service
SELECT 
  service,
  COUNT(*) as error_count
FROM "application-logs"
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '24 hours'
GROUP BY service
ORDER BY error_count DESC
LIMIT 10;

-- Response time percentiles
SELECT 
  percentile_cont(0.50) WITHIN GROUP (ORDER BY response_time) as p50,
  percentile_cont(0.95) WITHIN GROUP (ORDER BY response_time) as p95,
  percentile_cont(0.99) WITHIN GROUP (ORDER BY response_time) as p99
FROM "api-logs"
WHERE p_timestamp > NOW() - INTERVAL '1 hour';

Best Practices

1. Use Caching - Enable query caching for better performance
2. Optimize Queries - Use time filters to limit data scanned
3. Create Virtual Datasets - Pre-aggregate data for complex dashboards
4. Set Refresh Intervals - Configure appropriate auto-refresh rates

Troubleshooting

Connection Issues

1. Verify Parseable is accessible from Superset
2. Check credentials are correct
3. Ensure PostgreSQL port is exposed

Slow Queries

1. Add time range filters
2. Use LIMIT clauses
3. Enable query caching

Next Steps

• Create dashboards in Parseable
• Set up alerts for anomalies
• Explore Grafana integration