Apache Ignite

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Distributed in-memory computing platform for caching and processing.

Overview

Apache Ignite is a distributed in-memory computing platform that combines data caching, processing, and storage capabilities into a unified system. Originally developed by GridGain Systems and later donated to the Apache Software Foundation in 2014, Ignite transforms clusters of commodity hardware into high-performance distributed systems capable of handling massive datasets entirely in memory. The platform supports ACID transactions, SQL queries, machine learning, and distributed computing while maintaining horizontal scalability across hundreds of nodes. This Docker configuration establishes a single Ignite node that exposes multiple protocols including thin client connections on port 10800, Memcache compatibility on port 11211, and cluster communication ports for distributed operations. The setup enables both transient in-memory operations and optional persistent storage, making it suitable for applications requiring sub-millisecond response times with enterprise-grade reliability. Organizations dealing with real-time analytics, high-frequency trading, IoT data processing, or applications requiring distributed caching will find Apache Ignite particularly valuable. The platform's ability to act simultaneously as a database, cache, and compute engine eliminates the complexity of managing separate systems while providing linear scalability and fault tolerance through data replication and partitioning.

Key Features

SQL-92 compliant distributed SQL engine supporting complex joins across partitioned datasets
Native persistence with write-ahead logging and configurable durability modes
Multi-tier storage architecture supporting RAM, SSD, and HDD with automatic data tiering
Distributed ACID transactions with optimistic and pessimistic concurrency control
Compute grid enabling MapReduce, distributed closures, and continuous queries
Machine learning grid with distributed algorithms for classification and regression
Multi-protocol support including JDBC, ODBC, REST, and native thin clients
Automatic data rebalancing and recovery during cluster topology changes

Common Use Cases

1Real-time fraud detection systems requiring sub-millisecond transaction analysis
2High-frequency trading platforms processing millions of market data updates per second
3IoT data processing hubs aggregating sensor data from thousands of connected devices
4Distributed caching layer for microservices architectures replacing Redis clusters
5Real-time recommendation engines analyzing user behavior across web applications
6Financial risk management systems performing complex calculations on large portfolios
7Gaming backends managing player state and leaderboards across global server clusters

Prerequisites

Minimum 4GB RAM allocation for meaningful in-memory data storage and processing
Docker host with at least 2 CPU cores for handling concurrent client connections
Available ports 10800, 11211, 47100, and 47500 for client and cluster communication
Understanding of distributed systems concepts including partitioning and replication
Basic knowledge of SQL for querying in-memory datasets and cache operations
JVM tuning experience recommended for production deployments and performance optimization

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  ignite: 
3    image: apacheignite/ignite:latest
4    container_name: ignite
5    restart: unless-stopped
6    environment: 
7      IGNITE_QUIET: "false"
8    volumes: 
9      - ignite_data:/opt/ignite/apache-ignite/work
10    ports: 
11      - "10800:10800"
12      - "11211:11211"
13      - "47100:47100"
14      - "47500:47500"
15    networks: 
16      - ignite-network
17
18volumes: 
19  ignite_data: 
20
21networks: 
22  ignite-network: 
23    driver: bridge

.env Template

.env

1# Apache Ignite default configuration

Usage Notes

1Docs: https://ignite.apache.org/docs/latest/
2Thin client port 10800 | JDBC: jdbc:ignite:thin://localhost:10800
3SQL support - query in-memory data with standard SQL
4In-memory data grid with optional persistence
5Compute grid - run distributed computations across cluster
6REST API at http://localhost:8080/ignite?cmd=version

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  ignite:
5    image: apacheignite/ignite:latest
6    container_name: ignite
7    restart: unless-stopped
8    environment:
9      IGNITE_QUIET: "false"
10    volumes:
11      - ignite_data:/opt/ignite/apache-ignite/work
12    ports:
13      - "10800:10800"
14      - "11211:11211"
15      - "47100:47100"
16      - "47500:47500"
17    networks:
18      - ignite-network
19
20volumes:
21  ignite_data:
22
23networks:
24  ignite-network:
25    driver: bridge
26EOF
27
28# 2. Create the .env file
29cat > .env << 'EOF'
30# Apache Ignite default configuration
31EOF
32
33# 3. Start the services
34docker compose up -d
35
36# 4. View logs
37docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/apache-ignite/run | bash

Troubleshooting

java.net.BindException on startup: Ensure ports 10800, 11211, 47100, and 47500 are not in use by other services
OutOfMemoryError during data loading: Increase Docker container memory limits and configure Ignite data regions appropriately
Client connection timeouts: Verify firewall rules allow traffic on port 10800 and check IGNITE_CLIENT_CONNECTOR_PORT environment variable
Cluster discovery failures: Ensure network connectivity between nodes and verify multicast is enabled or configure static IP finder
Slow query performance: Check data distribution across partitions and consider creating indexes on frequently queried columns
Data loss after container restart: Verify persistent volumes are properly mounted and enable Ignite native persistence in configuration

Community Notes

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