Apache Ignite — Distributed In-Memory Computing Platform

Introduction

Apache Ignite is a distributed platform that unifies in-memory caching, SQL querying, key-value access, and distributed computing in a single system. Data is partitioned across cluster nodes and kept in memory for microsecond-level access, with optional disk persistence for durability. Ignite serves as both a distributed cache layer in front of existing databases and a standalone distributed database with full ANSI SQL support.

What Apache Ignite Does

• Distributes data across cluster nodes with automatic partitioning and rebalancing
• Provides ANSI SQL queries with distributed joins, indexes, and DML support
• Offers key-value, compute grid, and service grid APIs in Java, C#, C++, and Python
• Persists data to disk with write-ahead logging for crash recovery
• Supports ACID transactions across partitions with two-phase commit

Architecture Overview

Ignite organizes data into caches (tables) that are hash-partitioned across nodes. Each partition has configurable backup copies for fault tolerance. The SQL engine compiles queries into distributed execution plans that push computation to data-owning nodes and merge results at the coordinator. The native persistence layer stores data pages on disk with a WAL for crash recovery, allowing datasets larger than available RAM while keeping hot data in memory.

Self-Hosting & Configuration

• Start a cluster with the CLI or embed Ignite as a library in Java applications
• Configure caches, replication factor, and memory regions in XML or programmatically
• Enable native persistence with dataStorageConfiguration to survive full cluster restarts
• Set up thin client connections for Python, Node.js, or C# access to the cluster
• Use the REST API or control script for monitoring and cluster management

Key Features

• Co-located compute pushes processing to where data resides, reducing network overhead
• Transparent read-through and write-through integration with RDBMS and NoSQL stores
• Continuous queries notify applications of data changes in real time
• Machine learning library for distributed training directly on in-memory data
• Multi-model access: SQL, key-value, and compute through a unified API

Comparison with Similar Tools

• Redis — single-threaded in-memory store; Ignite is distributed with SQL and compute grid
• Hazelcast — similar in-memory grid; Ignite adds full SQL engine and native persistence
• Apache Spark — batch/stream processing; Ignite provides mutable in-memory storage and ACID transactions
• CockroachDB — distributed SQL on disk; Ignite keeps data in memory for lower latency
• Memcached — simple cache with no persistence; Ignite offers SQL, transactions, and disk durability

FAQ

Q: Does Ignite require all data to fit in memory? A: No. With native persistence enabled, Ignite stores data on disk and caches hot pages in memory, supporting datasets larger than total cluster RAM.

Q: Can I use Ignite as a cache in front of PostgreSQL? A: Yes. Configure a CacheStore with read-through and write-through to PostgreSQL, and Ignite transparently loads and persists data.

Q: What languages are supported? A: Java has the richest API. Thin clients are available for Python, C#, C++, Node.js, and Go with key-value and SQL access.

Q: How does Ignite handle node failures? A: Backup copies on other nodes serve requests for failed partitions. The cluster automatically rebalances data when nodes join or leave.

Sources

• https://github.com/apache/ignite
• https://ignite.apache.org/docs/latest/