Open-Source Databases · apache

ignite

Apache Ignite is an open-source, distributed in-memory database designed for high-performance computing and caching workloads. It supports ACID transactions, ANSI SQL queries, and multi-tier storage (memory, disk, Optane) with optional native persistence, allowing you to trade off speed and scale.

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Source: GitHub — github.com/apache/ignite

5.1k

GitHub stars

1.9k

Forks

Java

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	apache/ignite
Owner	apache
Primary language	Java
License	Apache-2.0 — OSI-approved
Stars	5.1k
Forks	1.9k
Open issues	883
Latest release	Unknown
Last updated	2026-07-08
Source	https://github.com/apache/ignite

What ignite is

Ignite is a Java-based distributed system providing horizontally scalable ACID-compliant SQL engine, native persistence layer, MapReduce compute APIs, and multi-language client support (Java, C#, C++, Python). Data lives in memory tier by default with optional spillover to disk or external databases via pluggable persistence.

Quickstart

Get the ignite source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/apache/ignite.git2cd ignite3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

High-Performance Caching Layer

Replace traditional single-node caches with a distributed cluster serving sub-millisecond latency for transactional reads/writes across multiple commodity machines. ACID guarantees make it suitable for financial services and inventory systems.

Real-Time Analytics & OLAP

Run distributed SQL queries on in-memory datasets for dashboards and reporting without moving data to a separate analytics cluster. Multi-tier storage lets you cache hot data while archiving cold data to disk.

High-Performance Computing Clusters

Use MapReduce APIs to distribute compute-intensive workloads (simulations, ML feature engineering) across a cluster, keeping intermediate results in memory to avoid network serialization overhead.

Implementation considerations

Capacity Planning: In-memory storage is expensive; estimate working set size upfront. Native persistence provides spillover but adds disk I/O latency—requires trade-off analysis.
JVM Tuning: Ignite runs on Java and requires careful heap sizing, GC tuning, and memory configuration to avoid stop-the-world pauses in production clusters.
Cluster Topology: Design for replication factor, partition count, and node coordination. Network latency between nodes directly impacts query performance.
Client Protocol Selection: Choose between JDBC/ODBC, native language APIs, or REST; each has different latency and throughput characteristics.
Persistence Strategy: Decide between native disk persistence (for fast cluster restarts) or external DB (for schema flexibility and backup integration).

When to avoid it — and what to weigh

Simple Key-Value Cache Needs — If you only need basic caching without transactions or SQL, lightweight alternatives (Redis, Memcached) have lower operational overhead and faster setup.
Strict Write-Once Data Immutability — Ignite is mutable and ACID-oriented. If your workload demands immutable logs or append-only semantics, event streaming systems (Kafka, Pulsar) are better suited.
Small Team, Limited DevOps Capacity — Ignite cluster management, tuning, and troubleshooting require deep distributed systems expertise. Teams unfamiliar with JVM, memory management, and network protocols will face steep learning curves.
Strict Data Privacy / Air-Gapped Environments — No clear statement of data residency or confidential computing features in provided data. Organizations with strict regulatory isolation requirements should evaluate separately.

License & commercial use

Apache License 2.0 (Apache-2.0) is an OSI-approved, permissive license. Requires attribution and provides patent protection for licensees. Modifications must be disclosed, but commercial use and distribution are permitted.

Apache-2.0 permits commercial use, including proprietary modifications, as long as the license and copyright notices are retained. Derivative works must state material changes. No commercial support guarantees are stated in the data; contact Apache Ignite community or consulting partners for production SLAs.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

Data is held in plaintext in-memory by default; security depends on network access controls and JVM process isolation. TLS/SSL encryption for client-node and node-node communication availability not clearly stated in provided data. Persistence layer security (disk encryption, access control) requires separate review. Sensitive data handling and compliance certifications not mentioned.

Alternatives to consider

Redis

Simpler, lower-latency key-value cache. Lacks native ACID transactions and SQL; suitable for high-throughput caching with fewer operational dependencies.

Apache Hazelcast

Similar in-memory data grid with ACID support and MapReduce APIs. Lighter footprint, easier clustering. Differs in persistence strategy and SQL maturity.

ClickHouse

Distributed OLAP engine optimized for analytical queries on immutable data. Not suitable for transactional workloads but excellent for real-time analytics at scale.

Software development agency

Build on ignite with DEV.co software developers

Apache Ignite is a powerful choice for distributed caching and real-time analytics, but requires mature DevOps and distributed systems expertise. If you're building a production system or need guidance on cluster design, persistence strategy, or integration, our enterprise software and cloud deployment teams can help you architect and deploy Ignite reliably at scale.

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ignite FAQ

Does Ignite require an external database?

No. Ignite has optional native persistence (disk-based store). You can use it purely in-memory, with native persistence, or wrap an external database as a fallback data source.

Can I use Ignite on Kubernetes?

Yes, but cluster discovery and networking require configuration. Ignite needs persistent discovery (ZooKeeper or DNS) and stable pod identities. Production deployments typically use Helm charts or operators; check community resources.

What happens if a node fails?

If replication is enabled (backup count > 0), data is replicated to other nodes. A failed primary will promote a backup. With no replication, data on that partition is lost unless restored from native persistence.

Is Ignite suitable for production use?

Yes, it is used in production by enterprises. Requires mature DevOps practices, monitoring, capacity planning, and incident response readiness. No SLA stated in open-source; commercial support is available separately.

Work with a software development agency

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ignite is part of your open-source databases roadmap, our team can implement, customize, migrate, and maintain it.

Evaluate Apache Ignite for your high-performance data platform.

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