Apache Ignite vs Redis as an In-Memory Computing Platform vs a Data Cache

Apache® Ignite is a powerful alternative to Redis® for demanding computing use cases. Redis is primarily an in-memory key-value store used for caching data, although the product is often promoted as a database. Apache Ignite is a full featured in-memory computing platform which includes an in-memory data grid, in-memory database, streaming analytics, and a continuous learning framework for machine and deep learning. When evaluating Apache Ignite versus Redis, companies that use or are considering Redis for demanding use cases may find that it cannot meet their needs in use cases where advanced, real-time performance is required.

Apache Ignite and Redis both have data grid capabilities which partition and cache data in memory. Both products can scale out across distributed clusters. However, there are many differences in the way Redis and Apache Ignite support caching, transactions, persistence, and data querying. The Apache Ignite in-memory computing platform is a powerful alternative to Redis because it includes many additional features not found in Redis that are highly valuable when moving to in-memory computing.

Apache Ignite vs Redis

Used as an in-memory data grid or in-memory database, Apache Ignite also provides the ability to partition data based on data affinity, and use massively parallel processing (MPP) to improve performance and scalability. Apache Ignite provides built-in stream processing, analytics and machine learning capabilities as well. Companies using Apache Ignite are able to deploy new types of applications for digital transformation or omnichannel customer experience initiatives that were too costly or too complex to deliver in the past. Collocated processing has helped reduce network traffic in deployments by as much as 100x which is one reason companies have selected Apache Ignite over Redis for some demanding use cases.

Apache Ignite vs Redis as an In-Memory Grid

When using an in-memory data grid to add speed and scale to existing applications:

  • Redis is primarily used by developers to improve the read performance of applications as an in-memory key-value cache. It requires coding within the application to add the cache, and coding and configuration to keep the data in the cache up to date relative to any underlying data sources. Redis does not fully support SQL, so relational data must typically be mapped to another model.
  • Apache Ignite can be used as more than a cache. More importantly, Apache Ignite can be used as an in-memory data grid which slides between the existing application and data layers without having to rip-and-replace or rewrite the applications or databases. Apache Ignite can sit in the path of SQL or other queries and support ACID transactions as an inline read- and write-through cache by replacing existing JDBC or ODBC drivers with the Apache Ignite drivers. It updates the cache following a successful commit by the underlying database to keep data in sync with the underlying database.
Apache Ignite vs Redis as a Distributed SQL Key-Value Database

When using the systems as a distributed SQL Key-Value Database:

  • Redis can be used as a key-value store for application data. Developers appreciate how simple it is to manage data within an application using the Redis APIs.
  • Apache Ignite can be used as a distributed key-value database. But Apache Ignite can also be used as an in-memory distributed SQL database. Its optional Persistent Store feature allows RAM to hold a subset of the full dataset which resides on disk. It provides immediate availability on restart without having to wait for data to be loaded into memory first. Apache Ignite also offers ACID transaction support with pessimistic locking, unlike Redis.
More Information About Apache Ignite vs Redis

Learn more about Apache Ignite and the GridGain in-memory computing platform as Redis alternatives by downloading our in-depth GridGain versus Redis feature comparison. It details how GridGain and Redis compare in 22 different categories including:

  • Distributed Caching, Data Structures, Queries, Compute, Messaging and Events
  • ACID Compliant Transactions and Locks
  • Persistence and Data Loading
  • Security and Audit
  • In-Memory Streaming
  • Configuration and Grid Management
  • Supported Platforms, Standards and Integrations
  • Cloud and Virtualization Support

Check out Apache Ignite vs Apache Spark: Integration using Ignite RDDs for information on using Apache Ignite and Apache Spark together.