Alternatives to Redis logo

Alternatives to Redis

Memcached, MongoDB, RabbitMQ, Hazelcast, and Cassandra are the most popular alternatives and competitors to Redis.
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What is Redis and what are its top alternatives?

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Redis is a tool in the In-Memory Databases category of a tech stack.
Redis is an open source tool with GitHub stars and GitHub forks. Here’s a link to Redis's open source repository on GitHub

Top Alternatives to Redis

  • Memcached
    Memcached

    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. ...

  • MongoDB
    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Hazelcast
    Hazelcast

    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution. ...

  • Cassandra
    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • SQLite
    SQLite

    SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. ...

  • Aerospike
    Aerospike

    Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees. ...

Redis alternatives & related posts

Memcached logo

Memcached

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High-performance, distributed memory object caching system
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PROS OF MEMCACHED
  • 139
    Fast object cache
  • 129
    High-performance
  • 91
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
  • 11
    Improved response time and throughput
  • 3
    Great for caching HTML
  • 2
    Putta
CONS OF MEMCACHED
  • 2
    Only caches simple types

related Memcached posts

Kir Shatrov
Engineering Lead at Shopify · | 17 upvotes · 1.2M views

At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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MongoDB logo

MongoDB

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The database for giant ideas
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PROS OF MONGODB
  • 827
    Document-oriented storage
  • 593
    No sql
  • 553
    Ease of use
  • 464
    Fast
  • 410
    High performance
  • 257
    Free
  • 218
    Open source
  • 180
    Flexible
  • 145
    Replication & high availability
  • 112
    Easy to maintain
  • 42
    Querying
  • 39
    Easy scalability
  • 38
    Auto-sharding
  • 37
    High availability
  • 31
    Map/reduce
  • 27
    Document database
  • 25
    Easy setup
  • 25
    Full index support
  • 16
    Reliable
  • 15
    Fast in-place updates
  • 14
    Agile programming, flexible, fast
  • 12
    No database migrations
  • 8
    Easy integration with Node.Js
  • 8
    Enterprise
  • 6
    Enterprise Support
  • 5
    Great NoSQL DB
  • 4
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 3
    Aggregation Framework
  • 3
    Schemaless
  • 2
    Fast
  • 2
    Managed service
  • 2
    Easy to Scale
  • 2
    Awesome
  • 2
    Consistent
  • 1
    Good GUI
  • 1
    Acid Compliant
CONS OF MONGODB
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 234
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
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    I like the admin interface
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    Easy to set-up and start with
  • 21
    Durable
  • 18
    Standard protocols
  • 18
    Intuitive work through python
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Scales to 1 million messages per second
  • 3
    Reliable
  • 2
    Distributed
  • 2
    Supports MQTT
  • 2
    Better than most traditional queue based message broker
  • 2
    Supports AMQP
  • 1
    Clusterable
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
  • 1
    Inubit Integration
  • 1
    Better routing system
  • 1
    High performance
  • 1
    Runs on Open Telecom Platform
  • 1
    Delayed messages
  • 1
    Reliability
  • 1
    Open-source
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.6M views
Shared insights
on
CeleryCeleryRabbitMQRabbitMQ
at

As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

#MessageQueue

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Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

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Hazelcast logo

Hazelcast

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Clustering and highly scalable data distribution platform for Java
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PROS OF HAZELCAST
  • 11
    High Availibility
  • 6
    Distributed Locking
  • 6
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Map-reduce functionality
  • 3
    Simple-to-use
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 3
    Sql query support in cluster wide
  • 2
    Optimis locking for map
  • 2
    Performance
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
  • 1
    Super Fast
CONS OF HAZELCAST
  • 4
    License needed for SSL

related Hazelcast posts

Cassandra logo

Cassandra

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A partitioned row store. Rows are organized into tables with a required primary key.
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PROS OF CASSANDRA
  • 119
    Distributed
  • 97
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
CONS OF CASSANDRA
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates

related Cassandra posts

Thierry Schellenbach
Shared insights
on
RedisRedisCassandraCassandraRocksDBRocksDB
at

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

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Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 429.8K views

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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MySQL logo

MySQL

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The world's most popular open source database
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PROS OF MYSQL
  • 800
    Sql
  • 679
    Free
  • 562
    Easy
  • 528
    Widely used
  • 489
    Open source
  • 180
    High availability
  • 160
    Cross-platform support
  • 104
    Great community
  • 78
    Secure
  • 75
    Full-text indexing and searching
  • 25
    Fast, open, available
  • 16
    SSL support
  • 15
    Reliable
  • 14
    Robust
  • 8
    Enterprise Version
  • 7
    Easy to set up on all platforms
  • 2
    NoSQL access to JSON data type
  • 1
    Relational database
  • 1
    Easy, light, scalable
  • 1
    Sequel Pro (best SQL GUI)
  • 1
    Replica Support
CONS OF MYSQL
  • 16
    Owned by a company with their own agenda
  • 3
    Can't roll back schema changes

related MySQL posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 23 upvotes · 2.3M views

Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

https://eng.uber.com/mysql-migration/

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SQLite logo

SQLite

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A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
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PROS OF SQLITE
  • 162
    Lightweight
  • 135
    Portable
  • 121
    Simple
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    Sql
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    Preinstalled on iOS and Android
  • 2
    Tcl integration
  • 1
    Free
  • 1
    Portable A database on my USB 'love it'
CONS OF SQLITE
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform

related SQLite posts

Dimelo Waterson
Shared insights
on
PostgreSQLPostgreSQLMySQLMySQLSQLiteSQLite

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

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Hi all. I want to rewrite my system. I was a complete newbie 4 years ago and have developed a comprehensive business / finance web application that has been running successfully for 3 years (I am a business person and not a developer primarily although it seems I have become a developer). Front-end is written in native PHP (no framework) and jQuery with backend and where many processes run in MySQL. Hosted on Linux and also sends emails with attachments etc. The system logic is great and the business has grown and the system is creaking and needs to be modernised. I feel I would stick with MySql as DB and update / use Django / Spring or Laravel (because its php which I understand). To me, PHP feels old fashioned. I don't mind learning new things and also I want to set the system up that it can be easily migrated to Android/iOS app with SQLite. I would probably employ an experienced developer while also doing some myself. Please provide advice -- from my research it seems Spring/Java is the way to go ... not sure. Thanks

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Aerospike logo

Aerospike

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48
Flash-optimized in-memory open source NoSQL database
199
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48
PROS OF AEROSPIKE
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
  • 3
    Performance better than Redis
  • 3
    Petabyte Scale
  • 2
    Ease of use
CONS OF AEROSPIKE
    Be the first to leave a con

    related Aerospike posts

    Gagan Jakhotiya
    Engineering Manager at BigBasket · | 5 upvotes · 55.7K views
    Shared insights
    on
    Tile38Tile38MySQLMySQLAerospikeAerospike

    I have a very limited but significant use case for spatial index in a routing service. I see these indexes not growing beyond 10,000 geometries for the next 1 year and maybe 100,000 for the next 3 years. The solution needs to be approached from a delivery timeline perspective mostly because the use case also comes with a slightly relaxed compute time SLA and cost optimum implementation PoV.

    We have chosen R-Tree based index as a suitable choice for our use case. We are already using Aerospike and MySQL in our stack. MySQL supports R-Tree and has good docs as well. I couldn't find anything specific to R-Tree with Aerospike. Also, generally would like to understand from the performance perspective how these two choices would fare with something like Tile38?

    Suggestions beside these are also most welcome.

    See more