Alternatives to Cassandra logo

Alternatives to Cassandra

HBase, Google Cloud Bigtable, Hadoop, Redis, and Couchbase are the most popular alternatives and competitors to Cassandra.
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What is Cassandra and what are its top alternatives?

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.
Cassandra is a tool in the Databases category of a tech stack.
Cassandra is an open source tool with 7.5K GitHub stars and 3.2K GitHub forks. Here’s a link to Cassandra's open source repository on GitHub

Top Alternatives to Cassandra

  • HBase
    HBase

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. ...

  • Google Cloud Bigtable
    Google Cloud Bigtable

    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail. ...

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Redis
    Redis

    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. ...

  • Couchbase
    Couchbase

    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands. ...

  • 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. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

Cassandra alternatives & related posts

HBase logo

HBase

405
457
15
The Hadoop database, a distributed, scalable, big data store
405
457
+ 1
15
PROS OF HBASE
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries
CONS OF HBASE
    Be the first to leave a con

    related HBase posts

    I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

    See more

    Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

    So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

    I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

    As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

    Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

    I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

    See more
    Google Cloud Bigtable logo

    Google Cloud Bigtable

    122
    326
    25
    The same database that powers Google Search, Gmail and Analytics
    122
    326
    + 1
    25
    PROS OF GOOGLE CLOUD BIGTABLE
    • 11
      High performance
    • 9
      Fully managed
    • 5
      High scalability
    CONS OF GOOGLE CLOUD BIGTABLE
      Be the first to leave a con

      related Google Cloud Bigtable posts

      Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

      Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

      Check Out My Architecture: CLICK ME

      Check out the GitHub repo attached

      See more
      Hadoop logo

      Hadoop

      2.2K
      2.2K
      56
      Open-source software for reliable, scalable, distributed computing
      2.2K
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      PROS OF HADOOP
      • 39
        Great ecosystem
      • 11
        One stack to rule them all
      • 4
        Great load balancer
      • 1
        Amazon aws
      • 1
        Java syntax
      CONS OF HADOOP
        Be the first to leave a con

        related Hadoop posts

        Shared insights
        on
        KafkaKafkaHadoopHadoop
        at

        The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

        For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

        Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.2M views

        Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

        Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

        https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

        (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

        See more
        Redis logo

        Redis

        50.9K
        38.6K
        3.9K
        Open source (BSD licensed), in-memory data structure store
        50.9K
        38.6K
        + 1
        3.9K
        PROS OF REDIS
        • 881
          Performance
        • 538
          Super fast
        • 510
          Ease of use
        • 441
          In-memory cache
        • 321
          Advanced key-value cache
        • 190
          Open source
        • 179
          Easy to deploy
        • 163
          Stable
        • 152
          Free
        • 120
          Fast
        • 40
          High-Performance
        • 39
          High Availability
        • 34
          Data Structures
        • 32
          Very Scalable
        • 23
          Replication
        • 20
          Pub/Sub
        • 20
          Great community
        • 17
          "NoSQL" key-value data store
        • 14
          Hashes
        • 12
          Sets
        • 10
          Sorted Sets
        • 9
          Lists
        • 8
          BSD licensed
        • 8
          NoSQL
        • 7
          Integrates super easy with Sidekiq for Rails background
        • 7
          Async replication
        • 7
          Bitmaps
        • 6
          Keys with a limited time-to-live
        • 6
          Open Source
        • 5
          Strings
        • 5
          Lua scripting
        • 4
          Hyperloglogs
        • 4
          Awesomeness for Free!
        • 3
          Transactions
        • 3
          Runs server side LUA
        • 3
          outstanding performance
        • 3
          Networked
        • 3
          LRU eviction of keys
        • 3
          Written in ANSI C
        • 3
          Feature Rich
        • 2
          Performance & ease of use
        • 2
          Data structure server
        • 1
          Simple
        • 1
          Channels concept
        • 1
          Scalable
        • 1
          Temporarily kept on disk
        • 1
          Dont save data if no subscribers are found
        • 1
          Automatic failover
        • 1
          Easy to use
        • 1
          Existing Laravel Integration
        • 1
          Object [key/value] size each 500 MB
        CONS OF REDIS
        • 14
          Cannot query objects directly
        • 2
          No secondary indexes for non-numeric data types
        • 1
          No WAL

        related Redis posts

        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.

        See more

        I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

        We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

        Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

        We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

        Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

        See more
        Couchbase logo

        Couchbase

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        551
        110
        Document-Oriented NoSQL Database
        439
        551
        + 1
        110
        PROS OF COUCHBASE
        • 18
          High performance
        • 18
          Flexible data model, easy scalability, extremely fast
        • 9
          Mobile app support
        • 7
          You can query it with Ansi-92 SQL
        • 6
          All nodes can be read/write
        • 5
          Open source, community and enterprise editions
        • 5
          Both a key-value store and document (JSON) db
        • 5
          Equal nodes in cluster, allowing fast, flexible changes
        • 4
          Local cache capability
        • 4
          Automatic configuration of sharding
        • 3
          Cross data center replication
        • 3
          Elasticsearch connector
        • 3
          Easy setup
        • 3
          Web based management, query and monitoring panel
        • 3
          Linearly scalable, useful to large number of tps
        • 3
          Easy cluster administration
        • 3
          SDKs in popular programming languages
        • 2
          Map reduce views
        • 2
          DBaaS available
        • 2
          NoSQL
        • 1
          FTS + SQL together
        • 1
          Buckets, Scopes, Collections & Documents
        CONS OF COUCHBASE
        • 3
          Terrible query language

        related Couchbase posts

        Gabriel Pa

        We implemented our first large scale EPR application from naologic.com using CouchDB .

        Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

        It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

        See more
        Ilias Mentzelos
        Software Engineer at Plum Fintech · | 9 upvotes · 83.9K views
        Shared insights
        on
        MongoDBMongoDBCouchbaseCouchbase

        Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

        See more
        MySQL logo

        MySQL

        103K
        85.4K
        3.7K
        The world's most popular open source database
        103K
        85.4K
        + 1
        3.7K
        PROS OF MYSQL
        • 796
          Sql
        • 675
          Free
        • 557
          Easy
        • 527
          Widely used
        • 487
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 78
          Secure
        • 75
          Full-text indexing and searching
        • 25
          Fast, open, available
        • 15
          SSL support
        • 14
          Reliable
        • 13
          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
        • 15
          Owned by a company with their own agenda
        • 2
          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.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 22 upvotes · 1.3M 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/

        See more
        PostgreSQL logo

        PostgreSQL

        80.8K
        65.6K
        3.5K
        A powerful, open source object-relational database system
        80.8K
        65.6K
        + 1
        3.5K
        PROS OF POSTGRESQL
        • 754
          Relational database
        • 508
          High availability
        • 435
          Enterprise class database
        • 379
          Sql
        • 303
          Sql + nosql
        • 171
          Great community
        • 145
          Easy to setup
        • 130
          Heroku
        • 128
          Secure by default
        • 112
          Postgis
        • 48
          Supports Key-Value
        • 46
          Great JSON support
        • 32
          Cross platform
        • 30
          Extensible
        • 26
          Replication
        • 24
          Triggers
        • 22
          Rollback
        • 21
          Multiversion concurrency control
        • 20
          Open source
        • 17
          Heroku Add-on
        • 14
          Stable, Simple and Good Performance
        • 13
          Powerful
        • 12
          Lets be serious, what other SQL DB would you go for?
        • 9
          Good documentation
        • 7
          Scalable
        • 7
          Intelligent optimizer
        • 6
          Reliable
        • 6
          Transactional DDL
        • 6
          Modern
        • 5
          Free
        • 5
          One stop solution for all things sql no matter the os
        • 4
          Relational database with MVCC
        • 3
          Faster Development
        • 3
          Full-Text Search
        • 3
          Developer friendly
        • 2
          Excellent source code
        • 2
          search
        • 2
          Great DB for Transactional system or Application
        • 1
          Full-text
        • 1
          Free version
        • 1
          Open-source
        • 1
          Text
        CONS OF POSTGRESQL
        • 9
          Table/index bloatings

        related PostgreSQL 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!

        See more
        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.

        See more
        Oracle logo

        Oracle

        2K
        1.5K
        109
        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
        2K
        1.5K
        + 1
        109
        PROS OF ORACLE
        • 42
          Reliable
        • 32
          Enterprise
        • 15
          High Availability
        • 5
          Hard to maintain
        • 5
          Expensive
        • 4
          Maintainable
        • 3
          Hard to use
        • 3
          High complexity
        CONS OF ORACLE
        • 14
          Expensive

        related Oracle posts

        Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

        We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

        We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

        ASP.NET / Node.js / Laravel. ......?

        Please guide us

        See more