Alternatives to Galera Cluster logo

Alternatives to Galera Cluster

Cassandra, Percona, Apache Aurora, PostgreSQL, and CockroachDB are the most popular alternatives and competitors to Galera Cluster.
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What is Galera Cluster and what are its top alternatives?

It’s an easy-to-use, high-availability solution, which provides high system up-time, no data loss and scalability for future growth. You can Keep it up and running 24/7. Putting our expertise to use will help you avoid trial and error.
Galera Cluster is a tool in the Database Tools category of a tech stack.
Galera Cluster is an open source tool with 375 GitHub stars and 159 GitHub forks. Here’s a link to Galera Cluster's open source repository on GitHub

Top Alternatives to Galera Cluster

  • 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. ...

  • Percona
    Percona

    It delivers enterprise-class software, support, consulting and managed services for both MySQL and MongoDB across traditional and cloud-based platforms. ...

  • Apache Aurora
    Apache Aurora

    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation. ...

  • 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. ...

  • CockroachDB
    CockroachDB

    CockroachDB is distributed SQL database that can be deployed in serverless, dedicated, or on-prem. Elastic scale, multi-active availability for resilience, and low latency performance. ...

  • 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. ...

  • 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. ...

  • Microsoft SQL Server
    Microsoft SQL Server

    Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...

Galera Cluster alternatives & related posts

Cassandra logo

Cassandra

3.3K
3.3K
494
A partitioned row store. Rows are organized into tables with a required primary key.
3.3K
3.3K
+ 1
494
PROS OF CASSANDRA
  • 115
    Distributed
  • 95
    High performance
  • 80
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Multi datacenter deployments
  • 26
    Reliable
  • 9
    OLTP
  • 7
    Open source
  • 7
    Schema optional
  • 2
    Workload separation (via MDC)
  • 1
    Fast
CONS OF CASSANDRA
  • 3
    Reliability of replication
  • 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

See more
Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 211.5K 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.

See more
Percona logo

Percona

93
83
0
With more than 3,000 customers worldwide, Percona delivers enterprise-class solutions for both MySQL and MongoDB across traditional and...
93
83
+ 1
0
PROS OF PERCONA
    Be the first to leave a pro
    CONS OF PERCONA
      Be the first to leave a con

      related Percona posts

      Apache Aurora logo

      Apache Aurora

      66
      86
      0
      An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
      66
      86
      + 1
      0
      PROS OF APACHE AURORA
        Be the first to leave a pro
        CONS OF APACHE AURORA
          Be the first to leave a con

          related Apache Aurora posts

          Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

          See more
          PostgreSQL logo

          PostgreSQL

          74.5K
          60.1K
          3.5K
          A powerful, open source object-relational database system
          74.5K
          60.1K
          + 1
          3.5K
          PROS OF POSTGRESQL
          • 754
            Relational database
          • 508
            High availability
          • 436
            Enterprise class database
          • 380
            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
          CockroachDB logo

          CockroachDB

          159
          261
          0
          A distributed SQL database for building global, scalable cloud services that survive disasters.
          159
          261
          + 1
          0
          PROS OF COCKROACHDB
            Be the first to leave a pro
            CONS OF COCKROACHDB
              Be the first to leave a con

              related CockroachDB posts

              MySQL logo

              MySQL

              95.5K
              78.6K
              3.7K
              The world's most popular open source database
              95.5K
              78.6K
              + 1
              3.7K
              PROS OF MYSQL
              • 795
                Sql
              • 673
                Free
              • 556
                Easy
              • 527
                Widely used
              • 485
                Open source
              • 180
                High availability
              • 160
                Cross-platform support
              • 104
                Great community
              • 78
                Secure
              • 75
                Full-text indexing and searching
              • 25
                Fast, open, available
              • 14
                SSL support
              • 13
                Robust
              • 13
                Reliable
              • 8
                Enterprise Version
              • 7
                Easy to set up on all platforms
              • 2
                NoSQL access to JSON data type
              • 1
                Replica Support
              • 1
                Relational database
              • 1
                Easy, light, scalable
              • 1
                Sequel Pro (best SQL GUI)
              CONS OF MYSQL
              • 14
                Owned by a company with their own agenda
              • 1
                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 · | 21 upvotes · 1.2M 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
              MongoDB logo

              MongoDB

              72.4K
              61.3K
              4.1K
              The database for giant ideas
              72.4K
              61.3K
              + 1
              4.1K
              PROS OF MONGODB
              • 828
                Document-oriented storage
              • 593
                No sql
              • 549
                Ease of use
              • 465
                Fast
              • 408
                High performance
              • 256
                Free
              • 215
                Open source
              • 180
                Flexible
              • 143
                Replication & high availability
              • 110
                Easy to maintain
              • 42
                Querying
              • 38
                Easy scalability
              • 37
                Auto-sharding
              • 36
                High availability
              • 31
                Map/reduce
              • 27
                Document database
              • 25
                Full index support
              • 25
                Easy setup
              • 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
              • 3
                Drivers support is good
              • 3
                Aggregation Framework
              • 3
                Support for many languages through different drivers
              • 2
                Awesome
              • 2
                Schemaless
              • 2
                Managed service
              • 2
                Fast
              • 2
                Easy to Scale
              • 1
                Consistent
              • 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!

              See more
              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
              Microsoft SQL Server logo

              Microsoft SQL Server

              15K
              11.1K
              539
              A relational database management system developed by Microsoft
              15K
              11.1K
              + 1
              539
              PROS OF MICROSOFT SQL SERVER
              • 139
                Reliable and easy to use
              • 102
                High performance
              • 95
                Great with .net
              • 65
                Works well with .net
              • 56
                Easy to maintain
              • 21
                Azure support
              • 17
                Always on
              • 17
                Full Index Support
              • 10
                Enterprise manager is fantastic
              • 9
                In-Memory OLTP Engine
              • 2
                Security is forefront
              • 1
                Columnstore indexes
              • 1
                Great documentation
              • 1
                Faster Than Oracle
              • 1
                Decent management tools
              • 1
                Easy to setup and configure
              • 1
                Docker Delivery
              CONS OF MICROSOFT SQL SERVER
              • 4
                Expensive Licensing
              • 2
                Microsoft

              related Microsoft SQL Server posts

              We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

              We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

              In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

              Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

              See more

              I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

              1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
              2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
              See more