Alternatives to MonetDB logo

Alternatives to MonetDB

Cassandra, MemSQL, MySQL, Clickhouse, and Vertica are the most popular alternatives and competitors to MonetDB.
8
20
+ 1
2

What is MonetDB and what are its top alternatives?

MonetDB innovates at all layers of a DBMS, e.g. a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture, automatic and self-tuning indexes, run-time query optimization, and a modular software architecture.
MonetDB is a tool in the Databases category of a tech stack.

Top Alternatives to MonetDB

  • 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. ...

  • MemSQL

    MemSQL

    MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines. ...

  • 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. ...

  • Clickhouse

    Clickhouse

    It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query. ...

  • Vertica

    Vertica

    It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure. ...

  • MariaDB

    MariaDB

    Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance. ...

  • 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. ...

  • 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. ...

MonetDB alternatives & related posts

Cassandra logo

Cassandra

3K
2.9K
463
A partitioned row store. Rows are organized into tables with a required primary key.
3K
2.9K
+ 1
463
PROS OF CASSANDRA
  • 107
    Distributed
  • 90
    High performance
  • 77
    High availability
  • 71
    Easy scalability
  • 50
    Replication
  • 25
    Reliable
  • 24
    Multi datacenter deployments
  • 6
    Schema optional
  • 6
    OLTP
  • 5
    Open source
  • 2
    Workload separation (via MDC)
CONS OF CASSANDRA
  • 1
    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 Vappar · | 3 upvotes · 13K 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
MemSQL logo

MemSQL

61
123
18
Database for real-time transactions and analytics.
61
123
+ 1
18
PROS OF MEMSQL
  • 5
    Distributed
  • 3
    Realtime
  • 2
    Sql
  • 2
    JSON
  • 2
    Concurrent
  • 2
    Columnstore
  • 1
    Scalable
  • 1
    Ultra fast
CONS OF MEMSQL
    Be the first to leave a con

    related MemSQL posts

    MySQL logo

    MySQL

    68.4K
    52.7K
    3.7K
    The world's most popular open source database
    68.4K
    52.7K
    + 1
    3.7K
    PROS OF MYSQL
    • 789
      Sql
    • 674
      Free
    • 557
      Easy
    • 527
      Widely used
    • 485
      Open source
    • 180
      High availability
    • 158
      Cross-platform support
    • 103
      Great community
    • 77
      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
    • 1
      Easy, light, scalable
    • 1
      Relational database
    • 1
      NoSQL access to JSON data type
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 13
      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 · | 20 upvotes · 916.4K 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
    Clickhouse logo

    Clickhouse

    187
    234
    56
    A column-oriented database management system
    187
    234
    + 1
    56
    PROS OF CLICKHOUSE
    • 13
      Fast, very very fast
    • 11
      Good compression ratio
    • 6
      Horizontally scalable
    • 5
      Utilizes all CPU resources
    • 5
      RESTful
    • 4
      Great CLI
    • 2
      Has no transactions
    • 2
      Great number of SQL functions
    • 1
      Open-source
    • 1
      In IDEA data import via HTTP interface not working
    • 1
      Server crashes its normal :(
    • 1
      Buggy
    • 1
      Highly available
    • 1
      Flexible compression options
    • 1
      Flexible connection options
    • 1
      ODBC
    CONS OF CLICKHOUSE
    • 2
      Slow insert operations

    related Clickhouse posts

    Vertica logo

    Vertica

    61
    65
    13
    Storage platform designed to handle large volumes of data
    61
    65
    + 1
    13
    PROS OF VERTICA
    • 1
      Shared nothing or shared everything architecture
    • 1
      Offers users the freedom to choose deployment mode
    • 1
      Flexible architecture suits nearly any project
    • 1
      End-to-End ML Workflow Support
    • 1
      All You Need for IoT, Clickstream or Geospatial
    • 1
      Freedom from Underlying Storage
    • 1
      Pre-Aggregation for Cubes (LAPS)
    • 1
      Automatic Data Marts (Flatten Tables)
    • 1
      Near-Real-Time Analytics in pure Column Store
    • 1
      Fully automated Database Designer tool
    • 1
      Query-Optimized Storage
    • 1
      Vertica is the only product which offers partition prun
    • 1
      Partition pruning and predicate push down on Parquet
    CONS OF VERTICA
      Be the first to leave a con

      related Vertica posts

      MariaDB logo

      MariaDB

      9.2K
      6.7K
      470
      An enhanced, drop-in replacement for MySQL
      9.2K
      6.7K
      + 1
      470
      PROS OF MARIADB
      • 149
        Drop-in mysql replacement
      • 101
        Great performance
      • 74
        Open source
      • 56
        Free
      • 45
        Easy setup
      • 16
        Easy and fast
      • 14
        Lead developer is "monty" widenius the founder of mysql
      • 6
        Also an aws rds service
      • 4
        Learning curve easy
      • 4
        Consistent and robust
      • 1
        Native JSON Support / Dynamic Columns
      CONS OF MARIADB
        Be the first to leave a con

        related MariaDB posts

        I'm researching what Technology Stack I should use to build my product (something like food delivery App) for Web, iOS, and Android Apps. Please advise which technologies you would recommend from a Scalability, Reliability, Cost, and Efficiency standpoint for a start-up. Here are the technologies I came up with, feel free to suggest any new technology even it's not in the list below.

        For Mobile Apps -

        1. native languages like Swift for IOS and Java/Kotlin for Android
        2. or cross-platform languages like React Native for both IOS and Android Apps

        For UI -

        1. React

        For Back-End or APIs -

        1. Node.js
        2. PHP

        For Database -

        1. PostgreSQL
        2. MySQL
        3. Cloud Firestore
        4. MariaDB

        Thanks!

        See more

        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
        MongoDB logo

        MongoDB

        51.6K
        41.3K
        4K
        The database for giant ideas
        51.6K
        41.3K
        + 1
        4K
        PROS OF MONGODB
        • 822
          Document-oriented storage
        • 585
          No sql
        • 544
          Ease of use
        • 462
          Fast
        • 404
          High performance
        • 251
          Free
        • 212
          Open source
        • 177
          Flexible
        • 139
          Replication & high availability
        • 107
          Easy to maintain
        • 39
          Querying
        • 35
          Easy scalability
        • 34
          Auto-sharding
        • 33
          High availability
        • 29
          Map/reduce
        • 26
          Document database
        • 24
          Easy setup
        • 24
          Full index support
        • 15
          Reliable
        • 14
          Fast in-place updates
        • 13
          Agile programming, flexible, fast
        • 11
          No database migrations
        • 7
          Enterprise
        • 7
          Easy integration with Node.Js
        • 5
          Enterprise Support
        • 4
          Great NoSQL DB
        • 3
          Aggregation Framework
        • 3
          Drivers support is good
        • 3
          Support for many languages through different drivers
        • 2
          Schemaless
        • 2
          Managed service
        • 2
          Easy to Scale
        • 2
          Fast
        • 2
          Awesome
        • 1
          Consistent
        CONS OF MONGODB
        • 5
          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
        PostgreSQL logo

        PostgreSQL

        51.7K
        39.8K
        3.5K
        A powerful, open source object-relational database system
        51.7K
        39.8K
        + 1
        3.5K
        PROS OF POSTGRESQL
        • 755
          Relational database
        • 506
          High availability
        • 437
          Enterprise class database
        • 379
          Sql
        • 299
          Sql + nosql
        • 171
          Great community
        • 145
          Easy to setup
        • 129
          Heroku
        • 128
          Secure by default
        • 111
          Postgis
        • 48
          Supports Key-Value
        • 46
          Great JSON support
        • 32
          Cross platform
        • 29
          Extensible
        • 25
          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
          Transactional DDL
        • 6
          Modern
        • 6
          Reliable
        • 5
          One stop solution for all things sql no matter the os
        • 5
          Free
        • 4
          Relational database with MVCC
        • 3
          Full-Text Search
        • 3
          Developer friendly
        • 3
          Faster Development
        • 2
          Excellent source code
        • 2
          Great DB for Transactional system or Application
        • 1
          Free version
        • 1
          Text
        • 1
          Open-source
        • 1
          search
        • 1
          Full-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