What is MonetDB and what are its top alternatives?
Top Alternatives to MonetDB
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 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
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
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
It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure. ...
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 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 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
- Distributed107
- High performance90
- High availability77
- Easy scalability71
- Replication50
- Reliable25
- Multi datacenter deployments24
- Schema optional6
- OLTP6
- Open source5
- Workload separation (via MDC)2
- Reliability of replication1
- Updates1
related Cassandra posts
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
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.
- Distributed5
- Realtime3
- Sql2
- JSON2
- Concurrent2
- Columnstore2
- Scalable1
- Ultra fast1
related MemSQL posts
- Sql789
- Free674
- Easy557
- Widely used527
- Open source485
- High availability180
- Cross-platform support158
- Great community103
- Secure77
- Full-text indexing and searching75
- Fast, open, available25
- SSL support14
- Robust13
- Reliable13
- Enterprise Version8
- Easy to set up on all platforms7
- Easy, light, scalable1
- Relational database1
- NoSQL access to JSON data type1
- Sequel Pro (best SQL GUI)1
- Replica Support1
- Owned by a company with their own agenda13
- Can't roll back schema changes1
related MySQL posts
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.
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:
- Fast, very very fast13
- Good compression ratio11
- Horizontally scalable6
- Utilizes all CPU resources5
- RESTful5
- Great CLI4
- Has no transactions2
- Great number of SQL functions2
- Open-source1
- In IDEA data import via HTTP interface not working1
- Server crashes its normal :(1
- Buggy1
- Highly available1
- Flexible compression options1
- Flexible connection options1
- ODBC1
- Slow insert operations2
related Clickhouse posts
- Shared nothing or shared everything architecture1
- Offers users the freedom to choose deployment mode1
- Flexible architecture suits nearly any project1
- End-to-End ML Workflow Support1
- All You Need for IoT, Clickstream or Geospatial1
- Freedom from Underlying Storage1
- Pre-Aggregation for Cubes (LAPS)1
- Automatic Data Marts (Flatten Tables)1
- Near-Real-Time Analytics in pure Column Store1
- Fully automated Database Designer tool1
- Query-Optimized Storage1
- Vertica is the only product which offers partition prun1
- Partition pruning and predicate push down on Parquet1
related Vertica posts
- Drop-in mysql replacement149
- Great performance101
- Open source74
- Free56
- Easy setup45
- Easy and fast16
- Lead developer is "monty" widenius the founder of mysql14
- Also an aws rds service6
- Learning curve easy4
- Consistent and robust4
- Native JSON Support / Dynamic Columns1
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 -
- native languages like Swift for IOS and Java/Kotlin for Android
- or cross-platform languages like React Native for both IOS and Android Apps
For UI -
- React
For Back-End or APIs -
- Node.js
- PHP
For Database -
- PostgreSQL
- MySQL
- Cloud Firestore
- MariaDB
Thanks!















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
- Document-oriented storage822
- No sql585
- Ease of use544
- Fast462
- High performance404
- Free251
- Open source212
- Flexible177
- Replication & high availability139
- Easy to maintain107
- Querying39
- Easy scalability35
- Auto-sharding34
- High availability33
- Map/reduce29
- Document database26
- Easy setup24
- Full index support24
- Reliable15
- Fast in-place updates14
- Agile programming, flexible, fast13
- No database migrations11
- Enterprise7
- Easy integration with Node.Js7
- Enterprise Support5
- Great NoSQL DB4
- Aggregation Framework3
- Drivers support is good3
- Support for many languages through different drivers3
- Schemaless2
- Managed service2
- Easy to Scale2
- Fast2
- Awesome2
- Consistent1
- Very slowly for connected models that require joins5
- Not acid compliant3
- Proprietary query language1
related MongoDB posts









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!
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.
- Relational database755
- High availability506
- Enterprise class database437
- Sql379
- Sql + nosql299
- Great community171
- Easy to setup145
- Heroku129
- Secure by default128
- Postgis111
- Supports Key-Value48
- Great JSON support46
- Cross platform32
- Extensible29
- Replication25
- Triggers24
- Rollback22
- Multiversion concurrency control21
- Open source20
- Heroku Add-on17
- Stable, Simple and Good Performance14
- Powerful13
- Lets be serious, what other SQL DB would you go for?12
- Good documentation9
- Scalable7
- Intelligent optimizer7
- Transactional DDL6
- Modern6
- Reliable6
- One stop solution for all things sql no matter the os5
- Free5
- Relational database with MVCC4
- Full-Text Search3
- Developer friendly3
- Faster Development3
- Excellent source code2
- Great DB for Transactional system or Application2
- Free version1
- Text1
- Open-source1
- search1
- Full-text1
- Table/index bloatings9
related PostgreSQL posts









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!
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.