Citus vs Microsoft SQL Server

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Citus
Citus

28
26
+ 1
8
Microsoft SQL Server
Microsoft SQL Server

3.8K
2.7K
+ 1
525
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Citus vs Microsoft SQL Server: What are the differences?

Citus: Worry-free Postgres for SaaS. Built to scale out. Citus is worry-free Postgres for SaaS. Made to scale out, Citus is an extension to Postgres that distributes queries across any number of servers. Citus is available as open source, as on-prem software, and as a fully-managed service; Microsoft SQL Server: A relational database management system developed by Microsoft. Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

Citus and Microsoft SQL Server can be primarily classified as "Databases" tools.

"Multi-core Parallel Processing" is the primary reason why developers consider Citus over the competitors, whereas "Reliable and easy to use" was stated as the key factor in picking Microsoft SQL Server.

Citus is an open source tool with 3.64K GitHub stars and 273 GitHub forks. Here's a link to Citus's open source repository on GitHub.

- No public GitHub repository available -

What is Citus?

It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.

What is Microsoft SQL Server?

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.
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Why do developers choose Citus?
Why do developers choose Microsoft SQL Server?

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    What tools integrate with Citus?
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    What are some alternatives to Citus and Microsoft SQL Server?
    TimescaleDB
    TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
    CockroachDB
    Cockroach Labs is the company building CockroachDB, an open source, survivable, strongly consistent, scale-out SQL database.
    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 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.
    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.
    See all alternatives
    Decisions about Citus and Microsoft SQL Server
    Dan Robinson
    Dan Robinson
    at Heap, Inc. · | 16 upvotes · 53.5K views
    atHeapHeap
    Citus
    Citus
    PostgreSQL
    PostgreSQL
    #Databases
    #DataStores

    PostgreSQL was an easy early decision for the founding team. The relational data model fit the types of analyses they would be doing: filtering, grouping, joining, etc., and it was the database they knew best.

    Shortly after adopting PG, they discovered Citus, which is a tool that makes it easy to distribute queries. Although it was a young project and a fork of Postgres at that point, Dan says the team was very available, highly expert, and it wouldn’t be very difficult to move back to PG if they needed to.

    The stuff they forked was in query execution. You could treat the worker nodes like regular PG instances. Citus also gave them a ton of flexibility to make queries fast, and again, they felt the data model was the best fit for their application.

    #DataStores #Databases

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    Dan Robinson
    Dan Robinson
    at Heap, Inc. · | 14 upvotes · 45.3K views
    atHeapHeap
    Heap
    Heap
    Node.js
    Node.js
    Kafka
    Kafka
    PostgreSQL
    PostgreSQL
    Citus
    Citus
    #FrameworksFullStack
    #Databases
    #MessageQueue

    At Heap, we searched for an existing tool that would allow us to express the full range of analyses we needed, index the event definitions that made up the analyses, and was a mature, natively distributed system.

    After coming up empty on this search, we decided to compromise on the “maturity” requirement and build our own distributed system around Citus and sharded PostgreSQL. It was at this point that we also introduced Kafka as a queueing layer between the Node.js application servers and Postgres.

    If we could go back in time, we probably would have started using Kafka on day one. One of the biggest benefits in adopting Kafka has been the peace of mind that it brings. In an analytics infrastructure, it’s often possible to make data ingestion idempotent.

    In Heap’s case, that means that, if anything downstream from Kafka goes down, we won’t lose any data – it’s just going to take a bit longer to get to its destination. We also learned that you want the path between data hitting your servers and your initial persistence layer (in this case, Kafka) to be as short and simple as possible, since that is the surface area where a failure means you can lose customer data. We learned that it’s a very good fit for an analytics tool, since you can handle a huge number of incoming writes with relatively low latency. Kafka also gives you the ability to “replay” the data flow: it’s like a commit log for your whole infrastructure.

    #MessageQueue #Databases #FrameworksFullStack

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    Amazon ElastiCache
    Amazon ElastiCache
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    AWS Elastic Load Balancing (ELB)
    AWS Elastic Load Balancing (ELB)
    Memcached
    Memcached
    Redis
    Redis
    Python
    Python
    AWS Lambda
    AWS Lambda
    Amazon RDS
    Amazon RDS
    Microsoft SQL Server
    Microsoft SQL Server
    MariaDB
    MariaDB
    Amazon RDS for PostgreSQL
    Amazon RDS for PostgreSQL
    Rails
    Rails
    Ruby
    Ruby
    Heroku
    Heroku
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk

    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

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    Interest over time
    Reviews of Citus and Microsoft SQL Server
    No reviews found
    How developers use Citus and Microsoft SQL Server
    Avatar of Brillium, Inc.
    Brillium, Inc. uses Microsoft SQL ServerMicrosoft SQL Server

    We've always counted on SQL Server as our database backend. It has served us well over the years. It isn't the cheapest part of our stack, but with the plethora of tools provided by 3rd parties, we have found an incredible and scalable method of keeping our data available and easy to maintain.

    Avatar of Andrew Miller
    Andrew Miller uses Microsoft SQL ServerMicrosoft SQL Server

    Defacto, industry standard for backend relational databases. Entity Framework makes designing, migrating & maintaining SQL Server databases a breeze. LocalDB is especially helpful during development.

    Avatar of David Flynn
    David Flynn uses Microsoft SQL ServerMicrosoft SQL Server

    Our core systems that we integrate with are using SQL Server 2012 / 2016 database servers. We use database views on core system databases to help build our domain model.

    Avatar of Olo
    Olo uses Microsoft SQL ServerMicrosoft SQL Server

    Main transactional database. SQL Server 2012 Enterprise with AlwaysOn Availability Groups for high availability and disaster recovery.

    Avatar of Arbor Health, LLC
    Arbor Health, LLC uses Microsoft SQL ServerMicrosoft SQL Server

    Managing script output and input, as well as data cleansing.

    How much does Citus cost?
    How much does Microsoft SQL Server cost?
    Pricing unavailable