Cassandra vs Citus

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Cassandra
Cassandra

2.5K
2K
+ 1
454
Citus
Citus

32
34
+ 1
8
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Cassandra vs Citus: What are the differences?

Cassandra: A partitioned row store. Rows are organized into tables with a required primary key. 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; 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.

Cassandra and Citus belong to "Databases" category of the tech stack.

"Distributed" is the top reason why over 96 developers like Cassandra, while over 3 developers mention "Multi-core Parallel Processing" as the leading cause for choosing Citus.

Cassandra and Citus are both open source tools. Cassandra with 5.27K GitHub stars and 2.35K forks on GitHub appears to be more popular than Citus with 3.64K GitHub stars and 273 GitHub forks.

What is 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.

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.
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Why do developers choose Cassandra?
Why do developers choose Citus?

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      What are some alternatives to Cassandra and Citus?
      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.
      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 is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
      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
      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.
      See all alternatives
      Decisions about Cassandra and Citus
      Dan Robinson
      Dan Robinson
      at Heap, Inc. · | 16 upvotes · 56.7K views
      atHeapHeap
      PostgreSQL
      PostgreSQL
      Citus
      Citus
      #DataStores
      #Databases

      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 · 66.9K views
      atHeapHeap
      Heap
      Heap
      Citus
      Citus
      PostgreSQL
      PostgreSQL
      Kafka
      Kafka
      Node.js
      Node.js
      #MessageQueue
      #Databases
      #FrameworksFullStack

      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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      Interest over time
      Reviews of Cassandra and Citus
      No reviews found
      How developers use Cassandra and Citus
      Avatar of Soundcloud
      Soundcloud uses CassandraCassandra

      Stitch is a wrapper around a Cassandra database. It has a web application that provides read-access to the counts through an HTTP API. The counts are written to Cassandra in two distinct ways, and it's possible to use either or both of them:

      • Real-time: For real-time updates, Stitch has a processor application that handles a stream of events coming from a broker and increments the appropriate counts in Cassandra.

      • Batch: The batch part is a MapReduce job running on Hadoop that reads event logs, calculates the overall totals, and bulk loads this into Cassandra.

      Avatar of Vital Labs, Inc.
      Vital Labs, Inc. uses CassandraCassandra

      Cassandra is our data management workhorse. It handles all our key-value services, supports time-series data storage and retrieval, securely stores all our audit trails, and backs our Datomic database.

      Avatar of SocialCops
      SocialCops uses CassandraCassandra

      While we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.

      Avatar of ShareThis
      ShareThis uses CassandraCassandra

      We are using Cassandra in a few of our apps. One of them is as a count service application to track the number of shares, clicks.. etc

      Avatar of Kaiko
      Kaiko uses CassandraCassandra
      How much does Cassandra cost?
      How much does Citus cost?
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