Citus vs CrateIO

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Citus
Citus

28
26
+ 1
8
CrateIO
CrateIO

9
14
+ 1
7
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Citus vs CrateIO: What are the differences?

Developers describe Citus as "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. On the other hand, CrateIO is detailed as "The Distributed Database for Docker". Crate is a distributed data store. Simply install Crate directly on your application servers and make the big centralized database a thing of the past. Crate takes care of synchronization, sharding, scaling, and replication even for mammoth data sets.

Citus and CrateIO can be categorized as "Databases" tools.

Some of the features offered by Citus are:

  • Multi-Node Scalable PostgreSQL
  • Built-in Replication and High Availability
  • Real-time Reads/Writes On Multiple Nodes

On the other hand, CrateIO provides the following key features:

  • Familiar SQL syntax
  • Semi-structured data
  • High availability, resiliency, and scalability in a distributed design

"Multi-core Parallel Processing" is the top reason why over 3 developers like Citus, while over 2 developers mention "Simplicity" as the leading cause for choosing CrateIO.

Citus and CrateIO are both open source tools. Citus with 3.64K GitHub stars and 273 forks on GitHub appears to be more popular than CrateIO with 2.49K GitHub stars and 333 GitHub forks.

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 CrateIO?

Crate is a distributed data store. Simply install Crate directly on your application servers and make the big centralized database a thing of the past. Crate takes care of synchronization, sharding, scaling, and replication even for mammoth data sets.
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Why do developers choose Citus?
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        What are some alternatives to Citus and CrateIO?
        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 CrateIO
        Dan Robinson
        Dan Robinson
        at Heap, Inc. · | 16 upvotes · 53.6K views
        atHeapHeap
        Citus
        Citus
        PostgreSQL
        PostgreSQL
        #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 · 46.4K views
        atHeapHeap
        Heap
        Heap
        Node.js
        Node.js
        Kafka
        Kafka
        PostgreSQL
        PostgreSQL
        Citus
        Citus
        #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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