Citus vs Riak

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Citus
Citus

34
39
+ 1
8
Riak
Riak

79
86
+ 1
35
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Citus vs Riak: 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; Riak: A distributed, decentralized data storage system. Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your client can reach one Riak server, it should be able to write data. In most failure scenarios, the data you want to read should be available, although it may not be the most up-to-date version of that data.

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

"Multi-core Parallel Processing" is the primary reason why developers consider Citus over the competitors, whereas "High Performance " was stated as the key factor in picking Riak.

Citus and Riak are both open source tools. Citus with 3.64K GitHub stars and 273 forks on GitHub appears to be more popular than Riak with 3.24K GitHub stars and 530 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 Riak?

Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your client can reach one Riak server, it should be able to write data. In most failure scenarios, the data you want to read should be available, although it may not be the most up-to-date version of that data.
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Why do developers choose Citus?
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      What are some alternatives to Citus and Riak?
      CockroachDB
      It allows you to deploy a database on-prem, in the cloud or even across clouds, all as a single store. It is a simple and straightforward bridge to your future, cloud-based data architecture.
      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.
      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 Riak
      Dan Robinson
      Dan Robinson
      at Heap, Inc. · | 16 upvotes · 57.5K 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 · 97K 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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