Citus vs PipelineDB

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Citus
Citus

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39
+ 1
8
PipelineDB
PipelineDB

6
13
+ 1
0
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Citus vs PipelineDB: What are the differences?

What is Citus? Worry-free Postgres for SaaS. 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 PipelineDB? The Streaming SQL Database. PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.

Citus and PipelineDB can be primarily classified 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, PipelineDB provides the following key features:

  • No Application Code
  • Runs on PostgreSQL
  • Eliminate ETL

Citus is an open source tool with 3.99K GitHub stars and 309 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 PipelineDB?

PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.
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        What are some alternatives to Citus and PipelineDB?
        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.
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        Decisions about Citus and PipelineDB
        Dan Robinson
        Dan Robinson
        at Heap, Inc. · | 16 upvotes · 57.4K 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 · 96.5K 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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