Citus vs MarkLogic

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Citus
Citus

28
26
+ 1
8
MarkLogic
MarkLogic

13
17
+ 1
23
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Citus vs MarkLogic: 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, MarkLogic is detailed as "Schema-agnostic Enterprise NoSQL database technology, coupled w/ powerful search & flexible application services". MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.

Citus and MarkLogic 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, MarkLogic provides the following key features:

  • Search and Query
  • ACID Transactions
  • High Availability and Disaster Recovery

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

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 MarkLogic?

MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.
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Why do developers choose Citus?
Why do developers choose MarkLogic?

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      What are some alternatives to Citus and MarkLogic?
      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 MarkLogic
      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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