Citus vs TimescaleDB

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Citus

45
81
+ 1
9
TimescaleDB

159
268
+ 1
41
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Citus vs TimescaleDB: 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, TimescaleDB is detailed as "Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension". TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.

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

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, TimescaleDB provides the following key features:

  • Packaged as a PostgreSQL extension
  • Full ANSI SQL
  • JOINs (e.g., across PostgreSQL tables)

Citus and TimescaleDB are both open source tools. It seems that TimescaleDB with 7.28K GitHub stars and 385 forks on GitHub has more adoption than Citus with 3.64K GitHub stars and 273 GitHub forks.

Advice on Citus and TimescaleDB
Umair Iftikhar
Technical Architect at Vappar · | 3 upvotes · 140K views
Needs advice
on
TimescaleDBTimescaleDBDruidDruid
and
CassandraCassandra

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Replies (1)
Recommends
MongoDBMongoDB

Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.

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Needs advice
on
TimescaleDBTimescaleDBMongoDBMongoDB
and
InfluxDBInfluxDB

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

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Replies (3)
Yaron Lavi
Recommends
PostgreSQLPostgreSQL

We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.

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Recommends
DruidDruid

Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.

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Ankit Malik
Software Developer at CloudCover · | 3 upvotes · 84.9K views
Recommends
Google BigQueryGoogle BigQuery

if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.

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Decisions about Citus and TimescaleDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 57.7K views

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

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Pros of Citus
Pros of TimescaleDB
  • 5
    Multi-core Parallel Processing
  • 2
    Drop-in PostgreSQL replacement
  • 2
    Distributed with Auto-Sharding
  • 8
    Open source
  • 7
    Easy Query Language
  • 6
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
  • 2
    Paid support for automatic Retention Policy
  • 2
    Fast and scalable
  • 2
    Chunk-based compression
  • 2
    Postgres integration
  • 2
    High-performance
  • 1
    Case studies

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Cons of Citus
Cons of TimescaleDB
    Be the first to leave a con
    • 3
      Licensing issues when running on managed databases

    Sign up to add or upvote consMake informed product decisions

    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 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.

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    What companies use TimescaleDB?
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    What tools integrate with Citus?
    What tools integrate with TimescaleDB?

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    What are some alternatives to Citus and TimescaleDB?
    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.
    Apache Aurora
    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
    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.
    Vitess
    It is a database solution for deploying, scaling and managing large clusters of MySQL instances. It’s architected to run as effectively in a public or private cloud architecture as it does on dedicated hardware. It combines and extends many important MySQL features with the scalability of a NoSQL database.
    Clickhouse
    It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
    See all alternatives