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Clickhouse

251
340
+ 1
58
TimescaleDB

162
277
+ 1
41
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Clickhouse vs TimescaleDB: What are the differences?

Clickhouse: A column-oriented database management system. 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; TimescaleDB: 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.

Clickhouse and TimescaleDB can be primarily classified as "Databases" tools.

TimescaleDB is an open source tool with 7.39K GitHub stars and 393 GitHub forks. Here's a link to TimescaleDB's open source repository on GitHub.

According to the StackShare community, TimescaleDB has a broader approval, being mentioned in 21 company stacks & 19 developers stacks; compared to Clickhouse, which is listed in 22 company stacks and 11 developer stacks.

Advice on Clickhouse and TimescaleDB
Needs advice
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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 · 97.3K 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 Clickhouse and TimescaleDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 61.5K 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 Clickhouse
Pros of TimescaleDB
  • 15
    Fast, very very fast
  • 10
    Good compression ratio
  • 5
    Horizontally scalable
  • 4
    RESTful
  • 4
    Utilizes all CPU resources
  • 4
    Great CLI
  • 3
    Has no transactions
  • 3
    Great number of SQL functions
  • 2
    Buggy
  • 2
    Open-source
  • 1
    In IDEA data import via HTTP interface not working
  • 1
    Server crashes its normal :(
  • 1
    Highly available
  • 1
    Flexible compression options
  • 1
    Flexible connection options
  • 1
    ODBC
  • 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 Clickhouse
Cons of TimescaleDB
  • 3
    Slow insert operations
  • 4
    Licensing issues when running on managed databases

Sign up to add or upvote consMake informed product decisions

- No public GitHub repository available -

What is 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.

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 Clickhouse?
What companies use TimescaleDB?
See which teams inside your own company are using Clickhouse or TimescaleDB.
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What tools integrate with Clickhouse?
What tools integrate with TimescaleDB?

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What are some alternatives to Clickhouse and TimescaleDB?
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.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
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.
InfluxDB
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
See all alternatives