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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.
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
PostgreSQLWe 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.
DruidDruid 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.
Google BigQueryif 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.
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
Pros of Clickhouse
- Fast, very very fast15
- Good compression ratio10
- Horizontally scalable5
- RESTful4
- Utilizes all CPU resources4
- Great CLI4
- Has no transactions3
- Great number of SQL functions3
- Buggy2
- Open-source2
- In IDEA data import via HTTP interface not working1
- Server crashes its normal :(1
- Highly available1
- Flexible compression options1
- Flexible connection options1
- ODBC1
Pros of TimescaleDB
- Open source8
- Easy Query Language7
- Time-series data analysis6
- Established postgresql API and support5
- Reliable4
- Paid support for automatic Retention Policy2
- Fast and scalable2
- Chunk-based compression2
- Postgres integration2
- High-performance2
- Case studies1
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Cons of Clickhouse
- Slow insert operations3
Cons of TimescaleDB
- Licensing issues when running on managed databases4
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