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OpenTSDB vs TimescaleDB: What are the differences?
What is OpenTSDB? A scalable time series database. It is a distributed, scalable time series database to store, index & serve metrics collected from computer systems at a large scale. It can store and serve massive amounts of time series data without losing granularity.
What is 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.
OpenTSDB and TimescaleDB can be categorized as "Databases" tools.
OpenTSDB and TimescaleDB are both open source tools. It seems that TimescaleDB with 7.39K GitHub stars and 393 forks on GitHub has more adoption than OpenTSDB with 3.81K GitHub stars and 1.11K GitHub forks.
WakaTime, ScreenAware, and AgFlow are some of the popular companies that use TimescaleDB, whereas OpenTSDB is used by Cloudinsight, SAYMON, and Server Density. TimescaleDB has a broader approval, being mentioned in 21 company stacks & 19 developers stacks; compared to OpenTSDB, which is listed in 4 company stacks and 6 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 OpenTSDB
Pros of TimescaleDB
- Open source8
- Easy Query Language7
- Time-series data analysis6
- Established postgresql API and support5
- Reliable4
- Chunk-based compression2
- High-performance2
- Paid support for automatic Retention Policy2
- Postgres integration2
- Fast and scalable2
- Case studies1
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Cons of OpenTSDB
Cons of TimescaleDB
- Licensing issues when running on managed databases5
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What is OpenTSDB?
What is TimescaleDB?
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What companies use OpenTSDB?
What companies use TimescaleDB?
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