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EdgeDB vs TimescaleDB: What are the differences?
What is EdgeDB? The Next Generation Object-Relational Database. An object-relational database that stores and describes the data as strongly typed objects and relationships between them.
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.
EdgeDB and TimescaleDB can be categorized as "Databases" tools.
Some of the features offered by EdgeDB are:
- Strict, strongly typed schema
- Powerful and clean query language
- Ability to easily work with complex hierarchical data
On the other hand, TimescaleDB provides the following key features:
- Packaged as a PostgreSQL extension
- Full ANSI SQL
- JOINs (e.g., across PostgreSQL tables)
EdgeDB 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 EdgeDB with 3.01K GitHub stars and 64 GitHub forks.
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 EdgeDB
Pros of TimescaleDB
- Open source8
- Easy Query Language7
- Time-series data analysis6
- Established postgresql API and support5
- Reliable4
- Postgres integration2
- Fast and scalable2
- High-performance2
- Chunk-based compression2
- Paid support for automatic Retention Policy2
- Case studies1
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Cons of EdgeDB
Cons of TimescaleDB
- Licensing issues when running on managed databases5
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What is EdgeDB?
What is TimescaleDB?
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