The rise of the time-series database
Which new startups are emerging?
New companies see an opportunity through focusing on adding the right amount of indexing and post-processing to make queries fast and effective.
InfluxDB began as an open source project and is now available as either a standalone installation or an elastic serverless option from the InfluxDB Cloud. The company’s Flux query language simplifies tasks like computing the moving averages of the data stream. The language is functional and designed to be easily composable so queries can be built up from other queries.
Timescale DB is a separate engine that is fully integrated with PostgreSQL for tasks that might need traditional relational tables and time-series data. The company’s benchmarks boast of speeding up ingesting data by a factor of 20. The queries for searching the data or identifying significant values like maxima can be thousands of times faster.
Prometheus stores all data with a timestamp automatically and provides a set of standard queries for analyzing changes in the data. Its PromQL bears some resemblance to the emerging data format for queries, GraphQL. This makes it simple for developers to set up alerts that could be triggered by data anomalies.
Redis created a special module for ingesting the rapid data flows into the database. The indexing routines build a set of average statistics about the data’s evolution. To save memory, it can also downsample or aggregate the elements.
The cloud companies are also adding data storage services for this market. AWS, for example, launched its Timestream service, a tool optimized for IoT data.
https://venturebeat.com/2021/01/15/database-trends-the-rise-of-the-time-series-database/
