How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Beringei is a high performance, in-memory storage engine for time series data.
The main features of facebookincubator/beringei are: Time Series Databases.
Projects with overlapping indexed features include: dalmatinerdb/dalmatinerdb — See gitlab: https://gitlab.com/Project-FiFo/DalmatinerDB/dalmatinerdb. druid-io/druid — Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It… improbable-eng/thanos — Thanos is a CNCF cloud native monitoring tool that provides a highly available and scalable extension to the… kairosdb/kairosdb — Fast scalable time series database. manahl/arctic — High performance datastore for time series and tick data. akumuli/akumuli — Time-series database.
See gitlab: https://gitlab.com/Project-FiFo/DalmatinerDB/dalmatinerdb
Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume
Thanos is a CNCF cloud native monitoring tool that provides a highly available and scalable extension to the Prometheus ecosystem. It functions as a global query engine, a long-term storage system, and a metric downsampler. The project enables a unified interface to aggregate and query metrics across multiple distributed clusters from a single view. It maintains historical data beyond local retention limits by persisting time-series metrics in object storage and eliminates data gaps by merging metrics from redundant server pairs. The system includes capabilities for reducing the resolution o
