aa4d10eef7
* feat/inverted-index-cache: Update dependencies and add caching for inverted index reader - Updated `atomic` to 0.6.0 and `uuid` to 1.9.1 in `Cargo.lock`. - Added `moka` and `uuid` dependencies in `Cargo.toml`. - Introduced `seek_read` method in `InvertedIndexBlobReader` for common seek and read operations. - Added `cache.rs` module to implement caching for inverted index reader using `moka`. - Updated `async-compression` to 0.4.11 in `puffin/Cargo.toml`. * feat/inverted-index-cache: Refactor InvertedIndexReader and Add Index Cache Support - Refactored `InvertedIndexReader` to include `seek_read` method and default implementations for `fst` and `bitmap`. - Implemented `seek_read` in `InvertedIndexBlobReader` and `CachedInvertedIndexBlobReader`. - Introduced `InvertedIndexCache` in `CacheManager` and `SstIndexApplier`. - Updated `SstIndexApplierBuilder` to accept and utilize `InvertedIndexCache`. - Added `From<FileId> for Uuid` implementation. * feat/inverted-index-cache: Update Cargo.toml and refactor SstIndexApplier - Moved `uuid.workspace` entry in Cargo.toml for better organization. * feat/inverted-index-cache: Refactor InvertedIndexCache to use type alias for Arc - Replaced `Arc<InvertedIndexCache>` with `InvertedIndexCacheRef` type alias. * feat/inverted-index-cache: Add Prometheus metrics and caching improvements for inverted index - Introduced `prometheus` and `puffin` dependencies for metrics. * feat/inverted-index-cache: Refactor InvertedIndexReader and Cache handling - Simplified `InvertedIndexReader` trait by removing seek-related comments. * feat/inverted-index-cache: Add configurable cache sizes for inverted index metadata and content - Introduced `index_metadata_size` and `index_content_size` in `CacheManagerBuilder`. * feat/inverted-index-cache: Refactor and optimize inverted index caching - Removed `metrics.rs` and integrated cache metrics into `index.rs`. * feat/inverted-index-cache: Remove unused dependencies from Cargo.lock and Cargo.toml - Removed `moka`, `prometheus`, and `puffin` dependencies from both Cargo.lock and Cargo.toml. * feat/inverted-index-cache: Replace Uuid with FileId in CachedInvertedIndexBlobReader - Updated `file_id` type from `Uuid` to `FileId` in `CachedInvertedIndexBlobReader` and related methods. * feat/inverted-index-cache: Refactor cache configuration for inverted index - Moved `inverted_index_metadata_cache_size` and `inverted_index_cache_size` from `MitoConfig` to `InvertedIndexConfig`. * feat/inverted-index-cache: Remove unnecessary conversion of `file_id` in `SstIndexApplier` - Simplified the initialization of `CachedInvertedIndexBlobReader` by removing the redundant `into()` conversion for `file_id`.
Cloud-scale, Fast and Efficient Time Series Database
Introduction
GreptimeDB is an open-source time-series database focusing on efficiency, scalability, and analytical capabilities. Designed to work on infrastructure of the cloud era, GreptimeDB benefits users with its elasticity and commodity storage, offering a fast and cost-effective alternative to InfluxDB and a long-term storage for Prometheus.
Why GreptimeDB
Our core developers have been building time-series data platforms for years. Based on our best-practices, GreptimeDB is born to give you:
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Easy horizontal scaling
Seamless scalability from a standalone binary at edge to a robust, highly available distributed cluster in cloud, with a transparent experience for both developers and administrators.
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Analyzing time-series data
Query your time-series data with SQL and PromQL. Use Python scripts to facilitate complex analytical tasks.
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Cloud-native distributed database
Fully open-source distributed cluster architecture that harnesses the power of cloud-native elastic computing resources.
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Performance and Cost-effective
Flexible indexing capabilities and distributed, parallel-processing query engine, tackling high cardinality issues down. Optimized columnar layout for handling time-series data; compacted, compressed, and stored on various storage backends, particularly cloud object storage with 50x cost efficiency.
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Compatible with InfluxDB, Prometheus and more protocols
Widely adopted database protocols and APIs, including MySQL, PostgreSQL, and Prometheus Remote Storage, etc. Read more.
Try GreptimeDB
1. GreptimePlay
Try out the features of GreptimeDB right from your browser.
2. GreptimeCloud
Start instantly with a free cluster.
3. Docker Image
To install GreptimeDB locally, the recommended way is via Docker:
docker pull greptime/greptimedb
Start a GreptimeDB container with:
docker run --rm --name greptime --net=host greptime/greptimedb standalone start
Read more about Installation on docs.
Getting Started
Build
Check the prerequisite:
- Rust toolchain (nightly)
- Protobuf compiler (>= 3.15)
- Python toolchain (optional): Required only if built with PyO3 backend. More detail for compiling with PyO3 can be found in its documentation.
Build GreptimeDB binary:
make
Run a standalone server:
cargo run -- standalone start
Extension
Dashboard
SDK
- GreptimeDB Go Ingester
- GreptimeDB Java Ingester
- GreptimeDB C++ Ingester
- GreptimeDB Erlang Ingester
- GreptimeDB Rust Ingester
- GreptimeDB JavaScript Ingester
Grafana Dashboard
Our official Grafana dashboard is available at grafana directory.
Project Status
The current version has not yet reached General Availability version standards. In line with our Greptime 2024 Roadmap, we plan to achieve a production-level version with the update to v1.0 in August. [Join Force]
Community
Our core team is thrilled to see you participate in any ways you like. When you are stuck, try to ask for help by filling an issue with a detailed description of what you were trying to do and what went wrong. If you have any questions or if you would like to get involved in our community, please check out:
- GreptimeDB Community on Slack
- GreptimeDB GitHub Discussions forum
- Greptime official website
In addition, you may:
License
GreptimeDB uses the Apache License 2.0 to strike a balance between open contributions and allowing you to use the software however you want.
Contributing
Please refer to contribution guidelines and internal concepts docs for more information.
Acknowledgement
Special thanks to all the contributors who have propelled GreptimeDB forward. For a complete list of contributors, please refer to AUTHOR.md.
- GreptimeDB uses Apache Arrow™ as the memory model and Apache Parquet™ as the persistent file format.
- GreptimeDB's query engine is powered by Apache Arrow DataFusion™.
- Apache OpenDAL™ gives GreptimeDB a very general and elegant data access abstraction layer.
- GreptimeDB's meta service is based on etcd.
- GreptimeDB uses RustPython for experimental embedded python scripting.