| ## RoadMap |
| |
| ### v0.1.0 |
| |
| - [x] Standalone version, local storage |
| - [x] Analytical storage format |
| - [x] Support SQL |
| |
| ### v0.2.0 |
| |
| - [x] Distributed version supports static topology defined in config file. |
| - [x] The underlying storage supports Aliyun OSS. |
| - [x] WAL implementation based on [OBKV](https://github.com/oceanbase/oceanbase). |
| |
| ### v0.3.0 |
| |
| - [x] Release multi-language clients, including Java, Rust and Python. |
| - [x] Static cluster mode with `HoraeMeta`. |
| - [x] Basic implementation of hybrid storage format. |
| |
| ### v0.4.0 |
| |
| - [x] Implement more sophisticated cluster solution that enhances reliability and scalability of HoraeDB. |
| - [x] Set up nightly benchmark with TSBS. |
| |
| ### v1.0.0-alpha (Released) |
| |
| - [x] Implement Distributed WAL based on `Apache Kafka`. |
| - [x] Release Golang client. |
| - [x] Improve the query performance for classic time series workloads. |
| - [x] Support dynamic migration of tables in cluster mode. |
| |
| ### v1.0.0 |
| |
| - [x] Formally release HoraeDB and its SDKs with all breaking changes finished. |
| - [x] Finish the majority of work related to `Table Partitioning`. |
| - [x] Various efforts to improve query performance, especially for cloud-native cluster mode. These works include: |
| - Multi-tier cache. |
| - Introduce various methods to reduce the data fetched from remote storage (improve the accuracy of SST data filtering). |
| - Increase the parallelism while fetching data from remote object-store. |
| - [x] Improve data ingestion performance by introducing resource control over compaction. |
| |
| ### Afterwards |
| |
| With an in-depth understanding of the time-series database and its various use cases, the majority of our work will focus on performance, reliability, scalability, ease of use, and collaborations with open-source communities. |
| |
| - [ ] Add utilities that support `PromQL`, `InfluxQL`, `OpenTSDB` protocol, and so on. |
| - [ ] Provide basic utilities for operation and maintenance. Specifically, the following are included: |
| - Deployment tools that fit well for cloud infrastructures like `Kubernetes`. |
| - Enhance self-observability, especially critical logs and metrics should be supplemented. |
| - [ ] Develop various tools that ease the use of HoraeDB. For example, data import and export tools. |
| - [ ] Explore new storage formats that will improve performance on hybrid workloads (analytical and time-series workloads). |
