kamu is done by a set of plug-in . This allows us to integrate many mature data processing frameworks, use them to transform data, while kamu coordinates all the advanced aspects of processing, tracks , ensures , etc.
The opinions below relate to ODF adapters implemented using the described engine, not the engines themselves. Engines featured here all have very different designs, making them more suitable for some tasks than others. Information below is intended as a rough guidance for engine choice within ODF and should be taken with a big grain of salt.
Known Engine Implementations
| Name | Technology | Query Dialect | Links | Notes |
|---|---|---|---|---|
spark | Apache Spark | Spark Streaming SQL with Sedona GIS Extensions | Repository Image | Spark is used in kamu-cli for all data ingestion and is default (but not only) engine for SQL shell. Spark is also used in combination with Livy to query data from embedded Jupyter Notebooks. It’s currently the only engine that supports GIS data via Apache Sedona integration. |
flink | Apache Flink | Flink Streaming SQL | Repository Image | Flink has most mature support for stream processing, like stream-to-stream and stream-to-table joins, windowed aggregations, watermarks etc. It’s thus the recommended engine for most derivative datasets. |
datafusion | Apache Arrow DataFusion | DataFusion SQL | Repository Image | An extremely fast and low-footprint batch processing engine. DataFusion is also embedded into kamu-cli and is used for data ingestion, ad-hoc SQL queries, and SQL console. Despite being a batch-only engine it can still be used in pipelines for simple map/filter/union operations where temporal semantics is not needed. |
risingwave | RisingWave | RisingWave SQL | Repository Image | Experimental stream process engine. There are ongoing attempts to add stream processing functionality. DataFusion is also embedded into kamu-cli and is used for executing ad-hoc SQL queries. We are aiming to make data ingest functionality use DataFusion for most cases instead of Spark. |
Schema Support
| Feature | kamu | Spark | Flink | DataFusion | RisingWave |
|---|---|---|---|---|---|
| Basic types | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| Decimal type | ✔️ | ✔️ | ✔️** | ✔️ | ✔️ |
| Nested types | ✔️* | ✔️ | ❌ | ❔*** | ❔*** |
| GIS types | ✔️* | ✔️ | ❌ | ❌ | ❌ |
Operation Types
Note that ODF always operates in , this all temporal aggregations and joins have to be supported by the engine in event-time processing mode.| Feature | Spark | Flink | DataFusion | RisingWave |
|---|---|---|---|---|
| Filter | ✔️ | ✔️ | ✅ | ✅ |
| Map | ✔️ | ✔️ | ✅ | ✅ |
| Aggregation: Window functions | ❌ | ✔️ | ❌ | ✅ |
| Aggregation: Tumbling windows | ❌ | ✔️ | ❌ | ✅ |
| Aggregation: Top-N | ❌ | ❔ | ❌ | ✅ |
| Join: Windowed | ❌ | ✔️ | ❌ | ❔ |
| Join: Temporal Table | ❌ | ✔️ | ❌ | ❔ |
| GIS extensions | ✅ | ❌ | ❌ | ❌ |
✅ - supported and recommended
❌ - not supported
❔ - engine capability exists but requires more integration testing