iox-community
ballista
| iox-community | ballista | |
|---|---|---|
| 3 | 20 | |
| 35 | 2,238 | |
| - | - | |
| 8.6 | 9.3 | |
| 3 months ago | about 3 years ago | |
| Shell | Rust | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
iox-community
-
InfluxDB 3.0 Infinite Observability with qryn-iox
Watch out for the AGPL minio <https://github.com/metrico/iox-community/blob/155a14bb5e8e32...> the almost certainly AGPL grafana <https://github.com/grafana/grafana/blob/v10.1.1/LICENSE> and always eye anyone who uses :latest images with healthy suspicion
That said, influx_iox itself appears to be Apache 2 (and/or MIT?) https://github.com/influxdata/influxdb_iox/blob/main/LICENSE...
-
InfluxDB Cloud shuts down in Belgium; some weren't notified before data deletion
For anyone interested, Self-Hosted InfluxDB3.0 IOx builds & containers are available here: https://github.com/metrico/iox-community
- Influxdb 3.0 “iox” static builds and slim Docker images for early integrators
ballista
- Ballista: Distributed compute platform implemented in Rust using Apache Arrow.
-
Open source contributions for a Data Engineer?
His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.
-
Best format to use for DataFrames in Rust and Python?
https://github.com/ballista-compute/ballista/blob/main/rust/executor/src/flight_service.rs#L193-L228
-
I wrote one of the fastest DataFrame libraries
I'm guessing Polars and Ballista (https://github.com/ballista-compute/ballista) have different goals, but I don't know enough about either to say what those might be. Does anyone know enough about either to explain the differences?
-
Introducing Kamu - World's first global collaborative data pipeline
In your article you mention looking for a faster data engine, have you looked at Ballista https://github.com/ballista-compute/ballista? It’s pretty young but it uses the Apache Arrow memory model and the maintainer did a bunch of work on Apache Spark I believe.
-
Rust for DE?
https://github.com/ballista-compute/ballista is also a cool project worth checking out.
-
Julia: A Post-Mortem
It’s mostly a personal favourite, but once Ballista [1] gets a bit more developed, I expect we’ll tear out our Java/Spark pipelines and replace them with that.
The ML ecosystem in Rust is a bit underdeveloped at the moment, but work is ticking along on packages like Linfa and SmartCore, so maybe it’ll get there? In my field I’m mostly about it’s potential for correct, high-performance data pipelines that are straightforward to write in reasonable time, and hopefully a model-serving framework: I hate that so many of the current tools require annotating and shipping Python when really model-serving shouldn’t really need any Python code.
[1] https://github.com/ballista-compute/ballista
- Ballista 0.4.0
-
Why isn't differential dataflow more popular?
I've looked at this and thought it looked amazing, but also haven't used it for anything. Some thoughts...
Rust is a blessing and curse. I seems like the obvious choice for data pipelines, but everything big currently exists in Java and the small stuff is in Javascript, Python or R. Maybe this will slowly change, but it's a big ship to turn. I'm hopeful that tools like this and Balista [1] will eventually get things moving.
Since the Rust community is relatively small, language bindings would be very helpful. Being able to configure pipelines from Java or Typescript(!) would be great.
Or maybe it's just that this form of computation is too foreign. By the time you need it, the project is so large that it's too late to redesign it to use it. I'm also unclear on how it would handle changing requirements and recomputing new aggregations over old data. Better docs with more convincing examples would be helpful here. The GitHub page showing counting isn't very compelling.
[1] https://github.com/ballista-compute/ballista
- ballista-compute/ballista proof-of-concept distributed compute platform primarily implemented in Rust, using Apache Arrow as the memory model.
What are some alternatives?
influxdb_iox - Pronounced (influxdb eye-ox), short for iron oxide. This is the new core of InfluxDB written in Rust on top of Apache Arrow.
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
ataraxia - Simple and lightweight source-based multi-platform Linux distribution with musl libc.
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
roapi - Create full-fledged APIs for slowly moving datasets without writing a single line of code.
delta-rs - A native Rust library for Delta Lake, with bindings into Python
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
dagster - An orchestration platform for the development, production, and observation of data assets.
sabotage - a radical and experimental distribution based on musl libc and busybox
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.