arrow-datafusion
arrow-rs
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arrow-datafusion | arrow-rs | |
---|---|---|
55 | 16 | |
4,924 | 2,176 | |
4.9% | 5.1% | |
9.9 | 9.8 | |
7 days ago | 3 days ago | |
Rust | Rust | |
Apache License 2.0 | 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.
arrow-datafusion
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Velox: Meta's Unified Execution Engine [pdf]
Python's Substrait seems like the biggest/most-used competitor-ish out there. I'd love some compare & contrast; my sense is that Substrait has a smaller ambition, and more wants to be a language for talking about execution rather than a full on execution engine. https://github.com/substrait-io/substrait
We can also see from the DataFusion discussion that they too see themselves as a bit of a Velox competitor. https://github.com/apache/arrow-datafusion/discussions/6441
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What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
Agree, substrait is a really cool project! Related: if you like substrait you might want to check out datafusion too. The project is a query execution engine built on top of Apache Arrow (with SQL parser, query planner & optimizer, execution engine, extensible user defined functions, among others) and it implements a substrait provider and consumer: https://github.com/apache/arrow-datafusion/tree/main/datafus...
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DuckDB performance improvements with the latest release
The draft contains some preliminary benchmark results, comparing it to DuckDB.
https://github.com/apache/arrow-datafusion/issues/6782
- Apache Arrow DataFusion
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GlareDB: An open source SQL database to query and analyze distributed data
Apache Arrow is a pretty common memory structure these days. Datafusion is an open query engine built in Rust started by Andy Grove.
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DuckDB 0.8.0
DuckDB is a great piece of software if you are
If you are looking for a query engine implemented in a safe language (Rust) I definitely suggest checking out DataFusion. It is comparable to DuckDB in performance, has all the standard built in SQL functionality, and is extensible in pretty much all areas (query language, data formats, catalogs, user defined functions, etc)
https://arrow.apache.org/datafusion/
Disclaimer I am a maintainer of DataFusion
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Data Engineering with Rust
https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb
- Polars: Computing a new column from multiple columns - there must be a better way
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Bridging Async and Sync Rust Code - A lesson learned while working with Tokio
Problem comes when you want to do this inside an async context since we couldn't block an async task. https://users.rust-lang.org/t/sync-function-invoking-async/43364/6 You might need to do it in another runtime/thread. It is not recommended to do this, but sometimes it is unavoidable while implementing a third-party trait. https://github.com/apache/arrow-datafusion/issues/3777 However, I believe this isn't a problem particular to tokio, or any specific runtime.
- Using Rust to write a Data Pipeline. Thoughts. Musings.
arrow-rs
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Rkyv: Rkyv zero-copy deserialization framework for rust
https://github.com/djkoloski/rust_serialization_benchmark
Apache/arrow-rs: https://github.com/apache/arrow-rs
From https://arrow.apache.org/faq/ :
> How does Arrow relate to Flatbuffers?
> Flatbuffers is a low-level building block for binary data serialization. It is not adapted to the representation of large, structured, homogenous data, and does not sit at the right abstraction layer for data analysis tasks.
> Arrow is a data layer aimed directly at the needs of data analysis, providing a comprehensive collection of data types required to analytics, built-in support for โnullโ values (representing missing data), and an expanding toolbox of I/O and computing facilities.
> The Arrow file format does use Flatbuffers under the hood to serialize schemas and other metadata needed to implement the Arrow binary IPC protocol, but the Arrow data format uses its own representation for optimal access and computation
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Polars: Company Formation Announcement
One of the interesting components of Polars that I've been watching is the use of the Apache Arrow memory format, which is a standard layout for data in memory that enables processing (querying, iterating, calculating, etc) in a language agnostic way, in particular without having to copy/convert it into the local object format first. This enables cross-language data access by mmaping or transferring a single buffer, with zero [de]serialization overhead.
For some history, there's has been a bit of contention between the official arrow-rs implementation and the arrow2 implementation created by the polars team which includes some extra features that they find important. I think the current status is that everyone agrees that having two crates that implement the same standard is not ideal, and they are working to port any necessary features to the arrow-rs crate and plan on eventually switching to it and deprecating arrow2. But that's not easy.
https://github.com/apache/arrow-rs/issues/1176
https://github.com/jorgecarleitao/arrow2/pull/1476
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InfluxDB 3.0 System Architecture
It's built around the arrow-rs library, which we've contributed to significantly: https://github.com/apache/arrow-rs
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best cache type for 5gb size tables
For loading Parquet in memory, probably worth a look at arrow-rs.
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The state of Apache Avro in Rust
From what I've seen, most of the Rust community seems to be adopting Apache Arrow as the go-to for data processing. It has strong community support and good interoperability with many cross-language tools. It is natively a columnar format. If row-oriented is a must for your use case, consider looking into alternatives like gRPC that might better suit your needs.
- Arrow-Rs - Official Rust implementation of Apache Arrow
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Apache Arrow Feature Parity Timeline?
That matrix doesn't seem up to date. For example looking at the rust crate it does seem to support things like map, float16, and IPC. The changelog shows an impressive development pace.
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Apache Arrow Flight SQL: Accelerating Database Access
Oh, and for anyone interested in pitching in on the Rust implementation, there's an issue logged here along with some discussion: https://github.com/apache/arrow-rs/issues/1323
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February 2022 Rust Apache Arrow and Parquet Highlights
There is more discussion about the decision here: https://github.com/apache/arrow-rs/issues/1120
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Arrow2 0.9 has been released
I'm still not sure how this differs from https://github.com/apache/arrow-rs. What does transmute even mean?
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
ClickHouse - ClickHouseยฎ is a free analytics DBMS for big data
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
db-benchmark - reproducible benchmark of database-like ops
arrow2 - Transmute-free Rust library to work with the Arrow format
databend - ๐๐ฎ๐๐ฎ, ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ & ๐๐. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
byo-sql - An in-memory SQL database in Rust.
nushell - A new type of shell
duckdb - DuckDB is an in-process SQL OLAP Database Management System
parquet2 - Fastest and safest Rust implementation of parquet. `unsafe` free. Integration-tested against pyarrow