Dandandan commented on a change in pull request #201:
URL: https://github.com/apache/arrow-site/pull/201#discussion_r829324787
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File path: _posts/2022-03-16-datafusion-contrib.md
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+---
+layout: post
+title: Introducing Apache Arrow DataFusion Contrib
+date: "2022-03-16 00:00:00"
+author: pmc
+categories: [release]
+---
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+
+# Introduction
+
+[DataFusion](https://arrow.apache.org/datafusion/) is an extensible query
execution framework, written in Rust, that uses Apache Arrow as its in-memory
format.
+
+When you want to extend your Rust project with [SQL
support](https://arrow.apache.org/datafusion/user-guide/sql/sql_status.html), a
DataFrame API, or the ability to read and process Parquet, JSON, Avro or CSV
data, DataFusion is definitely worth checking out.
+
+DataFusion's SQL, `DataFrame`, and manual `PlanBuilder` API let users access
a sophisticated query optimizer and execution engine capable of fast, resource
efficient, and parallel execution that takes optimal advantage of todays
multicore hardware. Being written in Rust means DataFusion can offer *both* the
safety of dynamic languages as well as the resource efficiency of a compiled
language.
+
+The Apache Arrow DataFusion team is pleased to announce the creation of the
[DataFusion-Contrib](https://github.com/datafusion-contrib) GitHub
organization. This organization was created to provide an unofficial testing
ground for new DataFusion features and as home for extensions to the core
DataFusion project. There are currently five active repositories within this
organization which are summarized below.
+
+## DataFusion-Python
+
+This [project](https://github.com/datafusion-contrib/datafusion-python)
provides Python bindings to the core Rust implementation of DataFusion. It
already provides many core methods for working with DataFusion such as:
+
+- Work with a SQL or DataFrame API for creating query plans, executing them in
a multi-threaded environment, and returning results in Python
+- Creation of User Defined Functions and User Defined Aggregate Functions for
complex operations
+- Provides zero copy between Python and underlying Rust based execution engine
+
+### Upcoming enhancements
+
+Going forward there will be a focus on exposing more features from the
underlying Rust implementation of DataFusion and improving documentation.
+
+### How to install
+
+From `pip`
+
+```bash
+pip install datafusion
+```
+
+Or
+
+```bash
+python -m pip install datafusion
+```
+
+## DataFusion-ObjectStore-S3
+
+This [crate](https://github.com/datafusion-contrib/datafusion-objectstore-s3)
provides an `ObjectStore` implementation for querying data stored in S3 or S3
compatible storage.
+
+- Ability to create `S3FileSystem` to register as part of DataFusion
`ExecutionContext`
+- Register files or directories stored on S3 with `ctx.register_listing_table`
+
+### Upcoming enhancements
+
+The current priority is adding python bindings for `S3FileSystem`. After that
there will be async improvements as DataFusion adopts more of that
functionality and we are looking into S3 Select functionality.
+
+### How to Install
+
+Add the below to your `Cargo.toml` in your Rust Project with DataFusion.
+
+```toml
+datafusion-objectstore-s3 = "0.1.0"
+```
+
+## DataFusion-Substrait
+
+Substrait provides a cross-language serialization format for relational
algebra, based on protocol buffers.
+
+This [crate](https://github.com/datafusion-contrib/datafusion-substrait)
provides a Substrait producer and consumer for DataFusion. A producer converts
a DataFusion logical plan into a Substrait protobuf and a consumer does the
reverse.
+
+Examples of how to use this crate can be found
[here](https://github.com/datafusion-contrib/datafusion-substrait/blob/main/src/lib.rs).
+
+### Potential Use Cases
+
+- Replace current DataFusion protobuf definition used in Ballista for passing
query plan fragments to executors.
+- Make it easier to pass query plans over FFI boundaries, such as from Python
to Rust
+- Allow Apache Calcite query plans to be executed in DataFusion
+
+## DataFusion-BigTable
+
+This [crate](https://github.com/datafusion-contrib/datafusion-bigtable)
implements [Bigtable](https://cloud.google.com/bigtable) as a data source and
physical executor for DataFusion queries. It currently supports both UTF-8
string and 64-bit big-endian signed integers in Bigtable. From a SQL
perspective it supports both simple and composite row keys with `=`, `IN`, and
`BETWEEN` operators as well as projection pushdown. The physical execution for
queries is handled by this crate while any subsequent aggregation, group bys,
or joins are handled in DataFusion.
+
+### Upcoming Enhancements
+
+- Predicate pushdown
+ - Value range
+ - Value Regex
+ - Timestamp range
+- Multithreaded
+- Partition aware execution
+- Production ready
+
+### How to Install
+
+Add the below to your `Cargo.toml` in your Rust Project with DataFusion.
+
+```toml
+datafusion-bigtable = "0.1.0"
+```
+
+## DataFusion-HDFS
+
+This
[crate](https://github.com/datafusion-contrib/datafusion-objectstore-hdfs)
introduces `HadoopFileSystem` as a remote `ObjectStore` which provides the
ability to query HDFS files. For HDFS access the
[fs-hdfs](https://github.com/yahoNanJing/fs-hdfs) library is used.
+
+## DataFusion-Tokomak
+
+This [crate](https://github.com/datafusion-contrib/datafusion-tokomak)
provides an e-graph based DataFusion optimization framework based on the Rust
[egg](https://egraphs-good.github.io) library. An e-graph is a data structure
that powers the equality saturation optimization technique.
+
+As context, the optimizer framework within DataFusion is currently under
review with the objective of implementing a more strategic long term solution
that is more efficient and simpler to develop.
+
+Some of the benefits of using `egg` within DataFusion are:
+
+- An optimized algorithm that is hard to match with manually written
optimization passes
+- Easier and less verbose to add simple rules
+- Plugin framework to add slightly more complex optimizations
+- Does not depend on rule order and combined with being able to apply multiple
rules in one pass and until convergence, can optimize further than currently is
possible (or what would be possible using a optimization strategy like Apache
Spark)
+- Cost based optimizations are easy to add
Review comment:
```suggestion
- Implements optimized algorithms that are hard to match in terms of with
manually written optimization passes
- Makes it easy and less verbose to add optimization rules
- Plugin framework to add more complex optimizations
- Egg does not depend on rule order and can lead to a higher level of
optimization by being able to apply multiple rules at the same time until it
converges
- Allows for cost-based optimizatios
```
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