README.md

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# BLAZE

[![TPC-DS](https://github.com/blaze-init/blaze/actions/workflows/tpcds.yml/badge.svg?branch=master)](https://github.com/blaze-init/blaze/actions/workflows/tpcds.yml)
[![master-ce7-builds](https://github.com/blaze-init/blaze/actions/workflows/build-ce7-releases.yml/badge.svg?branch=master)](https://github.com/blaze-init/blaze/actions/workflows/build-ce7-releases.yml)

The Blaze accelerator for Apache Spark leverages native vectorized execution to accelerate query processing. It combines
the power of the [Apache Arrow-DataFusion](https://arrow.apache.org/datafusion/) library and the scale of the Spark distributed
computing framework.

Blaze takes a fully optimized physical plan from Spark, mapping it into DataFusion's execution plan, and performs native
plan computation in Spark executors.

Blaze is composed of the following high-level components:

- **Spark Extension**: hooks the whole accelerator into Spark execution lifetime.
- **Spark Shims**: specialized codes for different versions of spark.
- **Native Engine**: implements the native engine in rust, including:
  - ExecutionPlan protobuf specification
  - JNI gateway
  - Customized operators, expressions, functions

Based on the inherent well-defined extensibility of DataFusion, Blaze can be easily extended to support:

- Various object stores.
- Operators.
- Simple and Aggregate functions.
- File formats.

We encourage you to [extend DataFusion](https://github.com/apache/arrow-datafusion) capability directly and add the
supports in Blaze with simple modifications in plan-serde and extension translation.

## Build from source

To build Blaze, please follow the steps below:

1. Install Rust

The native execution lib is written in Rust. So you're required to install Rust (nightly) first for
compilation. We recommend you to use [rustup](https://rustup.rs/).

2. Install JDK+Maven

Blaze has been well tested on jdk8 and maven3.5, should work fine with higher versions.

3. Check out the source code.

```shell
git clone git@github.com:blaze-init/blaze.git
cd blaze
```

4. Build the project.

_Specify shims package of which spark version that you would like to run on._
_You could either build Blaze in dev mode for debugging or in release mode to unlock the full potential of
Blaze._

```shell
SHIM=spark333 # or spark303/spark320/spark324/spark351
MODE=release # or pre
mvn package -P"${SHIM}" -P"${MODE}"
```

After the build is finished, a fat Jar package that contains all the dependencies will be generated in the `target`
directory.

## Build with docker

_You can use the following command to build a centos-7 compatible release:_
```shell
SHIM=spark333 MODE=release ./release-docker.sh
```

## Run Spark Job with Blaze Accelerator

This section describes how to submit and configure a Spark Job with Blaze support.

1. move blaze jar package to spark client classpath (normally `spark-xx.xx.xx/jars/`).

2. add the follow confs to spark configuration in `spark-xx.xx.xx/conf/spark-default.conf`:

```properties
spark.blaze.enable true
spark.sql.extensions org.apache.spark.sql.blaze.BlazeSparkSessionExtension
spark.shuffle.manager org.apache.spark.sql.execution.blaze.shuffle.BlazeShuffleManager
spark.memory.offHeap.enabled false

# suggested executor memory configuration
spark.executor.memory 4g
spark.executor.memoryOverhead 4096
```

3. submit a query with spark-sql, or other tools like spark-thriftserver:
```shell
spark-sql -f tpcds/q01.sql
```

## Performance

Check [Benchmark Results](./benchmark-results/20240701-blaze300.md) with the latest date for the performance
comparison with vanilla Spark 3.3.3. The benchmark result shows that Blaze save about 50% time on TPC-DS/TPC-H 1TB datasets.
Stay tuned and join us for more upcoming thrilling numbers.

TPC-DS Query time: ([How can I run TPC-DS benchmark?](./tpcds/README.md))
![20240701-query-time-tpcds](./benchmark-results/spark333-vs-blaze300-query-time-20240701.png)

TPC-H Query time:
![20240701-query-time-tpch](./benchmark-results/spark333-vs-blaze300-query-time-20240701-tpch.png)

We also encourage you to benchmark Blaze and share the results with us. 🤗

## Community

We're using [Discussions](https://github.com/blaze-init/blaze/discussions) to connect with other members
of our community. We hope that you:
- Ask questions you're wondering about.
- Share ideas.
- Engage with other community members.
- Welcome others who are open-minded. Remember that this is a community we build together 💪 .


## License

Blaze is licensed under the Apache 2.0 License. A copy of the license
[can be found here.](LICENSE.txt)