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chore(doc): Clean up deployment docs. (#1354)
* Rewords the introduction. * Rewords the cargo install section. * Rewords the docker section and updates builder Dockerfile. * Rewords the docker compose, kubernetes, and scheduler sections. * Rewords the docker compose, kubernetes, and scheduler sections. * Adds a link to the docker section for the docker compose section. * Adds a full stop * Adds full stop * Prettifies the docs. * Rolls back change to builder Dockerfile.
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docs/source/user-guide/deployment/cargo-install.md

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# Deploying a standalone Ballista cluster using cargo install
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A simple way to start a local cluster for testing purposes is to use cargo to install
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Another simple way to start a local cluster for testing purposes is to use cargo to install
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the scheduler and executor crates.
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```bash

docs/source/user-guide/deployment/docker-compose.md

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## Build Docker Images
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Run the following commands to download the [official Docker image](https://github.com/apache/datafusion-ballista/pkgs/container/datafusion-ballista-standalone):
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```bash
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docker pull ghcr.io/apache/datafusion-ballista-standalone:latest
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```
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Altenatively run the following commands to clone the source repository and build the Docker images from source:
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```bash
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git clone [email protected]:apache/datafusion-ballista.git -b latest
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cd datafusion-ballista
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./dev/build-ballista-docker.sh
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```
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This will create the following images:
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- `apache/datafusion-ballista-benchmarks:latest`
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- `apache/datafusion-ballista-cli:latest`
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- `apache/datafusion-ballista-executor:latest`
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- `apache/datafusion-ballista-scheduler:latest`
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- `apache/datafusion-ballista-standalone:latest`
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To create the required Docker images please refer to the [docker deployment page](docker.md).
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## Start a Cluster
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docs/source/user-guide/deployment/docker.md

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docker pull ghcr.io/apache/datafusion-ballista-standalone:latest
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```
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Altenatively run the following commands to clone the source repository and build the Docker images from source:
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Alternatively run the following commands to clone the source repository and build the Docker images from source:
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```bash
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git clone [email protected]:apache/datafusion-ballista.git

docs/source/user-guide/deployment/kubernetes.md

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## Build Docker Images
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Run the following commands to download the [official Docker image](https://github.com/apache/datafusion-ballista/pkgs/container/datafusion-ballista-standalone):
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```bash
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docker pull ghcr.io/apache/datafusion-ballista-standalone:0.12.0-rc4
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```
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Altenatively run the following commands to clone the source repository and build the Docker images from source:
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```bash
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git clone [email protected]:apache/datafusion-ballista.git -b 0.12.0
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cd datafusion-ballista
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./dev/build-ballista-docker.sh
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```
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This will create the following images:
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- `apache/datafusion-ballista-benchmarks:0.12.0`
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- `apache/datafusion-ballista-cli:0.12.0`
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- `apache/datafusion-ballista-executor:0.12.0`
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- `apache/datafusion-ballista-scheduler:0.12.0`
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- `apache/datafusion-ballista-standalone:0.12.0`
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To create the required Docker images please refer to the [docker deployment page](docker.md).
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## Publishing Docker Images
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## Autoscaling Executors
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Ballista supports autoscaling for executors through [Keda](http://keda.sh). Keda allows scaling a deployment
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through custom metrics which are exposed through the Ballista scheduler, and it can even scale the number of
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executors down to 0 if there is no activity in the cluster.
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Ballista supports autoscaling for executors through [Keda](http://keda.sh). Keda allows for the scaling of a
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deployment through custom metrics which are exposed through the Ballista scheduler, and it
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can even scale the number of executors down to 0 if there is no activity in the cluster.
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Keda can be installed in your kubernetes cluster through a single command line:
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docs/source/user-guide/introduction.md

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Ballista is a distributed compute platform primarily implemented in Rust, and powered by Apache DataFusion.
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Ballista has a scheduler and an executor process that are standard Rust executables and can be executed directly, but
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Dockerfiles are provided to build images for use in containerized environments, such as Docker, Docker Compose, and
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Kubernetes. See the [deployment guide](deployment.md) for more information
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Ballista has both scheduler and an executor component processes that are standard Rust executables.
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Dockerfiles are also provided to build images for use in containerized environments, such as Docker, Docker Compose,
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and Kubernetes. See the [deployment guide](deployment.md) for more information.
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SQL and DataFrame queries can be submitted from Python and Rust, and SQL queries can be submitted via the Arrow
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Flight SQL JDBC driver, supporting your favorite JDBC compliant tools such as [DataGrip](datagrip)
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or [tableau](tableau). For setup instructions, please see the [FlightSQL guide](flightsql.md).
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## How does this compare to Apache Spark?
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Although Ballista is largely inspired by Apache Spark, there are some key differences.
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Although Ballista is largely inspired by Apache Spark, there are some key differences:
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- The choice of Rust as the main execution language means that memory usage is deterministic and avoids the overhead
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of GC pauses.

docs/source/user-guide/scheduler.md

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The scheduler also provides a REST API that allows jobs to be monitored.
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> This is optional scheduler feature which should be enabled with `rest-api` feature
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> This is optional scheduler feature which should be enabled with the `rest-api` feature.
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| API | Method | Description |
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| ------------------------------------ | ------ | ----------------------------------------------------------------- |

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