pysparkcli 0.0.4

PySpark Project Buiding Tool

PySpark CLI

This will implement a PySpark Project boiler plate code based on user input.

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

PySpark is the Python API for Spark.

Installation Steps:

git clone https://github.com/qburst/PySparkCLI.git

cd PySparkCLI

pip3 install -e . --user

Create a PySpark Project

pysparkcli create [PROJECT_NAME] --master [MASTER_URL] --cores [NUMBER]

master - The URL of the cluster it connects to. You can also use -m instead of --master.
cores - You can also use -c instead of --cores.

Run a PySpark Project

pysparkcli run [PROJECT_NAME]

Project Structure

The basic project structure is as follows:

sample
├── __init__.py
├── src
│   ├── app.py
│   ├── configs
│   │   ├── etl_config.json
│   │   └── __init__.py
│   ├── __init__.py
│   ├── jobs
│   │   ├── etl_job.py
│   │   └── __init__.py
│   └── settings
│       ├── default.py
│       ├── __init__.py
│       ├── local.py
│       └── production.py
└── tests
    ├── __init__.py
    ├── test_data
    │   ├── employees
    │   │   └── part-00000-9abf32a3-db43-42e1-9639-363ef11c0d1c-c000.snappy.parquet
    │   └── employees_report
    │       └── part-00000-4a609ba3-0404-48bb-bb22-2fec3e2f1e68-c000.snappy.parquet
    └── test_etl_job.py

8 directories, 15 files

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