sparglim 0.1.0

sparglim

Sparglim

Sparglim is aimed at providing a clean solution for PySpark applications in cloud-native scenarios (On K8S、Connect Server etc.).

This is a fledgling project, looking forward to any PRs, Feature Requests and discussions!

🌟✨⭐ Start to support!

Quick Start

Run Jupyterlab with sparglim docker image:

docker run \
-it \
-p 8888:8888 \
wh1isper/jupyterlab-sparglim

Access http://localhost:8888 in browser to use jupyterlab with sparglim. Then you can try SQL Magic.

Run and Daemon a Spark Connect Server:

docker run \
-it \
-p 15002:15002 \
-p 4040:4040 \
wh1isper/sparglim-server

Access http://localhost:4040 for Spark-UI and sc://localhost:15002 for Spark Connect Server. Use sparglim to setup SparkSession to connect to Spark Connect Server.

Install: pip install sparglim[all]

• Install only for config and daemon spark connect server pip install sparglim
• Install for pyspark app pip install sparglim[pyspark]
• Install for using magic within ipython/jupyter (will also install pyspark) pip install sparglim[magic]
• Install for all above (such as using magic in jupyterlab on k8s) pip install sparglim[all]

Feature

• Config Spark via environment variables, see config spark
• %SQL and %%SQL magic for executing Spark SQL in IPython/Jupyter
  • SQL statement can be written in multiple lines, support using ; to separate statements
  • Support config connect client, see Spark Connect Overview
  • TODO: Visualize the result of SQL statement(Spark Dataframe)
• sparglim-server for daemon Spark Connect Server

User cases

PySpark App

To config Spark on k8s for Data explorations, see examples/jupyter-sparglim-on-k8s

(TODO)To config Spark for ELT Application/Service, see pyspark-sampling

Spark Connect Server on K8S

To daemon Spark Connect Server on K8S, see examples/sparglim-server

To daemon Spark Connect Server on K8S and Connect it in JupyterLab , see examples/jupyter-sparglim-sc

Connect to Spark Connect Server

Only thing need to do is to set SPARGLIM_REMOTE env, format is sc://host:port

Example Code:

import os
os.environ["SPARGLIM_REMOTE"] = "sc://localhost:15002" # or export SPARGLIM_REMOTE=sc://localhost:15002 before run python

from sparglim.config.builder import ConfigBuilder
from datetime import datetime, date
from pyspark.sql import Row


c = ConfigBuilder().config_connect_client()
spark = c.get_or_create()

df = spark.createDataFrame([
    Row(a=1, b=2., c='string1', d=date(2000, 1, 1), e=datetime(2000, 1, 1, 12, 0)),
    Row(a=2, b=3., c='string2', d=date(2000, 2, 1), e=datetime(2000, 1, 2, 12, 0)),
    Row(a=4, b=5., c='string3', d=date(2000, 3, 1), e=datetime(2000, 1, 3, 12, 0))
])
df.show()

SQL Magic

Install Sparglim with

pip install sparglim["magic"]

Load magic in IPython/Jupyter

%load_ext sparglim.sql

Create a view:

from sparglim.config.builder import ConfigBuilder


from datetime import datetime, date
from pyspark.sql import Row

c: ConfigBuilder = ConfigBuilder()
spark = c.get_or_create()


df = spark.createDataFrame([
            Row(a=1, b=2., c='string1', d=date(2000, 1, 1), e=datetime(2000, 1, 1, 12, 0)),
            Row(a=2, b=3., c='string2', d=date(2000, 2, 1), e=datetime(2000, 1, 2, 12, 0)),
            Row(a=4, b=5., c='string3', d=date(2000, 3, 1), e=datetime(2000, 1, 3, 12, 0))
        ])
df.createOrReplaceTempView("tb")

Query the view by %SQL:

%sql SELECT * FROM tb

%SQL result dataframe can be assigned to a variable:

df = %sql SELECT * FROM tb
df

or %%SQL can be used to execute multiple statements:

%%sql SELECT
        *
        FROM
        tb;

You can also using Spark SQL to load data from external data source, such as:

%%sql CREATE TABLE tb_people
USING json
OPTIONS (path "/path/to/file.json");
Show tables;

Develop

Install pre-commit before commit

pip install pre-commit
pre-commit install

Install package locally

pip install -e .[test]

Run unit-test before PR, ensure that new features are covered by unit tests

pytest -v

(Optional, python<=3.10) Use pytype to check typed

pytype ./sparglim