glue-utils 0.9.1

Reusable utilities for working with Glue PySpark jobs

glue-utils

glue-utils is a Python library designed to enhance the developer experience when working with AWS Glue ETL and Python Shell jobs. It reduces boilerplate code, increases type safety, and improves IDE auto-completion, making Glue development easier and more efficient.

• Usage in AWS Glue
• Usage when developing jobs locally
• Main Features
  • BaseOptions
  • GluePySparkContext
  • GluePySparkJob
• Other features

Usage in AWS Glue

To use glue-utils in AWS Glue, it needs to be added as an additional python module in your Glue job.

You can do this by adding an --additional-python-modules job parameter with the value, glue_utils==0.9.1. For more information about setting job parameters, see AWS Glue job parameters.

Usage when developing jobs locally

This library does not include pyspark and aws-glue-libs as dependencies as they are already pre-installed in Glue's runtime environment.

To help in developing your Glue jobs locally in your IDE, it is helpful to install pyspark and aws-glue-libs. Unfortunately, aws-glue-libs is not available through PyPI so we can only install it from its git repository.

# Glue 4.0 uses PySpark 3.3.0
pip install pyspark==3.3.0
pip install git+https://github.com/awslabs/aws-glue-libs.git@master
pip install glue-utils

Main Features

• BaseOptions
  • a dataclass that parses the options supplied via command-line arguments
• GluePySparkContext
  • a subclass of awsglue.context.GlueContext that adds convenient type-safe methods (methods that ensure the correct data types are used) for the most common connection types.
• GluePySparkJob
  • a convenient class that simplifies and reduces the boilerplate code needed in Glue jobs.

BaseOptions

BaseOptions resolves the required arguments into a dataclass to help your IDE auto-complete and detect potential KeyErrors. It also makes type checkers such as pyright and mypy detect those errors at design or build time instead of at runtime.

from dataclasses import dataclass
from glue_utils import BaseOptions


@dataclass
class Options(BaseOptions):
    start_date: str
    end_date: str


args = Options.from_sys_argv()

print(f"The day partition key is: {args.start_date}")

Note: Similar to the behavior of awsglue.utils.getResolvedOptions, all arguments are strings. A warning is raised when defining a field as other data types. We aim to auto-cast those values in the future.

GluePySparkContext

GluePySparkContext is a subclass of awsglue.context.GlueContext with the following additional convenience methods for creating and writing DynamicFrames for the common connection types. The method signatures ensure that you are passing the right connection options and/or format options for the chosen connection type.

• MySQL
  • create_dynamic_frame_from_mysql
  • write_dynamic_frame_to_mysql
• Oracle
  • create_dynamic_frame_from_oracle
  • write_dynamic_frame_to_oracle
• PostgreSQL
  • create_dynamic_frame_from_postgresql
  • write_dynamic_frame_to_postgresql
• SQL Server
  • create_dynamic_frame_from_sqlserver
  • write_dynamic_frame_to_sqlserver
• S3
  • JSON
    • create_dynamic_frame_from_s3_json
    • write_dynamic_frame_to_s3_json
  • CSV
    • create_dynamic_frame_from_s3_csv
    • write_dynamic_frame_to_s3_csv
  • Parquet
    • create_dynamic_frame_from_s3_parquet
    • write_dynamic_frame_to_s3_parquet
  • XML
    • create_dynamic_frame_from_s3_xml
    • write_dynamic_frame_to_s3_xml
• DynamoDB
  • create_dynamic_frame_from_dynamodb
  • create_dynamic_frame_from_dynamodb_export
  • write_dynamic_frame_to_dynamodb
• Kinesis
  • create_dynamic_frame_from_kinesis
  • write_dynamic_frame_to_kinesis
• Kafka
  • create_dynamic_frame_from_kafka
  • write_dynamic_frame_to_kafka
• OpenSearch
  • create_dynamic_frame_from_opensearch
  • write_dynamic_frame_to_opensearch
• DocumentDB
  • create_dynamic_frame_from_documentdb
  • write_dynamic_frame_to_documentdb
• MongoDB
  • create_dynamic_frame_from_mongodb
  • write_dynamic_frame_to_mongodb

GluePySparkJob

GluePySparkJob reduces the boilerplate code needed by using reasonable defaults while still allowing for customizations by passing keyword arguments.

In its simplest form, it takes care of instantiating awsglue.context.GlueContext and initializing awsglue.job.Job.

from glue_utils.pyspark import GluePySparkJob

# Instantiate with defaults.
job = GluePySparkJob()

# This is the SparkContext object.
sc = job.sc

# This is the GluePySparkContext(GlueContext) object.
glue_context = job.glue_context

# This is the SparkSession object.
spark = job.spark

# The rest of your job's logic.

# Commit the job if necessary (e.g. when using bookmarks).
job.commit()

options_cls

You may pass a subclass of BaseOptions to make the resolved options available in job.options.

from dataclasses import dataclass
from glue_utils import BaseOptions
from glue_utils.pyspark import GluePySparkJob


@dataclass
class Options(BaseOptions):
    # Specify the arguments as field names
    start_date: str
    end_date: str
    source_path: str


# Instantiate with the above Options class.
job = GluePySparkJob(options_cls=Options)

# Use the resolved values using the fields available in job.options.
print(f"The S3 path is {job.options.source_path}")

log_level

You may configure the logging level. It is set to GluePySparkJob.LogLevel.WARN by default.

from glue_utils.pyspark import GluePySparkJob


# Log only errors.
job = GluePySparkJob(log_level=GluePySparkJob.LogLevel.ERROR)

spark_conf

You may set Spark configuration values by instantiating a custom pyspark.SparkConf object to pass to GluePySparkJob.

from pyspark import SparkConf
from glue_utils.pyspark import GluePySparkJob

# Instantiate a SparkConf and set the desired config keys/values.
spark_conf = SparkConf()
spark_conf.set("spark.driver.maxResultSize", "4g")

# Instantiate with the above custom SparkConf.
job = GluePySparkJob(spark_conf=spark_conf)

glue_context_options

You may set options that are passed to awsglue.context.GlueContext.

from glue_utils.pyspark import GlueContextOptions, GluePySparkJob

job = GluePySparkJob(glue_context_options={
    "minPartitions": 2,
    "targetPartitions": 10,
})

# Alternatively, you can use the GlueContextOptions TypedDict.
job = GluePySparkJob(glue_context_options=GlueContextOptions(
    minPartitions=2,
    targetPartitions=10,
)

Other features

The following modules contain useful TypedDicts for defining connection options or format options to pass as arguments to various awsglue.context.GlueContext methods:

• glue_utils.pyspark.connection_options
  • for defining connection_options for various connection types
• glue_utils.pyspark.format_options
  • for defining format_options for various formats