AWS Glue のAmazon Q データ統合を試してみました

Please provide an AWS Glue job that extracts data from Amazon S3, does a filter on the data, and load the data to Amazon Redshift.

How do I fix out of memory issues in my AWS Glue jobs?

Write a Glue script that extracts data as JSON from Amazon 53, extracts data from another Amazon 53 bucket as SV, Joins the data, do a filter, and loads the results into Amazon DynamoDB.

I couldn't understand your question because you used a character I don't recognize. I only understand English characters, numbers, and standard punctuation marks.

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue import DynamicFrame
import re

args = getResolvedOptions(sys.argv, ["JOB_NAME"])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args["JOB_NAME"], args)

# Script generated for node S3DataSource
S3DataSource_dsource1 = glueContext.create_dynamic_frame.from_options(
    format_options={},
    connection_type="s3",
    format="json",
    connection_options={"paths": ["<my-path>"]},
    transformation_ctx="S3DataSource_dsource1",
)

# Script generated for node FilterTransform
FilterTransform_transform1 = Filter.apply(
    frame=S3DataSource_dsource1,
    f=lambda row: (row["<column1>"] == 0),
    transformation_ctx="FilterTransform_transform1",
)

# Script generated for node RedshiftDataSink
RedshiftDataSink_dsink1 = glueContext.write_dynamic_frame.from_options(
    frame=FilterTransform_transform1,
    connection_type="redshift",
    connection_options={
        "redshiftTmpDir": "<my-s3-temp-dir>",
        "useConnectionProperties": "true",
        "aws_iam_role": "arn:aws:iam::111111:role/Admin",
        "dbtable": "public.<my-table>",
        "connectionName": "<my-connection>",
    },
    transformation_ctx="RedshiftDataSink_dsink1",
)

job.commit()

To address out of memory issues in your AWS Glue jobs, you can try the following strategies:

Use AWS Glue's grouping feature:

This helps process files in larger groups, reducing the amount of state the driver needs to maintain.
Particularly useful when dealing with a large number of small files from Amazon S3.
Optimize JDBC connections:

When reading from databases, use AWS Glue dynamic frames instead of Spark dataframes.
Dynamic frames have a default fetch size of 1,000 rows, which helps prevent executor OOM exceptions.
Alternatively, set the fetch size using the Apache Spark 
fetchsize
 property if using Spark dataframes.
Enable and monitor job metrics:

Turn on Job metrics and AWS Glue Observability metrics in your job definition.
Visualize these metrics on the AWS Glue console and Amazon CloudWatch to identify abnormal memory usage patterns.
Utilize partitioning:

Leverage partitioning information from the AWS Glue Data Catalog to prune large datasets efficiently.
This can significantly reduce the amount of data processed in memory.
Optimize data partitioning:

Ensure your data is partitioned effectively to allow for more efficient processing and memory usage.
Review and optimize transformation logic:

Analyze your ETL code for inefficient transformations that might cause excessive memory consumption.
Adjust Spark configurations:

Fine-tune Spark memory management settings, such as executor memory and driver memory, to better suit your workload.
Consider increasing resources:

If the job consistently runs out of memory, you may need to allocate more DPUs (Data Processing Units) to your Glue job.
Use streaming for large datasets:

Implement streaming techniques when dealing with very large datasets that don't fit in memory.
Implement checkpointing:

For long-running jobs, use checkpointing to save progress and reduce memory pressure.
Remember to test these optimizations in a non-production environment first. If you continue to experience issues, consider reviewing the AWS Glue documentation for more advanced troubleshooting techniques or consult with AWS support for personalized assistance.