Aggiornamenti generativi dell'intelligenza artificiale per Apache Spark in Glue AWS

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["glue:GetJobUpgradeAnalysis"],
      "Resource": [
        "arn:aws:glue:us-east-1:123456789012:job/jobName"
      ]
    }
  ]
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["glue:StopJobUpgradeAnalysis",
                 "glue:BatchStopJobRun"
                 ],
      "Resource": [
        "arn:aws:glue:us-east-1:123456789012:job/jobName"
      ]
    }
  ]
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["glue:ListJobUpgradeAnalyses"],
      "Resource": [
        "arn:aws:glue:us-east-1:123456789012:job/jobName"
      ]
    }
  ]
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["glue:UpdateJob",
                 "glue:UpgradeJob"
                 ],
      "Resource": [
        "arn:aws:glue:us-east-1:123456789012:job/jobName"
      ]
    },
    {
      "Effect": "Allow",
      "Action": ["iam:PassRole"],
      "Resource": [
        "<Role arn associated with the job>"
      ]
    }
  ]
}

È necessaria l'autorizzazione (crittografia/decrittografia) per passare la chiave. Nell'esempio di policy riportato di seguito, l' AWS account o il ruolo specificato da <IAM Customer caller ARN> è autorizzato a eseguire le azioni consentite:

{
    "Effect": "Allow",
    "Principal":{
        "AWS": "<IAM Customer caller ARN>"
    },
    "Action": [
      "kms:Decrypt",
      "kms:GenerateDataKey",
    ],
    "Resource": "<key-arn-passed-on-start-api>"
}

È necessario autorizzare AWS Glue a utilizzare la AWS KMS chiave sia per la crittografia che per la decrittografia della chiave.

{
    "Effect": "Allow",
    "Principal":{
       "Service": "glue.amazonaws.com"
     },
     "Action": [
       "kms:Decrypt",
       "kms:GenerateDataKey",
      ],
     "Resource": "<key-arn>",
     "Condition": {
       "StringLike": {
         "aws:SourceArn": "arn:aws:glue:<region>:<aws_account_id>:job/job-name"
       }
     }
 }

Questa politica garantisce di disporre sia delle autorizzazioni di crittografia che di decrittografia sulla chiave. AWS KMS

{
    "Effect": "Allow",
    "Principal":{
        "AWS": "<IAM Customer caller ARN>"
    },
    "Action": [
      "kms:Decrypt",
      "kms:GenerateDataKey",
    ],
    "Resource": "<key-arn-passed-on-start-api>"
}

from awsglue.transforms import *
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from pyspark.sql.types import *
from pyspark.sql.functions import *
from awsglue.job import Job
import json
from pyspark.sql.types import StructType

sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)

gdc_database = "s3://aws-glue-scripts-us-east-1-gamma/demo-database/"
schema_location = (
    "s3://aws-glue-scripts-us-east-1-gamma/DataFiles/"
)

products_schema_string = spark.read.text(
    f"{schema_location}schemas/products_schema"
).first()[0]

product_schema = StructType.fromJson(json.loads(products_schema_string))

products_source_df = (
    spark.read.option("header", "true")
    .schema(product_schema)
    .option(
        "path",
        f"{gdc_database}products/",
    )
    .csv(f"{gdc_database}products/")
)

products_source_df.show()
products_temp_view_name = "spark_upgrade_demo_product_view"
products_source_df.createOrReplaceTempView(products_temp_view_name)

query = f"select {products_temp_view_name}.*, format_string('%0$s-%0$s', category, subcategory) as unique_category from {products_temp_view_name}"
products_with_combination_df = spark.sql(query)
products_with_combination_df.show()

products_with_combination_df.createOrReplaceTempView(products_temp_view_name)
product_df_attribution = spark.sql(
    f"""
SELECT *,
unbase64(split(product_name, ' ')[0]) as product_name_decoded,
unbase64(split(unique_category, '-')[1]) as subcategory_decoded
FROM {products_temp_view_name}
"""
)
product_df_attribution.show()


product_df_attribution.write.mode("overwrite").option("header", "true").option(
    "path", f"{gdc_database}spark_upgrade_demo_product_agg/"
).saveAsTable("spark_upgrade_demo_product_agg", external=True)

spark_upgrade_demo_product_agg_table_df = spark.sql(
    f"SHOW TABLE EXTENDED in default like 'spark_upgrade_demo_product_agg'"
)
spark_upgrade_demo_product_agg_table_df.show()
job.commit()

from awsglue.transforms import *
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from pyspark.sql.types import *
from pyspark.sql.functions import *
from awsglue.job import Job
import json
from pyspark.sql.types import StructType

sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
# change 1
spark.conf.set("spark.sql.adaptive.enabled", "false")
# change 2
spark.conf.set("spark.sql.legacy.pathOptionBehavior.enabled", "true")
job = Job(glueContext)

gdc_database = "s3://aws-glue-scripts-us-east-1-gamma/demo-database/"
schema_location = (
    "s3://aws-glue-scripts-us-east-1-gamma/DataFiles/"
)

products_schema_string = spark.read.text(
    f"{schema_location}schemas/products_schema"
).first()[0]

product_schema = StructType.fromJson(json.loads(products_schema_string))

products_source_df = (
    spark.read.option("header", "true")
    .schema(product_schema)
    .option(
        "path",
        f"{gdc_database}products/",
    )
    .csv(f"{gdc_database}products/")
)

products_source_df.show()
products_temp_view_name = "spark_upgrade_demo_product_view"
products_source_df.createOrReplaceTempView(products_temp_view_name)

# change 3
query = f"select {products_temp_view_name}.*, format_string('%1$s-%1$s', category, subcategory) as unique_category from {products_temp_view_name}"
products_with_combination_df = spark.sql(query)
products_with_combination_df.show()

products_with_combination_df.createOrReplaceTempView(products_temp_view_name)
# change 4
product_df_attribution = spark.sql(
    f"""
SELECT *,
try_to_binary(split(product_name, ' ')[0], 'base64') as product_name_decoded,
try_to_binary(split(unique_category, '-')[1], 'base64') as subcategory_decoded
FROM {products_temp_view_name}
"""
)
product_df_attribution.show()


product_df_attribution.write.mode("overwrite").option("header", "true").option(
    "path", f"{gdc_database}spark_upgrade_demo_product_agg/"
).saveAsTable("spark_upgrade_demo_product_agg", external=True)

spark_upgrade_demo_product_agg_table_df = spark.sql(
    f"SHOW TABLE EXTENDED in default like 'spark_upgrade_demo_product_agg'"
)
spark_upgrade_demo_product_agg_table_df.show()
job.commit()

In base al riepilogo, ci sono quattro modifiche proposte da AWS Glue per aggiornare correttamente lo script da AWS Glue 2.0 a AWS Glue 4.0: