Added Enhancement related to Apache Hudi and pyspark script from S3

Dockerfile

@@ -6,6 +6,7 @@ ARG HADOOP_VERSION=3.2.4
 ARG AWS_SDK_VERSION=1.11.901
 ARG PYSPARK_VERSION=3.3.0
 ARG SOURCE_REGION_NAME='us-east-1'
+ARG HUDI_VERSION=0.12.2
 
 
 # yum updates, security updates for zlib, java installation and pyspark installation
@@ -15,9 +16,11 @@ RUN yum update -y && \
     yum -y install yum-plugin-versionlock && \
     yum -y versionlock add java-1.8.0-openjdk-1.8.0.352.b08-0.amzn2.0.1.x86_64 && \
     yum -y install java-1.8.0-openjdk && \
+    pip install --upgrade pip && \
     pip install pyspark==$PYSPARK_VERSION && \
     yum clean all
 
+
 # setting the environment variable and Spark path
 ENV SPARK_HOME="/var/lang/lib/python3.8/site-packages/pyspark"
 ENV PATH=$PATH:$SPARK_HOME/bin
@@ -34,7 +37,8 @@ ENV PATH=$SPARK_HOME/python:$PATH
 
 RUN mkdir $SPARK_HOME/conf && \
     echo "SPARK_LOCAL_IP=127.0.0.1" > $SPARK_HOME/conf/spark-env.sh && \
-    wget -q https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/${HADOOP_VERSION}/hadoop-aws-${HADOOP_VERSION}.jar -P ${SPARK_HOME}/jars/ && \ 
+    wget -q https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/${HADOOP_VERSION}/hadoop-aws-${HADOOP_VERSION}.jar -P ${SPARK_HOME}/jars/ && \
+    wget -q https://repo1.maven.org/maven2/org/apache/hudi/hudi-spark3.3-bundle_2.12/${HUDI_VERSION}/hudi-spark3.3-bundle_2.12-${HUDI_VERSION}.jar -P ${SPARK_HOME}/jars/ && \
     wget -q https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/${AWS_SDK_VERSION}/aws-java-sdk-bundle-${AWS_SDK_VERSION}.jar -P ${SPARK_HOME}/jars/
 
 # JAVA_HOME depends upon the java version used
@@ -56,7 +60,7 @@ RUN chmod -R 755 $SPARK_HOME
 
 # Copy the Pyspark script to container
 
-COPY sparkOnAWSLambda.py ${LAMBDA_TASK_ROOT}
+COPY sparkLambdaHandler.py ${LAMBDA_TASK_ROOT}
 
 # calling the Lambda handler
-CMD [ "/var/task/sparkOnAWSLambda.lambda_handler" ]
+CMD [ "/var/task/sparkLambdaHandler.lambda_handler" ]

sparkOnAWSLambda.py → spark-scripts/sparkOnAWSLambda.py

@@ -42,4 +42,5 @@ def lambda_handler(event, context):
 
 
  print("Started Writing the CSV file to  Target S3 location ", target_path)
- df.write.format("csv").save(target_path)
+ #df.write.format("csv").save(target_path)
+ df.write.format("hudi").save(target_path)

spark-scripts/spark_script_hudi.py

@@ -0,0 +1,69 @@
+from pyspark.sql import SparkSession
+from pyspark.sql.types import *
+from pyspark.sql.functions import current_timestamp
+import sys
+import os
+
+def spark_script():
+ print("start...................")
+
+ input_path = os.environ['input_path'] 
+ target_path = os.environ['output_path']
+ s3_bucket  = os.environ['s3_bucket']
+
+ aws_region = os.environ['REGION'] 
+ aws_access_key_id = os.environ['ACCESS_KEY_ID'] 
+ aws_secret_access_key = os.environ['SECRET_ACCESS_KEY'] 
+ session_token = os.environ['SESSION_TOKEN']
+
+
+ input_path = "s3a://"+s3_bucket+"/"+input_path
+ target_path ="s3a://"+s3_bucket+"/"+target_path
+
+ print(" ******* Input path ",input_path)
+ print(" ******* Target path ",target_path)
+
+ spark = SparkSession.builder \
+ .appName("Spark-on-AWS-Lambda") \
+ .master("local[*]") \
+ .config("spark.driver.bindAddress", "127.0.0.1") \
+ .config("spark.driver.memory", "5g") \
+ .config("spark.executor.memory", "5g") \
+ .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
+ .config("spark.sql.hive.convertMetastoreParquet", "false") \
+ .config("spark.hadoop.hive.metastore.client.factory.class", "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory") \
+ .config("hoodie.meta.sync.client.tool.class", "org.apache.hudi.aws.sync.AwsGlueCatalogSyncTool") \
+ .config("spark.hadoop.fs.s3a.access.key", aws_access_key_id) \
+ .config("spark.hadoop.fs.s3a.secret.key", aws_secret_access_key) \
+ .config("spark.hadoop.fs.s3a.session.token",session_token) \
+ .config("spark.hadoop.fs.s3a.aws.credentials.provider","org.apache.hadoop.fs.s3a.TemporaryAWSCredentialsProvider") \
+ .enableHiveSupport().getOrCreate()
+
+
+
+ print("Started Reading the CSV file from S3 location ",input_path)
+
+ df=spark.read.option('header','true').csv(input_path)
+ df = df.withColumn("last_upd_timestamp", current_timestamp())
+ df.show()
+
+ hudi_options = {
+  'hoodie.table.name': 'customer_table',
+    'hoodie.datasource.write.recordkey.field': 'Customer_ID',
+    'hoodie.datasource.write.precombine.field': 'last_upd_timestamp',
+    'hoodie.insert.shuffle.parallelism': 2,
+    "hoodie.datasource.hive_sync.enable": "false",
+    "hoodie.datasource.hive_sync.database": "default",
+    "hoodie.datasource.hive_sync.table": "customer_table",
+    "hoodie.datasource.hive_sync.use_jdbc": "false",
+    "hoodie.datasource.hive_sync.mode": "hms",
+    "hoodie.write.markers.type":"direct", # It's not advisable to use this configuration. Working on workaround without using this config. 
+    "hoodie.embed.timeline.server":"false" # It's not advisable to use this configuration. Working on workaround without using this config. 
+ }
+
+ print("Started Writing the CSV file to  Target hudi table ", target_path)
+ df.write.format("hudi").options(**hudi_options).mode("overwrite").save(target_path)
+ # df.write.format("csv").save(target_path)
+
+if __name__ == '__main__':
+ spark_script()

sparkLambdaHandler.py

@@ -0,0 +1,16 @@
+import boto3
+import sys
+import os
+import subprocess
+
+def lambda_handler(event, context):
+ print("start...................")
+ s3_bucket_script = os.environ['SCRIPT_BUCKET']
+ input_script = os.environ['SPARK_SCRIPT']
+ s3_client = boto3.client("s3")
+ s3_client.download_file(s3_bucket_script, input_script, "/tmp/spark_script.py")
+ # Set the environment variables for the Spark application
+ os.environ["PYSPARK_SUBMIT_ARGS"] = "--master local pyspark-shell"
+ #ENV PYSPARK_SUBMIT_ARGS="--master local pyspark-shell"\
+ # Run the spark-submit command
+ subprocess.run(["spark-submit", "/tmp/spark_script.py"])