[GitHub] [hudi] soumilshah1995 commented on issue #8918: [SUPPORT] Unable to Remove Physical Files During Partition Deletion

["soumilshah1995 (via GitHub)" <gi...@apache.org>]

soumilshah1995 commented on issue #8918:
URL: https://github.com/apache/hudi/issues/8918#issuecomment-1590038680

   cleaner should clean or purge older files 
   are we talking about deletion of partition if yes 
   here is video https://www.youtube.com/watch?v=QqCiycIgSFk
   
   ```
   """
   Author : Soumil Nitin Shah
   Email shahsoumil519@gmail.com
   --additional-python-modules  | faker==11.3.0
   --conf  |  spark.serializer=org.apache.spark.serializer.KryoSerializer  --conf spark.sql.hive.convertMetastoreParquet=false --conf spark.sql.hive.convertMetastoreParquet=false --conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog --conf spark.sql.legacy.pathOptionBehavior.enabled=true --conf spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension
   
   --datalake-formats | hudi
   """
   
   try:
       import sys, os, ast, uuid, boto3, datetime, time, re, json
       from ast import literal_eval
       from dataclasses import dataclass
       from datetime import datetime
       from pyspark.sql.functions import lit, udf
       from pyspark.sql.types import StringType
       from pyspark.context import SparkContext
       from pyspark.sql.session import SparkSession
       from awsglue.context import GlueContext
       from awsglue.job import Job
       from awsglue.dynamicframe import DynamicFrame
       from pyspark.sql.functions import col, to_timestamp, monotonically_increasing_id, to_date, when
       from pyspark.sql.functions import *
       from awsglue.utils import getResolvedOptions
       from pyspark.sql.types import *
       from faker import Faker
   except Exception as e:
       print("Modules are missing : {} ".format(e))
   
   DYNAMODB_LOCK_TABLE_NAME = 'hudi-lock-table'
   curr_session = boto3.session.Session()
   curr_region = curr_session.region_name
   
   # Get command-line arguments
   args = getResolvedOptions(
       sys.argv, [
           'JOB_NAME',
       ],
   )
   
   spark = (SparkSession.builder.config('spark.serializer', 'org.apache.spark.serializer.KryoSerializer') \
            .config('spark.sql.hive.convertMetastoreParquet', 'false') \
            .config('spark.sql.catalog.spark_catalog', 'org.apache.spark.sql.hudi.catalog.HoodieCatalog') \
            .config('spark.sql.extensions', 'org.apache.spark.sql.hudi.HoodieSparkSessionExtension') \
            .config('spark.sql.legacy.pathOptionBehavior.enabled', 'true').getOrCreate())
   
   # Create a Spark context and Glue context
   sc = spark.sparkContext
   glueContext = GlueContext(sc)
   job = Job(glueContext)
   logger = glueContext.get_logger()
   job.init(args["JOB_NAME"], args)
   
   
   def upsert_hudi_table(glue_database, table_name,
                         record_id, precomb_key, table_type, spark_df,partition_feild,
                         enable_partition, enable_cleaner, enable_hive_sync, enable_dynamodb_lock,
                         use_sql_transformer, sql_transformer_query,
                         target_path, index_type, method='upsert'):
       """
       Upserts a dataframe into a Hudi table.
   
       Args:
           glue_database (str): The name of the glue database.
           table_name (str): The name of the Hudi table.
           record_id (str): The name of the field in the dataframe that will be used as the record key.
           precomb_key (str): The name of the field in the dataframe that will be used for pre-combine.
           table_type (str): The Hudi table type (e.g., COPY_ON_WRITE, MERGE_ON_READ).
           spark_df (pyspark.sql.DataFrame): The dataframe to upsert.
           enable_partition (bool): Whether or not to enable partitioning.
           enable_cleaner (bool): Whether or not to enable data cleaning.
           enable_hive_sync (bool): Whether or not to enable syncing with Hive.
           use_sql_transformer (bool): Whether or not to use SQL to transform the dataframe before upserting.
           sql_transformer_query (str): The SQL query to use for data transformation.
           target_path (str): The path to the target Hudi table.
           method (str): The Hudi write method to use (default is 'upsert').
           index_type : BLOOM or GLOBAL_BLOOM
       Returns:
           None
       """
       # These are the basic settings for the Hoodie table
       hudi_final_settings = {
           "hoodie.table.name": table_name,
           "hoodie.datasource.write.table.type": table_type,
           "hoodie.datasource.write.operation": method,
           "hoodie.datasource.write.recordkey.field": record_id,
           "hoodie.datasource.write.precombine.field": precomb_key,
       }
   
       # These settings enable syncing with Hive
       hudi_hive_sync_settings = {
           "hoodie.parquet.compression.codec": "gzip",
           "hoodie.datasource.hive_sync.enable": "true",
           "hoodie.datasource.hive_sync.database": glue_database,
           "hoodie.datasource.hive_sync.table": table_name,
           "hoodie.datasource.hive_sync.partition_extractor_class": "org.apache.hudi.hive.MultiPartKeysValueExtractor",
           "hoodie.datasource.hive_sync.use_jdbc": "false",
           "hoodie.datasource.hive_sync.mode": "hms",
       }
   
       # These settings enable automatic cleaning of old data
       hudi_cleaner_options = {
           "hoodie.clean.automatic": "true",
           "hoodie.clean.async": "true",
           "hoodie.cleaner.policy": 'KEEP_LATEST_FILE_VERSIONS',
           "hoodie.cleaner.fileversions.retained": "3",
           "hoodie-conf hoodie.cleaner.parallelism": '200',
           'hoodie.cleaner.commits.retained': 5
       }
   
       # These settings enable partitioning of the data
       partition_settings = {
           "hoodie.datasource.write.partitionpath.field": partition_feild,
           "hoodie.datasource.hive_sync.partition_fields": partition_feild,
           "hoodie.datasource.write.hive_style_partitioning": "true",
       }
   
       # Define a dictionary with the index settings for Hudi
       hudi_index_settings = {
           "hoodie.index.type": index_type,  # Specify the index type for Hudi
       }
   
       hudi_dynamo_db_based_lock = {
           'hoodie.write.concurrency.mode': 'optimistic_concurrency_control'
           , 'hoodie.cleaner.policy.failed.writes': 'LAZY'
           , 'hoodie.write.lock.provider': 'org.apache.hudi.aws.transaction.lock.DynamoDBBasedLockProvider'
           , 'hoodie.write.lock.dynamodb.table': DYNAMODB_LOCK_TABLE_NAME
           , 'hoodie.write.lock.dynamodb.partition_key': 'tablename'
           , 'hoodie.write.lock.dynamodb.region': '{0}'.format(curr_region)
           , 'hoodie.write.lock.dynamodb.endpoint_url': 'dynamodb.{0}.amazonaws.com'.format(curr_region)
           , 'hoodie.write.lock.dynamodb.billing_mode': 'PAY_PER_REQUEST'
   
       }
   
       hudi_file_size = {
           "hoodie.parquet.max.file.size": 512 * 1024 * 1024,  # 512MB
           "hoodie.parquet.small.file.limit": 104857600,  # 100MB
       }
   
       # Add the Hudi index settings to the final settings dictionary
       for key, value in hudi_index_settings.items():
           hudi_final_settings[key] = value  # Add the key-value pair to the final settings dictionary
   
       for key, value in hudi_file_size.items():
           hudi_final_settings[key] = value
   
           # If partitioning is enabled, add the partition settings to the final settings
       if enable_partition == "True" or enable_partition == "true" or enable_partition == True:
           for key, value in partition_settings.items(): hudi_final_settings[key] = value
   
       # if DynamoDB based lock enabled use dynamodb as lock table
       if enable_dynamodb_lock == "True" or enable_dynamodb_lock == "true" or enable_dynamodb_lock == True:
           for key, value in hudi_dynamo_db_based_lock.items(): hudi_final_settings[key] = value
   
       # If data cleaning is enabled, add the cleaner options to the final settings
       if enable_cleaner == "True" or enable_cleaner == "true" or enable_cleaner == True:
           for key, value in hudi_cleaner_options.items(): hudi_final_settings[key] = value
   
       # If Hive syncing is enabled, add the Hive sync settings to the final settings
       if enable_hive_sync == "True" or enable_hive_sync == "true" or enable_hive_sync == True:
           for key, value in hudi_hive_sync_settings.items(): hudi_final_settings[key] = value
   
       # If there is data to write, apply any SQL transformations and write to the target path
       if spark_df.count() > 0:
   
           if use_sql_transformer == "True" or use_sql_transformer == "true" or use_sql_transformer == True:
               spark_df.createOrReplaceTempView("temp")
               spark_df = spark.sql(sql_transformer_query)
   
           spark_df.write.format("hudi"). \
               options(**hudi_final_settings). \
               mode("append"). \
               save(target_path)
   
   
   global faker
   faker = Faker()
   
   
   def get_customer_data(total_customers=2):
       customers_array = []
       for i in range(0, total_customers):
           customer_data = {
               "customer_id": i,
               "name": faker.name(),
               "state": faker.state(),
               "city": faker.city(),
               "email": faker.email(),
               "ts": datetime.now().isoformat().__str__()
           }
           customers_array.append(customer_data)
       return customers_array
   
   
   # total_customers = 100
   #
   # customer_data = get_customer_data(total_customers=total_customers)
   # spark_df_customers = spark.createDataFrame(data=[tuple(i.values()) for i in customer_data],
   #                                            schema=list(customer_data[0].keys()))
   # print(spark_df_customers.show(truncate=False))
   
   path = "s3://soumilshah-hudi-demos/silver/table_name=customers"
   
   
   # upsert_hudi_table(
   #     glue_database="hudidb",
   #     table_name="customers",
   #     record_id="customer_id",
   #     precomb_key="ts",
   #     partition_feild='state',
   #     table_type="COPY_ON_WRITE",
   #     method='upsert',
   #     index_type="BLOOM",
   #     enable_partition="True",
   #     enable_cleaner="True",
   #     enable_hive_sync="True",
   #     enable_dynamodb_lock="False",
   #     use_sql_transformer="False",
   #     sql_transformer_query="default",
   #     target_path=path,
   #     spark_df=spark_df_customers,
   # )
   
   
   # ============DELETE PARTITION ================
   # ============DELETE PARTITION ================
   
   print(
       "Connecticut", spark.read.format("hudi").load(path).where("state='Connecticut'").count()
   )
   
   hudi_options = {}
   
   try:
       spark.createDataFrame([], StructType([])) \
           .write \
           .format("org.apache.hudi") \
           .options(**hudi_options) \
           .option("hoodie.datasource.hive_sync.enable", False) \
           .option("hoodie.datasource.write.operation", "delete_partition") \
           .option("hoodie.datasource.write.partitions.to.delete", "state=Connecticut") \
           .mode("append") \
           .save(path)
       print("2.. ")
   except Exception as e:
       print("Error 2", e)
   
   
   print(
       "Connecticut ** ", spark.read.format("hudi").load(path).where("state='Connecticut'").count()
   )
   ```
   


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