soumilshah1995 commented on issue #9493: URL: https://github.com/apache/hudi/issues/9493#issuecomment-1689045266
Here is Tutorial https://www.youtube.com/watch?v=QqCiycIgSFk&t=1s ``` """ Author : Soumil Nitin Shah Email shahsoumil...@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() ) ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@hudi.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
