postgres-connector-hudi

Flink CDC Connector: Joining and Upserting CDC Events to Hudi¶

flink.drawio.png

Step 1: Spin up Postgres Database¶

Mac¶

version: "3.7"

services:
  postgres:
    image: arm64v8/postgres:13  # Use a compatible ARM64 image
    ports:
      - 5432:5432
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=postgres

Windows¶

version: "3.7"

services:
  postgres:
    image: postgres:13  # Use a compatible x86_64 image
    ports:
      - 5432:5432
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=postgres

SQL Commands for Creating Tables, Inserting Data, and Enabling Replica Identity¶

-- Create the "customers" table if it does not exist
CREATE TABLE IF NOT EXISTS public.customers (
  customer_id INT PRIMARY KEY,
  name TEXT,
  city TEXT,
  state TEXT
);

-- Create the "orders" table if it does not exist
CREATE TABLE IF NOT EXISTS public.orders (
  order_id INT PRIMARY KEY,
  order_value DOUBLE PRECISION,
  customer_id INT
);

-- Insert data into the "customers" table in the public schema
INSERT INTO public.customers (customer_id, name, city, state)
VALUES
  (1, 'Alice', 'New York', 'NY'),
  (2, 'Bob', 'Los Angeles', 'CA'),
  (3, 'Charlie', 'Chicago', 'IL');

-- Insert data into the "orders" table in the public schema
INSERT INTO public.orders (order_id, order_value, customer_id)
VALUES
  (101, 100.50, 1),
  (102, 75.25, 2),
  (103, 200.75, 1);

-- Enable full replica identity for the "customers" table
ALTER TABLE public.customers REPLICA IDENTITY FULL;

-- Enable full replica identity for the "orders" table
ALTER TABLE public.orders REPLICA IDENTITY FULL;

In [2]:

import os
os.environ['AWS_ACCESS_KEY_ID'] = "XXX"
os.environ['AWS_ACCESS_KEY'] = "XXX"

os.environ['AWS_SECRET_ACCESS_KEY'] = "XXXF"
os.environ['AWS_SECRET_KEY'] = "XXXX"

Flink Code¶

Creating table Enviorment and loading JAR Files¶

In [3]:

from pyflink.table import EnvironmentSettings, TableEnvironment
import os
from faker import Faker

# Create a batch TableEnvironment
env_settings = EnvironmentSettings.in_streaming_mode()
table_env = TableEnvironment.create(env_settings)

# Get the current working directory
CURRENT_DIR = os.getcwd()

# Define a list of JAR file names you want to add
jar_files = [
    "flink-sql-connector-postgres-cdc-2.4.1.jar",
    "postgresql-42.6.0.jar",
    "flink-connector-jdbc-1.16.1.jar", 
    "flink-s3-fs-hadoop-1.16.1.jar",
    "hudi-flink1.16-bundle-0.13.1.jar"
]

# Build the list of JAR URLs by prepending 'file:///' to each file name
jar_urls = [f"file:///{CURRENT_DIR}/{jar_file}" for jar_file in jar_files]

table_env.get_config().get_configuration().set_string(
    "pipeline.jars",
    ";".join(jar_urls)
)
table_env.get_config().get_configuration().set_string(
    "execution.checkpointing.interval",
    "5000"
)

table_env.get_config().get_configuration().set_string(
    "parallelism.default",
    "4"
)

Out[3]:

<pyflink.common.configuration.Configuration at 0x13022ca90>

Create Source¶

In [4]:

# Create a source for the "customer_source" table
customer_source = f"""
CREATE TABLE IF NOT EXISTS customer_source (
    customer_id INT,
    name STRING,
    city STRING,
    state STRING,
    db_name STRING METADATA FROM 'database_name' VIRTUAL,
    table_name STRING METADATA  FROM 'table_name' VIRTUAL,
    operation_ts TIMESTAMP_LTZ(3) METADATA FROM 'op_ts' VIRTUAL,
    PRIMARY KEY (customer_id) NOT ENFORCED
 ) WITH (
   'connector' = 'postgres-cdc',
   'hostname' = 'localhost',
   'port' = '5432',
   'username' = 'postgres',
   'password' = 'postgres',
   'database-name' = 'postgres',
   'schema-name' = 'public',
   'slot.name' = 'customer_slot',
   'decoding.plugin.name' = 'pgoutput',
   'table-name' = 'customers'
 );
"""

# Create a source for the "order_source" table
order_source = f"""
CREATE TABLE IF NOT EXISTS order_source (
    order_id INT,
    order_value DOUBLE PRECISION,
    customer_id INT,
    db_name STRING METADATA FROM 'database_name' VIRTUAL,
    table_name STRING METADATA  FROM 'table_name' VIRTUAL,
    operation_ts TIMESTAMP_LTZ(3) METADATA FROM 'op_ts' VIRTUAL,
    PRIMARY KEY (order_id) NOT ENFORCED
 ) WITH (
   'connector' = 'postgres-cdc',
   'hostname' = 'localhost',
   'port' = '5432',
   'username' = 'postgres',
   'password' = 'postgres',
   'database-name' = 'postgres',
   'schema-name' = 'public',
   'slot.name' = 'order_slot',
   'decoding.plugin.name' = 'pgoutput',
   'table-name' = 'orders'
 );
"""

# Execute the SQL to create the sources
table_env.execute_sql(customer_source)
table_env.execute_sql(order_source)

print("Created customer_source and order_source tables.")
print("Created order_source and order_source tables.")

Created customer_source and order_source tables.
Created order_source and order_source tables.

Joining CDC Streams Preview¶

In [5]:

# table_env.execute_sql("""
#     SELECT DISTINCT
#       CAST(o.order_id AS STRING) AS order_id,
#       CAST(o.operation_ts AS STRING) AS operation_ts,
#       CAST(c.name AS STRING) AS customer_name,
#       CAST(c.city AS STRING) AS customer_city,
#       CAST(c.state AS STRING) AS customer_state,
#       CAST(o.order_value AS STRING) AS price
#     FROM
#       customer_source AS c
#     INNER JOIN
#       order_source AS o
#     ON
#       c.customer_id = o.customer_id;
# """).wait()

# print("Job started.")

o/p preview ¶

SINK Hudi SINK¶

In [6]:

# Define the Hudi sink table schema to match the query result schema
hudi_output_path = 's3a://XXX/enriched_orders'

hudi_sink = f"""
CREATE TABLE order_enriched_hudi_sink (
   order_id STRING,
   operation_ts STRING,
   customer_name STRING,
   customer_city STRING,
   customer_state STRING,
   price STRING,
   PRIMARY KEY (order_id) NOT ENFORCED
 )
  PARTITIONED BY (`customer_state`)
  WITH (
    'connector' = 'hudi',
    'path' = '{hudi_output_path}',
    'table.type' = 'MERGE_ON_READ',
    'hoodie.datasource.write.recordkey.field' = 'order_id',  -- Specify record key
    'hoodie.datasource.write.precombine.field' = 'operation_ts',  -- Use "operation_ts" as precombine key
    'hoodie.datasource.write.operation' = 'upsert'  -- Use UPSERT method
 );
"""

# Execute the SQL to create the Hudi table
table_env.execute_sql(hudi_sink)

Out[6]:

<pyflink.table.table_result.TableResult at 0x13023b820>

Read from Source and Write into SINK¶

In [ ]:

table_env.execute_sql("""
    INSERT INTO order_enriched_hudi_sink
    SELECT DISTINCT
      CAST(o.order_id AS STRING) AS order_id,
      CAST(o.operation_ts AS STRING) AS operation_ts,
      CAST(c.name AS STRING) AS customer_name,
      CAST(c.city AS STRING) AS customer_city,
      CAST(c.state AS STRING) AS customer_state,
      CAST(o.order_value AS STRING) AS price
    FROM
      customer_source AS c
    INNER JOIN
      order_source AS o
    ON
      c.customer_id = o.customer_id;
""").wait()

print("Job started.")

2023-09-26 20:13:16,235 WARN  org.apache.hadoop.metrics2.impl.MetricsConfig                [] - Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
2023-09-26 20:13:16,268 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - Scheduled Metric snapshot period at 10 second(s).
2023-09-26 20:13:16,269 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - s3a-file-system metrics system started
2023-09-26 20:13:29,380 WARN  org.apache.hadoop.metrics2.impl.MetricsConfig                [] - Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
2023-09-26 20:13:29,393 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=MetricsSystem,sub=Stats": Instance already exists.
2023-09-26 20:13:29,394 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - Scheduled Metric snapshot period at 10 second(s).
2023-09-26 20:13:29,395 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - s3a-file-system metrics system started
2023-09-26 20:13:29,400 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=MetricsSystem,sub=Control": Instance already exists.
2023-09-26 20:13:29,402 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=S3AMetrics1-datateam-sandbox-qa-demo": Instance already exists.
2023-09-26 20:13:39,978 WARN  org.apache.hadoop.metrics2.impl.MetricsConfig                [] - Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
2023-09-26 20:13:40,020 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=MetricsSystem,sub=Stats": Instance already exists.
2023-09-26 20:13:45,028 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - Scheduled Metric snapshot period at 10 second(s).
2023-09-26 20:13:45,029 INFO  org.apache.hadoop.metrics2.impl.MetricsSystemImpl            [] - s3a-file-system metrics system started
2023-09-26 20:13:45,036 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=MetricsSystem,sub=Control": Instance already exists.
2023-09-26 20:13:45,110 WARN  org.apache.hadoop.metrics2.util.MBeans                       [] - Failed to register MBean "Hadoop:service=s3a-file-system,name=S3AMetrics1-datateam-sandbox-qa-demo": Instance already exists.
# WARNING: Unable to get Instrumentation. Dynamic Attach failed. You may add this JAR as -javaagent manually, or supply -Djdk.attach.allowAttachSelf
# WARNING: Unable to get Instrumentation. Dynamic Attach failed. You may add this JAR as -javaagent manually, or supply -Djdk.attach.allowAttachSelf
# WARNING: Unable to attach Serviceability Agent. Unable to attach even with module exceptions: [org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed., org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed., org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed.]
# WARNING: Unable to attach Serviceability Agent. Unable to attach even with module exceptions: [org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed., org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed., org.apache.hudi.org.openjdk.jol.vm.sa.SASupportException: Sense failed.]
2023-09-26 20:15:39,041 WARN  org.apache.hadoop.fs.s3a.S3ABlockOutputStream                [] - Application invoked the Syncable API against stream writing to enriched_orders/NY/.hoodie_partition_metadata_2. This is unsupported

Output¶

¶

Clean UP¶

In [7]:

import boto3
def delete_all_items_in_bucket(bucket_name):
    # Initialize the S3 client
    s3 = boto3.client('s3')

    # List all objects in the bucket
    objects = s3.list_objects_v2(Bucket=bucket_name)

    # Check if the bucket is empty
    if 'Contents' in objects:
        # Delete each object in the bucket
        for obj in objects['Contents']:
            key = obj['Key']
            s3.delete_object(Bucket=bucket_name, Key=key)
            print(f"Deleted: {key}")
    else:
        print(f"The bucket {bucket_name} is already empty.")

# Usage
bucket_name = 'dXXX'
delete_all_items_in_bucket(bucket_name)

Deleted: silver/enriched_orders/.hoodie/.aux/
Deleted: silver/enriched_orders/.hoodie/.schema/
Deleted: silver/enriched_orders/.hoodie/.temp/
Deleted: silver/enriched_orders/.hoodie/archived/

Referneces¶

https://ververica.github.io/flink-cdc-connectors/master/content/quickstart/mysql-postgres-tutorial.html

Pythonist

Tuesday, September 26, 2023

Flink CDC Connector: Joining and Upserting CDC Events to Hudi

Flink CDC Connector: Joining and Upserting CDC Events to Hudi¶

Step 1: Spin up Postgres Database¶

Mac¶

Windows¶

SQL Commands for Creating Tables, Inserting Data, and Enabling Replica Identity¶

Flink Code¶

Creating table Enviorment and loading JAR Files¶

Create Source¶

Joining CDC Streams Preview¶

o/p preview ¶

SINK Hudi SINK¶

Read from Source and Write into SINK¶

Output¶

¶

Clean UP¶

Referneces¶

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