[SUPPORT] hoodie.cleaner.commits.retained Setting Overridden, Warning to Increase to 20

[Soumil Nitin Shah]

Issue Description:
I'm encountering an issue with Hudi configurations related to commit retention and cleaning. Despite explicitly setting hoodie.cleaner.commits.retained to 5, I'm receiving a warning suggesting it should be set to 20. It seems like the system is not acknowledging my provided value and is using some default instead.

hudi_options = {
    "hoodie.keep.min.commits": "5",
    "hoodie.cleaner.commits.retained": "5",
    "hoodie.keep.max.commits": "6"
}

Warning Message:

24/06/11 08:56:29 WARN HoodieWriteConfig: Increase hoodie.keep.min.commits=7 to be greater than hoodie.cleaner.commits.retained=20 (there is risk of incremental pull missing data from few instants based on the current configuration). The Hudi archiver will automatically adjust the configuration regardless.

Steps to Reproduce:
Use the above Hudi configuration.
Run the ingestion process using the provided code sample.
Expected Behavior:
The system should respect the explicitly set hoodie.cleaner.commits.retained value of 5 without suggesting an increase to 20.

Actual Behavior:
The system issues a warning to increase hoodie.keep.min.commits to be greater than hoodie.cleaner.commits.retained set to 20, despite hoodie.cleaner.commits.retained being explicitly set to 5.

Environment:
Hudi Version: 0.14.0
Spark Version: 3.4
OS: macOS
Code Sample:

import os
import sys
from pyspark.sql import SparkSession

HUDI_VERSION = '0.14.0'
SPARK_VERSION = '3.4'

os.environ["JAVA_HOME"] = "/opt/homebrew/opt/openjdk@11"
SUBMIT_ARGS = f"--packages org.apache.hudi:hudi-spark{SPARK_VERSION}-bundle_2.12:{HUDI_VERSION} pyspark-shell"
os.environ["PYSPARK_SUBMIT_ARGS"] = SUBMIT_ARGS
os.environ['PYSPARK_PYTHON'] = sys.executable

# Spark session
spark = SparkSession.builder \
    .config('spark.serializer', 'org.apache.spark.serializer.KryoSerializer') \
    .config('spark.sql.extensions', 'org.apache.spark.sql.hudi.HoodieSparkSessionExtension') \
    .config('className', 'org.apache.hudi') \
    .config('spark.sql.hive.convertMetastoreParquet', 'false') \
    .getOrCreate()

def write_to_hudi(spark_df,
                  table_name,
                  db_name,
                  method='upsert',
                  table_type='COPY_ON_WRITE',
                  recordkey='',
                  precombine='',
                  partition_fields='',
                  index_type='BLOOM',
                  curr_region='us-east-1'
                  ):
    path = f"file:///path/to/hudi/table/{db_name}/{table_name}"

    hudi_options = {
        'hoodie.table.name': table_name,
        'hoodie.datasource.write.table.type': table_type,
        'hoodie.datasource.write.table.name': table_name,
        'hoodie.datasource.write.operation': method,
        'hoodie.datasource.write.recordkey.field': recordkey,
        'hoodie.datasource.write.precombine.field': precombine,
        "hoodie.datasource.write.partitionpath.field": partition_fields,
        
        "hoodie.keep.min.commits": "5",
        "hoodie.cleaner.commits.retained": "5",
        "hoodie.keep.max.commits": "6",

        "hoodie.write.concurrency.mode": "optimistic_concurrency_control",
        "hoodie.cleaner.policy.failed.writes": "LAZY",
        "hoodie.write.lock.provider": "org.apache.hudi.client.transaction.lock.FileSystemBasedLockProvider",
    }
    print(hudi_options)
    print(path)

    spark_df.write.format("hudi"). \
        options(**hudi_options). \
        mode("append"). \
        save(path)

from pyspark.sql.types import StructType, StructField, StringType

schema = StructType([
    StructField("id", StringType(), True),
    StructField("message", StringType(), True)
])

# Loop to generate data and write to Hudi
for i in range(1, 10):
    # Create the data
    updated_data = [(str(i), "Batch : {} ".format(i))]

    # Create the DataFrame with the new data
    df = spark.createDataFrame(updated_data, schema)

    # Show the DataFrame with the updated "message" column
    df.show()

    # Write to Hudi
    write_to_hudi(
        spark_df=df,
        method="upsert",
        db_name="default",
        table_name="messages",
        recordkey="id",
        precombine="message"
    )

    import time
    time.sleep(1)

Additional Context:
This behavior seems to indicate an internal adjustment or default setting that overrides the user-defined configuration, potentially causing confusion and misconfiguration. Any insights or fixes to ensure that the provided configurations are respected would be greatly appreciated.

[Soumil Nitin Shah]

Slack Thread
https://apache-hudi.slack.com/archives/C4D716NPQ/p1718110790091359

[Aditya Goenka]

@soumilshah1995 You are right. when the number of cleaner commits is less than 20, it actually compares the archival commits with 20 only. When it's more than 20 then it is behaving fine. Though the code just directly compares the same -

Need to take a deeper look why can this happen

[Soumil Nitin Shah]

Roger that sir