0.13.0 (2025-02-24)

🎉 3 years since first release 0.1.0 🎉

Breaking Changes

• Add Python 3.13. support. (#298)

• Change the logic of FileConnection.walk and FileConnection.list_dir. (#327)

  Previously limits.stops_at(path) == True considered as "return current file and stop", and could lead to exceeding some limit. Not it means "stop immediately".

• Change default value for FileDFWriter.Options(if_exists=...) from error to append, to make it consistent with other .Options() classes within onETL. (#343)

Features

• Add support for FileModifiedTimeHWM HWM class (see etl-entities 2.5.0):

  from etl_entitites.hwm import FileModifiedTimeHWM
  from onetl.file import FileDownloader
  from onetl.strategy import IncrementalStrategy
  
  downloader = FileDownloader(
      ...,
      hwm=FileModifiedTimeHWM(name="somename"),
  )
  
  with IncrementalStrategy():
      downloader.run()

• Introduce FileSizeRange(min=..., max=...) filter class. (#325)

  Now users can set FileDownloader / FileMover to download/move only files with specific file size range:

  from onetl.file import FileDownloader
  from onetl.file.filter import FileSizeRange
  
  downloader = FileDownloader(
      ...,
      filters=[FileSizeRange(min="10KiB", max="1GiB")],
  )

• Introduce TotalFilesSize(...) limit class. (#326)

  Now users can set FileDownloader / FileMover to stop downloading/moving files after reaching a certain amount of data:

  from datetime import datetime, timedelta
  from onetl.file import FileDownloader
  from onetl.file.limit import TotalFilesSize
  
  downloader = FileDownloader(
      ...,
      limits=[TotalFilesSize("1GiB")],
  )

• Implement FileModifiedTime(since=..., until=...) file filter. (#330)

  Now users can set FileDownloader / FileMover to download/move only files with specific file modification time:

  from datetime import datetime, timedelta
  from onetl.file import FileDownloader
  from onetl.file.filter import FileModifiedTime
  
  downloader = FileDownloader(
      ...,
      filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))],
  )

• Add SparkS3.get_exclude_packages() and Kafka.get_exclude_packages() methods. (#341)

  Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark:

  from onetl.connection import SparkS3, Kafka
  
  maven_packages = [
      *SparkS3.get_packages(spark_version="3.5.4"),
      *Kafka.get_packages(spark_version="3.5.4"),
  ]
  exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages()
  spark = (
      SparkSession.builder.appName("spark_app_onetl_demo")
      .config("spark.jars.packages", ",".join(maven_packages))
      .config("spark.jars.excludes", ",".join(exclude_packages))
      .getOrCreate()
  )

Improvements

• All DB connections opened by JDBC.fetch(...), JDBC.execute(...) or JDBC.check() are immediately closed after the statements is executed. (#334)

  Previously Spark session with master=local[3] actually opened up to 5 connections to target DB - one for JDBC.check(), another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, as JDBC.check() does not hold opened connection.

  This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low.

• Set up ApplicationName (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. (#339, #248)

  Also update ApplicationName format for Greenplum, Postgres, Kafka and SparkS3. Now all connectors have the same ApplicationName format: ${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}

  The only connections not sending ApplicationName are Teradata and FileConnection implementations.

• Now DB.check() will test connection availability not only on Spark driver, but also from some Spark executor. (#346)

  This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not.

Bug Fixes

• Avoid suppressing Hive Metastore errors while using DBWriter. (#329)

  Previously this was implemented as:

  try:
      spark.sql(f"SELECT * FROM {table}")
      table_exists = True
  except Exception:
      table_exists = False

  If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting to full table override instead of append or override only partitions subset.

• Fix using onETL to write data to PostgreSQL or Greenplum instances behind pgbouncer with pool_mode=transaction. (#336)

  Previously Postgres.check() opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, and when DBWriter.run(df) executed in read-only connection, producing errors like:

  org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction
  org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction
  

  Added a workaround by passing readOnly=True to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly.

  After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. To this this, user can manually convert read-only connection to read-write:

  postgres.execute("BEGIN READ WRITE;")  # <-- add this line
  DBWriter(...).run()

  After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed.

  See Postgres JDBC driver documentation.

• Fix MSSQL.fetch(...) and MySQL.fetch(...) opened a read-write connection instead of read-only. (#337)

  • Now this is fixed:

    • MSSQL.fetch(...) establishes connection with ApplicationIntent=ReadOnly.
    • MySQL.fetch(...) calls SET SESSION TRANSACTION READ ONLY statement.

• Fixed passing multiple filters to FileDownloader and FileMover. (#338) If was caused by sorting filters list in internal logging method, but FileFilter subclasses are not sortable.

• Fix a false warning about a lof of parallel connections to Grenplum. (#342)

  Creating Spark session with .master("local[5]") may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), but onETL instead used number of CPU cores on the host as a number of parallel connections.

  This lead to showing a false warning that number of Greenplum connections is too high, which actually should be the case only if number of executors is higher than 30.

• Fix MongoDB trying to use current database name as authSource. (#347)

  Use default connector value which is admin database. Previous onETL versions could be fixed by:

  from onetl.connection import MongoDB
  
  mongodb = MongoDB(
      ...,
      database="mydb",
      extra={
          "authSource": "admin",
      },
  )

Dependencies

• Minimal etl-entities version is now 2.5.0. (#331)

• • Update DB connectors/drivers to latest versions: (#345)

    • Clickhouse 0.6.5 → 0.7.2
    • MongoDB 10.4.0 → 10.4.1
    • MySQL 9.0.0 → 9.2.0
    • Oracle 23.5.0.24.07 → 23.7.0.25.01
    • Postgres 42.7.4 → 42.7.5

Doc only Changes

• Split large code examples to tabs. (#344)