data_disposal

Do you have data in Apache Hadoop using Apache HDFS that is made available with Apache Hive? Do you spend too much time manually cleaning old data or maintaining multiple hacked scripts? If you answered yes to these questions, then the Data Disposal tool is for you!

The Java based Data Disposal tool takes in a simple yaml configuration specifying Apache HDFS directories and Apache Hive tables with customizable retention windows and date parsing from a partition or file path.

Apache HDFS Configurations

• int retentionDuration: How many units of granularity the retention should be.
• ChronoUnit granularity: A string that can be parsed into a ChronoUnit value for example DAYS or WEEKS.
• SimpleDateFormat dateFormat: A string that that matches the allowed patterns in the DateTimeFormatter spec.
• String path: A path in Apache HDFS. If specifying dateFormat, you need to specify %s where you expect the date string to be located. Note that it will match the regex even where a more precise one could be used (e.g. 'yyyyMMdd' will also match a path containing '20160229125500'). All paths will be treated as globs.
• HDFSRetentionType retentionType: Either MODIFICATION_TIME for retention based on the specified file/folders modification time or PATH_DATE for retention based on the dateFormat included in the path.
• boolean recursive: If the specified path/s up for disposal are directories, should they be deleted?

Apache Hive Configurations

• int retentionDuration: How many units of granularity the retention should be.
• ChronoUnit granularity: A string that can be parsed into a ChronoUnit value for example DAYS or WEEKS.
• SimpleDateFormat dateFormat: A string that that matches the allowed patterns in the DateTimeFormatter spec.
• String database: The database containing the table.
• String table: The table that needs some partitions removed.
• String partitionFilterKey: Which key contains the date stamp that matches dateFormat:
• boolean deleteExternalData: Delete data associated with the partions that are going to be deleted. NOTE: This only works for Apache Hive internal tables. For external tables, you need to delete the data files in addition to the partitions.

Example Config

Apache Hive Example

HiveConfigExample.yaml

entries:
  - database: somename
    table: users
    partitionFilterKey: date
    deleteExternalData: true
    retentionDuration: 14
    granularity: DAYS
    dateFormat: yyyy-MM

  - database: somename2
    table: comments
    partitionFilterKey: date
    deleteExternalData: false
    retentionDuration: 14
    granularity: WEEKS
    dateFormat: yyyy-MM

Apache HDFS Example

HDFSConfigExample.yaml

hdfsNamenode: "hdfs://namenode:8020"

entries:
  - path: hdfs://namenode:8020/somepath/data/date=%s
    retentionDuration: 14
    granularity: DAYS
    retentionType: PATH_DATE
    dateFormat: yyyy-MM-dd
    recursive: true

  - path: hdfs://namenode:8020/somepath/metadata/somepath
    retentionDuration: 14
    granularity: HOURS
    retentionType: MODIFICATION_TIME
    recursive: false

Install

Currently the jar is not distributed to any repositories. You can create a working jar by cloning the repo and running mvn clean package

Usage

You must specify the Apache Hive conf dir in your classpath and you also need to ensure that both Apache Hadoop and Hive jars are on the classpath. In the example here, hive --service jar is used to include all Hadoop and Hive dependencies.

The --dry_run option will allow you to run the script to just log all directories and partitions that would be deleted in an actual run of the tool.

HADOOP_CLASSPATH=/<your path to>/hive/conf hive --service jar /<your path to>/data_disposal.jar com.vz.disposal.DataDisposal --hive_conf /<your path to>/hive_config.yaml --hdfs_conf /<your path to>hdfs_config.yaml --dry_run

Retention for other datastores

The data disposal tool is designed with a config interface and a data deletion interface both of which could easily be extended to new datastores based on your requirements. We would be happy to accept contributions following the guidelines listed below.

Contribute

• See Contributing
• See Code-of-Conduct

License

• See LICENSE