July 30, 2023

tHiveOutput – Docs for ESB 7.x

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tHiveOutput

Connects to a given Hive database and writes the data it receives into a given Hive
table or a directory in HDFS.

When ACID is enabled on the Hive side, a Spark Job cannot delete or
update a table and unless data is compacted, this Job cannot correctly read
aggregated data from a Hive table, either. This is a known limitation described in
the Spark bug tracking system: https://issues.apache.org/jira/browse/SPARK-15348.

Depending on the Talend
product you are using, this component can be used in one, some or all of the following
Job frameworks:

tHiveOutput properties for Apache Spark Batch

These properties are used to configure tHiveOutput running in the Spark Batch Job framework.

The Spark Batch
tHiveOutput component belongs to the Databases family.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

Basic settings

Hive storage configuration

Select the tHiveConfiguration
component from which you want Spark to use the configuration details to connect to
Hive.

HDFS Storage configuration

Select the tHDFSConfiguration component from which you want Spark to use the configuration
details to connect to a given HDFS system and transfer the dependent jar files to this HDFS
system. This field is relevant only when you are using an on-premises distribution.

Schema and Edit Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this
    option to view the schema only.

  • Change to built-in property:
    choose this option to change the schema to Built-in for local changes.

  • Update repository connection:
    choose this option to change the schema stored in the repository and decide whether
    to propagate the changes to all the Jobs upon completion. If you just want to
    propagate the changes to the current Job, you can select No upon completion and choose this schema metadata
    again in the Repository Content
    window.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

Output source

Select the type of the output data you want tHiveOutput to change:

  • Hive table: the Database field, the Table name field, the Table format list and the Enable
    Hive partitions     check box are displayed. You need to enter
    the related information about the Hive database to be connected to and the
    Hive table you need to modify.

    By default, the format of the output data is JSON, but you can
    change it to ORC or Parquet by selecting the corresponding option from the
    Table format list.

  • ORC file: the Output folder field is displayed and the
    Hive storage configuration list is deactivated, because the ORC file should
    be stored in your HDFS system hosting Hive. You need to enter the directory
    in which the output data is written.

Save mode

Select the type of changes you want to make regarding the target Hive
table.

  • When filling a newly created table which is still empty, you must not only
    use the Append mode but also select the
    Retrieve table schema from Hive metastore check
    box.

Enable Hive partitions

Select the Enable Hive partitions check box and in
the Partition keys table, define partitions for
the Hive table you are creating or changing. In the Partition
keys table, select columns from the input schema of
tHiveOutput to use as partition keys.

Bear in mind that:

  • When the Save mode to be used is
    Append, meaning that you are
    adding data to an existing Hive table, the partition columns
    you select in the Partition keys
    table must be already partition keys of the Hive table to be
    updated.

  • A partitioned Hive table created by tHiveOutput can only be read
    by tHiveInput due to Spark specific
    limitations. If you need to read your partitioned table
    through Hive itself, it is recommended to use
    tHiveRow or
    tHiveCreateTable in a Standard Job to
    create this table and then use tHiveOutput to append data to it.

  • Defining columns as partition keys does not alter your data
    but only create subfolders using the partition keys and put
    data in them.

Advanced settings

Sort columns alphabetically Select this check box to sort the schema columns in the alphabetical order. If you leave this check box clear, these columns stick to the order defined in the schema editor.
Use Timestamp format for Date type

Select the check box to output dates, hours, minutes and seconds contained in your
Date-type data. If you clear this check box, only years, months and days are
outputted.

The format used by Deltalake is yyyy-MM-dd HH:mm:ss.

Usage

Usage rule

This component is used as a start component and requires an output
link..

This component should use a tHiveConfiguration component present in the same Job to connect to
Hive.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
 data integration Jobs.

Spark Connection

In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration
      tab.

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      configuration       area in the Spark
      configuration       tab.

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
      configuration       tab.
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.

    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or
    tS3Configuration.

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks
    Filesystem).

This connection is effective on a per-Job basis.

Related scenarios

For a scenario about how to use the same type of component in a Spark Batch Job, see Writing and reading data from MongoDB using a Spark Batch Job.

tHiveOutput properties for Apache Spark Streaming

These properties are used to configure tHiveOutput running in the Spark Streaming Job framework.

The Spark Streaming
tHiveOutput component belongs to the Databases family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

Hive storage configuration

Select the tHiveConfiguration
component from which you want Spark to use the configuration details to connect to
Hive.

HDFS Storage configuration

Select the tHDFSConfiguration component from which you want Spark to use the configuration
details to connect to a given HDFS system and transfer the dependent jar files to this HDFS
system. This field is relevant only when you are using an on-premises distribution.

Schema and Edit
Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this
    option to view the schema only.

  • Change to built-in property:
    choose this option to change the schema to Built-in for local changes.

  • Update repository connection:
    choose this option to change the schema stored in the repository and decide whether
    to propagate the changes to all the Jobs upon completion. If you just want to
    propagate the changes to the current Job, you can select No upon completion and choose this schema metadata
    again in the Repository Content
    window.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

Output source

Select the type of the output data you want tHiveOutput to change:

  • Hive table: the Database field, the Table name field, the Table format list and the Enable
    Hive partitions     check box are displayed. You need to enter
    the related information about the Hive database to be connected to and the
    Hive table you need to modify.

    By default, the format of the output data is JSON, but you can
    change it to ORC or Parquet by selecting the corresponding option from the
    Table format list.

  • ORC file: the Output folder field is displayed and the
    Hive storage configuration list is deactivated, because the ORC file should
    be stored in your HDFS system hosting Hive. You need to enter the directory
    in which the output data is written.

Save mode

Select the type of changes you want to make regarding the target Hive
table.

  • When filling a newly created table which is still empty, you must not only
    use the Append mode but also select the
    Retrieve table schema from Hive metastore check
    box.

Enable Hive partitions

Select the Enable Hive partitions check box and in
the Partition keys table, define partitions for
the Hive table you are creating or changing. In the Partition
keys table, select columns from the input schema of
tHiveOutput to use as partition keys.

Bear in mind that:

  • When the Save mode to be used is
    Append, meaning that you are
    adding data to an existing Hive table, the partition columns
    you select in the Partition keys
    table must be already partition keys of the Hive table to be
    updated.

  • A partitioned Hive table created by tHiveOutput can only be read
    by tHiveInput due to Spark specific
    limitations. If you need to read your partitioned table
    through Hive itself, it is recommended to use
    tHiveRow or
    tHiveCreateTable in a Standard Job to
    create this table and then use tHiveOutput to append data to it.

  • Defining columns as partition keys does not alter your data
    but only create subfolders using the partition keys and put
    data in them.

Advanced settings

Sort columns alphabetically Select this check box to sort the schema columns in the alphabetical order. If you leave this check box clear, these columns stick to the order defined in the schema editor.
Use Timestamp format for Date type

Select the check box to output dates, hours, minutes and seconds contained in your
Date-type data. If you clear this check box, only years, months and days are
outputted.

The format used by Deltalake is yyyy-MM-dd HH:mm:ss.

Usage

Usage rule

This component is used as a start component and requires an output link.

This component should use a tHiveConfiguration component present in the same Job to connect to
Hive.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents
only Standard Jobs, that is to say traditional
Talend
 data
integration Jobs.

Spark Connection

In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration
      tab.

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      configuration       area in the Spark
      configuration       tab.

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
      configuration       tab.
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.

    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or
    tS3Configuration.

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks
    Filesystem).

This connection is effective on a per-Job basis.

Related scenarios

For a scenario about how to use the same type of component in a Spark Streaming Job, see
Reading and writing data in MongoDB using a Spark Streaming Job.

Document get from Talend https://help.talend.com
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