# tMysqlInput properties for Apache Spark Batch These properties are used to configure tMysqlInput running in the Spark Batch Job framework. The Spark Batch tMysqlInput component belongs to the Databases family. This component also allows you to connect and read data from a RDS Aurora or a RDS MySQL database. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Property type | Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the
properties are stored. | | ![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/Save_Icon.png) | Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic settings view.

For more information about setting up and storing database connection parameters, see [Centralizing database metadata](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/centralizing-database-metadata). | | Use an existing connection | Select this check box and in the Component
List drop-down list, select the desired connection component to
reuse the connection details you already defined. | | DB version | Select the version of the database to be used.

When the database to be used is RDS Aurora, you need to select Mysql 5 (deprecated).

Information noteNote: MySQL 5 has been removed from Talend Studio 8.0 R2026-04. Use MySQL 8.0 or later. | | Host | Database server IP address. | | Port | Listening port number of DB server. | | Database | Name of the database. | | Username and Password | DB user authentication data.

To enter the password, click the [...] button
next to the password field, enter the password in double quotes in the pop-up dialog box,
and click OK to save the settings. | | 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.
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.
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.

| | Table Name | Type in the name of the table from which you need to read data.

This field is only available when you select Table
from the Read from drop-down list. | | Read from | Select the type of the source of the
data to be read.

Table: tMysqlInput reads the data of the table you specify in the Table name field.
Query: tMysqlInput reads the result of the query you write in the Query field.

| | Query type and Query | Specify the database query statement paying particularly attention to the sequence of the fields which must correspond to the schema definition.

If you are using Spark V2.0 onwards, the Spark SQL does not recognize the prefix of a database table anymore. This means that you must enter only the table name without adding any prefix that indicates for example the schema this table belongs to.

For example, if you need to perform a query in a table *system.mytable*, in which the *system* prefix indicates the schema that the *mytable* table belongs to, in the query, you must enter *mytable* only.

You can use pushdown predicate in the query to filter the data from the
database. Spark supports the following operators:

Filter
Limit

These fields are only available when you select
Query from the Read
from drop-down list. | ## Advanced settings | Properties | Description | | --- | --- | | Additional JDBC parameters | Specify additional connection properties for the database connection you are
creating. The properties are separated by semicolon and each property is a key-value
pair, for example, *encryption=1;clientname=Talend*.

This field is not available if the Use an existing
connection check box is selected. | | Spark SQL JDBC parameters | Add the JDBC properties supported by Spark SQL to this table. For a list of the user configurable properties, see [JDBC to other database](https://spark.apache.org/docs/latest/sql-programming-guide.html#jdbc-to-other-databases).

This component automatically set the *url*, *dbtable* and *driver* properties by using the configuration from the Basic settings tab. | | Trim all the String/Char columns | Select this check box to remove leading and trailing whitespace from
all the String/Char columns. | | Trim column | Remove leading and trailing whitespace from defined columns.

Information noteNote:

Clear Trim all the String/Char
columns to enable Trim columns in this field. | | Enable partitioning | Select this check box to read data in partitions.

Define, in double quotation marks, the following parameters to configure the
partitioning:

Partition column: the numeric column used as partition key.
Lower bound of the partition stride and Upper bound of the partition stride: enter the upper bounds and the lower bound to determine the partition stride. These bounds do not filter the table rows. All rows in the table are partitioned and returned.
Number of partitions: the number of partitions into which the table rows are split. Each Spark worker handles only one of the partitions at a time.

The average size of the partitions is the result of the difference between the
upper bound and the lower bound divided by the number of partitions, that is to say,
(upperBound - lowerBound)/partitionNumber, while the first and the last partitions also
include all the other rows that are not contained in the other partitions.

For example, to partition 1000 rows into 4 partitions, if you enter 0 for
the lower bound and 1000 for the upper bound, each partition will contain 250 rows
and so the partitioning is even. If you enter 250 for the lower bound and 750 for
the upper bound, the second and the third partition will each contain 125 rows and
the first and the last partitions each 375 rows. With this configuration, the
partitioning is skewed. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as a start component and requires an
output link.

This component should use a tMysqlConfiguration component
present in the same Job to connect to MySQL. You need to select the Use an existing connection check box and then select the tMysqlConfiguration component to be used.

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 on-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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. |