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The JDBC Load orchestration component allows you to run queries on a JDBC-compatible data source that you have a driver for. You can upload your drivers to , and so connect to any sources that Matillion doesn’t provide drivers for as standard. You do not need to set up a Create Table component before using this component.
  • This component is only available for use with Hybrid SaaS agents.
  • When adding new drivers, an agent restart is required to recognize the configuration. Once the agent has restarted, you can choose the driver from the Database Type property in the component.
  • For connecting to Oracle Autonomous Databases, read the Oracle Autonomous Database authentication guide.
To use this component, you must upload a suitable JDBC driver for your data source, following the process given in Uploading external drivers to the agent. You can upload drivers for multiple different data sources, and select the required source from a drop-down when configuring the component. You must also create a manifest file, which will contain configuration information for your data sources. If the component requires access to a cloud provider (AWS, Azure, or GCP), it will use the cloud credentials associated with your environment to access resources. To stage data to Azure Blob Storage, the Azure credentials associated with your environment must be assigned the Storage Blob Data Contributor role. For more information, read User assigned with the Storage Blob Data Contributor role.
For Snowflake projects, this component supersedes the JDBC component, which is no longer available for new pipelines.Existing pipelines with the JDBC component will continue to work as expected.

Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.

Connect

Database Type
drop-down
required
Select the database type. The drop-down will contain all databases that you have included in the manifest file.
Connection URL
string
required
The URL for your chosen JDBC database. If the field displays a template URL for the database, replace any placeholder values with the actual values of your database’s URL.Although many parameters and options can be added to the end of the URL, it’s generally easier to add them in the Connection Options property, documented below.
Authentication Type
drop-down
required
Currently only supports Username & Password or None.
Username
string
required
Your username for the JDBC data source.
Password
drop-down
required
Displays a drop-down list of secret definitions. Select the secret definition that references the password corresponding to the username.
Connection Options
column editor
required
  • Parameter: A JDBC parameter supported by the database driver. The available parameters are explained in the data model. Manual setup is not usually required, since sensible defaults are assumed.
  • Value: A value for the given parameter.
Click the Text Mode toggle at the bottom of the Connection Options dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode.

Configure

Load Type
drop-down
required
  • Full Load: Select this option to load your entire dataset.
  • Incremental Load: Select this option to only load new and updated records from your dataset.

Mode
drop-down
required
  • Basic: This mode will build a query for you using settings from the Schema, Data Source, Data Selection, Data Source Filter, Combine Filters, and Limit parameters. In most cases, this mode will be sufficient.
  • Advanced: This mode will require you to write an SQL-like query to call data from the service you’re connecting to. The available fields and their descriptions are documented in the data model.
Advanced mode is currently not supported when Incremental Load is selected.
There are some special pseudo columns that can form part of a query filter, but are not returned as data. This is fully described in the data model.
While the query is exposed in an SQL-like language, the exact semantics can be surprising, for example, filtering on a column can return more data than not filtering on it. This is an impossible scenario with regular SQL.

SQL Query
code editor
required
This is an SQL-like SELECT query. Treat collections as table names, and fields as columns. Only available in Advanced mode.For more information, read the Snowflake SELECT documentation.

Schema
drop-down
required
Select the database schema to load data from.

Data Source
drop-down
required
Select a single data source to be extracted from the source system and loaded into a table in the destination. The source system defines the data sources available. Use multiple components to load multiple data sources.

Data Selection
dual listbox
required
Choose one or more columns to return from the query. The columns available are dependent upon the data source selected. Move columns left-to-right to include in the query.To use grid variables, select the Use Grid Variable checkbox at the bottom of the Data Selection dialog.

Data Source Filter
column editor
required
Define one or more filter conditions that each row of data must meet to be included in the load.
  • Input Column: Select an input column. The available input columns vary depending upon the data source.
  • Qualifier: Is compares the column to the value using the comparator. Not reverses the effect of the comparison, so “Equals” becomes “Not equals”, “Less than” becomes “Greater than or equal to”, etc.
  • Comparator: Choose a method of comparing the column to the value. Possible comparators include: “Equal to”, “Greater than”, “Less than”, “Greater than or equal to”, “Less than or equal to”, “Like”, “Null”. Not all data sources support all comparators.
  • Value: The value to be compared.
Click the Text Mode toggle at the bottom of the dialog to open a multi-line editor. For more information, read Text mode.

Combine Filters
drop-down
required
The data source filters you have defined can be combined using either And or Or logic. If And, then all filter conditions must be satisfied to load the data row. If Or, then only a single filter condition must be satisfied. The default is And.If you have only one filter, or no filters, this parameter is essentially ignored.

Row Limit
integer
required
Set a numeric value to limit the number of rows that are loaded. The default is an empty field, which will load all rows.

High-Water Mark Selection
drop-down
required
When Incremental Load is selected, select a datetime field from your dataset that is always updated when your data changes, such as dateModified. The connector will record the maximum value of this field each time you run this pipeline. On subsequent runs when Incremental Load is selected, only data with a higher value in this field will be loaded.
If no rows in the source have a higher value than the stored high-water mark, the task will complete with a “task is skipped” message. This is expected behavior, indicating that no new or updated data was found since the last successful run.

Destination

Destination
drop-down
required
Select the destination for your data. This is either in Snowflake as a table or as files in cloud storage.
  • Snowflake: Load your data into a table in Snowflake. The data must first be staged via Snowflake or a cloud storage solution.
  • Cloud Storage: Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension so we suggest inspecting the output to determine the format of the data.
Warehouse
drop-down
required
The Snowflake warehouse used to run the queries. The special value [Environment Default] uses the warehouse defined in the environment. Read Overview of Warehouses to learn more.
Database
drop-down
required
The Snowflake database to access. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
Schema
drop-down
required
The Snowflake schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
Table Name
string
required
The name of the table to be created in your Snowflake database. You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.
Load Strategy
drop-down
required
Define what happens if the table name already exists in the specified Snowflake database and schema. Only available when Full Load is selected.
  • Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
  • Truncate and Insert: If the specified table name already exists, all rows within the table will be removed and new rows will be inserted per the next run of this pipeline.
  • Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
  • Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It’s appended onto the end of the existing data in the table. If the specified table name doesn’t exist, then the table will be created, and your data will be inserted into the table.
Primary Keys
dual listbox
Choose one or more columns to be designated as the table’s primary key.When Incremental Load is selected, if a primary key is selected, the loaded data will be merged with your existing data. If no primary key is selected, the loaded data will be appended to your existing data.
Clean Staged files
boolean
required
  • Yes: Staged files will be destroyed after data is loaded. This is the default setting.
  • No: Staged files are retained in the staging area after data is loaded.
Stage Access Strategy
drop-down
Select the stage access strategy. The strategies available depend on the cloud platform you select in Stage Platform.
  • Credentials: Connects to the external stage (AWS, Azure) using your configured cloud provider credentials. Not available for Google Cloud Storage.
  • Storage Integration: Use a Snowflake storage integration to grant access to Snowflake to read data from and write to a cloud storage location. This will reveal the Storage Integration property, through which you can select any of your existing Snowflake storage integrations.
Stage Platform
drop-down
required
Use the drop-down menu to choose where the data is staged before being loaded into your Snowflake table.
  • Amazon S3: Stage your data on an AWS S3 bucket.
  • Snowflake: Stage your data on a Snowflake internal stage.
  • Azure Storage: Stage your data in an Azure Blob Storage container.
  • Google Cloud Storage: Stage your data in a Google Cloud Storage bucket.

Advanced Settings

Parse 'Null' & Empty Strings as NULL
boolean
required
Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", “NULL”, “NUL”, “Null”, “null”. The default is No.
Currently, this property is only applicable when using Snowflake as your destination.

Trim String Columns
boolean
required
When Yes, remove leading and trailing characters from a string column. The default is No.

Manifest file

The manifest file is a JSON format text file that includes the configuration information needed to connect to the data source. Every data source that you upload an external driver for must have a corresponding entry in the manifest file. The manifest file must be named jdbc-driver-manifest.json and saved to the storage location that also holds the uploaded external driver files. A manifest file must be in valid JSON format and strictly follow this structure:
[
    {
      "name": "<database_type>",
      "driverClassName": "<driver_class_name>",
      "baseUrl": "<base_url>",
      "limitStrategy": "<limit_strategy>",
      "fetchSize": <fetch_size>,
      "defaultProperties": {
        "<property>": "<value>",
        ...
      },
      "blacklistedProperties": [
        "<property>",
        ...
      ],
      "propertyOverrides": {
        "<property>": "<value>",
        ...
      },
      "skipQueryValidation": "true"
    }
]
A single manifest file can include the configuration for several different database types by repeating this structure for each type. Replace the parameters shown within < > above with the actual values required by the driver you are using, in accordance with the following notes:
  • name: This should be a simple name for the driver that identifies the database type it’s used with. For example MySQL, Mailchimp, or PostgreSQL. The name will appear in the component’s Database Type drop-down.
  • driverClassName: The fully qualified name of a class that implements the java.sql.Driver interface in Java. The fully qualified name of the driver class includes the package name followed by the class name, ensuring it’s unique across all Java packages. For example, the driver class name for MySQL’s Connector/J driver is com.mysql.cj.jdbc.Driver (version 8.0 and above), and the class name for the PostgreSQL driver is org.postgresql.Driver.
  • baseUrl: A template URL used to prompt the user to enter the correct format of URL in the component’s Connection URL property. This should be the basic URL for the driver, such as jdbc:postgresql:// or jdbc:cdata:mailchimp:.
  • limitStrategy: Controls how the component gets metadata about the query. Different databases have different ways of supporting this. Enter the method required by the data source you are configuring:
    • limit-inline: The most straightforward and widely supported method for limiting query results, commonly used in MySQL, PostgreSQL, SQLite, and others.
    • top-n: Similar to limit-inline, but commonly used in SQL Server and Sybase.
    • fetch-first-n: Similar to limit-inline, but supported by newer versions of databases such as DB2, Oracle (12c and later), and PostgreSQL.
    • rownum: Uses a pseudocolumn in Oracle to limit the number of rows returned, and is particularly useful for complex queries or pagination.
    • limit-outer: A sophisticated technique to limit query results by wrapping the main query within an outer query where the limit is applied. This strategy is particularly useful for complex queries that require pre-processing steps before the application of the limit. Commonly used in databases that can use optimized nested queries.
    • none: No limit is applied to the query, either due to retrieving all records for processing or when the dataset is known to be within acceptable size limits. Commonly used in all SQL databases.
  • fetchSize: Used in conjunction with the limitStrategy, this is the number of rows fetched at once, if supported by the database driver. Enter this as a JSON integer value, i.e. not within " ".
  • defaultProperties: Define any required defaults for driver properties. These defaults can be overridden by the user when the component is configured (but see blacklistedProperties, below, for how to prevent overrides). List the name of each default property and its default value as a name:value pair inside the defaultProperties object. This is a JSON object requiring { } delimiters. For example:
    "defaultProperties": {
        "connectTimeout": "20000",
        "allowLoadLocalInfile": "false",
        "allowUrlInLocalInfile": "false",
        "allowLocalInfile": "false"
    },
    
    If there are no default properties, this element is still required and should be an empty object:
    "defaultProperties": {},
    
  • blacklistedProperties: List any properties that you don’t want to be accessible from the UI. For example, if you have set a default property here in the manifest file that you don’t want the pipeline creator to be able to override in the UI, you should list it here. List the name of each blacklisted property inside the blacklistedProperties array. This is a JSON array requiring [ ] delimiters. For example:
    "blacklistedProperties": [
        "allowLoadLocalInfile",
        "allowUrlInLocalInfile",
        "allowLocalInfile"
    ],
    
    If there are no blacklisted properties, this element is still required and should be an empty array:
    "blacklistedProperties": [],
    
  • propertyOverrides: If the driver is expecting property names that are different from those used by the component, you can map the expected names here. For example, MySQL expects “user” instead of “username” as used by the component, so in a manifest file for MySQL you would specify: "username": "user". List each required override inside the propertyOverrides object. This is a JSON object requiring { } delimiters. For example:
    "propertyOverrides": {
        "username": "user",
        "password": "pass"
    }
    
    If there are no required overrides, this element is still required and should be an empty object:
    "propertyOverrides": {}
    
  • skipQueryValidation: The component’s default behavior is to validate a query written in Advanced mode by fetching metadata from the data source. In some cases, returning metadata takes a considerable time, resulting in slow validation of the component. To avoid such delays, include this property in your manifest file and set it to true. This property is optional, and should be omitted if not needed.
The following sections include some examples of suitable manifest files for specific database types that Matillion has tested with this component. For other database types not covered here, you will need to refer to the database documentation, along with the guidance given above, to determine which properties and values are required in your manifest file.

MySQL

The following manifest can be used to configure MySQL as a data source.
[
    {
      "name": "MySQL",
      "driverClassName": "com.mysql.cj.jdbc.Driver",
      "baseUrl": "jdbc:mysql://",
      "limitStrategy": "limit-inline",
      "fetchSize": 500,
      "defaultProperties": {
        "connectTimeout": "20000",
        "allowLoadLocalInfile": "false",
        "allowUrlInLocalInfile": "false",
        "allowLocalInfile": "false"
      },
      "blacklistedProperties": [
      ],
      "propertyOverrides": {
        "username": "user",
        "password": "password"
      }
    }
]
You can download an example of this file here to use as a template for creating your own manifest file.

SAP Hana

The following manifest can be used to configure SAP Hana as a data source.
[
    {
      "name" : "SAPHana",
      "driverClassName" : "com.sap.db.jdbc.Driver",
      "baseUrl" : "jdbc:sap://",
      "fetchSize" : 500,
      "limitStrategy" : "top-n",
      "defaultProperties": {
      },
      "blacklistedProperties": [
      ],
      "propertyOverrides": {
        "username": "user",
        "password": "password"
      }
    }
]

InterSystems Caché

The following manifest can be used to configure InterSystems Caché as a data source.
[
   {
    "name": "Intersystems Cache",
    "driverClassName": "com.intersys.jdbc.CacheDriver",
    "baseUrl": "jdbc:Cache://",
    "limitStrategy": "top-n",
    "fetchSize": 500,
    "defaultProperties": {
      "connectTimeout": "20000"
    },
    "blacklistedProperties": [],
    "propertyOverrides": {
      "username": "user",
      "password": "password"
    }
  }
]

Mailchimp

The following manifest can be used to configure Mailchimp as a data source.
[
   {
    "name": "mailchimp",
    "baseUrl": "jdbc:cdata:mailchimp:",
    "limitStrategy": "limit-inline",
    "driverClassName": "cdata.jdbc.mailchimp.MailChimpDriver",
    "blacklistedProperties": [
        "logfile",
        "verbosity"
    ],
    "defaultProperties": {
        "rtk": "434D524A5641535552425641454E54503156474A3138323200000000000000000000000000000000384A36435347324400005239584145445A39344D545A0000"
    }
  }
]

Informix

The following manifest can be used to configure Informix as a data source.
[
   {
    "name": "Informix",
    "driverClassName": "com.informix.jdbc.IfxDriver",
    "baseUrl": "jdbc:informix-sqli://",
    "limitStrategy": "limit-inline",
    "fetchSize": 500,
    "defaultProperties": {},
    "blacklistedProperties": [],
    "propertyOverrides": {
    "username": "user"
    }
  }
]