S3 Reader
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S3 Reader component typically authenticate with S3 using AWS credentials, such as an access key ID and secret access key, to gain access to the S3 bucket and its contents. S3 Reader is designed to read and access data stored in an S3 bucket in AWS.
All component configurations are classified broadly into the following sections:
Meta Information
Check out the given demonstration to configure the S3 component and use it in a pipeline workflow.
Navigate to the Data Pipeline Editor.
Expand the Reader section provided under the Component Pallet.
Drag and drop the S3 Reader component to the Workflow Editor.
Click on the dragged S3 Reader to get the component properties tabs.
It is the default tab to open for the component while configuring it.
Invocation Type: Select an invocation mode out of ‘Real-Time’ or ‘Batch’ using the drop-down menu.
Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.
Container Image Version: It displays the image version for the docker container. This field comes pre-selected.
Failover Event: Select a failover Event from the drop-down menu.
Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 10).
Open the ‘Meta Information’ tab and fill in all the connection-specific details for the S3 Reader.·
Bucket Name (*): Enter S3 Bucket name where file is located.
Zone (*): S3 Zone location.
Access Key (*): Access key shared by AWS to login.
Secret Key (*): Secret key shared by AWS to login.
Table (*): Mention the Table or file name from S3 location which is to be read.
File Type (*): Select a file type from the drop-down menu (CSV, JSON, PARQUET, AVRO are the supported file types)
Limit: Set a limit for the number of records.
Query: Enter a Spark SQL query. Take inputDF/inputDf as table name.
Access Key (*): Access key shared by AWS to login.
Secret Key (*): Secret key shared by AWS to login
Table (*): Mention the Table or file name from S3 location which is to be read.
File Type (*): Select a file type from the drop-down menu (CSV, JSON, PARQUET, AVRO, XML are the supported file types)
Limit: Set limit for the number of records
Query: Enter a Spark SQL query. Take inputDF/inputDf as table name.
Sample Spark SQL query for S3 Reader:
There is also a section for the selected columns in the Meta Information tab if the user can select some specific columns from the table to read data instead of selecting a complete table so this can be achieved by using the ‘Selected Columns’ section. Select the columns which you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.
or
Use ‘Download Data’ and ‘Upload File’ options to select the desired columns.
Provide a unique Key column name to partition data in Spark.
Click the Save Component in Storage icon after doing all the configurations to save the reader component.
A notification message appears to inform about the component configuration success.
Please Note:
(*) the symbol indicates that the field is mandatory.
Either table or query must be specified for the data readers except for SFTP Reader.
Selected Columns- There should not be a data type mismatch in the Column Type for all the Reader components.
The Meta Information fields may vary based on the selected File Type.
All the possibilities are mentioned below:
CSV: ‘Header’ and ‘Infer Schema’ fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.
JSON: ‘Multiline’ and ‘Charset’ fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.
PARQUET: No extra field gets displayed with PARQUET as the selected File Type.
AVRO: This File Type provides two drop-down menus.
Compression: Select an option out of the ‘Deflate’ and ‘Snappy’ options.
Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.
XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:
Infer schema: Enable this option to get true schema of the column.
Path: Provide the path of the file.
Root Tag: Provide the root tag from the XML files.
Row Tags: Provide the row tags from the XML files.
Join Row Tags: Enable this option to join multiple row tags.