S3 Reader
Last updated
Last updated
All component configurations are classified broadly into the following sections:
Basic
Metadata
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 (*): Folder Name
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 object name 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: Insert an SQL query (it supports query containing a join statement as well)·
Access Key (*): Access key shared by AWS to login
Secret Key (*): Secret key shared by AWS to login
Table (*): Mention the Table or object name which has 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 limit for the number of records
Query: Insert an SQL query (it supports query containing a join statement as well)
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.
JSON: ‘Multiline’ and ‘Charset’ fields get displayed with JSON as the selected File Type.
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.