HDFS Reader
Last updated
Last updated
HDFS stands for Hadoop Distributed File System. It is a distributed file system designed to store and manage large data sets in a reliable, fault-tolerant, and scalable way. HDFS is a core component of the Apache Hadoop ecosystem and is used by many big data applications.
This task reads the file located in HDFS(Hadoop Distributed File System).
Please follow the below mentioned steps to configure the meta information of HDFS Reader task:
Host IP Address: Enter the host IP address for HDFS.
Port: Enter the Port.
Zone: Enter the Zone for HDFS. Zone is a special directory whose contents will be transparently encrypted upon write and transparently decrypted upon read.
File Type: Select the File Type from the drop down. The supported file types are:
CSV: The 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: The 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.
Path: Provide the path of the file.
Partition Columns: Provide a unique Key column name to partition data in Spark.
