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 component reads the file located in HDFS(Hadoop Distributed File System).
All component configurations are classified broadly into 3 section
Meta Information
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.
ORC: Select this option to read ORC file. If this option is selected, the following fields will get displayed:
Push Down: In ORC (Optimized Row Columnar) file format, "push down" typically refers to the ability to push down predicate filters to the storage layer for processing. There will be two options in it:
True: When push down is set to True, it indicates that predicate filters can be pushed down to the ORC storage layer for filtering rows at the storage level. This can improve query performance by reducing the amount of data that needs to be read into memory for processing.
False: When push down is set to False, predicate filters are not pushed down to the ORC storage layer. Instead, filtering is performed after the data has been read into memory by the processing engine. This may result in more data being read and potentially slower query performance compared to when push down is enabled.
Path: Provide the path of the file.
Partition Columns: Provide a unique Key column name to partition data in Spark.