In this short post I will show you how you can using Apache Spark rename or delete a file HDFS".
Table of Contents
Introduction
Apache Spark
Apache Spark" is an open-source, distributed computing system that is designed for fast, in-memory data processing. It was developed at the University of California, Berkeley, and is now maintained and supported by the Apache Software Foundation.
Spark is designed to be highly scalable, making it well-suited for large-scale data processing tasks. It can be used to process data from a variety of sources, including structured data stored in databases and unstructured data stored in distributed file systems. Spark is also designed to be easy to use, with a wide range of libraries and APIs that support a variety of programming" languages, including Java", Python", R, and Scala".
One of the key features of Spark" is its ability to perform in-memory data processing, which allows it to process large amounts of data much faster than traditional disk-based systems. It also has a number of other advanced features, such as support for stream processing, machine learning", and graph processing.
Spark is often used in a variety of applications, including data engineering, data analytics, and machine learning". It is widely used in industry, as well as in research and academia, and has a large and active community of users and developers.
Apache Hadoop
Apache Hadoop" is an open-source software framework that is designed for distributed storage and processing of large datasets. It was developed by the Apache Software Foundation and is now a top-level Apache project.
Hadoop" is based on the MapReduce programming model, which was developed by Google to enable distributed processing of large data sets across clusters of computers. Hadoop" consists of a number of components, including a distributed file system (HDFS"), a resource scheduler (YARN), and a number of data processing tools and libraries.
Hadoop" is designed to be highly scalable and fault-tolerant, making it well-suited for storing and processing large amounts of data. It is often used in a variety of applications, including data warehousing, data lakes, and big data" analytics. Hadoop" has a large and active community of users and developers, and is widely used in industry and research.
Hadoop Distributed File System (HDFS)
The Hadoop" Distributed File System (HDFS") is a distributed file system that is part of the Apache Hadoop" software framework. It is designed to store large amounts of data in a distributed and fault-tolerant manner, and to support the rapid processing of this data using the MapReduce programming model.
HDFS" is based on a master-slave architecture, with a central server called the NameNode that manages the file system namespace and the metadata for the files and directories stored in the file system. The NameNode is responsible for mapping the file names to the blocks of data that make up the files, and for keeping track of the locations of these blocks on the slaves.
The slaves in HDFS" are called DataNodes, and they are responsible for storing the actual data blocks and serving the data to clients. HDFS" is designed to store data across multiple DataNodes, with each block of data replicated on multiple nodes to provide fault tolerance.
HDFS" is designed to support high-throughput data access, and is well-suited for applications that require the processing of large amounts of data, such as batch processing and data warehousing. It is also highly scalable, making it well-suited for use in distributed computing environments.
Apache Spark Rename Or Delete A File HDFS
To delete a file from HDFS in Apache Spark", you can use the hadoop
module in the Python" API or the org.apache.hadoop.fs.FileSystem
class in the Java" API.
package com.bigdataetl import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.sql.SparkSession object Test extends App { val spark = SparkSession.builder // I set master to local[*], because I run it on my local computer. // I production mode master will be set rom s .master("local[*]") .appName("BigDataETL") .getOrCreate() // Create FileSystem object from Hadoop Configuration val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration) // Base path where Spark will produce output file val basePath = "/bigtadata_etl/spark/output" val newFileName = "renamed_spark_output" // Change file name from Spark generic to new one fs.rename(new Path(s"$basePath/part-00000"), new Path(s"$basePath/$newFileName")) }
Apache Spark Remove File / Files
Using DSL. (I described another example under the post: Scala": how to run a shell" command from the code level?) or use the FileSystem class from the org.apache.hadoop.fs package. (Apache Spark" rename or delete a file HDFS")
package com.bigdataetl import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.sql.SparkSession import scala.sys.process._ object SparkDeleteFile { val spark = SparkSession.builder // I set master to local[*], because I run it on my local computer. // I production mode master will be set rom s .master("local[*]") .appName("BigDataETL") .getOrCreate() // Create FileSystem object from Hadoop Configuration val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration) // Delete directories recursively using FileSystem class fs.delete(new Path("/bigdata_etl/data"), true) // Delete using Scala DSL s"hdfs dfs -rm -r /bigdata_etl/data/" ! // Delete file fs.removeAcl(new Path("/bigdata_etl/data/file_to_delete.dat")) // Delete using Scala DSL s"hdfs dfs -rm /bigdata_etl/data/file_to_delete.dat" ! }
It is important to note that these examples assume that you have the necessary Hadoop" configuration and dependencies set up in your Spark application. If you are using a different version of Hadoop", you may need to modify the code to use the appropriate classes and methods.
PySpark Delete File
To delete a file from HDFS in PySpark", you can use the os
module in the Python" standard library and the SparkContext
to access the Hadoop" configuration and the HDFS" file system.
Here is an example of how To delete a file from HDFS in PySpark":
import os from pyspark import SparkContext # Get the file path file_path = "hdfs:///path/to/file.txt" # Get the SparkContext sc = SparkContext.getOrCreate() # Get the Hadoop configuration hadoop_conf = sc._jsc.hadoopConfiguration() # Get the file system fs = sc._jvm.org.apache.hadoop.fs.FileSystem.get(hadoop_conf) # Check if the file exists if fs.exists(sc._jvm.org.apache.hadoop.fs.Path(file_path)): # Delete the file fs.delete(sc._jvm.org.apache.hadoop.fs.Path(file_path), True)
In this example, the SparkContext
is used to access the Hadoop" configuration and the HDFS" file system. The delete
method of the FileSystem
class is then used to delete the file from HDFS". The second argument to the delete
method specifies whether to delete the file recursively, if it is a directory.
Summary
Using described approach you can easily cover topics like:
- Spark Delete File
- PySpark" Delete File
- HDFS" Rename File
- Spark Rename File
- Rename Parquet" File in Spark / PySpark"
That’s all about topic: Apache Spark" rename or delete a file HDFS". Enjoy! 🙂
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