Scala Hadoop
Scala is a programming language that can be used in conjunction with Hadoop, a distributed computing framework, to build scalable and high-performance data processing applications. Scala is a versatile language that runs on the Java Virtual Machine (JVM) and is compatible with the Hadoop ecosystem. Here’s how Scala can be used with Hadoop:
Hadoop MapReduce: Scala can be used to write Hadoop MapReduce applications. MapReduce is a programming model and processing framework used to process large datasets in a distributed manner. You can write both map and reduce functions in Scala to process data stored in HDFS (Hadoop Distributed File System).
Hadoop Streaming: Hadoop Streaming is a feature that allows you to write MapReduce jobs in any programming language, including Scala. You can write Scala scripts to process data using Hadoop Streaming and submit them to a Hadoop cluster for execution.
Hadoop Ecosystem Integration: Scala can be integrated with various components of the Hadoop ecosystem. For example:
- Apache Hive: Hive provides a SQL-like query language for querying and analyzing data in Hadoop. You can write Hive queries in Scala using the Hive JDBC or ODBC driver.
- Apache Pig: Pig is a high-level platform for creating MapReduce programs using a scripting language called Pig Latin. You can write Pig scripts in Scala for data transformation tasks.
- Apache Spark: Apache Spark, a fast and general-purpose cluster computing framework, provides native support for Scala. You can write Spark applications in Scala to process large-scale data in-memory and perform batch processing, stream processing, machine learning, and graph processing tasks.
Hadoop Libraries: Scala can leverage various Hadoop libraries, such as Hadoop Common, Hadoop HDFS, and Hadoop YARN, to interact with Hadoop clusters, manage files in HDFS, and submit jobs for execution.
Scala and Functional Programming: Scala’s functional programming capabilities, such as immutability, pattern matching, and higher-order functions, make it well-suited for writing data processing code that can be parallelized and distributed effectively in a Hadoop cluster.
Here’s a simple example of writing a Hadoop MapReduce job in Scala:
import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.{IntWritable, LongWritable, Text}
import org.apache.hadoop.mapreduce.{Job, Mapper, Reducer}
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
object WordCount {
class Map extends Mapper[LongWritable, Text, Text, IntWritable] {
override def map(key: LongWritable, value: Text, context: Mapper[LongWritable, Text, Text, IntWritable]#Context): Unit = {
val line = value.toString
line.split(" ").foreach(word => context.write(new Text(word), new IntWritable(1)))
}
}
class Reduce extends Reducer[Text, IntWritable, Text, IntWritable] {
override def reduce(key: Text, values: java.lang.Iterable[IntWritable], context: Reducer[Text, IntWritable, Text, IntWritable]#Context): Unit = {
val sum = values.asScala.map(_.get()).sum
context.write(key, new IntWritable(sum))
}
}
def main(args: Array[String]): Unit = {
val conf = new Configuration()
val job = Job.getInstance(conf, "word count")
job.setJarByClass(classOf[WordCount])
job.setMapperClass(classOf[Map])
job.setCombinerClass(classOf[Reduce])
job.setReducerClass(classOf[Reduce])
job.setOutputKeyClass(classOf[Text])
job.setOutputValueClass(classOf[IntWritable])
FileInputFormat.addInputPath(job, new Path(args(0)))
FileOutputFormat.setOutputPath(job, new Path(args(1)))
System.exit(if (job.waitForCompletion(true)) 0 else 1)
}
}Hadoop Training Demo Day 1 Video:
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