What are the different methods to run Spark over Apache Hadoop?
What are the different methods to run Spark over Apache Hadoop?
There are three methods to run Spark in a Hadoop cluster: standalone, YARN, and SIMR. Standalone deployment: In Standalone Deployment, one can statically allocate resources on all or a subset of machines in a Hadoop cluster and run Spark side by side with Hadoop MR.
How does Spark integrate with Hadoop?
Monitor Your Spark Applications
- Create the log directory in HDFS: hdfs dfs -mkdir /spark-logs.
- Run the History Server: $SPARK_HOME/sbin/start-history-server.sh.
- Repeat steps from previous section to start a job with spark-submit that will generate some logs in the HDFS:
How would you pick between Hadoop MapReduce or Apache spark for a project?
In fact, the key difference between Hadoop MapReduce and Spark lies in the approach to processing: Spark can do it in-memory, while Hadoop MapReduce has to read from and write to a disk. However, the volume of data processed also differs: Hadoop MapReduce is able to work with far larger data sets than Spark.
What are map and reduce functions?
MapReduce serves two essential functions: it filters and parcels out work to various nodes within the cluster or map, a function sometimes referred to as the mapper, and it organizes and reduces the results from each node into a cohesive answer to a query, referred to as the reducer.
What is Apache spark framework?
Apache Spark is an open-source, distributed processing system used for big data workloads. Apache Spark has become one of the most popular big data distributed processing framework with 365,000 meetup members in 2017.
How does Spark read data from Hadoop?
Use textFile() and wholeTextFiles() method of the SparkContext to read files from any file system and to read from HDFS, you need to provide the hdfs path as an argument to the function. If you wanted to read a text file from an HDFS into DataFrame.