How do you perform performance tuning in Hadoop?

You can consider the following options to optimize the performance of an HDFS cluster: swapping disk drives on a DataNode, caching data, configuring rack awareness, customizing HDFS, optimizing NameNode disk space with Hadoop archives, identifying slow DataNodes and improving them, optimizing small write operations by …

How is Hadoop performance measured?

To measure the performance we will set up a Hadoop cluster with many nodes and use the file TestDFSIO. java of the Hadoop version 0.18. 3 which gives us the data throughput, average I/O rate and I/O rate standard deviation. The HDFS writing performance scales well on both small and big data set.

Which component of an Hadoop system is the primary cause of poor performance?

Bottlenecks in a subset of the hardware systems within the cluster can cause overall poor performance of the underlying Hadoop workload. Performance of Hadoop workloads is sensitive to every component of the stack – Hadoop, JVM, OS, network infrastructure, the underlying hardware, and possibly the BIOS settings.

What is the use of combiner in MapReduce?

A Combiner, also known as a semi-reducer, is an optional class that operates by accepting the inputs from the Map class and thereafter passing the output key-value pairs to the Reducer class. The main function of a Combiner is to summarize the map output records with the same key.

How do I tune a MapReduce job performance?

The best thumb rule for memory tuning to maximize the performance is to ensure that the MapReduce jobs do not trigger swapping. That means use as much memory as you can without triggering swapping. Softwares like Cloudera Manager, Nagios, or Ganglia can be used for monitoring the swap memory usage.

What is the use of ambari in Hadoop?

Apache Ambari is a software project of the Apache Software Foundation. Ambari enables system administrators to provision, manage and monitor a Hadoop cluster, and also to integrate Hadoop with the existing enterprise infrastructure.

What is Hadoop benchmark?

TeraSort. TeraSort Benchmark is used to test both, MapReduce and HDFS by sorting some amount of data as quickly as possible in order to measure the capabilities of distributing and mapreducing files in cluster. This benchmark consists of 3 components: TeraGen – generates random data.

How does the best performance can be measured in Hadoop and why?

The foremost step to ensure maximum performance for a Hadoop job, is to tune the best configuration parameters for memory, by monitoring the memory usage on the server. Apache Hadoop has various options on memory, disk, CPU and network that helps optimize the performance of the hadoop cluster.

Which type of data can Hadoop deal?

Hadoop can handle not only structured data that fits well into relational tables and arrays but also unstructured data. A partial list of this type of data Hadoop can deal with are: Computer logs. Spatial data/GPS outputs.

What is a combiner Hadoop?

What is Hadoop Combiner? Combiner is also known as “Mini-Reducer” that summarizes the Mapper output record with the same Key before passing to the Reducer. On a large dataset when we run MapReduce job. The Hadoop framework provides a function known as Combiner that plays a key role in reducing network congestion.

What is partitioner and combiner MapReduce?

The difference between a partitioner and a combiner is that the partitioner divides the data according to the number of reducers so that all the data in a single partition gets executed by a single reducer. However, the combiner functions similar to the reducer and processes the data in each partition.

What comes under performance enhancements in Hadoop?

Performance tuning in Hadoop will help in optimizing the Hadoop cluster performance. It will cover 7 important concepts like Memory Tuning in Hadoop, Map Disk spill in Hadoop, tuning mapper tasks, Speculative execution in Big data Hadoop and many other related concepts for Hadoop MapReduce performance tuning.

What is the use of Hadoop?

Hadoop [ 22] is a very popular and useful open-source software framework that enables distributed storage, including the capability of storing a large amount of big datasets across clusters. It is designed in such a way that it can scale up from a single server to thousands of nodes.

How do MapReduce and Hadoop work together?

MapReduce and Hadoop distributed file systems (HDFS) are core parts of the Hadoop system, so computing and storage work together across all nodes that compose a cluster of computers [ 7 ]. Apache Spark is an open-source cluster-computing framework [ 8 ].

Is spark or Hadoop better for big data?

The analysis of the results shows that Spark has better performance as compared to Hadoop when data sets are small, achieving up to two times speedup in WordCount workloads and up to 14 times in TeraSort workloads when default parameter values are reconfigured.