Hadoop Monitoring best Practices

This document provides guidelines for tuning Hadoop for performance. It discusses key factors that influence Hadoop performance like hardware configuration, application logic, and system bottlenecks. It also outlines various configuration parameters that can be tuned at the cluster and job level to optimize CPU, memory, disk throughput, and task granularity. Sample tuning gains are shown for a webmap application where tuning multiple parameters improved job execution time by up to 22%.

Three identical Apache Hadoop clusters were provisioned on Joyent infrastructure using different operating systems: SmartOS, Ubuntu, and KVM virtual machines. Monitoring showed the Ubuntu and KVM clusters spent more time in the OS kernel during I/O operations compared to the SmartOS cluster. The SmartOS cluster was able to utilize CPU resources more efficiently and scale to more mappers and reducers. Basic cluster configuration and tuning the number of map and reduce tasks are important to optimize Hadoop performance.

Performance is a thing that you can never have too much of. But performance is a nebulous concept in Hadoop. Unlike databases, there is no equivalent in Hadoop to TPC, and different use cases experience performance differently. This talk will discuss advances on how Hadoop performance is measured and will also talk about recent and future advances in performance in different areas of the Hadoop stack.

This document summarizes key Hadoop configuration parameters that affect MapReduce job performance and provides suggestions for optimizing these parameters under different conditions. It describes the MapReduce workflow and phases, defines important parameters like dfs.block.size, mapred.compress.map.output, and mapred.tasktracker.map/reduce.tasks.maximum. It explains how to configure these parameters based on factors like cluster size, data and task complexity, and available resources. The document also discusses other performance aspects like temporary space, JVM tuning, and reducing reducer initialization overhead.

Creating containers for an application is easy (even if it’s a goold old distributed application like Apache Hadoop), just a few steps of packaging. The hard part isn't packaging: it's deploying How can we run the containers together? How to configure them? How do the services in the containers find and talk to each other? How do you deploy and manage clusters with hundred of nodes? Modern cloud native tools like Kubernetes or Consul/Nomad could help a lot but they could be used in different way. It this presentation I will demonstrate multiple solutions to manage containerized clusters with different cloud-native tools including kubernetes, and docker-swarm/compose. No matter which tools you use, the same questions of service discovery and configuration management arise. This talk will show the key elements needed to make that containerized cluster work. Tools: kubernetes, docker-swam, docker-compose, consul, consul-template, nomad together with: Hadoop, Yarn, Spark, Kafka, Zookeeper, Storm…. References: https://github.com/flokkr Speaker Marton Elek, Lead Software Engineer, Hortonworks

Hadoop is emerging as the standard for big data processing and analytics. However, as usage of the Hadoop clusters grow, so do the demands of managing and monitoring these systems. In this full-day Strata Hadoop World tutorial, attendees will get an overview of all phases for successfully managing Hadoop clusters, with an emphasis on production systems — from installation, to configuration management, service monitoring, troubleshooting and support integration. We will review tooling capabilities and highlight the ones that have been most helpful to users, and share some of the lessons learned and best practices from users who depend on Hadoop as a business-critical system.

Hadoop is an open-source framework that processes large datasets in a distributed manner across commodity hardware. It uses a distributed file system (HDFS) and MapReduce programming model to store and process data. Hadoop is highly scalable, fault-tolerant, and reliable. It can handle data volumes and variety including structured, semi-structured and unstructured data.

1. The document provides 7 simple Linux configuration tips to improve Hadoop cluster performance. The tips include disabling swapping, mounting data disks with noatime, disabling root reserved space, enabling nscd, increasing file handle limits, using a dedicated OS disk, and ensuring proper name resolution. 2. It also discusses additional optional tips like checking disk I/O, disabling transparent huge pages, enabling jumbo frames, and monitoring systems. 3. The document recommends reading the Hadoop Operations book and taking questions.

This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.

This document provides tips for tuning Hadoop clusters and jobs. It recommends: 1) Choosing optimal numbers of mappers and reducers per node and oversubscribing CPUs slightly. 2) Adjusting memory allocations for tasks and ensuring they do not exceed total memory available. 3) Increasing buffers for sorting and shuffling, compressing intermediate data, and using combiners to reduce data sent to reducers.

This presentation provides a basic overview on Hadoop, Map-Reduce and HDFS related concepts, Configuration and Installation steps and a Sample code.

This document provides an overview of setting up a Hadoop cluster, including installing the Apache Hadoop distribution, configuring SSH keys for passwordless login between nodes, configuring environment variables and Hadoop configuration files, and starting and stopping the HDFS and MapReduce services. It also briefly discusses alternative Hadoop distributions from Cloudera and Yahoo, as well as using cloud platforms like Amazon EC2 for Hadoop clusters.

With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities. Know how to setup a Hadoop Cluster With HDFS High Availability here : www.edureka.co/blog/how-to-set-up-hadoop-cluster-with-hdfs-high-availability/

Are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? Inspired by real-world support cases, this talk discusses best practices and new features to help improve incident response and daily operations. Chances are that you’ll walk away from this talk with some new ideas to implement in your own clusters.

This document provides an overview of the Hadoop Distributed File System (HDFS), including its goals, design, daemons, and processes for reading and writing files. HDFS is designed for storing very large files across commodity servers, and provides high throughput and reliability through replication. The key components are the NameNode, which manages metadata, and DataNodes, which store data blocks. The Secondary NameNode assists the NameNode in checkpointing filesystem state periodically.

Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.

This document provides an overview and introduction to Hadoop, an open-source framework for storing and processing large datasets in a distributed computing environment. It discusses what Hadoop is, common use cases like ETL and analysis, key architectural components like HDFS and MapReduce, and why Hadoop is useful for solving problems involving "big data" through parallel processing across commodity hardware.

The document discusses optimizing performance in MapReduce jobs. It covers understanding bottlenecks through metrics and logs, tuning parameters to reduce spills during the map task sort and spill phase like io.sort.mb and io.sort.record.percent, and tips for reducer fetch tuning. The goal is to help developers understand and address bottlenecks in their MapReduce jobs to improve performance.

This document discusses benchmarking Hadoop and big data systems. It provides an overview of common Hadoop benchmarks including microbenchmarks like TestDFSIO, TeraSort, and NNBench which test individual Hadoop components. It also describes BigBench, a benchmark modeled after TPC-DS that aims to test a more complete big data analytics workload using techniques like MapReduce, Hive, and Mahout across structured, semi-structured, and unstructured data. The document emphasizes using Hadoop distributions for administration and both microbenchmarks and full benchmarks like BigBench for evaluation.

This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.

This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.

The document discusses backup and disaster recovery strategies for Hadoop. It focuses on protecting data sets stored in HDFS. HDFS uses data replication and checksums to protect against disk and node failures. Snapshots can protect against data corruption and accidental deletes. The document recommends copying data from the primary to secondary site for disaster recovery rather than teeing, and discusses considerations for large data movement like bandwidth needs and security. It also notes the importance of backing up metadata like Hive configurations along with core data.

Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.

Tips for Ganglia on Prototype, Test, and more.

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