HADOOP OPERATIONS ERIC SAMMER PDF

Jan 07, Ritesh Chhajer rated it liked it Traditional file systems like ext3 are implemented as kernel modules. HDFS instead is a user space file system meaning the file system code runs outside the kernel. Another difference is in block size. In HDFS, there is no concept of current working directory. Its more like a remote file system than a local OS file system.

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Jan 07, Ritesh Chhajer rated it liked it Traditional file systems like ext3 are implemented as kernel modules. HDFS instead is a user space file system meaning the file system code runs outside the kernel.

Another difference is in block size. In HDFS, there is no concept of current working directory. Its more like a remote file system than a local OS file system. Namenode stores its file system Traditional file systems like ext3 are implemented as kernel modules.

Namenode stores its file system metadata in fsimage Note: Block location not kept in fsimage and edits change log. Over time edits file grows and might take a long time to replay in event of a server failure, hence it is periodically checkpointed every hour or when the edits file reaches 64M with changes applied to fsimage file.

Mapreduce is relatively simple for developers in the sense no need to worry about threading, socket programming, etc. Simply operate on one record at at time. Map functions operate on these records and produce intermediate key-value pairs.

The reduce function then operates on the intermediate key-value pairs, groups the keys together and produces aggregated results. Default heap size for namenode is 1G for every 1 million blocks. Mapreduce was the original framework for writing Hadoop applications. Hive, Pig popular tools to use Mapreduce for interacting with Hadoop. Now Spark is the new programming framework for writing Hadoop applications. Node managers worker nodes communicates with resource manager by sending heartbeat providing status of nodes and launches application masters on request from resource manager.

Map tasks are almost always uniform in execution. For all the reasons you would not run a high performance relational database in a VM, you should not run Hadoop in a VM. Set it to 0. I absolutely recommend it to anyone doing anything with Hadoop. When I first flipped through it I though it would just be a regurgitation of what is online, and tables of configs and their definitions. This is not the case. From hardware and operating system tuning all the way to This book is fantastic.

From hardware and operating system tuning all the way to monitoring, Sammer explains the ins and outs of a Hadoop cluster without putting you to sleep.

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Hadoop Operations by Eric Sammer

Demand for operations-specific material has skyrocketed now that Hadoop is becoming the de facto standard for truly large-scale data processing in the data center. Eric Sammer, Principal Solution Architect at Cloudera, shows you the particulars of running Hadoop in production, from planning, installing, and configuring the system to providing ongoing maintenance. Rather than run through all possible scenarios, this pragmatic operations guide calls out what works, as demonstrated in critical deployments. Get a high-level overview of HDFS and MapReduce: why they exist and how they work Plan a Hadoop deployment, from hardware and OS selection to network requirements Learn setup and configuration details with a list of critical properties Manage resources by sharing a cluster across multiple groups Get a runbook of the most common cluster maintenance tasks Monitor Hadoop clusters—and learn troubleshooting with the help of real-world war stories Use basic tools and techniques to handle backup and catastrophic failure.

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Hadoop Operations

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