Cloudera Engineering Blog · Ops And DevOps Posts

How-to: Install Cloudera Manager and Cloudera Search with Ansible

The following guest post is re-published here courtesy of Gerd König, a System Engineer with YMC AG. Thanks, Gerd!

Cloudera Manager is a great tool to orchestrate your CDH-based Apache Hadoop cluster. You can use it from cluster installation, deploying configurations, restarting daemons to monitoring each cluster component. Starting with version 4.6, the manager supports the integration of Cloudera Search, which is currently in Beta state. In this post I’ll show you the required steps to set up a Hadoop cluster via Cloudera Manager and how to integrate Cloudera Search.

How Does Cloudera Manager Work?

At Cloudera, we believe that Cloudera Manager is the best way to install, configure, manage, and monitor your Apache Hadoop stack. Of course, most users prefer not to take our word for it — they want to know how Cloudera Manager works under the covers, first. 

In this post, I’ll explain some of its inner workings. 

The Vocabulary of Cloudera Manager

How-to: Automate Your Hadoop Cluster from Java

One of the complexities of Apache Hadoop is the need to deploy clusters of servers, potentially on a regular basis. At Cloudera, which at any time maintains hundreds of test and development clusters in different configurations, this process presents a lot of operational headaches if not done in an automated fashion. In this post, I’ll describe an approach to cluster automation that works for us, as well as many of our customers and partners.

Taming Complexity

At Cloudera engineering, we have a big support matrix: We work on many versions of CDH (multiple release trains, plus things like rolling upgrade testing), and CDH works across a wide variety of OS distros (RHEL 5 & 6, Ubuntu Precise & Lucid, Debian Squeeze, and SLES 11), and complex configuration combinations — highly available HDFS or simple HDFS, Kerberized or non-secure, using YARN or MR1 as the execution framework, etc. Clearly, we need an easy way to spin-up a new cluster that has the desired setup, which we can subsequently use for integration, testing, customer support, demos, and so on.

How-to: Use Vagrant to Set Up a Virtual Hadoop Cluster (For CDH 4)

This guest post comes to us from David Greco, CTO of Eligotech. For a how-to on this subject for CDH 5, see this post.

Vagrant is a very nice tool for programmatically managing many virtual machines (VMs) on a single physical machine. It natively supports VirtualBox and also provides plugins for VMware Fusion and Amazon EC2, supporting the management of VMs in those environments as well.

How-to: Create a CDH Cluster on Amazon EC2 via Cloudera Manager

Editor’s Note (added Feb. 28, 2014): The instructions below are deprecated for Cloudera Manager releases beyond 4.5. Please refer to this doc for instructions pertaining to releases 4.6 and later.

Cloudera Manager includes a new express installation wizard for Amazon Web Services (AWS) EC2. Its goal is to enable Cloudera Manager users to provision CDH clusters and Cloudera Impala (the open source distributed query engine for Apache Hadoop) on EC2 as easily as possible (for testing and development purposes only, not supported for production workloads) - and thus is currently the fastest way to provision a Cloudera Manager-managed cluster in EC2.

Cloudera Manager 4.1 Now Available; Supports Impala Beta Release

I am very pleased to announce the availability of Cloudera Manager 4.1. This release adds support for the Cloudera Impala beta release, and management and monitoring of key CDH features.

Here are the highlights of Cloudera Manager 4.1:

Exploring Compression for Hadoop: One DBA’s Story

This guest post comes to us courtesy of Gwen Shapira (@gwenshap), a database consultant for The Pythian Group (and an Oracle ACE Director).

Most western countries use street names and numbers to navigate inside cities. But in Japan, where I live now, very few streets have them.

How-to: Automate Your Cluster with Cloudera Manager API

API access was a new feature introduced in Cloudera Manager 4.0 (download free edition here.). Although not visible in the UI, this feature is very powerful, providing programmatic access to cluster operations (such as configuration and restart) and monitoring information (such as health and metrics). This article walks through an example of setting up a 4-node HDFS and MapReduce cluster via the Cloudera Manager (CM) API.

Cloudera Manager API Basics

The CM API is an HTTP REST API, using JSON serialization. The API is served on the same host and port as the CM web UI, and does not require an extra process or extra configuration. The API supports HTTP Basic Authentication, accepting the same users and credentials as the Web UI. API users have the same privileges as they do in the web UI world.

Cloudera Manager 4.0: Customer Feedback and Adoption

It’s been roughly three months since we announced GA of Cloudera Manager 4.0 (CM4) and I wanted to provide an update on its adoption and feedback from customers.

For those new to it, Cloudera Manager is the first and market-leading management platform for CDH (Cloudera’s Distribution Including Apache Hadoop). Enterprise customers are coming to expect an end-to-end tool that manages the entire lifecycle of their Hadoop operations. In fact, in a recent Cloudera customer survey, an overwhelming 95%  emphasized the need for this approach. 

What Do Real-Life Apache Hadoop Workloads Look Like?

Organizations in diverse industries have adopted Apache Hadoop-based systems for large-scale data processing. As a leading force in Hadoop development with customers in half of the Fortune 50 companies, Cloudera is in a unique position to characterize and compare real-life Hadoop workloads. Such insights are essential as developers, data scientists, and decision makers reflect on current use cases to anticipate technology trends.

Recently we collaborated with researchers at UC Berkeley to collect and analyze a set of Hadoop traces. These traces come from Cloudera customers in e-commerce, telecommunications, media, and retail (Table 1). Here I will explain a subset of the observations, and the thoughts they triggered about challenges and opportunities in the Hadoop ecosystem, both present and in the future.

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