Tag Archives: jmx

Metrics2: The New Hotness for Apache HBase Metrics

Categories: General HBase

The post below was originally published at blogs.apache.org/hbase. We re-publish it here for your convenience.

Apache HBase is a distributed big data store modeled after Google’s Bigtable paper. As with all distributed systems, knowing what’s happening at a given time can help  spot problems before they arise, debug on-going issues, evaluate new usage patterns, and provide insight into capacity planning.

Since October 2008, version 0.19.0 (HBASE-625),

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How-to: Use Apache ZooKeeper to Build Distributed Apps (and Why)

Categories: How-to ZooKeeper

It’s widely accepted that you should never design or implement your own cryptographic algorithms but rather use well-tested, peer-reviewed libraries instead. The same can be said of distributed systems: Making up your own protocols for coordinating a cluster will almost certainly result in frustration and failure.

Architecting a distributed system is not a trivial problem; it is very prone to race conditions, deadlocks, and inconsistency. Making cluster coordination fast and scalable is just as hard as making it reliable.

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What is Hadoop Metrics2?

Categories: CDH Hadoop

Metrics are collections of information about Hadoop daemons, events and measurements; for example, data nodes collect metrics such as the number of blocks replicated, number of read requests from clients, and so on. For that reason, metrics are an invaluable resource for monitoring Apache Hadoop services and an indispensable tool for debugging system problems. 

This blog post focuses on the features and use of the Metrics2 system for Hadoop, which allows multiple metrics output plugins to be used in parallel,

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Apache HBase + Apache Hadoop + Xceivers

Categories: Hadoop HBase


Some of the configuration properties found in Apache Hadoop have a direct effect on clients, such as Apache HBase. One of those properties is called “dfs.datanode.max.xcievers”, and belongs to the HDFS subproject. It defines the number of server side threads and – to some extent – sockets used for data connections. Setting this number too low can cause problems as you grow or increase utilization of your cluster. This post will help you to understand what happens between the client and server,

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Hadoop Graphing with Cacti

Categories: Data Ingestion Guest Hadoop

An important part of making sure Apache Hadoop works well for all users is developing and maintaining strong relationships with the folks who run Hadoop day in and day out. Edward Capriolo keeps About.com’s Hadoop cluster happy, and we frequently chew the fat with Ed on issues ranging from administrative best practices to monitoring. Ed’s been an invaluable resource as we beta test our distribution and chase down bugs before our official releases. Today’s article looks at some of Ed’s tricks for monitoring Hadoop with Cacti through JMX.

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