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3 Ways To Ensure Your Hadoop Cluster Runs Efficiently

Feb. 10, 2016 ⋅ Categories: Big Data, Hadoop

Configuring your Hadoop cluster is only half the battle. The real trick lies in getting it to run at its most efficient capacity, but that doesn’t have to be a trick at all. By knowing what red flags to look for and where to concentrate your efforts, you can easily optimize your Hadoop cluster performance.

1. Manage Your Memory

One of your main priorities for ensuring optimal Hadoop performance should be your memory usage. Your Hadoop server has different memory options so you can enhance your overall performance. You should take advantage of this by monitoring your memory usage, using software like Ganglia, Nagios or Cloudera Manager, and making sure your MapReduce jobs aren’t triggering swapping.

2. Get To the Root of Your Capacity Issues

One problem Hadoop cluster users can encounter is seeming to run out of capacity when it hasn’t all been used. Your jobs don’t run efficiently and if you try to run more applications, you can’t. Dataconomy explained that while most monitoring tools can show you when a network is busy, they can’t help you get to the root of the problem. However, this problem can often be attributed to a YARN architecture, which is configured to handle worst-case scenarios but doesn’t react quickly to changes.

3. Compress Output For Optimal Disk Usage

Controlling your MapReduce performance or disk IO is another way to optimize your Hadoop cluster. By default, your Map output is not compressed, but it can be configured to minimize disk spilling, a move that is likely to benefit any job with a large output. Monitoring your disk usage can help you when certain issues arise as well. Have you ever experienced your cluster coming to a grinding halt? You’re not alone. It’s a common warning sign for clusters used by multiple developers with a lot data. Without a visualization tool, it’s difficult to identify the root cause of your heavy disk usage. For this problem, Dataconomy recommends isolating the cause by using traditional monitoring tools to log nodes and a resource like isostat to keep an eye on processes with significant disk usage.

Overall, the theme of these tips is to invest in tools and resources that help you efficiently identify the root of your problems, instead of experimenting with different solutions and wasting time. As Hadoop continues to be widely adopted by different enterprises, it’s important to know how to maximize its performance.

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