Category Archives: Performance

Big Data Benchmarks: Toward Real-Life Use Cases

Categories: Guest Hadoop Ops and DevOps Performance

The Transaction Processing Council (TPC), working with Cloudera, recently announced the new TPCx-HS benchmark, a good first step toward providing a Big Data benchmark.

In this interview by Roberto Zicari with Francois Raab, the original author of the TPC-C Benchmark, and Yanpei Chen, a Performance Engineer at Cloudera, the interviewees share their thoughts on the next step for benchmarks that reflect real-world use cases.

This interview was originally published at ODBMS.org;

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New in CDH 5.1: HDFS Read Caching

Categories: CDH Hadoop HDFS Impala Performance

Applications using HDFS, such as Impala, will be able to read data up to 59x faster thanks to this new feature.

Server memory capacity and bandwidth have increased dramatically over the last few years. Beefier servers make in-memory computation quite attractive, since a lot of interesting data sets can fit into cluster memory, and memory is orders of magnitude faster than disk.

For the latest release of CDH 5.1,

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New SQL Choices in the Apache Hadoop Ecosystem: Why Impala Continues to Lead

Categories: Impala Parquet Performance

Impala continues to demonstrate performance leadership compared to alternatives (by 950% or more), while providing greater query throughput and with a far smaller CPU footprint.

In our previous post from January 2014, we reported that Impala had achieved query performance over Apache Hadoop equivalent to that of an analytic DBMS over its own proprietary storage system. We believed this was an important milestone because Impala’s objective has been to support a high-quality BI experience on Hadoop data,

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The Truth About MapReduce Performance on SSDs

Categories: Hadoop Hardware MapReduce Performance

Cost-per-performance, not cost-per-capacity, turns out to be the better metric for evaluating the true value of SSDs.

In the Big Data ecosystem, solid-state drives (SSDs) are increasingly considered a viable, higher-performance alternative to rotational hard-disk drives (HDDs). However, few results from actual testing are available to the public.

Recently, Cloudera engineers did such a study based on a combination of SSDs and HDDs, with the goal of determining to what extent SSDs accelerate different MapReduce workloads,

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How-to: Select the Right Hardware for Your New Hadoop Cluster

Categories: Hadoop Hardware How-to Performance Use Case

One of the first questions Cloudera customers raise when getting started with Apache Hadoop is how to select appropriate hardware for their new Hadoop clusters.

Although Hadoop is designed to run on industry-standard hardware, recommending an ideal cluster configuration is not as easy as delivering a list of hardware specifications. Selecting hardware that provides the best balance of performance and economy for a given workload requires testing and validation. (For example, users with IO-intensive workloads will invest in more spindles per core.)

In this blog post,

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