Learn how to set up a Hadoop cluster in a way that maximizes successful production-ization of Hadoop and minimizes ongoing, long-term adjustments.
Previously, we published some recommendations on selecting new hardware for Apache Hadoop deployments. That post covered some important ideas regarding cluster planning and deployment such as workload profiling and general recommendations for CPU, disk, and memory allocations. In this post, we’ll provide some best practices and guidelines for the next part of the implementation process: configuring the machines once they arrive.
Learn about BigBench, the new industrywide effort to create a sorely needed Big Data benchmark.
Benchmarking Big Data systems is an open problem. To address this concern, numerous hardware and software vendors are working together to create a comprehensive end-to-end big data benchmark suite called BigBench. BigBench builds upon and borrows elements from existing benchmarking efforts in the Big Data space (such as YCSB, TPC-xHS,
Thanks to Alexander Rubin of Percona for allowing us to re-publish the post below!
Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from MySQL to Hadoop, load the data to Cloudera Impala (columnar format),
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,
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,