Our “Top 10” list of blog posts published during a calendar year is a crowd favorite (see the 2013 version here), in particular because it serves as informal, crowdsourced research about popular interests. Page views don’t lie (although skew for publishing date—clearly, posts that publish earlier in the year have pole position—has to be taken into account).
In 2014, a strong interest in various new components that bring real time or near-real time capabilities to the Apache Hadoop ecosystem is apparent. And we’re particularly proud that the most popular post was authored by a non-employee.
Benchmarking Big Data systems is nontrivial. Avoid these traps!
Here at Cloudera, we know how hard it is to get reliable performance benchmarking results. Benchmarking matters because one of the defining characteristics of Big Data systems is the ability to process large datasets faster. “How large” and “how fast” drive technology choices, purchasing decisions, and cluster operations. Even with the best intentions, performance benchmarking is fraught with pitfalls—easy to get numbers,
Thanks to recent work upstream, YARN is now a highly available service. This post explains its architecture and configuration details.
YARN, the next-generation compute and resource management framework in Apache Hadoop, until recently had a single point of failure: the ResourceManager, which coordinates work in a YARN cluster. With planned (upgrades) or unplanned (node crashes) events, this central service, and YARN itself, could become unavailable.
This post details Cloudera’s recent work in the Hadoop community (YARN-149) to make the ResourceManager (and thus YARN) highly available.
Understanding some key differences between MR1 and MR2/YARN will make your migration much easier.
Here at Cloudera, we recently finished a push to get Cloudera Enterprise 5 (containing CDH 5.0.0 + Cloudera Manager 5.0.0) out the door along with more than 100 partner certifications.
CDH 5.0.0 is the first release of our software distribution where YARN and MapReduce 2 (MR2) is the default MapReduce execution framework,
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,