Cloudera Developer Blog
Big Data best practices, how-to's, and internals from Cloudera Engineering and the community
A concise look at the differences between how Spark and MapReduce manage cluster resources under YARN
The most popular Apache YARN application after MapReduce itself is Apache Spark. At Cloudera, we have worked hard to stabilize Spark-on-YARN (SPARK-1101), and CDH 5.0.0 added support for Spark on YARN clusters.
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, not to produce a “faster Apache Hive.” An enterprise-quality BI experience requires low latency and high concurrency (among other things), so surpassing a well-known proprietary MPP DBMS in these areas was important evidence of progress.
In the past nine months, we’ve also all seen additional public validation that the original technical design for Hive, while effective for batch processing, was a dead-end for BI workloads. Recent examples have included the launch of Facebook’s Presto engine (Facebook was the inventor and world’s largest user of Hive), the emergence of Shark (Hive running on the Apache Spark DAG), and the “Stinger” initiative (Hive running on the Apache Tez [incubating] DAG).
Given the introduction of a number of new SQL-on-Hadoop implementations it seemed like a good time to do a roundup of the latest versions of each engine to see how they differ. We find that Impala maintains a significant performance advantage over the various other open source alternatives — ranging from 5x to 23x depending on the workload and the implementations that are compared. This advantage is due to some inherent design differences among the various systems, which we’ll explain below. Impala’s advantage is strongest for multi-user workloads, which arguably is the most relevant measure for users evaluating their options for BI use cases.
Using an appropriate network representation and the right tool set are the key factors in successfully merging structured and time-series data for analysis.
In Part 1 of this series, you took your first steps for using Apache Giraph, the highly scalable graph-processing system, alongside Apache Hadoop. In this installment, you’ll explore a general use case for analyzing time-dependent, Big Data graphs using data from multiple sources. You’ll learn how to generate random large graphs and small-world networks using Giraph – as well as play with several parameters to probe the limits of your cluster.
In its relatively short lifetime (co-founded by Twitter and Cloudera in July 2013), Parquet has already become the de facto standard for columnar storage of Apache Hadoop data — with native support in Impala, Apache Hive, Apache Pig, Apache Spark, MapReduce, Apache Tajo, Apache Drill, Apache Crunch, and Cascading (and forthcoming in Presto and Shark). Parquet adoption is also broad-based, with employees of the following companies (partial list) actively contributing:
Learn how HiveServer, Apache Sentry, and Impala help make Hadoop play nicely with BI tools when Kerberos is involved.
In 2010, I wrote a simple pair of blog entries outlining the general considerations behind using Apache Hadoop with BI tools. The Cloudera partner ecosystem has positively exploded since then, and the technology has matured as well. Today, if JDBC is involved, all the pieces needed to expose Hadoop data through familiar BI tools are available:
Learn how to convert your data to the Parquet columnar format to get big performance gains.
Using a columnar storage format for your data offers significant performance advantages for a large subset of real-world queries. (Click here for a great introduction.)
Meet Alan Paulsen, among the first to earn the CCP: Data Scientist distinction.
Big Data success requires professionals who can prove their mastery with the tools and techniques of the Apache Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.
Cloudera’s new “Designing and Building Big Data Applications” is a great springboard for writing apps for an enterprise data hub.
Cloudera’s vision of an enterprise data hub as a central, scalable repository for all your data is changing the notion of data warehousing. The best way to gain value from all of your data is by bringing more workloads to where the data lives. That place is Apache Hadoop.
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working as a Principal Big Data Consultant at Allstate Insurance, for the guest post below about his experiences with Impala.
It started with a simple request from one of the managers in my group at Allstate to put together a demo of Tableau connecting to Cloudera Impala. I had previously worked on Impala with a large dataset about a year ago while it was still in beta, and was curious to see how Impala had improved since then in features and stability.
Did you know that using the Crunch API is a powerful option for doing time-series analysis?
Apache Crunch is a Java library for building data pipelines on top of Apache Hadoop. (The Crunch project was originally founded by Cloudera data scientist Josh Wills.) Developers can spend more time focused on their use case by using the Crunch API to handle common tasks such as joining data sets and chaining jobs together in a pipeline. At Cloudera, we are so enthusiastic about Crunch that we have included it in CDH 5! (You can get started with Apache Crunch here and here.)