Tag Archives: scheduling

RecordService: For Fine-Grained Security Enforcement Across the Hadoop Ecosystem

Categories: Hadoop Impala Platform Security & Cybersecurity Sentry

This new core security layer provides a unified data access path for all Hadoop ecosystem components, while improving performance.

We’re thrilled to announce the beta availability of RecordService, a distributed, scalable, data access service for unified access control and enforcement in Apache Hadoop. RecordService is Apache Licensed open source that we intend to transition to the Apache Software Foundation. In this post, we’ll explain the motivation, system architecture,

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Introduction to HDFS Erasure Coding in Apache Hadoop

Categories: Hadoop HDFS

Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works.

HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from.

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What’s Next for Impala: More Reliability, Usability, and Performance at Even Greater Scale

Categories: Impala

This year will close out with new features for reliability, usability, and nested types, and in 2016, performance-related enhancements promise >20x gains.

It’s been roughly a year since we provided an update about the Impala roadmap. During that time, a number of milestones have been reached:

  • Most Cloudera customers have deployed Impala to production across industries including financial services, retail, healthcare, gaming, government, advertising, and telecom.

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Data Processing with Apache Crunch at Spotify

Categories: Guest Spark

Many thanks to David Whiting of Spotify for allowing us to re-publish the following Spotify Labs post about its Apache Crunch use cases.

(Note: Since this post was originally published in November 2014, many of the library functions described have been added into crunch-core, so they’ll soon be available to all Crunch users by default.)

All of our lovely Spotify users generate many terabytes of data every day.

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New in CDH 5.2: Improvements for Running Multiple Workloads on a Single HBase Cluster

Categories: CDH HBase

These new Apache HBase features in CDH 5.2 make multi-tenant environments easier to manage.

Historically, Apache HBase treats all tables, users, and workloads with equal weight. This approach is sufficient for a single workload, but when multiple users and multiple workloads were applied on the same cluster or table, conflicts can arise. Fortunately, starting with HBase in CDH 5.2 (HBase 0.98 + backports), workloads and users can now be prioritized.

One can categorize the approaches to this multi-tenancy problem in three ways:

  • Physical isolation or partitioning

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