Category Archives: Performance

New Benchmarks for SQL-on-Hadoop: Impala 1.4 Widens the Performance Gap

Categories: Hadoop Impala Performance

With 1.4, Impala’s performance lead over the SQL-on-Hadoop ecosystem gets wider, especially under multi-user load.

As noted in our recent post about the Impala 2.x roadmap (“What’s Next for Impala: Focus on Advanced SQL Functionality”), Impala’s ecosystem momentum continues to accelerate, with nearly 1 million downloads since the GA of 1.0, deployment by most of Cloudera’s enterprise data hub customers, and adoption by MapR, Amazon, and Oracle as a shipping product.

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How Impala Supports Mixed Workloads in Multi-User Environments

Categories: Guest Impala Performance Use Case

Our thanks to Melanie Imhof, Jonas Looser, Thierry Musy, and Kurt Stockinger of the Zurich University of Applied Science in Switzerland for the post below about their research into the query performance of Impala for mixed workloads.

Recently, we were approached by an industry partner to research and create a blueprint for a new Big Data, near real-time, query processing architecture that would replace its current architecture based on a popular open source database system.

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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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