Tag Archives: HDFS

Introducing Apache HBase Medium Object Storage (MOB) compaction partition policies

Categories: HBase

Introduction

The Apache HBase Medium Object Storage (MOB) feature was introduced by HBASE-11339. This feature improves low latency read and write access for moderately-sized values (ideally from 100K to 10MB based on our testing results), making it well-suited for storing documents, images, and other moderately-sized objects [1]. The Apache HBase MOB feature achieves this improvement by separating IO paths for file references and MOB objects, applying different compaction policies to MOBs and thus reducing write amplification created by HBase’s compactions.

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Cloudera Enterprise 5.5 is Now Generally Available

Categories: CDH Cloudera Manager

Cloudera Enterprise 5.5 (comprising CDH 5.5, Cloudera Manager 5.5, and Cloudera Navigator 2.4) has been released.

Cloudera is excited to bring you news of Cloudera Enterprise 5.5. Our persistent emphasis on quality is especially pronounced in this release, with more than 500 issues identified and triaged during its development.

A highlight of this release is the inclusion of Cloudera Navigator Optimizer (available in limited beta for select Cloudera Enterprise customers;

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How-to: Ingest and Query “Fast Data” with Impala (Without Kudu)

Categories: Hadoop How-to Impala Kudu

Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.

In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,

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How-to: Use Impala with Kudu

Categories: How-to Impala Kudu

Learn the details about using Impala alongside Kudu.

Kudu (currently in beta), the new storage layer for the Apache Hadoop ecosystem, is tightly integrated with Impala, allowing you to insert, query, update, and delete data from Kudu tablets using Impala’s SQL syntax, as an alternative to using the Kudu APIs to build a custom Kudu application. In addition, you can use JDBC or ODBC to connect existing or new applications written in any language,

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