This is the second stable release of Apache Hadoop 2.10 line. It contains 218 bug fixes, improvements and enhancements since 2.10.0.
Users are encouraged to read the overview of major changes since 2.10.0.For details of 218 bug fixes, improvements, and other enhancements since the previous 2.10.0 release,please check release notes and changelogdetail the changes since 2.10.0.
First general available(GA) release of Apache Hadoop Ozone with OM HA, OFS, Security phase II, Ozone Filesystem performance improvement, security enabled Hadoop 2.x support, bucket link, Recon / Recon UI improvment, etc.
Download Apache Hadoop You can click here to download apache Hadoop 3.0.3 version or go to Apache site to download directly from there. Move the downloaded Hadoop. This is the second stable release of Apache Hadoop 2.10 line. It contains 218 bug fixes, improvements and enhancements since 2.10.0. Users are encouraged to read the overview of major changes since 2.10.0. For details of 218 bug fixes, improvements, and other enhancements since the previous 2.10.0 release, please check release notes and changelog detail the changes since 2.10.0. This is a maintenance release of Apache Hadoop 2.7. It addresses CVE-2018-8009. Download tar.gz (checksum signature) Download src (checksum signature) Documentation. Apache-zookeeper-X.Y.Z.tar.gz is standard source-only release, apache-zookeeper-X.Y.Z-bin.tar.gz is the convenience tarball which contains the binaries Thanks to the contributors for their tremendous efforts to make this release happen.
Apache Flink® 1.11.2 is our latest stable release. If you plan to use Apache Flink together with Apache Hadoop (run Flink on YARN, connect to HDFS, connect to HBase, or use some Hadoop-based file system connector), please check out the Hadoop Integration documentation. Apache Flink 1.11.2. Apache Flink 1.11.2 for Scala 2.11 (asc, sha512).
For more information check the ozone site.
This is the second stable release of Apache Hadoop 3.1 line. It contains 308 bug fixes, improvements and enhancements since 3.1.3.
Users are encouraged to read the overview of major changes since 3.1.3.For details of 308 bug fixes, improvements, and other enhancements since the previous 3.1.3 release,please check release notes and changelog.
This is the first release of Apache Hadoop 3.3 line. It contains 2148 bug fixes, improvements and enhancements since 3.2.
Users are encouraged to read the overview of major changes.For details of please check release notes and changelog.
First beta release of Apache Hadoop Ozone with GDPR Right to Erasure, Network Topology Awareness, O3FS, and improved scalability/stability.
For more information check the ozone site.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
To learn more about HADOOP, visit http://hadoop.apache.org/
Flume is a distributed, reliable and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
To learn more about Flume, visit http://flume.apache.org/
This tutorial describes how to store data in the Hadoop Distributed File System (HDFS) using a Hortonworks Single Node Data Platform (HDP). The HDP is a Hortonworks Sandbox hosted on a VirtualBox VM. In addition, the Sandbox includes management tools to enable you to view and manage the data.
This tutorial describes how to
- Download and setup VirtualBox on MAC OSX
- Import Hortonworks Sandbox as a VM
- Install Maven to build open source Java packages
- Setup a RabbitMQ Server as an Event Source for Flume
- Setup Flume to consume messages from RabbitMQ and
sink
the data to HDFS - Run a simple NodeJS RabbitMQ Producer to generate messages and route them to RabbitMQ
- Use HADOOP tools to
manage
data stored in HDFS 1. HCatalog – a table and storage management layer – http://hortonworks.com/hadoop/hcatalog/2. Hive – facilitates querying and managing large datasets – see https://hive.apache.org/ 3. Use the Hortonworks Management tools to manage the data
Download and setup VirtualBox
- Download VirtualBox for Mac OS X http://www.oracle.com/technetwork/server-storage/virtualbox/downloads/index.html
- Run the downloaded
dmg
package - Setup network adapters
Import Hortonworks Sandbox 2.1
Hadoop On Windows
- Download the Hortonworks OVA from http://hortonworks.com/products/hortonworks-sandbox/#install
- Follow the instructions in http://hortonworks.com/wp-content/uploads/2014/04/InstallingHortonworksSandbox2_1MacUsingVB.pdf
- Start the VirtualBox
- Logon to VirtualBox from the Terminal Window
Fn+Alt+F5
and get its “external” IP Address - ssh to the VirtualBox
Install
Start Server Automatically
Enable the Rabbit Management Plugin
Hadoop Client Download
Environment variables
Setup an environment variable for the rabbit install and put it on the PATH
Enable remote access to the Management Plugin
Create a virtual host
Enable administration of the Virtual Host from Management Console
Start RabbitMQ as a background process
Test the RabbitMQ Management console
A Flume dataflow consists of configuring a source, a sink and a channel. In this tutorial, the source consists of a RabbitMQ Server and the sink is the HDFS file system. The data flow works as follows
- The source consumes events delivered to it by RabbitMQ using the
RabbitMQ-Flume Plugin
- When the Flume source receives an event, it stores it into one or more channels. The channel is a passive store that keeps the event until it’s consumed by a Flume sink
- The sink removes the event from the channel and stores the data in HDFS (via Flume HDFS sink)
- The source and sink within the given agent run asynchronously with the events staged in the channel.
Install Flume
Install the RabbitMQ-Flume Plugin
Configure Flume to source from Rabbit and sink to HDFS
Setup a Flume source to consumer messages from RabbitMQ and sync the data to HDFS.
edit/create /etc/flume/conf/analytics.conf and enter the following
Start the flume service as a background process
View the Data
Create a Hive Table to access the HDFS data. The LOCATION value is the path specified in the HDFS sink
analytics.sinks.sink_to_hdfs.hdfs.path
in $FLUME_HOME/conf/analytics.confUse the Hortonworks Management console to view the list of tables, you should now see a table called
analytics
Install and run the NodeJS Message Producer
If you do not have NodeJS installed, checkout http://shapeshed.com/setting-up-nodejs-and-npm-on-mac-osx for instructions
On your MAC, checkout the code from git.
Edit etc/vars.env – this script setups ENV variables to connect to the RabbitMQ Server on the Hortonworks Sandbox and sepcifies the queue to be created and the Virtual host to access.
When configured, source the ENV variables as follows
Run the NodeJS script. The script connects to the RabbitMQ Server (specified in the
RABBIT_SERVER Environment Variable
), creates a queue (specified in the RABBIT_QUEUE Environment Variable
) and produces messages.The Flume service consumes the messages and stores them in HDFS.
View Flume Processing
You can view Flume processing progress by viewing the logs as follows
View the Data
Select the Beeswax Hive (UI) icon in the Hortonworks Management to view the data in the
analytics
table