blog/2013/04/03/15-minutes-to-live-druid.html - druid-website - Git at Google

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         <h1>15 Minutes to Live Druid</h1>
         <p class="text-muted">by <span class="author text-uppercase">Jaypal Sethi</span> · April  3, 2013</p>

         <p>Big Data reflects today’s world where data generating events are measured in
 the billions and business decisions based on insight derived from this data is
 measured in seconds. There are few tools that provide deep insight into both
 live and stationary data as business events are occurring; Druid was designed
 specifically to serve this purpose.</p>

 <p>If you’re not familiar with Druid, it’s a powerful, open source, real-time
 analytics database designed to allow queries on large quantities of streaming
 data – that means querying data as it’s being ingested into the system (see
 previous <a href="http://metamarkets.com/2012/metamarkets-open-sources-druid/">blog post</a>.
 Many databases claim they are real-time because they are
 “real fast;” this usually works for smaller workloads or for customers with
 infinite IT budgets. For companies like Netflix, whose engineers use Druid to
 cull through <a href="http://www.slideshare.net/g9yuayon/netflix-druidstrata2013">70 billion log events per day, ingesting over 2 TB per hour at
 peak times</a>
 (more on this in a later blog post), real-time means they have to
 query data as it’s being ingested into the system.</p>

 <p>Taking Druid a step further, the database provides benefits for both real-time
 and non-real-time uses by allowing arbitrary drill-downs and n-dimensional
 filtering without any impact on performance. Beyond being a key feature used by
 <a href="http://www.metamarkets.com/">Metamarkets</a> (average query times of less than
 500 milliseconds), it’s also a valuable capability for Netflix, and a key use
 case for the R community.</p>

 <p>Outside of features and functionality, the value of so many successful open
 source projects can be attributed to their user community. As a sponsor of this
 project, one of our core goals here at Metamarkets is to support our growing
 Druid Community. In fact, this blog post is a good example of responding to
 community feedback to make Druid immediately accessible to users who want to
 explore and become familiar with the database.</p>

 <p>Today, we’re excited to announce a ready to run Druid Personal Demo Cluster
 with a pre-loaded test workload: the Wikipedia edit stream. The DPDC (Druid
 Personal Demo Cluster) is available via AWS as a StackTemplate and is free to
 use and run; all that’s required is your own AWS account and 15 minutes.</p>

 <p>The DPDC is designed to provide a small, but realistic and fully functional
 Druid environment, allowing users to become familiar with a working example of
 a Druid system, write queries and understand how to manage the environment. The
 DPDC is also extensible; once users are familiar with Druid, we encourage them
 to load their own data and to continue learning. While the DPDC is far from an
 actual deployment, it’s designed to be an educational tool and an on-ramp
 towards your own deployment.</p>

 <p>The AWS (Amazon Web Services) <a href="http://aws.amazon.com/cloudformation/">CloudFormation</a>
 Template pulls together two Druid AMIs and creates a pre-configured Druid
 Cluster preloaded with the Wikipedia edit stream, and a basic query interface
 to help you become familiar with Druid capabilities like drill-downs on
 arbitrary dimensions and filters.</p>

 <p>What’s in this Druid Demo Cluster?</p>

 <ol>
 <li><p>A single Master node is based on a preconfigured AWS AMI (Amazon Machine
 Image) and also contains the Zookeeper broker, the Indexer, and a MySQL
 instance which keeps track of system metadata. You can read more about Druid
 architecture <a href="https://github.com/metamx/druid/wiki/Design">here</a>.</p></li>
 <li><p>Three compute nodes based on another AWS AMI; these compute nodes, have been
 pre-configured to work with the Master node and already contain the Wikipedia
 edit stream data (no specific setup is required).  How to Get Started:</p></li>
 </ol>

 <p>Our quick start guide is located on the Druid Github wiki:
 <a href="https://github.com/metamx/druid/wiki/Druid-Personal-Demo-Cluster">https://github.com/metamx/druid/wiki/Druid-Personal-Demo-Cluster</a></p>

 <p>For support, please join our mailing list (Google Groups):
 <a href="https://groups.google.com/d/forum/druid-development">https://groups.google.com/d/forum/druid-development</a>. We welcome your feedback
 and contributions as we consider adding more content for the DPDC.</p>

 <p>Need more?</p>

 <p>Try out our connectors – we recently open-sourced our RDruid connector and will
 be holding a Druid Meetup where we’ll conduct a hands-on mini-lab to get
 attendees working with Druid.</p>

 <p>The community also contributed a Ruby client
 (<a href="https://github.com/madvertise/ruby-druid">https://github.com/madvertise/ruby-druid</a>) and is rumored to be working on
 Python and SQL clients. And, a massive thanks to the team at
 <a href="http://skilledanalysts.com/">SkilledAnalysts</a> for their contributions to the
 DPDC and their continued involvement in the Druid community.</p>

 <p>Finally, if you’re looking for more information on Druid, you can find it on
 our <a href="http://metamarkets.com/product/technology/">technology page</a>.</p>

 <p>IMAGE: <a href="http://www.shutterstock.com/gallery-86570p1.html">PEDRO MIGUEL SOUSA</a> / <a href="http://www.shutterstock.com/">SHUTTERSTOCK</a></p>

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	<h1>15 Minutes to Live Druid</h1>
	<p class="text-muted">by <span class="author text-uppercase">Jaypal Sethi</span> · April 3, 2013</p>

	<p>Big Data reflects today’s world where data generating events are measured in
	the billions and business decisions based on insight derived from this data is
	measured in seconds. There are few tools that provide deep insight into both
	live and stationary data as business events are occurring; Druid was designed
	specifically to serve this purpose.</p>

	<p>If you’re not familiar with Druid, it’s a powerful, open source, real-time
	analytics database designed to allow queries on large quantities of streaming
	data – that means querying data as it’s being ingested into the system (see
	previous <a href="http://metamarkets.com/2012/metamarkets-open-sources-druid/">blog post</a>.
	Many databases claim they are real-time because they are
	“real fast;” this usually works for smaller workloads or for customers with
	infinite IT budgets. For companies like Netflix, whose engineers use Druid to
	cull through <a href="http://www.slideshare.net/g9yuayon/netflix-druidstrata2013">70 billion log events per day, ingesting over 2 TB per hour at
	peak times</a>
	(more on this in a later blog post), real-time means they have to
	query data as it’s being ingested into the system.</p>

	<p>Taking Druid a step further, the database provides benefits for both real-time
	and non-real-time uses by allowing arbitrary drill-downs and n-dimensional
	filtering without any impact on performance. Beyond being a key feature used by
	<a href="http://www.metamarkets.com/">Metamarkets</a> (average query times of less than
	500 milliseconds), it’s also a valuable capability for Netflix, and a key use
	case for the R community.</p>

	<p>Outside of features and functionality, the value of so many successful open
	source projects can be attributed to their user community. As a sponsor of this
	project, one of our core goals here at Metamarkets is to support our growing
	Druid Community. In fact, this blog post is a good example of responding to
	community feedback to make Druid immediately accessible to users who want to
	explore and become familiar with the database.</p>

	<p>Today, we’re excited to announce a ready to run Druid Personal Demo Cluster
	with a pre-loaded test workload: the Wikipedia edit stream. The DPDC (Druid
	Personal Demo Cluster) is available via AWS as a StackTemplate and is free to
	use and run; all that’s required is your own AWS account and 15 minutes.</p>

	<p>The DPDC is designed to provide a small, but realistic and fully functional
	Druid environment, allowing users to become familiar with a working example of
	a Druid system, write queries and understand how to manage the environment. The
	DPDC is also extensible; once users are familiar with Druid, we encourage them
	to load their own data and to continue learning. While the DPDC is far from an
	actual deployment, it’s designed to be an educational tool and an on-ramp
	towards your own deployment.</p>

	<p>The AWS (Amazon Web Services) <a href="http://aws.amazon.com/cloudformation/">CloudFormation</a>
	Template pulls together two Druid AMIs and creates a pre-configured Druid
	Cluster preloaded with the Wikipedia edit stream, and a basic query interface
	to help you become familiar with Druid capabilities like drill-downs on
	arbitrary dimensions and filters.</p>

	<p>What’s in this Druid Demo Cluster?</p>

	<ol>
	<li><p>A single Master node is based on a preconfigured AWS AMI (Amazon Machine
	Image) and also contains the Zookeeper broker, the Indexer, and a MySQL
	instance which keeps track of system metadata. You can read more about Druid
	architecture <a href="https://github.com/metamx/druid/wiki/Design">here</a>.</p></li>
	<li><p>Three compute nodes based on another AWS AMI; these compute nodes, have been
	pre-configured to work with the Master node and already contain the Wikipedia
	edit stream data (no specific setup is required). How to Get Started:</p></li>
	</ol>

	<p>Our quick start guide is located on the Druid Github wiki:
	<a href="https://github.com/metamx/druid/wiki/Druid-Personal-Demo-Cluster">https://github.com/metamx/druid/wiki/Druid-Personal-Demo-Cluster</a></p>

	<p>For support, please join our mailing list (Google Groups):
	<a href="https://groups.google.com/d/forum/druid-development">https://groups.google.com/d/forum/druid-development</a>. We welcome your feedback
	and contributions as we consider adding more content for the DPDC.</p>

	<p>Need more?</p>

	<p>Try out our connectors – we recently open-sourced our RDruid connector and will
	be holding a Druid Meetup where we’ll conduct a hands-on mini-lab to get
	attendees working with Druid.</p>

	<p>The community also contributed a Ruby client
	(<a href="https://github.com/madvertise/ruby-druid">https://github.com/madvertise/ruby-druid</a>) and is rumored to be working on
	Python and SQL clients. And, a massive thanks to the team at
	<a href="http://skilledanalysts.com/">SkilledAnalysts</a> for their contributions to the
	DPDC and their continued involvement in the Druid community.</p>

	<p>Finally, if you’re looking for more information on Druid, you can find it on
	our <a href="http://metamarkets.com/product/technology/">technology page</a>.</p>

	<p>IMAGE: <a href="http://www.shutterstock.com/gallery-86570p1.html">PEDRO MIGUEL SOUSA</a> / <a href="http://www.shutterstock.com/">SHUTTERSTOCK</a></p>

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