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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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