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         <h1>Druid, Part Deux: Three Principles for Fast, Distributed OLAP</h1>
         <p class="text-muted">by <span class="author text-uppercase">Eric Tschetter</span> · May 20, 2011</p>

         <p>In a <a href="/blog/2011/04/30/introducing-druid.html">previous blog
 post</a> we introduced the
 distributed indexing and query processing infrastructure we call Druid. In that
 post, we characterized the performance and scaling challenges that motivated us
 to build this system in the first place. Here, we discuss three design
 principles underpinning its architecture.</p>

 <p><strong>1. Partial Aggregates + In-Memory + Indexes =&gt; Fast Queries</strong></p>

 <p>We work with two representations of our data: <em>alpha</em> represents the raw,
 unaggregated event logs, while <em>beta</em> is its partially aggregated derivative.
 This <em>beta</em> is the basis against which all further queries are evaluated:</p>
 <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>2011-01-01T01:00:00Z  ultratrimfast.com  google.com  Male    USA  1800  25  15.70
 2011-01-01T01:00:00Z  bieberfever.com    google.com  Male    USA  2912  42  29.18
 2011-01-01T02:00:00Z  ultratrimfast.com  google.com  Male    UK   1953  17  17.31
 2011-01-01T02:00:00Z  bieberfever.com    google.com  Male    UK   3194  170 34.01
 </code></pre></div>
 <p>This is the most compact representation that preserves the finest grain of data,
 while enabling on-the-fly computation of all O(2^n) possible dimensional
 roll-ups.</p>

 <p>The key to Druid’s speed is maintaining the <em>beta</em> data entirely in memory. Full
 scans are several orders of magnitude faster in memory than via disk. What we
 lose in having to compute roll-ups on the fly, we make up for with speed.</p>

 <p>To support drill-downs on specific dimensions (such as results for only
 ‘bieberfever.com’), we maintain a set of inverted indices. This allows for fast
 calculation (using AND &amp; OR operations) of rows matching a search query. The
 inverted index enables us to scan a limited subset of rows to compute final
 query results – and these scans are themselves distributed, as we discuss next.</p>

 <p><strong>2. Distributed Data + Parallelizable Queries =&gt; Horizontal Scalability</strong></p>

 <p>Druid’s performance depends on having memory — lots of it. We achieve the requisite
 memory scale by dynamically distributing data across a cluster of nodes. As the
 data set grows, we can horizontally expand by adding more machines.</p>

 <p>To facilitate rebalancing, we take chunks of <em>beta</em> data and index them into
 segments based on time ranges. For high cardinality dimensions, distributing by
 time isn’t enough (we generally try to keep segments no larger than 20M rows),
 so we have introduced partitioning. We store metadata about segments within the
 query layer and partitioning logic within the segment generation code.</p>

 <p>We persist these segments in a storage system (currently S3) that is accessible
 from all nodes. If a node goes down, <a href="http://zookeeper.apache.org/">Zookeeper</a>
 coordinates the remaining live nodes to reconstitute the missing <em>beta</em> set.</p>

 <p>Downstream clients of the API are insulated from this rebalancing: Druid’s
 query API seamlessly handles changes in cluster topology.</p>

 <p>Queries against the Druid cluster are perfectly horizontal. We limited the
 aggregation operations we support – count, mean, variance and other parametric
 statistics – that are inherently parallelizable. While less parallelizable
 operations, such as median, are not supported, this limitation is offset by
 rich support of histogram and higher-order moment stores. The co-location of
 processing with in-memory data on each node reduces network load and
 dramatically improves performance.</p>

 <p>This architecture provides a number of extra benefits:</p>

 <ul>
 <li>Segments are read-only, so they can simultaneously serve multiple servers. If
 we have a hotspot in a particular index, we can replicate that index to
 multiple servers and load balance across them.</li>
 <li>We can provide tiered classes of service for our data, with servers occupying
 different points in the “query latency vs. data size” spectrum</li>
 <li>Our clusters can span data center boundaries</li>
 </ul>

 <p><strong>3. Real-Time Analytics: Immutable Past, Append-Only Future</strong></p>

 <p>Our system for real-time analytics is centered, naturally, on time. Because past events
 happen once and never change, they need not be re-writable. We need only be
 able to append new events.</p>

 <p>For real-time analytics, we have an event stream that flows into a set of
 real-time indexers. These are servers that advertise responsibility for the
 most recent 60 minutes of data and nothing more. They aggregate the real-time
 feed and periodically push an index segment to our storage system. The segment
 then gets loaded into memory of a standard server, and is flushed from the
 real-time indexer.</p>

 <p>Similarly, for long-range historical data that we want to make available, but
 not keep hot, we have deep-history servers. These use a memory mapping strategy
 for addressing segments, rather than loading them all into memory. This
 provides access to long-range data while maintaining the high-performance that
 our customers expect for near-term data.</p>

 <h2 id="summary">Summary</h2>

 <p>Druid’s power resides in providing users fast, arbitrarily deep
 exploration of large-scale transaction data. Queries over billions of rows,
 that previously took minutes or hours to run, can now be investigated directly
 with sub-second response times.</p>

 <p>We believe that the performance, scalability, and unification of real-time and
 historical data that Druid provides could be of broader interest. As such, we
 plan to open source our code base in the coming year.</p>

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	<h1>Druid, Part Deux: Three Principles for Fast, Distributed OLAP</h1>
	<p class="text-muted">by <span class="author text-uppercase">Eric Tschetter</span> · May 20, 2011</p>

	<p>In a <a href="/blog/2011/04/30/introducing-druid.html">previous blog
	post</a> we introduced the
	distributed indexing and query processing infrastructure we call Druid. In that
	post, we characterized the performance and scaling challenges that motivated us
	to build this system in the first place. Here, we discuss three design
	principles underpinning its architecture.</p>

	<p><strong>1. Partial Aggregates + In-Memory + Indexes => Fast Queries</strong></p>

	<p>We work with two representations of our data: <em>alpha</em> represents the raw,
	unaggregated event logs, while <em>beta</em> is its partially aggregated derivative.
	This <em>beta</em> is the basis against which all further queries are evaluated:</p>
	<div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>2011-01-01T01:00:00Z ultratrimfast.com google.com Male USA 1800 25 15.70
	2011-01-01T01:00:00Z bieberfever.com google.com Male USA 2912 42 29.18
	2011-01-01T02:00:00Z ultratrimfast.com google.com Male UK 1953 17 17.31
	2011-01-01T02:00:00Z bieberfever.com google.com Male UK 3194 170 34.01
	</code></pre></div>
	<p>This is the most compact representation that preserves the finest grain of data,
	while enabling on-the-fly computation of all O(2^n) possible dimensional
	roll-ups.</p>

	<p>The key to Druid’s speed is maintaining the <em>beta</em> data entirely in memory. Full
	scans are several orders of magnitude faster in memory than via disk. What we
	lose in having to compute roll-ups on the fly, we make up for with speed.</p>

	<p>To support drill-downs on specific dimensions (such as results for only
	‘bieberfever.com’), we maintain a set of inverted indices. This allows for fast
	calculation (using AND & OR operations) of rows matching a search query. The
	inverted index enables us to scan a limited subset of rows to compute final
	query results – and these scans are themselves distributed, as we discuss next.</p>

	<p><strong>2. Distributed Data + Parallelizable Queries => Horizontal Scalability</strong></p>

	<p>Druid’s performance depends on having memory — lots of it. We achieve the requisite
	memory scale by dynamically distributing data across a cluster of nodes. As the
	data set grows, we can horizontally expand by adding more machines.</p>

	<p>To facilitate rebalancing, we take chunks of <em>beta</em> data and index them into
	segments based on time ranges. For high cardinality dimensions, distributing by
	time isn’t enough (we generally try to keep segments no larger than 20M rows),
	so we have introduced partitioning. We store metadata about segments within the
	query layer and partitioning logic within the segment generation code.</p>

	<p>We persist these segments in a storage system (currently S3) that is accessible
	from all nodes. If a node goes down, <a href="http://zookeeper.apache.org/">Zookeeper</a>
	coordinates the remaining live nodes to reconstitute the missing <em>beta</em> set.</p>

	<p>Downstream clients of the API are insulated from this rebalancing: Druid’s
	query API seamlessly handles changes in cluster topology.</p>

	<p>Queries against the Druid cluster are perfectly horizontal. We limited the
	aggregation operations we support – count, mean, variance and other parametric
	statistics – that are inherently parallelizable. While less parallelizable
	operations, such as median, are not supported, this limitation is offset by
	rich support of histogram and higher-order moment stores. The co-location of
	processing with in-memory data on each node reduces network load and
	dramatically improves performance.</p>

	<p>This architecture provides a number of extra benefits:</p>

	<ul>
	<li>Segments are read-only, so they can simultaneously serve multiple servers. If
	we have a hotspot in a particular index, we can replicate that index to
	multiple servers and load balance across them.</li>
	<li>We can provide tiered classes of service for our data, with servers occupying
	different points in the “query latency vs. data size” spectrum</li>
	<li>Our clusters can span data center boundaries</li>
	</ul>

	<p><strong>3. Real-Time Analytics: Immutable Past, Append-Only Future</strong></p>

	<p>Our system for real-time analytics is centered, naturally, on time. Because past events
	happen once and never change, they need not be re-writable. We need only be
	able to append new events.</p>

	<p>For real-time analytics, we have an event stream that flows into a set of
	real-time indexers. These are servers that advertise responsibility for the
	most recent 60 minutes of data and nothing more. They aggregate the real-time
	feed and periodically push an index segment to our storage system. The segment
	then gets loaded into memory of a standard server, and is flushed from the
	real-time indexer.</p>

	<p>Similarly, for long-range historical data that we want to make available, but
	not keep hot, we have deep-history servers. These use a memory mapping strategy
	for addressing segments, rather than loading them all into memory. This
	provides access to long-range data while maintaining the high-performance that
	our customers expect for near-term data.</p>

	<h2 id="summary">Summary</h2>

	<p>Druid’s power resides in providing users fast, arbitrarily deep
	exploration of large-scale transaction data. Queries over billions of rows,
	that previously took minutes or hours to run, can now be investigated directly
	with sub-second response times.</p>

	<p>We believe that the performance, scalability, and unification of real-time and
	historical data that Druid provides could be of broader interest. As such, we
	plan to open source our code base in the coming year.</p>

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