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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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