Druid, Part Deux: Three Principles for Fast, Distributed OLAP

In a previous blog post 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.

1. Partial Aggregates + In-Memory + Indexes => Fast Queries

We work with two representations of our data: alpha represents the raw, unaggregated event logs, while beta is its partially aggregated derivative. This beta is the basis against which all further queries are evaluated:

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.

The key to Druid’s speed is maintaining the beta 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.

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.

2. Distributed Data + Parallelizable Queries => Horizontal Scalability

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.

To facilitate rebalancing, we take chunks of beta 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.

We persist these segments in a storage system (currently S3) that is accessible from all nodes. If a node goes down, Zookeeper coordinates the remaining live nodes to reconstitute the missing beta set.

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

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.

This architecture provides a number of extra benefits:

  • 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.
  • We can provide tiered classes of service for our data, with servers occupying different points in the “query latency vs. data size” spectrum
  • Our clusters can span data center boundaries

3. Real-Time Analytics: Immutable Past, Append-Only Future

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.

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.

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.


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.

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.

Looking for more Druid information? Learn more about our core technology.

Interested in tackling distributed systems challenges like this one? Come join the Metamarkets Team.

Filed in Druid, Technology