1) The presentation discusses Druid, an open source analytics engine that can perform aggregations on memory mapped data in sub-second time. 2) It describes how Druid fits into their software stack at the API layer and how they extend its capabilities through a SQL interface and addressing limitations like limited querying and missing features like distinct counts. 3) Examples of SQL queries against Druid are shown to demonstrate its capabilities like group by, filtering, joins, and handling of timeseries data.

1. Yahoo! Presentation, Confidential 16 July 2014
July 16, 2014
Pushing the limits of
Realtime analytics with Druid
Reza Iranmanesh
Srikalyan Chandrashekar
By realtime we mean subsecond response, highly concurrent and
realtime ingestion too

2. Yahoo! Presentation, Confidential 16 July 2014
Agenda
1. What is Druid ?
2. Fitting Druid into our Software Stack; Druid in the API layer
3. The SQL 4 Druid suite (compiler, driver and client).
4. Contrast with traditional RDBMS SQL.
5. Features of SQL 4 Druid suite.
6. Current state.
7. Demo.
8. Future plans.

3. Yahoo! Presentation, Confidential 16 July 2014
What is Analytics?
Analytics The process of accessing, cleaning, transforming and
modeling data with the goal of discovering information.
Business Intelligence analytics focused on business information.
Star Schema
Data Cubes / OLAP Systems

4. Yahoo! Presentation, Confidential 16 July 2014
What is Druid?
- A google dremel inspired, open source OLAP-like engine that
can do aggregate operations in sub second (most of them) on
memory mapped data.
- de-normalized data
- Time-based segments
- Timeseries/GroupBy/TopN
- Plays well with Hadoop

5. Yahoo! Presentation, Confidential 16 July 2014
What is Druid?
Lambda Architecture in a box – a relatively self-contained system

6. Yahoo! Presentation, Confidential 16 July 2014
Main Components
Indexing Service
• Realtime ingestion
• Hadoop batch ingestion
• Local batch ingestion
• Aggregates are defined at indexing stage
• Final output: segments of data that will eventually live on deep store. Each segment holds
a timerange of data.
Coordinator Node
• The Coordinator! Takes care of reading metadata from MySQL and looking at zookeeper to see who’s
there and putting segment distribution information for Historicals to pick, etc.
Broker Node
• Forwards the queries to the nodes who have the segments that fall into the given time interval
• Takes care of aggregating the partial aggregates from historical nodes
Historical Node
• Loads segments of immutable indexed data that live on the deep store (usually Grid)
• Each historical node
Realtime Node

7. Yahoo! Presentation, Confidential 16 July 2014
Why Druid?
Pros:
• Horizontal Scalability with linear performance gains
• Sub-second response time for most use cases
• Native time zone support
• Relatively self-contained (batch + real-time ingestion/query support,
distributed memcached support, multi-tier replication/load support)
• Active and responsive community
Cons:
• Limited query power compared with SQL/MDX
• Lack of joins
• Missing distinct count
• Memory bounds for GroupBy Query

8. Yahoo! Presentation, Confidential 16 July 2014
Horizontal Scalability

9. Yahoo! Presentation, Confidential 16 July 2014
Some Numbers

10. Yahoo! Presentation, Confidential 16 July 2014
Some Numbers

11. Yahoo! Presentation, Confidential 16 July 2014
Druid in our API Layer
Limitation Solution
groupBy memory bounded - Replace with TopN
- Not a real limit in Analytics world
Limited Query power - Extend Druid’s limits
- Some extra work in the API
layer
- Implement a SQL layer
Missing exact Distinct Count - Use HyperLogLog
implementation
- Create pre-aggregates on the
grid when we need exact values;
create a multiplexer on the API
side
The problem of mutable dimension
data; supporting star schema
- Query pipeline in the API layer
Missing joins - SQL layer

12. Yahoo! Presentation, Confidential 16 July 2014
Grid
RDBMS
Druid
Druid in our API Layer
Star Schema
Solving the problem of mutable dimension tables with a Druid query pipeline and one-to-one joins:
filter on pre-aggregates / join on post-aggregates
- uuid
-
name
- url
- uuid
-
name
- url
-
views
- clicks
First Druid groupby
First result
as input to
the next Query
Select sum(uuid), ..
From …
Join …
Where …
API

13. Yahoo! Presentation, Confidential 16 July 2014
Druid in our API Layer
Unique Counts
Unique_count_1_hour Unique_count_7_day
Unique_count_24_hour Unique_count_14_day
Unique_count_30_day
use hyperLogLog
aggregate
What is the granularity?
hour? day? all?
Hybrid approach to take care of distinct counts:

14. Yahoo! Presentation, Confidential 16 July 2014
SQL 4 Druid suite
1. JDBC driver
2. Command line client
the driver is powered
by the DCompiler.

15. Yahoo! Presentation, Confidential 16 July 2014
Demo

16. Yahoo! Presentation, Confidential 16 July 2014
Sql features
1. GroupBy, Having clause supported.
2. Post aggregation including javascript functions
accepted.
3. Order BY and LIMIT which essentially is Top N.
4. Where clause translates into filters.
5. Aggregators: count, double_sum, long_sum, unique,
max, min etc.
6. BREAK BY translates into granularity.
7. HINT timeseries if dimension is timestamp.

17. Yahoo! Presentation, Confidential 16 July 2014
Sql features continued
8. Specify micro and adhoc interval ranges.
9. Select-type query(no aggregation) just plain
dimension (and/or) metrics retrieval.

18. Yahoo! Presentation, Confidential 16 July 2014
Sample SQLs

19. Yahoo! Presentation, Confidential 16 July 2014
Driver features.
1. Can do JOIN(Inner, Left and Right), can go
only two level deep right now.
2. Template parameters.

20. Yahoo! Presentation, Confidential 16 July 2014
Driver features continued.
3. Map data to list .
4. Map data to bean.
5. Dynamic column type inference on select queries(Ex:
dimension/metric is found from Coordinator)

21. Yahoo! Presentation, Confidential 16 July 2014
Client Features.
1. GroupBy,TS and TopN, search queries.
2. Generate Bean source code based on previous SQL
executed.
3. See all tables(data sources), schema of table etc.
4. Navigate through command history.
5. MySQL like pretty print.

22. Yahoo! Presentation, Confidential 16 July 2014
We Are Hiring
srikalyan@yahoo-inc.com
reza1@yahoo-inc.com
Contact us:

23. Yahoo! Presentation, Confidential 16 July 2014
Appendix A
Timeseries with adhoc interval spec

24. Yahoo! Presentation, Confidential 16 July 2014
Appendix B
Select Query on dimensions and metrics

25. Yahoo! Presentation, Confidential 16 July 2014
Appendix C
GroupBy With Join