An Introduction to Druid

1 © Hortonworks Inc. 2011–2018. All rights reserved
An Introduction to Druid
Nishant Bangarwa
Software Developer

© Hortonworks Inc. 2011 – 2016. All Rights Reserved
2
Agenda
History and Motivation
Introduction
Data Storage Format
Druid Architecture – Indexing and Querying Data
Druid In Production
Recent Improvements

HISTORY
• Druid Open sourced in late 2012
• Initial Use case
• Power ad-tech analytics product
• Requirements
• Query any combination of metrics and
dimensions
• Scalability : trillions of events/day
• Real-time : data freshness
• Streaming Ingestion
• Interactive : low latency queries

4
How Big is the initial use case ?

5
MOTIVATION
• Business Intelligence Queries
• Arbitrary slicing and dicing of data
• Interactive real time visualizations on
Complex data streams
• Answer BI questions
• How many unique male visitors visited my
website last month ?
• How many products were sold last quarter
broken down by a demographic and product
category ?
• Not interested in dumping entire dataset

Introduction

7
What is Druid ?
• Column-oriented distributed datastore
• Sub-Second query times
• Realtime streaming ingestion
• Arbitrary slicing and dicing of data
• Automatic Data Summarization
• Approximate algorithms (hyperLogLog, theta)
• Scalable to petabytes of data
• Highly available

8
Companies Using Druid

Druid Architecture

1
Node Types
• Realtime Nodes
• Historical Nodes
• Broker Nodes
• Coordinator Nodes

Realtime
Nodes
Historical
Nodes
1
Druid Architecture
Batch Data
Event
Historical
Nodes
Broker
Nodes
Realtime
Index Tasks
Streaming
Data
Historical
Nodes
Handoff

1
Druid Architecture
Batch Data
Queries
Metadata
Store
Coordinator
Nodes
Zookeepe
r
Historical
Nodes
Broker
Nodes
Realtime
Index Tasks
Streaming
Data
Handoff

Storage Format

Druid: Segments
• Data in Druid is stored in Segment Files.
• Partitioned by time
• Ideally, segment files are each smaller than 1GB.
• If files are large, smaller time partitions are needed.
Time
Segment 1:
Monday
Segment 2:
Tuesday
Segment 3:
Wednesday
Segment 4:
Thursday
Segment 5_2:
Friday
Segment 5_1:
Friday

1
Example Wikipedia Edit Dataset
timestamp page language city country … added deleted
2011-01-01T00:01:35Z Justin Bieber en SF USA 10 65
2011-01-01T00:05:35Z Ke$ha en Calgary CA 17 87
2011-01-02T00:08:35Z Selena Gomes en Calgary CA 12 53
Timestamp Dimensions Metrics

1
Data Rollup
timestamp page language city country count sum_added sum_deleted min_added max_added ….
2011-01-01T00:00:00Z Justin Bieber en SF USA 3 57 172 10 32
2011-01-01T00:00:00Z Ke$ha en Calgary CA 2 60 186 17 43
2011-01-02T00:00:00Z Selena Gomes en Calgary CA 1 12 53 12 12
Rollup by hour

1
Dictionary Encoding
• Create and store Ids for each value
• e.g. page column
⬢ Values - Justin Bieber, Ke$ha, Selena Gomes
⬢ Encoding - Justin Bieber : 0, Ke$ha: 1, Selena Gomes: 2
⬢ Column Data - [0 0 0 1 1 2]
• city column - [0 0 0 1 1 1]

1
Bitmap Indices
• Store Bitmap Indices for each value
⬢ Justin Bieber -> [0, 1, 2] -> [1 1 1 0 0 0]
⬢ Ke$ha -> [3, 4] -> [0 0 0 1 1 0]
⬢ Selena Gomes -> [5] -> [0 0 0 0 0 1]
• Queries
⬢ Justin Bieber or Ke$ha -> [1 1 1 0 0 0] OR [0 0 0 1 1 0] -> [1 1 1 1 1 0]
⬢ language = en and country = CA -> [1 1 1 1 1 1] AND [0 0 0 1 1 1] -> [0 0 0 1 1 1]
• Indexes compressed with Concise or Roaring encoding

1
Approximate Sketch Columns
timestamp page userid language city country … added deleted
2011-01-01T00:01:35Z Justin Bieber user1111111 en SF USA 10 65
2011-01-01T00:05:35Z Ke$ha user3333333 en Calgary CA 17 87
2011-01-01T00:06:41Z Ke$ha user4444444 en Calgary CA 43 99
2011-01-02T00:08:35Z Selena Gomes user1111111 en Calgary CA 12 53
timestamp page language city country count sum_added sum_delete
d
min_added Userid_sket
ch
….
2011-01-01T00:00:00Z Justin Bieber en SF USA 3 57 172 10 {sketch}
2011-01-01T00:00:00Z Ke$ha en Calgary CA 2 60 186 17 {sketch}
2011-01-02T00:00:00Z Selena Gomes en Calgary CA 1 12 53 12 {sketch}
Rollup by hour

Approximate Sketch Columns
• Better rollup for high cardinality columns e.g userid
• Reduced storage size
• Use Cases
• Fast approximate distinct counts
• Approximate histograms
• Funnel/retention analysis
• Limitation
• Not possible to do exact counts
• filter on individual row values

Indexing Data

Indexing Service
• Indexing is performed by
• Overlord
• Middle Managers
• Peons
• Middle Managers spawn peons which runs ingestion
tasks
• Each peon runs 1 task
• Task definition defines which task to run and its
properties

2
Streaming Ingestion : Realtime Index Tasks
• Ability to ingest streams of data
• Stores data in write-optimized structure
• Periodically converts write-optimized structure
to read-optimized segments
• Event query-able as soon as it is ingested
• Both push and pull based ingestion

Streaming Ingestion : Tranquility
• Helper library for coordinating
streaming ingestion
• Simple API to send events to
druid
• Transparently Manages
• Realtime index Task Creation
• Partitioning and Replication
• Schema Evolution
• Can be used with your favourite
ETL framework e.g Flink, Nifi,
Samza, Spark, Storm
• At-least once ingestion

Kafka Indexing Service (experimental)
• Supports Exactly once ingestion
• Messages pulled by Kafka Index Tasks
• Each Kafka Index Task consumes from a set
of partitions with specific start and end
offset
• Each message verified to ensure sequence
• Kafka Offsets and corresponding segments
persisted in same metadata transaction
atomically
• Kafka Supervisor
• embedded inside overlord
• Manages kafka index tasks
• Retry failed tasks
Task 1
Task 2
Task 3

Batch Ingestion
• HadoopIndexTask
• Peon launches Hadoop MR job
• Mappers read data
• Reducers create Druid segment files
• Index Task
• Runs in single JVM i.e peon
• Suitable for data sizes(<1G)
• Integrations with Apache HIVE and Spark for Batch Ingestion

Querying Data

Querying Data from Druid
• Druid supports
• JSON Queries over HTTP
• In built SQL (experimental)
• Querying libraries available for
• Python
• R
• Ruby
• Javascript
• Clojure
• PHP
• Multiple Open source UI tools

2
JSON Over HTTP
• HTTP Rest API
• Queries and results expressed in JSON
• Multiple Query Types
• Time Boundary
• Timeseries
• TopN
• GroupBy
• Select
• Segment Metadata

In built SQL (experimental)
• Apache Calcite based parser and planner
• Ability to connect druid to any BI tool that supports JDBC
• SQL via JSON over HTTP
• Supports Approximate queries
• APPROX_COUNT_DISTINCT(col)
• Ability to do Fast Approx TopN queries
• APPROX_QUANTILE(column, probability)

Integrated with multiple Open Source UI tools
• Superset –
• Developed at AirBnb
• In Apache Incubation since May 2017
• Grafana – Druid plugin
• Metabase
• With in-built SQL, connect with any BI tool supporting JDBC

Druid in Production

Druid in Production
 Is Druid suitable for my Use case ?
 Will Druid meet my performance requirements at scale ?
 How complex is it to Operate and Manage Druid cluster ?
 How to monitor a Druid cluster ?
 High Availability ?
 How to upgrade Druid cluster without downtime ?
 Security ?

Suitable Use Cases
• Powering Interactive user facing applications
• Arbitrary slicing and dicing of large datasets
• User behavior analysis
• measuring distinct counts
• retention analysis
• funnel analysis
• A/B testing
• Exploratory analytics/root cause analysis
• Not interested in dumping entire dataset

Performance and Scalability : Fast Facts
Most Events per Day
300 Billion Events / Day
(Metamarkets)
Most Computed Metrics
1 Billion Metrics / Min
(Jolata)
Largest Cluster
200 Nodes
(Metamarkets)
Largest Hourly Ingestion
2TB per Hour
(Netflix)

3
Performance Numbers
• Query Latency
• average - 500ms
• 90%ile < 1sec
• 95%ile < 5sec
• 99%ile < 10 sec
• Query Volume
• 1000s queries per minute
• Benchmarking code
• https://github.com/druid-
io/druid-benchmark

Simplified Druid Cluster Management with Ambari
 Install, configure and manage Druid and all external dependencies from Ambari
 Easy to enable HA, Security, Monitoring …

Simplified Druid Cluster Management with Ambari

Monitoring a Druid Cluster
• Each Druid Node emits metrics for
• Query performance
• Ingestion Rate
• JVM Health
• Query Cache performance
• System health
• Emitted as JSON objects to a runtime log file or over HTTP to other services
• Emitters available for Ambari Metrics Server, Graphite, StatsD, Kafka
• Easy to implement your own metrics emitter

Monitoring using Ambari Metrics Server
• HDP 2.6.1 contains pre-defined grafana dashboards
• Health of Druid Nodes
• Ingestion
• Query performance
• Easy to create new dashboards and setup alerts
• Auto configured when both Druid and Ambari Metrics Server are installed

High Availability
• Deploy Coordinator/Overlord on multiple instances
• Leader election in zookeeper
• Broker – install multiple brokers
• Use druid Router/ Any Load balancer to route queries to brokers
• Realtime Index Tasks – create redundant tasks.
• Historical Nodes – create load rule with replication factor >= 2 (default = 2)

4
Rolling Upgrades
 Shared Nothing Architecture
⬢ Maintain backwards compatibility
⬢ Data redundancy
 Upgrade one Druid Component at a time
⬢ No Downtime
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1
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Security
• Supports Authentication via Kerberos/ SPNEGO
• Easy Wizard based kerberos security enablement via Ambari
Druid
KDC server
User
Browser1 kinit user
2 Token

4
Summary
• Easy installation and management via Ambari
• Real-time
• Ingestion latency < seconds.
• Query latency < seconds.
• Arbitrary slice and dice big data like ninja
• No more pre-canned drill downs.
• Query with more fine-grained granularity.
• High availability and Rolling deployment capabilities
• Secure and Production ready
• Vibrant and Active community
• Available as Tech Preview in HDP 2.6.1

4
• Druid website – http://druid.io
• Druid User Group - dev@druid.incubator.apache.org
• Druid Dev Group – users@druid.incubator.apache.org
Useful Resources

Thank you
Twitter - @NishantBangarwa
Email - nbangarwa@hortonworks.com

Questions?

Extending Core Druid
• Plugin Based Architecture
• leverage Guice in order to load extensions at runtime
• Possible to add extension to
• Add a new deep storage implementation
• Add a new Firehose for Ingestion
• Add Aggregators
• Add Complex metrics
• Add new Query types
• Add new Jersey resources
• Bundle your extension with all the other Druid extensions

Performance : Approximate Algorithms
• Ability to Store Approximate Data Sketches for high cardinality columns e.g userid
• Reduced storage size
• Use Cases
• Fast approximate distinct counts
• Approximate Top-K queries
• Approximate histograms
• Funnel/retention analysis
• Limitation
• Not possible to do exact counts
• filter on individual row values

Superset
• Python backend
• Flask app builder
• Authentication
• Pandas for rich analytics
• SqlAlchemy for SQL toolkit
• Javascript frontend
• React, NVD3
• Deep integration with Druid

Superset Rich Dashboarding Capabilities: Treemaps

Superset Rich Dashboarding Capabilities: Sunburst

Superset UI Provides Powerful Visualizations
Rich library of dashboard visualizations:
Basic:
• Bar Charts
• Pie Charts
• Line Charts
Advanced:
• Sankey Diagrams
• Treemaps
• Sunburst
• Heatmaps
And More!

An Introduction to Druid

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