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Real-time Insights into Application
Events
Who Are We?
Software Engineers in Netflix’s
Platform Engineering team,
working on very large scale data
infrastructure
Building and operating Netflix’s
cloud real-time query service
Why We Are Here?
No Monitoring Metrics Today
213 event processingtalk-deviewkorea.key
Netflix is a log generating company
that also happens to stream movies
- Adrian Cockroft

photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/o/in/photostream/
1,500,000
70,000,000,000
Making Sense of Billions of Events
A Humble Beginning
Things Changed
Application

Application

Application
Application

Application

Application

Application

Application

Application

Application
So We Evolved

hgrep -C 10 -k 5,2,3 'users.*[1-9]{3}' *catalina.out s3//bucket
213 event processingtalk-deviewkorea.key
213 event processingtalk-deviewkorea.key
What Is Missing?
Interactive Exploration
Use Cases
Real-time or Product
Business Operational
Metrics
Insignts
Getting Results Back in Seconds

150,000
Querying Data Along Different Dimensions
Discover Outstanding Data

HTTP 500
Discover Outstanding Data
See Trends Over Time
See Data Distributions
It’s All about Extracting Small Data
Out of Big Data
But Then What?
Intelligent Alerts
Guided Debugging in the Right Context
Guided Debugging in the Right Context
Guided Debugging in the Right Context
Technical Challenges
Problem:
Minimizing programming effort

Solution:

-Homogeneous architecture
- Separating producing logs from
consuming logs
Field Name

Field Value

Client

“API”

Server

“Cryptex”

StatusCode

200

ResponseTime

73
A Single Data Pipeline

Log data

Log Filter

Collector
Agent
Log Collectors

LogManager.logEvent(anEvent)
Reliable/Flexible Data Pipeline

Log Filter

Sink Plugin

Log Filter

Sink Plugin

Log Filter

Sink Plugin

Server Farm

Server Farm
Log Collectors

Hadoop

Kafka
Druid

Server Farm

photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/m/in/photostream/

Kafka
ElasticSearch
Problem:
Not All Logs Are Worth Processing

Solution:
Dynamic Filtering
213 event processingtalk-deviewkorea.key
Problem:
Realtime Ingestion

Solution:
Druid & ElasticSearch
ElasticSearch

-Distributed restful search analytics
- Lucene based, Full text search
- High availability
- Faceted search, a little slow
Druid

-Real-time indexing and querying
- Arbitrary slicing and dicing, rolling
up and drilling down

- Packaged queries - TopN, Time
Series, Histograms, Cardinalities
Druid Architecture
RealTime Nodes

Hand off data

Historical Nodes

Deep Storage
Query API

Query API
Query Rewrite
Scatter/Gatter

Broker Nodes

slide credit: Eric Cheddar @Metamx
Colmum Compression
timestamp
2011-01-01T00:01:35Z
2011-01-01T00:03:63Z
2011-01-01T00:04:51Z
2011-01-01T01:00:00Z
2011-01-01T02:00:00Z
2011-01-01T02:00:00Z
...

publisher
advertiser gender country
bieberfever.com
google.com Male
USA
bieberfever.com
google.com Male
USA
bieberfever.com
google.com Male
USA
ultratrimfast.com google.com Female UK
ultratrimfast.com google.com Female UK
ultratrimfast.com google.com Female UK

Create Ids:
bieberfever.com -> 0, ultratrimfast.com-> 1

Store:
publisher -> [0, 0, 0, 1, 1, 1]
advertiser -> [0, 0, 0, 0, 0, 0]

slide credit: Eric Cheddar @Metamx

...

0.65
0.62
0.45
0.87
0.99
1.53
Bitmap Index
timestamp
2011-01-01T00:01:35Z
2011-01-01T00:03:63Z
2011-01-01T00:04:51Z
2011-01-01T01:00:00Z
2011-01-01T02:00:00Z
2011-01-01T02:00:00Z
...

publisher
bieberfever.com
bieberfever.com
bieberfever.com
ultratrimfast.com
ultratrimfast.com
ultratrimfast.com

advertiser
google.com
google.com
google.com
google.com
google.com
google.com

gender
Male
Male
Male
Female
Female
Female

country
USA
USA
USA
UK
UK
UK

...

0.65
0.62
0.45
0.87
0.99
1.53

bieberfever.com -> [0, 1, 2] -> [111000]
ultratrimfast.com -> [3, 4, 5] -> [000111]
Compress
CONCISE
http://ricerca.mat.uniroma3.it/users/colanton/co
slide credit: Eric Cheddar @Metamx
Problem:
JSON Payload Is Tedious

Solution:
Build a parser
curl -X POST http://druid -d @data
There’s More
System Monitoring
System Resilience
System Operability
Problem:
So many combinations of configurations

Solution:
Build a flexible load testing tool
213 event processingtalk-deviewkorea.key
Problem:
Managing data sources can be hairy

Solution:
Use cell-like deployment
Druid

Kafka

Druid

Druid

Kafka

Kafka

Log Data Pipeline
Problem:
How do we know everything of the
new systems?

Solution:
Extensive instrumentation with
Servo and Atlas
213 event processingtalk-deviewkorea.key
Problem:
Many open-sourced solutions
assume static configuration

Solution:
Integrating with Netflix platform,
particularly Eureka
Problem:
Zookeeper goes down, and so does
connections for Kakfa clients

Solution:
Replacing zkClient with Apache
Curator
Technology Stacks
- Netflix OSS: Powerful cloud computation
- Suro: Internal main data pipeline
- Kafka: High-throughput and durable

message queue
- Druid: Efficient real-time multi-dimensional
database on large-scale data
- ElasticSearch: Distributed search engine
- Kibana: ElasticSearch UI
- Zookeeper: Distributed coordinator
Thank You!

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