Amihay Zer-Kavod discusses how LivePerson uses Apache Avro to maintain consistent data across services. Avro provides a unified event schema and tools for serialization, enabling events to be sent between services and stored in Hadoop. LivePerson's use of an event-driven system with a common Avro schema allows over 320,000 events per second to be processed and over 2TB of data to be stored daily.

1. Apache Avro in LivePerson
Collecting and saving data is easy
keeping it consistent is tough
Sandwich club, Sep 2014
Amihay Zer-Kavod, Software Architect

2. Who am I?
Amihay Zer-Kavod
Software Architect
Been in software Since 1989

3. LivePerson Echo System
M/R

4. Communication & Meaning
● Consistent but decoupled communication
between services, such as:
o Monitoring, Interaction
o Predictive, Sentiment
o RT Reporting & Analysis
o Visitor History
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● Consistent meaning over time
o BigData Store (Hadoop)
o Offline Reporting & Analysis

5. What shouldn’t we use?
Don’t use Direct APIs!
They are completely wrong for this subject:
• They produce too much coupling between services
• APIs are synchronous by nature
• Adds irrelevant complexity to the called service

6. What is needed?
The Message is the API!
● A unified event model (schema) for all reported events
● Management tools for the unified schema
● Tools for sending events over the wire
● Tools for reading/writing event in big data
● Backward and forward compatibility

7. The Event model
From generic to specific structure with:
• Common header - all common data to all events
• Logical Entities - common header to all logical entities
(such as Visitor)
• Dynamic Specific headers
• Specific Event body

8. Apache Avro to the rescue
● Avro - a schema based serialization/deserialization
framework
● Avro idl - schema definition language
● Avro file - Hadoop integration
● Avro schema resolution
● Apache Avro created by Doug Cutting

9. Avro 101 - Data Structures
● Rich data structures
○ Primitives
■ null, int, long, boolean, float, double, bytes, string
○ Records
○ Map (string, Schema)
○ Arrays (Schema)
○ Enums
○ Unions

10. Avro 101 - JSON Schema
{
"type": "record",
"name": "Event",
"namespace": "com.liveperson.example",
"doc": "Example event",
"fields":[
{ "name": "id", "type": "string", "default": "Unknown"},
{ "name": "time", "type": "long", "default": -1},
{ "name": "color", "type":
{ "type": "enum", "name": "Color",
"symbols": ["NO_COLOR", "BLUE", "BLACK", "WHITE", "PINK"]
},
"default": "NO_COLOR" }
]
}

11. Avro 101 - Avro IDL Schema
@namespace("com.liveperson.example")
enum Color { NO_COLOR, BLUE, BLACK, WHITE, PINK }
/**
Example event
*/
@namespace("com.liveperson.example")
record Event {
string id = “Unknown”;
long time = -1;
Color color = "NO_COLOR";
}

12. Avro 101 - Serialization
● JSON Serialization
● Binary serialization
○ int, long - variable length, Zig-zag encoding
○ float, double - 4,8 bytes respectively
○ string - long followed by UTF-8 bytes
○ map, array - unlimited size, use blocks
○ Unions - long index of the type

13. Avro 101 - Generic vs. Specific
● SpecificDatumReader/Writer <T>
○ Static types
○ Code Generation: Java, C, C++, C#, Python, Ruby...
● GenericDatumReader/Writer <GenericRecord>
○ Dynamic types & access

14. Avro 101 - Schema Resolution
● Writer schema must be always provided for decoding
● Reader can use its own schema
● Allows the reader and writer schema to evolve
independently

15. Avro vs...
Technologies Protobuf Thrift Avro
Created 2001 (2008) 2007 2009
Creator / Maintainer Google / Google Facebook / Apache
Doug cutting /
Apache
Schema evolution Field Tag Field Tag Schema
Static/Dynamic Yes/No Yes/No Yes/Yes
Hadoop support No No Yes
RPC No Yes Yes
Used by Google Facebook, Cassandra Hadoop, Liveperson
Lang support Good Great Good

16. Backward & Forward Compatibility
Avro schema evolution
● Avro supports resolution between two schemes
● Need to follow a set of rules:
● Every field must have a default value
● A field can be added (make sure to put a default value)
● Field types can not be changed (add a new field
instead)
● enum symbols can be added but never removed

17. Avro IDL - LivePerson Event
/** Base for all LivePerson Events
*/
@namespace("com.liveperson.global")
record LPEvent {
/** Common Header of the event */
CommonHeader header = null;
/** Logical entity details participating in this event - Visitor, Agent, etc... */
array<Participant> participants = null;
/** Holding specific platform info as node name (machine) cluster Id etc... */
PlatformHeader platformSpecificHeader = null;
/** Auditing Header, Optional - adds data for auditing of the events flow in the platform*/
union {null, AuditingHeader } auditingHeader = null;
/** The event body */
EventBody eventBody = null;
}

18. Wait there is (much) more!
M/R
Migdalor

19. How good does it work?
● Cyber Monday 2013 (one day)
o More than 320,000 events per second
o 7 Storm topologies consuming the events seconds from
real time
o 2TB of data saved to Hadoop
● 2014 preparation:
o x2 number of events per second to ~640,000

20. So how did we do it?
1. Use an event driven system, don’t use direct APIs
2. Create a unified schema for all events
3. Use Avro to implement the schema
4. Add some supporting infrastructure

21. Questions
????
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22. Amihay Zer-Kavod
You can contact me at:
amihayz@liveperson.com
LivePerson is hiring!

23. Thank You