You Must Construct Additional Pipelines: Pub-Sub on Kafka at Blizzard

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(Stephen Parente + Jeff Field, Blizzard) Kafka Summit SF 2018 Blizzard’s global data platform has become a driving force in both business and operational analytics. As more internal customers onboard with the system, there is increasing demand for custom applications to access this data in near real time. In order to avoid many independent teams with varying levels of Kafka expertise all accessing the firehose from our critical production Kafkas, we developed our own pub-sub system on top of Kafka to provide specific datasets to customers on their own cloud deployed Kafka clusters.

AT
PUBSUB ON KAFKA
YOU MUST CONSTRUCT
ADDITIONAL PIPELINES!

Who We Are
Stephen Parente
Senior Software Engineer I
Global Data Platform
@webmakersteve
Jeff Field
Senior Systems Engineer I
Global Data Platform
@jfield

BIAPI (<2016 – Now)
RabbitMQ
MimironGame Server
Schema
Exchange

Telemetry V1 (2016-2017)
Sink
(Ingestion)
Drain (ETL)
Elasticsearch
Schema
Repository
Game
Server
BEAM
(Alerting)

Telemetry V2 (2017-Now)
HTTP
Ingest
Enrichment
Near Real-Time Data
(Elasticsearch Proc)
Longterm Data (Mimiron
V2)
Senders
(SDK)
Ingest Enrich Process Store Access
Metrics (Chromie DB)
RSYSLOG
Log
Enrichment
Logs
(Elasticsearch Proc)
Server
Logs

Pipeline Usage
• BIAPI
• 645 discrete message types
• 5.4 TB written / day
• 6.1 billion messages / day
• Telemetry V2
• 1200 discrete message types
• 1.5 TB written / day
• 14 billion messages / day

Emerging Data Problems – “Play Nice, Play Fair”
• Disruptive Player Behavior
• Analyze chat data and action accounts immediately
• Risk Forwarding
• Detect abusive and anomalous behavior

Challenges
• Data culture
• Two production pipelines
• Centralization
• Isolation
• Latency

Core Concepts
FORWARD
Data needs to be available in a central
location.
ISOLATE
Each customer should read their
own stream of data.
FILTER
Data should be filtered as close to the
edge as possible, in all pipes.

Telemetry V2 Architecture (Again!)
HTTP
Ingest
Enrichment
Near Real-Time Data
(Elasticsearch Proc)
Longterm Data (Mimiron
V2)
Senders
(SDK)
Ingest Enrich Process Store Access
Metrics (Chromie DB)
RSYSLOG
Log
Enrichment
Logs
(Elasticsearch Proc)
Server
Logs

Platform Integration
HTTP
Ingest
Enrichment Stream Filterer
Streaming
Platform
Senders
(SDK)
Ingest Enrich Process Store Access
RSYSLOG
Log
Enrichment
Server
Logs
… … …

Streaming Platform
Stream
Splitter
Topic: Stream 1
Data
Pipeline
Topic: Stream 3
Canned
Jobs
Topic: Stream 2
Stream
Rules API
Central Rules
API
Platform
UI

Kafka's Role in the Platform
• Compacted topics to store rules
• Data partitioning
• Buffer cache reads
• Blue/green job deployments

Disruptive Player Behavior
• Low latency requirements
• Single centralized deployment
• Supports supervised actions

Risk Forwarding
• Pub-sub use case
• Broker to broker forwarding
• Single centralized deployment
• Requires high reliability

Tapping Into Legacy Data
• De-structure summaries
• Batch
• Embrace "unstructure"
• API Driven

Future Platform Work
• Canaries
• Bad-actor detection
• Pre-partitioning

Takeaways
• Be a carrot, not a stick
• Partition & isolate
• Provide a good user experience
• Integrate with existing services
• Don’t name your platform Telemetry

THANK YOU
…AND WE’RE HIRING!
https://careers.blizzard.com/en-us/
Please check out node-rdkafka!
https://github.com/Blizzard/node-rdkafka

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