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Operating Kafka on Auto
Pilot
Arpit Dubey
VP, Streaming Platform, DBS Bank
Motivation
Thou shalt automate self
out of the job!!!
- Some wise and bored developer
Simplistic view of a typical stream development lifecycle
WHAT ?
❖ A Self-service Streaming Framework
WHY ?
❖ To Enable, Govern, Control, Recommend, Review, Implement and Test a
Kafka App
❖ To remove blind spots for kafka’s numerous subtleties and bring them to
fore
HOW ?
❖ UI + REST API + Kafka Admin API + DevOps Tools + ML + Bouquet of Data
Engines + Language of your choice + Lots of ideating and Continuous
observation
Solution
Metadata
Management
GraphDB
Stream
Discovery
Configuration
Management
Topic ConnectSchema
Recommendation
Validation
Meta &
Audit Store
Capacity
Management
Planner
Recommendation
Cluster
Management
Provisioning
Deployment
Chargeback
Security
ManagementImplementation
API
Kafka - DEV
Policy
Store
Artefact
Lifecycle
Management
Kafka - UAT Kafka - PROD
Performance
Engineering
Chaos
Engineering
Quota Manager
Load SimulatorEvent Simulator
Client Simulator Fault Simulator
Metric
Store
OLAP
Store
Dashboards Incident Manager HealthCheck API
Logging Solution
Dashboard-as-a-
Service
Email/ SMS
HealthReport
Prediction Prescription
Anomaly
Detection
Observability
Platform
Log
Store
Benchmarking
Self-service Streaming Platform - Conceptual View
Auto Archival
Data Lake
Streams Discovery using Knowledge Graph
❖ Event Discovery
❖ Explore Service Inter dependencies
❖ Traverse Topic/ Event Lineage and Data Flows
❖ Schema Linkage and Exploration
❖ Search Events by Source System, Country, Product,
Use-case, Event Fields etc
Is the event of interest already
Published ? Who is the owner ?
How and what should I
subscribe ?
How do we use card events today ?
Are we doing Fraud analytics or we
also use it for Campaigns ?
Events in two topics looks
similar ? How and why are
they different ? would be
good to compare schema ?
My consumer service is mission
critical, what all services I depend on ?
can they meet my SLA ?
Solution - A Search Engine which leverages property Graph Engine
Myriad of question every app navigates through
Simplistic schema of Stream Graph
❖ Be sure of your SLAs
❖ Determine % Availability in real-time
❖ Detect faults in Data Pipeline
❖ Determine end to end Latency of Data Pipeline
❖ Untangle Service dependencies
❖ Manage complex cluster inventory
My consumer service hosted on
K8s consumes from kafka on
bare metal published by a
Producer on PCF getting events
from a System on VM, what is
broken ?
I see kafka cluster is in bad health, Is
my service impacted ? what is the
Availability % of my service ?
My event goes through several
processing pipeline, what is impacting
end to end latency ?
Myriad of question every app navigates through
System Observability using Graph Analytics
Sample Observability Graph
Save the Saviour!!! - Bring Machine Learning for your Cluster Health
★ Train model on flagged and known anomalies
★ Discover anomalies with similar traits (App,
Cluster, Infra)
★ Learn correlation between alert produced
And the cause
★ Distinguish false-positives from true-positives
★ Identify Outliers - e.g Broker with highest Open FD
★ Predict anomalies - e.g running out of
resources, Broker shutdown
★ Prescribe a resolution - e.g. increase
Num.network.thread, suggest
partition increase, skewness or
imbalance, suggest under or over
provisioning
★ Learn scenarios for Failure
Categories
★ Forecast periodic growth patterns
and evaluate trends in resource
utilization - CPU, Memory, FS,
Network utilization etc
★ Act on failures in real-time
★ E.g AD latency will cause kerberos
re-login error, restart Broker to fix
★ Sudden FD surge and drop in
network traffic in a particular
broker indicate issues with the
node, could move the broker POD
to a different host
★ Discover general patterns of
pipeline and cluster failures
Machine Learning in Operations
Business value Proposition
❖ High returns on minimal investment and low risks
❖ High Serviceability, Availability and Reliability - with less MTTD and MTTR
❖ 100% self-service platform usage
❖ Unified governance and less error prone lifecycles
❖ Cost efficient Kafka clusters
❖ Minimize operational overheads
❖ Better maintainability - with no under or over cluster provisioning
❖ Distributed ownership and reduced dependency on Kafka SME
❖ Enhanced Developer and Operator Experience
❖ No manual toils and reduced operational costs
And many many more…...
Thank you !!!
Time for questions
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Operating Kafka on AutoPilot mode @ DBS Bank (Arpit Dubey, DBS Bank) Kafka Summit 2020

  • 1. Operating Kafka on Auto Pilot Arpit Dubey VP, Streaming Platform, DBS Bank
  • 2. Motivation Thou shalt automate self out of the job!!! - Some wise and bored developer Simplistic view of a typical stream development lifecycle
  • 3. WHAT ? ❖ A Self-service Streaming Framework WHY ? ❖ To Enable, Govern, Control, Recommend, Review, Implement and Test a Kafka App ❖ To remove blind spots for kafka’s numerous subtleties and bring them to fore HOW ? ❖ UI + REST API + Kafka Admin API + DevOps Tools + ML + Bouquet of Data Engines + Language of your choice + Lots of ideating and Continuous observation Solution
  • 4. Metadata Management GraphDB Stream Discovery Configuration Management Topic ConnectSchema Recommendation Validation Meta & Audit Store Capacity Management Planner Recommendation Cluster Management Provisioning Deployment Chargeback Security ManagementImplementation API Kafka - DEV Policy Store Artefact Lifecycle Management Kafka - UAT Kafka - PROD Performance Engineering Chaos Engineering Quota Manager Load SimulatorEvent Simulator Client Simulator Fault Simulator Metric Store OLAP Store Dashboards Incident Manager HealthCheck API Logging Solution Dashboard-as-a- Service Email/ SMS HealthReport Prediction Prescription Anomaly Detection Observability Platform Log Store Benchmarking Self-service Streaming Platform - Conceptual View Auto Archival Data Lake
  • 5. Streams Discovery using Knowledge Graph ❖ Event Discovery ❖ Explore Service Inter dependencies ❖ Traverse Topic/ Event Lineage and Data Flows ❖ Schema Linkage and Exploration ❖ Search Events by Source System, Country, Product, Use-case, Event Fields etc Is the event of interest already Published ? Who is the owner ? How and what should I subscribe ? How do we use card events today ? Are we doing Fraud analytics or we also use it for Campaigns ? Events in two topics looks similar ? How and why are they different ? would be good to compare schema ? My consumer service is mission critical, what all services I depend on ? can they meet my SLA ? Solution - A Search Engine which leverages property Graph Engine Myriad of question every app navigates through Simplistic schema of Stream Graph
  • 6. ❖ Be sure of your SLAs ❖ Determine % Availability in real-time ❖ Detect faults in Data Pipeline ❖ Determine end to end Latency of Data Pipeline ❖ Untangle Service dependencies ❖ Manage complex cluster inventory My consumer service hosted on K8s consumes from kafka on bare metal published by a Producer on PCF getting events from a System on VM, what is broken ? I see kafka cluster is in bad health, Is my service impacted ? what is the Availability % of my service ? My event goes through several processing pipeline, what is impacting end to end latency ? Myriad of question every app navigates through System Observability using Graph Analytics Sample Observability Graph
  • 7. Save the Saviour!!! - Bring Machine Learning for your Cluster Health
  • 8. ★ Train model on flagged and known anomalies ★ Discover anomalies with similar traits (App, Cluster, Infra) ★ Learn correlation between alert produced And the cause ★ Distinguish false-positives from true-positives ★ Identify Outliers - e.g Broker with highest Open FD ★ Predict anomalies - e.g running out of resources, Broker shutdown ★ Prescribe a resolution - e.g. increase Num.network.thread, suggest partition increase, skewness or imbalance, suggest under or over provisioning ★ Learn scenarios for Failure Categories ★ Forecast periodic growth patterns and evaluate trends in resource utilization - CPU, Memory, FS, Network utilization etc ★ Act on failures in real-time ★ E.g AD latency will cause kerberos re-login error, restart Broker to fix ★ Sudden FD surge and drop in network traffic in a particular broker indicate issues with the node, could move the broker POD to a different host ★ Discover general patterns of pipeline and cluster failures Machine Learning in Operations
  • 9. Business value Proposition ❖ High returns on minimal investment and low risks ❖ High Serviceability, Availability and Reliability - with less MTTD and MTTR ❖ 100% self-service platform usage ❖ Unified governance and less error prone lifecycles ❖ Cost efficient Kafka clusters ❖ Minimize operational overheads ❖ Better maintainability - with no under or over cluster provisioning ❖ Distributed ownership and reduced dependency on Kafka SME ❖ Enhanced Developer and Operator Experience ❖ No manual toils and reduced operational costs And many many more…...
  • 10. Thank you !!! Time for questions

Editor's Notes

  • #9: Restart Broker on receiving kerberos re-login issue Sudden Open FD surges may indicate network issues, move the broker POD to a different host or DC
  • #10: Z
  • #11: Z