Today data is big part of very bit of decision making be it parents deciding on where there children need to go for Karate classes to formulating the design for a batter driven aircraft. The speed at which data is delivered from producer to consumers is equally important. Any loss of data can potentially cause loss of millions of dollars. Walmart is no different. The world’s largest retailer caters to the need of millions of its Online and Walk in customers by ensuring optimal availability of needed assortment and timely delivery on Online fulfillment through a suit of well orchestrated event streaming platforms leveraging Kafka and its ecosystem. One of the application that plays an extremely key role to customer satisfaction is the real time Replenishment system . This system on a given day processes more than 4 billion messages in less than 3 hours leveraging an array of processors to generates an order plan for the entire network of Walmart stores with great accuracy. While doing so it also ensures no data loss through event tracking and necessary replays and retries. Through this session, we will highlight various aspects of Design, Architecture, Deployment strategy, Kafka settings, Optimization techniques etc that was paramount to achieve this rate of processing and certainly with no accidents.

1. ©2021 Walmart Inc. All Rights Reserved.
SENSITIVE INFORMATION CLASSIFICATION
A Walmart Story
- Suman Pattnaik
- Dhawal Soni
Speed Without
Accidents : Kafka
Exemplified

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SENSITIVE INFORMATION CLASSIFICATION
Table of
contents
1
About Us
2
Use Case
Introduction
3
The Ask & Our
Approach
4
Architectural
Decisions
5
Kafka’s Role in
Our Use Case
6
Key Learnings

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SENSITIVE INFORMATION CLASSIFICATION
About Us
What started small, with a single discount store and the simple idea of selling more for less, has grown over the last
50 years into the largest retailer in the world. Each week, approximately 220 million customers and members visit
approximately 10,500 stores and clubs under 48 banners in 24 countries and eCommerce websites. With fiscal year
2021 revenue of $559 billion, Walmart employs over 2.3 million associates worldwide. Walmart continues to be a
leader in sustainability, corporate philanthropy and employment opportunity. It’s all part of our unwavering
commitment to creating opportunities and bringing value to customers and communities around the world.

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SENSITIVE INFORMATION CLASSIFICATION
Walmart Supply Chain Network
• 150+ Distribution
Centers
• Serving 5000+
stores across the
country

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SENSITIVE INFORMATION CLASSIFICATION
Our Use - Case: Near Realtime Replenishment

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SENSITIVE INFORMATION CLASSIFICATION
The Ask – Near Real Time
Replenishment
Cycle Time Reduction
Accuracy
Speed
Reduced Complexity
Elasticity and Scalability
Improved Resiliency
Reduced Cost

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SENSITIVE INFORMATION CLASSIFICATION
Walmart US - At Scale
Online Transactions
1 Million+
Trucks on-road everyday
100+
B&M Customers
1 Million +
Stores
5000+
Distribution Centers
150+
Vendors
1000+

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SENSITIVE INFORMATION CLASSIFICATION
Our Use Case: At Scale

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Our Solve : A leap into Event Streaming and Micro batch Architecture

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Replenishment Workflow

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Stats & Facts
• Currently processing 60 GB
messages/Min across the
pipeline. Expected to serve 10x
more in near future
• Platform supports running across
Multiple Virtual Rings
41000000
62000000
98000000
165000000
230000000
400000000
July, 2021 Oct, 2021 Nov, 2021 Dec, 2021 Feb, 2022 Nov, 2022
PROCESSED SKUS

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SENSITIVE INFORMATION CLASSIFICATION
Kafka’s Role in Our Ecosystem

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SENSITIVE INFORMATION CLASSIFICATION
Key architectural Goals and Challenges
• Real Time Replenishment Powered by Real time changes to inputs like inventory positions,
forecasts etc.
• Multi tenancy
• Horizontal Scaling based on future needs
• Data and System Resiliency leading up to Reliable replenishment orders and plans
• Cost Optimizations with necessary elasticity
• Real Time decision making by diverse downstream consumers on near real time replenishment
orders and plans
• Tighter contracts between inputs and the replenishment engine

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Key Tuning and Optimizations
• Producer Config changes
• Consumer Config changes
• Key Architectural decisions

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Producer Config
• partition.assignment.strategy customization
• linger.ms limiting
• batch.size management
• ack

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Consumer Config
• max.poll.records & max.poll.interval.ms tuning
• enable.auto.commit
• session.timeout.ms & heartbeat.interval.ms optimization

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Key Architectural Decisions
• Active-Passive and path forward with Active-Active
• Resiliency upfront
• Retry and Failover strategy
• Sufficient Alerting and Notifications
• Ring Strategy
• Cloud strategy
• Path forward with Kubernetes

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SENSITIVE INFORMATION CLASSIFICATION
Reference
https://corporate.walmart.com/our-story

19. Thank you!();