Let’s explore an inventory use case and discover how it’s possible to use an eventually consistent data store like Cassandra / DSE to support scalability, consistency, continuous availability. Combine that with analytics and I’ll show you how to build an inventory system for the future.

1. Jeff Carpenter
Can My Inventory Survive Eventual Consistency?

2. Who am I?
• Developer
• Architect
• Author
• Evangelist
• Defense
• Hospitality
• R&D
• Distributed Systems
• Large Scale
• Cassandra
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3. DataStax Enterprise / Cassandra Use Cases
• Customer 360
• Personalization
• IoT/Sensors
• Security
• Inventory Management!
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4. What Is Inventory?
A tangible object that can be sold…
Shirt, Clif Bar, Used Car
CREATE TABLE physical_inventory
(product_id uuid PRIMARY KEY,
int amount_available,
int amount_consumed
);
A space that can be used for some time
Hotel Room, My house, Restaurant table
CREATE TABLE temporal_inventory
(product_id uuid,
date date,
int amount_available,
int amount_consumed,
PRIMARY KEY ((product_id), date)
);
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5. Inventory in Context
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rates
temporal
inventory
products
reservations
physical
inventory
purchase
orders
prices
Retail
Reservation
and rental

6. Why use DSE/Cassandra for inventory?
• Effortless scale
• Geographic distribution
• Real-time access / Low latency
• Continuous availability
• Consistency
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7. Design Considerations
• Systems of Record
• Microservices
• Tuneable Consistency
• Compensating Transactions
• Data Maintenance via TTL (for Temporal Inventory)
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8. Systems of Record
• Are there other systems that track this inventory data?
• What is the level of uncertainty in my system vs. external
systems?
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Property
Management
System
Reservation
Systeminventory
synchronization
Walk-in
customer
Online
customerProperty
manager

9. Notional Microservice Architecture –
Temporal Inventory
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Product
Service
Booking
Service
Rates
Service
Shopping
Service
Data Maintenance
Apps
Inventory
Service
Reservation
Service
Inventory
keyspace
Rates
keyspace
Product
keyspace
Reservations
keyspace

10. Consistency Error Cases
• Undercounting sold inventory
– Reservations – Overbooking
• Bumping, walking
– Retail
• Back order, cancel order
• Overcounting sold inventory
– Lost opportunity to sell, wasted resource
• Inconsistencies across data types
– Booked reservation without decrementing inventory
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11. Inventory Service
Instance 1
Strong Consistency via Quorum on Read/Write
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C*
node
node
node
node
Inventory keyspace
Replication Factor = 3
Inventory Service
Instance 2
Write @ QUORUM
(2 nodes)
Read @ QUORUM
(2 nodes)

12. Distributed Transaction?
Consistencies Across Data Types
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Commit
the
contract
Reserve
the
inventory
Booking
Service
Inventory
Service
Reservation
Service
inventoryreservations
Monolithic Reservation System

13. Tuneable Consistency Revisited
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Approach Example Scope
Lightweight Transaction Updating inventory counts Data Tier
Logged Batch
Writing to multiple denormalized
tables
Data Tier
Retrying failed calls
Data synchronization,
reservation processing
Service
Compensating
processes
Verifying reservation processing System
Eventual
consistency
Strong
consistency

14. Compensating Transactions
• Technical
– Run repairs more frequently
– Consistency checking apps
• Business Processes
– Reservation: reschedule, “walk”
– Retail: back order, refund
It’s all about the tradeoffs…
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Development and operations
cost
Customer satisfaction and
retention cost

15. TTL for Temporal Inventory Cleanup
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Now
Time
Yesterday’s data is
ancient history
Rate + Inventory Data

16. Tomorrow’s Inventory Management System
• Leverage DSE Search / Analytics / Graph for value
added real-time use cases
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Identify
undersold
product
Personalize
shopping
experience Upsell
popular
products
Offer
discount

17. Plug
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Cassandra: The Definitive Guide, 2nd Edition
Completely reworked for Cassandra 3.X:
• Data modeling in CQL
• SASI indexes
• Materialized views
• Lightweight Transactions
• DataStax drivers
• New chapters on security, deployment, and integration

18. Contact
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jeff.carpenter@datastax.com
@jscarp
jeffreyscarpenter