A talk on scalability at Walmart Labs

1. Scalability
Single Machine in a Garage
vs
Thousands of Clusters in Multiple Data Centers
Deepak Goyal
@WalmartLabs

2. Assumptions
• Network is reliable
• Latency does not exist
• Bandwidth is unlimited
• Network is secure
• Topology never changes
• Administrators are always available
• Network is homogeneous

3. cAP Theorem (Eric Brewer*)
Every read receives the most recent write or an error
Consistency
Partition Tolerance
The system continues to operate despite an
arbitrary number of messages being dropped (or
delayed) by the network between nodes
Availability
Every request receives a (non-error)
response – without guarantee that it
contains the most recent write
screw you and pick 2*

4. Core Architecture of an application
1. Application or Web Server
• Application Server: responds to requests on any protocol
• Web Server: responds to requests primarily on HTTP/S
2. Database Server
• Responds to requests for manipulate data

5. Concepts
• Vertical Scaling (scale-up)
• Horizontal Scaling (scale-out)
• Vertical Partitioning
• Horizontal Partitioning
• Master-Master and Master Slave

6. Master-Slave and Master-Master

7. Vertical Scaling vs Horizontal Scaling
1. Processing power
• more cores
• more cache
2. Memory
• more RAM
• better RAMs
3. More disk space
• more HDDS
• moving to SSDs
Adding more
(commodity/cheap)
machines into the pool of
resources.

8. Failed Architectures*
If a design can be failed on paper, it is BOUND TO fail in practice.
learn from the mistakes of others*

9. Failed Architecture
• A single machine with an app sever and a DB server

10. Polyglot Persistence
Different kind of data are best dealt with different DB solutions
1. Key-Value
• Shopping Cart, Session Data
2. Document Store
• Completed Orders, Archival
3. RDBMS (SQL)
• Inventory Management
4. Graph Store
• Customer social graph

11. Sharding

12. Consistent Hashing (Caching)