How to Scale .NET Apps with Distributed Caching
summary
Shows how adding a distributed cache (examples: NCache or Redis) between .NET app tier and relational DB removes DB bottlenecks so 80% reads to cache and ~20% DB; typical cache nodes are 8-core, 16–32 GB, favor more small nodes; common wins are sessions/viewstate/output-cache offload, SQL dependency or CLR-trigger sync, and read-through/write-through with async write-behind to avoid DB latency. Demo patterns include client libraries, cache-as-master with replication, query/indexing in-cache, and client-side near cache to regain in‑process speed.
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chapters (38)
- 0:00Introduction
- 1:05Scalability
- 1:43Linear Scalability
- 2:15Nonlinear Scalability
- 2:34Which Applications Need Scalability
- 4:19The Scalability Problem
- 5:40Distributed Caching
- 10:19Worst Case Scenario
- 10:40Use Cases
- 16:09Runtime Data Sharing
- 18:22Frameworks
- 20:01ASPnet Provider
- 21:37Sanity Testing
- 22:33No Programming Needed
- 22:52Application Data Cache
- 23:06NCache
- 25:17Application Data caching
- 25:30Explorations
- 26:46Sliding Expressions
- 33:10Cache Dependency
- 36:11Database Client
- 41:35Keeping the Caching Fresh
- 41:53Read Through
- 42:04Read Through Example
- 43:24Caching Tier
- 43:48Readthrough
- 45:42Readthrough Benefits
- 46:24RightThrough Benefits
- 47:10RightBehind Benefits
- 49:37NCash
- 50:06Partition Replication
- 52:10Secular Scalability
- 52:23Keep Cache Fresh
- 53:03Querying
- 55:27Linked Query
- 57:01Grouping
- 58:14Indexing
- 59:25Client Cache
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