LRU vs FIFO (with Lazy Promotion and Quick Demotion)

Sprinkling some lazy promotion and quick demotion on FIFO

Notes on 'A large scale analysis of hundreds of in-memory cache clusters at Twitter'

TTLs are prevalent, object sizes are small, metadata overhead can be large, object sizes change, FIFO is better than LRU, you’ve got to address memory fragmentation

Leanstore: High Performance Low-Overhead Buffer Pool

A dash of pointer swizzling, a sprinkle of optimistic locking and a touch of lean eviction, that’s the secret to a high performance buffer pool!

Tiered Storage via 2-Tree

Split a data-structure into two: a memory-optimized ’top’-tree and a disk optimized ‘bottom’-tree. Implement a lightweight migration protocol for hot records to move up and cold records down.

Pointer Swizzling Buffer Pools

Switching pointers as the data pointed to moves to and fro memory and secondary storage

Compacting Transactional Data in HyPer

Keep hot tuples uncompressed, organize cold data into chunks of columns then use lightweight compression, handle both OLTP and OLAP workloads

Virtual Memory Hot/Cold Data Re-organization for OLTP

Hot/Cold aware memory allocation with locking of the hot region to physical memory and letting the OS swap out cold LRU pages as needed.

Offline (but Faster and more Accurate) Classification of Hot and Cold Data

Hint, it’s based on exponential smoothing

Anti-Caching

Track hot/cold data at tuple-level granularity. Evict LRU cold data in blocks.

Optimizing Data Placement for Distributed OLAP Systems

Using MIP solvers to model and optimize shard placement

End-to-End Arguments in System Design

Paper Review

Distributed Reference Counting