
LSM trees buffer writes in memory for write-heavy workloads
Image: U.S. Department of Agriculture, Public domain, via Wikimedia Commons
LSM trees buffer writes in memory for write-heavy workloads
LSM trees are designed to handle high insert volumes efficiently. They maintain data in separate structures optimized for their respective storage mediums. This design allows LSM trees to buffer writes in memory, which is particularly beneficial for write-heavy workloads.
Example
A database with a high volume of write operations can use an LSM tree to buffer writes in memory, reducing the need for immediate disk writes and improving overall performance.
Remember this
Understanding how LSM trees buffer writes in memory helps in choosing the right data structure for applications with heavy write workloads.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
B-trees optimize: disk-based sorted data with O(log n) reads per query
B-trees optimize disk-based sorted data with O(log n) reads per query
BFS vs DFS: BFS finds shortest path in unweighted graphs, DFS uses less memory
BFS finds shortest path in unweighted graphs; DFS uses less memory
CPU cache
L1/L2 cache hierarchy reduces global memory latency
Delay-line memory
CPU speed grows faster than memory speed
Memory hierarchy
Memory hierarchy levels: registers → L1 → L2 → L3 → RAM → SSD → HDD (each ~10× slower)
consistent hashing does: minimizes remapping when nodes join/leave
How can we efficiently share resources without constant reorganization?
Swipe through 100 ML concepts daily
Open Pocket Polymath