Optimizing the SPR Library in LinkedIn Feed Mixer: Reducing Memory Usage and Latency

While browsing technical blogs, the author found a LinkedIn post describing the Feed Mixer, a middle‑layer that serves multiple distribution channels, and the SPR library used within it.

1. Careful loop traversal – Using an iterator (code A) creates an iterator object, while directly accessing elements with _bars.get(i) (code B) avoids that overhead; however, the choice depends on the list implementation (ArrayList vs LinkedList) and the overall time‑complexity.

2. Pre‑size collections – Initialize a HashMap with an estimated capacity (input size / load factor ≈ 0.7) to avoid rehashing during bulk insertion.

3. Lazy evaluation – Comparison chains evaluate arguments left‑to‑right and stop early; implementing a custom comparator for Bar objects prevents unnecessary creation of temporary String objects.

4. Pre‑compile regular expressions – Compile a constant pattern once with Pattern.compile(_regex) and reuse the Matcher instead of calling String.replaceAll each time.

5. Cache results – Use a Guava LoadingCache with a maximum size to store expensive operation results, reducing repeated computation.

6. Use String.intern() cautiously – Interning can act as a cache but may cause unbounded memory growth; LinkedIn switched to an LRU cache to limit size.

The optimizations cut SPR’s memory footprint by 75%, halved Feed‑Mixer memory usage, and reduced service latency by about 25%.