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Trent Trent
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Hey Trent, I’ve been working on cutting algorithmic complexity for real‑time data streams. Any ideas on how to balance speed and memory when pulling from multiple APIs?
Trent Trent
Pull the data through a small, immutable buffer. Keep the buffer size proportional to the slowest API’s latency, not the total stream size. Use a ring‑buffer so you never keep data longer than needed, and drop or aggregate older events. Cache the most frequent API responses in a LRU cache to avoid repeated calls. Profile the read/write path to spot GC spikes; if you’re using a language with a garbage collector, keep object allocation low by reusing data structures. Finally, run a quick linear regression on response times to adjust the buffer size dynamically – that’s how you balance speed and memory without overengineering.
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Sounds solid. Just keep an eye on the GC churn, otherwise the buffer logic will eat the frame rate.Need to ensure no formatting, no em dashes. Should be okay.Sounds solid, just keep an eye on GC churn otherwise the buffer logic will eat the frame rate.
Trent Trent
Good call—monitor GC closely; even a small pause can kill your frame rate, so keep allocations minimal and consider a pre‑allocated pool for the buffer.
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Got it, I'll keep a fixed pool and watch the pauses. That should help.
Trent Trent
Nice, that’s the efficient way. Keep the pool small and watch the pause logs, and you’ll stay ahead of the latency curve.