python
14 minPython Runtime Advanced: Native Memory, ASGI Lifecycles, and Worker Failure
Debug memory outside Python objects, choose ASGI worker boundaries, and find silent hangs in background consumers.
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14 matching resultsArticles and interview answers
Q&A
Q&AWhat happens when a Java service creates too many threads?
Native stack memory, scheduling overhead, context switching, and file descriptors can be exhausted.
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Each platform thread consumes native resources, including stack reservation, and excessive runnable threads add scheduling and context-switching overhead. The service may hit native-thread creation errors or degrade before that point. Bound executors, measure queue depth and task latency, and apply backpressure rather than creating one thread per unbounded unit of work.
unix
9 minDNS Troubleshooting: Trace Name Resolution Before Blaming the Network
Separate resolver configuration, caching, search domains, authoritative records, and application-level DNS behavior.
java
9 minJava Memory Model Foundations: Visibility, Ordering, and Safe Publication
Build the mental model behind happens-before, volatile fields, monitors, and safely publishing objects between threads.
Q&A
Q&AWhat evidence distinguishes high allocation from a Java memory leak?
Compare post-GC live-set size and object retention paths over time.
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High allocation can cause frequent collections while post-GC occupancy remains broadly stable. A leak tends to retain objects that should have become unreachable, so compare heap dumps and dominator paths across time. Check allocation rate, live set, GC logs, and process/container memory together before changing heap settings.
java
8 minJava class-loader retention: why an undeployed app can remain in memory
Trace plugin and application class loaders from long-lived roots before blaming metaspace.
Q&A
Q&AIs Python map eager or lazy?
In Python 3, map returns an iterator that computes values as it is consumed.
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Python 3 map is lazy: it returns an iterator and evaluates each mapped value as the iterator advances. Convert it to a list only when you need materialized results or repeated traversal. Lazy evaluation saves memory but exceptions may occur later when a consumer iterates.
Q&A
Q&AWhy can epoll handle many mostly-idle sockets more efficiently than select?
epoll tracks interest and reports readiness changes instead of requiring repeated full-set scans.
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select requires rebuilding and scanning descriptor sets on each wait and has platform-specific descriptor limits; poll also examines a supplied list. epoll maintains kernel-side interest and ready structures, which suits large mostly-idle sets. It does not remove per-connection memory costs, application scheduling, or edge-triggered drain requirements.