Garbage Collected Memory Management Questions

Covers memory management in managed runtimes that use garbage collection. Topics include the memory model distinguishing value types and reference types, stack and heap allocation patterns, how common garbage collection algorithms work and their runtime impacts such as pause times and allocation throughput, causes of allocation pressure, and strategies to reduce garbage collection overhead. Practical techniques include avoiding boxing, reusing and preallocating collections, using value types or structs for small frequently instantiated data, object pooling, and data oriented design trade offs versus object oriented design for performance. Candidates should also know profiling tools, memory budgeting for constrained platforms such as mobile or game consoles, and platform specific considerations for engines like Unity.

HardTechnical

95 practiced

A Java service exhibits occasional very long GC pauses caused by promotion failures and full GCs. Propose a detailed set of tuning steps (heap sizing, GC selection/settings, object allocation reduction, escape analysis, and code changes) to reduce pause times while maintaining throughput. Explain the reasoning behind each change.

EasyTechnical

83 practiced

List common causes of allocation pressure in data-science workloads and give a short example for each (e.g., temporary Python lists in map operations, expensive eager copies in NumPy, frequent small object creation, excessive string concatenation). How would you detect which of these is dominant in a job?

HardTechnical

82 practiced

Describe the effect of OS-level features such as large memory pages (hugepages) and NUMA on GC and memory performance for JVM or Python processes. When would enabling hugepages improve GC behavior and when might it make things worse (e.g., increased internal fragmentation)?

HardTechnical

96 practiced

When implementing custom TensorFlow ops in C++ that allocate memory, what allocator APIs and ownership conventions must you follow to avoid leaks and excessive host<->device copies? Discuss lifetime, shape metadata, and best practices to minimize GC/allocator churn from the Python side.

EasyTechnical

101 practiced

You're trying to load a 200M-row CSV into pandas and hit a MemoryError. Describe immediate practical steps you'd take to reduce memory usage and get useful output quickly (developer troubleshooting checklist: dtype coercion, category dtypes, chunking, selective columns, use of parquet, Dask/out-of-core).

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