Performance smells & optimizations in python

Python is a high-level, versatile programming language known for its simplicity, readability, and wide range of applications. It is used in web development, data analysis, AI/ML, automation, and more.

1. Performance Smells &
Optimizations in Python

1. Inefficient Loops
Nested loops or unnecessary iterations can
dramatically slow down code.
Fix: Replace with list comprehensions,
generator expressions, or NumPy
vectorization.

2. Unnecessary Object Creation
Objects like strings, lists, or connections
recreated in hot paths increase CPU and
memory load.
Fix: Use object pooling or caching, and
avoid creating objects inside tight loops.

3. Blocking I/O Operations

2. Synchronous file and network I/O creates
bottlenecks in web and data apps.
Fix: Switch to asyncio, aiohttp, or use
threads for I/O concurrency.

4. Memory Leaks
Retaining unused references prevents
garbage collection.
Fix: Track memory with tracemalloc, clear
unused objects, and avoid circular
references.

5. Repeated Database Queries (N+1
Problem)
Fetching one record at a time inside loops
instead of batching queries.
Fix: Use JOINs, batch queries, or ORM tools
like select_related.

3. 6. Inefficient Data Structures
Wrong data structures lead to wasted
time/space (e.g., using lists for lookups).
Fix: Use dict/set for O(1) lookups, deque
for queues, and heapq for priority
queues.

7. Lack of Caching
Recomputing expensive function results
unnecessarily.
Fix: Add functools.lru_cache, memoization,
or distributed caching like Redis.

8. Poor Use of Concurrency
Only running tasks sequentially, even when
parallelization is possible.
Fix:

4. • CPU-bound → multiprocessing, joblib,
or Cython
• I/O-bound → asyncio, threading

9. No Profiling or Monitoring
Optimizing blindly without identifying real
bottlenecks.
Fix: Use cProfile, py-spy, line_profiler, or
APM tools like Datadog.

10. Unoptimized External Libraries
Using slow built-ins when optimized ones
exist.
Fix: Prefer NumPy, Pandas, Numba,
TensorFlow, etc. for performance-critical
work.

11. Overuse of Global Variables

5. Global state increases memory use and
reduces modularity.
Fix: Prefer local variables or dependency
injection.

12. Inefficient String Handling
Concatenating strings in loops with + is very
slow.
Fix: Use str.join(), f-strings, or StringIO.

13. Inefficient Logging
Synchronous or excessive logging in loops
slows execution.
Fix: Use asynchronous logging, buffer logs,
or log at appropriate levels.

14. Large JSON/XML Parsing in Pure
Python

6. Parsing big files into memory at once
consumes huge memory.
Fix: Use streaming parsers (ijson, orjson,
lxml).

15. Ignoring Lazy Evaluation
Loading large datasets entirely into memory
when not needed.
Fix: Use generators, yield, itertools, or
frameworks like Dask.

16. Inefficient Sorting
Sorting repeatedly or using default sorts
when optimized approaches exist.
Fix: Use sorted() with key functions, or
heapq/nlargest for partial sorts.

17. Using Python for Heavy Math Directly

7. Pure Python loops for math are slower than
C-level operations.
Fix: Use NumPy, SciPy, or Numba for
vectorized math.

18. Not Using Multiprocessing for Parallel
Work
CPU-heavy tasks run on a single core by
default.
Fix: Use multiprocessing.Pool, Ray, or
joblib for parallelism.

19. Inefficient File Handling
Reading/writing files repeatedly with small
chunks causes overhead.
Fix: Use buffered I/O, memory-mapped
files (mmap), or batch writes.

8. 20. Overuse of Exceptions for Control
Flow
Catching exceptions in hot loops is slower
than conditional checks.
Fix: Use if-else checks where possible;
reserve exceptions for rare cases.

21. Poor Garbage Collection Management
Default GC may cause performance hits in
long-running apps.
Fix: Tune Python’s gc module, or disable
GC in critical sections if safe.

22. Ignoring Compiler/Interpreter
Alternatives
Sticking only to CPython for heavy
workloads.
Fix: Try PyPy, Cython, or Mojo for
performance improvements