Modify a word count script to output results in descending frequency order.

Why Descending Order Matters: Top-N by frequency is the core of term frequency analysis, log analysis, and recommendation features (most-viewed items).

Scalability Tiers: (1) Single file < 1GB: Counter + most_common()—in-memory, O(n log k) for top-k. (2) Multi-file/large: MapReduce pattern—map emits (word,1), reduce sums, final sort. (3) Spark: reduceByKey, then sortBy(col('count').desc()). (4) Streaming: maintain heap of top-K—O(n log k) space.

Cost: Full sort of 10M distinct words vs heap of top 1000—heap wins. Use most_common(1000) to avoid full sort when only top-N needed.

from collections import Counter
counts = Counter(words)
for word, count in counts.most_common():
print(f'{word}: {count}')