How to Extract Top 100 Search Keywords from Billion‑Scale Logs Efficiently

When building SEO, social media trend analysis, or e‑commerce recommendation systems, you often need to analyze the most popular search terms from internal logs.

For large sites the daily keyword logs can reach tens or hundreds of millions, making it impossible to load the entire file into memory. A divide‑and‑conquer approach solves this.

(1) Split the massive log file into many small files, e.g., each 512 KB.

(2) Create a hash‑table array of length n (e.g., 2048) to count keyword frequencies. Use multiple threads to traverse the small files, hash each keyword, and update the corresponding bucket.

(3) After counting, scan the hash table and maintain a min‑heap of size 100 to keep the top‑100 keywords. When a keyword’s count exceeds the heap’s minimum, replace the root and re‑heapify.

Finally, the min‑heap contains the 100 most frequent search terms.

Summary:

Divide the large log into small chunks (hash‑based splitting).

Use multithreading to count keyword occurrences in each chunk.

Apply a min‑heap to extract the top‑N frequent keywords efficiently.