perf: optimization for homepage referral activity tracking

[ankushchk]

What does this PR do?

The previous implementation of the index view ran a separate database query for every single top referrer to calculate their click counts. This is a classic N+1 query problem.

Furthermore, because these queries used path__contains (a SQL LIKE scan) on the WebRequest table which is not indexed for text containment and grows rapidly, the homepage load time would degrade exponentially as the site traffic increased.

Fix:

1. Replaced the per-referrer loop with a single batched query using a combined Q expression.
2. Resolved exact counts in-memory using Python.
3. This reduces the number of database queries by up to 10x on every homepage load and avoids repeated expensive table scans.

Checklist

• Did you run the pre-commit?
• Did you test the change? (Verified referral click counts match correctly locally)
• [/] Added screenshots to the PR description (not applicable - performance improvement)

Summary by CodeRabbit

• Refactor
  • Optimized homepage performance by streamlining database queries: consolidated multiple referrer lookup operations into a single batch query and moved click count calculations to in-memory processing, reducing database load and improving page responsiveness.

[github-actions]

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📥 Commits

Reviewing files that changed from the base of the PR and between 644e45f and a33f917.

📒 Files selected for processing (1)

• web/views.py

Walkthrough

Query performance optimization in the homepage view that consolidates N separate click-count queries into a single batched database query, then computes click totals in memory rather than issuing individual LIKE-scans per referral code.

Changes

Cohort / File(s)	Summary
Query Batching Optimization web/views.py	Replaced per-referrer click-count queries (N queries in a loop) with single batched query; builds in-memory mapping of click counts per referral code instead of issuing separate queries per ref code.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~15 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title accurately summarizes the main change: a performance optimization for referral activity tracking on the homepage by addressing an N+1 query issue.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@web/views.py`:
- Around line 294-309: The current substring matching of referral codes
(variables all_codes, combined_q, matching_paths, click_counts) misattributes
clicks for codes that share prefixes and includes empty codes; fix by first
filtering out empty/None codes from all_codes, then build exact-match conditions
using path boundaries (e.g., require "/ref/{code}/" or "?ref={code}" followed by
end-of-string or "&") or use a regex-based Q (path__regex) that enforces
whole-code boundaries so "ABC" won't match "ABC1"; remove the fragile
first-match break and instead rely on the precise match to count each path to
the correct code when populating click_counts.

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ℹ️ Review info

Configuration used: Organization UI

Review profile: ASSERTIVE

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between e694ce8 and 644e45f.

📒 Files selected for processing (1)

• web/views.py