Fix N+1 queries and optimize bulk updates across contributor, hackathon etc #5333
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WalkthroughReplaces many per-item DB queries with bulk ORM operations (annotations, prefetch_related, select_related, in_bulk, bulk_update, bulk_create) across views; refactors vote handling, bulk-maps PRs to profiles, updates tag/recommender logic, per-domain reporting, challenge progress rendering, and wallet/leaderboard access to reduce N+1 queries. (44 words) Changes
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website/views/ossh.py (1)
278-305: Bug:discussion_channel_recommendertreats tag counts as languages
user_tagsis a list of(tag, count)pairs (as produced bypreprocess_user_dataand used elsewhere), but here it’s unpacked as(tag, lang):
language_weight = sum(language_weights.get(lang, 0) for tag, lang in user_tags if tag in channel_tag_names)matching_languages = [lang for tag, lang in user_tags if tag in channel_tag_names and lang in language_weights]Since the second element is an integer count, not a language string, language weights and matches will almost always be zero/empty, degrading recommendations.
A safer approach is to intersect channel tag names with
language_weightskeys.def discussion_channel_recommender(user_tags, language_weights, top_n=5): - matching_channels = ( - OsshDiscussionChannel.objects.filter(Q(tags__name__in=[tag[0] for tag in user_tags])) - .distinct() - .prefetch_related("tags") - ) + tag_names = [tag for tag, _ in user_tags] + matching_channels = ( + OsshDiscussionChannel.objects.filter(Q(tags__name__in=tag_names)) + .distinct() + .prefetch_related("tags") + ) @@ - recommended_channels = [] - for channel in matching_channels: - channel_tag_names = {tag.name for tag in channel.tags.all()} - tag_matches = sum(1 for tag, _ in user_tags if tag in channel_tag_names) - - language_weight = sum(language_weights.get(lang, 0) for tag, lang in user_tags if tag in channel_tag_names) + recommended_channels = [] + tag_weight_map = dict(user_tags) + + for channel in matching_channels: + channel_tag_names = {tag.name for tag in channel.tags.all()} + + # Number of user tags present on this channel + tag_matches = sum(1 for tag in channel_tag_names if tag in tag_weight_map) + + # Sum weights for languages that appear as tags on this channel + language_weight = sum(language_weights.get(tag, 0) for tag in channel_tag_names) @@ - if relevance_score > 0: - matching_tags = [tag.name for tag in channel.tags.all() if tag.name in dict(user_tags)] - matching_languages = [ - lang for tag, lang in user_tags if tag in channel_tag_names and lang in language_weights - ] + if relevance_score > 0: + matching_tags = [tag for tag in channel_tag_names if tag in tag_weight_map] + matching_languages = [lang for lang in channel_tag_names if lang in language_weights]
🧹 Nitpick comments (4)
website/views/teams.py (1)
269-299: TeamChallenges progress logic and prefetch look solidThe prefetch on
team_participantsplus the per-challenge progress calculation is correct and removes the obvious N+1. Using a singlecircumferenceconstant also simplifies the stroke calculations. If you ever need to squeeze more perf, you could avoid repeatedchallenge.team_participants.all()membership checks by building asetof participant IDs per challenge first, but that’s optional here.website/views/hackathon.py (1)
228-255: Efficient merged PR counting via annotation looks correctThe
merged_pr_countannotation onhackathon.repositoriescorrectly applies the merged‑window and bot filters and removes the per‑repo counting query. The subsequent use ofrepo.merged_pr_countinrepos_with_pr_countsis consistent with that change. The earlierrepositories = hackathon.repositories.all()assignment is now redundant and can be dropped for cleanliness.website/views/ossh.py (1)
93-108: Normalized tag set usage is fineDeriving
ALLOWED_NORMALIZED_TAGSfromTAG_NORMALIZATION.values()and checking membership against it is a reasonable way to keep tag handling consistent and efficient withinpreprocess_user_data. You could hoist this set to module scope to avoid rebuilding it on every call, but that’s just a micro‑optimization.website/views/organization.py (1)
237-281: Weekly report prefetch + in‑memory aggregation are appropriatePrefetching
issue_setwith a restricted field set and then computing open/closed/total counts plus per‑issue lines in Python is a good way to remove the per‑domain issue queries. One thing to consider is skipping or logging domains that have no validdomain.emailbefore callingsend_mail, to avoid attempting deliveries to empty/invalid recipients.
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website/views/hackathon.py(1 hunks)website/views/issue.py(2 hunks)website/views/organization.py(1 hunks)website/views/ossh.py(2 hunks)website/views/teams.py(1 hunks)website/views/user.py(5 hunks)
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website/views/teams.py (1)
website/models.py (1)
Challenge(2038-2060)
website/views/hackathon.py (1)
website/api/views.py (1)
repositories(706-716)
website/views/organization.py (1)
website/models.py (4)
Domain(386-484)Issue(590-739)open_issues(419-420)closed_issues(423-424)
website/views/ossh.py (1)
website/models.py (1)
OsshDiscussionChannel(2460-2473)
website/views/issue.py (2)
website/models.py (3)
Issue(590-739)UserProfile(896-1109)GitHubIssue(2090-2328)website/api/views.py (9)
get(282-287)get(341-346)get(365-368)get(462-475)get(488-494)get(550-576)get(609-633)filter(375-414)filter(844-911)
website/views/user.py (1)
website/models.py (6)
IssueScreenshot(780-783)Issue(590-739)Wallet(1152-1173)Contributor(1351-1363)Points(849-859)UserProfile(896-1109)
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🔇 Additional comments (7)
website/views/issue.py (1)
2636-2678: Excellent bulk update optimization for PR-profile mapping.This refactoring effectively eliminates N+1 queries by:
- Collecting all GitHub URLs from PRs without profiles (lines 2647-2658)
- Bulk fetching matching UserProfiles using
in_bulkpattern (line 2661)- Building a list of PRs to update in memory (lines 2663-2676)
- Applying all updates in a single
bulk_updatecall (line 2678)This is a significant performance improvement over the previous per-PR save pattern and aligns well with the PR objectives.
Minor observation: The code safely handles cases where
user_profileis None (line 2671 check), ensuring robustness when profiles don't exist for all GitHub URLs.website/views/user.py (6)
440-441: LGTM: Efficient screenshot bulk fetch and mapping.Lines 440-441 replace per-activity screenshot queries with a single bulk fetch using
select_related("issue")and dictionary comprehension. This eliminates N+1 queries when displaying activity screenshots and aligns perfectly with the PR's performance objectives.
460-475: Excellent consolidation of profile-related queries.This section implements multiple optimizations:
- Lines 460-462: Build
bug_qsonce and reuse it in the loop, avoiding repeated base query construction- Lines 465-466: Use
select_related("user")to fetch follower users in one query, then extract to list- Lines 469-470: Same optimization for following relationships
- Line 472: Derive emails from already-fetched follower data instead of querying each profile
- Line 475: Simplified tag access using direct relationship
These changes collectively eliminate multiple N+1 query patterns and significantly reduce database round-trips for profile pages with many followers/following.
1000-1030: Excellent leaderboard optimization using in_bulk pattern.Lines 1000-1030 transform the leaderboard query from N queries to just 3:
- Lines 1003-1008: Single annotated query for points aggregation
- Line 1012: Bulk fetch users with
in_bulk(user_ids)- Line 1013: Bulk fetch profiles as a dictionary keyed by user_id
The loop (lines 1015-1030) then assembles leaderboard entries entirely from pre-fetched data, with safe handling for missing users or profiles (line 1021 check).
This pattern is consistent with the contributor_stats optimization and represents a significant performance improvement for leaderboard rendering.
1063-1067: Optimal wallet creation using bulk_create.This function replaces a looped
get_or_createpattern with a singlebulk_createoperation:
- Line 1063: Efficiently identify existing wallet user IDs using
values_list- Line 1064: Filter users without wallets using exclusion
- Line 1065: Build list of Wallet instances in memory
- Line 1066: Create all wallets in a single database operation
This is a textbook example of bulk operation optimization and aligns perfectly with the PR's stated objective to "replace per-user get_or_create() wallet loop with bulk_create."
946-983: Strong in_bulk optimization with proper defensive handling.This refactoring replaces per-contributor queries with a bulk fetch pattern:
- Line 946: Extract contributor IDs from aggregated stats
- Line 948: Fetch all contributors at once using
in_bulk(contributor_ids)- Lines 950-983: Loop through stats and retrieve pre-fetched contributors from the map
- Lines 952-953: Skip stats for contributors not found in the map
The safe handling of missing contributors (lines 952-953) is good defensive programming. In practice, orphaned stats should not occur since the
ContributorStatsmodel defines the contributor FK withon_delete=models.CASCADE, meaning contributors cannot be deleted without their stats being automatically removed. The.get()check safeguards against edge cases like data corruption or race conditions.The impact score calculation and level assignment logic remains unchanged and correct.
447-447: The wallet context variable set in this view is never used in any template. No template accesses it via{{ wallet }}or conditional checks, so returningNonefromfilter().first()poses no risk to template rendering. The change from.get()to.filter().first()is safe and improves error handling by avoidingDoesNotExistexceptions.
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Actionable comments posted: 2
🧹 Nitpick comments (4)
website/views/organization.py (2)
238-243: Prefetch optimization looks good.The use of
prefetch_relatedwith a restricted field set effectively addresses the N+1 query issue mentioned in the PR objectives. The selected fields match those used in the report generation loop.Minor optimization:
domain_idin theonly()clause may be redundant since you're already iterating overdomain.issue_set.all(), which implicitly filters by domain.
247-260: Consider the memory trade-off of Python-side filtering.The current approach loads all issues for each domain into memory, then filters by status in Python. For domains with thousands of issues, this could be memory-intensive.
Consider using database-level aggregation instead:
- domains = Domain.objects.prefetch_related( - Prefetch( - "issue_set", - queryset=Issue.objects.only("description", "views", "label", "status", "domain_id"), - ) - ) + domains = Domain.objects.prefetch_related( + Prefetch( + "issue_set", + queryset=Issue.objects.only("description", "views", "label", "status"), + ) + ).annotate( + open_count=Count("issue", filter=Q(issue__status="open")), + closed_count=Count("issue", filter=Q(issue__status="closed")) + )Then use the annotated counts in the report:
open_issues = [i for i in issues if i.status == "open"] closed_issues = [i for i in issues if i.status == "closed"] # Use domain.open_count and domain.closed_count for the countsThis way you get both efficient counts and the actual issue objects for iteration.
website/views/issue.py (2)
95-138: LGTM with minor suggestions.The prefetch_related optimization and toggle logic are well-implemented. The past review concerns about
issue.userprofile_sethave been correctly resolved (line 137 now usesUserProfile.objects.filter()).However, two minor points:
- Line 137:
total_votesis computed but never used or returned. Consider removing this line or returning the value if needed by the caller.- Lines 128-134: Email is sent synchronously, which may slow down the response. For better performance, consider using Django's async email sending or a task queue (Celery) for notifications.
142-166: LGTM, but clean up unused variables.The prefetch_related optimization mirrors
like_issuecorrectly, addressing the past review concern. However:
- Lines 158, 161:
is_dislikedis assigned but never used.- Line 164:
total_votesis computed but never returned or used.Consider removing these unused variables to keep the code clean.
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🧬 Code graph analysis (3)
website/views/organization.py (1)
website/models.py (4)
Domain(386-484)Issue(590-739)open_issues(419-420)closed_issues(423-424)
website/views/issue.py (2)
website/models.py (3)
Issue(590-739)UserProfile(896-1109)GitHubIssue(2090-2328)website/api/views.py (9)
get(282-287)get(341-346)get(365-368)get(462-475)get(488-494)get(550-576)get(609-633)filter(375-414)filter(844-911)
website/views/ossh.py (1)
website/models.py (1)
OsshDiscussionChannel(2460-2473)
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🔇 Additional comments (4)
website/views/organization.py (1)
270-272: Good fix for the None email bug.The email validation guard correctly addresses the issue identified in the past review comments where
domain.emailcould beNone, causing aTypeErrorinsend_mail.The implementation properly:
- Checks for missing email before attempting to send
- Logs a warning for visibility
- Continues processing other domains instead of failing entirely
website/views/ossh.py (3)
94-94: Minor optimization: pre-compute allowed tags set.Creating
ALLOWED_NORMALIZED_TAGSonce per function call avoids repeated.values()calls and provides O(1) membership testing.
279-282: Effective query optimization with prefetch_related.The addition of
prefetch_related("tags")eliminates N+1 queries when accessing channel tags in the loop. The filter bytag_namesand use ofdistinct()are both appropriate for M2M relationships.
297-312: Improved matching logic using set operations.The refactored logic using
channel_tag_namesas a set improves clarity and efficiency. The derivation ofmatching_tagsandmatching_languagesthrough intersection is clean and correct.However, this code will only work after fixing the critical
relevance_scoreissue flagged in the previous comment.
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🧹 Nitpick comments (2)
website/views/issue.py (2)
95-157: Like/dislike toggle logic is sound; prefetch usage could be simplifiedThe new like/dislike implementation looks correct:
- Fetches
UserProfileonce per request.- Ensures upvotes and downvotes are mutually exclusive.
- Sends the notification email only on a new upvote and only when
issue.userexists, which avoids noisy repeats.A small performance nit:
UserProfile.objects.prefetch_related("issue_upvoted", "issue_downvoted")plususerprof.issue_* .filter(pk=issue.pk).exists()still hits the DB for the.filter()checks, so the prefetch doesn’t buy you much here. If this endpoint is on a hot path, you could either:
- Drop
prefetch_relatedand just useUserProfile.objects.get(user=request.user)(simpler, fewer queries), or- Leverage the prefetched collections directly (e.g., check membership on
userprof.issue_upvoted.all()/issue_downvoted.all()), accepting the in‑memory iteration trade‑off.Not blocking, but worth considering given the PR’s performance focus.
2635-2671: Bulk linking PRs to UserProfiles is correct; minor reuse and normalization opportunitiesThis block cleanly switches from per‑PR updates to a per‑repo bulk update:
- Only processes repos that actually exist in
Repo.- Restricts to merged PRs since
since_datethat already have acontributorbut lackuser_profile.- Collects contributor GitHub URLs, fetches all matching
UserProfilerows in one query, and thenbulk_updates only PRs with a mapped profile.Two minor follow‑ups you might consider:
- You already compute
repo_objs = Repo.objects.filter(name__in=[...])earlier; using a{r.name: r for r in repo_objs}map here would avoid one extraRepo.objects.filter(name=repo_name).first()query per repo.- Matching is done on raw
contributor.github_url; if any profiles store GitHub URLs with different normalization (trailing slash, case), a simple normalization step (e.g., lowercasing and stripping trailing/on both sides) before buildinggithub_urls/profiles_mapwould make the linking more robust.Functionally this achieves the PR objective of batching PR→profile linkage.
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OsshDiscussionChannel(2460-2473)
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Domain(386-484)Issue(590-739)open_issues(419-420)closed_issues(423-424)
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website/views/ossh.py (4)
279-282: Good optimization to eliminate N+1 queries.The query now uses
prefetch_related("tags")to fetch all channel tags in a single query, avoiding N+1 when accessingchannel.tags.all()in the loop. This aligns well with the PR's performance objectives.
285-286: Good optimization for fast lookups.Converting
user_tagsto a dictionary enables O(1) lookups in the subsequent loop instead of O(n) list scans.
288-296: Correctly fixes the criticalrelevance_scorebug.The
relevance_scorevariable is now properly calculated at line 296 before being used in the conditional at line 298. This resolves theNameErrorthat previous reviewers flagged.The logic correctly:
- Extracts channel tag names from prefetched tags
- Counts matching tags between channel and user
- Sums language weights for channel tags that match user languages
- Combines both into a single relevance score
299-300: Efficient extraction of matching tags and languages.The list comprehensions efficiently identify which specific tags and languages matched, using the optimized
tag_weight_mapandlanguage_weightsdictionaries for fast lookups.
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Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: Length of output: 2665 🏁 Script executed: Length of output: 59013 🏁 Script executed: Length of output: 1482 @Nachiket-Roy: The test failed due to a typo in the field name in Root CauseThe code is using Error: The ProblemIn repositories = hackathon.repositories.annotate(
merged_pr_count=Count(
"githubissue", # ❌ Wrong - should be "github_issues"
filter=Q(
githubissue__type="pull_request", # ❌ Wrong
githubissue__is_merged=True, # ❌ Wrong
githubissue__merged_at__gte=hackathon.start_time, # ❌ Wrong
githubissue__merged_at__lte=hackathon.end_time, # ❌ Wrong
)
& ~Q(githubissue__contributor__contributor_type="Bot") # ❌ Wrong
& ~Q(githubissue__contributor__name__endswith="[bot]") # ❌ Wrong
& ~Q(githubissue__contributor__name__icontains="bot"), # ❌ Wrong
)
)Fix RequiredReplace all occurrences of repositories = hackathon.repositories.annotate(
merged_pr_count=Count(
"github_issues", # ✅ Correct
filter=Q(
github_issues__type="pull_request", # ✅ Correct
github_issues__is_merged=True, # ✅ Correct
github_issues__merged_at__gte=hackathon.start_time, # ✅ Correct
github_issues__merged_at__lte=hackathon.end_time, # ✅ Correct
)
& ~Q(github_issues__contributor__contributor_type="Bot") # ✅ Correct
& ~Q(github_issues__contributor__name__endswith="[bot]") # ✅ Correct
& ~Q(github_issues__contributor__name__icontains="bot"), # ✅ Correct
)
)Once you fix this typo, the |
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🔇 Additional comments (7)
website/views/project.py (1)
1266-1273: LGTM! Efficient bulk contributor fetch eliminates N+1 queries.The refactor replaces per-stat
Contributor.objects.get()calls with a single bulk fetch viain_bulk(), then uses dictionary lookup. Skipping stats with missing contributors is appropriate and prevents errors.website/views/issue.py (4)
103-107: LGTM! Efficient M2M membership checks using.exists().The refactor from
issue in userprof.issue_downvoted.all()touserprof.issue_downvoted.filter(pk=issue.pk).exists()avoids loading entire querysets just to check membership, eliminating unnecessary database overhead. This pattern is correctly applied at both lines 103 and 107.
112-129: LGTM! Email notification sent only on new upvotes with proper validation.The email logic correctly:
- Sends only when adding a new upvote (line 110 else branch), not when removing
- Validates both
issue.userandissue.user.emailexist (line 113) before callingsend_mail
142-146: LGTM! Logic corrected and M2M checks optimized.The function now correctly:
- Removes upvote if present (line 142-143)
- Toggles downvote (line 146-149)
Both membership checks use the efficient
.exists()pattern consistent withlike_issue.
2629-2672: LGTM! Efficient bulk update pattern replaces per-PR saves.The refactor correctly:
- Uses
pr.contributor.github_urldirectly (lines 2641-2649), addressing past review comments about incorrect URL parsing- Filters out bot accounts with safe null checks (lines 2644-2648)
- Bulk-fetches UserProfile objects once (line 2652) instead of per-PR queries
- Uses dictionary lookup
profiles_map.get()for O(1) profile retrieval (line 2658)- Applies updates via
bulk_update()(line 2665) instead of per-PR.save()callsThis eliminates N+1 queries and reduces database writes, consistent with the PR's performance objectives.
website/views/organization.py (2)
239-296: LGTM! All critical issues from previous reviews have been resolved.The weekly report function now correctly:
- Requires staff authentication via
@staff_member_required- Imports and handles
SMTPExceptionproperly- Uses per-domain try/except blocks to prevent one failure from stopping the entire batch
- Validates
domain.emailbefore sending- Optimizes queries with
annotate()andprefetch_related()The optimization effectively eliminates N+1 queries by computing counts in SQL and prefetching filtered issues in a single query per batch.
349-355: Excellent use ofin_bulkto eliminate N+1 queries.The code now fetches all required users in a single query using
in_bulk()and retrieves them from an in-memory dictionary during iteration, avoiding per-user database hits. The graceful handling of missing users withcontinueensures robustness.
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🔇 Additional comments (4)
website/views/organization.py (4)
10-10: LGTM: Required imports added.The addition of
SMTPExceptionandstaff_member_requiredimports addresses critical issues from previous reviews.Also applies to: 17-17
239-296: Good refactoring: N+1 queries eliminated and previous concerns addressed.The
weekly_reportrefactoring successfully addresses multiple performance and security issues from previous reviews:
- ✅ Authentication required via
@staff_member_required(line 239)- ✅ Per-domain SMTP error handling prevents cascade failures (lines 292-294)
- ✅ Skips domains without email addresses (lines 257-259)
- ✅ Uses annotations (
open_count,closed_count) to avoid Python-side counting (lines 241-243, 263-265)- ✅ Uses
prefetch_relatedwith filtered queryset to reduce queries (lines 244-250)The only remaining concern is the
issue_setaccess pattern at line 261 (see separate comment).
314-317: LGTM: bulk_update moved outside loop.The
bulk_updatecall is now correctly placed outside the loop (line 317), eliminating N database calls and addressing the critical issue from previous reviews. This changes the update pattern from one query per issue to a single batched query for all issues.
349-356: Good optimization: in_bulk eliminates per-user queries.The code now fetches winner/runner users in a single query using
in_bulk(line 350), replacing the previous pattern of repeatedUser.objects.get()calls. This reduces the query count from N to 1 for user retrieval and addresses the N+1 concern from previous reviews.The logic correctly fetches only the first 3 user IDs (line 349) since the loop only processes winners at indices 1-3 and breaks at index 4.
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⚠️ Outside diff range comments (1)
website/views/organization.py (1)
340-341: Potential bug: Orphaned issue.save() in publish branch.Line 341 calls
issue.save()on theissuevariable from the loop that ended at line 336. This saves whichever issue happened to be last in the iteration overrequest.POST.items(), which doesn't logically relate to the "publish" action or winner creation that follows.This appears to be leftover code. Either remove it, or if a specific issue needs to be saved here, clarify which one and why.
🔎 Apply this diff to remove the orphaned save:
if request.POST["submit"] == "save": pass elif request.POST["submit"] == "publish": - issue.save() winner = Winner()
🧹 Nitpick comments (1)
website/views/organization.py (1)
314-318: Good: bulk_update moved outside loop, fixing N+1 writes.The
bulk_updateat line 318 is now correctly placed outside the loop, addressing the critical issue flagged in previous reviews. This eliminates N database writes and replaces them with a single batch operation.Note: Lines 320-336 still call
issue.save()individually for each updated issue. This is better than before (the initial reset is batched), but a fully optimal approach would collect all changes in memory during the loop and perform a singlebulk_updateat the end covering both the reset and the POST updates.
Based on previous review identifying
bulk_updateinside the loop as a critical performance issue.
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website/views/organization.py (3)
10-10: LGTM: Required imports added.These imports correctly support the security and error-handling improvements in
weekly_report:
SMTPExceptionis now properly imported and caught at line 293staff_member_requireddecorator protects the endpoint at line 239Both address critical issues flagged in previous reviews.
Also applies to: 17-17
239-297: Excellent refactor: All critical issues from previous reviews resolved.This implementation comprehensively addresses every concern raised in past reviews:
✅ Security (Critical):
@staff_member_requireddecorator added (line 239), preventing unauthorized mass email sending✅ Error handling (Critical):
SMTPExceptionis now imported (line 10) and caught inside the per-domain loop (line 293), so SMTP failures no longer abort the entire run✅ Data consistency (Major): Prefetch uses
to_attr="filtered_issues"(line 250) and is accessed correctly (line 262), ensuring the report's issue list matches the header counts✅ Optimization (Major): Annotated
open_countandclosed_count(lines 242-243) are now used (lines 264-265) instead of redundant Python list comprehensions✅ Validation (High): Email check (line 258) now skips domains without email before attempting to send
The refactor eliminates N+1 queries via annotations and prefetching while fixing all previously identified bugs. Well done!
Based on previous review feedback indicating multiple critical and major issues in this function.
350-356: Excellent: Batch user fetch eliminates N+1 queries.Replacing per-row
User.objects.get()calls within_bulk()(line 351) eliminates N queries. The.get(obj["user"])pattern (line 354) with the None check (lines 355-356) safely handles missing users without raising exceptions.This directly addresses the N+1 issue in organization.py:348 mentioned in the PR objectives and previous review comments.
Based on PR objectives noting "per-row User.objects.get() in organization.py:348; recommended in_bulk() for users."
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website/views/organization.py (1)
316-319: Critical fix: bulk_update now correctly placed outside the loop.The
bulk_update()call is now properly positioned after the loop completes, ensuring all issues are modified in memory first and then updated in a single database operation. This addresses the previous critical N+1 issue.Optional micro-optimization:
🔎 Guard against empty list
for issue in issues: issue.verified = False issue.score = 0 - Issue.objects.bulk_update(issues, ["verified", "score"]) + if issues: + Issue.objects.bulk_update(issues, ["verified", "score"])While Django's
bulk_update()handles empty lists gracefully, explicitly checking avoids an unnecessary database call.
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website/views/organization.py (2)
240-298: Excellent work addressing all critical security and correctness issues!This refactor successfully resolves all previously identified critical problems:
✅ Authentication: Added
@staff_member_requiredto prevent unauthorized mass email sending
✅ Exception handling:SMTPExceptionis now imported and caught per-domain (not aborting the entire batch)
✅ Prefetch usage: Correctly usingto_attr="filtered_issues"and accessing the cached queryset
✅ Email validation: Skipping domains without email addresses
✅ Annotations: Usingopen_countandclosed_countefficiently
✅ Consistency: Counts and listed issues both use the same open/closed filterThe implementation is clean, efficient, and correctly handles errors without stopping the batch process.
342-368: Excellent optimization usingin_bulk()to eliminate N+1 queries.The refactor correctly:
- Wraps winner assignment in
transaction.atomic()for data consistency- Aggregates and orders users by total score in SQL
- Fetches exactly the top 3 users in a single
in_bulk()call (previously wasUser.objects.get()per winner)- Guards each winner assignment with
len(top_users) >checksThis eliminates the N+1 query pattern and batches user access as intended by the PR objectives.
Note: If a user is deleted between the aggregation query and the
in_bulk()call,user_map.get()will returnNoneand the corresponding winner field will be set toNone. This is acceptable behavior for a rare edge case.
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Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: Length of output: 2350 🏁 Script executed: Length of output: 26183 @Nachiket-Roy: The migration label test failed due to a GitHub API issue, not a problem with your code. Root CauseThe test tried to fetch the list of changed files from GitHub's API but received a 500 Internal Server Error: What This Test DoesThe
Why It FailedGitHub's API experienced a temporary server-side failure when trying to list your PR's files. The request timed out after 10 seconds and returned HTTP 500 (server error). SolutionRe-run the workflow - this is a transient infrastructure issue that should resolve on retry:
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CLOSES: #5277
Summary
This PR addresses multiple performance issues caused by N+1 query patterns and per-row database updates in contributor stats, hackathon pages, team challenges, and GSoC project refresh logic. The changes reduce query counts, improve scalability, and keep existing behavior unchanged.
Key Changes
Query Efficiency Improvements
Code Cleanup & Safety
Impact
Summary by CodeRabbit
Refactor
Bug Fixes
Chores
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