
Community platforms
حوالہ جاتی نمونہFifty thousand concurrent voters without touching the database
A live polling platform that counts votes in Redis and flushes to PostgreSQL in batches — cutting database write load by 97% while acknowledging votes in under 20 milliseconds.
ایک نظر میں
- مدت
- 35 ہفتے
- ٹیم کا حجم
- 3 افراد
- معاہدے کی نوعیت
- نئی تعمیر
- پروجیکٹ کی قسم
- ویب ایپلیکیشن
- شعبہ
- میڈیا اور تفریح
کن چیزوں سے بنا
صورتحال
مسئلہ
Naive vote counting collapses at scale: 50,000 concurrent voters means 50,000 simultaneous write transactions and COUNT queries against PostgreSQL. Multiple accounts and bots threatened the credibility of the results on top of that.
ہم نے کیا کیا
Count where counting is cheap, persist on a schedule, and accept that the database being five seconds behind is invisible to everyone.
وہ فیصلے جو اہم تھے
Redis counts, PostgreSQL records
INCR for O(1) tallying and SADD for deduplication, with Lua scripts making check-and-increment atomic. A five-second batch flush to PostgreSQL removed 97% of the write load.
Diff-based broadcasts on a fixed cadence
500ms broadcasts carrying only what changed. Pushing full tallies per vote is what turns a popular poll into a self-inflicted denial of service.
کیا بدلا
- total votes processed
- 1M+total votes processed
- concurrent voters
- 50Kconcurrent voters
- p99 vote acknowledgement
- <20msp99 vote acknowledgement
- reduction in database write load
- 97%reduction in database write load
- 50K — without degradation
A million votes processed, 50,000 concurrent voters held without degradation, sub-20ms acknowledgement, and no vote corruption incidents.
استعمال شدہ خدمات
- Redis-first vote counting with atomic Lua operations
- Batched persistence layer and reconciliation
- Layered anti-fraud: fingerprinting, rate limits, challenges
ہم کیا مختلف کرتے
ہر منصوبے میں ایسی ایک بات ہوتی ہے۔ اسے شائع کرنا ہی اصل نکتہ ہے — جس کیس اسٹڈی میں کوئی پچھتاوا نہ ہو وہ ثبوت نہیں، تشہیر ہے۔
Scaling here was about accepting eventual consistency in the right place. The flush delay makes database records briefly stale, but users read results from Redis and perceive them as live — decoupling the two let each be optimised on its own terms.
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