Content par jayen

Community platforms

Reference build

Fifty 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.

1M+votes processed

Aik nazar mein

Muddat
35 hafte
Team ka hajm
3 afraad
Muahide ki naueeyat
Nayi tameer
Project ki qism
Web application
Shoba
Media aur Entertainment

Soorat-e-haal

Challenge kya tha

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.

Hum ne kya kiya

Count where counting is cheap, persist on a schedule, and accept that the database being five seconds behind is invisible to everyone.

Wo faisle jo aham the

  • 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.

Kya badla

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.

Istemal shuda khidmaat

  • Redis-first vote counting with atomic Lua operations
  • Batched persistence layer and reconciliation
  • Layered anti-fraud: fingerprinting, rate limits, challenges

Hum kya mukhtalif karte

Har mansoobe mein aisi aik baat hoti hai. Ise shaya karna hi asal nukta hai — jis case study mein koi pachhtawa na ho wo saboot nahi, tashheer hai.

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.

Istemal shuda khidmaat

Mazeed kaam

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