Stack Overflow#

Q: How are nested comment threads modeled?

Each comment stores parent_id and a precomputed path or depth. Queries fetch all comments for a post, then the client tree builds the thread by walking parent links.

Q: How is the Reddit Hot ranking computed?

Hot combines log-scaled score with a time-decay term: log(votes) plus age-in-seconds divided by a constant. Older posts with high scores still float above brand-new ones.

Q: How do you count votes for a hot post?

Aggregate vote events into per-post counters via a streaming pipeline. A short-window in-memory counter handles bursts, and periodic flushes update the canonical store.

Q: How does subreddit isolation work?

Each subreddit is a logical partition keyed by name. Listings and search are filtered by subreddit ID, and per-subreddit moderation rules apply at write and read time.

Q: How does Reddit handle 2B page views per day?

Read-through caches like Redis or Memcached serve hot post listings, CDNs front anonymous traffic, and the write path goes through a queue to absorb voting bursts.

Problem statement (interviewer prompt)

Design Reddit (or Quora / Stack Overflow): users post questions/threads in communities, others comment in nested threads, everyone votes. Listings (hot/new/top) must be ordered by a ranking algorithm. Support 50M+ DAU and 2B+ page views/day.

flowchart LR
  U([User])
  W[Write API]
  POST[(Posts / Questions)]
  COMM[(Comments / Answers)]
  VOTE[Vote Service]
  RANK[Ranking Service]
  FEED[Feed / Listing API]
  SRCH[Search]
  U -->|post| W --> POST
  U -->|comment| W --> COMM
  U -->|vote| W --> VOTE
  U -->|read| FEED --> RANK
  RANK --> POST
  POST --> SRCH

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class U client;
    class W,VOTE,RANK,FEED,SRCH service;
    class POST,COMM datastore;

flowchart TB
  subgraph Clients
    Web
    Mobile
    API
  end

  subgraph Edge
    CDN
    LB
    GW[API Gateway]
  end

  subgraph Write
    POST[Post / Question Service]
    COM[Comment / Answer Service]
    VOTE[Vote Service]
    EDIT[Edit history / versioning]
    FLAG[Flag / Report]
  end

  subgraph Stores
    PDB[(Posts Postgres / Cassandra<br/>sharded by subreddit / topic)]
    CDB[(Comments tree per post)]
    VDB[(Votes - Redis counters)]
    USER[(Users)]
    META[(Communities / Tags)]
    HIST[(Edit history)]
    SEARCH[(Elasticsearch)]
  end

  subgraph Read
    LIST[Listings: hot / new / top]
    THREAD[Thread / Question view]
    USERFEED[Home feed]
    NOTIF[[Notifications / Inbox]]
  end

  subgraph Ranking[Ranking Algorithms]
    HOT[Hot - Reddit log decay]
    BEST[Best - Wilson confidence]
    CON[Controversial]
    TOP[Top in window]
    SOFT[SO score - score + age decay]
    QPER[Quora personalized DNN]
  end

  subgraph Async
    K[[Kafka]]
    SC[Score recomputer]
    SPAM([Spam classifier])
    BAD[Bad actor detector]
    EMB([Embeddings job])
    REC([Recommendations])
  end

  subgraph Cache
    R1[(Hot post cache)]
    R2[(Vote count cache)]
    R3[(Comment tree cache)]
  end

  Clients --> CDN --> LB --> GW
  GW --> POST --> PDB
  GW --> COM --> CDB
  GW --> VOTE --> VDB
  POST --> K
  COM --> K
  VOTE --> K
  K --> SC --> R2
  K --> SPAM
  K --> BAD
  K --> EMB --> REC
  POST --> SEARCH
  GW --> LIST --> R1
  R1 -. miss .-> PDB
  LIST --> Ranking
  GW --> THREAD --> R3
  R3 -. miss .-> CDB
  GW --> USERFEED --> REC
  GW --> SRCH[Search] --> SEARCH
  Ranking --> SC
  FLAG --> SPAM

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class GW edge;
    class POST,COM,VOTE,EDIT,FLAG,LIST,THREAD,USERFEED,HOT,BEST,CON,TOP,SOFT,QPER,SC,BAD,SRCH service;
    class PDB,CDB,USER,META,HIST,SEARCH,R1,R2,R3 datastore;
    class VDB cache;
    class NOTIF,K queue;
    class SPAM,EMB,REC compute;

Ranking formulas#

Reddit Hot: score = log10(max(|ups - downs|, 1)) + sign × seconds_since_epoch / 45000.
Reddit Best: Wilson lower-bound of upvote ratio for comment ranking.
Stack Overflow: (upvotes - downvotes) + acceptance + age decay.
Quora: personalized DNN over content + viewer features.

Comment tree#

Stored materialized-path or adjacency list with path encoded.
Paginated with "load more replies" to avoid huge trees.
Vote counts cached separately to allow O(1) update.

Subreddit / community#

Sharded by subreddit_id - locality benefit (listings & posts together).
Hot listings per community precomputed every few seconds.

Search#

Elasticsearch index per content type (posts, comments).
Real-time CDC from Postgres binlog.

Spam / abuse#

ML classifier on submit + behavioral signals (vote ring detection).
Shadow-ban + report queues.

Glossary & fundamentals#

Concepts referenced in this design. Each row links to its canonical page; the tag column shows whether it is a high-level (HLD) or low-level (LLD) concept.

Tag	Concept	What it is	Page
`HLD`	CDN	edge caching for static assets	cdn
`HLD`	API gateway / BFF	single ingress, auth, rate limit, routing	api-gateway
`HLD`	Sharding	horizontal partitioning across nodes	database-sharding
`HLD`	Pub/Sub & message brokers	topics, consumer groups, delivery semantics	pub-sub-pattern
`HLD`	Leader/follower replication	sync/semi-sync/async replication, failover	replication-leader-follower
`HLD`	Change Data Capture	WAL/binlog tailing, outbox publishing	change-data-capture

Quick reference#

Functional#

Post / question with title, body, tags, media.
Threaded comments / answers (deeply nested).
Voting (up/down or accept).
Listings: hot, new, top, controversial.
Per-user feed, notifications, mentions.
Moderation (community + global).

Non-functional#

Read-heavy 50:1; comment voting bursts.
p99 listing < 200 ms.
Eventual consistency on vote counts OK; user must see own vote immediately.

Capacity (Reddit-scale)#

50M+ DAU, 1B+ posts/comments total.
~2B page views/day → ~25k/s avg.
Storage: text ~2 KB avg × 1B posts = 2 TB + comments many TB.

Schema highlights#

posts(id PK, subreddit_id, author, ts, title, body, score, num_comments)
comments(id, post_id, parent_id, path, author, body, score) materialized path
votes(user_id, target_id, value, ts) Cassandra; counters in Redis
subreddits(id, name, type, rules)

Trade-offs#

Materialized-path comments simplify thread queries; cost: rewrite path on move.
Eventually consistent vote counters allow huge scale; show user-personal vote from local cache.
Personalized vs algorithm-only: SO uses simple formulas (signals trust); Quora goes full ML.
Sharding by subreddit localizes hot subs but can hotspot one shard.

Refs#

Reddit "Hot" ranking blog post (Amir Salihefendic).
Quora engineering posts (Webnode, Quora Search).
Stack Overflow architecture (Nick Craver's blog series).
ByteByteGo "Design Reddit".

FAQ#

How are nested comment threads modeled?#

Each comment stores parent_id and a precomputed path or depth. Queries fetch all comments for a post, then the client tree builds the thread by walking parent links.

How is the Reddit Hot ranking computed?#

Hot combines log-scaled score with a time-decay term: log(votes) plus age-in-seconds divided by a constant. Older posts with high scores still float above brand-new ones.

How do you count votes for a hot post?#

Aggregate vote events into per-post counters via a streaming pipeline. A short-window in-memory counter handles bursts, and periodic flushes update the canonical store.

How does subreddit isolation work?#

Each subreddit is a logical partition keyed by name. Listings and search are filtered by subreddit ID, and per-subreddit moderation rules apply at write and read time.

How does Reddit handle 2B page views per day?#

Read-through caches like Redis or Memcached serve hot post listings, CDNs front anonymous traffic, and the write path goes through a queue to absorb voting bursts.

Video walkthrough

Design Reddit: System Design Mock Interview : via Exponent