# Low Level Design: Reading List Service

Source: https://www.techinterview.org/post/3233471004/lld-reading-list/
Updated: 2026-04-17 · techinterview.org

## Reading List Service: Core Schema

The reading list service saves articles for later reading with content extraction and offline availability.

### ReadingItem Table


```
id                   BIGSERIAL PRIMARY KEY
user_id              BIGINT NOT NULL
url                  TEXT NOT NULL
title                TEXT
author               TEXT
word_count           INT
estimated_read_minutes INT
status               TEXT DEFAULT 'unread'  -- unread/reading/completed/archived
progress_pct         INT DEFAULT 0          -- 0-100
content_s3_key       TEXT                   -- extracted article content in S3
saved_at             TIMESTAMPTZ DEFAULT now()
read_at              TIMESTAMPTZ

UNIQUE (user_id, url)
```


## Content Extraction Pipeline

An async worker extracts the readable article body from each saved URL, stripping ads, navigation, and boilerplate.


```
Worker flow:
1. Dequeue item_id from extraction queue
2. HTTP GET url
3. Run Mozilla Readability algorithm on HTML
   → extract article body, title, author, word count
4. Strip remaining ads/nav/scripts
5. Store cleaned HTML in S3 (content_s3_key = users/{uid}/items/{id}.html)
6. UPDATE reading_items SET content_s3_key=..., word_count=...,
       estimated_read_minutes=CEIL(word_count/238.0) WHERE id=item_id
```

Word count drives `estimated_read_minutes` using an average adult reading speed of 238 words per minute.

## Offline Reading

Mobile clients download extracted content over WiFi for offline access. A local tracking table prevents redundant downloads.

### ContentDownload Table (client-side)


```
item_id       BIGINT PRIMARY KEY
downloaded_at TIMESTAMPTZ
local_path    TEXT
size_bytes    INT
```

The client syncs the list of items with `content_s3_key` set, compares against local downloads, and fetches missing content only on WiFi by default.

## Progress Tracking

The reading client sends scroll position updates to the server periodically to persist reading progress across devices.


```
-- Client sends every 30s while reading:
PATCH /api/items/{id}/progress
Body: { "progress_pct": 42 }

-- Server:
UPDATE reading_items SET progress_pct=$1, status='reading'
WHERE id=$2 AND user_id=$3;
-- If progress_pct >= 95: SET status='completed', read_at=now()
```


## Re-Surface and Daily Digest

Items that remain unread for more than 7 days are surfaced in a daily digest email to re-engage the user.


```
-- Nightly job query:
SELECT * FROM reading_items
WHERE user_id=$1 AND status='unread'
  AND saved_at < now() - INTERVAL '7 days'
ORDER BY saved_at ASC
LIMIT 5;
```

The digest email lists up to 5 stale unread items with title, estimated read time, and a direct link to the reader view.

## Tagging and Full-Text Search

Tags and full-text search follow the same pattern as the bookmark service: JSONB tags column with GIN index, plus a generated tsvector column on title and extracted author for keyword search.


```
CREATE INDEX idx_reading_items_tags ON reading_items USING GIN (tags);
ALTER TABLE reading_items ADD COLUMN fts tsvector
    GENERATED ALWAYS AS (
        to_tsvector('english', coalesce(title,'') || ' ' || coalesce(author,''))
    ) STORED;
CREATE INDEX idx_reading_items_fts ON reading_items USING GIN (fts);
```


## Reading Statistics

Aggregate stats are computed on read from the reading_items table. For high-traffic deployments, a nightly rollup into a stats table reduces query cost.


```
SELECT
    COUNT(*) FILTER (WHERE status='completed')    AS articles_read,
    SUM(word_count) FILTER (WHERE status='completed') AS total_words_read,
    AVG(estimated_read_minutes) FILTER (WHERE status='completed') AS avg_read_minutes
FROM reading_items WHERE user_id=$1;
```


## Smart Recommendations

After a user completes an article, the system extracts its tags and finds unread items with overlapping tags, ranked by tag overlap count.


```
-- Find unread items sharing tags with recently completed items:
SELECT ri.id, ri.title, COUNT(*) AS tag_overlap
FROM reading_items ri,
     jsonb_array_elements_text(ri.tags) t
WHERE ri.user_id=$1 AND ri.status='unread'
  AND t IN (SELECT jsonb_array_elements_text(tags)
            FROM reading_items
            WHERE user_id=$1 AND status='completed'
              AND read_at > now() - INTERVAL '30 days')
GROUP BY ri.id, ri.title
ORDER BY tag_overlap DESC
LIMIT 10;
```


## Auto-Archive

Items completed more than 90 days ago are automatically archived to reduce inbox clutter. A nightly batch job performs the transition.


```
UPDATE reading_items
SET status='archived'
WHERE status='completed'
  AND read_at < now() - INTERVAL '90 days';
```


See also: [Netflix Interview Guide 2026: Streaming Architecture, Recommendation Systems, and Engineering Excellence](https://www.techinterview.org/post/3233460274/netflix-interview-guide-2026-streaming-architecture-recommendation-systems-and-engineering-excellence/)

See also: [Apple Interview Guide 2026: iOS Systems, Hardware-Software Integration, and iCloud Architecture](https://www.techinterview.org/post/3233460277/apple-interview-guide-2026-ios-systems-hardware-software-integration-and-icloud-architecture/)

See also: [Scale AI Interview Guide 2026: Data Infrastructure, RLHF Pipelines, and ML Engineering](https://www.techinterview.org/post/3233460275/scale-ai-interview-guide-2026-data-infrastructure-rlhf-pipelines-and-ml-engineering/)