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News Platform at Scale: Dainik Bhaskar’s 5M+ Daily Readers

Dainik Bhaskar is India’s largest Hindi newspaper (5M+ daily readers). When Bhaskar went digital, they needed a backend that could handle peak traffic (60K concurrent readers), deliver fresh content in < 500ms, and monetize through targeted advertising.

See also: BigQuery analytics

Cloud Spanner database

Observability metrics

Kubernetes deployment

Go concurrency

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The Architecture

Content Writers
    ↓
Article API (Go, gRPC)
    ↓
PostgreSQL (read replicas for scale)
    ↓
Redis Cache (60-second TTL)
    ↓
CDN (CloudFlare for global distribution)
    ↓
Mobile App / Web

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Multi-Edition API in Go

type Article struct {
    ID        string    `json:"id"`
    Title     string    `json:"title"`
    Body      string    `json:"body"`
    Edition   string    `json:"edition"`   // Hindi/English/Regional
    CreatedAt time.Time `json:"created_at"`
}

func (a *ArticleService) GetArticle(ctx context.Context, id string) (*Article, error) {
    // 1. Check cache
    cached, err := redis.Get(ctx, fmt.Sprintf("article:%s", id))
    if err == nil {
        return unmarshalshal(cached)
    }
    
    // 2. Query database (read replica for scale)
    article, err := db.QueryRow(ctx, `
        SELECT id, title, body, edition FROM articles WHERE id = $1
    `, id).Scan(&a.ID, &a.Title, &a.Body, &a.Edition)
    
    if err != nil {
        return nil, err
    }
    
    // 3. Cache for 60 seconds
    redis.Set(ctx, fmt.Sprintf("article:%s", id), marshal(article), 60*time.Second)
    
    return article, nil
}

---

Ad Revenue Analytics

import bigquery

def analyze_ad_revenue():
    """Track ad impressions, clicks, and revenue."""
    
    query = """
    SELECT
        DATE(event_time) as date,
        edition,
        device_type,
        COUNT(*) as impressions,
        SUM(CASE WHEN clicked THEN 1 ELSE 0 END) as clicks,
        SUM(revenue) as total_revenue,
        SUM(revenue) / COUNT(*) as cpm
    FROM ad_events
    WHERE DATE(event_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY date, edition, device_type
    ORDER BY total_revenue DESC
    """
    
    results = bigquery.query(query)
    
    for row in results:
        print(f"""
        Date: {row['date']}
        Edition: {row['edition']}
        CPM: ${row['cpm']:.2f}
        Revenue: ${row['total_revenue']:.2f}
        """)

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Tags: #ContentPlatform #Scaling #Caching #BigData #MultiLanguage

Published: June 2026
Author: Pratik Dhanave
Related Projects: DB Corp (Dainik Bhaskar)