Building Real-Time Analytics Counters with Redis in Spring Boot

February 23, 2026

Many modern applications need to track events in real time, such as page views, product clicks, downloads, or API usage. Traditional databases can struggle with very high write rates, but Redis handles these workloads efficiently.
Redis provides atomic counters, which allow applications to increment values safely and quickly even under heavy traffic. This article explains how to build real-time analytics counters using Redis and Spring Boot.

1. What Are Redis Counters

A Redis counter is simply a numeric value stored in Redis that can be incremented or decremented atomically.
Example counters:

pageviews:homepage
clicks:product:101
api:requests

Each event increases the counter.
Advantages:

Extremely fast
Atomic operations
Works well under high concurrency

2. Creating a Counter Service

Create a service to manage counters.

@Service
public class AnalyticsService {

    @Autowired
    private RedisTemplate<String, Long> redisTemplate;

    public Long incrementCounter(String key) {
        return redisTemplate.opsForValue().increment(key);
    }
}

This method increments the counter every time an event occurs.

3. Tracking Page Views

Example for counting page visits.

public Long trackPageView(String page) {
    String key = "pageviews:" + page;
    return redisTemplate.opsForValue().increment(key);
}

Example keys:

pageviews:home
pageviews:products
pageviews:contact

Each request increases the page view count.

4. Tracking Product Clicks

Example for product interaction analytics.

public Long trackProductClick(Long productId) {
    String key = "clicks:product:" + productId;
    return redisTemplate.opsForValue().increment(key);
}

Example result:

clicks:product:101 = 250
clicks:product:102 = 180

5. Setting Counter Expiration

For time-based analytics, counters can expire automatically.
Example:

redisTemplate.opsForValue()
    .increment("pageviews:daily");

redisTemplate.expire("pageviews:daily",
        Duration.ofDays(1));

This creates daily counters that reset automatically.

6. Exposing Analytics via REST API

Example controller:

@RestController
@RequestMapping("/analytics")
public class AnalyticsController {

    @Autowired
    private AnalyticsService analyticsService;

    @PostMapping("/pageview")
    public Long trackPage(@RequestParam String page) {
        return analyticsService.trackPageView(page);
    }
}

Example request:

POST /analytics/pageview?page=home

Each request increases the counter.

7. Building Trending Metrics

Combine counters with Redis Sorted Sets to create trending data.
Example:

redisTemplate.opsForZSet()
        .incrementScore("trending-products",
                productId.toString(),
                1);

Now Redis automatically ranks the most popular products.

8. Scaling Analytics Systems

Redis analytics can handle millions of events per second.
For large systems:

Use Redis clusters
Partition counters by date
Archive historical data to a database

Example key design:

pageviews:2026-03-09
clicks:product:101:2026-03-09
api:requests:minute

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