# Real-time Aggregation

> Maintains continuously updated aggregations over streaming data for instant insights.

- **Category**: Data Science
- **Subcategory**: Real-time
- **Canonical URL**: https://designpattern.fyi/patterns/real-time-aggregation/

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## Description
**Context**: Pre-computing aggregations on streaming data enables instant queries without scanning the entire dataset. Windowing functions allow temporal aggregations.


## Use Cases
Live dashboards, real-time metrics, monitoring systems, and applications requiring instant aggregation results.



## Implementation Example

```python
# Real-time Aggregation Pattern from collections import defaultdict
class RealTimeAggregator: def __init__(self): self.counters = defaultdict(int)
def process_event(self, event): key = event["category"] value = event["value"] self.counters[key] += value return self.counters[key]
def get_aggregation(self, key): return self.counters[key]
aggregator = RealTimeAggregator()
# Process streaming events aggregator.process_event({"category": "sales", "value": 100}) aggregator.process_event({"category": "sales", "value": 50})
```



## Trade-offs


### Advantages

- - Instant query response

- - Reduced compute at query time

- - Always current data

- - Efficient for repeated queries




### Considerations & Drawbacks

- - State management complexity

- - Memory requirements

- - Late data handling

- - Resource intensive







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**Reference**: [Original Source](https://kafka.apache.org/documentation/streams/)