# Metric Aggregations: avg, percentiles, cardinality — Elasticsearch

Source: https://www.skillbyai.com/en/elasticsearch/g-metric

> Summarise numbers.

## Exact and approximate metrics

**Metric** aggregations compute values over a set of documents: `avg`, `sum`, `min`, `max`, `value_count`, and `stats` for several at once. Some metrics are **approximate by design** to stay fast and memory-bounded at scale: `percentiles` uses the TDigest algorithm (very accurate at the extremes like p99, less so in the middle on huge datasets), and `cardinality` (distinct count) uses HyperLogLog++, exact up to roughly the configurable `precision_threshold` and approximate beyond it. Metrics can sit inside bucket aggregations, and pipeline aggregations such as `bucket_sort` or `derivative` work on the output of other aggregations.

## Latency and unique users per endpoint

Kibana Dev Tools console syntax; send the same requests with curl or a client library.

```http
GET /requests/_search
{
  "size": 0,
  "aggs": {
    "by_endpoint": {
      "terms": { "field": "endpoint", "size": 20 },
      "aggs": {
        "avg_ms":        { "avg": { "field": "duration_ms" } },
        "latency":       { "percentiles": { "field": "duration_ms", "percents": [ 50, 95, 99 ] } },
        "unique_users":  { "cardinality": { "field": "user_id", "precision_threshold": 3000 } }
      }
    }
  }
}
```

## Do not report approximate as exact

If finance needs an exact distinct count, compute it in the system of record. Cardinality from Elasticsearch is ideal for dashboards and trends, not invoices.

**Quiz:** What does the cardinality aggregation return?

- [ ] The number of shards
- [ ] The exact number of documents
- [ ] The largest value in a field
- [x] An approximate count of distinct values

*Answer:* An approximate count of distinct values. It uses HyperLogLog++ with a precision_threshold.