# Terms Aggregation vs Field Collapse for Search Suggestions at Scale (100K+ unique values)

**URL:** https://discuss.elastic.co/t/terms-aggregation-vs-field-collapse-for-search-suggestions-at-scale-100k-unique-values/383182
**Category:** Elasticsearch
**Created:** [November 3, 2025, 11:54am UTC](https://discuss.elastic.co/t/terms-aggregation-vs-field-collapse-for-search-suggestions-at-scale-100k-unique-values/383182 "2025-11-03T11:54:55Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![Yousif\_Alneamy](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/yousif_alneamy/32/139986_2.png) [@Yousif\_Alneamy](https://discuss.elastic.co/u/Yousif_Alneamy)
#### Post date: [November 3, 2025, 11:54am UTC](https://discuss.elastic.co/t/terms-aggregation-vs-field-collapse-for-search-suggestions-at-scale-100k-unique-values/383182/1 "2025-11-03T11:54:55Z")

</div>

I'm building a search suggestion feature and need help choosing between **terms aggregation** and **field collapse** for my use case.

**My Dataset:**

- **3 million items** in the index

- **100,000+ unique product names** (`brandName` in the mappings)

- Users search by typing partial names (autocomplete)

- I only need to return **unique name strings** (not full documents)

```json
{
  "mappings": {
    "properties": {
      "completionField": {
        "type": "search_as_you_type",
        "max_shingle_size": 3
      },
      "product": {
        "type": "object",
        "properties": {
          "brandName": {
            "type": "text",
            "analyzer": "product_name_analyzer",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          }
        }
      }
    }
  }
}

```

Response Needed:

```json
[
  { "text": "Panadol" },
  { "text": "Advil" },
  { "text": "Aspirin" }
]

```

### **Approach 1: Terms Aggregation**

```json
{
  "size": 0,
  "_source": false,
  "query": {
    "bool": {
      "should": [
        {
          "multi_match": {
            "query": "pana",
            "type": "bool_prefix",
            "fields": ["completionField", "completionField._2gram", "completionField._3gram"]
          }
        },
        {
          "multi_match": {
            "query": "pana",
            "fields": ["product.brandName^4"]
          }
        }
      ]
    }
  },
  "aggs": {
    "unique_brand_names": {
      "terms": {
        "field": "product.brandName.keyword",
        "size": 5,
        "order": { "max_score": "desc" }
      },
      "aggs": {
        "max_score": {
          "max": { "script": "_score" }
        }
      }
    }
  }
}

```

### **Approach 2: Field Collapse**

```json
{
  "size": 5,
  "_source_includes": ["product.brandName"],
  "query": {
    "bool": {
      "should": [
        {
          "multi_match": {
            "query": "pana",
            "type": "bool_prefix",
            "fields": ["completionField", "completionField._2gram", "completionField._3gram"]
          }
        },
        {
          "multi_match": {
            "query": "pana",
            "fields": ["product.brandName^4"]
          }
        }
      ]
    }
  },
  "collapse": {
    "field": "product.brandName.keyword"
  },
  "sort": ["_score"]
}

```

### **Questions:**

1. **For returning only unique string values (not documents), which approach is more efficient at this scale?**
2. **Which uses less memory per query?**
3. **Which provides more accurate relevance ordering?**
4. **Are there alternative approaches I should consider?**

**Benchmark results so far:**  
Terms aggregation is ~2x faster than collapse on average