Scroll API for deep pagination in Elasticsearch

Introduction

The Scroll API helps you get many search results in small parts, so your app doesn't slow down or crash.

You want to get thousands of search results without waiting a long time.

You need to process all matching items step by step, like reading pages in a book.

You want to export large data sets from Elasticsearch safely.

You have a web app that shows search results page by page beyond the first few pages.

You want to avoid heavy memory use by loading all results at once.

Syntax

Elasticsearch

POST /index_name/_search?scroll=1m
{
  "size": 100,
  "query": {
    "match_all": {}
  }
}

scroll=1m means keep the search context alive for 1 minute.

size controls how many results you get per batch.

Examples

Start scrolling to get 50 book products at a time, keeping the context for 2 minutes.

Elasticsearch

POST /products/_search?scroll=2m
{
  "size": 50,
  "query": {
    "match": { "category": "books" }
  }
}

Use this request to get the next batch of results using the scroll ID.

Elasticsearch

POST /_search/scroll
{
  "scroll": "2m",
  "scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAA..."
}

Sample Program

This program gets search results from Elasticsearch in two batches using the Scroll API. It first requests 3 results, then uses the scroll ID to get the next 3.

Elasticsearch

import requests

# Step 1: Start scroll search
url = 'http://localhost:9200/products/_search?scroll=1m'
query = {
    "size": 3,
    "query": {"match_all": {}}
}
response = requests.post(url, json=query)
data = response.json()
scroll_id = data['_scroll_id']
hits = data['hits']['hits']

print('First batch:')
for hit in hits:
    print(hit['_source'])

# Step 2: Get next batch
scroll_url = 'http://localhost:9200/_search/scroll'
scroll_query = {
    "scroll": "1m",
    "scroll_id": scroll_id
}
response2 = requests.post(scroll_url, json=scroll_query)
data2 = response2.json()
hits2 = data2['hits']['hits']

print('\nSecond batch:')
for hit in hits2:
    print(hit['_source'])

Important Notes

Always keep the scroll alive time (like 1m) long enough to fetch all batches.

Scroll API is not for real-time user searches but for deep data processing.

Remember to clear scroll contexts if you stop early to save resources.

Summary

Scroll API helps get large search results in small parts safely.

Use scroll ID to fetch next batches step by step.

Keep scroll context alive with the scroll parameter.

Practice

1. What is the main purpose of the Scroll API in Elasticsearch?

easy

A. To retrieve large sets of search results in small, manageable batches.

B. To update documents in bulk efficiently.

C. To delete old indices automatically.

D. To create new indices with custom mappings.

Solution

1. Step 1: Understand Scroll API usage

   The Scroll API is designed to handle large result sets by breaking them into smaller parts.

2. Step 2: Compare options with Scroll API purpose

   Options B, C, and D relate to other Elasticsearch features, not scrolling.

3. Final Answer:

   To retrieve large sets of search results in small, manageable batches. -> Option A

4. Quick Check:

   Scroll API = batch retrieval [OK]

Hint: Scroll API = fetch big results in small parts [OK]

Common Mistakes:

• Confusing Scroll API with bulk update operations
• Thinking Scroll API deletes or creates indices
• Assuming Scroll API returns all results at once

2. Which of the following is the correct way to start a scroll search request in Elasticsearch using JSON?

easy

A. {"scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAA", "size": 100}

B. {"query": {"match_all": {}}, "scroll": "1m", "size": 100}

C. {"query": {"match": {"field": "value"}}, "timeout": "1m"}

D. {"scroll": "1m", "update": true}

Solution

1. Step 1: Identify scroll search syntax

   Starting a scroll requires a query, a scroll time, and size for batch size.

2. Step 2: Analyze options

   {"query": {"match_all": {}}, "scroll": "1m", "size": 100} includes query, scroll time, and size correctly. {"scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAA", "size": 100} uses scroll_id which is for continuing scroll, not starting. {"query": {"match": {"field": "value"}}, "timeout": "1m"} lacks scroll parameter. {"scroll": "1m", "update": true} has invalid update field.

3. Final Answer:

   {"query": {"match_all": {}}, "scroll": "1m", "size": 100} -> Option B

4. Quick Check:

   Start scroll = query + scroll + size [OK]

Hint: Start scroll with query + scroll + size keys [OK]

Common Mistakes:

• Using scroll_id to start scroll instead of continue
• Omitting the scroll parameter
• Confusing scroll with timeout or update

3. Given the following scroll response snippet, what is the correct next step to fetch more results?

{
  "_scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAA",
  "hits": {"hits": [{"_id": "1"}, {"_id": "2"}]}
}

medium

A. Send a new search request without scroll_id.

B. Delete the scroll_id to reset the scroll context.

C. Use the scroll_id in a subsequent scroll request with the scroll parameter.

D. Use the hits array to manually fetch documents by ID.

Solution

1. Step 1: Understand scroll continuation

   To get next batch, use the scroll_id from previous response with scroll parameter.

2. Step 2: Evaluate options

   Use the scroll_id in a subsequent scroll request with the scroll parameter. correctly describes using scroll_id and scroll to continue. Send a new search request without scroll_id. restarts search, losing context. Delete the scroll_id to reset the scroll context. is incorrect as deleting scroll_id is not valid. Use the hits array to manually fetch documents by ID. is manual and inefficient.

3. Final Answer:

   Use the scroll_id in a subsequent scroll request with the scroll parameter. -> Option C

4. Quick Check:

   Next scroll = scroll_id + scroll [OK]

Hint: Use scroll_id + scroll param to get next batch [OK]

Common Mistakes:

• Restarting search instead of continuing scroll
• Ignoring scroll parameter in next request
• Trying to fetch documents manually by ID

4. You wrote this scroll request but get an error: {"scroll_id": "abc123"}. What is the likely cause?

medium

A. Missing the scroll parameter to keep the scroll context alive.

B. The scroll_id is invalid and must be a number.

C. You cannot use scroll_id in a scroll request.

D. The size parameter is required with scroll_id.

Solution

1. Step 1: Check scroll request requirements

   When continuing a scroll, the scroll parameter (time) must be included to keep context alive.

2. Step 2: Analyze error cause

   Missing the scroll parameter to keep the scroll context alive. correctly identifies missing scroll parameter. The scroll_id is invalid and must be a number. is wrong; scroll_id is a string. You cannot use scroll_id in a scroll request. is false; scroll_id is needed. The size parameter is required with scroll_id. is incorrect; size is not required in scroll continuation.

3. Final Answer:

   Missing the scroll parameter to keep the scroll context alive. -> Option A

4. Quick Check:

   Scroll continuation needs scroll param [OK]

Hint: Always include scroll param with scroll_id [OK]

Common Mistakes:

• Omitting scroll parameter in scroll continuation
• Assuming scroll_id must be numeric
• Thinking size is needed every scroll request

5. You want to retrieve 10,000 documents using the Scroll API. Which approach is best to avoid memory issues and ensure all documents are retrieved?

hard

A. Use the Scroll API but do not specify the scroll parameter to speed up retrieval.

B. Set size to 10,000 in a single search request without scrolling.

C. Fetch documents by IDs one by one using separate queries.

D. Use a scroll time of 1 minute and fetch batches of 100 documents repeatedly until no hits remain.

Solution

1. Step 1: Understand deep pagination with Scroll API

   Scroll API is designed to fetch large results in small batches with a scroll timeout to keep context alive.

2. Step 2: Evaluate options for best practice

   Use a scroll time of 1 minute and fetch batches of 100 documents repeatedly until no hits remain. correctly uses scroll time and batch size to safely retrieve all documents. Set size to 10,000 in a single search request without scrolling. risks memory overload. Use the Scroll API but do not specify the scroll parameter to speed up retrieval. is invalid because scroll param is required. Fetch documents by IDs one by one using separate queries. is inefficient and slow.

3. Final Answer:

   Use a scroll time of 1 minute and fetch batches of 100 documents repeatedly until no hits remain. -> Option D

4. Quick Check:

   Scroll API + batch + scroll time = safe deep pagination [OK]

Hint: Fetch in batches with scroll time to avoid overload [OK]

Common Mistakes:

• Requesting all documents at once causing memory errors
• Omitting scroll parameter to speed up
• Fetching documents individually instead of batches