Scroll API for deep pagination in Elasticsearch - Step-by-Step Execution

Concept Flow - Scroll API for deep pagination

Start Scroll Search

↓

Receive initial batch of results + scroll_id

↓

Use scroll_id to request next batch

↓

Receive next batch + updated scroll_id

↓

No more results?

Yes→End Scroll

↩Back to Use scroll_id

The Scroll API starts a search and returns a batch of results with a scroll ID. You use this ID to fetch the next batch repeatedly until no results remain.

Execution Sample

Elasticsearch

POST /_search/scroll
{
  "scroll": "1m",
  "scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAA..."
}

This request uses the scroll ID to get the next batch of search results within the scroll context.

Execution Table

Step	Action	Input	Output	scroll_id	Notes
1	Start scroll search	{"scroll":"1m","size":2,"query":{"match_all":{}}}	Batch 1 results (2 items)	scroll_id_1	Initial search returns first 2 results and scroll_id_1
2	Request next batch	{"scroll":"1m","scroll_id":"scroll_id_1"}	Batch 2 results (2 items)	scroll_id_2	Use scroll_id_1 to get next 2 results and new scroll_id_2
3	Request next batch	{"scroll":"1m","scroll_id":"scroll_id_2"}	Batch 3 results (1 item)	scroll_id_3	Next batch has 1 result, scroll_id_3 returned
4	Request next batch	{"scroll":"1m","scroll_id":"scroll_id_3"}	No results	null	No more results, scroll ends

💡 No more results returned, scroll_id is null, scroll session ends

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4
scroll_id	null	scroll_id_1	scroll_id_2	scroll_id_3	null
results_count	0	2	2	1	0

Key Moments - 3 Insights

Why do we need to keep using the scroll_id for each next request?

Each scroll_id represents the current position in the search results. Using it tells Elasticsearch where to continue fetching results, as shown in execution_table steps 2 and 3.

What happens when the scroll returns no results?

When no results are returned (step 4), it means we reached the end of the data. The scroll session ends and no further requests are needed.

Why do we specify a scroll time like "1m" in each request?

The scroll time keeps the search context alive on the server for that duration. Each request refreshes this timer to avoid losing the scroll session, as seen in all requests.

Visual Quiz - 3 Questions

Look at the execution_table, what is the scroll_id after step 2?

A scroll_id_1

B scroll_id_3

C scroll_id_2

D null

💡 Hint

Check the scroll_id column in execution_table row for step 2

At which step does the scroll API return no results?

A Step 4

B Step 1

C Step 3

D Step 2

💡 Hint

Look at the Output column in execution_table for the step with 'No results'

If the scroll time was not specified in the requests, what would happen?

A Scroll session would stay open indefinitely

B Scroll session might expire too soon and cause errors

C Scroll_id would change automatically

D Results would be returned faster

💡 Hint

Refer to key_moments about the importance of the scroll time parameter

Concept Snapshot

Scroll API lets you fetch large search results in batches.
Start with a search request specifying scroll time and size.
Get a scroll_id with each batch to request the next batch.
Repeat until no results remain.
Always include scroll time to keep the session alive.

Full Transcript

The Scroll API in Elasticsearch helps you get large sets of search results in small pieces. First, you send a search request with a scroll time and batch size. Elasticsearch returns the first batch of results and a scroll_id. You then use this scroll_id in the next request to get the next batch. This process repeats, each time using the new scroll_id returned, until no more results come back. The scroll time keeps the search context alive on the server. When no results are returned, the scroll session ends. This method is useful for deep pagination where normal paging is inefficient.

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