Scroll API for deep pagination in Elasticsearch - Mini Project: Build & Apply

Scroll API for deep pagination

📖 Scenario: You work with a large collection of documents in Elasticsearch. You want to retrieve all documents matching a query, but the number of results is too big to get in one request. Elasticsearch's Scroll API helps you fetch results in batches, like flipping pages in a book, so you can see all data without missing any.

🎯 Goal: Build a program that uses Elasticsearch's Scroll API to fetch all documents matching a query in batches, handling deep pagination efficiently.

📋 What You'll Learn

Create an initial search request with a scroll parameter

Store the scroll ID returned by Elasticsearch

Use the scroll ID to fetch the next batch of results

Repeat fetching until no more results remain

Print the total number of documents retrieved

💡 Why This Matters

🌍 Real World

When working with very large datasets in Elasticsearch, normal pagination can be inefficient or limited. The Scroll API lets you retrieve all matching documents in manageable batches, like reading pages of a book, without missing any data.

💼 Career

Many data engineer and backend developer roles require handling large search results efficiently. Knowing how to use Elasticsearch's Scroll API is important for building scalable search and analytics applications.

1

Create initial search request with scroll

Write code to send an initial search request to Elasticsearch index products with a match_all query and a scroll time of 1m. Store the response in a variable called response.

Elasticsearch

# Your code here

Hint

Use client.search with scroll='1m' and a match_all query inside body.

2

Store scroll ID and prepare to fetch batches

Extract the _scroll_id from response and store it in a variable called scroll_id. Also, create a list called all_hits and add the hits from response to it.

Elasticsearch

response = client.search(
    index='products',
    scroll='1m',
    body={
        'query': {
            'match_all': {}
        }
    }
)
# Your code here

Hint

Access _scroll_id from response and assign it to scroll_id. Then get hits from response['hits']['hits'] and assign to all_hits.

3

Fetch all batches using scroll ID

Use a while loop to keep fetching batches using client.scroll with scroll_id and scroll='1m'. Update scroll_id with the new scroll ID from each response. Add the hits from each batch to all_hits. Stop when the batch has no hits.

Elasticsearch

response = client.search(
    index='products',
    scroll='1m',
    body={
        'query': {
            'match_all': {}
        }
    }
)
scroll_id = response['_scroll_id']
all_hits = response['hits']['hits']

# Your code here

Hint

Use a while True loop. Inside, call client.scroll with current scroll_id and scroll='1m'. Update scroll_id. Get hits and break if empty. Otherwise, add hits to all_hits.

4

Print total number of documents retrieved

Write a print statement to display the total number of documents retrieved by printing the length of all_hits.

Elasticsearch

response = client.search(
    index='products',
    scroll='1m',
    body={
        'query': {
            'match_all': {}
        }
    }
)
scroll_id = response['_scroll_id']
all_hits = response['hits']['hits']

while True:
    response = client.scroll(scroll_id=scroll_id, scroll='1m')
    scroll_id = response['_scroll_id']
    hits = response['hits']['hits']
    if not hits:
        break
    all_hits.extend(hits)

# Your code here

Hint

Use print(len(all_hits)) to show how many documents were fetched in total.

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