Elasticsearch Scroll

What you are looking for is pagination.

You can achieve your objective by querying for a fixed size and setting the from parameter. Since you want to set display in batches of 250 results, you can set size = 250 and with each consecutive query, increment the value of from by 250.

GET /_search?size=250                     ---- return first 250 results
GET /_search?size=250&from=250            ---- next 250 results 
GET /_search?size=250&from=500            ---- next 250 results

On the contrary, Scan & scroll lets you retrieve a large set of results with a single search and is ideally meant for operations like re-indexing data into a new index. Using it for displaying search results in real-time is not recommended.

To explain Scan & scroll briefly, what it essentially does is that it scans the index for the query provided with the scan request and returns a scroll_id. This scroll_id can be passed to the next scroll request to return the next batch of results.

Consider the following example-

    # Initialize the scroll
    page = es.search(
      index = 'yourIndex',
      doc_type = 'yourType',
      scroll = '2m',
      search_type = 'scan',
      size = 1000,
      body = {
        # Your query's body
        }
    )
    sid = page['_scroll_id']
    scroll_size = page['hits']['total']
      
    # Start scrolling
    while (scroll_size > 0):
      print "Scrolling..."
      page = es.scroll(scroll_id = sid, scroll = '2m')
      # Update the scroll ID
      sid = page['_scroll_id']
      # Get the number of results that we returned in the last scroll
      scroll_size = len(page['hits']['hits'])
      print "scroll size: " + str(scroll_size)
      # Do something with the obtained page

In above example, following events happen-

• Scroller is initialized. This returns the first batch of results along with the scroll_id
• For each subsequent scroll request, the updated scroll_id (received in the previous scroll request) is sent and next batch of results is returned.
• Scroll time is basically the time for which the search context is kept alive. If the next scroll request is not sent within the set timeframe, the search context is lost and results will not be returned. This is why it should not be used for real-time results display for indexes with a huge number of docs.

You are understanding wrong the purpose of the scroll property. It does not mean that elasticsearch will fetch next page data after 30 seconds. When you are doing first scroll request you need to specify when scroll context should be closed. scroll parameter is telling to close scroll context after 30 seconds.

After doing first scroll request you will get back scroll_idparameter in response. For next pages you need to pass that value to get next page of the scroll response. If you will not do the next scroll request within 30 seconds, the scroll request will be closed and you will not be able to get next pages for that scroll request.

What you described as an example use case is actually search results pagination, which is available for any search query and is limited by 10k results. scroll requests are needed for the cases when you need to go over that 10k limit, with scroll query you can fetch even the entire collection of documents.

Probably the source of confusion here is that scroll term is ambiguous: it means the type of a query, and also it is a name of a parameter of such query (as was mentioned in other comments, it is time ES will keep waiting for you to fetch next chunk of scrolling).

scroll queries are heavy, and should be avoided until absolutely necessary. In fact, in the docs it says:

Scrolling is not intended for real time user requests, but rather for processing large amounts of data, ...

Now regarding your another question:

In elasticsearch is it possible to call search API everytime whenever the user scrolls on the result set?

Yes, even several parallel scroll requests are possible:

Each scroll is independent and can be processed in parallel like any scroll request.

The documentation of the Scroll API at elastic explains this behaviour also.

The result size of 10k is a default value and can be overwritten during runtime, if necessary:

PUT { "index" : { "max_result_window" : 500000} }

The life time of the scroll id is defined in each scroll request with the parameter "scroll", e.g.

..
  "scroll" : "5m"
  ..

In recent versions of Elasticsearch, you'll use search_after. The keep_alive you set there, much like the timeout in the scroll, is only the time needed for you to process one page.

That's because Elasticsearch will keep your "search context" alive for that amount of time, then removes it. Also, Elasticsearch won't fetch the next page for you automatically, you'll have to do that by sending requests with the ID from the last request.

It is wise to use the scroll api as one can not get more than 10K data at a time in elasticsearch.