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How to implement pagination in OpenSearch with Logit.io?
How to implement pagination in OpenSearch with Logit.io?

Learn How to implement pagination in OpenSearch with Logit.io

Eleanor Bennett avatar
Written by Eleanor Bennett
Updated over a week ago

Navigating through large volumes of data in OpenSearch often requires an efficient approach to pagination. This guide demonstrates how to implement pagination in your OpenSearch queries within the Logit.io environment, ensuring you can access and analyze your data effectively, without overwhelming your application or the OpenSearch cluster.

OpenSearch offers two primary methods for pagination, which we will describe in more detail below:

From/Size Pagination: Suitable for small to medium-sized datasets, allowing you to specify the starting point (from) and the number of results (size) to return.

Search After: Recommended for deep pagination over large datasets. It uses a sort key to retrieve results after a certain point, which is more efficient than from/size for large volumes of data.

Step 1: Using From/Size Pagination

The from/size method is straightforward, making it a good starting point for basic pagination needs.

Access the Dev Tools in your OpenSearch Dashboards on Logit.io.

Execute a Paginated Query: Here’s a basic template:

GET /your_index_name/_search
{
  "from": 0,
  "size": 10,
  "query": {
    "match_all": {}
  },
  "sort": [
    { "your_sort_field": { "order": "asc" } }
  ]
}

Replace your_index_name with the name of your index.

The from parameter specifies the starting point (offset), and size is the number of records to return.

Adjust your_sort_field to the field you wish to sort by.

Iterate Over Pages: To retrieve the next set of results, increment the from parameter by the size used in the previous query.

Note: The from/size approach has performance limitations when dealing with large datasets, as it becomes slower and more resource-intensive.

Step 2: Implementing Search After for Deep Pagination

For larger datasets or deeper pagination, search_after is the preferred method.

Specify a Sort Order: Ensure your query has a unique sort order to use as the pagination key.

Initial Query: Start with a query that specifies the sort field(s):

GET /your_index_name/_search
{
  "size": 10,
  "query": {
    "match_all": {}
  },
  "sort": [
    { "timestamp": { "order": "asc" } },
    { "_id": "asc" }
  ]
}

Use the Last Result as the Starting Point: The response will include a sort value for each document. Use the sort value of the last document as the search_after parameter in your next query.

GET /your_index_name/_search
{
  "size": 10,
  "query": {
    "match_all": {}
  },
  "search_after": ["last_doc_timestamp", "last_doc_id"],
  "sort": [
    { "timestamp": { "order": "asc" } },
    { "_id": "asc" }
  ]
}

Replace last_doc_timestamp and last_doc_id with the sort values of the last document from the previous response.

Step 3: Automate and Optimize Pagination

Automate Pagination: Implement logic in your application to automatically adjust the from (for from/size) or search_after parameters based on the user's navigation or API calls.

Optimize Performance: Use filters to narrow down results and reduce the volume of data paginated through, improving response times.

By using from/size for smaller datasets or search_after for larger, more complex datasets, you can efficiently navigate through your data. Remember to tailor your pagination strategy to the size of your dataset and the specific needs of your application for optimal performance. If you have any questions at all about this topic just reach out to a member of Logit.io’s support team for further assistance.

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