Конвейерные агрегации

Welcome to Pipeline Aggregations! So far, we've learned about metric and bucket aggregations.

Pipeline aggregations take things a step further. They operate on the results of other aggregations, allowing you to chain them together for more sophisticated analytics.

Think of basic aggregations as giving you statistics within each bucket (e.g., total sales per product category).

Pipeline aggregations, on the other hand, allow you to calculate statistics across those buckets or over time. For example, finding the overall total of 'total sales per category'.

The core idea is chaining. A pipeline aggregation receives its input from another aggregation's output.

• It processes the results of 'sibling' or 'parent' aggregations.
• This enables multi-stage analysis, where one calculation feeds into the next.

One common pipeline aggregation is sum_bucket. It calculates the sum of a specified metric across all sibling buckets.

Use case: You've grouped sales by product category and calculated the total sales for each category. Now you want to find the grand total sales across all categories.

This example first gets total sales per category, then uses sum_bucket to sum those category totals into an overall total. You can send this JSON to Elasticsearch using curl -XGET 'localhost:9200/your_index/_search?pretty' -H 'Content-Type: application/json' -d'...'

Similar to sum_bucket, the avg_bucket aggregation calculates the average of a specific metric across all sibling buckets.

Use case: After getting the total sales for each product category, you might want to know the average total sales amount *per category*.

Here, we use avg_bucket to calculate the average of the 'total sales per category' values. Notice the buckets_path remains the same, pointing to the aggregation whose results we want to average.

Elasticsearch also provides min_bucket and max_bucket pipeline aggregations.

• min_bucket: Finds the minimum value among the specified metric from all sibling buckets.
• max_bucket: Finds the maximum value among the specified metric from all sibling buckets.

These work just like sum_bucket and avg_bucket, simply changing the operation.

The buckets_path parameter is crucial for pipeline aggregations. It tells Elasticsearch which aggregation's output to use as input.

• It uses a > separator for nested aggregations (e.g., parent_agg>child_agg).
• You can also reference the document count of a bucket using _count (e.g., sales_by_category>_count).

Let's check your understanding of pipeline aggregations!

Great job! In this lesson, you learned about:

• What pipeline aggregations are and why they're powerful.
• How they allow you to perform calculations across the results of other aggregations.
• Key types like sum_bucket, avg_bucket, min_bucket, and max_bucket.
• The importance of the buckets_path parameter for referencing aggregation outputs.

Pipeline aggregations enable a deeper level of data analysis in Elasticsearch!