Elasticsearch Extended Stats Bucket Aggregation - Syntax, Example, and Tips

The Extended Stats Bucket Aggregation is a sibling pipeline aggregation that calculates extended statistics over numeric values extracted from the child buckets of a specified metric in a parent bucket aggregation. It provides a comprehensive set of statistical measures, including count, min, max, avg, sum, sum_of_squares, variance, std_deviation, and std_deviation_bounds.

Syntax

{
  "extended_stats_bucket": {
    "buckets_path": "string"
  }
}

For detailed syntax and options, refer to the official Elasticsearch documentation.

Example Usage

{
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        }
      }
    },
    "sales_stats": {
      "extended_stats_bucket": {
        "buckets_path": "sales_per_month>sales"
      }
    }
  }
}

This example calculates extended statistics for monthly sales.

Common Issues

1. Incorrect buckets_path: Ensure the path correctly points to the metric in the parent aggregation.
2. Non-numeric data: The aggregation works only on numeric values.
3. Empty buckets: Consider how to handle buckets with no data.

Best Practices

1. Use extended_stats_bucket when you need a comprehensive statistical overview.
2. Combine with other aggregations for more complex analyses.
3. Consider using gap_policy to handle missing data points.
4. Be mindful of performance impact on large datasets.

Frequently Asked Questions

Q: How does Extended Stats Bucket Aggregation differ from regular Stats Aggregation?
A: Extended Stats Bucket Aggregation is a pipeline aggregation that operates on the results of other aggregations, while regular Stats Aggregation works directly on document fields. Extended Stats also provides additional metrics like sum_of_squares and std_deviation_bounds.

Q: Can I use Extended Stats Bucket Aggregation with non-numeric data?
A: No, Extended Stats Bucket Aggregation only works with numeric data. Attempting to use it with non-numeric data will result in an error.

Q: How can I handle missing values in Extended Stats Bucket Aggregation?
A: You can use the gap_policy parameter to specify how to handle missing values. Options include "skip" (default), "insert_zeros", or using a custom value.

Q: Is there a performance impact when using Extended Stats Bucket Aggregation?
A: While generally efficient, Extended Stats Bucket Aggregation can impact performance on very large datasets or when used in complex nested aggregations. Monitor your cluster's performance and optimize as needed.

Q: Can Extended Stats Bucket Aggregation be used in combination with other aggregations?
A: Yes, it's often used in combination with other aggregations like date_histogram or terms aggregations to provide statistical insights across different dimensions of your data.