Elasticsearch Derivative Aggregation - Syntax, Example, and Tips

The Derivative Aggregation in Elasticsearch calculates the rate of change between consecutive data points in a time series. It's particularly useful for analyzing trends, identifying anomalies, and understanding the velocity of change in metrics over time.

Syntax

{
  "derivative": {
    "buckets_path": "the_sum"
  }
}

For more details, refer to the official Elasticsearch documentation on Derivative Aggregation.

Example Usage

Here's an example that calculates the rate of change in daily sales:

{
  "aggs": {
    "sales_per_day": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "day"
      },
      "aggs": {
        "daily_sales": {
          "sum": { "field": "sales" }
        }
      }
    },
    "sales_derivative": {
      "derivative": {
        "buckets_path": "sales_per_day>daily_sales"
      }
    }
  }
}

Common Issues

1. Missing data points: The derivative aggregation requires consecutive data points. Missing data can lead to inaccurate results.
2. Unit mismatch: Ensure that the units of your time intervals match the units of your metric for meaningful results.
3. Outliers: Extreme values can significantly skew derivative calculations.

Best Practices

1. Use with date_histogram for time-based analysis.
2. Consider using gap_policy to handle missing data points.
3. Normalize your data or use normalize parameter for comparing different scales.
4. Combine with moving averages to smooth out short-term fluctuations.

Frequently Asked Questions

Q: How does the Derivative Aggregation handle gaps in data?
A: By default, it skips gaps. You can use the gap_policy parameter to specify how to handle missing data points, such as inserting zeros or carrying forward the last known value.

Q: Can Derivative Aggregation be used on non-numeric fields?
A: No, Derivative Aggregation is designed for numeric fields only. It calculates the difference between numeric values in consecutive buckets.

Q: How is the derivative calculated for the first data point?
A: The derivative for the first data point is always null because there's no previous point to calculate the difference from.

Q: Can I use Derivative Aggregation with non-time-based data?
A: While it's most commonly used with time-based data, you can use it with any ordered sequence of numeric values. However, interpretation may be less intuitive for non-time-based data.

Q: How can I calculate percentage change instead of absolute change?
A: You can use the normalize parameter in combination with Derivative Aggregation to calculate percentage change. Set normalize to the desired interval (e.g., 1 for percentage).