The ThirdEye drift operators are used to measure population drift across each value of a dimension.

Thirdeye has two drift operators:

  1. Static Drift Operator
  2. Lookback Drift Operator

The output of the dimension drift operators can be passed into any anomaly detector.

​
Sample alert configuration

How it works (video)

{
  "name": "USStoreSales-drift-lookback-dx",
  "description": "",
  "template": {
    "name": "startree-drift-lookback-threshold-dx"
  },
  "templateProperties": {
    "dataSource": "mypinot",
    "dataset": "USStoreSalesOrderData",
    "aggregationColumn": "*",
    "aggregationFunction": "COUNT",
    "seasonalityPeriod": "P7D",
    "lookback": "P28D",
    "monitoringGranularity": "P1D",
    "sensitivity": "3",
    "bucketSize": "${bucketSize}",
    "bucketCount": "${bucketCount}",
    "bucketStart": "${bucketStart}",
    "encoder": "${encoder}",
    "max": "${max}",
    "min": "0",
    "enumerationItems": [
      {
        "name": "DiscountApplied",
        "params": {
          "dimension": "DiscountApplied",
          "encoder": "LINEAR_BUCKETS",
          "bucketSize": 0.05,
          "bucketCount": 10,
          "bucketStart": 0,
          "max": 0.5
        }
      },
      {
        "name": "UnitPrice",
        "params": {
          "dimension": "UnitPrice",
          "encoder": "LINEAR_BUCKETS",
          "bucketSize": 50,
          "bucketCount": 120,
          "bucketStart": 0,
          "max": 0.5
        }
      },
      {
        "name": "UnitCost",
        "params": {
          "dimension": "UnitCost",
          "encoder": "LINEAR_BUCKETS",
          "bucketSize": 50,
          "bucketCount": 120,
          "bucketStart": 0,
          "max": 0.5
        }
      },
      {
        "name": "OrderQuantity",
        "params": {
          "dimension": "OrderQuantity",
          "encoder": "LINEAR_BUCKETS",
          "bucketSize": 1,
          "bucketCount": 10,
          "bucketStart": 0,
          "max": 0.7
        }
      },
      {
        "name": "SalesChannel",
        "params": {
          "dimension": "SalesChannel",
          "encoder": "ENUMERATE",
          "bucketSize": 0,
          "bucketCount": 0,
          "bucketStart": 0,
          "max": 0.5
        }
      },
      {
        "name": "StoreLocation",
        "params": {
          "dimension": "StoreLocation",
          "encoder": "ENUMERATE",
          "bucketSize": 0,
          "bucketCount": 0,
          "bucketStart": 0,
          "max": 0.9
        }
      },
      {
        "name": "SalesRegion",
        "params": {
          "dimension": "SalesRegion",
          "encoder": "ENUMERATE",
          "bucketSize": 0,
          "bucketCount": 0,
          "bucketStart": 0,
          "max": 0.5
        }
      },
      {
        "name": "WarehouseCode",
        "params": {
          "dimension": "WarehouseCode",
          "encoder": "ENUMERATE",
          "bucketSize": 0,
          "bucketCount": 0,
          "bucketStart": 0,
          "max": 0.5
        }
      }
    ],
    "dimension": "${dimension}",
    "dayHoursOfWeek": {}
  },
  "cron": "0 0 5 ? * MON-FRI *",
}

​
How does this work?

​
What it does?

  • Detects Changes in Data: This operator is like a microscope for your data. It looks for shifts or unusual patterns in specific parts of your data (dimensions). For example, imagine you’re tracking website traffic by country – this operator could alert you if there’s a sudden spike in visitors from a new location.
  • Compares to the Past: The “lookback” part means it compares your current data to a historical snapshot. This helps it figure out what’s normal and what’s an unexpected change.
  • Gives You a “Drift Score”: The operator calculates how different your current data is from the past. A higher score means a potentially significant change.

​
How it Works (simplified)?

  • Choose Your Focus: You tell the operator which dimension to watch (e.g., country of website visitors, product category, customer age group).
  • Lookback Window: You set how far back in time it should compare (e.g., last week, last month).
  • Calculates Differences: It compares the distribution of values in your current data against the historical data. Is the mix of countries, product types, or ages roughly the same?
  • Assigns “Drift Score”: Based on the calculated difference, it gives you a score to indicate the level of change or “drift”.

​
Why This Matters?

  • Spotting problems early: If a critical part of your data suddenly changes, it could signal a problem (website outage in a certain country, a product becoming unexpectedly popular) or a new opportunity.
  • Keeping track of trends: It helps you understand if your data is gradually shifting over time, even if the changes aren’t dramatic at any one moment.