Transformation examples

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You can transform your CloudEvents data by writing transformation expressions using CEL. For more information, see Transform received events.

The following are some common use cases and examples that show you how to write CEL expressions to transform your event data.

Note: To simplify the examples, required CloudEvents context attributes have been omitted.

Standard use cases

The following are some standard use cases when transforming event data.

Data normalization

You need to flatten a nested data structure in your event message to allow for easier processing by a downstream service.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "orderId": "12345",
    "customer": {
      "firstName": "Alex",
      "lastName": "Taylor",
      "address": {
        "street": "1800 Amphibious Blvd.",
        "city": "Mountain View"
      }
    }
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "orderId": "12345",
    "customerFirstName": "Alex",
    "customerLastName": "Taylor",
    "customerStreet": "1800 Amphibious Blvd.",
    "customerCity": "Mountain View"
  }
}

Solution 1:

Format the output data manually. This lets you list the field names and pick only the elements needed in the output. This is a reasonable approach when the input is predictable and when the number of fields is low. For example:

message.setField("data",
{
  "orderId": message.data.orderId,
  "customerFirstName": message.data.customer.firstName,
  "customerLastName": message.data.customer.lastName,
  "customerStreet": message.data.customer.address.street,
  "customerCity": message.data.customer.address.city,
})

Solution 2:

Use a function in your expression. The denormalize function flattens deep structures to a list of key and value pairs. Field names are delimited using a period (.) to segment the structure hierarchy. For example:

message.setField("data", message.data.denormalize())

This results in the following output which differs slightly from the expected payload. However, the advantages include a shorter CEL expression which operates on any input, and which automatically includes any number of incoming fields.

{
  "data": {
    "orderId": "12345",
    "customer.firstName": "Alex",
    "customer.lastName": "Taylor",
    "customer.address.street": "1800 Amphibious Blvd.",
    "customer.address.city": "Mountain View"
  }
}

Data masking

You need to mask sensitive data in an event payload before it's sent on to a less secure environment.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "userId": "user123",
    "email": "[e-mail]",
    "creditCardNumber": "1234-5678-9012-3456"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "userId": "user123",
    "email": "a***@example.com",
    "creditCardNumber": "xxxx-xxxx-xxxx-3456"
  }
}

Solution:

Use an expression to mask any sensitive information such as the email address and credit card number. For example:

message
      .setField("data.email",
          re.extract(message.data.email,
                    "(^.).*@(.*)",
                    "\\1***@\\2"))

      .setField("data.creditCardNumber",
          re.extract(message.data.creditCardNumber,
                    "(\\d{4})\\D*$",
                    "xxxx-xxxx-xxxx-\\1"))

Data redaction

You need to remove specific fields from an event payload based upon certain conditions.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "orderId": "12345",
    "customerType": "gold",
    "discountCode": "VIP"
  }
}

You want to write a CEL expression that results in the following output:

{
  {
  "orderId": "12345",
  "customerType": "gold"
  }
}

Solution:

Use an expression that redacts the discountCode field if the customerType is "gold". For example:

message.data.customerType == "gold" ?
      message.removeFields(["data.discountCode"]) :
      message

Data conversion

You need to convert data from one format or type to another.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "orderDate": "2024-10-31T12:00:00Z",
    "totalAmount": "1500"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "orderDate": 1704086400,
    "totalAmount": 1500.00
  }
}

Solution:

Use an expression which converts orderDate to a UNIX timestamp, and the totalAmount type from a string to a double (floating-point number). For example:

message
      .setField("data.orderDate", int(timestamp(message.data.orderDate)))
      .setField("data.totalAmount", double(message.data.totalAmount))

Conditional routing

You need to route events to different destinations based on the event data.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "eventType": "order.created",
    "orderValue": 200
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "eventType": "order.created",
    "orderValue": 200,
    "routingKey": "highValue"
  }
}

Solution:

Use an expression that adds a routingKey field with a "highValue" if the orderValue is greater than 100; otherwise, "normal". The routingKey field can be used to determine the routing path. For example:

message.data.orderValue > 100 ?
      message.setField("data.routingKey", "highValue") :
      message.setField("data.routingKey", "normal")

Default value handling

You need to ensure that certain fields in the event payload have default values if they are not present.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "itemName": "Product A"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "itemName": "Product A",
    "quantity": 1
  }
}

Solution:

Use an expression that adds a quantity field with a default value of 1 if the field doesn't already exist. For example:

has(message.data.quantity)  ?
    message :
    message.setField("data.quantity", 1)

String manipulation

You need to extract or modify parts of a string field in the event data.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "customerEmail": "[e-mail]"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "customerEmail": "[e-mail]",
    "emailDomain": "example.com"
  }
}

Solution:

Use an expression that extracts the domain name ("example.com") from the customerEmail field and stores it in a new emailDomain field. For example:

message
  .setField("data.emailDomain",
re.extract(message.data.customerEmail, "(^.*@)(.*)", "\\2"))

List and map operations

You need to work with lists or maps in the event data.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "productIds": [
      "product123",
      "product456"
    ]
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "productIds": [
      "product123",
      "product456"
    ],
    "productFound": true
  }
}

Solution:

Use an expression that checks if "product456" exists in the productIds list and stores the result (true or false) in a new productFound field. For example:

message.setField("data.productFound",
        message.data.productIds.exists(id, id == "product123"))

Error handling

You need to gracefully handle potential errors or unexpected data in the event payload.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "quantity": "abc"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "quantity": 0,
    "error": "Invalid quantity"
  }
}

Solution:

Use an expression that attempts to convert the quantity field to an integer. If the conversion fails, set the quantity field to 0, and add a new error field with the value "Invalid quantity". For example:

// Check if data.quantity exists
has(message.data.quantity) &&
// Check if data.quantity is a string
type(message.data.quantity) == string &&
// Check if string consists of digits
message.data.quantity.matches(r'^-?[0-9]+$') ?
  // If data.quantity is valid, use message
  message :
  // If data.quantity is invalid, set to 0 and generate error
  message
    .setField("data.quantity", 0)
    .setField("data.error", "Invalid quantity")

Complex use cases

The following are some complex use cases when transforming event data.

Data transformation

You need to perform multiple transformations on nested event data.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "orderId": "12345",
    "customer": {
      "firstName": "Alex",
      "lastName": "Taylor",
      "email": "[e-mail]",
      "address": {
        "street": "1800 Amphibious Blvd.",
        "city": "Mountain View",
        "state": "CA"
      }
    },
    "items": [
      {
        "itemId": "item1",
        "price": 10.00,
        "quantity": 2
      },
      {
        "itemId": "item2",
        "price": 5.00,
        "quantity": 1
      }
    ]
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "orderId": "12345",
    "customer.firstName": "Alex",
    "customer.lastName": "Taylor",
    "customer.email": "a***@example.com",
    "customer.address.city": "Mountain View",
    "customer.address.state": "CA"
  }
}

Solution:

Use an expression that extracts the city and state from the address, and that masks the email address. For example:

message
.setField("data",
  message.data.setField("customer.address",
    message.data.customer.address.map(key, key == "city" || key == "state",
          { key: message.data.customer.address[key] }).toMap())
  .setField("customer.email",
        re.extract(message.data.customer.email, "(^..?).*@(.*)", "\\1***@\\2"))
  .removeFields(["items"])
  .denormalize()
)

Data formatting and routing

You need to format event data, add product information, and then route the event message.

Scenario:

Given the following CloudEvents data:

{
  "data": {
    "productId": "p123",
    "productName": "Example Product",
    "category": "electronics"
  }
}

You want to write a CEL expression that results in the following output:

{
  "data": {
    "productId": "electronics-p123",
    "productName": "EXAMPLE PRODUCT",
    "category": "electronics",
    "routingKey": "electronics"
  }
}

Solution:

Use an expression that formats the product name to uppercase, adds a prefix to the product ID based on its category, and includes a routing key for downstream processing. For example:

message
.setField("data.productId",
message.data.category + "-" + message.data.productId)
.setField("data.productName", message.data.productName.upperAscii())
.setField("data.routingKey", message.data.category)