Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
Accounts
Apps
Audiences
Calculated Attributes
Data Points
Feeds
Field Transformations
Services
Users
Workspaces
Warehouse Sync API Overview
Warehouse Sync API Tutorial
Warehouse Sync API Reference
Data Mapping
Warehouse Sync SQL Reference
Warehouse Sync Troubleshooting Guide
ComposeID
Warehouse Sync API v2 Migration
Bulk Profile Deletion API Reference
Calculated Attributes Seeding API
Custom Access Roles API
Data Planning API
Group Identity API Reference
Pixel Service
Profile API
Events API
mParticle JSON Schema Reference
IDSync
AMP SDK
Initialization
Configuration
Network Security Configuration
Event Tracking
User Attributes
IDSync
Screen Events
Commerce Events
Location Tracking
Media
Kits
Application State and Session Management
Data Privacy Controls
Error Tracking
Opt Out
Push Notifications
WebView Integration
Logger
Preventing Blocked HTTP Traffic with CNAME
Linting Data Plans
Troubleshooting the Android SDK
API Reference
Upgrade to Version 5
Cordova Plugin
Identity
Direct URL Routing FAQ
Web
Android
iOS
Initialization
Configuration
Event Tracking
User Attributes
IDSync
Screen Tracking
Commerce Events
Location Tracking
Media
Kits
Application State and Session Management
Data Privacy Controls
Error Tracking
Opt Out
Push Notifications
Webview Integration
Upload Frequency
App Extensions
Preventing Blocked HTTP Traffic with CNAME
Linting Data Plans
Troubleshooting iOS SDK
Social Networks
iOS 14 Guide
iOS 15 FAQ
iOS 16 FAQ
iOS 17 FAQ
iOS 18 FAQ
API Reference
Upgrade to Version 7
Getting Started
Identity
Upload Frequency
Getting Started
Opt Out
Initialize the SDK
Event Tracking
Commerce Tracking
Error Tracking
Screen Tracking
Identity
Location Tracking
Session Management
Initialization
Configuration
Content Security Policy
Event Tracking
User Attributes
IDSync
Page View Tracking
Commerce Events
Location Tracking
Media
Kits
Application State and Session Management
Data Privacy Controls
Error Tracking
Opt Out
Custom Logger
Persistence
Native Web Views
Self-Hosting
Multiple Instances
Web SDK via Google Tag Manager
Preventing Blocked HTTP Traffic with CNAME
Facebook Instant Articles
Troubleshooting the Web SDK
Browser Compatibility
Linting Data Plans
API Reference
Upgrade to Version 2 of the SDK
Getting Started
Identity
Web
Alexa
Node SDK
Go SDK
Python SDK
Ruby SDK
Java SDK
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
Introduction
Outbound Integrations
Firehose Java SDK
Inbound Integrations
Compose ID
Data Hosting Locations
Glossary
Rules Developer Guide
API Credential Management
The Developer's Guided Journey to mParticle
Create an Input
Start capturing data
Connect an Event Output
Create an Audience
Connect an Audience Output
Transform and Enhance Your Data
The new mParticle Experience
The Overview Map
Introduction
Data Retention
Connections
Activity
Live Stream
Data Filter
Rules
Tiered Events
mParticle Users and Roles
Analytics Free Trial
Troubleshooting mParticle
Usage metering for value-based pricing (VBP)
Introduction
Sync and Activate Analytics User Segments in mParticle
User Segment Activation
Welcome Page Announcements
Project Settings
Roles and Teammates
Organization Settings
Global Project Filters
Portfolio Analytics
Analytics Data Manager Overview
Events
Event Properties
User Properties
Revenue Mapping
Export Data
UTM Guide
Data Dictionary
Query Builder Overview
Modify Filters With And/Or Clauses
Query-time Sampling
Query Notes
Filter Where Clauses
Event vs. User Properties
Group By Clauses
Annotations
Cross-tool Compatibility
Apply All for Filter Where Clauses
Date Range and Time Settings Overview
Understanding the Screen View Event
Analyses Introduction
Getting Started
Visualization Options
For Clauses
Date Range and Time Settings
Calculator
Numerical Settings
Assisted Analysis
Properties Explorer
Frequency in Segmentation
Trends in Segmentation
Did [not] Perform Clauses
Cumulative vs. Non-Cumulative Analysis in Segmentation
Total Count of vs. Users Who Performed
Save Your Segmentation Analysis
Export Results in Segmentation
Explore Users from Segmentation
Getting Started with Funnels
Group By Settings
Conversion Window
Tracking Properties
Date Range and Time Settings
Visualization Options
Interpreting a Funnel Analysis
Group By
Filters
Conversion over Time
Conversion Order
Trends
Funnel Direction
Multi-path Funnels
Analyze as Cohort from Funnel
Save a Funnel Analysis
Explore Users from a Funnel
Export Results from a Funnel
Saved Analyses
Manage Analyses in Dashboards
Dashboards––Getting Started
Manage Dashboards
Dashboard Filters
Organize Dashboards
Scheduled Reports
Favorites
Time and Interval Settings in Dashboards
Query Notes in Dashboards
User Aliasing
The Demo Environment
Keyboard Shortcuts
Analytics for Marketers
Analytics for Product Managers
Compare Conversion Across Acquisition Sources
Analyze Product Feature Usage
Identify Points of User Friction
Time-based Subscription Analysis
Dashboard Tips and Tricks
Understand Product Stickiness
Optimize User Flow with A/B Testing
User Segments
IDSync Overview
Use Cases for IDSync
Components of IDSync
Store and Organize User Data
Identify Users
Default IDSync Configuration
Profile Conversion Strategy
Profile Link Strategy
Profile Isolation Strategy
Best Match Strategy
Aliasing
Overview
Create and Manage Group Definitions
Introduction
Catalog
Live Stream
Data Plans
Blocked Data Backfill Guide
Predictive Attributes Overview
Create Predictive Attributes
Assess and Troubleshoot Predictions
Use Predictive Attributes in Campaigns
Predictive Audiences Overview
Using Predictive Audiences
Introduction
Profiles
Warehouse Sync
Data Privacy Controls
Data Subject Requests
Default Service Limits
Feeds
Cross-Account Audience Sharing
Approved Sub-Processors
Import Data with CSV Files
CSV File Reference
Glossary
Video Index
Single Sign-On (SSO)
Setup Examples
Introduction
Introduction
Introduction
Rudderstack
Google Tag Manager
Segment
Advanced Data Warehouse Settings
AWS Kinesis (Snowplow)
AWS Redshift (Define Your Own Schema)
AWS S3 (Snowplow Schema)
AWS S3 Integration (Define Your Own Schema)
BigQuery (Snowplow Schema)
BigQuery Firebase Schema
BigQuery (Define Your Own Schema)
GCP BigQuery Export
Snowflake (Snowplow Schema)
Snowplow Schema Overview
Snowflake (Define Your Own Schema)
Aliasing
mParticle’s Rules are JavaScript functions that manipulate an incoming batch object from an mParticle input. See mParticle’s Platform Guide for help setting up rules in the mParticle dashboard.
Rules take the form of an AWS Lambda function, running in Node.js 16. The function takes an incoming batch
argument to be manipulated and a context
argument containing immutable metadata. The context
argument is required, but for an mParticle rule is effectively null
.
The callback
takes a message and a data output. For mParticle Rules, the message will always be null
. The output should either be an object in the same format as the original batch
argument, or null
.
exports.handler=(batch,context,callback)=>{
//Do something with batch
callback(null, batch); // or callback(null, null); to drop the batch completely
}
Note that standard AWS Lambda naming convention calls for
(event,context,callback)
; here we have used(batch,context,callback)
to avoid confusion with mParticle’s event object.
While all rules have the same basic syntax, there are two main use cases for rules:
There are two basic kinds of non-event rules:
The first is a simple ‘filter’. Based on some attribute/s of the batch, the callback either contains the original batch object, unaltered, or null
, effectively dropping the batch, altogether:
exports.handler=(batch,context,callback)=>{
/*
A support feed contains batches from internal and external users.
We can create a rule to drop batches from internal users.
*/
if(batch.user_attributes.internal) {
callback(null, null);
}
else{
callback(null, batch);
}
};
Alternatively, the callback can contain a modified version of the original batch, with some attributes added, changed or dropped.
exports.handler=(batch,context,callback)=>{
/*
An input has firstname and lastname attributes for a user.
Our output platform expects a full name, so we can use a rule to create one.
*/
if(batch.user_attributes.$firstname && batch.user_attributes.$lastname) {
var firstname = batch.user_attributes.$firstname;
var lastname = batch.user_attributes.$lastname;
batch.user_attributes.name = `${firstname} ${lastname}`;
}
callback(null, batch);
};
If you want to update a user attribute via a rule and that rule is connected to a platform input (Android, iOS, Web, Roku, etc.), you must use user attribute update events in the rule.
The following example of an inbound rule adds a new user attribute to the user_attributes
JSON in the event batch. Because the data is sent via platform input (SDK), the example also includes a user attribute change (UAC) event in the event batch for the new user attributes being set.
// User attribute added to user_attributes
batch.user_attributes["foo"] = "this is the new value";
// UAC event added to events
batch.events.push(
{
"data":{
"user_attribute_name":"foo",
"new":"this is the new value",
"old":"this is an old value", //this is optional
"deleted":false,
"is_new_attribute":true
},
"event_type":"user_attribute_change"
}
);
If the UAC event isn’t present in SDK batches, the user attribute shows up in Live Stream, in event batches in the User Activity view, and possibly downstream, but it won’t be written to the user’s mParticle profile. Thus, it won’t be usable for audiences or journeys.
UAC events aren’t required for server-to-server data. An SDK uploads an event whenever a user attribute changes to denote new attributes, changing attributes, and removed attributes. This allows for calculation of the current user attribute state for each event within an mParticle upload.
The batch object contains an events
array, which can have any number of events. If you want to handle each event individually, you will need to define a handler function and use it to iterate over the events
array.
exports.handler=(batch,context,callback)=>{
/*
An input sends events with the event name 'Signup'.
We can create a rule to change it to 'subscribe', to tailor it to a specific Output service.
*/
function event_handler(event) {
if (event.data.event_name === 'Signup') {
event.data.event_name = 'subscribe';
}
return event;
}
var events = batch.events;
var newEvents = [];
events.forEach(function(currentEvent){
try {
newEvents.push(event_handler(currentEvent));
}
catch(err){ }
});
batch.events = newEvents;
callback(null, batch);
};
Rules can be applied in two places. ‘All Output’ rules are applied first, and their output is passed along to all Output services connected to that input. ‘Specific Output’ Rules are applied as part of a particular Input/Output connection and affect only that Output service.
In most ways the two types of rules operate in the same way. Both take the same arguments and return a batch
object in the same format. However, different fields can be accessed and altered in ‘All Outputs’ and ‘Specific Outputs’ rules. See Batch format for details.
When creating a rule in the mParticle dashboard, you must select a Failure Action. This determines what will happen if your rule throws an unhandled exception.
Discard
, an unhandled exception will cause your rule return null
, effectively dropping the batch.Proceed
, an unhandled exception will cause your rule to return the unaltered batch object, proceeding as if the rule had not been applied.Regardless of which option you choose, it’s best practice to handle all exceptions in your code, rather than falling back on the above defaults. This is especially true if your rule deals with events, where an unhandled exception from just one event could lead to all events in the batch being dropped.
See the main mParticle docs for full JSON batch examples. There are a few limitations on what is available and what can be changed in Rules:
Limitations applying to all Rules:
batch.batch_id
) and for each event (event.event_id
) cannot be altered.null
in the events array will cause a serialization error. If you want to drop individual events, make sure your handler does not push null
to the event array.Limitations applying only to ‘All Outputs’ Rules:
batch.batch_id
) and for each event (event.event_id
) will not be populated.batch.ip
) cannot be accessed in a rule. If a value is set for batch.ip
it will be accepted only if it is a valid IP address.batch.application_info
) object cannot be accessed or altered.batch.deleted_user_attributes
) will not be applied to the user profile.AWS supports multiple Node runtime versions. If using Node for your Rule, be sure to select a runtime that supports the Javascript features you want to use.
To control costs, you can combine multiple transformations into a single rule.
The Batch object is deeply nested. Be careful when accessing nested keys, as the rule will error if one of the parent keys does not exist. Likewise, different event types have different properties. If part of your rule only applies to a specific event type, wrap it in a conditional statement.
/*
If applied to an event that is not a Commerce event,
this condition will cause an error and prevent
the entire batch from being processed.
*/
if (event.data.product_action.action === "add_to_cart") {
//do something
}
/*
You can add a condition to only apply this part of
the rule to Commerce events.
*/
if (event.event_type === "commerce_event") {
if (event.data.product_action.action === "add_to_cart") {
//do something
}
}
/*
The above rule will still fail if the commerce event does
not contain a product action. To be safer, check each nested key.
*/
if (event.event_type === "commerce_event") {
if (event.data && event.data.product_action && event.data.product_action.action === "add_to_cart") {
//do something
}
}
While the Rules UI supports two basic error handling mechanisms (either dropping the batch or forwarding it as is), it’s preferable for your rule to handle errors wherever possible to avoid unexpected behavior. If your rule applies multiple transformations to a batch, wrap each section in a try...catch
statement so that an error in one part of your rule doesn’t stop other parts from being applied.
/*
This event handler function has two parts to deal with commerce events and custom events.
By using try...catch, we ensure that, if one part of the rule causes an unforseen error,
the other part can still be applied.
*/
function event_handler(event) {
try {
if (event.event_type === "commerce_event") {
if (event.data && event.data.product_action && event.data.product_action.action === "add_to_cart") {
//do something
}
}
}
catch (error) {
// handle error
}
try {
if (event.event_type === "custom_event") {
if (event.data && event.data.event_name && event.data.event_name === "level_up") {
event.data.event_name = "increase_level";
}
}
}
catch (error) {
// handle error
}
return event;
}
Converts event names from an input platform still using an older version of those names.
exports.handler = (batch, context, callback) => {
// Define mapping of legacy names to new names
const mappings = {
"legacy_name1": "new_name1",
"legacy_name2": "new_name2",
"legacy_name3": "new_name3"
};
// If the event name is in mappings, update it to the new name
function rename_test_events(eventItem, mappings) {
const keys = Object.keys(mappings);
if (keys.indexOf(eventItem.data.event_name) != -1) {
eventItem.data.event_name = mappings[eventItem.data.event_name];
}
}
// Create updated events array
const newEvents = []
batch.events.forEach(item => {
try {
rename_test_events(item, mappings);
newEvents.push(item)
}
catch (err) { } // if an error occurs, exclude the event from the updated events array
});
// Replace original events with updated events
batch.events = newEvents;
callback(null, batch);
};
Apply multiple transformations to events and user attributes within a single rule.
exports.handler = (batch, context, callback) => {
// Change "United States" to "USA"
function changeCountryName(data) {
if (!data.user_attributes || !data.user_attributes.$Country) {
return;
}
const countryLower = data.user_attributes.$Country.toLowerCase();
if (countryLower === 'united states' || countryLower === 'united states of america') {
data.user_attributes.$Country = 'USA';
}
}
// If an event name is received as "Play Video", change it to "Viewed Video"
function rename_test_events(eventItem) {
if (eventItem.data.event_name === 'Play Video') {
eventItem.data.event_name = 'Viewed Video';
}
}
// Create an attribute to record speed in seconds based on an existing "timing" attribute
function create_speed_attribute(eventItem) {
if (eventItem.data.custom_attributes && eventItem.data.custom_attributes.timing) {
eventItem.data.custom_attributes.speed = eventItem.data.custom_attributes.timing / 1000;
}
}
// Update events
newEvents = [];
batch.events.forEach(item => {
// Basic error handling is included here, to avoid dropping or passing through
// a whole batch due to errors processing one event.
try {
rename_test_events(item);
create_speed_attribute(item);
newEvents.push(item);
}
catch (err) { }
});
batch.events = newEvents;
// Update batch
changeCountryName(batch);
callback(null, batch);
};
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