To apply an event structure you validated with test logs to the actual game, send the event with actual game data right after the player's action is confirmed. Hive Analytics supports both client and server sending, and if required property names or the eventTime format are sent incorrectly, the log is stored in the quarantine table t_raw_event_log_rescue.
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- Sending Game Data as Event Logs: Sending Event Logs, Part 3
- Validating Your Collection Structure and Metrics with Test Logs: Sending Event Logs, Part 2
Sending test logs before developing the event-sending feature lets you check in advance whether events and properties are collected in the intended structure and data types, and whether the collected data can produce the metrics you need. In Hive Analytics, send test logs with ETL Simulation in Data > Event, check the results in Data Preview and BigQuery, and check metric setup in Create.
- Defining Your Log Collection Structure with Event Templates: Sending Event Logs, Part 1
Hive Analytics provides event templates for player actions commonly needed in game analysis. Event names and properties are predefined, so you can define your log collection structure without designing it from scratch. Select an event in Data > Event to see the metrics it supports, edit or add properties in Property Setting, then check the source code for your development environment in Source Generation.
- Grouping Dimension Values by the Criteria You Need
Raw dimension values such as country codes, prices, and levels often need to be regrouped to fit your analysis. Learn how to use Bucket settings in Hive Analytics charts to group text values and split numeric values into manual or equal-width buckets, without changing the raw logs.
- Building the Metrics You Need with Multiple Calculations
In Hive Analytics charts, you apply a primary calculation to events and properties to build a measure, then use a secondary calculation to recalculate the maximum, minimum, average, or latest value of the results at each interval. This lets you build metrics in a chart that a single aggregation can't give you, such as monthly average daily AU or today's peak concurrent users.
