Analyze RT event tables to identify the most common values for stitching keys. Use when inspecting what ID values are actually flowing into RT 2.0, debugging unexpected stitching behavior, or checking for data quality issues like nulls or empty stitching keys.
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This skill is used to inspect the actual data flowing into RT 2.0 event tables and analyze the distribution of stitching key values. It helps debug ID stitching issues by showing the most common values for each configured stitching key, including nulls and empty values that might cause problems. This is what's seen in plazma and may not be different than what the rt system ignores by configuration.
Use this skill when you want to understand:
In order to analyze key values we need:
This skill will:
First, inspect the current RT configuration to understand what to analyze:
# Get RT configuration via CDP API
tdx api "/audiences/<parent_segment_id>/realtime_setting" --type cdp --method GET
# Example response:
# {
# "keyColumns": [
# { "name": "td_client_id" }
# ],
# "eventTables": [
# { "database": "engage_in_app_message", "table": "be_users" }
# ],
# "status": "ok"
# }Based on your RT configuration, generate a query like this example for parent segment 508396:
RT Configuration:
engage_in_app_message.be_userstd_client_id-- Most common values in last 3 hours (including nulls/empties)
SELECT
key_name,
value,
event_count
FROM (
SELECT 'td_client_id' as key_name, td_client_id as value, COUNT(*) as event_count
FROM engage_in_app_message.be_users
WHERE td_interval(time, '-3h/now')
GROUP BY td_client_id
)
ORDER BY event_count DESC
LIMIT 100;1a0845f
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