RT Journey Monitoring & Debugging

Monitor RT journey performance, track activation success, and debug failures.

Prerequisites

• RT journeys created and enabled
• Activations configured

Monitor Journeys

List Journeys

# List all RT journeys for parent segment
tdx api "/entities/realtime_journeys?parent_segment_id=<parent_segment_id>" --type cdp

# View journey details
tdx api "/entities/realtime_journeys/<journey_id>" --type cdp

# Check journey status
tdx api "/entities/realtime_journeys/<journey_id>/status" --type cdp

Journey Metrics (API)

# View activation logs
tdx api "/entities/realtime_journeys/<journey_id>/activations" --type cdp

# Check recent triggers
tdx api "/entities/realtime_journeys/<journey_id>/triggers?limit=100" --type cdp

# View errors
tdx api "/entities/realtime_journeys/<journey_id>/errors" --type cdp

Activation Logs Table

Query activation logs directly:

select
  time,
  delivered,
  status,
  activation_name,
  journey_name,
  error,
  response
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and journey_name = 'welcome_new_users'
order by time desc
limit 100

Activation Log Schema

Column	Type	Description
time	int	Event timestamp (epoch)
delivered	string	"true" or "false"
status	long	HTTP status code (200, 404, 500, etc.)
timestamp	long	Processing timestamp
activation_type	string	webhook, salesforce, email
log_time	long	Log entry timestamp
journey_name	string	Journey identifier
journey_stage_name	string	Stage name (if applicable)
activation_name	string	Activation name
rid	string	Request ID
error	string	Error message (if failed)
activation_id	string	Unique activation ID
event_id	string	Event that triggered activation
response	string	Response from destination system

Monitor Activation Success

Success Rate

select
  journey_name,
  count(*) as total_activations,
  count(case when delivered = 'true' then 1 end) as successful,
  count(case when delivered = 'false' then 1 end) as failed,
  round(100.0 * count(case when delivered = 'true' then 1 end) / count(*), 2) as success_rate_pct
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
group by journey_name
order by total_activations desc

Activation Volume

select
  td_time_string(time, 'yyyy-MM-dd HH:00:00') as hour,
  count(*) as activation_count,
  count(case when delivered = 'true' then 1 end) as successful,
  count(case when delivered = 'false' then 1 end) as failed
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and journey_name = 'abandoned_cart'
group by td_time_string(time, 'yyyy-MM-dd HH:00:00')
order by hour

Status Code Distribution

select
  status,
  count(*) as count,
  round(100.0 * count(*) / sum(count(*)) over (), 2) as percentage
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and journey_name = 'welcome_new_users'
group by status
order by count desc

Debug Failures

Error Analysis

select
  error,
  status,
  count(*) as error_count,
  max(time) as last_occurrence
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and delivered = 'false'
group by error, status
order by error_count desc
limit 20

Failed Activation Details

select
  td_time_string(time, 'yyyy-MM-dd HH:mm:ss') as timestamp,
  activation_name,
  error,
  status,
  response,
  event_id
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1h')
  and delivered = 'false'
  and journey_name = 'cart_recovery'
order by time desc
limit 50

Webhook Response Inspection

select
  td_time_string(time, 'yyyy-MM-dd HH:mm:ss') as timestamp,
  status,
  response,
  error
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1h')
  and activation_type = 'td_webhook_out'
  and journey_name = 'product_recommendations'
order by time desc
limit 20

Common Issues & Solutions

Webhook Timeouts

-- Find timeout errors
select
  count(*) as timeout_count,
  activation_name
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and error like '%timeout%'
group by activation_name

Solution: Increase webhook timeout_ms or optimize endpoint response time

Authentication Failures

-- Find auth errors
select
  error,
  count(*) as count
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and (status = 401 or status = 403)
group by error

Solution: Verify API keys and credentials in secrets

Rate Limiting

-- Find rate limit errors
select
  td_time_string(time, 'yyyy-MM-dd HH:mm:ss') as timestamp,
  count(*) as count
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1h')
  and status = 429
group by td_time_string(time, 'yyyy-MM-dd HH:mm:ss')
order by timestamp

Solution: Reduce activation frequency or increase rate limits with destination

Invalid Payload

-- Find validation errors
select
  error,
  response,
  count(*) as count
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and status = 400
group by error, response
order by count desc

Solution: Verify payload template matches destination API requirements

Performance Monitoring

Activation Latency

Track time from event to activation:

select
  journey_name,
  avg(timestamp - time) as avg_latency_seconds,
  min(timestamp - time) as min_latency_seconds,
  max(timestamp - time) as max_latency_seconds
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1h')
group by journey_name

Activation Volume Trends

select
  td_time_string(time, 'yyyy-MM-dd') as date,
  journey_name,
  count(*) as daily_activations
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-7d')
group by td_time_string(time, 'yyyy-MM-dd'), journey_name
order by date, journey_name

Debugging Commands

# Check RT status
tdx ps view <parent_segment_id> --json | jq '.realtime_config.status'

# Verify key event exists
tdx api "/audiences/<parent_segment_id>/realtime_key_events" --type cdp | jq '.[]'

# Test webhook manually
curl -X POST "https://your-webhook.com/endpoint" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "test": "payload",
    "user_id": "test_123"
  }'

# Check journey is enabled
tdx api "/entities/realtime_journeys/<journey_id>" --type cdp | jq '.enabled'

# View journey configuration
tdx api "/entities/realtime_journeys/<journey_id>" --type cdp | jq '.activations'

Activation Success Checklist

✅ Journey enabled: Check journey status is "enabled"
✅ RT status OK: Verify RT config status is "ok"
✅ Events flowing: Confirm events are being ingested
✅ Key event configured: Verify key event exists
✅ Webhook accessible: Test webhook endpoint responds
✅ Credentials valid: Check API keys/secrets are current
✅ Payload valid: Verify payload matches destination format
✅ No rate limits: Check destination rate limits not exceeded

Alert Queries

Set up monitoring alerts:

High Error Rate

-- Alert if error rate > 10%
select
  journey_name,
  count(*) as total,
  count(case when delivered = 'false' then 1 end) as failed,
  round(100.0 * count(case when delivered = 'false' then 1 end) / count(*), 2) as error_rate
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-15m')
group by journey_name
having error_rate > 10

No Recent Activations

-- Alert if no activations in last hour
select
  'welcome_journey' as journey_name,
  max(time) as last_activation_time,
  td_time_format(max(time), 'yyyy-MM-dd HH:mm:ss', 'UTC') as last_activation
from cdp_audience_<parent_segment_id>_rt.activations
where journey_name = 'welcome_journey'
  and td_interval(time, '-24h')
having max(time) < td_time_add(td_scheduled_time(), '-1h')

Specific Error Spike

-- Alert if specific error occurs frequently
select
  error,
  count(*) as count
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-15m')
  and delivered = 'false'
group by error
having count > 10

Performance Optimization

Reduce Activation Volume

-- Find high-volume journeys
select
  journey_name,
  count(*) as activation_count,
  count(distinct event_id) as unique_events
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
group by journey_name
order by activation_count desc

Optimization: Add filters to reduce trigger frequency

Identify Slow Activations

-- Find slowest activations
select
  journey_name,
  activation_name,
  avg(timestamp - time) as avg_latency_sec
from cdp_audience_<parent_segment_id>_rt.activations
where td_interval(time, '-1d')
  and delivered = 'true'
group by journey_name, activation_name
having avg_latency_sec > 60
order by avg_latency_sec desc

Optimization: Optimize webhook endpoint or use async processing

Best Practices

Monitoring

• Regular checks: Review activation logs daily
• Alert setup: Configure alerts for error rates
• Trend analysis: Track activation volume trends
• Response inspection: Review webhook responses

Debugging

• Recent errors first: Start with last hour of failures
• Group by error: Identify common failure patterns
• Check destination: Verify destination system health
• Test manually: Test webhooks outside TD first

Maintenance

• Rotate secrets: Update API keys regularly
• Clean up: Disable unused journeys
• Optimize filters: Refine event filters to reduce volume
• Document issues: Keep log of common problems

Tools & Dashboards

Consider creating:

• Success rate dashboard: Real-time activation success metrics
• Error alerting: Slack/email alerts for failures
• Volume monitoring: Track activation trends
• Latency tracking: Monitor end-to-end latency

Next Steps

After monitoring setup:

• Optimize: Refine filters and targeting
• Scale: Adjust as volume grows
• Document: Document common issues and solutions

Resources

• Activation Logs Documentation
• Monitoring Best Practices
• Troubleshooting Guide