Rt Journey Monitor

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

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