Monitoring and Observability Specification
Overview
The Monitoring and Observability system provides comprehensive visibility into pipeline execution, performance, and health. This specification defines the architecture for collecting, processing, analyzing, and visualizing metrics, logs, traces, and events across the entire pipeline ecosystem.
Core Principles
1. Observability Pillars
observability_pillars:
metrics:
definition: "Numerical measurements over time"
examples:
- execution_duration
- token_usage
- error_rates
- resource_consumption
characteristics:
- aggregatable
- time_series_based
- low_cardinality
- efficient_storage
logs:
definition: "Discrete events with detailed context"
examples:
- execution_logs
- error_messages
- audit_trails
- debug_information
characteristics:
- high_detail
- searchable
- structured_format
- contextual
traces:
definition: "Request flow through distributed systems"
examples:
- pipeline_execution_flow
- component_interactions
- dependency_chains
- performance_bottlenecks
characteristics:
- distributed_context
- causal_relationships
- latency_analysis
- dependency_mapping
events:
definition: "Significant state changes or occurrences"
examples:
- pipeline_started
- component_failed
- threshold_exceeded
- configuration_changed
characteristics:
- point_in_time
- business_relevant
- actionable
- correlatable
Architecture
1. Collection Layer
collection_layer:
agents:
pipeline_agent:
type: "embedded"
responsibilities:
- metric_collection
- log_forwarding
- trace_generation
- event_emission
features:
auto_instrumentation: true
sampling_support: true
buffering: true
compression: true
protocols:
- otlp # OpenTelemetry Protocol
- prometheus
- statsd
- fluentd
infrastructure_agent:
type: "standalone"
targets:
- compute_resources
- storage_systems
- network_components
- external_services
collection_methods:
- pull_based # Prometheus style
- push_based # Telegraf style
- streaming # Kafka/Kinesis
collection_strategies:
metrics:
interval: "15s"
retention_raw: "7d"
aggregation_levels:
- "1m": "30d"
- "5m": "90d"
- "1h": "1y"
- "1d": "5y"
logs:
sampling:
error_logs: "100%"
warning_logs: "100%"
info_logs: "10%"
debug_logs: "1%"
batching:
size: "1MB"
timeout: "5s"
traces:
sampling_strategy:
type: "adaptive"
base_rate: 0.1
error_rate: 1.0
slow_request_rate: 1.0
rate_limiting: 100 # per second
2. Processing Layer
processing_layer:
stream_processing:
engine: "apache_flink | spark_streaming"
pipelines:
metric_aggregation:
operations:
- deduplication
- aggregation
- anomaly_detection
- threshold_checking
windowing:
- tumbling: "1m, 5m, 1h"
- sliding: "5m/1m, 1h/5m"
- session: "inactivity_30m"
log_enrichment:
operations:
- parsing
- field_extraction
- correlation_id_injection
- contextual_enrichment
- pii_masking
enrichment_sources:
- service_registry
- user_database
- configuration_store
trace_analysis:
operations:
- span_correlation
- service_dependency_mapping
- critical_path_analysis
- error_propagation_tracking
derived_metrics:
- service_latency
- dependency_health
- error_rates
- throughput
data_transformation:
schemas:
metric_schema:
name: string
value: float
timestamp: timestamp
labels: map<string, string>
unit: string
log_schema:
timestamp: timestamp
level: enum
message: string
context: map<string, any>
trace_id: string
span_id: string
trace_schema:
trace_id: string
spans: array<span>
service_map: object
critical_path: array<span_id>
3. Storage Layer
storage_layer:
time_series_database:
technology: "prometheus | influxdb | timescaledb"
configuration:
retention_policies:
hot_storage: "7d"
warm_storage: "30d"
cold_storage: "1y"
compaction:
enabled: true
levels: [2h, 1d, 1w]
replication:
factor: 3
consistency: "quorum"
log_storage:
technology: "elasticsearch | loki | cloudwatch"
configuration:
index_strategy:
pattern: "logs-{pipeline}-{date}"
shards: 5
replicas: 1
lifecycle_management:
hot_phase: "7d"
warm_phase: "30d"
delete_phase: "90d"
trace_storage:
technology: "jaeger | tempo | x-ray"
configuration:
sampling_storage: true
adaptive_sampling: true
retention: "72h"
object_storage:
technology: "s3 | gcs | azure_blob"
usage:
- long_term_archive
- large_payload_storage
- backup_destination
- report_storage
Metrics Framework
1. Pipeline Metrics
pipeline_metrics:
execution_metrics:
pipeline_duration:
type: histogram
unit: milliseconds
labels: [pipeline_name, version, environment]
buckets: [100, 250, 500, 1000, 2500, 5000, 10000]
pipeline_status:
type: counter
labels: [pipeline_name, status, error_type]
states: [started, completed, failed, timeout]
active_pipelines:
type: gauge
labels: [pipeline_name, environment]
description: "Currently executing pipelines"
component_metrics:
component_duration:
type: histogram
unit: milliseconds
labels: [component_name, component_type, pipeline_name]
component_errors:
type: counter
labels: [component_name, error_type, severity]
component_retries:
type: counter
labels: [component_name, retry_reason]
resource_metrics:
memory_usage:
type: gauge
unit: bytes
labels: [pipeline_name, component_name]
cpu_usage:
type: gauge
unit: percentage
labels: [pipeline_name, component_name]
io_operations:
type: counter
labels: [operation_type, pipeline_name]
business_metrics:
tokens_consumed:
type: counter
labels: [provider, model, pipeline_name]
api_calls:
type: counter
labels: [api_name, endpoint, pipeline_name]
cost_estimate:
type: gauge
unit: dollars
labels: [cost_category, pipeline_name]
2. SLI/SLO Framework
sli_slo_framework:
sli_definitions:
availability:
definition: "Percentage of successful pipeline executions"
formula: "successful_executions / total_executions"
measurement_window: "5m"
latency:
definition: "95th percentile execution time"
formula: "histogram_quantile(0.95, pipeline_duration)"
measurement_window: "5m"
error_rate:
definition: "Percentage of failed executions"
formula: "failed_executions / total_executions"
measurement_window: "5m"
throughput:
definition: "Executions per minute"
formula: "rate(pipeline_completions[1m])"
measurement_window: "1m"
slo_definitions:
- name: "Pipeline Availability"
sli: availability
target: 99.9
window: "30d"
budget_burn_rate_alerts:
- rate: 2
window: "1h"
severity: "warning"
- rate: 10
window: "5m"
severity: "critical"
- name: "Pipeline Latency"
sli: latency
target:
value: 5000 # ms
percentile: 95
window: "30d"
- name: "Error Budget"
sli: error_rate
target: 0.1 # 0.1%
window: "30d"
error_budget_policy:
actions:
budget_remaining_25:
- reduce_deployment_velocity
- increase_testing_requirements
budget_remaining_10:
- freeze_non_critical_changes
- mandatory_post_mortems
budget_exhausted:
- halt_all_changes
- incident_response_mode
- executive_escalation
Logging Framework
1. Structured Logging
structured_logging:
log_format:
standard_fields:
timestamp: iso8601
level: enum[DEBUG, INFO, WARN, ERROR, FATAL]
message: string
logger: string
thread_id: string
contextual_fields:
trace_id: string
span_id: string
pipeline_id: string
component_id: string
user_id: string
correlation_id: string
custom_fields:
execution_stage: string
input_hash: string
output_hash: string
duration_ms: number
log_levels:
DEBUG:
description: "Detailed diagnostic information"
retention: "24h"
sampling: "1%"
INFO:
description: "General operational information"
retention: "7d"
sampling: "10%"
WARN:
description: "Warning conditions"
retention: "30d"
sampling: "100%"
ERROR:
description: "Error conditions"
retention: "90d"
sampling: "100%"
FATAL:
description: "Critical failures"
retention: "1y"
sampling: "100%"
sensitive_data_handling:
pii_detection:
patterns:
- ssn_pattern
- credit_card_pattern
- email_pattern
- phone_pattern
action: "mask"
mask_character: "*"
secrets_detection:
patterns:
- api_key_pattern
- password_pattern
- token_pattern
action: "remove"
2. Log Aggregation
log_aggregation:
collection_pipeline:
sources:
- application_logs
- system_logs
- audit_logs
- security_logs
processors:
- name: parser
type: regex | json | kv
error_handling: "send_to_dlq"
- name: enricher
enrichments:
- add_service_metadata
- add_environment_info
- add_geographic_data
- name: filter
rules:
- drop_debug_in_production
- drop_health_check_logs
- sample_high_volume_logs
outputs:
primary:
type: elasticsearch
index_pattern: "logs-{service}-{date}"
archive:
type: s3
format: compressed_json
partition: "year/month/day/hour"
Tracing Framework
1. Distributed Tracing
distributed_tracing:
instrumentation:
automatic:
frameworks:
- http_clients
- grpc_clients
- database_drivers
- message_queues
- cache_clients
trace_propagation:
formats:
- w3c_trace_context
- b3_multi_header
- jaeger
manual:
span_attributes:
required:
- service.name
- span.kind
- component.name
recommended:
- pipeline.id
- pipeline.version
- user.id
- environment
custom:
- business_operation
- feature_flag
- experiment_id
sampling_strategies:
head_based:
rules:
- sample_all_errors
- sample_slow_requests
- sample_percentage: 0.1
tail_based:
decision_wait: "30s"
rules:
- error_traces: 1.0
- latency_threshold:
threshold_ms: 1000
sample_rate: 1.0
- default: 0.1
trace_analysis:
service_maps:
generation: "automatic"
update_interval: "1m"
edge_metrics:
- request_rate
- error_rate
- latency_p50_p95_p99
critical_path_analysis:
identify:
- bottlenecks
- redundant_calls
- n_plus_one_queries
- synchronous_chains
2. Trace Correlation
trace_correlation:
correlation_strategies:
log_trace_correlation:
method: "inject_trace_context"
fields:
- trace_id
- span_id
- trace_flags
metric_trace_correlation:
method: "exemplars"
sampling_rate: 0.1
event_trace_correlation:
method: "event_attributes"
required_fields:
- trace_id
- timestamp
- event_type
correlation_queries:
logs_for_trace:
query: "trace_id:{trace_id}"
time_window: "trace_duration + 1m"
metrics_for_trace:
query: "exemplar_trace_id:{trace_id}"
aggregation: "by_span"
events_for_trace:
query: "attributes.trace_id:{trace_id}"
order: "timestamp"
Alerting Framework
1. Alert Configuration
alerting_framework:
alert_rules:
- name: "High Error Rate"
condition: |
rate(pipeline_errors[5m]) / rate(pipeline_total[5m]) > 0.05
duration: "5m"
severity: "critical"
annotations:
summary: "Pipeline error rate exceeds 5%"
description: "Pipeline {{ $labels.pipeline_name }} has error rate of {{ $value }}%"
runbook_url: "https://runbooks.io/pipeline-errors"
actions:
- notify_oncall
- create_incident
- scale_down_traffic
- name: "SLO Burn Rate"
condition: |
slo_burn_rate > 10 AND slo_time_window = "5m"
severity: "critical"
annotations:
summary: "SLO budget burning too fast"
impact: "User-facing service degradation"
actions:
- page_oncall
- trigger_rollback
- notify_stakeholders
notification_channels:
pagerduty:
integration_key: "${PAGERDUTY_KEY}"
routing:
critical: "immediate_page"
warning: "low_priority"
slack:
webhook_url: "${SLACK_WEBHOOK}"
channels:
critical: "#incidents"
warning: "#alerts"
info: "#monitoring"
message_template: |
:warning: *{{ .Alert.Name }}*
Severity: {{ .Alert.Severity }}
Pipeline: {{ .Labels.pipeline_name }}
Description: {{ .Alert.Description }}
[View Dashboard]({{ .DashboardURL }})
email:
smtp_config:
server: "smtp.example.com"
port: 587
recipients:
critical: ["[email protected]", "[email protected]"]
warning: ["[email protected]"]
alert_routing:
rules:
- match:
severity: "critical"
environment: "production"
receivers: ["pagerduty", "slack-critical"]
- match:
severity: "warning"
receivers: ["slack-warning", "email"]
- match:
team: "data-pipeline"
receivers: ["slack-data-team"]
alert_suppression:
maintenance_windows:
- name: "Weekly Maintenance"
schedule: "0 2 * * 0" # Sunday 2 AM
duration: "2h"
deduplication:
group_by: ["pipeline_name", "error_type"]
interval: "5m"
throttling:
max_alerts_per_hour: 50
max_alerts_per_severity:
critical: 10
warning: 20
info: 100
2. Incident Management
incident_management:
incident_lifecycle:
detection:
sources:
- automated_alerts
- manual_reports
- anomaly_detection
triage:
severity_matrix:
critical:
user_impact: "total_outage"
revenue_impact: ">$10k/hour"
major:
user_impact: "partial_outage"
revenue_impact: ">$1k/hour"
minor:
user_impact: "degraded_performance"
revenue_impact: "<$1k/hour"
response:
roles:
incident_commander:
responsibilities:
- coordinate_response
- make_decisions
- external_communication
technical_lead:
responsibilities:
- investigate_issue
- implement_fixes
- coordinate_engineers
communications_lead:
responsibilities:
- stakeholder_updates
- status_page_updates
- post_mortem_scheduling
resolution:
steps:
- identify_root_cause
- implement_fix
- verify_resolution
- monitor_stability
post_incident:
timeline:
post_mortem_draft: "24h"
post_mortem_meeting: "48h"
action_items_due: "2w"
post_mortem_template:
- incident_summary
- timeline
- root_cause_analysis
- impact_assessment
- what_went_well
- what_went_wrong
- action_items
Visualization and Dashboards
1. Dashboard Architecture
dashboard_architecture:
dashboard_types:
executive_dashboard:
purpose: "High-level business metrics"
refresh_rate: "5m"
widgets:
- slo_status_grid
- cost_trends
- usage_statistics
- incident_summary
operational_dashboard:
purpose: "Real-time system health"
refresh_rate: "15s"
widgets:
- pipeline_status_map
- error_rate_trends
- latency_heatmap
- resource_utilization
investigation_dashboard:
purpose: "Deep dive analysis"
refresh_rate: "on_demand"
widgets:
- log_search
- trace_explorer
- metric_correlations
- time_comparisons
team_dashboards:
purpose: "Team-specific metrics"
customizable: true
templates:
- pipeline_development_team
- infrastructure_team
- data_science_team
- business_analytics_team
visualization_types:
time_series:
use_cases:
- metric_trends
- rate_calculations
- predictions
features:
- multi_axis
- annotations
- threshold_lines
- anomaly_highlighting
heatmaps:
use_cases:
- latency_distribution
- error_patterns
- usage_patterns
features:
- time_buckets
- value_buckets
- drill_down
- tooltips
topology_maps:
use_cases:
- service_dependencies
- pipeline_flow
- error_propagation
features:
- auto_layout
- edge_metrics
- node_health
- filtering
2. Reporting Framework
reporting_framework:
scheduled_reports:
daily_summary:
schedule: "0 9 * * *"
recipients: ["[email protected]"]
content:
- pipeline_execution_summary
- error_summary
- cost_summary
- slo_status
weekly_analytics:
schedule: "0 9 * * 1"
recipients: ["[email protected]"]
content:
- trend_analysis
- capacity_planning
- incident_summary
- improvement_recommendations
monthly_executive:
schedule: "0 9 1 * *"
recipients: ["[email protected]"]
content:
- business_metrics
- cost_analysis
- reliability_metrics
- strategic_insights
ad_hoc_reports:
incident_report:
triggers:
- incident_resolved
- post_mortem_completed
content:
- incident_timeline
- impact_analysis
- root_cause
- remediation_steps
- prevention_measures
performance_report:
parameters:
- time_range
- pipeline_filter
- metric_selection
content:
- performance_trends
- bottleneck_analysis
- optimization_opportunities
- benchmark_comparisons
report_formats:
email:
template: "html"
attachments:
- pdf_summary
- csv_data
slack:
format: "markdown"
interactive: true
dashboard:
format: "embedded"
sharing: "link_with_auth"
Cost Monitoring
1. Cost Attribution
cost_attribution:
cost_dimensions:
infrastructure:
compute:
metrics:
- cpu_hours
- memory_gb_hours
- gpu_hours
attribution:
- pipeline_id
- component_id
- team_id
- project_id
storage:
metrics:
- storage_gb_hours
- io_operations
- bandwidth_gb
attribution:
- data_type
- retention_class
- access_pattern
network:
metrics:
- data_transfer_gb
- api_calls
- load_balancer_hours
attribution:
- source_region
- destination_region
- service_type
external_services:
ai_providers:
metrics:
- tokens_consumed
- api_calls
- model_type
attribution:
- provider
- model
- pipeline_id
- use_case
data_services:
metrics:
- queries_executed
- data_scanned_gb
- compute_units
attribution:
- service_type
- query_complexity
- user_id
cost_optimization:
recommendations:
- underutilized_resources
- oversized_instances
- inefficient_queries
- unnecessary_api_calls
- data_retention_optimization
automated_actions:
- scale_down_idle_resources
- switch_to_spot_instances
- archive_old_data
- cache_expensive_operations
Integration Ecosystem
1. Data Export
data_export:
export_formats:
prometheus:
endpoint: "/metrics"
format: "prometheus_text"
opentelemetry:
protocol: "otlp"
formats:
- grpc
- http/protobuf
- http/json
custom_webhooks:
format: "json"
batching: true
compression: true
export_destinations:
monitoring_platforms:
- datadog
- new_relic
- grafana_cloud
- elastic_cloud
data_lakes:
- s3
- bigquery
- snowflake
- databricks
streaming_platforms:
- kafka
- kinesis
- pubsub
- event_hubs
2. API Access
api_access:
query_api:
endpoints:
metrics:
path: "/api/v1/metrics/query"
methods: ["GET", "POST"]
logs:
path: "/api/v1/logs/search"
methods: ["POST"]
traces:
path: "/api/v1/traces/{trace_id}"
methods: ["GET"]
authentication:
methods:
- api_key
- oauth2
- mutual_tls
rate_limiting:
default: "100/minute"
by_tier:
free: "10/minute"
standard: "100/minute"
enterprise: "1000/minute"
webhook_api:
event_types:
- alert_triggered
- alert_resolved
- slo_breach
- incident_created
- cost_threshold_exceeded
delivery:
retry_policy:
max_attempts: 3
backoff: "exponential"
security:
- signature_verification
- ip_allowlist
- tls_required
Performance Optimization
1. Data Pipeline Optimization
optimization_strategies:
data_reduction:
sampling:
adaptive_sampling:
- high_value_retention: 100%
- normal_sampling: 10%
- verbose_sampling: 1%
aggregation:
pre_aggregation:
- service_level_metrics
- component_summaries
- time_based_rollups
compression:
algorithms:
- zstd
- snappy
- gzip
compression_levels:
hot_data: "fast"
warm_data: "balanced"
cold_data: "maximum"
query_optimization:
indexing_strategy:
primary_indexes:
- timestamp
- pipeline_id
- trace_id
secondary_indexes:
- user_id
- error_type
- component_name
caching:
levels:
- memory_cache: "1GB"
- redis_cache: "10GB"
- cdn_cache: "100GB"
cache_keys:
- query_hash
- time_range
- filters
query_planning:
- partition_pruning
- predicate_pushdown
- join_optimization
- parallel_execution
Security and Compliance
1. Data Security
data_security:
encryption:
at_rest:
algorithm: "AES-256-GCM"
key_management: "KMS"
key_rotation: "90d"
in_transit:
protocol: "TLS 1.3"
cipher_suites:
- TLS_AES_256_GCM_SHA384
- TLS_CHACHA20_POLY1305_SHA256
access_control:
rbac:
roles:
- viewer: "read_only"
- operator: "read_write_metrics"
- admin: "full_access"
attribute_based:
attributes:
- team_membership
- data_classification
- environment_access
audit_logging:
events:
- access_granted
- access_denied
- configuration_changed
- data_exported
compliance:
frameworks:
- gdpr:
pii_handling: "anonymization"
retention_limits: true
right_to_erasure: true
- hipaa:
encryption_required: true
access_logging: true
data_integrity: true
- sox:
audit_trail: true
change_control: true
separation_of_duties: true
Disaster Recovery
1. Backup and Recovery
disaster_recovery:
backup_strategy:
frequency:
metrics: "continuous"
logs: "hourly"
configuration: "on_change"
retention:
daily: "7d"
weekly: "4w"
monthly: "12m"
yearly: "7y"
verification:
automated_restore_test: "weekly"
checksum_validation: "daily"
recovery_procedures:
rto_targets: # Recovery Time Objective
critical_metrics: "15m"
recent_logs: "1h"
historical_data: "4h"
rpo_targets: # Recovery Point Objective
metrics: "1m"
logs: "5m"
traces: "5m"
failover:
automatic_triggers:
- region_failure
- availability_zone_failure
- service_degradation
manual_procedures:
- verification_steps
- failover_commands
- validation_checks
- rollback_plan
Future Enhancements
1. AI-Powered Operations
- Anomaly detection using machine learning
- Predictive alerting
- Automated root cause analysis
- Intelligent capacity planning
2. Advanced Analytics
- Pipeline performance predictions
- Cost optimization recommendations
- User behavior analytics
- Business impact correlation
3. Enhanced Visualization
- 3D service topology maps
- AR/VR monitoring dashboards
- Real-time collaboration features
- Mobile-first monitoring apps
4. Automation Extensions
- Self-healing pipelines
- Automated incident response
- Dynamic resource allocation
- Proactive optimization