aws-cloudwatch-investigation

name aws-cloudwatch-investigation description Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage. AWS CloudWatch Investigation Skill Reusable patterns for investigating production incidents using CloudWatch Logs, Metrics, and Alarms. These patterns are designed to be composed together during incident triage. Pattern 1: Logs Insights Query Templates Error Spike Detection Find the top errors in a time window, grouped by error type: fields @timestamp, @message, @logStream | filter @message like /(?i)(error|exception|fatal|critical)/ | stats count(*) as errorCount by bin(5m), @logStream | sort errorCount desc | limit 20 P99 Latency Breakdown by Operation Identify which operations are driving latency spikes: fields @timestamp, @duration, operation | filter ispresent(@duration) | stats avg(@duration) as avgMs, pct(@duration, 50) as p50Ms, pct(@duration, 95) as p95Ms, pct(@duration, 99) as p99Ms, count(*) as invocations by operation | sort p99Ms desc | limit 15 Lambda Cold Start Detection Quantify cold start impact during an incident: fields @timestamp, @duration, @initDuration, @memorySize, @maxMemoryUsed | filter ispresent(@initDuration) | stats count(*) as coldStarts, avg(@initDuration) as avgInitMs, max(@initDuration) as maxInitMs, avg(@duration) as avgDurationMs by bin(5m) | sort @timestamp desc Out-of-Memory (OOM) Detection Find Lambda functions or containers killed by memory pressure: fields @timestamp, @message, @logStream, @memorySize, @maxMemoryUsed | filter @message like /Runtime exited|out of memory|OOMKilled|Cannot allocate memory|MemoryError/ | stats count(*) as oomEvents by @logStream, bin(10m) | sort oomEvents desc | limit 10 For memory utilization trending before OOM: fields @timestamp, @maxMemoryUsed, @memorySize | filter ispresent(@maxMemoryUsed) | stats max(@maxMemoryUsed / @memorySize * 100) as peakMemPct, avg(@maxMemoryUsed / @memorySize * 100) as avgMemPct by bin(5m) | sort @timestamp desc Timeout Detection Find invocations that hit the configured timeout: fields @timestamp, @duration, @logStream, @requestId | filter @message like /Task timed out/ or @duration > 28000 | stats count(*) as timeouts by @logStream, bin(5m) | sort timeouts desc Pattern 2: Alarm History to Deploy-Event Correlation Process Get alarm transition time — note the exact timestamp when the alarm entered ALARM state. Query CloudTrail for deployment-related events in a window of [alarm_time - 30min, alarm_time]: # CloudTrail Lake query for deployment events SELECT eventTime, eventName, userIdentity.arn, requestParameters FROM <event-data-store-id> WHERE eventTime > '<alarm_time_minus_30m>' AND eventTime < '<alarm_time>' AND eventName IN ( 'UpdateFunctionCode', 'UpdateFunctionConfiguration', 'UpdateService', 'CreateDeployment', 'RegisterTaskDefinition', 'CreateChangeSet', 'ExecuteChangeSet', 'StartPipelineExecution', 'PutImage' ) ORDER BY eventTime DESC Correlation criteria — a deploy is "correlated" if: It targets the same service/resource as the alarm It completed within 15 minutes before the alarm transition The deployer identity matches a CI/CD role (not a human applying a hotfix) Strengthening the correlation: Check if the same alarm was healthy in the previous deployment cycle Verify no other environmental changes (scaling events, config changes) in the same window Look for canary/synthetic monitor failures that started at the same time Output Format Deploy Correlation: Event: UpdateFunctionCode Time: 2024-03-15T14:23:07Z (12 min before alarm) Actor: arn:aws:sts::123456789012:assumed-role/github-actions-deploy/session Resource: arn:aws:lambda:us-east-1:123456789012:function:payment-processor Correlation: STRONG — same resource, CI/CD actor, alarm was OK prior cycle Pattern 3: Narrow the Blast Radius Decision Tree Use this tree to systematically scope an incident from broadest to most specific: START | v [1] ACCOUNT — Which account(s) show the alarm? | - Check: Are alarms firing in multiple accounts? | - If yes → suspect shared service (SSO, networking, shared deployment pipeline) | - If no → proceed to Region v [2] REGION — Which region(s) are affected? | - Check: Same alarm in other regions? | - If multi-region → suspect global service (IAM, Route53, S3 global) | - If single-region → proceed to Service v [3] SERVICE — Which service namespace shows degradation? | - Check CloudWatch namespace: AWS/Lambda, AWS/ECS, AWS/ApiGateway, etc. | - If multiple services → suspect shared dependency (VPC, NAT, DNS, IAM) | - If single service → proceed to Operation v [4] OPERATION — Which API action or function is failing? | - For Lambda: which function name? | - For ECS: which service/task definition? | - For API GW: which stage/resource/method? | - If all operations → suspect service-level issue (throttling, quota) | - If specific operation → proceed to Resource v [5] RESOURCE — Which specific resource instance? - Function ARN, Task ID, DB instance identifier - This is your investigation target - Proceed to log and trace analysis scoped to this resource Shared Dependency Investigation When blast radius spans multiple services, investigate in this order: VPC/Networking — NAT Gateway ErrorPortAllocation, packet drops, DNS resolution failures IAM/STS — ThrottlingException on AssumeRole, token vending latency Downstream dependency — shared database, cache, or external API Deployment pipeline — simultaneous deploys across services from same pipeline run AWS service event — check AWS Health Dashboard and Service Health for the region Pattern 4: PromQL-Style Metric Query Patterns These patterns use CloudWatch metric math and GetMetricData to build composite signals. Express them as metric queries for dashboards or programmatic retrieval. Error Rate as Percentage MetricDataQueries: - Id: errors MetricStat: Metric: Namespace: AWS/Lambda MetricName: Errors Dimensions: [{Name: FunctionName, Value: TARGET}] Period: 60 Stat: Sum - Id: invocations MetricStat: Metric: Namespace: AWS/Lambda MetricName: Invocations Dimensions: [{Name: FunctionName, Value: TARGET}] Period: 60 Stat: Sum - Id: error_rate Expression: "errors / invocations * 100" Label: "Error Rate %" Latency Anomaly Detection (Compare to Baseline) MetricDataQueries: - Id: current_p99 MetricStat: Metric: Namespace: AWS/Lambda MetricName: Duration Dimensions: [{Name: FunctionName, Value: TARGET}] Period: 300 Stat: p99 - Id: baseline_p99 MetricStat: Metric: Namespace: AWS/Lambda MetricName: Duration Dimensions: [{Name: FunctionName, Value: TARGET}] Period: 300 Stat: p99 # Use StartTime/EndTime set to same window last week - Id: anomaly_ratio Expression: "current_p99 / baseline_p99" Label: "Latency vs Baseline (ratio > 2 = anomaly)" Throttling Pressure Score Combine multiple throttling signals into a single pressure metric: MetricDataQueries: - Id: lambda_throttles MetricStat: Metric: {Namespace: AWS/Lambda, MetricName: Throttles} Period: 60 Stat: Sum - Id: api_gw_429s MetricStat: Metric: {Namespace: AWS/ApiGateway, MetricName: 4XXError, Dimensions: [{Name: ApiName, Value: TARGET}]} Period: 60 Stat: Sum - Id: dynamo_throttles MetricStat: Metric: {Namespace: AWS/DynamoDB, MetricName: ThrottledRequests, Dimensions: [{Name: TableName, Value: TARGET}]} Period: 60 Stat: Sum - Id: throttle_pressure Expression: "lambda_throttles + api_gw_429s + dynamo_throttles" Label: "Combined Throttle Pressure" Concurrent Execution Headroom MetricDataQueries: - Id: concurrent MetricStat: Metric: {Namespace: AWS/Lambda, MetricName: ConcurrentExecutions} Period: 60 Stat: Maximum - Id: headroom Expression: "1000 - concurrent" Label: "Remaining Concurrency (account limit 1000)" Pattern 5: Incident Timeline Reconstruction Process Reconstruct a precise timeline by merging data from multiple sources: Collect timestamps: Source Query Yields CloudWatch Alarms Alarm history API State transition times CloudWatch Metrics GetMetricData with 1-min period First anomaly point CloudWatch Logs Logs Insights with earliest(@timestamp) First error occurrence CloudTrail LookupEvents filtered by time Deployment/change events AWS Health DescribeEvents AWS-side incidents Build the timeline: fields @timestamp, @message | filter @message like /ERROR|WARN|timeout|refused|denied/ | stats earliest(@timestamp) as firstSeen, latest(@timestamp) as lastSeen, count(*) as occurrences by @message | sort firstSeen asc | limit 20 Identify the sequence: Timeline: T-15m: CloudTrail — UpdateFunctionCode by CI/CD role T-12m: Logs — first error "Connection refused to payments-api.internal" T-10m: Metrics — Error count crosses 5/min threshold T-8m: Alarm — PaymentProcessorErrors enters ALARM T-5m: Metrics — p99 latency spikes to 28s (timeout) T-0: Current — error rate at 45%, alarm still firing Determine root event — the earliest change that preceded all symptoms. Walk backward from the first symptom to the most recent mutation (deploy, config change, scaling event, or external dependency shift). Gotchas CloudWatch metric timestamps are end-of-period. A 1-minute datapoint at 14:05 covers 14:04-14:05. CloudTrail events can have up to 15-minute delivery delay. Use eventTime , not ingestion time. Log group timestamps depend on the agent/SDK flush interval. Allow for 30-60s of clock skew. Alarm state changes have a built-in evaluation delay (periods x evaluation periods). The actual anomaly started earlier.