Ready-made dashboards

Guardrail coverage by service table

Trace coverage per service - share of LLM traces that included at least one guardrail span. A service with low trace coverage may have guardrails configured but not applied to all requests.

Trace guardrail coverage donut chart

Overall trace coverage - share of all LLM traces in the selected period that included at least one guardrail span, across all selected services.

LLM traces over time by guardrail status bar chart

LLM request volume over time, split by whether the trace was covered by a guardrail. A trace counts as covered when at least one guardrail span was observed within it.

Bedrock calls - guardrail coverage donut chart

Bedrock trace coverage - Bedrock LLM spans split by whether a guardrail ID was attached. Spans without a guardrail ID were processed without going through a configured guardrail.

Guardrail inventory table

All Bedrock guardrails observed, with call volume and per-policy activation counts for content, PII/sensitive info, topics, and words policies. Requires AWS CloudWatch monitoring with the "Amazon Bedrock Guardrails" namespace enabled - see AWS CloudWatch metrics setup for instructions.

Policy activations by category donut chart

Policy activation counts by category - how often each Bedrock guardrail policy detected content matching its rules. An activation does not necessarily mean the request was blocked.

Activations by category over time bar chart

How often each Bedrock guardrail policy was activated over time. An activation means the policy evaluated and flagged content - not necessarily that the request was blocked.

Input filter - activation rate donut chart

Share of Azure LLM spans where the content filter triggered on any category (hate, violence, sexual, self-harm, jailbreak). Safe = all categories returned severity 'safe' with no jailbreak detected.

Input filter - detections by category over time bar chart

Azure Content Safety prompt detections per category over time. Counts spans where hate/violence/sexual/self-harm severity was not 'safe', or jailbreak attempt was detected - not necessarily that the request was blocked.

Last execution of alert configs honeycomb chart

Overall of all last execution result for each selected alert configuration.

Last execution - Success single value

Count the last success execution result for each selected alert configuration.

Last execution - Warning single value

Count the last warning execution result for each selected alert configuration.

Last execution - Failed single value

Count the last failed execution result for each selected alert configuration.

Warning or Failure Events bar chart

Shows all warning or failure events for each selected alert configuration.

FAILED Message table

WARNING Message table

Integration latency line chart

The time between when API Gateway relays a request to the backend and when it receives a response from the backend.

Latency line chart

The time between when API Gateway receives a request from a client and when it returns a response to the client. The latency includes the integration latency and other API Gateway overhead.

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query visualizes pre-counted log status metrics (dt.log.status_per_entity_count) grouped by status for selected HTTP/WebSocket APIs, filtered by Smartscape ID ($SmartscapeId). It sorts the statuses in descending order based on their severity.

Error and warning log patterns table

Query Explanation: This DQL query identifies recurring error and warning log patterns for selected HTTP/WebSocket APIs, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Raw log messages are normalized by replacing volatile data (UUIDs, timestamps, IP addresses, numbers) with placeholders, then grouped by the resulting pattern and status to surface the most frequently occurring issues.

Error and warning log stream table

Query Explanation: This DQL query fetches the 100 most recent error and warning logs from the last 15 minutes for selected HTTP/WebSocket APIs, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Results are sorted by timestamp descending and display only the most relevant fields: time, status, content, API name and ID.

Integration latency line chart

The time between when API Gateway relays a request to the backend and when it receives a response from the backend.

Latency line chart

The time between when API Gateway receives a request from a client and when it returns a response to the client. The latency includes the integration latency and other API Gateway overhead.

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query visualizes pre-counted log status metrics (dt.log.status_per_entity_count) grouped by status for selected REST APIs, filtered by Smartscape ID ($SmartscapeId). It sorts the statuses in descending order based on their severity.

Error and warning log patterns table

Query Explanation: This DQL query identifies recurring error and warning log patterns for selected REST APIs, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Raw log messages are normalized by replacing volatile data (UUIDs, timestamps, IP addresses, numbers) with placeholders, then grouped by the resulting pattern and status to surface the most frequently occurring issues.

Error and warning log stream table

Query Explanation: This DQL query fetches the 100 most recent error and warning logs from the last 15 minutes for selected REST APIs, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Results are sorted by timestamp descending and display only the most relevant fields: time, status, content, API name and ID.

Average Total Time single value

The time it took for the server to process the request.

Total Time line chart

The time it took for the server to process the request.

Top $Limit models per latency line chart

Total errors single value

The time it took for the server to process the request.

Client vs Server errors line chart

Invocation Throttles line chart

Agent throttles

Invocations line chart

Successful agent invocations

Invocations Intervened line chart

Successful agent invocations

Findings count line chart

Successful agent invocations

Tables single value

Total DynamoDB tables across selected accounts and regions.

Throttled requests single value

Total throttled requests across all selected tables. Any value > 0 indicates capacity or hot partition issues.

System errors (5xx) single value

HTTP 500 errors — DynamoDB service-side issues.

User errors (4xx) single value

HTTP 400 errors — client-side issues (malformed requests, validation failures).

Conditional check failed requests single value

The number of failed attempts to perform conditional writes.

Transaction conflicts single value

Rejected item-level requests due to transactional conflicts between concurrent requests.

Active Problems single value

Active warning signals single value

Read capacity utilization % line chart

Consumed read capacity as a percentage of provisioned read capacity per table. Only applicable to provisioned capacity mode tables.

Write capacity utilization % line chart

Consumed write capacity as a percentage of provisioned write capacity per table. Only applicable to provisioned capacity mode tables.

Max request latency single value

Maximum successful request latency across operations — reveals throttle-induced retry delays.

Successful request latency line chart

Average time (ms) for successful requests per operation. Does not include SDK retry time — correlate with Throttled Requests for full latency picture.

Average request latency single value

Maximum successful request latency across operations — reveals throttle-induced retry delays.

Latency by operation (avg) line chart

Average latency per DynamoDB operation. Latency varies by operation: GetItem ~5ms, Query ~10-50ms, Scan ~100ms+.

Latency by operation (max) line chart

Maximum latency per operation. High max latency often reveals throttle-induced SDK retries with exponential backoff.

Consumed read capacity by GSI line chart

Consumed read capacity per GSI. GSIs have independent capacity from the base table.

Consumed write capacity by GSI line chart

Consumed write capacity per GSI. GSI write throttling causes ProvisionedThroughputExceededException on base table writes.

Active EC2 instances single value

Active Problems single value

Active warning signals single value

Instances per region categorical chart

Instances per type categorical chart

Instances with highest CPU usage categorical chart

The most recent percentage of physical CPU time that Amazon EC2 uses to run the EC2 instance, which includes time spent to run both the user code and the Amazon EC2 code.

Status Check failures line chart

Network input line chart

Network output line chart

Total network input single value

The total number of bytes received by the instance on all network interfaces.

Total network output single value

The total number of bytes sent out by the instance on all network interfaces.

Volumes idle time bar chart

The total number of seconds in a specified period of time when no read or write operations were submitted. High idle time indicates underutilized resources like EBS volume attached to an EC2 instance that is not actively used.

Volumes queue length bar chart

The number of read and write operation requests waiting to be completed in a specified period of time.

Burst balance percentage line chart

Percentage of I/O credits (for gp2) or throughput credits (for st1 and sc1) remaining in the burst bucket.

Auto Scaling groups by group max size categorical chart

Auto Scaling groups by desired capacity categorical chart

The number of instances that the Auto Scaling groups attempt to maintain.

In-service instances line chart

The number of instances that are running as part of the Auto Scaling group.

Pending instances bar chart

The number of instances that are pending. A pending instance is not yet in service.

Standby instances bar chart

The number of instances that are in a Standby state. Instances in this state are still running but are not actively in service.

Terminating instances line chart

The number of instances that are in the process of terminating. This metric does not include instances that are in service, pending, or returning to a warm pool after Auto Scaling group scale in.

Clusters single value

Average CPU utilization single value

Average memory utilization single value

The total percentage of memory being used by containers in the resource in a given period.

Active Problems single value

Active warning signals single value

CPU utilization by cluster line chart

Memory utilization by cluster line chart

CPU utilization by cluster and service line chart

Memory utilization by cluster and service line chart

Services single value

The number of services in the clusters in a given period.

Container instances single value

Deployments single value

Task sets single value

Tasks single value

Pending tasks single value

Desired tasks single value

Running tasks single value

Total bytes downloaded single value

Bytes downloaded line chart

The total number of bytes downloaded by viewers for GET and HEAD requests.

Total bytes uploaded single value

Bytes uploaded line chart

The total number of bytes that viewers uploaded to CloudFront, using OPTIONS, POST and PUT requests.

4xx error rate bar chart

The percentage of all viewer requests for which the response's HTTP status code is 4xx.

5xx error rate bar chart

The percentage of all viewer requests for which the response's HTTP status code is 5xx.

Average connection time single value

Connection time line chart

The average time, in milliseconds, that it took Route 53 health checkers to establish a TCP connection with the endpoint.

Average time to first byte single value

Time to first byte line chart

The average time, in milliseconds, that it took Route 53 health checkers to receive the first byte of the response to an HTTP or HTTPS request.

Average percentage of permitted throughput utilization single value

Percentage of permitted throughput utilization line chart

Ratio between metered IO bytes and total permitted throughput, in percentage. If you are reaching maximum capacity, then you are consuming the entire amount of throughput allocated to your file system. In this situation, you might consider changing the file system's throughput mode to get higher throughput.

Total IO processed bytes single value

Total IO bytes line chart

The actual number of bytes for each file system operation processed by Amazon EFS, without any read discounts.

Burst credit balance line chart

The number of burst credits that a file system has. Burst credits allow a file system to burst to throughput levels above a file system’s baseline level for periods of time.

Active Problems single value

Active warning signals single value

Clusters single value

Number of EKS clusters matching the current filters

Pending pods single value

Total number of pods pending scheduling — a rising trend indicates insufficient cluster capacity or scheduling constraints blocking pod placement

API 5XX errors single value

Total API server 5XX error rate — any non-zero value means the control plane is failing requests; investigate etcd connectivity and API server logs immediately

Error logs bar chart

Query Explanation: This DQL query shows the number of ERROR logs over time for the selected AWS EKS clusters. It uses the dt.log.status_per_entity_count metric and groups the result by Smartscape source ID, so each selected EKS cluster is represented as a separate time series. The query filters the data to the selected Smartscape IDs ($SmartscapeId) and includes only logs with ERROR status. The number of returned series is controlled by the $Limit dashboard variable. To make the result easier to read in a dashboard, the query performs a lookup against AWS_EKS_CLUSTER Smartscape nodes and resolves each Smartscape source ID to the corresponding EKS cluster name. The resolved cluster name is exposed as aws.resource.name, while intermediate lookup fields and the raw Smartscape source ID are removed from the final result.

Total API requests single value

Total API server request rate across selected clusters — a sudden drop may indicate the control plane is unreachable or severely throttled

Scheduler errors single value

Total scheduler error rate — errors mean pods could not be assigned to a node; non-zero values require investigation of node capacity, taints, or resource quotas

API Requests by Cluster line chart

Total API server request rate per cluster — sorted by highest traffic to identify clusters under load

API Errors by Cluster line chart

Client (4XX) and server (5XX) error rates per cluster — sorted by highest 5XX rate to surface the most unreliable clusters first

etcd storage size by cluster line chart

etcd database size per cluster — a growing trend approaching the storage quota causes write failures and switches the cluster to read-only mode; consider compacting history or reviewing resource object retention policies

Schedule Attempts by Cluster line chart

Scheduler attempt rate split by outcome — error attempts mean the scheduler tried but failed to place a pod; sustained errors require investigation of resource constraints, taints, or affinity rules

Pending Pods by Cluster line chart

Breakdown of pending pods by queue state — unschedulable pods have no fitting node; backoff pods hit repeated failures; gated pods are held by scheduling gates; activeq pods are queued for immediate scheduling

Webhook Requests by Cluster line chart

Admission webhook request rate split by type — mutating (admit) webhooks run first and may modify objects; validating webhooks enforce policy; a spike in either can slow or block deployments

Webhook Rejections by Cluster line chart

Admission webhook rejection count per cluster — rejections block resource creation or updates; a spike indicates policy violations or misconfigured webhooks causing deployment failures

Webhook Admission Duration by Cluster line chart

Average admission webhook processing time per cluster — slow webhooks (>1 s) directly extend API request latency and can cause controller timeouts; identify and optimize high-latency webhooks

Total Nodes single value

Total number of worker nodes across selected clusters

Failed Nodes single value

Number of failed worker nodes — any non-zero value requires immediate investigation of node health and availability

Running Pods single value

Total number of running pods across selected clusters

Node CPU Utilization by Cluster line chart

Average CPU utilization per node per cluster — sustained high values indicate under-provisioned capacity or resource-intensive workloads

Node Memory Utilization by Cluster line chart

Average memory utilization per node per cluster — high values risk OOM kills; consider scaling nodes or adjusting workload memory limits

Node Filesystem Utilization by Cluster line chart

Average filesystem utilization per node per cluster — nodes approaching full disk risk pod evictions and log pipeline failures

Node Network Traffic by Cluster line chart

Total network bytes across nodes per cluster — unexpected spikes may indicate data exfiltration, traffic amplification, or storage-intensive workloads

Running Pods & Containers by Cluster line chart

Running pods and containers per cluster — a large gap between the two indicates multi-container pods or sidecar overhead

Pod CPU Utilization by Cluster line chart

Average CPU utilization across pods per cluster — high values combined with pending pods indicate a need for cluster scaling

Pod Memory Utilization by Cluster line chart

Average memory utilization across pods per cluster — sustained high values combined with restart spikes typically indicate OOM kills

Pod Network Traffic by Cluster line chart

Network receive and transmit bytes across pods per cluster — correlate with application activity to identify traffic anomalies or bandwidth saturation

Container Restarts by Cluster bar chart

Total container restart count per cluster — high restart counts indicate crashlooping or OOM-killed workloads; clusters at the top need immediate investigation

Node GPU Usage by Cluster line chart

Total GPU units in use across nodes per cluster — only populated for clusters with GPU-accelerated instance types

Pod GPU Usage by Cluster line chart

Total GPU units in use across pods per cluster — only populated for clusters running GPU-accelerated workloads

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query shows log volume over time for the selected AWS EKS Smartscape entities. It uses the dt.log.status_per_entity_count metric to calculate the number of logs grouped by log status, such as ERROR, WARN, INFO and NONE. The query filters the data to the selected Smartscape ID ($SmartscapeId), making the chart specific to the selected EKS cluster or related Kubernetes entity. A custom sort_order field is added to control the display order of statuses by severity, with higher-severity statuses shown first. This helps quickly identify whether the selected EKS resources are producing warning or error logs during the selected timeframe.

Error and warning log patterns (last 15 minutes) table

Query Explanation: This DQL query identifies recurring ERROR and WARN log patterns for selected AWS EKS clusters. It fetches logs for the selected Smartscape IDs, AWS account and region, then normalizes the log content to reduce high-cardinality noise caused by values such as UUIDs, ARNs, container runtime IDs, image digests, timestamps, IP addresses, hexadecimal values, Kubernetes-generated suffixes and standalone numbers. Logs are grouped by normalized pattern, status and cluster name. For each recurring pattern, the query keeps the EKS cluster with the highest number of occurrences, helping identify which cluster is most affected by a repeated issue. Results are sorted by frequency and include a sample raw log entry for investigation.

Live log stream (last 15 minutes) table

Query Explanation: This DQL query fetches the 100 most recent logs from a fixed 15-minute timeframe for selected AWS EKS clusters. It filters logs by selected Smartscape ID ($SmartscapeId), AWS account ($AccountId) and AWS region ($Region). The query displays the raw log content as Log message and derives the cluster name from available EKS, Kubernetes or AWS resource metadata. Results are sorted by timestamp descending and show the most relevant fields for live troubleshooting: time, status, log message, cluster name and Smartscape source ID.

Active Problems single value

Active warning signals single value

Available cache clusters donut chart

Cache clusters by engine categorical chart

Current connections line chart

Evictions by cache categorical chart

Number of keys that have been evicted due to max memory limit.

Engine memory usage line chart

CPU utilization (host) line chart

The percentage of CPU utilization for the entire host.

Freeable memory (host) line chart

The amount of free memory available on the host.

Total network bytes in (host) single value

Network bytes in (host) line chart

Network bytes out (host) line chart

Total network bytes out (host) single value

Cache hit rate line chart

Efficiency of the cache instance. If the cache ratio is lower than about 0.8, it means that a significant number of keys are evicted, expired, or don't exist.

Hits and misses by cache categorical chart

Number of successful and unsuccessful key lookups in the cache.

Engine CPU utilization line chart

Bytes used line chart

Average successful read request latency single value

Successful read request latency line chart

Successful write request latency line chart

Average successful write request latency single value

Cache hit rate line chart

Efficiency of the cache instance. If the cache ratio is lower than about 0.8, it means that a significant number of keys are evicted, expired, or don't exist.

Hits and misses by cache categorical chart

Number of successful and unsuccessful key lookups in the cache.

Unused memory line chart

Bytes used table

Total network bytes in single value

Network bytes in line chart

Network bytes out line chart

Total network bytes out single value

Elastic load balancers single value

ALB unhealthy rate single value

Percentage of targets that are considered unhealthy in a given period.

NLB unhealthy rate single value

Percentage of targets that are considered unhealthy in a given period.

CLB unhealthy rate single value

Percentage of unhealthy instances registered with your load balancer in a given period.

Active Problems single value

Active warning signals single value

ALB error rate single value

Percentage of errors hat originate from the load balancer in a given period.

ALB target error rate single value

Percentage of errors generated by the targets in a given period.

CLB backend error rate single value

Percentage of HTTP response codes generated by registered instances in a given period.

Active connections single value

Healthy and unhealthy hosts by load balancer categorical chart

Healthy hosts single value

Unhealthy hosts single value

Requests line chart

The number of requests processed over IPv4 and IPv6 through time in a given period. This metric is only incremented for requests where the load balancer node was able to choose a target. Requests that are rejected before a target is chosen are not reflected in this metric.

Target response time line chart

The time elapsed, in seconds, after the request leaves the load balancer until the target starts to send the response headers through time in a given period.

Active flows (TCP+TLS+UDP) single value

The total number of concurrent flows (or connections) from clients to targets in a given period, summed across TCP, TLS, and UDP protocols.

Total TCP client resets single value

The total number of reset (RST) packets sent from a client to a target in a given period. These resets are generated by the client and forwarded by the load balancer.

Total TCP ELB resets single value

The total number of reset (RST) packets generated by the load balancer in a given period.

Total TCP target resets single value

The total number of reset (RST) packets sent from a target to a client in a given period. These resets are generated by the target and forwarded by the load balancer.

Healthy hosts single value

Healthy and unhealthy hosts by load balancer categorical chart

Unhealthy hosts single value

Healthy hosts single value

Healthy and unhealthy hosts by load balancer categorical chart

Distribution of healthy and unhealthy instances registered with your load balancer in a given period.

Unhealthy hosts single value

Requests line chart

The number of requests completed or connections made during the specified interval through time in a given period.

Latency line chart

The total time elapsed, in seconds, from the time the load balancer sent the request to a registered instance until the instance started to send the response headers through time in a given period.

ELB 4xx responses single value

ELB 5xx responses single value

Backend error and successful requests by load balancer categorical chart

Backend 4xx responses single value

Backend 5xx responses single value

Backend connection errors line chart

The number of connections that were not successfully established between the load balancer and the registered instances through time in a given period.

Backend connection errors by load balancer categorical chart

Active Problems single value

Active warning signals single value

Active EventBridge instances single value

Number of all active event buses in the environment.

invocation attempts categorical chart

Number of times each target EventBus was successfully invoked.

Successful invocation attempts single value

A percentage of times target was successfully invoked.

Invocation attempts line chart

Number of times EventBridge attempted invoking a target.

Matched events donut chart

The number of events that matched with any rule.

Triggered rules donut chart

The number of rules that have run and matched with any event.

Throttled rules donut chart

The number of times rule execution was throttled.

Ingestion to invocation start latency line chart

The time to process events, measured from when an event is ingested by EventBridge to the first invocation of a target.

Ingestion to invocation success latency line chart

The time taken from event ingestion to successful target delivery, using the invocation end time as cutoff.

Ingestion to invocation complete latency line chart

The time taken from event ingestion to completion of the first invocation attempt.

Percentage of established connections through NAT gateways single value

The percentage established connections made through the NAT gateway in a given period.

NAT gateways single value

Number of NAT Gateways in the environment.

PrivateLink connections single value

The number of endpoints connected to all endpoint services.

Total bytes received from source single value

Bytes received from source line chart

The number of bytes received by the NAT gateway from clients in your VPC.

Bytes sent to source line chart

The number of bytes sent through the NAT gateway to the clients in your VPC.

Total bytes sent to source single value

Total bytes sent to destination single value

Bytes sent to destination line chart

The number of bytes sent out through the NAT gateway to the destination.

Bytes received from destination line chart

The number of bytes received by the NAT gateway from the destination.

Total bytes received from destination single value

Active connections by endpoint service ID categorical chart

Bytes processed line chart

The number of bytes exchanged between endpoint services and endpoints, in both directions.

Reset packets sent line chart

Active connections by service name bar chart

Bytes processed line chart

The number of bytes exchanged between endpoints and endpoint services, aggregated in both directions. This is the number of bytes billed to the owner of the endpoint.

Packets dropped line chart

Reset packets received line chart

Errors % single value

Percentage value of invocations that resolved in errors for every Lambda function that fits filtering.

Errors single value

Count of invocations that resolved in errors for every Lambda function that fits filtering.

Throttles single value

Percentage value of execution of Lambda functions to that were limiting to prevent overwhelming the function.

Invocations single value

Total number of invocations for every Lambda function that fits filtering.

Active Problems single value

Active warning signals single value

Errors line chart

Time series of invocations that result in a function error.

Function invocations and error count categorical chart

The invocations count in comparison to the invocation that resulted in an error.

Error logs bar chart

Duration line chart

The amount of time that function code spends processing an event - does not include cold start time.

Concurrent executions line chart

Number of function instances that are actively processing events at given time.

Throttles line chart

The number of invocation requests that were throttled because the concurrency limit was exceeded.

Async events dropped table

The number of asynchronous events that were dropped without being successfully processed.

Post runtime extensions duration table

The time spent by Lambda Extensions to complete final tasks, after your function's code has finished executing.

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query visualizes pre-counted log status metrics (dt.log.status_per_entity_count) grouped by status for selected Lambda functions, filtered by Smartscape ID ($SmartscapeId). It sorts the statuses in descending order based on their severity. Why this visualization is useful: Using a bar chart to display this data provides a clear visual representation of log severity distribution across the selected Lambda functions. It helps quickly identify the prevalence of critical issues (ERROR) compared to less severe statuses (INFO, WARN or NONE), enabling efficient prioritization of troubleshooting efforts.

Error and warning log patterns (last 15 minutes) table

Query Explanation: This DQL query identifies recurring error and warning log patterns for selected Lambda functions, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Raw log messages are normalized by replacing volatile data (UUIDs, timestamps, IP addresses, numbers) with placeholders, then grouped by the resulting pattern and status to surface the most frequently occurring issues. Why this visualization is useful: Using a table to display this data provides a clear overview of the most common error and warning patterns across the selected Lambda functions. By normalizing volatile fields, similar log messages are grouped together rather than appearing as unique entries - making it easy to spot recurring issues, prioritize the most impactful problems by occurrence count, and focus troubleshooting efforts where they matter most.

Live log stream (last 15 minutes) table

Query Explanation: This DQL query fetches the 100 most recent error and warning logs from the last 15 minutes for selected Lambda functions, filtered by AWS account ($AccountId), region ($Region) and Smartscape ID ($SmartscapeId). Results are sorted by timestamp descending and display only the most relevant fields: time, status, message, function name and ID. Why this visualization is useful: Using a table to display this data provides a real-time stream of the latest critical log entries across selected Lambda functions. It enables immediate visibility into ongoing errors and warnings, allowing teams to quickly react to live issues, trace problems to specific functions via the Function Name column, and monitor the health of their Lambda environment as it happens.

Active problems single value

Problems by region pie chart

Active problem details pie chart

Error logs bar chart

Query Explanation: This DQL query shows the number of ERROR logs over time for AWS-related Smartscape entities. It uses the dt.log.status_per_entity_count metric and groups the result by Smartscape source ID, so each AWS resource is represented as a separate time series. The query includes only sources whose Smartscape type starts with AWS_ and filters the data to logs with ERROR status. To make the result easier to read in an AWS overview dashboard, the query performs a lookup against AWS Smartscape nodes and resolves each Smartscape source ID to the corresponding resource name. The resolved name is exposed as aws.resource.name, while intermediate lookup fields and the raw Smartscape source ID are removed from the final result.

Active warning signals single value

Active problems details table

Active warnings details table

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query shows AWS log volume over time grouped by log status. It uses the dt.log.status_per_entity_count metric and includes only logs from Smartscape sources whose type starts with AWS_, making it suitable for an AWS overview dashboard across multiple AWS services and resource types. The query groups the time series by status, such as ERROR, WARN, INFO and NONE. A custom sort_order field is added to control the display order by severity. ERROR logs are assigned the highest priority, followed by WARN, INFO, NONE and any other statuses. The result is sorted by this severity order in descending order, so the most important log statuses appear first in the dashboard visualization.

Error and warning log patterns (last 15 minutes) table

Query Explanation: This DQL query identifies recurring ERROR and WARN log patterns across AWS-related Smartscape entities. It fetches logs from sources whose Smartscape type starts with AWS_, making it suitable for an AWS overview dashboard that spans multiple AWS services and resource types. The query derives key AWS context fields, including source type, resource, account and region. It then normalizes the raw log content to reduce high-cardinality noise caused by values such as UUIDs, ARNs, timestamps, IP addresses, hexadecimal values, alphanumeric identifiers and standalone numbers. Logs are grouped by normalized pattern, status and AWS resource metadata. For each recurring pattern, the query keeps the AWS resource with the highest number of occurrences, helping identify which resource is most affected by a repeated warning or error. Results are sorted by frequency and include a sample raw log entry to support investigation.

Live log stream (last 15 minutes) table

Query Explanation: This DQL query fetches the 100 most recent logs from AWS-related Smartscape entities. It selects logs where the Smartscape source type starts with AWS_, making the result suitable for an AWS overview dashboard that spans multiple AWS services and resource types. The query displays the raw log content as Log message and derives a readable AWS Resource value from available AWS metadata, using the resource name first, then the resource ID, and finally the Smartscape source ID as a fallback. Additional AWS context is included, such as source type, account and region. Results are sorted by timestamp descending and show the key fields needed for live troubleshooting: time, status, log message, AWS source type, AWS resource, AWS account, AWS region and Smartscape source ID.

Active Problems single value

Active warning signals single value

Database instances single value

Average network receive throughput single value

Average read latency single value

Error logs bar chart

Query Explanation: This DQL query shows the number of ERROR logs over time for the selected Amazon RDS instances. It uses the dt.log.status_per_entity_count metric and groups the result by Smartscape source ID, so each selected RDS instance is represented as a separate time series. The query filters the data to the selected RDS Smartscape IDs ($InstanceSmartscapeId) and includes only logs with ERROR status. The number of returned series is controlled by the $Limit dashboard variable. To make the result easier to read in a dashboard, the query performs a lookup against AWS RDS Smartscape nodes and resolves each Smartscape source ID to the corresponding database instance name. The resolved name is exposed as aws.resource.name, while intermediate lookup fields and the raw Smartscape source ID are removed from the final result.

Average network transmit throughput single value

Average write latency single value

Database instances by class categorical chart

Database instances by engine categorical chart

CPU utilization line chart

Database connections line chart

Free storage space line chart

Freeable memory line chart

The amount of available random access memory.

Swap usage line chart

Read IO operations line chart

The number of billed read I/O operations from a cluster volume within a 5-minute interval.

Write IO operations line chart

The number of write disk I/O operations to the cluster volume, reported at 5-minute intervals.

Volume bytes used line chart

Log level distribution donut chart

Share of log records per severity level.

Log volume bar chart

Query Explanation: This DQL query shows log volume over time by status for the selected Amazon RDS instances. It uses the dt.log.status_per_entity_count metric and groups results by log status, such as ERROR, WARN, INFO and NONE. The query filters the data to the selected RDS Smartscape IDs ($InstanceSmartscapeId), making the result specific to the selected database instances. A custom sort_order field is added to control how statuses are displayed in the dashboard. Statuses are ordered by severity, with ERROR and WARN prioritized above informational or empty-status logs. This makes it easier to quickly assess whether selected RDS instances are producing warning or error logs during the selected timeframe.

Error and warning log patterns (last 15 minutes) table

Query Explanation: This DQL query identifies recurring ERROR and WARN log patterns for selected Amazon RDS instances. It fetches logs for the selected RDS Smartscape IDs, AWS account and region, then normalizes the raw log content to reduce high-cardinality noise caused by values such as UUIDs, ARNs, timestamps, client IP addresses, hexadecimal values, durations and numeric identifiers. Logs are grouped by normalized pattern, status and DB instance. For each recurring pattern, the query keeps the RDS instance with the highest number of occurrences, helping identify which database instance is most affected by a repeated issue. Results are sorted by frequency and include a sample raw log entry to support investigation.

Live log stream (last 15 minutes) table

Query Explanation: This DQL query fetches the 100 most recent logs for selected Amazon RDS instances. It filters logs by selected RDS Smartscape ID, AWS account and AWS region. The query displays the raw log content as Log message without parsing or transforming the payload. It also derives a readable DB instance value from available AWS resource metadata, falling back to the Smartscape source ID when no resource name is available. Results are sorted by timestamp descending and show the key fields needed for live troubleshooting: time, status, log message, DB instance, AWS resource ID and Smartscape source ID.