Monitor Armory Enterprise with Prometheus and Grafana.
global vision
Armory recommends monitoring the Armory Enterprise health on each production instance. This document describes how to set up a BasicPrometheuseGrafanastack and enable monitoring for Armory Enterprise services.
Additional configuration of Prometheus and Grafana is required to make them production ready, and that configuration is not part of this document. Also note that monitoring the Pipelines as Code service (Dinghy) and the TerraformIntegration service (Terraformer) are not covered on this page.
before you start
- You know Prometheus and Grafana
- Armory Enterprise is deployed on
Spinnaker
namespace - Prometheus and Grafana are in action
monitoring
namespace - You haveMonitoring configured with the observability plugin.
To usekube-prometheus
to create a monitoring stack
You can skip this section if you already have a trace stack.
A quick and easy way to set up a cluster monitoring solution is to use thiskube-prometheus
🇧🇷 This project builds a monitoring stack that includes cluster monitoring with Prometheus and dashboards with Grafana.
To create the stack, follow thekube-prometheus quick startStatements starting withcompatibility matrixSection.
After following the instructions, the pods will run onmonitoring
namespace:
% kubectl get pods --namespace monitoramentoNAME READY STATUS RESTARTS AGEalertmanager-main-0 2/2 Läuft044salertmanager-main-1 2/2 running044salertmanager-main-2 2/2 running044sgrafana-77978cbbdc-x5rsq 1/1 Running040skube-state-metrics-7f6d7b46b4-crzx2 3/3 Running040snode-exporter-nrc88 2/2 Running041sprometheus-adapter-68698bc948-bl7p8 1/1 running040sprometheus-k8s-0 3/3 Running139sprometheus-k8s-1 3/3 Running139sprometheus-operator-6685db5c6-qfpbj 1/1 Running0106s
Access the Prometheus web interface using thekubectl port-forward
Command. If you want to make this interface available to others, create an Ingress service. Make sure you are using security controls that follow Prometheus best practices.
% kubectl --namespace port forwarding monitoring svc/prometheus-k8s9090 &
navigate tohttp://localhost:9090/targets
.
Grant Prometheus RBAC permissions
There are two steps to configuring Prometheus to monitor Armory Enterprise:
- Add Prometheus permissions to communicate with the Spinnaker namespace
- Configure Prometheus to discover Armory Enterprise endpoints
Add Prometheus permissions by applying the following configuration to your cluster. You can learn more about this process atPrometheus-Operator-Homepage.
Configuration example:
apiVersion: rbac.authorization.k8s.io/v1beta1liquid: ClusterRoleBindingmetadata: # name can be prometheus or prometheus-k8s depending on the Prometheus operator version Name: PrometheusRollenRef: apiGroup: rbac.authorization.k8s.io liquid: ClusterRolle # name can be prometheus or prometheus-k8s depending on the Prometheus operator version Name: Prometheusmatters: -liquid: service account # name can be prometheus or prometheus-k8s depending on the Prometheus operator version Name: prometheus-k8s namespace: monitoring---apiVersion: rbac.authorization.k8s.io/v1beta1liquid: ClusterRollemetadata: # name can be prometheus or prometheus-k8s depending on the Prometheus operator version Name: PrometheusRules:-apiGroups: [""] Resources: - we - Nodes/Metrics - Services - Endpoints - Capsules verbs: ["to receive", "List", "Ver"]-apiGroups: [""] Resources: - Configuration cards verbs: ["to receive"]-nonResourceURLs: ["/Metrics"] verbs: ["to receive"]---apiVersion: v1liquid: service accountmetadata: namespace: monitoring # name can be prometheus or prometheus-k8s depending on the Prometheus operator version Name: prometheus-k8s
Add the ServiceMonitor
Prometheus Operator uses a "ServiceMonitor" to add targets that are copied for monitoring. The following example configuration shows how to monitor pods that use the observability plugin to monitor theaop-prometheus
full stop. Note that the example includes excluding specific services (e.g. Redis) and changing the gate endpoint to show different options.
These are examples of possible configurations. Use them as a starting point. Armory recommends that you understand how they work and find services. Adjust them according to your environment.
apiVersion: monitoring.coreos.com/v1liquid: Service Monitormetadata: labels: Application: to turn # This label is here to match the serviceMonitorSelector attribute of the Prometheus operator # prometheus.prometheusSpec.serviceMonitorSelector. For more information, see # https://github.com/helm/charts/tree/master/stable/prometheus-operator Publication: Prometheus Operator Name: All Spinnaker Metrics namespace: Spinnakerspecification: Endpoints: -Break: 10s A way: /aop-prometheus voters: matching expressions: -key: Set Operator: Baden values: - sling gate - Spin-Gate-API - Custom revolving gate - Spin-Deck - Spin-Deck-Custom - spin-redis - Spin-Terraformer - Slingshot matchLabels: Application: to turn
The example excludes Gate, the API service, because Gate restricts access to endpoints unless authenticated (except health).
The example below is for a service monitor for Gate on a different path and with TLS.
Once applied, you can pass Prometheus on and verify that Prometheus has detected and removed the targets.
apiVersion: monitoring.coreos.com/v1liquid: Service Monitormetadata: Name: The internal spinnaker metric namespace: Spinnaker labels: Application: to turn # This label is here to match the serviceMonitorSelector attribute of the Prometheus operator # prometheus.prometheusSpec.serviceMonitorSelector # https://github.com/helm/charts/tree/master/stable/prometheus-operator Publication: Prometheus Operatorspecification: voters: matchLabels: Set: Spin Tor Endpoints: -Break: 10s A way: "/api/v1/aop-prometheus" # If Prometheus returns error "http: server returned HTTP response to HTTPS client". # Replace schema with targetPort: # Note that "port" is just a string. "targetPort" is an integer or a string. # For example targetPort: 8084 the plan: "https" tlsConfig: unsafeIgnoreVerify: Is correct
Research Armory Enterprise objectives on Prometheus
After applying these changes, you should be able to see Armory Enterprise targets in Prometheus. It may take 3-5 minutes for this to appear, depending on where Prometheus is in your configuration polling range.
Access Grafana
Configure port forwarding for Grafana:
$ kubectl --namespace monitor port forwarding svc/grafana3000
Access the Grafana web interface via http://localhost:3000 and use the default Grafana username and passwordadmin: admin
.
Add Arsenal Panels to Grafana
Armory provides some sample dashboards (in JSON format) that you can import into Grafana as a starting point for metrics that can be graphed for monitoring. Armory has additional panels available to Armory customers. You can skip this section if you are a Grafana expert.
Follow these steps to import sample dashboards:
- Git clone this repository to your local workstation :(https://github.com/uneeq-oss/spinnaker-mixin)
- Access the Grafana web interface (as shown above)
- Navigate to Dashboards and then Manage
- click on itto importTaste
- Upload one or more sample dashboard files from the repository you cloned
After importing the dashboards, you can browse the charts for each service by clicking onDashboards > Manage > Spinnaker Kubernetes Details.
Metrics available per service
Disclaimer: The tables below may not contain all available metrics for each service.
cloud driver
metric name | Basic unit | Description |
---|---|---|
amazonClientProvider_rateLimitDelayMillis | ||
to allow | ||
aws_request_clientExecuteTime | milliseconds | |
aws_request_credentialsRequestTime | milliseconds | |
aws_request_httpClientReceiveResponseTime | milliseconds | |
aws_request_httpClientSendRequestTime | milliseconds | |
aws_request_httpRequestTime | milliseconds | |
aws_request_requestCount | ||
aws_request_requestMarshallTime | milliseconds | |
aws_request_requestSigningTime | milliseconds | |
aws_request_responseProcessingTime | milliseconds | |
aws_request_retryPauseTime | milliseconds | |
aws_request_throttling | ||
awsSdkClientSupplier_averageLoadPenalty | ||
awsSdkClientSupplier_hitCount | ||
awsSdkClientSupplier_loadExceptionCount | ||
awsSdkClientSupplier_missRate | ||
cats_sqlCache_evict_deleteOperations | ||
cats_sqlCache_evict_itemCount | ||
cats_sqlCache_evict_itemsGelöscht | ||
cats_sqlCache_get_itemCount | ||
cats_sqlCache_get_relationshipsRequested | ||
cats_sqlCache_get_requestedSize | ||
cats_sqlCache_get_selectOperations | ||
cats_sqlCache_merge_deleteOperations | ||
cats_sqlCache_merge_itemCount | ||
cats_sqlCache_merge_itemsStored | ||
cats_sqlCache_merge_relationshipCount | ||
cats_sqlCache_merge_relationshipsStored | ||
cats_sqlCache_merge_selectOperations | ||
cats_sqlCache_merge_writeOperations | ||
cf_okhttp_requests | milliseconds | OkHttp operation timer |
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
runtime | milliseconds | |
health_kubernetes_errors | ||
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads in the BLOCKED state |
kubernetes_api | milliseconds | |
logback_events | events | Number of debug level events that made it to the logs |
onDemand_cache | milliseconds | |
onDemand_count | ||
onDemand_error | ||
onDemand_evict | milliseconds | |
onDemand_read | milliseconds | |
onDemand_store | milliseconds | |
onDemand_total | milliseconds | |
onDemand_transform | milliseconds | |
The operation | milliseconds | |
orchestrations | milliseconds | |
process_files_max | files | The maximum number of file descriptors |
reservedInstances_surplusByAccountClassic | ||
reservedInstances_surplusByAccountVpc | ||
reservedinstances_excesstotal | ||
resiliência4j_retry_calls | The number of failed calls after a retry | |
sql_cacheCleanupAgent_dataTypeCleanupDuration | milliseconds | |
sql_cacheCleanupAgent_dataTypeRecordsGelöscht | ||
sql_healthProvider_invocations | ||
sql_taskCleanupAgent_deleted | ||
sql_taskCleanupAgent_timing | milliseconds | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tasks | ||
tasks | ||
tomcat_sessions_active_current | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
Eco
metric name | Basic unit | Description |
---|---|---|
aws_request_httpClientGetConnectionTime | milliseconds | |
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
echo_events_processed | ||
echo_triggers_sync_executionTimeMillis | milliseconds | |
fiat_enabled | ||
fiat_getPermission | ||
fiat_legacyFallback_enabled | ||
fiat_permissionsCache_evictions | ||
fiat_permissionsCache_evictions-weight | ||
fiat_permissionsCache_hits | ||
fiat_permissionsCache_loads | milliseconds | |
fiat_permissionsCache_loads-failure | ||
fiat_permissionsCache_loads-success | ||
fiat_permissionsCache_misses | ||
front50_lastPoll | ||
front50_requests | ||
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_buffer_total_capacity | bytes | An estimate of the total capacity of the buffers in this pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_memory_promoted | bytes | Number of positive increments in storage pool size from pre-GC to post-GC |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads with NEW status |
logback_events | events | Number of information level events that made it to the logs |
okhttp_requests | milliseconds | |
orca_requests | ||
orca_trigger_success | ||
pipelines_triggered | ||
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
quietPeriod_tests | ||
resiliência4j_circuitbreaker_buffered_calls | The number of buffered failed calls stored in the ring buffer | |
resiliência4j_circuitbreaker_calls | milliseconds | Total number of calls that failed but where the exception was ignored |
resiliência4j_circuitbreaker_failure_rate | The breaker failure rate | |
resiliência4j_circuitbreaker_slow_call_rate | The Slow Circuit Breaker Call | |
resiliência4j_circuitbreaker_state | The circuit breaker states | |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
Fiat
metric name | Basic unit | Description |
---|---|---|
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
fiat_getUserPermission | ||
fiat_userRoles_syncAnonymous | milliseconds | |
fiat_userRoles_syncCount | ||
fiat_userRoles_syncTime | milliseconds | |
fiat_userRoles_syncUsers | milliseconds | |
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_buffer_total_capacity | bytes | An estimate of the total capacity of the buffers in this pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_memory_promoted | bytes | Number of positive increments in storage pool size from pre-GC to post-GC |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads in the TERMINATED state |
kork_lock_acquire | ||
kork_lock_acquire_duration | ||
kork_lock_heartbeat | ||
kork_lock_release | ||
logback_events | events | Number of debug level events that made it to the logs |
okhttp_requests | milliseconds | |
permissionsRepository_get1_invocations | ||
permissionsRepository_get1_timing | ||
permissionsRepository_getAllById_invocations | ||
permissionsRepository_getAllById_timing | ||
permissionsRepository_put1_invocations | ||
permissionsRepository_put1_timing | ||
permissionsRepository_putAllById1_invocations | ||
permissionsRepository_putAllById1_timing | ||
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
redis_command_invocation_del | ||
redis_command_invocation_eval | ||
redis_command_invocation_get | ||
redis_command_invocation_hgetAll | ||
redis_command_invocation_hmset | ||
redis_command_invocation_hscan | ||
redis_command_invocation_pipelined | ||
redis_command_invocation_rename | ||
redis_command_invocation_sadd | ||
redis_command_invocation_set | ||
redis_command_invocation_sismember | ||
return_command_invocation_srem | ||
redis_command_invocation_sscan | ||
redis_command_invocation_time | ||
redis_command_latency_del | ||
redis_command_latency_eval | milliseconds | |
redis_command_latency_get | milliseconds | |
redis_command_latency_get | ||
redis_command_latency_hgetAll | ||
redis_command_latency_hmset | ||
redis_command_latency_hscan | ||
redis_command_latency_pipelined | ||
redis_command_latency_rename | ||
redis_command_latency_sadd | ||
redis_command_latency_set | ||
redis_command_latency_sismember | ||
redis_command_latency_srem | ||
redis_command_latency_sscan | ||
redis_command_latency_time | ||
redis_command_payloadSize_eval | ||
redis_command_payloadSize_eval_summary | ||
redis_command_payloadSize_sadd | ||
redis_command_payloadSize_sadd_summary | ||
redis_command_payloadSize_set | ||
redis_command_payloadSize_set_summary | ||
resiliência4j_circuitbreaker_buffered_calls | The number of buffered failed calls stored in the ring buffer | |
resiliência4j_circuitbreaker_calls | milliseconds | |
resiliência4j_circuitbreaker_failure_rate | The breaker failure rate | |
resiliência4j_circuitbreaker_slow_call_rate | The Slow Circuit Breaker Call | |
resiliência4j_circuitbreaker_state | The circuit breaker states | |
resiliência4j_retry_calls | The number of failed calls after a retry | |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_cpu_usage | Current CPU usage for the entire system | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
Front50
metric name | Basic unit | Description |
---|---|---|
aws_request_clientExecuteTime | milliseconds | |
aws_request_credentialsRequestTime | milliseconds | |
aws_request_httpClientGetConnectionTime | milliseconds | |
aws_request_httpClientReceiveResponseTime | milliseconds | |
aws_request_httpClientSendRequestTime | milliseconds | |
aws_request_httpRequestTime | milliseconds | |
aws_request_requestCount | ||
aws_request_requestSigningTime | milliseconds | |
aws_request_responseProcessingTime | milliseconds | |
aws_request_retryPauseTime | milliseconds | |
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
fiat_enabled | ||
fiat_getPermission | ||
fiat_legacyFallback_enabled | ||
fiat_permissionsCache_evictions | ||
fiat_permissionsCache_evictions-weight | ||
fiat_permissionsCache_hits | ||
fiat_permissionsCache_loads | milliseconds | |
fiat_permissionsCache_loads-failure | ||
fiat_permissionsCache_loads-success | ||
fiat_permissionsCache_misses | ||
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_buffer_total_capacity | bytes | An estimate of the total capacity of the buffers in this pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_memory_promoted | bytes | Number of positive increments in storage pool size from pre-GC to post-GC |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads in the WAITING state |
logback_events | events | Number of error level events that made it to the logs |
okhttp_requests | milliseconds | |
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
resiliência4j_circuitbreaker_buffered_calls | ||
resiliência4j_circuitbreaker_calls | milliseconds | |
resiliência4j_circuitbreaker_failure_rate | The breaker failure rate | |
resiliência4j_circuitbreaker_slow_call_rate | The Slow Circuit Breaker Call | |
resiliência4j_circuitbreaker_slow_calls | The number of failed slow calls that were slower than a given threshold | |
resiliência4j_circuitbreaker_state | The circuit breaker states | |
storageServiceSupport_autoRefreshTime | milliseconds | |
storageServiceSupport_cacheAge | ||
storageServiceSupport_cacheRefreshTime | milliseconds | |
storageServiceSupport_cacheSize | ||
storageServiceSupport_mismatchedIds | ||
storageServiceSupport_num Added | ||
storageServiceSupport_numEntfernt | ||
storageServiceSupport_numAtualizado | ||
storageServiceSupport_scheduledRefreshTime | milliseconds | |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_cpu_usage | Current CPU usage for the entire system | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
Tor
metric name | Basic unit | Description |
---|---|---|
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
fiat_enabled | ||
fiat_getPermission | ||
fiat_legacyFallback_enabled | ||
fiat_login | ||
fiat_permissionsCache_evictions | ||
fiat_permissionsCache_evictions-weight | ||
fiat_permissionsCache_hits | ||
fiat_permissionsCache_loads | milliseconds | |
fiat_permissionsCache_loads-failure | ||
fiat_permissionsCache_loads-success | ||
fiat_permissionsCache_misses | ||
http_server_requests | milliseconds | |
http_server_requests | milliseconds | |
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_buffer_total_capacity | bytes | An estimate of the total capacity of the buffers in this pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_memory_promoted | bytes | Number of positive increments in storage pool size from pre-GC to post-GC |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads in the RUNNABLE state |
logback_events | events | Number of error level events that made it to the logs |
okhttp_requests | milliseconds | |
plugins_deckAssets_hits | ||
plugins_deckCache_downloadDuration | milliseconds | |
plugins_deckCache_hits | ||
plugins_deckCache_misses | ||
plugins_deckCache_refreshDuration | milliseconds | |
plugins_deckCache_versions | ||
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_cpu_usage | Current CPU usage for the entire system | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
Igor
metric name | Basic unit | Description |
---|---|---|
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
fiat_enabled | ||
fiat_getPermission | ||
fiat_legacyFallback_enabled | ||
fiat_permissionsCache_evictions | ||
fiat_permissionsCache_evictions-weight | ||
fiat_permissionsCache_hits | ||
fiat_permissionsCache_loads | milliseconds | |
fiat_permissionsCache_loads-failure | ||
fiat_permissionsCache_loads-success | ||
fiat_permissionsCache_misses | ||
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | The current number of threads with NEW status |
logback_events | events | |
okhttp_requests | milliseconds | |
pollingMonitor_docker_retrieveImagesByAccount | milliseconds | |
pollingMonitor_jenkins_retrieveProjects | milliseconds | |
pollingMonitor_pollTiming | milliseconds | |
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
resiliência4j_circuitbreaker_buffered_calls | The number of buffered failed calls stored in the ring buffer | |
resiliência4j_circuitbreaker_calls | Total number of unauthorized calls | |
resiliência4j_circuitbreaker_failure_rate | The breaker failure rate | |
resiliência4j_circuitbreaker_slow_call_rate | The Slow Circuit Breaker Call | |
resiliência4j_circuitbreaker_state | The circuit breaker states | |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_cpu_usage | Current CPU usage for the entire system | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
her clothes
metric name | Basic unit | Description |
---|---|---|
canary_pipelines_initiated | ||
canary_telemetry_query | ||
controller_invocations | milliseconds | |
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
active_executions | ||
Completed_Executions | ||
Executions_started | ||
http_server_requests | milliseconds | |
jvm_gc_allocationRate | ||
jvm_gc_liveDataSize | ||
jvm_gc_maxDataSize | ||
jvm_gc_pause | milliseconds | |
jvm_gc_promotionRate | ||
okhttp_requests | milliseconds | |
orca_task_result | ||
queue_acknowledged_messages | ||
queue depth | ||
queue_duplicate_messages | ||
queue_last_poll_age | ||
queue_last_retry_check_age | ||
queue_message_lag | milliseconds | |
queue_orphaned_messages | ||
queue_pusshed_messages | ||
queue_ready_depth | ||
queue_unacked_depth | ||
redis_command_invocation_exists | ||
redis_command_invocation_hdel | ||
redis_command_invocation_hget | ||
redis_command_invocation_hgetAll | ||
redis_command_invocation_hmset | ||
redis_command_invocation_hset | ||
redis_command_invocation_multi | ||
redis_command_invocation_sadd | ||
return_command_invocation_srem | ||
redis_command_invocation_zadd | ||
redis_command_latency_exists | ||
redis_command_latency_exists | ||
redis_command_latency_hdel | ||
redis_command_latency_hget | ||
redis_command_latency_hgetAll | ||
redis_command_latency_hmset | milliseconds | |
redis_command_latency_hset | ||
redis_command_latency_multi | ||
redis_command_latency_sadd | ||
redis_command_latency_srem | ||
redis_command_latency_zadd | ||
redis_command_payloadSize_hmset | ||
redis_command_payloadSize_hmset_summary | ||
redis_command_payloadSize_hset | ||
redis_command_payloadSize_hset_summary | ||
redis_command_payloadSize_sadd | ||
redis_command_payloadSize_sadd_summary | ||
redis_command_payloadSize_srem | ||
redis_command_payloadSize_srem_summary | ||
redis_connectionPool_maxIdle | ||
redis_connectionPool_minIdle | ||
redis_connectionPool_numActive | ||
redis_connectionPool_numIdle | ||
redis_connectionPool_numWaiters | ||
redis_executionRepository_store1_invocations | ||
redis_executionRepository_store1_timing | milliseconds | |
redis_executionRepository_storeStage1_invocations | ||
redis_executionRepository_storeStage1_timing | ||
redis_executionRepository_updateStatus1_invocations | ||
redis_executionRepository_updateStatus1_timing | milliseconds | |
retrieveById_redis_executionRepository_invocations | ||
retrieveById_redis_executionRepository_timing | ||
stage_invocations | ||
stage_invocations_duration | ||
task_completions_duration | milliseconds | |
task_completions_duration_withType | milliseconds | |
task_invocations_duration | milliseconds | |
task_invocations_duration_withType | milliseconds | |
threadpool_activeCount | ||
threadpool_blockingQueueSize | ||
threadpool_corePoolSize | ||
threadpool_maximumPoolSize | ||
threadpool_poolSize | ||
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
orca
metric name | Basic unit | Description |
---|---|---|
aws_request_httpClientGetConnectionTime | milliseconds | |
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
active_executions | ||
Completed_Executions | ||
Executions_started | ||
Executions_total time | milliseconds | |
fiat_enabled | ||
fiat_getPermission | ||
fiat_legacyFallback_enabled | ||
fiat_permissionsCache_loads | milliseconds | |
fiat_permissionsCache_loads-failure | ||
http_server_requests | milliseconds | |
jdbc_connections_active | ||
jdbc_connections_idle | ||
jdbc_connections_max | ||
jvm_gc_allocationRate | ||
jvm_gc_pause | milliseconds | |
jvm_gc_promotionRate | ||
mpt_requests | ||
okhttp_requests | milliseconds | |
orca_task_result | ||
queue_acknowledged_messages | ||
queue depth | ||
queue_duplicate_messages | ||
queue_last_poll_age | ||
queue_message_notfound | ||
queue_orphaned_messages | ||
queue_pusshed_messages | ||
queue_retried_messages | ||
queue_unacked_depth | ||
redis_connectionPool_maxIdle | ||
redis_connectionPool_numActive | ||
redis_connectionPool_numIdle | ||
resiliência4j_retry_calls | The number of successful calls after a retry | |
retrieveById_sql_executions_invocations | ||
retrieveById_sql_executions_timing | ||
sql_executions_addStage1_timing | ||
sql_executions_cancel4_invocations | ||
sql_executions_cancel4_timing | ||
sql_executions_countActiveExecutions_invocations | ||
sql_executions_countActiveExecutions_timing | ||
sql_executions_handlesPartition1_invocations | ||
sql_executions_handlesPartition1_timing | milliseconds | |
sql_executions_retrieveByCorrelationId2_timing | ||
sql_executions_retrieveOrchestrationsForApplication3_timing | ||
sql_executions_store1_timing | ||
sql_executions_storeStage1_invocations | ||
sql_executions_storeStage1_timing | ||
sql_executions_updateStatus1_invocations | ||
sql_executions_updateStatus1_timing | ||
sql_healthProvider_invocations | ||
sql_pool_default_connectionAcquiredTiming | milliseconds | |
sql_queueActivator_invocations | ||
stage_invocations | ||
stage_invocations_duration | ||
task_completions_duration | milliseconds | |
task_completions_duration_withType | milliseconds | |
task_invocations_duration | milliseconds | |
task_invocations_duration_withType | milliseconds | |
task_serverGroupCacheForceRefresh | ||
threadpool_activeCount | ||
threadpool_blockingQueueSize | ||
threadpool_corePoolSize | ||
threadpool_corePoolSize | ||
threadpool_maximumPoolSize | ||
threadpool_poolSize | ||
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_rejected | sessions |
Rosko
metric name | Basic unit | Description |
---|---|---|
backtActive | ||
bake done | milliseconds | |
controller_invocations | ||
controller_invocations_contentLength | ||
controller_invocations_contentLength_summary | ||
http_server_requests | milliseconds | |
jvm_buffer_count | pufferfish | An estimate of the number of buffers in the pool |
jvm_buffer_memory_used | bytes | An estimate of the memory the Java Virtual Machine will use for this buffer pool |
jvm_buffer_total_capacity | bytes | An estimate of the total capacity of the buffers in this pool |
jvm_classes_loaded | Klassen | The number of classes currently loaded in the Java Virtual Machine |
jvm_classes_unloaded | Klassen | The total number of classes that have been unloaded since the Java Virtual Machine started running |
jvm_gc_allocationRate | ||
jvm_gc_live_data_size | bytes | Old generation storage pool size after a full GC |
jvm_gc_liveDataSize | ||
jvm_gc_max_data_size | bytes | Legacy storage pool maximum size |
jvm_gc_maxDataSize | ||
jvm_gc_memory_allocated | bytes | Increments to increase storage pool size from younger generation after one GC to before the next |
jvm_gc_memory_promoted | bytes | Number of positive increments in storage pool size from pre-GC to post-GC |
jvm_gc_pause | milliseconds | Time spent on GC pause |
jvm_gc_promotionRate | ||
jvm_memory_committed | bytes | The amount of memory, in bytes, committed to use by the Java Virtual Machine |
jvm_memory_max | bytes | The maximum amount of memory, in bytes, that can be used for memory management |
jvm_memory_used | bytes | The amount of memory used |
jvm_threads_daemon | topics | The current number of active daemon threads |
jvm_threads_live | topics | The current number of active threads, including daemon and non-daemon threads |
jvm_threads_peak | topics | Highest active thread count since Java Virtual Machine started or peaked |
jvm_threads_states | topics | |
logback_events | events | |
okhttp_requests | milliseconds | |
process_cpu_usage | The current CPU usage for the Java Virtual Machine process | |
process_files_max | files | The maximum number of file descriptors |
process_files_open | files | The number of open file descriptors |
process_start_time | milliseconds | Process start time since Unix_ days |
process_uptime | milliseconds | The Java Virtual Machine uptime |
system_cpu_count | The number of processors available to the Java Virtual Machine | |
system_cpu_usage | Current CPU usage for the entire system | |
system_load_average_1m | The sum of the number of executable entities queued in the available processors queue and the number of executable entities running on available processors, averaged over a period of time | |
tomcat_sessions_active_current | sessions | |
tomcat_sessions_active_max | sessions | |
tomcat_sessions_alive_max | milliseconds | |
tomcat_sessions_created | sessions | |
tomcat_sessions_expired | sessions | |
tomcat_sessions_rejected | sessions |
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Last modified on April 12, 2022:(37ef2950)
FAQs
What can I monitor with Prometheus? ›
Prometheus is an open-source technology designed to provide monitoring and alerting functionality for cloud-native environments, including Kubernetes. It can collect and store metrics as time-series data, recording information with a timestamp. It can also collect and record labels, which are optional key-value pairs.
When not to use Prometheus? ›If you need 100% accuracy, such as for per-request billing, Prometheus is not a good choice as the collected data will likely not be detailed and complete enough. In such a case you would be best off using some other system to collect and analyze the data for billing, and Prometheus for the rest of your monitoring.
How do I monitor public endpoints using Prometheus? ›- On the monitoring server, open the Prometheus configuration file.
- Add the Alertmanager endpoint.
- Add the Grafana endpoint.
- Restart Prometheus.
- Ensure all three endpoints are listed on the Targets page.
Configuring Prometheus to monitor itself
Since Prometheus exposes data in the same manner about itself, it can also scrape and monitor its own health.
A very good tool for continuous monitoring.
Best cloud native and metrics-based tool support, the best monitoring tool that I have been using for months and continuing. Prometheus can also be combined with Grafana to expose a nice counter and use Alertmanager to produce complicated alert rules.
Prometheus can handle millions of time series. However, with the above mentioned default setting for storage. local. target-heap-size , you will be limited to about 200,000 time series simultaneously present in memory.
What problem does Prometheus solve? ›Prometheus solves the problem of how devs can monitor highly dynamic container environments. In this article, Frederick Ryckbosch goes over the advantages and disadvantages of using Prometheus, and just how scalable it really is.
Is Prometheus push or pull? ›Data Acquisition Method: Push (CollectD, Zabbix, and InfluxDB); Pull (Prometheus, SNMP, and JMX).
What language is Prometheus written in? ›What language is Prometheus written in? Most Prometheus components are written in Go. Some are also written in Java, Python, and Ruby.
Can Prometheus monitor API? ›Prometheus is an open-source API monitoring tool that scrapes API data from intermediary pushes for short-lived jobs. It can be used to collect and store data with a timestamp of when an event occurs, or the API is called.
Can Prometheus monitor logs? ›
You can configure the Logging operator to expose metrics endpoints for FluentD and Fluent-bit using ServiceMonitor resources. That way, a Prometheus operator running in the same cluster can automatically fetch your logging metrics.
How can I monitor public IP traffic? ›- IP address management. ...
- Network performance monitors. ...
- Ping-based tools. ...
- SolarWinds ipMonitor (FREE TRIAL) ...
- SolarWinds IP Address Manager (FREE TRIAL) ...
- Paessler PRTG Ping Monitor (FREE TRIAL) ...
- ManageEngine OpUtils. ...
- Angry IP Scanner.
Prometheus & Grafana: Better Together
Prometheus collects rich metrics and provides a powerful querying language; Grafana transforms metrics into meaningful visualizations. Both are compatible with many, if not most, data source types. In fact, it is very common for DevOps teams to run Grafana on top of Prometheus.
Prometheus is easy to setup but it barely does anything. It gets really messy when one needs to gather application metrics (postgresql, cassandra, haproxy, java, etc...) and have dashboards and alerts going to different places (slack, email, etc...).
What is alternative to Prometheus? ›Datadog, like Prometheus, allows users to capture critical information including metrics. Datadog is a managed SaaS offering that collects core metrics for virtually any system or service. With Datadog, users are able to monitor and visualize usage metrics across their entire environment.
Is Prometheus better than Nagios? ›Prometheus is useful for monitoring app functionality, while Nagios is a very powerful platform for application networks and security. However, Prometheus has the edge in performance metrics.
Which is better Nagios or Prometheus? ›Abilities: The primary focus of Nagios is more on application network traffic and security, while Prometheus mainly focuses on the applicative aspects of the application and its infrastructure. Prometheus is developed to collect data from applications that push metrics to their API endpoints (or exporters).
Is Prometheus and splunk the same? ›Prometheus is open source, and free. Splunk is a one-stop-shop for everything related to data management, and they can be used in a broad range of fields (IoT, IT, security, and business metrics). Splunk is a proprietary service that can't be used for free.
How much RAM does Prometheus need? ›The Prometheus agent requires about 1 CPU core and 1 GB memory to collect 1 million metrics, as shown in the Stress test report for the Prometheus agent. The recommended CPU utilization and memory usage of the Prometheus agent are both 50% for the agent to properly collect data.
How long does Prometheus keep data? ›By default the retention is configured to 15 days. The amounts of data stored on disk depends on retention — higher retention means more data on disk. The lowest supported retention in Prometheus is 2 hours (2h).
How often does Prometheus scrape? ›
In this case the global setting is to scrape every 15 seconds. The evaluation_interval option controls how often Prometheus will evaluate rules. Prometheus uses rules to create new time series and to generate alerts. The rule_files block specifies the location of any rules we want the Prometheus server to load.
What is the weakness of Prometheus? ›Prometheus's weakness was not fearing the Gods. Some of his powers were also his weaknesses. He used to it to outsmart and deceive Zeus and other Gods and Goddesses. There for, Zeus punished him by arresting him and bound him to a stake on Mount Kaukasos where the eagles were to feed on him.
Does Prometheus get punished? ›How did Zeus punish Prometheus? According to one tale told by Hesiod, Zeus avenged himself on Prometheus by having him nailed to a mountain in the Caucasus and then sent an eagle to eat his immortal liver, which constantly replenished itself.
What is good about Prometheus? ›Prometheus is best known for defying the gods by stealing fire from them and giving it to humanity in the form of technology, knowledge, and more generally, civilization. In some versions of the myth, he is also credited with the creation of humanity from clay.
Does Prometheus use TCP or UDP? ›Prometheus will connect to Node Exporter using TCP port 9100 and HTTP protocol at given intervals to retrieve these metrics.
Is Prometheus strong? ›Prometheus is far more powerful than normal homies, being made from a fragment of Big Mom's very own soul. This gives the sun a will strong enough to resist the soul projection of the Yomi Yomi no Mi's user. However, Prometheus is still vulnerable to being injured by the user of the fruit.
Why is Prometheus called Prometheus? ›According to Edith Hamilton's book "Mythology," the name Prometheus means "forethought," and he was "very wise, wiser even than the gods." Prometheus was said to have been so enthralled by his human creation that he stole fire from the gods to help the humans along.
What companies use Prometheus? ›...
852 companies reportedly use Prometheus in their tech stacks, including Uber, Robinhood, and Slack.
- Uber.
- Robinhood.
- Slack.
- Nubank.
- medium.com.
- Hepsiburada.
- trivago.
- Alibaba Travels.
Prometheus is a free tool, though many companies provide Prometheus as a service covering, hosted Prometheus, Graphite, and Grafana. Whereas, Datadog charges a minimum of $15 per month for one host and other services, including log management and APM.
Is Prometheus owned by Grafana? ›Prometheus is an open source monitoring system for which Grafana provides out-of-the-box support. This topic walks you through the steps to create a series of dashboards in Grafana to display system metrics for a server monitored by Prometheus.
Can I use Prometheus without Kubernetes? ›
To taste Hue prometheus metrics, you may set up a Prometheus server to scrape the metrics endpoint /metrics on a Hue server (which may not need to run in docker or Kubernetes). Here is the set up example on Ubuntu 16.4. Prerequisites: a Hue server running at localhost:8000.
Can Prometheus scrape https endpoint? ›Prometheus supports TLS and basic authentication over its HTTP endpoints. Scraping target using HTTPS instead of HTTP has been supported for a long time. You could fetch metrics using HTTPS, client-certificate authentication, and basi contentc authentication.
Can I use Prometheus as database? ›Prometheus includes a local on-disk time series database, but also optionally integrates with remote storage systems.
How does Prometheus monitoring work? ›Prometheus is an open-source tool that provides monitoring and alerting for cloud-native systems, such as Kubernetes. It can collect and store measurements as time-series data, including a timestamp for each entry. Labels, which are optional key-value pairs, can also be collected and recorded.
How do I monitor application logs in Prometheus? ›The log files are matches line by line using a regular expression, and the matched data is stored in different variables. Then, the data of these variables are exported via grok_exporter. Once you add the grok_exporter server to Prometheus as a target, Prometheus can monitor the desired log files via grok_exporter.
How do I monitor Python application using Prometheus? ›Prometheus is an open-source monitoring and alerting tool. Prometheus can be used to monitor Python applications. Prometheus has an official Python client library that you can use on your Python project to export metrics (i.e. number of visitors, bytes sent or received).
Can police track your IP? ›So, If you're wondering if the police can track your phone numbers and IP addresses, the answer is - yes, they can. While the police cannot search your phone without a warrant, they have a legal right to watch what you do in public, on social media, and in your home and business.
How do hackers find IP addresses? ›Specialized IoT search engines like Rapid7 and MITRE track vulnerabilities known to specific devices. Using yet another IoT search engine like Shodan and ZoomEye, hackers can find devices connected to the internet, geolocation, port/operating system, services/host, and IP address.
Can police check IP address? ›In short, not really. But if they have a court order, they can request usage or connection logs. Overall, being completely anonymous and untraceable online is virtually impossible. A VPN is a great tool that gives you more privacy by masking your IP address, minimizing commercial advertising, and hacking attempts.
Can I use Grafana without Prometheus? ›Grafana can only use data from external systems like Prometheus, MySQL, Azure Monitor and Amazon CloudWatch. That means Grafana has no means to collect data on its own, through agents or other data pipelines, and is thus dependent on other systems to provide data.
Is Prometheus a DevOps tool? ›
Prometheus is a tool that every DevOps professional should be familiar with. It's an open-source system for monitoring services and alerts based on a time series data model. Prometheus collects data and metrics from different services and stores them according to a unique identifier—the metric name—and a time stamp.
Is Prometheus a CI CD tool? ›The easiest way to monitor any Kubernetes workload is using Prometheus. It also holds true for a CI/CD pipeline.
How long does it take to learn Prometheus? ›Typically, Prometheus and Grafana training by DevOpsSchool will let you master the concepts in about 2 months if you devote 2 to 4 hours per week. The training takes place in such a way that you become proficient in almost all the concepts of Prometheus and Grafana can easily add certification to your resume.
Is Prometheus kid friendly? ›Age Appropriate for: 15+. Cursing, implied sexual situations (kissing, but no nudity), sexual talk, and bunches of murder and bloodshed, including a Caesarean section-like procedure.
What age rating is Prometheus? ›Prometheus is rated R by the MPAA for sci-fi violence including some intense images, and brief language.
Does Prometheus delete old data? ›Data will be automatically deleted after the storage retention time has passed. By default it is 15 days. If you want to delete specific data earlier, then you are able. You need to enable the admin api in Prometheus before you can.
Why is Prometheus good for Kubernetes? ›Prometheus discovers targets to scrape from by using Service Discovery. A Kubernetes cluster already has labels and annotations and an excellent mechanism for keeping track of changes and the status of its elements. Hence, Prometheus uses the Kubernetes API to discover targets.
What can Prometheus scrape? ›Prometheus gathers metrics from different systems by scraping data from HTTP endpoints. It uses this information to identify issues, such as when an endpoint is missing or should not exist or when a time-series pattern indicates a problem.
What are the 4 metric types Prometheus support? ›...
Counter
- Go.
- Java.
- Python.
- Ruby.
- . Net.
Prometheus is an open-source systems monitoring and alerting toolkit. You can configure Docker as a Prometheus target. This topic shows you how to configure Docker, set up Prometheus to run as a Docker container, and monitor your Docker instance using Prometheus.
How do you expose data in Prometheus? ›
...
Exposing and scraping metrics
- Built-in metrics for the language (e.g. JVM metrics, Go metrics)
- The ability to registry custom metrics.
- Some HTTP handler to create the /metrics endpoint.
Prometheus collects rich metrics and provides a powerful querying language; Grafana transforms metrics into meaningful visualizations. Both are compatible with many, if not most, data source types. In fact, it is very common for DevOps teams to run Grafana on top of Prometheus.