skywalking/oap-server/server-starter/src/main/resources/otel-rules/flink/flink-taskManager.yaml

90 lines
5.1 KiB
YAML

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This will parse a textual representation of a duration. The formats
# accepted are based on the ISO-8601 duration format {@code PnDTnHnMn.nS}
# with days considered to be exactly 24 hours.
# <p>
# Examples:
# <pre>
# "PT20.345S" -- parses as "20.345 seconds"
# "PT15M" -- parses as "15 minutes" (where a minute is 60 seconds)
# "PT10H" -- parses as "10 hours" (where an hour is 3600 seconds)
# "P2D" -- parses as "2 days" (where a day is 24 hours or 86400 seconds)
# "P2DT3H4M" -- parses as "2 days, 3 hours and 4 minutes"
# "P-6H3M" -- parses as "-6 hours and +3 minutes"
# "-P6H3M" -- parses as "-6 hours and -3 minutes"
# "-P-6H+3M" -- parses as "+6 hours and -3 minutes"
# </pre>
filter: "{ tags -> tags.job_name == 'flink-taskManager-monitoring' }" # The OpenTelemetry job name
expSuffix: tag({tags -> tags.cluster = 'flink::' + tags.cluster}).instance(['cluster'], ['taskManager_node'], Layer.FLINK)
metricPrefix: meter_flink_taskManager
metricsRules:
# jvm
- name: jvm_cpu_load
exp: flink_taskmanager_Status_JVM_CPU_Load.sum(['cluster','taskManager_node'])*1000
- name: jvm_cpu_time
exp: flink_taskmanager_Status_JVM_CPU_Time.sum(['cluster','taskManager_node']).increase('PT1M')
- name: jvm_memory_heap_used
exp: flink_taskmanager_Status_JVM_Memory_Heap_Used.sum(['cluster','taskManager_node'])
- name: jvm_memory_heap_available
exp: flink_taskmanager_Status_JVM_Memory_Heap_Max.sum(['cluster','taskManager_node'])-flink_taskmanager_Status_JVM_Memory_Heap_Used.sum(['cluster','taskManager_node'])
- name: jvm_thread_count
exp: flink_taskmanager_Status_JVM_Threads_Count.sum(['cluster','taskManager_node'])
- name: jvm_memory_metaspace_available
exp: flink_taskmanager_Status_JVM_Memory_Metaspace_Max.sum(['cluster','taskManager_node'])-flink_taskmanager_Status_JVM_Memory_Metaspace_Used.sum(['cluster','taskManager_node'])
- name: jvm_memory_metaspace_used
exp: flink_taskmanager_Status_JVM_Memory_Metaspace_Used.sum(['cluster','taskManager_node'])
- name: jvm_memory_nonHeap_used
exp: flink_taskmanager_Status_JVM_Memory_NonHeap_Used.sum(['cluster','taskManager_node'])
- name: jvm_memory_nonHeap_available
exp: flink_taskmanager_Status_JVM_Memory_NonHeap_Max.sum(['cluster','taskManager_node'])-flink_taskmanager_Status_JVM_Memory_NonHeap_Used.sum(['cluster','taskManager_node'])
# # records
- name: numRecordsIn
exp: flink_taskmanager_job_task_numRecordsIn.sum(['cluster','taskManager_node','flink_job_name','task_name']).increase('PT1M')
- name: numRecordsOut
exp: flink_taskmanager_job_task_numRecordsOut.sum(['cluster','taskManager_node','flink_job_name','task_name']).increase('PT1M')
- name: numBytesInPerSecond
exp: flink_taskmanager_job_task_numBytesInPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])
- name: numBytesOutPerSecond
exp: flink_taskmanager_job_task_numBytesOutPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])
#
# # network
- name: netty_usedMemory
exp: flink_taskmanager_Status_Shuffle_Netty_UsedMemory.sum(['cluster','taskManager_node'])
- name: netty_availableMemory
exp: flink_taskmanager_Status_Shuffle_Netty_AvailableMemory.sum(['cluster','taskManager_node'])
- name: inPoolUsage
exp: flink_taskmanager_job_task_Shuffle_Netty_Input_Buffers_inPoolUsage.sum(['cluster','taskManager_node','flink_job_name','task_name'])*100
- name: outPoolUsage
exp: flink_taskmanager_job_task_Shuffle_Netty_Output_Buffers_outPoolUsage.sum(['cluster','taskManager_node','flink_job_name','task_name'])*100
# backPressured
- name: isBackPressured
exp: flink_taskmanager_job_task_isBackPressured.sum(['cluster','taskManager_node','flink_job_name','task_name'])
- name: idleTimeMsPerSecond
exp: flink_taskmanager_job_task_idleTimeMsPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])
- name: busyTimeMsPerSecond
exp: flink_taskmanager_job_task_busyTimeMsPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])
- name: softBackPressuredTimeMsPerSecond
exp: flink_taskmanager_job_task_softBackPressuredTimeMsPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])
- name: hardBackPressuredTimeMsPerSecond
exp: flink_taskmanager_job_task_hardBackPressuredTimeMsPerSecond.sum(['cluster','taskManager_node','flink_job_name','task_name'])