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pyQueryConstructor.py
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"""
Copyright 2019, Institute e-Austria, Timisoara, Romania
http://www.ieat.ro/
Developers:
* Gabriel Iuhasz, iuhasz.gabriel@info.uvt.ro
Licensed 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.
"""
from addict import Dict
import os
import json
class QueryConstructor():
def __init__(self, queryDir):
self.author = 'Constructor for EDE ES and PR connector querys'
self.queryDir = queryDir
def loadString(self, host):
qstring = "collectd_type:\"load\" AND host:\"%s\"" % host
file = "Load_%s.csv" % host
return qstring, file
def memoryString(self, host):
qstring = "collectd_type:\"memory\" AND host:\"%s\"" % host
file = "Memory_%s.csv" % host
return qstring, file
def interfaceString(self, host):
qstring = "plugin:\"interface\" AND collectd_type:\"if_octets\" AND host:\"%s\"" % host
file = "Interface_%s.csv" % host
return qstring, file
def packetString(self, host):
qstring = "plugin:\"interface\" AND collectd_type:\"if_packets\" AND host:\"%s\"" % host
file = "Packets_%s.csv" % host
return qstring, file
def dfsString(self):
qstring = "serviceType:\"dfs\""
file = "DFS.csv"
return qstring, file
def dfsFString(self):
qstring = "serviceType:\"dfs\" AND serviceMetrics:\"FSNamesystem\""
file = "DFSFS.csv"
return qstring, file
def jvmnodeManagerString(self, host):
qstring = "serviceType:\"jvm\" AND ProcessName:\"NodeManager\" AND hostname:\"%s\"" % host
file = "JVM_NM_%s.csv" % host
return qstring, file
def jvmNameNodeString(self):
qstring = "serviceType:\"jvm\" AND ProcessName:\"NameNode\""
file = "JVM_NN.csv"
return qstring, file
def nodeManagerString(self, host):
qstring = "serviceType:\"yarn\" AND serviceMetrics:\"NodeManagerMetrics\" AND hostname:\"%s\"" % host
file = "NM_%s.csv" % host
return qstring, file
def queueResourceString(self):
qstring = "type:\"resourcemanager-metrics\" AND serviceMetrics:\"QueueMetrics\""
file = "ResourceManagerQueue.csv"
return qstring, file
def clusterMetricsSring(self):
qstring = "type:\"resourcemanager-metrics\" AND ClusterMetrics:\"ResourceManager\""
file = "ClusterMetrics.csv"
return qstring, file
def jvmMapTask(self, host):
qstring = "hostname:\"%s\" AND type:\"maptask-metrics\"" % host # TODO add per process name
file = "JVM_MapTask_%s.csv" % host
return qstring, file
def jvmResourceManagerString(self):
qstring = "type:\"resourcemanager-metrics\" AND serviceType:\"jvm\""
file = "JVM_RM.csv"
return qstring, file
def datanodeString(self, host):
qstring = "type:\"datanode-metrics\" AND serviceType:\"dfs\" AND hostname:\"%s\"" % host
file = 'DN_%s.csv' % host
return qstring, file
def mrappmasterString(self):
qstring = "type:\"mrappmaster-metrics\" AND serviceMetrics:\"MRAppMetrics\""
file = 'MRAPP.csv'
return qstring, file
def jvmMrappmasterString(self):
qstring = "type:\"mrappmaster-metrics\" AND serviceMetrics:\"JvmMetrics\""
file = "JVM_MRAPP.csv"
return qstring, file
def fsopDurationsString(self):
qstring = "type:\"resourcemanager-metrics\" AND serviceMetrics:\"FSOpDurations\""
file = 'FSOP.csv'
return qstring, file
def shuffleString(self, host):
qstring = "serviceMetrics:\"ShuffleMetrics\" AND serviceType:\"mapred\" AND hostname:\"%s\"" % host
file = 'Shuffle_%s.csv' % host
return qstring, file
def jvmRedProcessString(self, host):
qstring = "type:\"reducetask-metrics\" AND serviceType:\"jvm\" AND hostname:\"%s\"" % host
return qstring
def jvmMapProcessingString(self, host):
qstring = "type:\"maptask-metrics\" AND serviceType:\"jvm\" AND hostname:\"%s\"" % host
return qstring #TODO
def jvmRedProcessbyNameString(self, host, process):
qstring = "type:\"reducetask-metrics\" AND serviceType:\"jvm\" AND hostname:\"%s\" AND ProcessName:\"%s\"" %(host, process)
file = 'JVM_ReduceTask_%s_%s.csv' %(host, process)
return qstring, file
def jvmMapProcessbyNameString(self, host, process):
qstring = "type:\"maptask-metrics\" AND serviceType:\"jvm\" AND hostname:\"%s\" AND ProcessName:\"%s\"" % (
host, process)
file = 'JVM_MapTasksTask_%s_%s.csv' % (host, process)
return qstring, file
def stormString(self):
qstring = "type:\"storm-topology\""
file = 'Storm.csv'
return qstring, file
def cassandraCounterString(self, host):
qstring = "plugin:\"GenericJMX\" AND collectd_type:\"counter\" host:\"%s\"" % host
file = "Cassandra_counter_%s.csv" % host
return qstring, file
def cassandraGaugeString(self, host):
qstring = "plugin:\"GenericJMX\" AND collectd_type:\"gauge\" host:\"%s\"" % host
file = "Cassandra_gauge_%s.csv" % host
return qstring, file
def mongodbCounterString(self, host):
qstring = "plugin:mongo AND collectd_type:counter AND host:\"%s\"" % host
file = "MongoDB_Counter_%s.csv" % host
return qstring, file
def mongodbGaugeString(self, host):
qstring = "plugin:mongo AND collectd_type:gauge AND host:\"%s\"" % host
file = "MongoDB_Gauge_%s.csv" % host
return qstring, file
def sideQueryString(self):
file = "query_response.csv"
return file
def sparkString(self):
qstring = "*"
file = "Spark.csv"
return qstring, file
def cepQueryString(self):
qstring = "type:cep-posidonia AND DComp:DMON"
file = "CEP.csv"
return qstring, file
def loadAverage(self): # TODO
return "Average load across all nodes!"
def memoryAverage(self):
return "Average memory across all nodes!"
def interfaceAverage(self): # TODO
return "Average interface across all nodes!"
def packetAverage(self): # TODO
return "Average packets across all nodes!"
def pr_query_node(self, time='1h'):
qstr = '''{{__name__=~"node.+"}}[{}]'''.format(time)
prquery = Dict()
prquery.query = qstr
return prquery
def yarnNodeManager(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["3"].date_histogram.field = "@timestamp"
cquery.aggs["3"].date_histogram.interval = qinterval
cquery.aggs["3"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["3"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["3"].date_histogram.extended_bounds.min = qgte
cquery.aggs["3"].date_histogram.extended_bounds.max = qlte
# Specify precise metrics, and the average value expressed by 'avg' key
cquery.aggs["3"].aggs["40"].avg.field = "ContainersLaunched"
cquery.aggs["3"].aggs["41"].avg.field = "ContainersCompleted"
cquery.aggs["3"].aggs["42"].avg.field = "ContainersFailed"
cquery.aggs["3"].aggs["43"].avg.field = "ContainersKilled"
cquery.aggs["3"].aggs["44"].avg.field = "ContainersIniting"
cquery.aggs["3"].aggs["45"].avg.field = "ContainersRunning"
cquery.aggs["3"].aggs["47"].avg.field = "AllocatedGB"
cquery.aggs["3"].aggs["48"].avg.field = "AllocatedContainers"
cquery.aggs["3"].aggs["49"].avg.field = "AvailableGB"
cquery.aggs["3"].aggs["50"].avg.field = "AllocatedVCores"
cquery.aggs["3"].aggs["51"].avg.field = "AvailableVCores"
cquery.aggs["3"].aggs["52"].avg.field = "ContainerLaunchDurationNumOps"
cquery.aggs["3"].aggs["53"].avg.field = "ContainerLaunchDurationAvgTime"
cqueryd = cquery.to_dict()
return cqueryd
def systemLoadQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
# Specifc system metrics for cpu load
cquery.aggs["2"].aggs["1"].avg.field = "shortterm"
cquery.aggs["2"].aggs["3"].avg.field = "midterm"
cquery.aggs["2"].aggs["4"].avg.field = "longterm"
cqueryd = cquery.to_dict()
return cqueryd
def systemMemoryQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
cquery.aggs["2"].aggs["3"].terms.field = "type_instance.raw"
cquery.aggs["2"].aggs["3"].terms.size = 0
cquery.aggs["2"].aggs["3"].terms.order["1"] = "desc"
cquery.aggs["2"].aggs["3"].aggs["1"].avg.field = "value"
cqueryd = cquery.to_dict()
return cqueryd
def systemInterfaceQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["3"].date_histogram.field = "@timestamp"
cquery.aggs["3"].date_histogram.interval = qinterval
cquery.aggs["3"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["3"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["3"].date_histogram.extended_bounds.min = qgte
cquery.aggs["3"].date_histogram.extended_bounds.max = qlte
cquery.aggs["3"].aggs["1"].avg.field = "tx"
cquery.aggs["3"].aggs["2"].avg.field = "rx"
cqueryd = cquery.to_dict()
return cqueryd
def dfsQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["3"].date_histogram.field = "@timestamp"
cquery.aggs["3"].date_histogram.interval = qinterval
cquery.aggs["3"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["3"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["3"].date_histogram.extended_bounds.min = qgte
cquery.aggs["3"].date_histogram.extended_bounds.max = qlte
#DFS metrics
cquery.aggs["3"].aggs["2"].avg.field = "CreateFileOps"
cquery.aggs["3"].aggs["4"].avg.field = "FilesCreated"
cquery.aggs["3"].aggs["5"].avg.field = "FilesAppended"
cquery.aggs["3"].aggs["6"].avg.field = "GetBlockLocations"
cquery.aggs["3"].aggs["7"].avg.field = "FilesRenamed"
cquery.aggs["3"].aggs["8"].avg.field = "GetListingOps"
cquery.aggs["3"].aggs["9"].avg.field = "DeleteFileOps"
cquery.aggs["3"].aggs["10"].avg.field = "FilesDeleted"
cquery.aggs["3"].aggs["11"].avg.field = "FileInfoOps"
cquery.aggs["3"].aggs["12"].avg.field = "AddBlockOps"
cquery.aggs["3"].aggs["13"].avg.field = "GetAdditionalDatanodeOps"
cquery.aggs["3"].aggs["14"].avg.field = "CreateSymlinkOps"
cquery.aggs["3"].aggs["15"].avg.field = "GetLinkTargetOps"
cquery.aggs["3"].aggs["16"].avg.field = "FilesInGetListingOps"
cquery.aggs["3"].aggs["17"].avg.field = "AllowSnapshotOps"
cquery.aggs["3"].aggs["18"].avg.field = "DisallowSnapshotOps"
cquery.aggs["3"].aggs["19"].avg.field = "CreateSnapshotOps"
cquery.aggs["3"].aggs["20"].avg.field = "DeleteSnapshotOps"
cquery.aggs["3"].aggs["21"].avg.field = "RenameSnapshotOps"
cquery.aggs["3"].aggs["22"].avg.field = "ListSnapshottableDirOps"
cquery.aggs["3"].aggs["23"].avg.field = "SnapshotDiffReportOps"
cquery.aggs["3"].aggs["24"].avg.field = "BlockReceivedAndDeletedOps"
cquery.aggs["3"].aggs["25"].avg.field = "StorageBlockReportOps"
cquery.aggs["3"].aggs["26"].avg.field = "TransactionsNumOps"
cquery.aggs["3"].aggs["27"].avg.field = "TransactionsAvgTime"
cquery.aggs["3"].aggs["28"].avg.field = "SnapshotNumOps"
cquery.aggs["3"].aggs["29"].avg.field = "SyncsAvgTime"
cquery.aggs["3"].aggs["30"].avg.field = "TransactionsBatchedInSync"
cquery.aggs["3"].aggs["31"].avg.field = "BlockReportNumOps"
cquery.aggs["3"].aggs["32"].avg.field = "BlockReportAvgTime"
cquery.aggs["3"].aggs["33"].avg.field = "SafeModeTime"
cquery.aggs["3"].aggs["34"].avg.field = "FsImageLoadTime"
cquery.aggs["3"].aggs["35"].avg.field = "GetEditNumOps"
cquery.aggs["3"].aggs["36"].avg.field = "GetGroupsAvgTime"
cquery.aggs["3"].aggs["37"].avg.field = "GetImageNumOps"
cquery.aggs["3"].aggs["38"].avg.field = "GetImageAvgTime"
cquery.aggs["3"].aggs["39"].avg.field = "PutImageNumOps"
cquery.aggs["3"].aggs["40"].avg.field = "PutImageAvgTime"
cqueryd = cquery.to_dict()
return cqueryd
def dfsFSQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["34"].date_histogram.field = "@timestamp"
cquery.aggs["34"].date_histogram.interval = qinterval
cquery.aggs["34"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["34"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["34"].date_histogram.extended_bounds.min = qgte
cquery.aggs["34"].date_histogram.extended_bounds.max = qlte
#DFS FS metrics
cquery.aggs["34"].aggs["1"].avg.field = "BlocksTotal"
cquery.aggs["34"].aggs["2"].avg.field = "MissingBlocks"
cquery.aggs["34"].aggs["3"].avg.field = "MissingReplOneBlocks"
cquery.aggs["34"].aggs["4"].avg.field = "ExpiredHeartbeats"
cquery.aggs["34"].aggs["5"].avg.field = "TransactionsSinceLastCheckpoint"
cquery.aggs["34"].aggs["6"].avg.field = "TransactionsSinceLastLogRoll"
cquery.aggs["34"].aggs["7"].avg.field = "LastWrittenTransactionId"
cquery.aggs["34"].aggs["8"].avg.field = "LastCheckpointTime"
cquery.aggs["34"].aggs["9"].avg.field = "UnderReplicatedBlocks"
cquery.aggs["34"].aggs["10"].avg.field = "CorruptBlocks"
cquery.aggs["34"].aggs["11"].avg.field = "CapacityTotal"
cquery.aggs["34"].aggs["12"].avg.field = "CapacityTotalGB"
cquery.aggs["34"].aggs["13"].avg.field = "CapacityUsed"
#cquery.aggs["34"].aggs["14"].avg.field = "CapacityTotalGB" ####
#cquery.aggs["34"].aggs["15"].avg.field = "CapacityUsed"
cquery.aggs["34"].aggs["16"].avg.field = "CapacityUsedGB"
cquery.aggs["34"].aggs["17"].avg.field = "CapacityRemaining"
cquery.aggs["34"].aggs["18"].avg.field = "CapacityRemainingGB"
cquery.aggs["34"].aggs["19"].avg.field = "CapacityUsedNonDFS"
cquery.aggs["34"].aggs["20"].avg.field = "TotalLoad"
cquery.aggs["34"].aggs["21"].avg.field = "SnapshottableDirectories"
cquery.aggs["34"].aggs["22"].avg.field = "Snapshots"
cquery.aggs["34"].aggs["23"].avg.field = "FilesTotal"
cquery.aggs["34"].aggs["24"].avg.field = "PendingReplicationBlocks"
cquery.aggs["34"].aggs["25"].avg.field = "ScheduledReplicationBlocks"
cquery.aggs["34"].aggs["26"].avg.field = "PendingDeletionBlocks"
cquery.aggs["34"].aggs["27"].avg.field = "ExcessBlocks"
cquery.aggs["34"].aggs["28"].avg.field = "PostponedMisreplicatedBlocks"
cquery.aggs["34"].aggs["29"].avg.field = "PendingDataNodeMessageCount"
cquery.aggs["34"].aggs["30"].avg.field = "MillisSinceLastLoadedEdits"
cquery.aggs["34"].aggs["31"].avg.field = "BlockCapacity"
cquery.aggs["34"].aggs["32"].avg.field = "StaleDataNodes"
cquery.aggs["34"].aggs["33"].avg.field = "TotalFiles"
cqueryd = cquery.to_dict()
return cqueryd
def jvmNNquery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["13"].date_histogram.field = "@timestamp"
cquery.aggs["13"].date_histogram.interval = qinterval
cquery.aggs["13"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["13"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["13"].date_histogram.extended_bounds.min = qgte
cquery.aggs["13"].date_histogram.extended_bounds.max = qlte
#NN JVM Metrics
cquery.aggs["13"].aggs["1"].avg.field = "MemNonHeapUsedM"
cquery.aggs["13"].aggs["2"].avg.field = "MemNonHeapCommittedM"
cquery.aggs["13"].aggs["3"].avg.field = "MemHeapUsedM"
cquery.aggs["13"].aggs["4"].avg.field = "MemHeapCommittedM"
cquery.aggs["13"].aggs["5"].avg.field = "MemHeapMaxM"
cquery.aggs["13"].aggs["6"].avg.field = "MemMaxM"
cquery.aggs["13"].aggs["7"].avg.field = "GcCountParNew"
cquery.aggs["13"].aggs["8"].avg.field = "GcTimeMillisParNew"
cquery.aggs["13"].aggs["9"].avg.field = "GcCountConcurrentMarkSweep"
cquery.aggs["13"].aggs["10"].avg.field = "GcTimeMillisConcurrentMarkSweep"
cquery.aggs["13"].aggs["11"].avg.field = "GcCount"
cquery.aggs["13"].aggs["12"].avg.field = "GcTimeMillis"
cquery.aggs["13"].aggs["14"].avg.field = "GcNumWarnThresholdExceeded"
cquery.aggs["13"].aggs["15"].avg.field = "GcNumInfoThresholdExceeded"
cquery.aggs["13"].aggs["16"].avg.field = "GcTotalExtraSleepTime"
cquery.aggs["13"].aggs["17"].avg.field = "ThreadsNew"
cquery.aggs["13"].aggs["18"].avg.field = "ThreadsRunnable"
cquery.aggs["13"].aggs["19"].avg.field = "ThreadsBlocked"
cquery.aggs["13"].aggs["20"].avg.field = "ThreadsWaiting"
cquery.aggs["13"].aggs["21"].avg.field = "ThreadsTimedWaiting"
cquery.aggs["13"].aggs["22"].avg.field = "ThreadsTerminated"
cquery.aggs["13"].aggs["23"].avg.field = "LogError"
cquery.aggs["13"].aggs["24"].avg.field = "LogFatal"
cquery.aggs["13"].aggs["25"].avg.field = "LogWarn"
cquery.aggs["13"].aggs["26"].avg.field = "LogInfo"
cqueryd = cquery.to_dict()
return cqueryd
def jvmMRQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["13"].date_histogram.field = "@timestamp"
cquery.aggs["13"].date_histogram.interval = qinterval
cquery.aggs["13"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["13"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["13"].date_histogram.extended_bounds.min = qgte
cquery.aggs["13"].date_histogram.extended_bounds.max = qlte
# NN JVM Metrics
cquery.aggs["13"].aggs["1"].avg.field = "MemNonHeapUsedM"
cquery.aggs["13"].aggs["2"].avg.field = "MemNonHeapCommittedM"
cquery.aggs["13"].aggs["3"].avg.field = "MemHeapUsedM"
cquery.aggs["13"].aggs["4"].avg.field = "MemHeapCommittedM"
cquery.aggs["13"].aggs["5"].avg.field = "MemHeapMaxM"
cquery.aggs["13"].aggs["6"].avg.field = "MemMaxM"
cquery.aggs["13"].aggs["7"].avg.field = "GcCountParNew"
cquery.aggs["13"].aggs["8"].avg.field = "GcTimeMillisParNew"
cquery.aggs["13"].aggs["9"].avg.field = "GcCountConcurrentMarkSweep"
cquery.aggs["13"].aggs["10"].avg.field = "GcTimeMillisConcurrentMarkSweep"
cquery.aggs["13"].aggs["11"].avg.field = "GcCount"
cquery.aggs["13"].aggs["12"].avg.field = "GcTimeMillis"
cquery.aggs["13"].aggs["17"].avg.field = "ThreadsNew"
cquery.aggs["13"].aggs["18"].avg.field = "ThreadsRunnable"
cquery.aggs["13"].aggs["19"].avg.field = "ThreadsBlocked"
cquery.aggs["13"].aggs["20"].avg.field = "ThreadsWaiting"
cquery.aggs["13"].aggs["21"].avg.field = "ThreadsTimedWaiting"
cquery.aggs["13"].aggs["22"].avg.field = "ThreadsTerminated"
cquery.aggs["13"].aggs["23"].avg.field = "LogError"
cquery.aggs["13"].aggs["24"].avg.field = "LogFatal"
cquery.aggs["13"].aggs["25"].avg.field = "LogWarn"
cquery.aggs["13"].aggs["26"].avg.field = "LogInfo"
cqueryd = cquery.to_dict()
return cqueryd
def resourceQueueQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["23"].date_histogram.field = "@timestamp"
cquery.aggs["23"].date_histogram.interval = qinterval
cquery.aggs["23"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["23"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["23"].date_histogram.extended_bounds.min = qgte
cquery.aggs["23"].date_histogram.extended_bounds.max = qlte
# Resource Manager Queue Metrics
cquery.aggs["23"].aggs["1"].avg.field = "running_0"
cquery.aggs["23"].aggs["2"].avg.field = "running_60"
cquery.aggs["23"].aggs["3"].avg.field = "running_300"
cquery.aggs["23"].aggs["4"].avg.field = "running_1440"
cquery.aggs["23"].aggs["5"].avg.field = "AppsSubmitted"
cquery.aggs["23"].aggs["6"].avg.field = "AppsPending"
cquery.aggs["23"].aggs["7"].avg.field = "AppsCompleted"
cquery.aggs["23"].aggs["8"].avg.field = "AllocatedMB"
cquery.aggs["23"].aggs["9"].avg.field = "AllocatedVCores"
cquery.aggs["23"].aggs["10"].avg.field = "AllocatedContainers"
cquery.aggs["23"].aggs["11"].avg.field = "AggregateContainersAllocated"
cquery.aggs["23"].aggs["12"].avg.field = "AggregateContainersReleased"
cquery.aggs["23"].aggs["13"].avg.field = "AvailableMB"
cquery.aggs["23"].aggs["14"].avg.field = "AvailableVCores"
cquery.aggs["23"].aggs["15"].avg.field = "PendingVCores"
cquery.aggs["23"].aggs["16"].avg.field = "PendingContainers"
cquery.aggs["23"].aggs["17"].avg.field = "ReservedMB"
cquery.aggs["23"].aggs["18"].avg.field = "ReservedContainers"
cquery.aggs["23"].aggs["19"].avg.field = "ActiveUsers"
cquery.aggs["23"].aggs["20"].avg.field = "ActiveApplications"
cquery.aggs["23"].aggs["21"].avg.field = "AppAttemptFirstContainerAllocationDelayNumOps"
cquery.aggs["23"].aggs["22"].avg.field = "AppAttemptFirstContainerAllocationDelayAvgTime"
cqueryd = cquery.to_dict()
return cqueryd
def clusterMetricsQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
# Cluster Metrics
cquery.aggs["2"].aggs["1"].avg.field = "NumActiveNMs"
cquery.aggs["2"].aggs["3"].avg.field = "NumDecommissionedNMs"
cquery.aggs["2"].aggs["4"].avg.field = "NumLostNMs"
cquery.aggs["2"].aggs["5"].avg.field = "NumUnhealthyNMs"
cquery.aggs["2"].aggs["6"].avg.field = "AMLaunchDelayNumOps"
cquery.aggs["2"].aggs["7"].avg.field = "AMLaunchDelayAvgTime"
cquery.aggs["2"].aggs["8"].avg.field = "AMRegisterDelayNumOps"
cquery.aggs["2"].aggs["9"].avg.field = "AMRegisterDelayAvgTime"
cquery.aggs["2"].aggs["10"].avg.field = "NumRebootedNMs"
cqueryd = cquery.to_dict()
return cqueryd
def datanodeMetricsQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["12"].date_histogram.field = "@timestamp"
cquery.aggs["12"].date_histogram.interval = qinterval
cquery.aggs["12"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["12"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["12"].date_histogram.extended_bounds.min = qgte
cquery.aggs["12"].date_histogram.extended_bounds.max = qlte
# DataNode Metrics
cquery.aggs["12"].aggs["1"].avg.field = "BytesWritten"
cquery.aggs["12"].aggs["2"].avg.field = "TotalWriteTime"
cquery.aggs["12"].aggs["3"].avg.field = "BytesRead"
cquery.aggs["12"].aggs["4"].avg.field = "TotalReadTime"
cquery.aggs["12"].aggs["5"].avg.field = "BlocksWritten"
cquery.aggs["12"].aggs["6"].avg.field = "BlocksRead"
cquery.aggs["12"].aggs["7"].avg.field = "BlocksReplicated"
cquery.aggs["12"].aggs["8"].avg.field = "BlocksRemoved"
cquery.aggs["12"].aggs["9"].avg.field = "BlocksVerified"
cquery.aggs["12"].aggs["10"].avg.field = "BlockVerificationFailures"
cquery.aggs["12"].aggs["11"].avg.field = "BlocksCached"
cquery.aggs["12"].aggs["13"].avg.field = "BlocksUncached"
cquery.aggs["12"].aggs["14"].avg.field = "ReadsFromLocalClient"
cquery.aggs["12"].aggs["15"].avg.field = "ReadsFromRemoteClient"
cquery.aggs["12"].aggs["16"].avg.field = "WritesFromLocalClient"
cquery.aggs["12"].aggs["17"].avg.field = "WritesFromRemoteClient"
cquery.aggs["12"].aggs["18"].avg.field = "BlocksGetLocalPathInfo"
cquery.aggs["12"].aggs["19"].avg.field = "RemoteBytesRead"
cquery.aggs["12"].aggs["20"].avg.field = "RemoteBytesWritten"
cquery.aggs["12"].aggs["21"].avg.field = "RamDiskBlocksWrite"
cquery.aggs["12"].aggs["22"].avg.field = "RamDiskBlocksWriteFallback"
cquery.aggs["12"].aggs["23"].avg.field = "RamDiskBytesWrite"
cquery.aggs["12"].aggs["24"].avg.field = "RamDiskBlocksReadHits"
cquery.aggs["12"].aggs["25"].avg.field = "RamDiskBlocksEvicted"
cquery.aggs["12"].aggs["27"].avg.field = "RamDiskBlocksEvictedWithoutRead"
cquery.aggs["12"].aggs["28"].avg.field = "RamDiskBlocksEvictionWindowMsNumOps"
cquery.aggs["12"].aggs["29"].avg.field = "RamDiskBlocksEvictionWindowMsAvgTime"
cquery.aggs["12"].aggs["30"].avg.field = "RamDiskBlocksLazyPersisted"
cquery.aggs["12"].aggs["31"].avg.field = "RamDiskBlocksDeletedBeforeLazyPersisted"
cquery.aggs["12"].aggs["32"].avg.field = "RamDiskBytesLazyPersisted"
cquery.aggs["12"].aggs["33"].avg.field = "RamDiskBlocksLazyPersistWindowMsNumOps"
cquery.aggs["12"].aggs["34"].avg.field = "RamDiskBlocksLazyPersistWindowMsAvgTime"
cquery.aggs["12"].aggs["35"].avg.field = "FsyncCount"
cquery.aggs["12"].aggs["36"].avg.field = "VolumeFailures"
cquery.aggs["12"].aggs["37"].avg.field = "DatanodeNetworkErrors"
cquery.aggs["12"].aggs["38"].avg.field = "ReadBlockOpNumOps"
cquery.aggs["12"].aggs["39"].avg.field = "ReadBlockOpAvgTime"
cquery.aggs["12"].aggs["40"].avg.field = "CopyBlockOpNumOps"
cquery.aggs["12"].aggs["41"].avg.field = "CopyBlockOpAvgTime"
cquery.aggs["12"].aggs["42"].avg.field = "ReplaceBlockOpNumOps"
cquery.aggs["12"].aggs["43"].avg.field = "ReplaceBlockOpAvgTime"
cquery.aggs["12"].aggs["44"].avg.field = "HeartbeatsNumOps"
cquery.aggs["12"].aggs["45"].avg.field = "HeartbeatsAvgTime"
cquery.aggs["12"].aggs["46"].avg.field = "BlockReportsNumOps"
cquery.aggs["12"].aggs["47"].avg.field = "BlockReportsAvgTime"
cquery.aggs["12"].aggs["48"].avg.field = "IncrementalBlockReportsNumOps"
cquery.aggs["12"].aggs["49"].avg.field = "IncrementalBlockReportsAvgTime"
cquery.aggs["12"].aggs["50"].avg.field = "CacheReportsNumOps"
cquery.aggs["12"].aggs["51"].avg.field = "CacheReportsAvgTime"
cquery.aggs["12"].aggs["52"].avg.field = "PacketAckRoundTripTimeNanosNumOps"
cquery.aggs["12"].aggs["53"].avg.field = "FlushNanosNumOps"
cquery.aggs["12"].aggs["54"].avg.field = "FlushNanosAvgTime"
cquery.aggs["12"].aggs["55"].avg.field = "FsyncNanosNumOps"
cquery.aggs["12"].aggs["56"].avg.field = "FsyncNanosAvgTime"
cquery.aggs["12"].aggs["57"].avg.field = "SendDataPacketBlockedOnNetworkNanosNumOps"
cquery.aggs["12"].aggs["58"].avg.field = "SendDataPacketBlockedOnNetworkNanosAvgTime"
cquery.aggs["12"].aggs["59"].avg.field = "SendDataPacketTransferNanosNumOps"
cquery.aggs["12"].aggs["60"].avg.field = "SendDataPacketTransferNanosAvgTime"
cquery.aggs["12"].aggs["61"].avg.field = "WriteBlockOpNumOps"
cquery.aggs["12"].aggs["62"].avg.field = "WriteBlockOpAvgTime"
cquery.aggs["12"].aggs["63"].avg.field = "BlockChecksumOpNumOps"
cquery.aggs["12"].aggs["64"].avg.field = "BlockChecksumOpAvgTime"
cqueryd = cquery.to_dict()
return cqueryd
def fsopDurationsQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
# FSOpDuration metrics
cquery.aggs["2"].aggs["1"].avg.field = "ContinuousSchedulingRunNumOps"
cquery.aggs["2"].aggs["3"].avg.field = "ContinuousSchedulingRunAvgTime"
cquery.aggs["2"].aggs["4"].avg.field = "ContinuousSchedulingRunStdevTime"
# cquery.aggs["2"].aggs["5"].avg.field = "ContinuousSchedulingRunIMinTime"
# cquery.aggs["2"].aggs["6"].avg.field = "ContinuousSchedulingRunIMaxTime"
# cquery.aggs["2"].aggs["7"].avg.field = "ContinuousSchedulingRunMinTime"
# cquery.aggs["2"].aggs["8"].avg.field = "ContinuousSchedulingRunMaxTime"
cquery.aggs["2"].aggs["9"].avg.field = "ContinuousSchedulingRunINumOps"
cquery.aggs["2"].aggs["10"].avg.field = "NodeUpdateCallNumOps"
cquery.aggs["2"].aggs["11"].avg.field = "NodeUpdateCallAvgTime"
cquery.aggs["2"].aggs["12"].avg.field = "NodeUpdateCallStdevTime"
cquery.aggs["2"].aggs["13"].avg.field = "NodeUpdateCallMinTime"
cquery.aggs["2"].aggs["14"].avg.field = "NodeUpdateCallIMinTime"
cquery.aggs["2"].aggs["15"].avg.field = "NodeUpdateCallMaxTime"
cquery.aggs["2"].aggs["16"].avg.field = "NodeUpdateCallINumOps"
cquery.aggs["2"].aggs["17"].avg.field = "UpdateThreadRunNumOps"
cquery.aggs["2"].aggs["18"].avg.field = "UpdateThreadRunAvgTime"
cquery.aggs["2"].aggs["19"].avg.field = "UpdateThreadRunStdevTime"
cquery.aggs["2"].aggs["20"].avg.field = "UpdateThreadRunIMinTime"
cquery.aggs["2"].aggs["21"].avg.field = "UpdateThreadRunMinTime"
cquery.aggs["2"].aggs["22"].avg.field = "UpdateThreadRunMaxTime"
cquery.aggs["2"].aggs["23"].avg.field = "UpdateThreadRunINumOps"
cquery.aggs["2"].aggs["24"].avg.field = "UpdateCallNumOps"
cquery.aggs["2"].aggs["25"].avg.field = "UpdateCallAvgTime"
cquery.aggs["2"].aggs["26"].avg.field = "UpdateCallStdevTime"
cquery.aggs["2"].aggs["27"].avg.field = "UpdateCallIMinTime"
cquery.aggs["2"].aggs["28"].avg.field = "UpdateCallMinTime"
cquery.aggs["2"].aggs["29"].avg.field = "UpdateCallMaxTime"
cquery.aggs["2"].aggs["30"].avg.field = "UpdateCallINumOps"
cquery.aggs["2"].aggs["31"].avg.field = "PreemptCallNumOps"
cquery.aggs["2"].aggs["32"].avg.field = "PreemptCallAvgTime"
cquery.aggs["2"].aggs["33"].avg.field = "PreemptCallStdevTime"
cquery.aggs["2"].aggs["34"].avg.field = "PreemptCallINumOps"
cqueryd = cquery.to_dict()
return cqueryd
def shuffleQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
#Shuffle metrics
cquery.aggs["2"].aggs["1"].avg.field = "ShuffleConnections"
cquery.aggs["2"].aggs["3"].avg.field = "ShuffleOutputBytes"
cquery.aggs["2"].aggs["4"].avg.field = "ShuffleOutputsFailed"
cquery.aggs["2"].aggs["5"].avg.field = "ShuffleOutputsOK"
cqueryd = cquery.to_dict()
return cqueryd
def queryByProcess(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
cquery.fields = ["*", "_source"]
cquery.script_fields = {}
cquery.fielddata_fields = ["@timestamp"]
cquery.sort = [{"@timestamp": {"order": "desc", "unmapped_type": "boolean"}}]
cqueryd = cquery.to_dict()
return cqueryd
def stormQuery(self, qstring, qgte, qlte, qsize, qinterval, bolts, spouts, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["1"].date_histogram.field = "@timestamp"
cquery.aggs["1"].date_histogram.interval = qinterval
cquery.aggs["1"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["1"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["1"].date_histogram.extended_bounds.min = qgte
cquery.aggs["1"].date_histogram.extended_bounds.max = qlte
#Storm metrics
cquery.aggs["1"].aggs["2"].avg.field = "executorsTotal"
cquery.aggs["1"].aggs["3"].avg.field = "msgTimeout"
cquery.aggs["1"].aggs["4"].avg.field = "tasksTotal"
cquery.aggs["1"].aggs["5"].avg.field = "workersTotal"
cquery.aggs["1"].aggs["6"].avg.field = "topologyStats_10m_acked"
cquery.aggs["1"].aggs["7"].avg.field = "topologyStats_10m_completeLatency"
cquery.aggs["1"].aggs["8"].avg.field = "topologyStats_10m_emitted"
cquery.aggs["1"].aggs["9"].avg.field = "topologyStats_10m_failed"
cquery.aggs["1"].aggs["10"].avg.field = "topologyStats_10m_transferred"
cquery.aggs["1"].aggs["11"].avg.field = "topologyStats_10m_window"
cquery.aggs["1"].aggs["12"].avg.field = "topologyStats_1d_acked"
cquery.aggs["1"].aggs["13"].avg.field = "topologyStats_1d_completeLatency"
cquery.aggs["1"].aggs["14"].avg.field = "topologyStats_1d_emitted"
cquery.aggs["1"].aggs["15"].avg.field = "topologyStats_1d_failed"
cquery.aggs["1"].aggs["16"].avg.field = "topologyStats_1d_transferred"
cquery.aggs["1"].aggs["17"].avg.field = "topologyStats_1d_window"
cquery.aggs["1"].aggs["18"].avg.field = "topologyStats_3h_acked"
cquery.aggs["1"].aggs["19"].avg.field = "topologyStats_3h_completeLatency"
cquery.aggs["1"].aggs["20"].avg.field = "topologyStats_3h_emitted"
cquery.aggs["1"].aggs["21"].avg.field = "topologyStats_3h_failed"
cquery.aggs["1"].aggs["22"].avg.field = "topologyStats_3h_transferred"
cquery.aggs["1"].aggs["23"].avg.field = "topologyStats_3h_window"
cquery.aggs["1"].aggs["24"].avg.field = "topologyStats_all_acked"
cquery.aggs["1"].aggs["25"].avg.field = "topologyStats_all_completeLatency"
cquery.aggs["1"].aggs["26"].avg.field = "topologyStats_all_emitted"
cquery.aggs["1"].aggs["27"].avg.field = "topologyStats_all_failed"
cquery.aggs["1"].aggs["28"].avg.field = "topologyStats_all_transferred"
gcounter = 29
for n in range(0, bolts):
cquery.aggs["1"].aggs[str(gcounter+1)].avg.field = "bolts_%s_acked" % str(n)
cquery.aggs["1"].aggs[str(gcounter+2)].avg.field = "bolts_%s_capacity" % str(n)
cquery.aggs["1"].aggs[str(gcounter+3)].avg.field = "bolts_%s_emitted" % str(n)
cquery.aggs["1"].aggs[str(gcounter+4)].avg.field = "bolts_%s_executed" % str(n)
cquery.aggs["1"].aggs[str(gcounter+5)].avg.field = "bolts_%s_executors" % str(n)
cquery.aggs["1"].aggs[str(gcounter+6)].avg.field = "bolts_%s_failed" % str(n)
cquery.aggs["1"].aggs[str(gcounter+7)].avg.field = "bolts_%s_tasks" % str(n)
cquery.aggs["1"].aggs[str(gcounter+8)].avg.field = "bolts_%s_transferred" % str(n)
cquery.aggs["1"].aggs[str(gcounter+9)].avg.field = "bolts_%s_executeLatency" % str(n)
cquery.aggs["1"].aggs[str(gcounter+10)].avg.field = "bolts_%s_processLatency" % str(n)
gcounter += 10
for t in range(0, spouts):
cquery.aggs["1"].aggs[str(gcounter+1)].avg.field = "spouts_%s_acked" % str(t)
cquery.aggs["1"].aggs[str(gcounter+2)].avg.field = "spouts_%s_emitted" % str(t)
cquery.aggs["1"].aggs[str(gcounter+3)].avg.field = "spouts_%s_executors" % str(t)
cquery.aggs["1"].aggs[str(gcounter+4)].avg.field = "spouts_%s_failed" % str(t)
cquery.aggs["1"].aggs[str(gcounter+5)].avg.field = "spouts_%s_tasks" % str(t)
cquery.aggs["1"].aggs[str(gcounter+6)].avg.field = "spouts_%s_transferred" % str(t)
cquery.aggs["1"].aggs[str(gcounter+7)].avg.field = "spouts_%s_completeLatency" % str(t)
gcounter += 7
cqueryd = cquery.to_dict()
return cqueryd
def cassandraQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["1"].date_histogram.field = "@timestamp"
cquery.aggs["1"].date_histogram.interval = qinterval
cquery.aggs["1"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["1"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["1"].date_histogram.extended_bounds.min = qgte
cquery.aggs["1"].date_histogram.extended_bounds.max = qlte
#Cassandra Metrics
cquery.aggs["1"].aggs["3"].terms.field = "type_instance"
cquery.aggs["1"].aggs["3"].terms.size = 0
cquery.aggs["1"].aggs["3"].terms.order["1"] = "desc"
cquery.aggs["1"].aggs["3"].aggs["1"].avg.field = "value"
cqueryd = cquery.to_dict()
return cqueryd
def mongoDBCounterQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["1"].date_histogram.field = "@timestamp"
cquery.aggs["1"].date_histogram.interval = qinterval
cquery.aggs["1"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["1"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["1"].date_histogram.extended_bounds.min = qgte
cquery.aggs["1"].date_histogram.extended_bounds.max = qlte
# Cassandra Metrics
cquery.aggs["1"].aggs["3"].terms.field = "type_instance"
cquery.aggs["1"].aggs["3"].terms.size = 0
cquery.aggs["1"].aggs["3"].terms.order["1"] = "desc"
cquery.aggs["1"].aggs["3"].aggs["1"].avg.field = "value"
cqueryd = cquery.to_dict()
return cqueryd
def mongoDBGaugeQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["1"].date_histogram.field = "@timestamp"
cquery.aggs["1"].date_histogram.interval = qinterval
cquery.aggs["1"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["1"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["1"].date_histogram.extended_bounds.min = qgte
cquery.aggs["1"].date_histogram.extended_bounds.max = qlte
# Cassandra Metrics
cquery.aggs["1"].aggs["3"].terms.field = "type_instance"
cquery.aggs["1"].aggs["3"].terms.size = 0
cquery.aggs["1"].aggs["3"].terms.order["1"] = "desc"
cquery.aggs["1"].aggs["3"].aggs["1"].avg.field = "value"
cqueryd = cquery.to_dict()
return cqueryd
def cepQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.highlight.pre_tags = ["@kibana-highlighted-field@"]
cquery.highlight.post_tags = ["@/kibana-highlighted-field@"]
cquery.highlight.fields = {"*":{}}
cquery.highlight.require_field_match = False
cquery.highlight.fragment_size = 2147483647
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.sort = [{"@timestamp": {"order": "desc", "unmapped_type": "boolean"}}]
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
cquery.fields = ["*", "_source"]
cquery.script_fields = {}
cquery.fielddata_fields = ["@timestamp"]
cqueryd = cquery.to_dict()
return cqueryd
def sparkQuery(self, qstring, qgte, qlte, qsize, qinterval, wildCard=True, qtformat="epoch_millis",
qmin_doc_count=1):
cquery = Dict()
cquery.query.filtered.query.query_string.query = qstring
cquery.query.filtered.query.query_string.analyze_wildcard = wildCard
cquery.query.filtered.filter.bool.must = [
{"range": {"@timestamp": {"gte": qgte, "lte": qlte, "format": qtformat}}}]
cquery.query.filtered.filter.bool.must_not = []
cquery.size = qsize
cquery.aggs["2"].date_histogram.field = "@timestamp"
cquery.aggs["2"].date_histogram.interval = qinterval
cquery.aggs["2"].date_histogram.time_zone = "Europe/Helsinki"
cquery.aggs["2"].date_histogram.min_doc_count = qmin_doc_count
cquery.aggs["2"].date_histogram.extended_bounds.min = qgte
cquery.aggs["2"].date_histogram.extended_bounds.max = qlte
# Spark metrics
cquery.aggs["2"].aggs["1"].avg.field = "jvm_total_committed"
cquery.aggs["2"].aggs["3"].avg.field = "jvm_heap_init"
cquery.aggs["2"].aggs["4"].avg.field = "jvm_total_used"
cquery.aggs["2"].aggs["5"].avg.field = "jvm_pools_Tenured-Gen_committed"
cquery.aggs["2"].aggs["6"].avg.field = "jvm_pools_Tenured-Gen_init"
cquery.aggs["2"].aggs["7"].avg.field = "jvm_pools_Tenured-Gen_max"
cquery.aggs["2"].aggs["8"].avg.field = "jvm_pools_Tenured-Gen_usage"
cquery.aggs["2"].aggs["9"].avg.field = "jvm_pools_Tenured-Gen_used"
cquery.aggs["2"].aggs["10"].avg.field = "jvm_pools_Survivor-Space_committed"
cquery.aggs["2"].aggs["11"].avg.field = "jvm_pools_Survivor-Space_init"
cquery.aggs["2"].aggs["12"].avg.field = "jvm_pools_Survivor-Space_max"
cquery.aggs["2"].aggs["13"].avg.field = "jvm_pools_Survivor-Space_usage"
cquery.aggs["2"].aggs["14"].avg.field = "jvm_pools_Survivor-Space_used"
cquery.aggs["2"].aggs["15"].avg.field = "jvm_pools_Metaspace_committed"
cquery.aggs["2"].aggs["16"].avg.field = "jvm_pools_Metaspace_init"
cquery.aggs["2"].aggs["17"].avg.field = "jvm_pools_Metaspace_max"
cquery.aggs["2"].aggs["18"].avg.field = "jvm_pools_Metaspace_usage"
cquery.aggs["2"].aggs["19"].avg.field = "jvm_pools_Metaspace_used"
cquery.aggs["2"].aggs["20"].avg.field = "jvm_pools_Eden-Space_committed"
cquery.aggs["2"].aggs["21"].avg.field = "jvm_pools_Eden-Space_init"
cquery.aggs["2"].aggs["22"].avg.field = "jvm_pools_Eden-Space_max"
cquery.aggs["2"].aggs["23"].avg.field = "jvm_pools_Eden-Space_usage"
cquery.aggs["2"].aggs["24"].avg.field = "jvm_pools_Eden-Space_used"
cquery.aggs["2"].aggs["25"].avg.field = "jvm_pools_Compressed-Class-Space_committed"
cquery.aggs["2"].aggs["26"].avg.field = "jvm_pools_Compressed-Class-Space_init"
cquery.aggs["2"].aggs["27"].avg.field = "jvm_pools_Compressed-Class-Space_max"
cquery.aggs["2"].aggs["28"].avg.field = "jvm_pools_Compressed-Class-Space_usage"
cquery.aggs["2"].aggs["29"].avg.field = "jvm_pools_Compressed-Class-Space_used"
cquery.aggs["2"].aggs["30"].avg.field = "jvm_pools_Code-Cache_committed"
cquery.aggs["2"].aggs["31"].avg.field = "jvm_pools_Code-Cache_init"
cquery.aggs["2"].aggs["32"].avg.field = "jvm_pools_Code-Cache_max"
cquery.aggs["2"].aggs["33"].avg.field = "jvm_pools_Code-Cache_usage"
cquery.aggs["2"].aggs["34"].avg.field = "jvm_pools_Code-Cache_used"
cquery.aggs["2"].aggs["35"].avg.field = "jvm_non-heap_committed"