def aggregateTxnsByRoutes(self, txnSubCollection, ancestry):
"""
Aggregates transactions in a given subcollection to child collections
in an ancestry node with conflatable routes
:param txnSubCollection: Transaction subcollection to be aggregated
:param ancestry: A node in the tree collection
"""
from xpedite.types.route import conflateRoutes
srcRouteMap = self.srcTree.getChildren(ancestry)
routes = srcRouteMap.keys() if srcRouteMap else []
routeMap = {}
for txn in txnSubCollection:
for dstRoute in routes:
if conflateRoutes(txn.route, dstRoute):
addTxn(routeMap, txnSubCollection, dstRoute, txn)
return routeMap
def conflateTimelineStats(self, route, src, dst):
"""
Conflates timelines from source timeline stats to destination timeline stats
:param route: route used for conflation
:param src: Source timeline stats
:param dst: Destination timeline stats
"""
routeIndices = conflateRoutes(src.route, route)
if routeIndices:
topdownMetrics = self.getTopdownMetrics(src.cpuInfo.cpuId, src.topdownKeys)
self.conflateTimelineCollection(
routeIndices, src.timelineCollection, dst.timelineCollection,
src.buildEventsMap(), topdownMetrics
)
return True
return None
def diffTxn(lhs, rhs, profiles):
"""
Compares duration/pmc values for a pair of transactions
:param lhs: Transaction id (lhs value of comparison)
:type lhs: int
:param rhs: Transaction id (rhs value of comparison)
:type rhs: int
:param profiles: Transactions from the current profile session
:type profiles: xpedite.report.profile.Profiles
"""
from xpedite.analytics.timelineFilter import locateTimeline
from xpedite.report.diff import DiffBuilder
from xpedite.types.route import conflateRoutes
from xpedite.analytics.conflator import Conflator
timeline1 = locateTimeline(profiles, lhs)
timeline2 = locateTimeline(profiles, rhs)
if timeline1 and timeline2:
lhs, rhs = (timeline1, timeline2) if len(timeline1) > len(timeline2) else (timeline2, timeline1)
routeIndices = conflateRoutes(lhs.txn.route, rhs.txn.route)
if not routeIndices:
display(HTML(ERROR_TEXT.format('Transactions {} and {} are not comparable'.format(lhs.txnId, rhs.txnId))))
return
topdownMetrics = Conflator().getTopdownMetrics(profiles.cpuInfo.cpuId, profiles.topdownKeys)
conflatedTimeline = Conflator().conflateTimeline(lhs, routeIndices, profiles.eventsMap, topdownMetrics)
display(HTML(str(DiffBuilder().buildDiffTable(conflatedTimeline, rhs))))
else:
if not (timeline1 or timeline2):
display(HTML(ERROR_TEXT.format(
'Can\'t find transactions. Are these ({} and {}) valid txn id\'s?'.format(lhs, rhs)
)))
else:
txnId = rhs if timeline1 else lhs
display(HTML(ERROR_TEXT.format('Can\'t find transaction {}, is the txn id valid?'.format(txnId))))
def buildTimelineStats(category, route, probes, txnSubCollection): # pylint: disable=too-many-locals
"""
Builds timeline statistics from a subcollection of transactions
:param probes: List of probes enabled for a profiling session
:param txnSubCollection: A subcollection of transactions
"""
from xpedite.types import InvariantViloation
begin = time.time()
cpuInfo = txnSubCollection.cpuInfo
topdownMetrics = txnSubCollection.topdownMetrics
timelineCollection = []
topdownKeys = topdownMetrics.topdownKeys() if topdownMetrics else []
deltaSeriesRepo = DeltaSeriesRepo(txnSubCollection.events, topdownKeys, probes)
pmcNames = deltaSeriesRepo.pmcNames
eventsMap = deltaSeriesRepo.buildEventsMap()
timelineStats = TimelineStats(
txnSubCollection.name, cpuInfo, category, route,
probes, timelineCollection, deltaSeriesRepo
)
tscDeltaSeriesCollection = deltaSeriesRepo.getTscDeltaSeriesCollection()
pmcCount = len(txnSubCollection.events) if txnSubCollection.events else 0
inceptionTsc = None
defaultIndices = range(len(route))
totalTxnCount = len(txnSubCollection)
for txnCount, txn in enumerate(txnSubCollection):
timeline = Timeline(txn)
indices = conflateRoutes(txn.route, route) if len(txn) > len(route) else defaultIndices
firstCounter = prevCounter = None
maxTsc = 0
i = -1
endpoint = TimePoint('end', 0, deltaPmcs=([0]* pmcCount if pmcCount > 0 else None))
for j in indices:
i += 1
probe = probes[i]
counter = txn[j]
if not compareProbes(probe, counter.probe):
raise InvariantViloation('category [{}] has mismatch of probes '
'"{}" vs "{}" in \n\ttransaction {}]\n\troute {}'.format(
category, probe, counter.probe, txn.txnId, probes
)
)
if counter:
tsc = counter.tsc
maxTsc = max(maxTsc, tsc)
if not firstCounter:
firstCounter = prevCounter = counter
elif tsc:
duration = cpuInfo.convertCyclesToTime(tsc - prevCounter.tsc)
point = cpuInfo.convertCyclesToTime(prevCounter.tsc - firstCounter.tsc)
timePoint = TimePoint(probes[i-1].name, point, duration, data=prevCounter.data)
if len(counter.pmcs) < pmcCount:
raise InvariantViloation(
'category [{}] has transaction {} with counter {} '
'missing pmc samples {}/{}'.format(
category, txn.txnId, counter, len(counter.pmcs), pmcCount
)
)
if pmcCount != 0:
timePoint.pmcNames = pmcNames
timePoint.deltaPmcs = []
for k in range(pmcCount):
deltaPmc = counter.pmcs[k] - prevCounter.pmcs[k] if counter.threadId == prevCounter.threadId else NAN
endpoint.deltaPmcs[k] += (deltaPmc if counter.threadId == prevCounter.threadId else 0)
timePoint.deltaPmcs.append(deltaPmc)
deltaSeriesRepo[pmcNames[k]][i-1].addDelta(deltaPmc)
if topdownMetrics:
counterMap = CounterMap(eventsMap, timePoint.deltaPmcs)
timePoint.topdownValues = topdownMetrics.compute(counterMap)
for td in timePoint.topdownValues:
deltaSeriesRepo[td.name][i-1].addDelta(td.value)
timeline.addTimePoint(timePoint)
tscDeltaSeriesCollection[i-1].addDelta(duration)
prevCounter = counter
else:
raise InvariantViloation(
'category [{}] has transaction {} with missing tsc for probe {}/counter {}'.format(
category, txn.txnId, probe, counter
)
)
else:
raise InvariantViloation(
'category [{}] has transaction {} with probe {} missing counter data'.format(
category, probe, txn.txnId
)
)
if prevCounter:
point = cpuInfo.convertCyclesToTime(prevCounter.tsc - firstCounter.tsc)
timeline.addTimePoint(TimePoint(probes[-1].name, point, 0, data=prevCounter.data))
endpoint.duration = cpuInfo.convertCyclesToTime(maxTsc - firstCounter.tsc)
if pmcCount != 0:
endpoint.pmcNames = pmcNames
for k, deltaPmc in enumerate(endpoint.deltaPmcs):
deltaSeriesRepo[pmcNames[k]][-1].addDelta(deltaPmc)
if topdownMetrics:
counterMap = CounterMap(eventsMap, endpoint.deltaPmcs)
endpoint.topdownValues = topdownMetrics.compute(counterMap)
for td in endpoint.topdownValues:
deltaSeriesRepo[td.name][-1].addDelta(td.value)
timeline.endpoint = endpoint
timelineCollection.append(timeline)
tscDeltaSeriesCollection[-1].addDelta(endpoint.duration)
elapsed = time.time() - begin
if elapsed >= 5:
LOGGER.completed(
'\n\tprocessed %d out of %d transactions | %0.2f%% complete |',
txnCount, totalTxnCount, float(100 * float(txnCount)/float(totalTxnCount))
)
begin = time.time()
if not inceptionTsc:
inceptionTsc = firstCounter.tsc
timeline.inception = 0
else:
timeline.inception = int(cpuInfo.convertCyclesToTime(firstCounter.tsc - inceptionTsc) / 1000)
return timelineStats