def executeJob(self, job):
time.sleep(5)
job['status'] = 'RUNNING'
(responseCode, job) = self.service.saveJob(job)
if responseCode != 200: raise Exception("could not save job status.")
filePath = conf.MOCK_DATA_PATH[job['queryType']]
with open(filePath, 'r') as handle:
data = json.loads(handle.read())
result_set = GeqeAPI.rawResultToResultDocument(job, data)
(response, result_set) = self.service.saveResultset(result_set)
if response != 200:
job['status'] = 'FAILED'
self.service.saveJob(job)
print str(result_set)
raise Exception("Could not save result set. error: " +
str(response))
if 'modelSavePath' in job and job['modelSavePath'] is not None:
# save the model meta data
modelData = {
"name": job['name'],
"username": job["username"],
"queryType": job["queryType"],
"modelSavePath": job['modelSavePath'],
"siteListId": job["siteListId"],
"datasetId": job["datasetId"]
}
(response, modelData) = self.service.saveModelData(modelData)
if response != 200:
job['status'] = 'FAILED'
self.service.saveJob(job)
raise Exception("Could not save model metadata: " +
str(response) + " \n" + str(modelData))
# save the results set into elastic search
if conf.ES_HOST is not None:
elaticsearchConnetor = GeqeAPI.ElasticSearchHelper(
conf.ES_HOST, port=conf.ES_PORT)
(response,
es_result) = elaticsearchConnetor.addResultSet(result_set)
if response != 201:
job['status'] = 'FAILED'
self.service.saveJob(job)
raise Exception("Could not save result set to es. error: " +
str(response) + " \n" + str(result_set))
time.sleep(5)
job['status'] = 'SUCCESS'
job['resultsetId'] = result_set['id']
(response, job) = self.service.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
return True
def executeJob(self, job):
time.sleep(5)
job["status"] = "RUNNING"
(responseCode, job) = self.service.saveJob(job)
if responseCode != 200:
raise Exception("could not save job status.")
filePath = conf.MOCK_DATA_PATH[job["queryType"]]
with open(filePath, "r") as handle:
data = json.loads(handle.read())
result_set = GeqeAPI.rawResultToResultDocument(job, data)
(response, result_set) = self.service.saveResultset(result_set)
if response != 200:
job["status"] = "FAILED"
self.service.saveJob(job)
print str(result_set)
raise Exception("Could not save result set. error: " + str(response))
if "modelSavePath" in job and job["modelSavePath"] is not None:
# save the model meta data
modelData = {
"name": job["name"],
"username": job["username"],
"queryType": job["queryType"],
"modelSavePath": job["modelSavePath"],
"siteListId": job["siteListId"],
"datasetId": job["datasetId"],
}
(response, modelData) = self.service.saveModelData(modelData)
if response != 200:
job["status"] = "FAILED"
self.service.saveJob(job)
raise Exception("Could not save model metadata: " + str(response) + " \n" + str(modelData))
# save the results set into elastic search
if conf.ES_HOST is not None:
elaticsearchConnetor = GeqeAPI.ElasticSearchHelper(conf.ES_HOST, port=conf.ES_PORT)
(response, es_result) = elaticsearchConnetor.addResultSet(result_set)
if response != 201:
job["status"] = "FAILED"
self.service.saveJob(job)
raise Exception("Could not save result set to es. error: " + str(response) + " \n" + str(result_set))
time.sleep(5)
job["status"] = "SUCCESS"
job["resultsetId"] = result_set["id"]
(response, job) = self.service.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
return True
}
]
}
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("serviceUrl", help="loop back data service url ie http://localhost:5500")
parser.add_argument("inputFile", help="polygon file to upload")
parser.add_argument("--name", help="name of the dataset")
parser.add_argument("--username", help="name of the user account.")
args = parser.parse_args()
dataConnector = GeqeAPI.GeqeRestHelper(args.serviceUrl)
if args.name is None or args.username is None:
print 'name and username are required'
parser.print_help()
sys.exit(1)
with open(args.inputFile) as handle:
data = json.loads(handle.read())
data['name'] = args.name
data['username'] = args.username
for site in data['sites']:
if 'dates' in site and len(site['dates']) > 0:
for daterange in site['dates']:
def executeJob(dataConnector, elaticsearchConnetor, job, dataset, sitelist):
#Declare Spark Context
conf = SparkConf().setAppName("GeqeRunner")
conf.set('spark.driver.maxResultSize', '0')
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
#Create polygon list and broadcast variable based on it
lPolygon = shapeReader.readInShapeDocument(
sitelist) if sitelist is not None else None
queryType = job['queryType']
jobname = job['username'] + '_' + job['name']
modelSavePath = job['modelSavePath'] if 'modelSavePath' in job else None
datasetPath = None
inputPartitions = None
if job['queryType'] == 'location':
datasetPath = dataset['location_path']
inputPartitions = dataset['location_partitions']
elif job['queryType'] == 'event':
datasetPath = dataset['event_path']
inputPartitions = dataset['event_partitions']
else:
raise ValueError("invalid query type: " + str(job['queryType']))
dictionaryPath = dataset['dictionaryPath']
nDataType = int(dataset['type'])
modelData = None
if 'geqeModelId' in job and job['geqeModelId'] != "":
(response, modelData) = dataConnector.getModelData(job['geqeModelId'])
if response != 200: raise Exception("Could not load geqeModel")
#iif modelData['dictionaryPath'] != dictionaryPath:
# raise ValueError("dataset dictionary and model dictionary do not match")
sNum = job['limit'] if 'limit' in job else 25
result = None
if modelData:
modelPath = modelData['modelSavePath'] + '/' + modelData['name']
result = run_refindSimilarPlaces(sc,
sqlContext,
jobname,
datasetPath,
dictionaryPath,
inputPartitions,
sNum,
modelPath,
bByDate=queryType == 'event')
elif 'location' == queryType:
result = run_findSimilarPlaces(sc, sqlContext, jobname, datasetPath,
dictionaryPath, nDataType,
inputPartitions, sNum, lPolygon,
modelSavePath)
elif 'event' == queryType:
result = run_findSimilarEvent(sc, sqlContext, jobname, datasetPath,
dictionaryPath, nDataType,
inputPartitions, sNum, lPolygon,
modelSavePath)
else:
raise Exception("Invalid query type: " + queryType)
if modelSavePath is not None:
# save the model meta data
modelData = {
"name": jobname,
"username": job["username"],
"queryType": job["queryType"],
"modelSavePath": modelSavePath,
"siteListId": job["siteListId"],
"datasetId": job["datasetId"]
}
(response, modelData) = dataConnector.saveModelData(modelData)
if response != 200:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save model metadata: " + str(response) +
" \n" + str(modelData))
result = GeqeAPI.rawResultToResultDocument(job, result)
# save the result
(response, result_set) = dataConnector.saveResultset(result)
if response != 200:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save result set. error: " + str(response) +
" \n" + str(result_set))
# save the results set into elastic search
if elaticsearchConnetor is not None:
(response, es_result) = elaticsearchConnetor.addResultSet(result_set)
if response != 201:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save result set to es. error: " +
str(response) + " \n" + str(result_set))
# update the job info
job['status'] = 'SUCCESS'
job['resultsetId'] = result_set['id']
(response, job) = dataConnector.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
raise Exception("could not save job status.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("serviceUrl", help="loop back data service url")
parser.add_argument("--elasticsearchHost",
help="Host name or ip address for elastic search.")
parser.add_argument("--elasticsearchPort",
type=int,
help="Port for elastic search defaults to 9200",
default=9200)
parser.add_argument("jobId", help="The job to execute")
args = parser.parse_args()
dataConnector = GeqeAPI.GeqeRestHelper(args.serviceUrl)
elaticsearchConnetor = GeqeAPI.ElasticSearchHelper(
args.elasticsearchHost,
port=args.elasticsearchPort) if args.elasticsearchHost else None
# Job
(responseCode, job) = dataConnector.getJob(args.jobId)
if 200 != responseCode:
raise Exception("Could not read job: " + args.jobId + ' response: ' +
str(responseCode))
job['status'] = 'RUNNING'
(responseCode, job) = dataConnector.saveJob(job)
if 200 != responseCode:
raise Exception("Could not save job: " + args.jobId + ' response: ' +
str(responseCode))
print 'JOB: ', job
def executeJob(dataConnector,elaticsearchConnetor,job,dataset,sitelist):
#Declare Spark Context
conf = SparkConf().setAppName("GeqeRunner")
conf.set('spark.driver.maxResultSize','0')
sc = SparkContext(conf = conf)
sqlContext = SQLContext(sc)
#Create polygon list and broadcast variable based on it
lPolygon = shapeReader.readInShapeDocument(sitelist) if sitelist is not None else None
queryType = job['queryType']
jobname = job['username']+'_'+job['name']
modelSavePath = job['modelSavePath'] if 'modelSavePath' in job else None
datasetPath = None
inputPartitions = None
if job['queryType'] == 'location':
datasetPath = dataset['location_path']
inputPartitions = dataset['location_partitions']
elif job['queryType'] == 'event':
datasetPath = dataset['event_path']
inputPartitions = dataset['event_partitions']
else: raise ValueError("invalid query type: "+str(job['queryType']))
dictionaryPath = dataset['dictionaryPath']
nDataType = int(dataset['type'])
modelData = None
if 'geqeModelId' in job and job['geqeModelId'] != "":
(response,modelData) = dataConnector.getModelData(job['geqeModelId'])
if response != 200: raise Exception("Could not load geqeModel")
#iif modelData['dictionaryPath'] != dictionaryPath:
# raise ValueError("dataset dictionary and model dictionary do not match")
sNum = job['limit'] if 'limit' in job else 25
result = None
if modelData:
modelPath = modelData['modelSavePath']+'/'+modelData['name']
result = run_refindSimilarPlaces(sc,sqlContext,jobname,datasetPath,dictionaryPath,inputPartitions,sNum,modelPath,bByDate= queryType == 'event')
elif 'location' == queryType:
result = run_findSimilarPlaces(sc,sqlContext,jobname,datasetPath,dictionaryPath,nDataType,inputPartitions,sNum,lPolygon,modelSavePath)
elif 'event' == queryType:
result = run_findSimilarEvent(sc,sqlContext,jobname,datasetPath,dictionaryPath,nDataType,inputPartitions,sNum,lPolygon,modelSavePath)
else:
raise Exception("Invalid query type: "+queryType)
if modelSavePath is not None:
# save the model meta data
modelData = {
"name" : jobname,
"username": job["username"],
"queryType": job["queryType"],
"modelSavePath": modelSavePath,
"siteListId" : job["siteListId"],
"datasetId" : job["datasetId"]
}
(response,modelData) = dataConnector.saveModelData(modelData)
if response != 200:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save model metadata: "+str(response)+" \n"+str(modelData))
result = GeqeAPI.rawResultToResultDocument(job,result)
# save the result
(response,result_set) = dataConnector.saveResultset(result)
if response != 200:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save result set. error: "+str(response)+" \n"+str(result_set))
# save the results set into elastic search
if elaticsearchConnetor is not None:
(response,es_result) = elaticsearchConnetor.addResultSet(result_set)
if response != 201:
job['status'] = 'FAILED'
dataConnector.saveJob(job)
raise Exception("Could not save result set to es. error: "+str(response)+" \n"+str(result_set))
# update the job info
job['status'] = 'SUCCESS'
job['resultsetId'] = result_set['id']
(response,job) = dataConnector.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
job["resultsetId"] = result_set["id"]
(response, job) = self.service.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
return True
if __name__ == "__main__":
global CLUSTER_STATUS
service = GeqeAPI.GeqeRestHelper(conf.LOOPBACK_SERVICE)
# insert mock dataset for front end testing, ensures a usable UI
for shape in [1, 2]:
dataset = GeqeAPI.getMockDataSet(shape=shape)
(response, dataset) = service.saveDataset(dataset)
if response != 200:
print "response: ", response
print dataset
raise Exception("Could not save MOCK dataset.")
(response, clusterStatus) = service.putStatus({"host": platform.node(), "status": "RUNNING"})
if response != 200:
print "response: ", response
print clusterStatus
raise Exception("Could not save cluster status.")
thread = JobRunner(conf.LOOPBACK_SERVICE)
thread.setDaemon(True)
try:
def __init__(self, serviceUrl):
super(JobRunner, self).__init__()
self.url = serviceUrl
self.service = GeqeAPI.GeqeRestHelper(self.url)
])
command.extend([self.service.serviceURL, job['id']])
command = map(str, command)
with open('lastcommand.sh', 'w') as handle:
handle.write(' '.join(command))
result = subprocess.call(command, stdout=stdoutFile, stderr=stderrFile)
print 'result: ', str(result)
stderrFile.close()
stdoutFile.close()
return int(result) == 0
if __name__ == '__main__':
global CLUSTER_STATUS
service = GeqeAPI.GeqeRestHelper(conf.LOOPBACK_SERVICE)
(response, clusterStatus) = service.putStatus({
'host': platform.node(),
'status': 'RUNNING'
})
if response != 200:
print 'response: ', response
print clusterStatus
raise Exception("Could not save cluster status.")
thread = JobRunner(conf.LOOPBACK_SERVICE)
thread.setDaemon(True)
try:
thread.start()
while thread.isAlive():
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"serviceUrl",
help="loop back data service url ie http://localhost:5500")
parser.add_argument("username", help="geqe username")
parser.add_argument("name", help="name of the dataset / job")
parser.add_argument("resultSetPath", help="path to the result file.")
parser.set_defaults(delete=False)
args = parser.parse_args()
dataConnector = GeqeAPI.GeqeRestHelper(args.serviceUrl)
# create a job object
job = {
"name": args.name,
"status": "WAITING",
"queryType": "unkown",
"limit": -1,
"username": args.username
}
(response, job) = dataConnector.saveJob(job)
if response != 200:
print "ERROR: Could not save job. = ", response, job
sys.exit(1)
# save the result set
"name": args.name,
"status": "WAITING",
"queryType": "unkown",
"limit" : -1,
"username": args.username
}
(response,job) = dataConnector.saveJob(job)
if response != 200:
print "ERROR: Could not save job. = ",response,job
sys.exit(1)
# save the result set
with open(args.resultSetPath,'r') as handle:
data = json.loads(handle.read())
result_set = GeqeAPI.rawResultToResultDocument(job,data)
(response,result_set) = dataConnector.saveResultset(result_set)
if response != 200:
print "ERROR: Could not save result. = ",response,result_set
sys.exit(1)
job['status'] = 'SUCCESS'
job['queryType'] = result_set['type']
job['resultsetId'] = result_set['id']
(response,job) = dataConnector.saveJob(job)
if response != 200:
print "ERROR: Could not save job. = ",response,job
sys.exit(1)
print 'success'
str(response) + " \n" + str(result_set))
time.sleep(5)
job['status'] = 'SUCCESS'
job['resultsetId'] = result_set['id']
(response, job) = self.service.saveJob(job)
if response != 200:
raise Exception("could not save job status.")
return True
if __name__ == '__main__':
global CLUSTER_STATUS
service = GeqeAPI.GeqeRestHelper(conf.LOOPBACK_SERVICE)
# insert mock dataset for front end testing, ensures a usable UI
for shape in [1, 2]:
dataset = GeqeAPI.getMockDataSet(shape=shape)
(response, dataset) = service.saveDataset(dataset)
if response != 200:
print 'response: ', response
print dataset
raise Exception("Could not save MOCK dataset.")
(response, clusterStatus) = service.putStatus({
'host': platform.node(),
'status': 'RUNNING'
})
if response != 200:
