def read_app_started_csv(path):
df = pd.read_csv(path)
jobs = []
for index, row in df.iterrows():
job = Job()
job.name = row['queue']
job.run_time = row['elapsedTime'] * 0.001
job.memory_seconds = row['memorySeconds']
jobs.append(job)
return jobs
def read_app_csv(path):
df = pd.read_csv(path)
jobs = []
for index, row in df.iterrows():
job = Job()
job.name = row['queue']
job.run_time = row['elapsedTime'] * 0.001
job.memory_seconds = row[
'allocatedMB'] * 300 # five minute per sampling
jobs.append(job)
return jobs
def read_app_started_csv(path):
df = pd.read_csv(path)
jobs = []
for index, row in df.iterrows():
job = Job()
job.name = row['queue']
job.run_time = row['elapsedTime'] * 0.001
job.memory_seconds = row['memorySeconds']
jobs.append(job)
"""
for i in range(df.shape[1]):
print(i, cols[i], row[i])
print( '-----------------------------------')
"""
return jobs
def read_app_csv(path):
df = pd.read_csv(path)
# cols = df.columns.tolist()
jobs = []
for index, row in df.iterrows():
job = Job()
job.name = row['queue']
job.run_time = row['elapsedTime'] * 0.001
job.memory_seconds = row['allocatedMB']*300 # five minute per sampling
jobs.append(job)
"""
for i in range(df.shape[1]):
print(i, cols[i], row[i])
print( '-----------------------------------')
"""
return jobs
def read_app_csv(path):
job_count = np.random.randint(10, 50)
queue = ['spark', 'hive', 'ProgrammerAlliance']
# queue = ['spark', 'hive']
jobs = []
for i in range(job_count):
job = Job()
job.name = queue[np.random.randint(0,3)]
job.wait_time = np.random.randint(0, 25)
job.run_time = np.random.randint(10, 40)
job.memory_seconds = 1024*job.run_time*0.05
jobs.append(job)
"""
print '%d: queue: %s, wait time: %d, run time: %d, memory seconds: %d' %(i, job.name, job.wait_time, job.run_time, job.memory_seconds)
"""
print('%d jobs finished during this interval' % job_count)
return jobs
def read_app_stopped_csv(path):
df = pd.read_csv(path)
# cols = df.columns.tolist()
# print(cols)
jobs = []
for index, row in df.iterrows():
job = Job()
job.name = row['queue']
# job.wait_time = np.random.randint(50) #暂时用随机数模拟
job.run_time = row['elapsedTime'] * 0.001
job.memory_seconds = row['memorySeconds']
if job.run_time > 150:
job.memory_seconds = job.memory_seconds * 150 / job.run_time
# print("STOPPED: ", job.memory_seconds)
jobs.append(job)
"""
for i in range(df.shape[1]):
print(i, cols[i], row[i])
print( '-----------------------------------')
"""
return jobs