def test_group_aggregate_all_forward(dtstart, delta, N, step):
"""Aggregate all data and check result"""
nseries = 10
begin = dtstart
end = dtstart + delta*(N + 1)
agg_funcs = ["min", "max", "count", "sum"]
query = att.make_group_aggregate_query("test", begin, end,
agg_funcs,
step,
output=dict(format='csv'),
where={"tag3": "D", "tag2": "C"})
queryurl = "http://{0}:{1}/api/query".format(HOST, HTTPPORT)
response = urlopen(queryurl, json.dumps(query))
iterations = 0
for line in response:
try:
columns = line.split(',')
timestamp = att.parse_timestamp(columns[1].strip())
tserrormsg = "Unexpected timestamp value: {0}".format(columns[1].strip())
if timestamp.second != dtstart.second:
raise ValueError(tserrormsg)
if timestamp.microsecond != dtstart.microsecond:
raise ValueError(tserrormsg)
iterations += 1
except:
print("Error at line: {0}".format(line))
raise
if iterations == 0:
raise ValueError("Results incomplete")
def group_aggregate_query(metric, begin, end, agg_funcs, step, **extra_args):
'''Query database, return series of tuples'''
query = att.make_group_aggregate_query(metric, begin, end,
agg_funcs,
"{0}ms".format(step),
output=dict(format='csv'),
**extra_args)
queryurl = "http://{0}:{1}/api/query".format(HOST, HTTPPORT)
response = urlopen(queryurl, json.dumps(query))
tuple_len = len(agg_funcs)
for line in response:
columns = line.split(',')
tagline = columns[0].strip()
timestamp = att.parse_timestamp(columns[1].strip())
output = {"seris": tagline, "timestamp": timestamp}
for i in range(0, tuple_len):
value = float(columns[2 + i].strip())
output[agg_funcs[i]] = value
yield output
def group_aggregate_query(metric, begin, end, agg_funcs, step, **extra_args):
'''Query database, return series of tuples'''
query = att.make_group_aggregate_query(metric,
begin,
end,
agg_funcs,
"{0}ms".format(step),
output=dict(format='csv'),
**extra_args)
queryurl = "http://{0}:{1}/api/query".format(HOST, HTTPPORT)
response = urlopen(queryurl, json.dumps(query))
tuple_len = len(agg_funcs)
for line in response:
columns = line.split(',')
tagline = columns[0].strip()
timestamp = att.parse_timestamp(columns[1].strip())
output = {"seris": tagline, "timestamp": timestamp}
for i in range(0, tuple_len):
value = float(columns[2 + i].strip())
output[agg_funcs[i]] = value
yield output
def group_aggregate_nonexistent_time_range(dtstart, delta, N):
begin = dtstart + delta*(N*2)
end = dtstart + delta*(N*3)
query = att.make_group_aggregate_query("test", begin, end, ["sum"], "10ms")
require_empty_response(query)
def group_aggregate_nonexistent_metric(dtstart, delta, N):
begin = dtstart
end = dtstart + delta*(N + 1)
query = att.make_group_aggregate_query("err", begin, end, ["sum"], "10ms")
msg = "-not found"
check_error_message(dtstart, delta, N, query, msg)
def test_group_aggregate_all_backward(dtstart, delta, N, nsteps):
"""Aggregate all data and check result"""
nseries = 10
begin = dtstart + delta*(N-1)
end = dtstart - delta
step = int((delta * N * 1000).total_seconds() / nsteps)
agg_funcs = ["min", "max", "count", "sum"]
query = att.make_group_aggregate_query("test", begin, end,
agg_funcs,
"{0}ms".format(step),
output=dict(format='csv'))
queryurl = "http://{0}:{1}/api/query".format(HOST, HTTPPORT)
response = urlopen(queryurl, json.dumps(query))
expected_tags = [
"tag3=D",
"tag3=E",
"tag3=F",
"tag3=G",
"tag3=H",
]
registerd_values = {}
iterations = 0
for line in response:
try:
columns = line.split(',')
tagline = columns[0].strip()
timestamp = att.parse_timestamp(columns[1].strip())
min_value = float(columns[2].strip())
max_value = float(columns[3].strip())
cnt_value = float(columns[4].strip())
sum_value = float(columns[4].strip())
max_index = len(expected_tags) - 1
exp_tag = expected_tags[max_index - (iterations % len(expected_tags))]
if tagline.endswith(exp_tag) == False:
msg = "Unexpected tag value: {0}, expected: {1}".format(tagline, exp_tag)
raise ValueError(msg)
cnt_expected = N/nsteps/nseries
if cnt_expected == 0:
# expected count is less then 1 but not 0
# there is more than 1 step per value in raw series
cnt_expected = 1
if cnt_value != cnt_expected:
msg = "Invalid cnt value, expected: {0}, actual: {1}".format(cnt_expected, cnt_value)
raise ValueError(msg)
prev_val = registerd_values.get(tagline)
if prev_val is not None:
if abs(prev_val['min'] - max_value) - nseries > 10E-5:
msg = "Invalid value, expected: {0}, actual: {1}".format(prev_val['min'], max_value)
raise ValueError(msg)
new_val = dict(max=max_value, min=min_value, cnt=cnt_value, sum=sum_value)
registerd_values[tagline] = new_val
iterations += 1
except:
print("Error at line: {0}".format(line))
raise
if iterations == 0:
raise ValueError("Results incomplete")
