Description
-----------
-Dashboard tables list a summary of per test-case VPP MRR performance trend
-values and detected anomalies (Maximum Receive Rate - received packet rate
-under line rate load). Data comes from trending MRR jobs executed every 12 hrs
-(2:00, 14:00 UTC). Trend and anomaly calculations are done over a rolling
-window of <N> samples, currently with N=14 covering last 7 days. Separate
-tables are generated for tested VPP worker-thread-core combinations (1t1c,
-2t2c, 4t4c).
+Dashboard tables list a summary of per test-case VPP MRR performance trend
+values and detected anomalies (Maximum Receive Rate - received packet rate
+under line rate load). Data comes from trending MRR jobs executed every 12
+hrs (2:00, 14:00 UTC). Trend, trend compliance and anomaly calculations are
+based on a rolling window of <N> samples, currently N=14 covering last 7 days.
+Separate tables are generated for tested VPP worker-thread-core combinations
+(1t1c, 2t2c, 4t4c).
Legend to table:
- - "Test case": name of CSIT test case, naming convention here
- `CSIT/csit-test-naming <https://wiki.fd.io/view/CSIT/csit-test-naming>`_
- - "Thput trend [Mpps]": last value of trend over rolling window.
- - "Anomaly value [Mpps]": in precedence - i) highest outlier if 3
- consecutive outliers, ii) highest regression if regressions detected,
- iii) highest progression if progressions detected, iv) nil if normal i.e.
- within trend.
- - "Anomaly vs. Trend [%]": anomaly value vs. trend value.
- - "Classification": outlier, regression, progression, normal - observed
- over a rolling window.
- - "# Outliers": number of outliers detected.
+ - "Test Case": name of CSIT test case, naming convention on
+ `CSIT wiki <https://wiki.fd.io/view/CSIT/csit-test-naming>`_.
+ - "Throughput Trend [Mpps]": last value of trend calculated over a
+ rolling window.
+ - "Trend Compliance": calculated based on detected anomalies, listed in
+ precedence order - i) "failure" if 3 consecutive outliers,
+ ii) "regression" if any regressions, iii) "progression" if any
+ progressions, iv) "normal" if data compliant with trend.
+ - "Anomaly Value [Mpps]": i) highest outlier if "failure", ii) highest
+ regression if "regression", iii) highest progression if "progression",
+ iv) "-" if normal i.e. within trend.
+ - "Change [%]": "Anomaly Value" vs. "Throughput Trend", "-" if normal.
+ - "# Outliers": number of outliers detected within a rolling window.
Tables are listed in sections 1.x. Followed by daily trending graphs in
sections 2.x. Daily trending data used to generate the graphs is listed in
------------------
.. include:: ../../../_build/_static/vpp/performance-trending-dashboard-4t4c.rst
-
if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ]
+ results = [0.66, ]
median = in_data.rolling(window=win_size, min_periods=2).median()
stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
- m_vals = median.values
- s_vals = stdev_t.values
- d_vals = in_data.values
- t_vals = trimmed_data.values
- for day in range(1, in_data.size):
- if np.isnan(t_vals[day]) \
- or np.isnan(m_vals[day]) \
- or np.isnan(s_vals[day]) \
- or np.isnan(d_vals[day]):
+
+ first = True
+ for build_nr, value in in_data.iteritems():
+ if first:
+ first = False
+ continue
+ if np.isnan(trimmed_data[build_nr]) \
+ or np.isnan(median[build_nr]) \
+ or np.isnan(stdev_t[build_nr]) \
+ or np.isnan(value):
results.append(0.0)
- elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
results.append(0.33)
- elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \
- (m_vals[day] + 3 * s_vals[day]):
- results.append(0.66)
- else:
+ elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
results.append(1.0)
+ else:
+ results.append(0.66)
else:
results = [0.0, ]
try:
in_data = _select_data(in_data, period,
fill_missing=fill_missing,
use_first=use_first)
- # try:
- # data_x = ["{0}/{1}".format(key, build_info[str(key)][1].split("~")[-1])
- # for key in in_data.keys()]
- # except KeyError:
- # data_x = [key for key in in_data.keys()]
- hover_text = ["vpp-build: {0}".format(x[1].split("~")[-1])
- for x in build_info.values()]
- data_x = [key for key in in_data.keys()]
+ data_x = [key for key in in_data.keys()]
data_y = [val for val in in_data.values()]
+
+ hover_text = list()
+ for idx in data_x:
+ hover_text.append("vpp-build: {0}".
+ format(build_info[str(idx)][1].split("~")[-1]))
+
data_pd = pd.Series(data_y, index=data_x)
t_data, outliers = find_outliers(data_pd, outlier_const=1.5)
-
results = _evaluate_results(data_pd, t_data, window=moving_win_size)
anomalies = pd.Series()
anomalies_res = list()
for idx, item in enumerate(in_data.items()):
- # item_pd = pd.Series([item[1], ],
- # index=["{0}/{1}".
- # format(item[0],
- # build_info[str(item[0])][1].split("~")[-1]),
- # ])
item_pd = pd.Series([item[1], ], index=[item[0], ])
if item[0] in outliers.keys():
anomalies = anomalies.append(item_pd)
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = ["Test case",
- "Thput trend [Mpps]",
- "Anomaly [Mpps]",
+ "Throughput Trend [Mpps]",
+ "Trend Compliance",
+ "Anomaly Value [Mpps]",
"Change [%]",
- "Classification"]
+ "#Outliers"
+ ]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
"-".join(tst_data["name"].
split("-")[1:]))
tbl_dict[tst_name] = {"name": name,
- "data": list()}
+ "data": dict()}
try:
- tbl_dict[tst_name]["data"]. \
- append(tst_data["result"]["throughput"])
+ tbl_dict[tst_name]["data"][str(build)] = \
+ tst_data["result"]["throughput"]
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict.keys():
if len(tbl_dict[tst_name]["data"]) > 2:
- sample_lst = tbl_dict[tst_name]["data"]
- pd_data = pd.Series(sample_lst)
+
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
win_size = pd_data.size \
if pd_data.size < table["window"] else table["window"]
# Test name:
name = tbl_dict[tst_name]["name"]
- # Trend list:
- trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
- median())
- # Stdevs list:
- t_data, _ = find_outliers(pd_data)
- t_data_lst = list(t_data)
- stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
- std())
+ median = pd_data.rolling(window=win_size, min_periods=2).median()
+ trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
+ stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
rel_change_lst = [None, ]
classification_lst = [None, ]
- for idx in range(1, len(trend_lst)):
+ median_lst = [None, ]
+ sample_lst = [None, ]
+ first = True
+ for build_nr, value in pd_data.iteritems():
+ if first:
+ first = False
+ continue
# Relative changes list:
- if not isnan(sample_lst[idx]) \
- and not isnan(trend_lst[idx])\
- and trend_lst[idx] != 0:
+ if not isnan(value) \
+ and not isnan(median[build_nr]) \
+ and median[build_nr] != 0:
rel_change_lst.append(
- int(relative_change(float(trend_lst[idx]),
- float(sample_lst[idx]))))
+ int(relative_change(float(median[build_nr]),
+ float(value))))
else:
rel_change_lst.append(None)
+
# Classification list:
- if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
+ if isnan(trimmed_data[build_nr]) \
+ or isnan(median[build_nr]) \
+ or isnan(stdev_t[build_nr]) \
+ or isnan(value):
classification_lst.append("outlier")
- elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
+ elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")
+ sample_lst.append(value)
+ median_lst.append(median[build_nr])
- last_idx = len(sample_lst) - 1
+ last_idx = len(classification_lst) - 1
first_idx = last_idx - int(table["evaluated-window"])
if first_idx < 0:
first_idx = 0
else:
classification = None
+ nr_outliers = 0
+ consecutive_outliers = 0
+ failure = False
+ for item in classification_lst[first_idx:]:
+ if item == "outlier":
+ nr_outliers += 1
+ consecutive_outliers += 1
+ if consecutive_outliers == 3:
+ failure = True
+ else:
+ consecutive_outliers = 0
+
idx = len(classification_lst) - 1
while idx:
if classification_lst[idx] == classification:
break
idx -= 1
- trend = round(float(trend_lst[-2]) / 1000000, 2) \
- if not isnan(trend_lst[-2]) else ''
+ if failure:
+ classification = "failure"
+ elif classification == "outlier":
+ classification = "normal"
+
+ trend = round(float(median_lst[-1]) / 1000000, 2) \
+ if not isnan(median_lst[-1]) else ''
sample = round(float(sample_lst[idx]) / 1000000, 2) \
if not isnan(sample_lst[idx]) else ''
rel_change = rel_change_lst[idx] \
if rel_change_lst[idx] is not None else ''
tbl_lst.append([name,
trend,
- sample,
- rel_change,
- classification])
+ classification,
+ '-' if classification == "normal" else sample,
+ '-' if classification == "normal" else rel_change,
+ nr_outliers])
# Sort the table according to the classification
tbl_sorted = list()
- for classification in ("regression", "progression", "outlier", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[4] == classification]
+ for classification in ("failure", "regression", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_lst if item[2] == classification]
tbl_tmp.sort(key=lambda rel: rel[0])
tbl_sorted.extend(tbl_tmp)
# Table header:
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
for idx, item in enumerate(csv_lst[0]):
- alignment = "left" if idx == 0 else "right"
+ alignment = "left" if idx == 0 else "center"
th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
th.text = item
for c_idx, item in enumerate(row):
alignment = "left" if c_idx == 0 else "center"
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
- if c_idx == 4:
+ if c_idx == 2:
if item == "regression":
td.set("bgcolor", "#eca1a6")
- elif item == "outlier":
+ elif item == "failure":
td.set("bgcolor", "#d6cbd3")
elif item == "progression":
td.set("bgcolor", "#bdcebe")
Code Review / csit.git / commitdiff