X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;ds=inline;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=16cb5a2cb35631f0191c609242a7f7c955b9b864;hb=54b40c5fbfa6102d3e71502ebcb9ca56cb5ef80c;hp=a20ce8efd4120eeed97c1bc3ed250a5abf05381a;hpb=47962ee624efeaec469473a5569b59bfd230babf;p=csit.git
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index a20ce8efd4..16cb5a2cb3 100644
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -14,113 +14,449 @@
"""
"""
-
import plotly.graph_objects as go
import pandas as pd
import re
+import hdrh.histogram
+import hdrh.codec
+
from datetime import datetime
+from numpy import isnan
+
+from ..jumpavg import classify
+
+
+_COLORS = (
+ u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
+ u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
+ u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
+ u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
+)
+_ANOMALY_COLOR = {
+ u"regression": 0.0,
+ u"normal": 0.5,
+ u"progression": 1.0
+}
+_COLORSCALE_TPUT = [
+ [0.00, u"red"],
+ [0.33, u"red"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"green"],
+ [1.00, u"green"]
+]
+_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
+_COLORSCALE_LAT = [
+ [0.00, u"green"],
+ [0.33, u"green"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"red"],
+ [1.00, u"red"]
+]
+_TICK_TEXT_LAT = [u"Progression", u"Normal", u"Regression"]
+_VALUE = {
+ "mrr": "result_receive_rate_rate_avg",
+ "ndr": "result_ndr_lower_rate_value",
+ "pdr": "result_pdr_lower_rate_value",
+ "pdr-lat": "result_latency_forward_pdr_50_avg"
+}
+_UNIT = {
+ "mrr": "result_receive_rate_rate_unit",
+ "ndr": "result_ndr_lower_rate_unit",
+ "pdr": "result_pdr_lower_rate_unit",
+ "pdr-lat": "result_latency_forward_pdr_50_unit"
+}
+_LAT_HDRH = ( # Do not change the order
+ "result_latency_forward_pdr_0_hdrh",
+ "result_latency_reverse_pdr_0_hdrh",
+ "result_latency_forward_pdr_10_hdrh",
+ "result_latency_reverse_pdr_10_hdrh",
+ "result_latency_forward_pdr_50_hdrh",
+ "result_latency_reverse_pdr_50_hdrh",
+ "result_latency_forward_pdr_90_hdrh",
+ "result_latency_reverse_pdr_90_hdrh",
+)
+# This value depends on latency stream rate (9001 pps) and duration (5s).
+# Keep it slightly higher to ensure rounding errors to not remove tick mark.
+PERCENTILE_MAX = 99.999501
-from dash import no_update
+_GRAPH_LAT_HDRH_DESC = {
+ u"result_latency_forward_pdr_0_hdrh": u"No-load.",
+ u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
+ u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
+ u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
+ u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
+ u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
+ u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
+ u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
+}
-def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime,
- end: datetime):
+def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
"""
"""
- if not sel:
- return no_update, no_update
-
- def _generate_trace(ttype: str, name: str, df: pd.DataFrame,
- start: datetime, end: datetime):
-
- value = {
- "mrr": "result_receive_rate_rate_avg",
- "ndr": "result_ndr_lower_rate_value",
- "pdr": "result_pdr_lower_rate_value"
- }
- unit = {
- "mrr": "result_receive_rate_rate_unit",
- "ndr": "result_ndr_lower_rate_unit",
- "pdr": "result_pdr_lower_rate_unit"
- }
-
- x_axis = [
- d for d in df["start_time"] if d >= start and d <= end
- ]
- hover_txt = list()
- for _, row in df.iterrows():
- hover_itm = (
- f"date: "
- f"{row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
- f"average [{row[unit[ttype]]}]: "
- f"{row[value[ttype]]}
"
- f"{row['dut_type']}-ref: {row['dut_version']}
"
- f"csit-ref: {row['job']}/{row['build']}"
- )
- if ttype == "mrr":
- stdev = (
- f"stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']}
"
- )
- else:
- stdev = ""
- hover_itm = hover_itm.replace("", stdev)
- hover_txt.append(hover_itm)
+ latencies = {"name": name}
+ for key in _LAT_HDRH:
+ try:
+ latencies[key] = row[key]
+ except KeyError:
+ return None
- return go.Scatter(
- x=x_axis,
- y=df[value[ttype]],
- name=name,
- mode="markers+lines",
- text=hover_txt,
- hoverinfo=u"text+name"
+ return latencies
+
+
+def _classify_anomalies(data):
+ """Process the data and return anomalies and trending values.
+
+ Gather data into groups with average as trend value.
+ Decorate values within groups to be normal,
+ the first value of changed average as a regression, or a progression.
+
+ :param data: Full data set with unavailable samples replaced by nan.
+ :type data: OrderedDict
+ :returns: Classification and trend values
+ :rtype: 3-tuple, list of strings, list of floats and list of floats
+ """
+ # NaN means something went wrong.
+ # Use 0.0 to cause that being reported as a severe regression.
+ bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
+ # TODO: Make BitCountingGroupList a subclass of list again?
+ group_list = classify(bare_data).group_list
+ group_list.reverse() # Just to use .pop() for FIFO.
+ classification = list()
+ avgs = list()
+ stdevs = list()
+ active_group = None
+ values_left = 0
+ avg = 0.0
+ stdv = 0.0
+ for sample in data.values():
+ if isnan(sample):
+ classification.append(u"outlier")
+ avgs.append(sample)
+ stdevs.append(sample)
+ continue
+ if values_left < 1 or active_group is None:
+ values_left = 0
+ while values_left < 1: # Ignore empty groups (should not happen).
+ active_group = group_list.pop()
+ values_left = len(active_group.run_list)
+ avg = active_group.stats.avg
+ stdv = active_group.stats.stdev
+ classification.append(active_group.comment)
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ continue
+ classification.append(u"normal")
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ return classification, avgs, stdevs
+
+
+def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
+ """
+ """
+
+ phy = itm["phy"].split("-")
+ if len(phy) == 4:
+ topo, arch, nic, drv = phy
+ if drv == "dpdk":
+ drv = ""
+ else:
+ drv += "-"
+ drv = drv.replace("_", "-")
+ else:
+ return None
+ cadence = \
+ "weekly" if (arch == "aws" or itm["testtype"] != "mrr") else "daily"
+ sel_topo_arch = (
+ f"csit-vpp-perf-"
+ f"{itm['testtype'] if itm['testtype'] == 'mrr' else 'ndrpdr'}-"
+ f"{cadence}-master-{topo}-{arch}"
+ )
+ df_sel = data.loc[(data["job"] == sel_topo_arch)]
+ regex = (
+ f"^.*{nic}.*\.{itm['framesize']}-{itm['core']}-{drv}{itm['test']}-"
+ f"{'mrr' if itm['testtype'] == 'mrr' else 'ndrpdr'}$"
+ )
+ df = df_sel.loc[
+ df_sel["test_id"].apply(
+ lambda x: True if re.search(regex, x) else False
)
+ ].sort_values(by="start_time", ignore_index=True)
- # Generate graph:
- fig = go.Figure()
- for itm in sel:
- phy = itm["phy"].split("-")
- if len(phy) == 4:
- topo, arch, nic, drv = phy
- if drv in ("dpdk", "ixgbe"):
- drv = ""
- else:
- drv += "-"
+ return df
+
+
+def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
+ start: datetime, end: datetime, color: str) -> list:
+ """
+ """
+
+ df = df.dropna(subset=[_VALUE[ttype], ])
+ if df.empty:
+ return list()
+
+ x_axis = [d for d in df["start_time"] if d >= start and d <= end]
+ if not x_axis:
+ return list()
+
+ anomalies, trend_avg, trend_stdev = _classify_anomalies(
+ {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
+ )
+
+ hover = list()
+ customdata = list()
+ for _, row in df.iterrows():
+ hover_itm = (
+ f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}
"
+ f""
+ f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"csit-ref: {row['job']}/{row['build']}
"
+ f"hosts: {', '.join(row['hosts'])}"
+ )
+ if ttype == "mrr":
+ stdev = (
+ f"stdev [{row['result_receive_rate_rate_unit']}]: "
+ f"{row['result_receive_rate_rate_stdev']}
"
+ )
else:
- continue
- cadence = \
- "weekly" if (arch == "aws" or itm["testtype"] != "mrr") else "daily"
- sel_topo_arch = (
- f"csit-vpp-perf-"
- f"{itm['testtype'] if itm['testtype'] == 'mrr' else 'ndrpdr'}-"
- f"{cadence}-master-{topo}-{arch}"
+ stdev = ""
+ hover_itm = hover_itm.replace(
+ "", "latency" if ttype == "pdr-lat" else "average"
+ ).replace("", stdev)
+ hover.append(hover_itm)
+ if ttype == "pdr-lat":
+ customdata.append(_get_hdrh_latencies(row, name))
+
+ hover_trend = list()
+ for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
+ hover_itm = (
+ f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f"trend [pps]: {avg}
"
+ f"stdev [pps]: {stdev}
"
+ f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"csit-ref: {row['job']}/{row['build']}
"
+ f"hosts: {', '.join(row['hosts'])}"
)
- df_sel = data.loc[(data["job"] == sel_topo_arch)]
- regex = (
- f"^.*{nic}.*\.{itm['framesize']}-{itm['core']}-{drv}{itm['test']}-"
- f"{'mrr' if itm['testtype'] == 'mrr' else 'ndrpdr'}$"
+ if ttype == "pdr-lat":
+ hover_itm = hover_itm.replace("[pps]", "[us]")
+ hover_trend.append(hover_itm)
+
+ traces = [
+ go.Scatter( # Samples
+ x=x_axis,
+ y=df[_VALUE[ttype]],
+ name=name,
+ mode="markers",
+ marker={
+ u"size": 5,
+ u"color": color,
+ u"symbol": u"circle",
+ },
+ text=hover,
+ hoverinfo=u"text+name",
+ showlegend=True,
+ legendgroup=name,
+ customdata=customdata
+ ),
+ go.Scatter( # Trend line
+ x=x_axis,
+ y=trend_avg,
+ name=name,
+ mode="lines",
+ line={
+ u"shape": u"linear",
+ u"width": 1,
+ u"color": color,
+ },
+ text=hover_trend,
+ hoverinfo=u"text+name",
+ showlegend=False,
+ legendgroup=name,
)
- df = df_sel.loc[
- df_sel["test_id"].apply(
- lambda x: True if re.search(regex, x) else False
+ ]
+
+ if anomalies:
+ anomaly_x = list()
+ anomaly_y = list()
+ anomaly_color = list()
+ hover = list()
+ for idx, anomaly in enumerate(anomalies):
+ if anomaly in (u"regression", u"progression"):
+ anomaly_x.append(x_axis[idx])
+ anomaly_y.append(trend_avg[idx])
+ anomaly_color.append(_ANOMALY_COLOR[anomaly])
+ hover_itm = (
+ f"date: {x_axis[idx].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f"trend [pps]: {trend_avg[idx]}
"
+ f"classification: {anomaly}"
+ )
+ if ttype == "pdr-lat":
+ hover_itm = hover_itm.replace("[pps]", "[us]")
+ hover.append(hover_itm)
+ anomaly_color.extend([0.0, 0.5, 1.0])
+ traces.append(
+ go.Scatter(
+ x=anomaly_x,
+ y=anomaly_y,
+ mode=u"markers",
+ text=hover,
+ hoverinfo=u"text+name",
+ showlegend=False,
+ legendgroup=name,
+ name=name,
+ marker={
+ u"size": 15,
+ u"symbol": u"circle-open",
+ u"color": anomaly_color,
+ u"colorscale": _COLORSCALE_LAT \
+ if ttype == "pdr-lat" else _COLORSCALE_TPUT,
+ u"showscale": True,
+ u"line": {
+ u"width": 2
+ },
+ u"colorbar": {
+ u"y": 0.5,
+ u"len": 0.8,
+ u"title": u"Circles Marking Data Classification",
+ u"titleside": u"right",
+ # u"titlefont": {
+ # u"size": 14
+ # },
+ u"tickmode": u"array",
+ u"tickvals": [0.167, 0.500, 0.833],
+ u"ticktext": _TICK_TEXT_LAT \
+ if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
+ u"ticks": u"",
+ u"ticklen": 0,
+ u"tickangle": -90,
+ u"thickness": 10
+ }
+ }
)
- ].sort_values(by="start_time", ignore_index=True)
+ )
+
+ return traces
+
+
+def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
+ start: datetime, end: datetime) -> tuple:
+ """
+ """
+
+ if not sel:
+ return None, None
+
+ fig_tput = None
+ fig_lat = None
+ for idx, itm in enumerate(sel):
+
+ df = select_trending_data(data, itm)
+ if df is None:
+ continue
+
name = (
f"{itm['phy']}-{itm['framesize']}-{itm['core']}-"
f"{itm['test']}-{itm['testtype']}"
)
- fig.add_trace(_generate_trace(itm['testtype'], name, df, start, end))
- style={
- "vertical-align": "top",
- "display": "inline-block",
- "width": "80%",
- "padding": "5px"
- }
+ traces = _generate_trending_traces(
+ itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
+ )
+ if traces:
+ if not fig_tput:
+ fig_tput = go.Figure()
+ fig_tput.add_traces(traces)
+
+ if itm["testtype"] == "pdr":
+ traces = _generate_trending_traces(
+ "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
+ )
+ if traces:
+ if not fig_lat:
+ fig_lat = go.Figure()
+ fig_lat.add_traces(traces)
+
+ if fig_tput:
+ fig_tput.update_layout(layout.get("plot-trending-tput", dict()))
+ if fig_lat:
+ fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
- layout = layout.get("plot-trending", dict())
- fig.update_layout(layout)
+ return fig_tput, fig_lat
+
+
+def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
+ """
+ """
+
+ fig = None
+
+ traces = list()
+ for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
+ try:
+ decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
+ except (hdrh.codec.HdrLengthException, TypeError) as err:
+ continue
+ previous_x = 0.0
+ prev_perc = 0.0
+ xaxis = list()
+ yaxis = list()
+ hovertext = list()
+ for item in decoded.get_recorded_iterator():
+ # The real value is "percentile".
+ # For 100%, we cut that down to "x_perc" to avoid
+ # infinity.
+ percentile = item.percentile_level_iterated_to
+ x_perc = min(percentile, PERCENTILE_MAX)
+ xaxis.append(previous_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"Direction: {(u'W-E', u'E-W')[idx % 2]}
"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ next_x = 100.0 / (100.0 - x_perc)
+ xaxis.append(next_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"Direction: {(u'W-E', u'E-W')[idx % 2]}
"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ previous_x = next_x
+ prev_perc = percentile
+
+ traces.append(
+ go.Scatter(
+ x=xaxis,
+ y=yaxis,
+ name=_GRAPH_LAT_HDRH_DESC[lat_name],
+ mode=u"lines",
+ legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
+ showlegend=bool(idx % 2),
+ line=dict(
+ color=_COLORS[int(idx/2)],
+ dash=u"solid",
+ width=1 if idx % 2 else 2
+ ),
+ hovertext=hovertext,
+ hoverinfo=u"text"
+ )
+ )
+ if traces:
+ fig = go.Figure()
+ fig.add_traces(traces)
+ layout_hdrh = layout.get("plot-hdrh-latency", None)
+ if lat_hdrh:
+ fig.update_layout(layout_hdrh)
- return fig, style
+ return fig