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Diffstat (limited to 'csit.infra.dash/app/cdash/trending/graphs.py')
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/graphs.py | 655 |
1 files changed, 655 insertions, 0 deletions
diff --git a/csit.infra.dash/app/cdash/trending/graphs.py b/csit.infra.dash/app/cdash/trending/graphs.py new file mode 100644 index 0000000000..ede3a06fd4 --- /dev/null +++ b/csit.infra.dash/app/cdash/trending/graphs.py @@ -0,0 +1,655 @@ +# Copyright (c) 2024 Cisco and/or its affiliates. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Implementation of graphs for trending data. +""" + +import logging +import plotly.graph_objects as go +import pandas as pd + +from ..utils.constants import Constants as C +from ..utils.utils import get_color, get_hdrh_latencies +from ..utils.anomalies import classify_anomalies + + +def select_trending_data(data: pd.DataFrame, itm: dict) -> pd.DataFrame: + """Select the data for graphs from the provided data frame. + + :param data: Data frame with data for graphs. + :param itm: Item (in this case job name) which data will be selected from + the input data frame. + :type data: pandas.DataFrame + :type itm: dict + :returns: A data frame with selected data. + :rtype: pandas.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 + + if itm["testtype"] in ("ndr", "pdr"): + test_type = "ndrpdr" + elif itm["testtype"] == "mrr": + test_type = "mrr" + elif itm["area"] == "hoststack": + test_type = "hoststack" + df = data.loc[( + (data["test_type"] == test_type) & + (data["passed"] == True) + )] + df = df[df.job.str.endswith(f"{topo}-{arch}")] + core = str() if itm["dut"] == "trex" else f"{itm['core']}" + ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"] + df = df[df.test_id.str.contains( + f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$", + regex=True + )].sort_values(by="start_time", ignore_index=True) + + return df + + +def graph_trending( + data: pd.DataFrame, + sel: dict, + layout: dict, + normalize: bool=False + ) -> tuple: + """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences + (result_latency_forward_pdr_50_avg). + + :param data: Data frame with test results. + :param sel: Selected tests. + :param layout: Layout of plot.ly graph. + :param normalize: If True, the data is normalized to CPU frquency + Constants.NORM_FREQUENCY. + :type data: pandas.DataFrame + :type sel: dict + :type layout: dict + :type normalize: bool + :returns: Trending graph(s) + :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure) + """ + + if not sel: + return None, None + + + def _generate_trending_traces( + ttype: str, + name: str, + df: pd.DataFrame, + color: str, + nf: float + ) -> list: + """Generate the trending traces for the trending graph. + + :param ttype: Test type (MRR, NDR, PDR). + :param name: The test name to be displayed as the graph title. + :param df: Data frame with test data. + :param color: The color of the trace (samples and trend line). + :param nf: The factor used for normalization of the results to + CPU frequency set to Constants.NORM_FREQUENCY. + :type ttype: str + :type name: str + :type df: pandas.DataFrame + :type color: str + :type nf: float + :returns: Traces (samples, trending line, anomalies) + :rtype: list + """ + + df = df.dropna(subset=[C.VALUE[ttype], ]) + if df.empty: + return list(), list() + + hover = list() + customdata = list() + customdata_samples = list() + name_lst = name.split("-") + for _, row in df.iterrows(): + h_tput, h_band, h_lat = str(), str(), str() + if ttype in ("mrr", "mrr-bandwidth"): + h_tput = ( + f"tput avg [{row['result_receive_rate_rate_unit']}]: " + f"{row['result_receive_rate_rate_avg'] * nf:,.0f}<br>" + f"tput stdev [{row['result_receive_rate_rate_unit']}]: " + f"{row['result_receive_rate_rate_stdev'] * nf:,.0f}<br>" + ) + if pd.notna(row["result_receive_rate_bandwidth_avg"]): + h_band = ( + f"bandwidth avg " + f"[{row['result_receive_rate_bandwidth_unit']}]: " + f"{row['result_receive_rate_bandwidth_avg'] * nf:,.0f}" + "<br>" + f"bandwidth stdev " + f"[{row['result_receive_rate_bandwidth_unit']}]: " + f"{row['result_receive_rate_bandwidth_stdev']* nf:,.0f}" + "<br>" + ) + elif ttype in ("ndr", "ndr-bandwidth"): + h_tput = ( + f"tput [{row['result_ndr_lower_rate_unit']}]: " + f"{row['result_ndr_lower_rate_value'] * nf:,.0f}<br>" + ) + if pd.notna(row["result_ndr_lower_bandwidth_value"]): + h_band = ( + f"bandwidth [{row['result_ndr_lower_bandwidth_unit']}]:" + f" {row['result_ndr_lower_bandwidth_value'] * nf:,.0f}" + "<br>" + ) + elif ttype in ("pdr", "pdr-bandwidth", "latency"): + h_tput = ( + f"tput [{row['result_pdr_lower_rate_unit']}]: " + f"{row['result_pdr_lower_rate_value'] * nf:,.0f}<br>" + ) + if pd.notna(row["result_pdr_lower_bandwidth_value"]): + h_band = ( + f"bandwidth [{row['result_pdr_lower_bandwidth_unit']}]:" + f" {row['result_pdr_lower_bandwidth_value'] * nf:,.0f}" + "<br>" + ) + if pd.notna(row["result_latency_forward_pdr_50_avg"]): + h_lat = ( + f"latency " + f"[{row['result_latency_forward_pdr_50_unit']}]: " + f"{row['result_latency_forward_pdr_50_avg'] / nf:,.0f}" + "<br>" + ) + elif ttype in ("hoststack-cps", "hoststack-rps", + "hoststack-cps-bandwidth", + "hoststack-rps-bandwidth", "hoststack-latency"): + h_tput = ( + f"tput [{row['result_rate_unit']}]: " + f"{row['result_rate_value'] * nf:,.0f}<br>" + ) + h_band = ( + f"bandwidth [{row['result_bandwidth_unit']}]: " + f"{row['result_bandwidth_value'] * nf:,.0f}<br>" + ) + h_lat = ( + f"latency [{row['result_latency_unit']}]: " + f"{row['result_latency_value'] / nf:,.0f}<br>" + ) + elif ttype in ("hoststack-bps", ): + h_band = ( + f"bandwidth [{row['result_bandwidth_unit']}]: " + f"{row['result_bandwidth_value'] * nf:,.0f}<br>" + ) + hover_itm = ( + f"dut: {name_lst[0]}<br>" + f"infra: {'-'.join(name_lst[1:5])}<br>" + f"test: {'-'.join(name_lst[5:])}<br>" + f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"{h_tput}{h_band}{h_lat}" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}<br>" + f"hosts: {', '.join(row['hosts'])}" + ) + hover.append(hover_itm) + if ttype == "latency": + customdata_samples.append(get_hdrh_latencies(row, name)) + customdata.append({"name": name}) + else: + customdata_samples.append( + {"name": name, "show_telemetry": True} + ) + customdata.append({"name": name}) + + x_axis = df["start_time"].tolist() + if "latency" in ttype: + y_data = [(v / nf) for v in df[C.VALUE[ttype]].tolist()] + else: + y_data = [(v * nf) for v in df[C.VALUE[ttype]].tolist()] + units = df[C.UNIT[ttype]].unique().tolist() + + try: + anomalies, trend_avg, trend_stdev = classify_anomalies( + {k: v for k, v in zip(x_axis, y_data)} + ) + except ValueError as err: + logging.error(err) + return list(), list() + + hover_trend = list() + for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()): + hover_itm = ( + f"dut: {name_lst[0]}<br>" + f"infra: {'-'.join(name_lst[1:5])}<br>" + f"test: {'-'.join(name_lst[5:])}<br>" + f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"trend [{row[C.UNIT[ttype]]}]: {avg:,.0f}<br>" + f"stdev [{row[C.UNIT[ttype]]}]: {stdev:,.0f}<br>" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}<br>" + f"hosts: {', '.join(row['hosts'])}" + ) + if ttype == "latency": + hover_itm = hover_itm.replace("[pps]", "[us]") + hover_trend.append(hover_itm) + + traces = [ + go.Scatter( # Samples + x=x_axis, + y=y_data, + name=name, + mode="markers", + marker={ + "size": 5, + "color": color, + "symbol": "circle", + }, + text=hover, + hoverinfo="text", + showlegend=True, + legendgroup=name, + customdata=customdata_samples + ), + go.Scatter( # Trend line + x=x_axis, + y=trend_avg, + name=name, + mode="lines", + line={ + "shape": "linear", + "width": 1, + "color": color, + }, + text=hover_trend, + hoverinfo="text", + showlegend=False, + legendgroup=name, + customdata=customdata + ) + ] + + if anomalies: + anomaly_x = list() + anomaly_y = list() + anomaly_color = list() + hover = list() + for idx, anomaly in enumerate(anomalies): + if anomaly in ("regression", "progression"): + anomaly_x.append(x_axis[idx]) + anomaly_y.append(trend_avg[idx]) + anomaly_color.append(C.ANOMALY_COLOR[anomaly]) + hover_itm = ( + f"dut: {name_lst[0]}<br>" + f"infra: {'-'.join(name_lst[1:5])}<br>" + f"test: {'-'.join(name_lst[5:])}<br>" + f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"trend [pps]: {trend_avg[idx]:,.0f}<br>" + f"classification: {anomaly}" + ) + if ttype == "latency": + 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="markers", + text=hover, + hoverinfo="text", + showlegend=False, + legendgroup=name, + name=name, + customdata=customdata, + marker={ + "size": 15, + "symbol": "circle-open", + "color": anomaly_color, + "colorscale": C.COLORSCALE_LAT \ + if ttype == "latency" else C.COLORSCALE_TPUT, + "showscale": True, + "line": { + "width": 2 + }, + "colorbar": { + "y": 0.5, + "len": 0.8, + "title": "Circles Marking Data Classification", + "titleside": "right", + "tickmode": "array", + "tickvals": [0.167, 0.500, 0.833], + "ticktext": C.TICK_TEXT_LAT \ + if ttype == "latency" else C.TICK_TEXT_TPUT, + "ticks": "", + "ticklen": 0, + "tickangle": -90, + "thickness": 10 + } + } + ) + ) + + return traces, units + + + fig_tput = None + fig_lat = None + fig_band = None + y_units = set() + for idx, itm in enumerate(sel): + df = select_trending_data(data, itm) + if df is None or df.empty: + continue + + if normalize: + phy = itm["phy"].split("-") + topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str() + norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY.get(topo_arch, 1.0)) \ + if topo_arch else 1.0 + else: + norm_factor = 1.0 + + if itm["area"] == "hoststack": + ttype = f"hoststack-{itm['testtype']}" + else: + ttype = itm["testtype"] + + traces, units = _generate_trending_traces( + ttype, + itm["id"], + df, + get_color(idx), + norm_factor + ) + if traces: + if not fig_tput: + fig_tput = go.Figure() + fig_tput.add_traces(traces) + + if ttype in ("ndr", "pdr", "mrr", "hoststack-cps", "hoststack-rps"): + traces, _ = _generate_trending_traces( + f"{ttype}-bandwidth", + itm["id"], + df, + get_color(idx), + norm_factor + ) + if traces: + if not fig_band: + fig_band = go.Figure() + fig_band.add_traces(traces) + + if ttype in ("pdr", "hoststack-cps", "hoststack-rps"): + traces, _ = _generate_trending_traces( + "latency" if ttype == "pdr" else "hoststack-latency", + itm["id"], + df, + get_color(idx), + norm_factor + ) + if traces: + if not fig_lat: + fig_lat = go.Figure() + fig_lat.add_traces(traces) + + y_units.update(units) + + if fig_tput: + fig_layout = layout.get("plot-trending-tput", dict()) + fig_layout["yaxis"]["title"] = \ + f"Throughput [{'|'.join(sorted(y_units))}]" + fig_tput.update_layout(fig_layout) + if fig_band: + fig_band.update_layout(layout.get("plot-trending-bandwidth", dict())) + if fig_lat: + fig_lat.update_layout(layout.get("plot-trending-lat", dict())) + + return fig_tput, fig_band, fig_lat + + +def graph_tm_trending( + data: pd.DataFrame, + layout: dict, + all_in_one: bool=False + ) -> list: + """Generates one trending graph per test, each graph includes all selected + metrics. + + :param data: Data frame with telemetry data. + :param layout: Layout of plot.ly graph. + :param all_in_one: If True, all telemetry traces are placed in one graph, + otherwise they are split to separate graphs grouped by test ID. + :type data: pandas.DataFrame + :type layout: dict + :type all_in_one: bool + :returns: List of generated graphs together with test names. + list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...) + :rtype: list + """ + + if data.empty: + return list() + + def _generate_traces( + data: pd.DataFrame, + test: str, + all_in_one: bool, + color_index: int + ) -> list: + """Generates a trending graph for given test with all metrics. + + :param data: Data frame with telemetry data for the given test. + :param test: The name of the test. + :param all_in_one: If True, all telemetry traces are placed in one + graph, otherwise they are split to separate graphs grouped by + test ID. + :param color_index: The index of the test used if all_in_one is True. + :type data: pandas.DataFrame + :type test: str + :type all_in_one: bool + :type color_index: int + :returns: List of traces. + :rtype: list + """ + traces = list() + metrics = data.tm_metric.unique().tolist() + for idx, metric in enumerate(metrics): + if "-pdr" in test and "='pdr'" not in metric: + continue + if "-ndr" in test and "='ndr'" not in metric: + continue + + df = data.loc[(data["tm_metric"] == metric)] + x_axis = df["start_time"].tolist() + y_data = [float(itm) for itm in df["tm_value"].tolist()] + hover = list() + for i, (_, row) in enumerate(df.iterrows()): + if row["test_type"] == "mrr": + rate = ( + f"mrr avg [{row[C.UNIT['mrr']]}]: " + f"{row[C.VALUE['mrr']]:,.0f}<br>" + f"mrr stdev [{row[C.UNIT['mrr']]}]: " + f"{row['result_receive_rate_rate_stdev']:,.0f}<br>" + ) + elif row["test_type"] == "ndrpdr": + if "-pdr" in test: + rate = ( + f"pdr [{row[C.UNIT['pdr']]}]: " + f"{row[C.VALUE['pdr']]:,.0f}<br>" + ) + elif "-ndr" in test: + rate = ( + f"ndr [{row[C.UNIT['ndr']]}]: " + f"{row[C.VALUE['ndr']]:,.0f}<br>" + ) + else: + rate = str() + else: + rate = str() + hover.append( + f"date: " + f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"value: {y_data[i]:,.2f}<br>" + f"{rate}" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}<br>" + ) + if any(y_data): + anomalies, trend_avg, trend_stdev = classify_anomalies( + {k: v for k, v in zip(x_axis, y_data)} + ) + hover_trend = list() + for avg, stdev, (_, row) in \ + zip(trend_avg, trend_stdev, df.iterrows()): + hover_trend.append( + f"date: " + f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"trend: {avg:,.2f}<br>" + f"stdev: {stdev:,.2f}<br>" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}" + ) + else: + anomalies = None + if all_in_one: + color = get_color(color_index * len(metrics) + idx) + metric_name = f"{test}<br>{metric}" + else: + color = get_color(idx) + metric_name = metric + + traces.append( + go.Scatter( # Samples + x=x_axis, + y=y_data, + name=metric_name, + mode="markers", + marker={ + "size": 5, + "color": color, + "symbol": "circle", + }, + text=hover, + hoverinfo="text+name", + showlegend=True, + legendgroup=metric_name + ) + ) + if anomalies: + traces.append( + go.Scatter( # Trend line + x=x_axis, + y=trend_avg, + name=metric_name, + mode="lines", + line={ + "shape": "linear", + "width": 1, + "color": color, + }, + text=hover_trend, + hoverinfo="text+name", + showlegend=False, + legendgroup=metric_name + ) + ) + + anomaly_x = list() + anomaly_y = list() + anomaly_color = list() + hover = list() + for idx, anomaly in enumerate(anomalies): + if anomaly in ("regression", "progression"): + anomaly_x.append(x_axis[idx]) + anomaly_y.append(trend_avg[idx]) + anomaly_color.append(C.ANOMALY_COLOR[anomaly]) + hover_itm = ( + f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}" + f"<br>trend: {trend_avg[idx]:,.2f}" + f"<br>classification: {anomaly}" + ) + hover.append(hover_itm) + anomaly_color.extend([0.0, 0.5, 1.0]) + traces.append( + go.Scatter( + x=anomaly_x, + y=anomaly_y, + mode="markers", + text=hover, + hoverinfo="text+name", + showlegend=False, + legendgroup=metric_name, + name=metric_name, + marker={ + "size": 15, + "symbol": "circle-open", + "color": anomaly_color, + "colorscale": C.COLORSCALE_TPUT, + "showscale": True, + "line": { + "width": 2 + }, + "colorbar": { + "y": 0.5, + "len": 0.8, + "title": "Circles Marking Data Classification", + "titleside": "right", + "tickmode": "array", + "tickvals": [0.167, 0.500, 0.833], + "ticktext": C.TICK_TEXT_TPUT, + "ticks": "", + "ticklen": 0, + "tickangle": -90, + "thickness": 10 + } + } + ) + ) + + unique_metrics = set() + for itm in metrics: + unique_metrics.add(itm.split("{", 1)[0]) + return traces, unique_metrics + + tm_trending_graphs = list() + graph_layout = layout.get("plot-trending-telemetry", dict()) + + if all_in_one: + all_traces = list() + + all_metrics = set() + all_tests = list() + for idx, test in enumerate(data.test_name.unique()): + df = data.loc[(data["test_name"] == test)] + traces, metrics = _generate_traces(df, test, all_in_one, idx) + if traces: + all_metrics.update(metrics) + if all_in_one: + all_traces.extend(traces) + all_tests.append(test) + else: + graph = go.Figure() + graph.add_traces(traces) + graph.update_layout(graph_layout) + tm_trending_graphs.append((graph, [test, ], )) + + if all_in_one: + graph = go.Figure() + graph.add_traces(all_traces) + graph.update_layout(graph_layout) + tm_trending_graphs.append((graph, all_tests, )) + + return tm_trending_graphs, list(all_metrics) |