1 files changed, 150 insertions, 0 deletions
diff --git a/fluent-bit/lib/librdkafka-2.1.0/tests/tools/stats/graph.py b/fluent-bit/lib/librdkafka-2.1.0/tests/tools/stats/graph.py
new file mode 100755
index 00000000..3eeaa154
--- /dev/null
+++ b/fluent-bit/lib/librdkafka-2.1.0/tests/tools/stats/graph.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+#
+# Use pandas + bokeh to create graphs/charts/plots for stats CSV (to_csv.py).
+#
+
+import os
+import pandas as pd
+from bokeh.io import curdoc
+from bokeh.models import ColumnDataSource, HoverTool
+from bokeh.plotting import figure
+from bokeh.palettes import Dark2_5 as palette
+from bokeh.models.formatters import DatetimeTickFormatter
+
+import pandas_bokeh
+import argparse
+import itertools
+from fnmatch import fnmatch
+
+datecolumn = '0time'
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Graph CSV files')
+    parser.add_argument('infiles', nargs='+', type=str,
+                        help='CSV files to plot.')
+    parser.add_argument('--cols', type=str,
+                        help='Columns to plot (CSV list)')
+    parser.add_argument('--skip', type=str,
+                        help='Columns to skip (CSV list)')
+    parser.add_argument('--group-by', type=str,
+                        help='Group data series by field')
+    parser.add_argument('--chart-cols', type=int, default=3,
+                        help='Number of chart columns')
+    parser.add_argument('--plot-width', type=int, default=400,
+                        help='Per-plot width')
+    parser.add_argument('--plot-height', type=int, default=300,
+                        help='Per-plot height')
+    parser.add_argument('--out', type=str, default='out.html',
+                        help='Output file (HTML)')
+    args = parser.parse_args()
+
+    outpath = args.out
+    if args.cols is None:
+        cols = None
+    else:
+        cols = args.cols.split(',')
+        cols.append(datecolumn)
+
+    if args.skip is None:
+        assert cols is None, "--cols and --skip are mutually exclusive"
+        skip = None
+    else:
+        skip = args.skip.split(',')
+
+    group_by = args.group_by
+
+    pandas_bokeh.output_file(outpath)
+    curdoc().theme = 'dark_minimal'
+
+    figs = {}
+    plots = []
+    for infile in args.infiles:
+
+        colors = itertools.cycle(palette)
+
+        cols_to_use = cols
+
+        if skip is not None:
+            # First read available fields
+            avail_cols = list(pd.read_csv(infile, nrows=1))
+
+            cols_to_use = [c for c in avail_cols
+                           if len([x for x in skip if fnmatch(c, x)]) == 0]
+
+        df = pd.read_csv(infile,
+                         parse_dates=[datecolumn],
+                         index_col=datecolumn,
+                         usecols=cols_to_use)
+        title = os.path.basename(infile)
+        print(f"{infile}:")
+
+        if group_by is not None:
+
+            grp = df.groupby([group_by])
+
+            # Make one plot per column, skipping the index and group_by cols.
+            for col in df.keys():
+                if col in (datecolumn, group_by):
+                    continue
+
+                print("col: ", col)
+
+                for _, dg in grp:
+                    print(col, " dg:\n", dg.head())
+                    figtitle = f"{title}: {col}"
+                    p = figs.get(figtitle, None)
+                    if p is None:
+                        p = figure(title=f"{title}: {col}",
+                                   plot_width=args.plot_width,
+                                   plot_height=args.plot_height,
+                                   x_axis_type='datetime',
+                                   tools="hover,box_zoom,wheel_zoom," +
+                                   "reset,pan,poly_select,tap,save")
+                        figs[figtitle] = p
+                        plots.append(p)
+
+                        p.add_tools(HoverTool(
+                            tooltips=[
+                                ("index", "$index"),
+                                ("time", "@0time{%F}"),
+                                ("y", "$y"),
+                                ("desc", "$name"),
+                            ],
+                            formatters={
+                                "@0time": "datetime",
+                            },
+                            mode='vline'))
+
+                        p.xaxis.formatter = DatetimeTickFormatter(
+                            minutes=['%H:%M'],
+                            seconds=['%H:%M:%S'])
+
+                    source = ColumnDataSource(dg)
+
+                    val = dg[group_by][0]
+                    for k in dg:
+                        if k != col:
+                            continue
+
+                        p.line(x=datecolumn, y=k, source=source,
+                               legend_label=f"{k}[{val}]",
+                               name=f"{k}[{val}]",
+                               color=next(colors))
+
+            continue
+
+        else:
+            p = df.plot_bokeh(title=title,
+                              kind='line', show_figure=False)
+
+        plots.append(p)
+
+    for p in plots:
+        p.legend.click_policy = "hide"
+
+    grid = []
+    for i in range(0, len(plots), args.chart_cols):
+        grid.append(plots[i:i + args.chart_cols])
+
+    pandas_bokeh.plot_grid(grid)