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/*
 * Copyright (c) 2015 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.
 */
/*
 * error.c: VLIB error handler
 *
 * Copyright (c) 2008 Eliot Dresselhaus
 *
 * Permission is hereby granted, free of charge, to any person obtaining
 * a copy of this software and associated documentation files (the
 * "Software"), to deal in the Software without restriction, including
 * without limitation the rights to use, copy, modify, merge, publish,
 * distribute, sublicense, and/or sell copies of the Software, and to
 * permit persons to whom the Software is furnished to do so, subject to
 * the following conditions:
 *
 * The above copyright notice and this permission notice shall be
 * included in all copies or substantial portions of the Software.
 *
 *  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 *  EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 *  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
 *  NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
 *  LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
 *  OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
 *  WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

#include <vlib/vlib.h>
#include <vppinfra/heap.h>
#include <vlib/stat_weak_inlines.h>

uword
vlib_error_drop_buffers (vlib_main_t * vm,
			 vlib_node_runtime_t * node,
			 u32 * buffers,
			 u32 next_buffer_stride,
			 u32 n_buffers,
			 u32 next_index,
			 u32 drop_error_node, u32 drop_error_code)
{
  u32 n_left_this_frame, n_buffers_left, *args, n_args_left;
  vlib_error_t drop_error;
  vlib_node_t *n;

  n = vlib_get_node (vm, drop_error_node);
  drop_error = n->error_heap_index + drop_error_code;

  n_buffers_left = n_buffers;
  while (n_buffers_left > 0)
    {
      vlib_get_next_frame (vm, node, next_index, args, n_args_left);

      n_left_this_frame = clib_min (n_buffers_left, n_args_left);
      n_buffers_left -= n_left_this_frame;
      n_args_left -= n_left_this_frame;

      while (n_left_this_frame >= 4)
	{
	  u32 bi0, bi1, bi2, bi3;
	  vlib_buffer_t *b0, *b1, *b2, *b3;

	  args[0] = bi0 = buffers[0];
	  args[1] = bi1 = buffers[1];
	  args[2] = bi2 = buffers[2];
	  args[3] = bi3 = buffers[3];

	  b0 = vlib_get_buffer (vm, bi0);
	  b1 = vlib_get_buffer (vm, bi1);
	  b2 = vlib_get_buffer (vm, bi2);
	  b3 = vlib_get_buffer (vm, bi3);

	  b0->error = drop_error;
	  b1->error = drop_error;
	  b2->error = drop_error;
	  b3->error = drop_error;

	  buffers += 4;
	  args += 4;
	  n_left_this_frame -= 4;
	}

      while (n_left_this_frame >= 1)
	{
	  u32 bi0;
	  vlib_buffer_t *b0;

	  args[0] = bi0 = buffers[0];

	  b0 = vlib_get_buffer (vm, bi0);
	  b0->error = drop_error;

	  buffers += 1;
	  args += 1;
	  n_left_this_frame -= 1;
	}

      vlib_put_next_frame (vm, node, next_index, n_args_left);
    }

  return n_buffers;
}

/* Reserves given number of error codes for given node. */
void
vlib_register_errors (vlib_main_t * vm,
		      u32 node_index, u32 n_errors, char *error_strings[])
{
  vlib_error_main_t *em = &vm->error_main;
  vlib_node_main_t *nm = &vm->node_main;

  vlib_node_t *n = vlib_get_node (vm, node_index);
  uword l;
  void *oldheap;

  ASSERT (vlib_get_thread_index () == 0);

  /* Free up any previous error strings. */
  if (n->n_errors > 0)
    heap_dealloc (em->error_strings_heap, n->error_heap_handle);

  n->n_errors = n_errors;
  n->error_strings = error_strings;

  if (n_errors == 0)
    return;

  n->error_heap_index =
    heap_alloc (em->error_strings_heap, n_errors, n->error_heap_handle);

  l = vec_len (em->error_strings_heap);

  clib_memcpy (vec_elt_at_index (em->error_strings_heap, n->error_heap_index),
	       error_strings, n_errors * sizeof (error_strings[0]));

  vec_validate (vm->error_elog_event_types, l - 1);

  /* Switch to the stats segment ... */
  oldheap = vlib_stats_push_heap (0);

  /* Allocate a counter/elog type for each error. */
  vec_validate (em->counters, l - 1);

  /* Zero counters for re-registrations of errors. */
  if (n->error_heap_index + n_errors <= vec_len (em->counters_last_clear))
    clib_memcpy (em->counters + n->error_heap_index,
		 em->counters_last_clear + n->error_heap_index,
		 n_errors * sizeof (em->counters[0]));
  else
    clib_memset (em->counters + n->error_heap_index,
		 0, n_errors * sizeof (em->counters[0]));

  /* Register counter indices in the stat segment directory */
  {
    int i;
    u8 *error_name = 0;

    for (i = 0; i < n_errors; i++)
      {
	vec_reset_length (error_name);
	error_name =
	  format (error_name, "/err/%v/%s%c", n->name, error_strings[i], 0);
	vlib_stats_register_error_index (oldheap, error_name, em->counters,
					 n->error_heap_index + i);
      }

    vec_free (error_name);
  }

  /* (re)register the em->counters base address, switch back to main heap */
  vlib_stats_pop_heap2 (em->counters, vm->thread_index, oldheap, 1);

  {
    elog_event_type_t t;
    uword i;

    clib_memset (&t, 0, sizeof (t));
    if (n_errors > 0)
      vec_validate (nm->node_by_error, n->error_heap_index + n_errors - 1);
    for (i = 0; i < n_errors; i++)
      {
	t.format = (char *) format (0, "%v %s: %%d",
				    n->name, error_strings[i]);
	vm->error_elog_event_types[n->error_heap_index + i] = t;
	nm->node_by_error[n->error_heap_index + i] = n->index;
      }
  }
}

static clib_error_t *
show_errors (vlib_main_t * vm,
	     unformat_input_t * input, vlib_cli_command_t * cmd)
{
  vlib_error_main_t *em = &vm->error_main;
  vlib_node_t *n;
  u32 code, i, ni;
  u64 c;
  int index = 0;
  int verbose = 0;
  u64 *sums = 0;

  if (unformat (input, "verbose %d", &verbose))
    ;
  else if (unformat (input, "verbose"))
    verbose = 1;

  vec_validate (sums, vec_len (em->counters));

  if (verbose)
    vlib_cli_output (vm, "%=10s%=40s%=20s%=6s", "Count", "Node", "Reason",
		     "Index");
  else
    vlib_cli_output (vm, "%=10s%=40s%=6s", "Count", "Node", "Reason");


  /* *INDENT-OFF* */
  foreach_vlib_main(({
    em = &this_vlib_main->error_main;

    if (verbose)
      vlib_cli_output(vm, "Thread %u (%v):", index,
                      vlib_worker_threads[index].name);

    for (ni = 0; ni < vec_len (this_vlib_main->node_main.nodes); ni++)
      {
	n = vlib_get_node (this_vlib_main, ni);
	for (code = 0; code < n->n_errors; code++)
	  {
	    i = n->error_heap_index + code;
	    c = em->counters[i];
	    if (i < vec_len (em->counters_last_clear))
	      c -= em->counters_last_clear[i];
	    sums[i] += c;

	    if (c == 0 && verbose < 2)
	      continue;

            if (verbose)
              vlib_cli_output (vm, "%10Ld%=40v%=20s%=6d", c, n->name,
                               em->error_strings_heap[i], i);
            else
              vlib_cli_output (vm, "%10d%=40v%s", c, n->name,
                               em->error_strings_heap[i]);
	  }
      }
    index++;
  }));
  /* *INDENT-ON* */

  if (verbose)
    vlib_cli_output (vm, "Total:");

  for (ni = 0; ni < vec_len (vm->node_main.nodes); ni++)
    {
      n = vlib_get_node (vm, ni);
      for (code = 0; code < n->n_errors; code++)
	{
	  i = n->error_heap_index + code;
	  if (sums[i])
	    {
	      if (verbose)
		vlib_cli_output (vm, "%10Ld%=40v%=20s%=10d", sums[i], n->name,
				 em->error_strings_heap[i], i);
	    }
	}
    }

  vec_free (sums);

  return 0;
}

/* *INDENT-OFF* */
VLIB_CLI_COMMAND (vlib_cli_show_errors) = {
  .path = "show errors",
  .short_help = "Show error counts",
  .function = show_errors,
};
/* *INDENT-ON* */

/* *INDENT-OFF* */
VLIB_CLI_COMMAND (cli_show_node_counters, static) = {
  .path = "show node counters",
  .short_help = "Show node counters",
  .function 
# Copyright (c) 2019 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.

"""Algorithms to generate tables.
"""


import logging
import csv
import re

from string import replace
from collections import OrderedDict
from numpy import nan, isnan
from xml.etree import ElementTree as ET
from datetime import datetime as dt
from datetime import timedelta

from utils import mean, stdev, relative_change, classify_anomalies, \
    convert_csv_to_pretty_txt, relative_change_stdev


REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')


def generate_tables(spec, data):
    """Generate all tables specified in the specification file.

    :param spec: Specification read from the specification file.
    :param data: Data to process.
    :type spec: Specification
    :type data: InputData
    """

    logging.info("Generating the tables ...")
    for table in spec.tables:
        try:
            eval(table["algorithm"])(table, data)
        except NameError as err:
            logging.error("Probably algorithm '{alg}' is not defined: {e
s) with algorithm: table_detailed_test_results specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table) # Prepare the header of the tables header = list() for column in table["columns"]: header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) # Generate the data for the table according to the model in the table # specification job = table["data"].keys()[0] build = str(table["data"][job][0]) try: suites = input_data.suites(job, build) except KeyError: logging.error(" No data available. The table will not be generated.") return for suite_longname, suite in suites.iteritems(): # Generate data suite_name = suite["name"] table_lst = list() for test in data[job][build].keys(): if data[job][build][test]["parent"] in suite_name: row_lst = list() for column in table["columns"]: try: col_data = str(data[job][build][test][column["data"]. split(" ")[1]]).replace('"', '""') if column["data"].split(" ")[1] in ("conf-history", "show-run"): col_data = replace(col_data, " |br| ", "", maxreplace=1) col_data = " |prein| {0} |preout| ".\ format(col_data[:-5]) row_lst.append('"{0}"'.format(col_data)) except KeyError: row_lst.append("No data") table_lst.append(row_lst) # Write the data to file if table_lst: file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, table["output-file-ext"]) logging.info(" Writing file: '{}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in table_lst: file_handler.write(",".join(item) + "\n") logging.info(" Done.") def table_merged_details(table, input_data): """Generate the table(s) with algorithm: table_merged_details specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table) data = input_data.merge_data(data) data.sort_index(inplace=True) logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) suites = input_data.filter_data(table, data_set="suites") suites = input_data.merge_data(suites) # Prepare the header of the tables header = list() for column in table["columns"]: header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) for _, suite in suites.iteritems(): # Generate data suite_name = suite["name"] table_lst = list() for test in data.keys(): if data[test]["parent"] in suite_name: row_lst = list() for column in table["columns"]: try: col_data = str(data[test][column["data"]. split(" ")[1]]).replace('"', '""') col_data = replace(col_data, "No Data", "Not Captured ") if column["data"].split(" ")[1] in ("conf-history", "show-run"): col_data = replace(col_data, " |br| ", "", maxreplace=1) col_data = " |prein| {0} |preout| ".\ format(col_data[:-5]) row_lst.append('"{0}"'.format(col_data)) except KeyError: row_lst.append('"Not captured"') table_lst.append(row_lst) # Write the data to file if table_lst: file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, table["output-file-ext"]) logging.info(" Writing file: '{}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in table_lst: file_handler.write(",".join(item) + "\n") logging.info(" Done.") def table_performance_comparison(table, input_data): """Generate the table(s) with algorithm: table_performance_comparison specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables try: header = ["Test case", ] if table["include-tests"] == "MRR": hdr_param = "Receive Rate" else: hdr_param = "Throughput" history = table.get("history", None) if history: for item in history: header.extend( ["{0} {1} [Mpps]".format(item["title"], hdr_param), "{0} Stdev [Mpps]".format(item["title"])]) header.extend( ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param), "{0} Stdev [Mpps]".format(table["reference"]["title"]), "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param), "{0} Stdev [Mpps]".format(table["compare"]["title"]), "Delta [%]"]) header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". format(err)) return # Prepare data to the table: tbl_dict = dict() for job, builds in table["reference"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\ replace("-ndrpdr", "").replace("-pdrdisc", "").\ replace("-ndrdisc", "").replace("-pdr", "").\ replace("-ndr", "").\ replace("1t1c", "1c").replace("2t1c", "1c").\ replace("2t2c", "2c").replace("4t2c", "2c").\ replace("4t4c", "4c").replace("8t4c", "4c") if "across topologies" in table["title"].lower(): tst_name_mod = tst_name_mod.replace("2n1l-", "") if tbl_dict.get(tst_name_mod, None) is None: groups = re.search(REGEX_NIC, tst_data["parent"]) nic = groups.group(0) if groups else "" name = "{0}-{1}".format(nic, "-".join(tst_data["name"]. split("-")[:-1])) if "across testbeds" in table["title"].lower() or \ "across topologies" in table["title"].lower(): name = name.\ replace("1t1c", "1c").replace("2t1c", "1c").\ replace("2t2c", "2c").replace("4t2c", "2c").\ replace("4t4c", "4c").replace("8t4c", "4c") tbl_dict[tst_name_mod] = {"name": name, "ref-data": list(), "cmp-data": list()} try: # TODO: Re-work when NDRPDRDISC tests are not used if table["include-tests"] == "MRR": tbl_dict[tst_name_mod]["ref-data"]. \ append(tst_data["result"]["receive-rate"].avg) elif table["include-tests"] == "PDR": if tst_data["type"] == "PDR": tbl_dict[tst_name_mod]["ref-data"]. \ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["ref-data"].append( tst_data["throughput"]["PDR"]["LOWER"]) elif table["include-tests"] == "NDR": if tst_data["type"] == "NDR": tbl_dict[tst_name_mod]["ref-data"]. \ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["ref-data"].append( tst_data["throughput"]["NDR"]["LOWER"]) else: continue except TypeError: pass # No data in output.xml for this test for job, builds in table["compare"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \ replace("-ndrpdr", "").replace("-pdrdisc", ""). \ replace("-ndrdisc", "").replace("-pdr", ""). \ replace("-ndr", "").\ replace("1t1c", "1c").replace("2t1c", "1c").\ replace("2t2c", "2c").replace("4t2c", "2c").\ replace("4t4c", "4c").replace("8t4c", "4c") if "across topologies" in table["title"].lower(): tst_name_mod = tst_name_mod.replace("2n1l-", "") try: # TODO: Re-work when NDRPDRDISC tests are not used if table["include-tests"] == "MRR": tbl_dict[tst_name_mod]["cmp-data"]. \ append(tst_data["result"]["receive-rate"].avg) elif table["include-tests"] == "PDR": if tst_data["type"] == "PDR": tbl_dict[tst_name_mod]["cmp-data"]. \ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["cmp-data"].append( tst_data["throughput"]["PDR"]["LOWER"]) elif table["include-tests"] == "NDR": if tst_data["type"] == "NDR": tbl_dict[tst_name_mod]["cmp-data"]. \ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["cmp-data"].append( tst_data["throughput"]["NDR"]["LOWER"]) else: continue except KeyError: pass except TypeError: tbl_dict.pop(tst_name_mod, None) if history: for item in history: for job, builds in item["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \ replace("-ndrpdr", "").replace("-pdrdisc", ""). \ replace("-ndrdisc", "").replace("-pdr", ""). \ replace("-ndr", "").\ replace("1t1c", "1c").replace("2t1c", "1c").\ replace("2t2c", "2c").replace("4t2c", "2c").\ replace("4t4c", "4c").replace("8t4c", "4c") if "across topologies" in table["title"].lower(): tst_name_mod = tst_name_mod.replace("2n1l-", "") if tbl_dict.get(tst_name_mod, None) is None: continue if tbl_dict[tst_name_mod].get("history", None) is None: tbl_dict[tst_name_mod]["history"] = OrderedDict() if tbl_dict[tst_name_mod]["history"].get(item["title"], None) is None: tbl_dict[tst_name_mod]["history"][item["title"]] = \ list() try: # TODO: Re-work when NDRPDRDISC tests are not used if table["include-tests"] == "MRR": tbl_dict[tst_name_mod]["history"][item["title" ]].append(tst_data["result"]["receive-rate"]. avg) elif table["include-tests"] == "PDR": if tst_data["type"] == "PDR": tbl_dict[tst_name_mod]["history"][ item["title"]].\ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["history"][item[ "title"]].append(tst_data["throughput"][ "PDR"]["LOWER"]) elif table["include-tests"] == "NDR": if tst_data["type"] == "NDR": tbl_dict[tst_name_mod]["history"][ item["title"]].\ append(tst_data["throughput"]["value"]) elif tst_data["type"] == "NDRPDR": tbl_dict[tst_name_mod]["history"][item[ "title"]].append(tst_data["throughput"][ "NDR"]["LOWER"]) else: continue except (TypeError, KeyError): pass tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if history: if tbl_dict[tst_name].get("history", None) is not None: for hist_data in tbl_dict[tst_name]["history"].values(): if hist_data: item.append(round(mean(hist_data) / 1000000, 2)) item.append(round(stdev(hist_data) / 1000000, 2)) else: item.extend([None, None]) else: item.extend([None, None]) data_t = tbl_dict[tst_name]["ref-data"] if data_t: item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) data_t = tbl_dict[tst_name]["cmp-data"] if data_t: item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if item[-4] is not None and item[-2] is not None and item[-4] != 0: item.append(int(relative_change(float(item[-4]), float(item[-2])))) if len(item) == len(header): tbl_lst.append(item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) # Generate csv tables: csv_file = "{0}.csv".format(table["output-file"]) with open(csv_file, "w") as file_handler: file_handler.write(header_str) for test in tbl_lst: file_handler.write(",".join([str(item) for item in test]) + "\n") convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"])) def table_nics_comparison(table, input_data): """Generate the table(s) with algorithm: table_nics_comparison specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables try: header = ["Test case", ] if table["include-tests"] == "MRR": hdr_param = "Receive Rate" else: hdr_param = "Throughput" header.extend( ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param), "{0} Stdev [Mpps]".format(table["reference"]["title"]), "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param), "{0} Stdev [Mpps]".format(table["compare"]["title"]), "Delta [%]"]) header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". format(err)) return # Prepare data to the table: tbl_dict = dict() for job, builds in table["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\ replace("-ndrpdr", "").replace("-pdrdisc", "").\ replace("-ndrdisc", "").replace("-pdr", "").\ replace("-ndr", "").\ replace("1t1c", "1c").replace("2t1c", "1c").\ replace("2t2c", "2c").replace("4t2c", "2c").\ replace("4t4c", "4c").replace("8t4c", "4c") tst_name_mod = re.sub(REGEX_NIC, "", tst_name_mod) if tbl_dict.get(tst_name_mod, None) is None: name = "-".join(tst_data["name"].split("-")[:-1]) tbl_dict[tst_name_mod] = {"name": name, "ref-data": list(), "cmp-data": list()} try: if table["include-tests"] == "MRR": result = tst_data["result"]["receive-rate"].avg elif table["include-tests"] == "PDR": result = tst_data["throughput"]["PDR"]["LOWER"] elif table["include-tests"] == "NDR": result = tst_data["throughput"]["NDR"]["LOWER"] else: result = None if result: if table["reference"]["nic"] in tst_data["tags"]: tbl_dict[tst_name_mod]["ref-data"].append(result) elif table["compare"]["nic"] in tst_data["tags"]: tbl_dict[tst_name_mod]["cmp-data"].append(result) except (TypeError, KeyError) as err: logging.debug("No data for {0}".format(tst_name)) logging.debug(repr(err)) # No data in output.xml for this test tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] data_t = tbl_dict[tst_name]["ref-data"] if data_t: item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) data_t = tbl_dict[tst_name]["cmp-data"] if data_t: item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if item[-4] is not None and item[-2] is not None and item[-4] != 0: item.append(int(relative_change(float(item[-4]), float(item[-2])))) if len(item) == len(header): tbl_lst.append(item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) # Generate csv tables: csv_file = "{0}.csv".format(table["output-file"]) with open(csv_file, "w") as file_handler: file_handler.write(header_str) for test in tbl_lst: file_handler.write(",".join([str(item) for item in test]) + "\n") convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"])) def table_soak_vs_ndr(table, input_data): """Generate the table(s) with algorithm: table_soak_vs_ndr specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the table try: header = [ "Test case", "{0} Throughput [Mpps]".format(table["reference"]["title"]), "{0} Stdev [Mpps]".format(table["reference"]["title"]), "{0} Throughput [Mpps]".format(table["compare"]["title"]), "{0} Stdev [Mpps]".format(table["compare"]["title"]), "Delta [%]", "Stdev of delta [%]"] header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". format(err)) return # Create a list of available SOAK test results: tbl_dict = dict() for job, builds in table["compare"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): if tst_data["type"] == "SOAK": tst_name_mod = tst_name.replace("-soak", "") if tbl_dict.get(tst_name_mod, None) is None: groups = re.search(REGEX_NIC, tst_data["parent"]) nic = groups.group(0) if groups else "" name = "{0}-{1}".format(nic, "-".join(tst_data["name"]. split("-")[:-1])) tbl_dict[tst_name_mod] = { "name": name, "ref-data": list(), "cmp-data": list() } try: tbl_dict[tst_name_mod]["cmp-data"].append( tst_data["throughput"]["LOWER"]) except (KeyError, TypeError): pass tests_lst = tbl_dict.keys() # Add corresponding NDR test results: for job, builds in table["reference"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): tst_name_mod = tst_name.replace("-ndrpdr", "").\ replace("-mrr", "") if tst_name_mod in tests_lst: try: if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"): if table["include-tests"] == "MRR": result = tst_data["result"]["receive-rate"].avg elif table["include-tests"] == "PDR": result = tst_data["throughput"]["PDR"]["LOWER"] elif table["include-tests"] == "NDR": result = tst_data["throughput"]["NDR"]["LOWER"] else: result = None if result is not None: tbl_dict[tst_name_mod]["ref-data"].append( result) except (KeyError, TypeError): continue tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] data_r = tbl_dict[tst_name]["ref-data"] if data_r: data_r_mean = mean(data_r) item.append(round(data_r_mean / 1000000, 2)) data_r_stdev = stdev(data_r) item.append(round(data_r_stdev / 1000000, 2)) else: data_r_mean = None data_r_stdev = None item.extend([None, None]) data_c = tbl_dict[tst_name]["cmp-data"] if data_c: data_c_mean = mean(data_c) item.append(round(data_c_mean / 1000000, 2)) data_c_stdev = stdev(data_c) item.append(round(data_c_stdev / 1000000, 2)) else: data_c_mean = None data_c_stdev = None item.extend([None, None]) if data_r_mean and data_c_mean: delta, d_stdev = relative_change_stdev( data_r_mean, data_c_mean, data_r_stdev, data_c_stdev) item.append(round(delta, 2)) item.append(round(d_stdev, 2)) tbl_lst.append(item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) # Generate csv tables: csv_file = "{0}.csv".format(table["output-file"]) with open(csv_file, "w") as file_handler: file_handler.write(header_str) for test in tbl_lst: file_handler.write(",".join([str(item) for item in test]) + "\n") convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"])) def table_performance_trending_dashboard(table, input_data): """Generate the table(s) with algorithm: table_performance_trending_dashboard specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables header = ["Test Case", "Trend [Mpps]", "Short-Term Change [%]", "Long-Term Change [%]", "Regressions [#]", "Progressions [#]" ] header_str = ",".join(header) + "\n" # Prepare data to the table: tbl_dict = dict() for job, builds in table["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): if tst_name.lower() in table.get("ignore-list", False): continue if tbl_dict.get(tst_name, None) is None: groups = re.search(REGEX_NIC, tst_data["parent"]) if not groups: continue nic = groups.group(0) tbl_dict[tst_name] = { "name": "{0}-{1}".format(nic, tst_data["name"]), "data": OrderedDict()} try: tbl_dict[tst_name]["data"][str(build)] = \ tst_data["result"]["receive-rate"] except (TypeError, KeyError): pass # No data in output.xml for this test tbl_lst = list() for tst_name in tbl_dict.keys(): data_t = tbl_dict[tst_name]["data"] if len(data_t) < 2: continue classification_lst, avgs = classify_anomalies(data_t) win_size = min(len(data_t), table["window"]) long_win_size = min(len(data_t), table["long-trend-window"]) try: max_long_avg = max( [x for x in avgs[-long_win_size:-win_size] if not isnan(x)]) except ValueError: max_long_avg = nan last_avg = avgs[-1] avg_week_ago = avgs[max(-win_size, -len(avgs))] if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0: rel_change_last = nan else: rel_change_last = round( ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2) if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0: rel_change_long = nan else: rel_change_long = round( ((last_avg - max_long_avg) / max_long_avg) * 100, 2) if classification_lst: if isnan(rel_change_last) and isnan(rel_change_long): continue if (isnan(last_avg) or isnan(rel_change_last) or isnan(rel_change_long)): continue tbl_lst.append( [tbl_dict[tst_name]["name"], round(last_avg / 1000000, 2), rel_change_last, rel_change_long, classification_lst[-win_size:].count("regression"), classification_lst[-win_size:].count("progression")]) tbl_lst.sort(key=lambda rel: rel[0]) tbl_sorted = list() for nrr in range(table["window"], -1, -1): tbl_reg = [item for item in tbl_lst if item[4] == nrr] for nrp in range(table["window"], -1, -1): tbl_out = [item for item in tbl_reg if item[5] == nrp] tbl_out.sort(key=lambda rel: rel[2]) tbl_sorted.extend(tbl_out) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) for test in tbl_sorted: file_handler.write(",".join([str(item) for item in test]) + '\n') txt_file_name = "{0}.txt".format(table["output-file"]) logging.info(" Writing file: '{0}'".format(txt_file_name)) convert_csv_to_pretty_txt(file_name, txt_file_name) def _generate_url(base, testbed, test_name): """Generate URL to a trending plot from the name of the test case. :param base: The base part of URL common to all test cases. :param testbed: The testbed used for testing. :param test_name: The name of the test case. :type base: str :type testbed: str :type test_name: str :returns: The URL to the plot with the trending data for the given test case. :rtype str """ url = base file_name = "" anchor = ".html#" feature = "" if "lbdpdk" in test_name or "lbvpp" in test_name: file_name = "link_bonding" elif "114b" in test_name and "vhost" in test_name: file_name = "vts" elif "testpmd" in test_name or "l3fwd" in test_name: file_name = "dpdk" elif "memif" in test_name: file_name = "container_memif" feature = "-base" elif "srv6" in test_name: file_name = "srv6" elif "vhost" in test_name: if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name: file_name = "vm_vhost_l2" if "114b" in test_name: feature = "" elif "l2xcbase" in test_name and "x520" in test_name: feature = "-base-l2xc" elif "l2bdbasemaclrn" in test_name and "x520" in test_name: feature = "-base-l2bd" else: feature = "-base" elif "ip4base" in test_name: file_name = "vm_vhost_ip4" feature = "-base" elif "ipsecbasetnlsw" in test_name: file_name = "ipsecsw" feature = "-base-scale" elif "ipsec" in test_name: file_name = "ipsec" feature = "-base-scale" elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name: file_name = "ip4_tunnels" feature = "-base" elif "ip4base" in test_name or "ip4scale" in test_name: file_name = "ip4" if "xl710" in test_name: feature = "-base-scale-features" elif "iacl" in test_name: feature = "-features-iacl" elif "oacl" in test_name: feature = "-features-oacl" elif "snat" in test_name or "cop" in test_name: feature = "-features" else: feature = "-base-scale" elif "ip6base" in test_name or "ip6scale" in test_name: file_name = "ip6" feature = "-base-scale" elif "l2xcbase" in test_name or "l2xcscale" in test_name \ or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \ or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name: file_name = "l2" if "macip" in test_name: feature = "-features-macip" elif "iacl" in test_name: feature = "-features-iacl" elif "oacl" in test_name: feature = "-features-oacl" else: feature = "-base-scale" if "x520" in test_name: nic = "x520-" elif "x710" in test_name: nic = "x710-" elif "xl710" in test_name: nic = "xl710-" elif "xxv710" in test_name: nic = "xxv710-" elif "vic1227" in test_name: nic = "vic1227-" elif "vic1385" in test_name: nic = "vic1385-" else: nic = "" anchor += nic if "64b" in test_name: framesize = "64b" elif "78b" in test_name: framesize = "78b" elif "imix" in test_name: framesize = "imix" elif "9000b" in test_name: framesize = "9000b" elif "1518b" in test_name: framesize = "1518b" elif "114b" in test_name: framesize = "114b" else: framesize = "" anchor += framesize + '-' if "1t1c" in test_name: anchor += "1t1c" elif "2t2c" in test_name: anchor += "2t2c" elif "4t4c" in test_name: anchor += "4t4c" elif "2t1c" in test_name: anchor += "2t1c" elif "4t2c" in test_name: anchor += "4t2c" elif "8t4c" in test_name: anchor += "8t4c" return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \ anchor + feature def table_performance_trending_dashboard_html(table, input_data): """Generate the table(s) with algorithm: table_performance_trending_dashboard_html specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: dict :type input_data: InputData """ testbed = table.get("testbed", None) if testbed is None: logging.error("The testbed is not defined for the table '{0}'.". format(table.get("title", ""))) return logging.info(" Generating the table {0} ...". format(table.get("title", ""))) try: with open(table["input-file"], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') csv_lst = [item for item in csv_content] except KeyError: logging.warning("The input file is not defined.") return except csv.Error as err: logging.warning("Not possible to process the file '{0}'.\n{1}". format(table["input-file"], err)) return # Table: dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) # Table header: tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) for idx, item in enumerate(csv_lst[0]): alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item # Rows: colors = {"regression": ("#ffcccc", "#ff9999"), "progression": ("#c6ecc6", "#9fdf9f"), "normal": ("#e9f1fb", "#d4e4f7")} for r_idx, row in enumerate(csv_lst[1:]): if int(row[4]): color = "regression" elif int(row[5]): color = "progression" else: color = "normal" background = colors[color][r_idx % 2] tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) # Columns: for c_idx, item in enumerate(row): alignment = "left" if c_idx == 0 else "center" td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) # Name: if c_idx == 0: url = _generate_url("../trending/", testbed, item) ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item else: td.text = item try: with open(table["output-file"], 'w') as html_file: logging.info(" Writing file: '{0}'".format(table["output-file"])) html_file.write(".. raw:: html\n\n\t") html_file.write(ET.tostring(dashboard)) html_file.write("\n\t<p><br><br></p>\n") except KeyError: logging.warning("The output file is not defined.") return def table_last_failed_tests(table, input_data): """Generate the table(s) with algorithm: table_last_failed_tests specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) if data is None or data.empty: logging.warn(" No data for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) return tbl_list = list() for job, builds in table["data"].items(): for build in builds: build = str(build) try: version = input_data.metadata(job, build).get("version", "") except KeyError: logging.error("Data for {job}: {build} is not present.". format(job=job, build=build)) return tbl_list.append(build) tbl_list.append(version) for tst_name, tst_data in data[job][build].iteritems(): if tst_data["status"] != "FAIL": continue groups = re.search(REGEX_NIC, tst_data["parent"]) if not groups: continue nic = groups.group(0) tbl_list.append("{0}-{1}".format(nic, tst_data["name"])) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: for test in tbl_list: file_handler.write(test + '\n') def table_failed_tests(table, input_data): """Generate the table(s) with algorithm: table_failed_tests specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data logging.info(" Creating the data set for the {0} '{1}'.". format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables header = ["Test Case", "Failures [#]", "Last Failure [Time]", "Last Failure [VPP-Build-Id]", "Last Failure [CSIT-Job-Build-Id]"] # Generate the data for the table according to the model in the table # specification now = dt.utcnow() timeperiod = timedelta(int(table.get("window", 7))) tbl_dict = dict() for job, builds in table["data"].items(): for build in builds: build = str(build) for tst_name, tst_data in data[job][build].iteritems(): if tst_name.lower() in table.get("ignore-list", False): continue if tbl_dict.get(tst_name, None) is None: groups = re.search(REGEX_NIC, tst_data["parent"]) if not groups: continue nic = groups.group(0) tbl_dict[tst_name] = { "name": "{0}-{1}".format(nic, tst_data["name"]), "data": OrderedDict()} try: generated = input_data.metadata(job, build).\ get("generated", "") if not generated: continue then = dt.strptime(generated, "%Y%m%d %H:%M") if (now - then) <= timeperiod: tbl_dict[tst_name]["data"][build] = ( tst_data["status"], generated, input_data.metadata(job, build).get("version", ""), build) except (TypeError, KeyError) as err: logging.warning("tst_name: {} - err: {}". format(tst_name, repr(err))) max_fails = 0 tbl_lst = list() for tst_data in tbl_dict.values(): fails_nr = 0 for val in tst_data["data"].values(): if val[0] == "FAIL": fails_nr += 1 fails_last_date = val[1] fails_last_vpp = val[2] fails_last_csit = val[3] if fails_nr: max_fails = fails_nr if fails_nr > max_fails else max_fails tbl_lst.append([tst_data["name"], fails_nr, fails_last_date, fails_last_vpp, "mrr-daily-build-{0}".format(fails_last_csit)]) tbl_lst.sort(key=lambda rel: rel[2], reverse=True) tbl_sorted = list() for nrf in range(max_fails, -1, -1): tbl_fails = [item for item in tbl_lst if item[1] == nrf] tbl_sorted.extend(tbl_fails) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for test in tbl_sorted: file_handler.write(",".join([str(item) for item in test]) + '\n') txt_file_name = "{0}.txt".format(table["output-file"]) logging.info(" Writing file: '{0}'".format(txt_file_name)) convert_csv_to_pretty_txt(file_name, txt_file_name) def table_failed_tests_html(table, input_data): """Generate the table(s) with algorithm: table_failed_tests_html specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ testbed = table.get("testbed", None) if testbed is None: logging.error("The testbed is not defined for the table '{0}'.". format(table.get("title", ""))) return logging.info(" Generating the table {0} ...". format(table.get("title", ""))) try: with open(table["input-file"], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') csv_lst = [item for item in csv_content] except KeyError: logging.warning("The input file is not defined.") return except csv.Error as err: logging.warning("Not possible to process the file '{0}'.\n{1}". format(table["input-file"], err)) return # Table: failed_tests = ET.Element("table", attrib=dict(width="100%", border='0')) # Table header: tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#7eade7")) for idx, item in enumerate(csv_lst[0]): alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item # Rows: colors = ("#e9f1fb", "#d4e4f7") for r_idx, row in enumerate(csv_lst[1:]): background = colors[r_idx % 2] tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor=background)) # Columns: for c_idx, item in enumerate(row): alignment = "left" if c_idx == 0 else "center" td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) # Name: if c_idx == 0: url = _generate_url("../trending/", testbed, item) ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item else: td.text = item try: with open(table["output-file"], 'w') as html_file: logging.info(" Writing file: '{0}'".format(table["output-file"])) html_file.write(".. raw:: html\n\n\t") html_file.write(ET.tostring(failed_tests)) html_file.write("\n\t<p><br><br></p>\n") except KeyError: logging.warning("The output file is not defined.") return