added option to support input from elk in analyticsWebReport, added TRexDataAnalysisV2 that has OOP design, added details to asciidoc that provide more info on how we calculate MPPS/Core

Signed-off-by: itraviv <itraviv@cisco.com>

3 files changed, 69 insertions, 22 deletions

diff --git a/doc/AnalyticsWebReport.py b/doc/AnalyticsWebReport.py
index e3f6504b..04058a02 100755
--- a/doc/AnalyticsWebReport.py
+++ b/doc/AnalyticsWebReport.py
@@ -6,21 +6,24 @@ import time
 import datetime

 

 

-def main(verbose=False, detailed_test_stats=''):

-    if verbose:

-        print('Retrieving data from Google Analytics')

-    analytics = ac.initialize_analyticsreporting()

-    current_date = time.strftime("%Y-%m-%d")

-    k_days_ago = datetime.datetime.now() - datetime.timedelta(days=15)

-    start_date = str(k_days_ago.date())

-    response = ac.get_report(analytics, start_date, current_date)

-    ga_all_data_dict, setups = ac.export_to_tuples(response)

+def main(verbose=False, source='ga', detailed_test_stats='yes'):

+    if source == 'ga':

+        if verbose:

+            print('Retrieving data from Google Analytics')

+        analytics = ac.initialize_analyticsreporting()

+        current_date = time.strftime("%Y-%m-%d")

+        k_days_ago = datetime.datetime.now() - datetime.timedelta(days=15)

+        start_date = str(k_days_ago.date())

+        response = ac.get_report(analytics, start_date, current_date)

+        all_data_dict, setups = ac.export_to_tuples(response)

+    if source == 'elk':

+        all_data_dict = 0  # INSERT JSON FROM ELK HERE

     dest_path = os.path.join(os.getcwd(), 'build', 'images')

     if verbose:

         print('Saving data to %s' % dest_path)

         if detailed_test_stats:

             print('generating detailed table for test results')

-    tr.create_all_data(ga_all_data_dict, start_date, current_date, save_path=dest_path,

+    tr.create_all_data(all_data_dict, start_date, current_date, save_path=dest_path,

                        detailed_test_stats=detailed_test_stats)

     if verbose:

         print('Done without errors.')

diff --git a/doc/TRexDataAnalysisV2.py b/doc/TRexDataAnalysisV2.py
index e7e82b20..cd49538d 100755
--- a/doc/TRexDataAnalysisV2.py
+++ b/doc/TRexDataAnalysisV2.py
@@ -2,19 +2,48 @@
 import pandas as pd

 import numpy as np

 import matplotlib

+from enum import IntEnum

 

 matplotlib.use('Agg')

 from matplotlib import pyplot as plt

 import os

 import time

 

+"""

+This Module is structured to work with a raw data at the following JSON format:

+

+ {'setup_name': {'test1_name':[QUERY1,QUERY2,QUERY3],

+                'test2_name':[QUERY1,QUERY2,QUERY3]

+                }

+  'setup_name2': {'test1_name':[QUERY1,QUERY2,QUERY3],

+                'test2_name':[QUERY1,QUERY2,QUERY3]

+                }

+ }

+

+ The Query structure is set (currently) to this:

+ (test_name,state, date,hour,minute,mpps_result,mpps_min,mpps_max,build_id)

+

+ it can be changed to support other formats of queries, simply change the enum class to support your desired structure

+ the enums specify the indexes of the data within the query tuple

+

+"""

+

+

+class TestQuery(IntEnum):

+    QUERY_DATE = 2  # date format is yyyymmdd

+    QUERY_HOUR = 3

+    QUERY_MINUTE = 4

+    QUERY_MPPS_RESULT = 5

+    QUERY_TEST_MIN = 6

+    QUERY_TEST_MAX = 7

+    QUERY_BUILD_ID = 8

 

-### TODO: insert a description of a test query

 

 class Test:

-    def __init__(self, name, setup_name):

+    def __init__(self, name, setup_name, end_date):

         self.name = name

         self.setup_name = setup_name

+        self.end_date = end_date

         self.stats = []  # tuple

         self.results_df = []  # dataFrame

         self.latest_result = []  # float

@@ -27,15 +56,17 @@ class Test:
         test_mins = set()

         test_maxs = set()

         for query in raw_test_data:

-            test_results.append(float(query[5]))

-            date_formatted = time.strftime("%d-%m-%Y", time.strptime(query[2], "%Y%m%d"))

-            time_of_res = date_formatted + '-' + query[3] + ':' + query[4]

+            date_formatted = time.strftime("%d-%m-%Y", time.strptime(query[int(TestQuery.QUERY_DATE)], "%Y%m%d"))

+            time_of_res = date_formatted + '-' + query[int(TestQuery.QUERY_HOUR)] + ':' + query[

+                int(TestQuery.QUERY_MINUTE)]

             test_dates.append(time_of_res)

-            test_build_ids.append(query[8])

-            test_mins.add(float(query[6]))

-            test_maxs.add(float(query[7]))

-        test_results_df = pd.DataFrame({self.name: test_results, (self.name + ' Date'): test_dates,

-                                        "Setup": ([self.setup_name] * len(test_results)), "Build Id": test_build_ids})

+            test_results.append(float(query[int(TestQuery.QUERY_MPPS_RESULT)]))

+            test_build_ids.append(query[int(TestQuery.QUERY_BUILD_ID)])

+            test_mins.add(float(query[int(TestQuery.QUERY_TEST_MIN)]))

+            test_maxs.add(float(query[int(TestQuery.QUERY_TEST_MAX)]))

+        test_results_df = pd.DataFrame({self.name: test_results, self.name + ' Date': test_dates,

+                                        "Setup": ([self.setup_name] * len(test_results)), "Build Id": test_build_ids},

+                                       dtype='str')

         stats = tuple(

             [float(test_results_df[self.name].mean()), min(test_mins), max(test_maxs)])  # stats = (avg_mpps,min,max)

         self.latest_result = float(test_results_df[self.name].iloc[-1])

@@ -58,7 +89,7 @@ class Setup:
 

     def analyze_all_tests(self):

         for test_name in self.test_names:

-            t = Test(test_name, self.name)

+            t = Test(test_name, self.name, self.end_date)

             t.analyze_all_test_data(self.raw_setup_data[test_name])

             self.tests.append(t)

 

@@ -93,7 +124,7 @@ class Setup:
 

     def plot_trend_graph_all_tests(self, save_path='', file_name='_trend_graph.png'):

         for test_name in self.test_names:

-            self.all_tests_data_table[test_name].plot()

+            self.all_tests_data_table[test_name].plot(style=['.-'])

             plt.legend(fontsize='small', loc='best')

         plt.ylabel('MPPS/Core (Norm)')

         plt.title('Setup: ' + self.name)

diff --git a/doc/trex_analytics.asciidoc b/doc/trex_analytics.asciidoc
index 10681925..b9dbf4c6 100755
--- a/doc/trex_analytics.asciidoc
+++ b/doc/trex_analytics.asciidoc
@@ -22,6 +22,19 @@ endif::backend-xhtml11[]
 
 
 = TRex performance trend 
+Performance is measured in a metric called MPPS/Core (Norm), which stands for Mega-Packet per second (Norm). +
+ +
+How the calculation is made? +
+ +
+We measure the MPPS of the dut in it's best working point. +
+The measurement is then scaled to fit 100% CPU utilization, and finally divided by the number of cores the device has,
+to get a normalized MPPS/Core which is denoted as MPPS/Core (Norm) +
+ +
+How to run these tests? +
+[source,python]
+----
+       trex-core/scripts> ./run_regression --stl -t <name of test> --cfg setups/<name of setup>
+----
 
 == Setup: XL710 (4x40GbE)  trex-08