Parser module API
The file common.py is the python module for performing benchmark log file processing, and results processing and aggregation.
It is used by the parser.py program from the test directory, to process the log after each test run. The data from a test run is processed to:
Check numeric values for pass/fail result(by checking against a reference threshold values)
Determine the overall result of the test, based on potentially complex results criteria
Save the data for use in history and comparison charts
Parser API
The following are functions used during log processing, by a test’s parser.py program.
parse_log() - parse the data from a test log
This routine takes a regular expression, with one or more groups, and results a list of tuples for lines that matched the expression
The tuples consist of the strings from the matching line corresponding to the regex groups
process() - process results from a test
This routine taks a dictionary of test results, and does 3 things:
Formats them into the run.json file (run results file)
Detects pass or fail by using the specified pass criteria
Formats the data into charts (plots and tables)
split_output_per_testcase()
Split testlog into chunks accessible from the Jenkins user interface (one per testcase)
In general, a parser module will normally call parse_log()
, then
take the resulting list of matching groups to construct a dictionary
to pass to the process()
routine.
If the log file format is amendable, the parser module may also call
split_output_per_testcase()
to generate a set of files from the
testlog, that can be referenced from the charts generated by the
charting module.
Please see parser.py for more details and examples of use of the API.
Deprecated API
Note
The following information is for historical purposes only. Although the API is still present in Fuego, these APIs are deprecated.
In Fuego version 1.1 and prior, the following functions were used. These are still available for backwards compatibility with tests written for these versions of Fuego.
parse()
process_data()
(see parser.py for invocation details)
parse()
input:
cur_search_pattern - compiled re search pattern
output:
list of regular expression matches for each line matching the specified pattern
This routine scans the current log file, using a regular expression. It returns an re match object for each line of the log file that matches the expression.
This list is used to populate a dictionary of metric/value pairs that can be passed to the process_data function.
process_data
This is the main routine of the module. It processes the list of metrics, and populates various output files for test.
input:
ref_section_pat - regular expression used to read reference.log
cur_dict - dictionary of metric/value pairs
m - indicates the size of the plot. It should be one of: ‘s’, ‘m’, ‘l’, ‘xl’
if ‘m’, ‘l’, or ‘xl’ are used, then a multiplot is created
label - label for the plot
This routine has the following outline:
write_report_results
read the reference thresholds
check the values against the reference thresholds
store the plot data to a file (plot.data)
create the plot
save the plot to an image file (plot.png)
Developer notes
functions in common.py
hls - print a big warning or error message
parse_log(regex_str) - specify a regular expression string to use to parse lines in the log
this is a helper function that returns a list of matches (with groups) that the parser.py can use to populate its dictionary of measurements
parse(regex_compiled_object)
similar to parse_log, but it takes a compiled regular expression object, and returns a list of matches (with groups)
this is deprecated, but left to support legacy tests
split_tguid()
split_test_id()
get_test_case()
add_results()
init_run_data()
get_criterion()
check_measure()
decide_status()
convert_reference_log_to_criteria()
load_criteria()
apply_criteria()
create_default_ref()
prepare_run_data()
extract_test_case_ids()
update_results_json()
delete()
save_run_json()
process(results)
results is a dictionary with
key=test_case_id (not including measure name)
for a functional test, the test_case_id is usually “default.<test_name>”
value=list of measures (for a benchmark)
or value=string (PASS|FAIL|SKIP) (for a functional test)
process_data(ref_sections_pat, test_results, plot_type, label)
call trees
process_data(ref_section_pat, test_results, plot_type, label)
process_data(measurements)
prepare_run_data(results)
run_data = (prepare non-results data structure)
ref = read reference.json
or ref = create_default_ref(results)
init_run_data(run_data, ref)
(put ref into run_data structure)
(mark some items as SKIP)
add_results(results, run_data)
for each item in results dictionary:
(check for results type: list or str)
if list, add measure
if str, set status for test_case
apply_criteria(run_data)
load_criteria()
(load criteria.json)
or convert_reference_log_to_criteria()
check_measure()
get_criterion()
decide_status()
get_criterion()
save_run_json(run_data)
update_results_json()
(return appropriate status)
miscellaneous notes
create_default_ref_tim (for docker.hello-fail.Functional.hello_world)
ref={‘test_sets’: [{‘test_cases’: [{‘measurements’: [{‘status’: ‘FAIL’, ‘name’: ‘Functional’}], ‘name’: ‘default’}], ‘name’: ‘default’}]}
create_default_ref
ref={‘test_sets’: [{‘test_cases’: [{‘status’: ‘FAIL’, ‘name’: ‘default’}], ‘name’: ‘default’}]}
data format and tguid rules
The current API and the old parser API take different data and allow different test identifiers. This sections explains the difference:
Data format for benchmark test with new API
measurements[test_case_id] = [{“name”: measure_name, “measure”: value}]
Data format for benchmark test with old API:
in reference.log
if tguid is a single word, then use that word as the measure name and “default” as the test_case.
e.g. for benchmark.arm, the reference.log has “short”. This becomes the fully-qualified tguid: arm.default.arm.short:
test_name = arm, test_case = default, test_case_id = arm, measure = short
Data format for functional tests with new API and the old API is the same:
e.g. measurements[“status”] = “PASS|FAIL”