Package: pyftdc
pyftdcLinkstatusConda buildshttps://anaconda.org/jimper/pyftdcPypi buildshttps://pypi.org/project/pyftdc/A MongoDB FTDC files parser written in C++ that provides Python bindings usingpybind11and scikit-build.RequisitesTo build the source distribution, you will need Python 3.8 or newer, git, python3-dev/python3-devel installed.Please read thebuild documentfor more details.InstallationYou can install the source distributions via pip or conda:pip3 install pyftdcconda install -c jimper pyftdcBuildingBuilding on Unix (Ubuntu, Centos, macOS)clone this repository and change to the top level directory.git clone git@gitlab.com:jimper/mongo_ftdc.git 
cd mongo_ftdcInstall Python libraries to build binaries. Create a virtual environment to make your life easier.python3 -m venv venv
source venv/bin/activate
pip3 install --user-pep517 .You will now have built and installed in your virtual environment.Alternatively, you can use setup.py directly, but for that you will need to manually install the required libraries into your virtual environment by runningcd mongo_ftdc
 pip install -r requirements.txtAfter which you can create a source distribution or a binary wheel:python3 setup.py sdist
 python3 setup.py bdist_wheelThese will reside in thedistdirectory.Building on WindowsNot tested yet, but 'It should work'(TM)LicenseApache V2UsageThe module provides two classes, FTDCParser and Dataset.
The proper usage is to obtain a Dataset object from a file by calling FTDCParse.parse_file(), or a list of Dataset objects from the FTDCParse.parse_dir(), and then call the methods on these objects.FTDCParserset_verbose(bool).Set verbosity flag.parse_dir(dir, only_metadata, only_metrics_names, lazy)Returns a list of Dataset objects.parse_file(file_path, only_metadata, only_metrics_names, lazy)Returns a single Dataset object.get_parsed_file_info():Returns information on the last parsed file (absolute path, samples, start timestamp, end timestamp).dump_file_as_json(input_file, output_file)Dumps the contents of an FTDC file to a JSON file. (WIP)dump_file_as_csv(input_file, output_file)
Dumps file contents to an FTDC file as CSV file. (WIP)get_metric(metric_name, start, end, rated_metric).Returns a list of the values of a metric for the last file parsed. If rated_metric is true, will convert values to metrics per second. start and end are ignored.get_metrics_list(metric_name_list)Returns a list of lists of values for the metrics specified in the list metric_name_list for the last file parsed. If rated_metric is true, will convert values to metrics per second. start and end are ignored.get_timestamps(start, end)Returns the timestamps of the last file parsed.metadata.Contains a string with the metadata of the last file parsed.get_metric_numpy(metric_name, start, end, rated_metric)Returns a list of the values of a metric for the last file parsed as a numpy array. If rated_metric is true, will convert values to metrics per second. start and end are ignored.get_metrics_list_numpy(metric_names_list, start, end, rated_metric)Returns a list of numpy arrays of the values of a metric for the last file parsed . If rated_metric is true, will convert values to metrics per second. start and end are ignored.DatasetinterinmContains a boolean that tells if this is an interim FTDC file.metadataContains the metadata associated with this datased.metrics_namesContains a list of the metrics names contained in the dataset.fileContains the file name from which this dataset was parsed.pathContains the full path to the file from which this dataset was parsed.get_metric(metric_name, start, end, rated_metric)Returns a list of the values of a metric for this dataset. If rated_metric is true, will convert values to metrics per second. start and end are ignored.get_metric_numpy(metric_name, start, end, rated_metric)Returns a list of the values of a metric for the last file parsed as a numpy array. If rated_metric is true, will convert values to metrics per second. start and end are ignored.get_metrics_list_numpy(metric_names_list, start, end, rated_metric)Returns a list of numpy arrays of the values of a metric for the last file parsed . If rated_metric is true, will convert values to metrics per second. start and end are ignored.Exampleimportpyftdcimportnumpydefget_prefixed_metrics_names(param,ds):ops_counters=[]fornameinds.metrics_names:ifname.startswith(param):ops=ds.get_metric(name)ops_counters.append((name,ops))returnops_countersdefopen_files_in_dir(dir_path,prefix):fromosimportlistdirfiles_read=[]try:dir_list=listdir(dir_path)forfile_nameindir_list:iffile_name.startswith(prefix):parser=pyftdc.FTDCParser()ds=parser.parse_file(dir_path+'/'+file_name)ifds:print(f'File:{ds.file}')print(f'{ds.metadata}')ts=ds.get_metric("start")ifts:ts_size=len(ts)print(f'Timestamp count{ts_size}. Start:{ts[0]}Last:{ts[-1]}')op_counter_names=get_prefixed_metrics_names('serverStatus.opcounters',ds)cpu=get_prefixed_metrics_names('systemMetrics.cpu',ds)disk=get_prefixed_metrics_names('systemMetrics.disks.nvme1n1',ds)xxx=ds.get_metric_list_numpy(['systemMetrics.cpu.iowait_ms','xxx','systemMetrics.cpu.num_cpus'])disk_n=ds.get_metric_numpy('systemMetrics.disks.nvme1n1.writes')files_read.append(file_name)else:print(f'No timestamps on this dataset.')exceptFileNotFoundErrorasnot_found:print('Path not found.')returnfiles_readif__name__=="__main__":files=open_files_in_dir('/somepath/diagnostic.data/','metrics.2022-11-13T21')print(files)