PATROL Appendix 1: BMC Patrol Support
This document walks through the process of recording BMC's Patrol data into IT Service vision. The following stages will be covered along with working examples.
BMC Patrol must be installed and collecting data in the UNIX and/or Windows NT environment. The data read into IT Service Vision comes from parameter history data maintained by the PATROL Agent. Refer to your PATROL documentation for more details. The extracted data can come from the Patrol History Loader KM, if installed, or extracted directly from the Patrol agent using the dump_hist command, both formats are recognized by IT Service Vision.
Patrol allows each metric to be sampled at it's own interval, typically 30 seconds, 1 minute, 5 minutes etc. This interval can be set by the Patrol administrator. IT Service Vision requires that the sample rates be specified on minute boundaries, the only exception being that we also recognize 30 second sample rates. (Please refer to Notes on Patrol data and it's summarization into IT Service Vision )
IT Service Vision Server must be installed at Release 2.2 or higher on MVS, Unix or Windows NT Server.
There are two approaches to collecting the PATROL history data to a central location.
Although these 2 methods produce slightly different output, either or both can be processed by IT Service Vision.
It is the PATROL Operator Console that retrieves the historical data stored by the Agent, and the dump_hist line command that dumps the parameter history data maintained by the PATROL Agents. The PATROL Agent Reference Manual contains more detailed information on the dump_hist command.
The following command dumps parameter history data for 1 day a file using the start and end switches for the dump_hist command, the format of which are ddmmhhmm[yy]. Additional switches can be specified that further restrict the amount of data that is extracted. :-
dump_hist -s 0723000098 -e 0723235998 > filename
The following is a small example of the format of the text file created by the above dump_hist command. This is the file that will be passed to %CPPROCES.
For MVS use the following %cpstart.
Once the tables have been added, dictionary characteristics (age limits, variables kept status) can be modified either using the interactive interface or the %CPDDUTL macro.
The dumped PATROL data can be processed on any platform, MVS, UNIX or Windows NT Server, irrespective of which platform it originated. Once that data is moved to the appropriate platform the processing is identical.
Transferring the Data
The text file containing the dumped history data should be transferred to the platform on which it will be processed. If using FTP ensure that the data is transferred in ASCII mode.
Note MVS: Typically, PATROL data has variable length records, however, they are assumed not to exceed 200 bytes in length. So allocated an appropriate MVS file with an LRECL of 200.
If you decide to use Input Filtering, we recommend that you do the following before running your first %CPPROCES :-
If you do not do the above, the first run of %cpproces with Input Filtering active will copy the default %CPDUPCHK invocation into the admin library automatically, and you will receive the following warning message recommending that you review the %CPDUPCHK parameter values.
WARNING: *** WARNING *** WARNING *** WARNING *** WARNING *** WARNING ***
If you require more information on Input Filtering please refer to the How To/Macro section from the online help for IT Service Vision.
Processing and Reducing the Data
The following process example should be run after a %CPSTART. For the purpose of this example I have included input filtering in this process run.
%put 'CPPROCES return code is ' &cpprc;
%put 'CPREDUCE return code is ' &cprrc;
In the SAS log you can expect to see the following :-
The above message will only appear when Input Filtering is active. The message shown will depend on whether the input data is considered duplicate.
This report is always produced when processing PATROL data. It reports the objects that were found in the rawdata that were not processed. If an object appears in this report for which you want to collect the data for, then you should add the appropriate table to the PDB. If you do not want to keep the data for an object, you can update your collection process to no longer keep the history data. If an object appears for which there is no supplied table, then one can be constructed using the GENERATE SOURCE function of the %CPDDUTL macro and an INTYPE= parameter of PATROL.
Patrol history data has several issues with regards to processing the data into a historical PDB.
Different Sample Rates for each Metric
Two metrics 'A' and 'B' do not have to be sampled at the same rate. 'A' may be sampled every 1 minute interval and 'B' every 5 minute interval. To combine these 2 metrics into the same observation in the PDB would be invalid as each value should eventually be weighted by the duration (depending on the interpretation type of the metric). To resolve this problem, the staging code of IT Service Vision includes a variable in each Patrol table called DURGRP. DURGRP is a string that represents the duration group that a metric belongs, e.g. in this example, 'A' which is sampled every minute is included in the observation with a DURGRP value of 60 (60 seconds) and 'B' in an observation with a DURGRP of 300 (300 seconds).
The DURGRP variable is only used at the DETAIL level in the BY list to ensure that the metrics are reduced and summarized by their respective DURATION value (assuming that they are weighted by DURATION).
At first, Patrol data in IT Service Vision may appear peculiar as there is the possibility of numerous null values appearing in each observation. The number of DURGRP's and null values will depend on the number of different sample rates applied to metrics that belong to the same table.
DATETIME Stamps of Samples that are not exactly aligned
In this example, two metrics 'A' and 'B' are both sampled at 1 minute intervals. From the example history data below you can see that the first sample occurred at x for both metrics, however the second sample the datetime stamps are out by a second with 'B' being sample later than 'A'. Obviously the first sample for each metric will be combined into a single observation as the duration and datetime stamps are the same, however this is not the case for the second sample.
During the staging of the raw data, IT Service Vision detects that this second sample has related datetime values and collapses the data into one observation. The result of this is that the data in the PDB table is much less sparse, however, the DATETIME and DURATION values are going to be near approximations.