README.md #3

## Perforce Server Log Analyzers

Log analyzers are useful to identify common issues on a Perforce
server quickly.

For more background information, please see:

http://answers.perforce.com/articles/KB/5470/

http://answers.perforce.com/articles/KB/2514/


## log_analyzer.php

This is a PHP script that turns a `P4LOG` into a SQLite database
and generate canned reports from it.

The report is useful to identify common performance issues on a
Perforce Helix server and further queries can be done against the
SQLite database created.

For more information, please refer to the following KB
article:

http://answers.perforce.com/articles/KB/1266/


## p4clog.py

A Python script that scans through a `P4LOG` file quickly and
generate data suitable for generating graphs.

More information and examples can be found in:

http://answers.perforce.com/articles/KB/3706/

## log2sql.py

The new kid on the block - similar to log_analyzer.php.
Big advantage is that it has a fairly comprehensive test harness
and processes all manner of recently observed log file scenarios.

In addition, there is an associated Python Flask-based web app
which runs various pre-canned queries and charts. It is setup to 
run inside a Docker container for ease of isolation of environment.

Pre-requisite: Docker installed

Use the Makefile (first build from scratch will take 5-10 mins):

    make build
    make up

The latter runs the web app by default on http://localhost:5000

Note that there is a page to upload a log file into the psla/logs directory
inside the container. But the container is run with the logs directory in
the current directory mounted as /logs - so files will persist when
container is not running. Any generated database will also be put in this 
same directory.

Especially if you have largeish log files, it is better to run the analysis
first manually and put the results in log directory. Then run the container 
to analyse the results.

So:

    cp p4d.log psla/logs
    cd psla/logs
    ../log2sql.py -d dbname psla.logs

View progress information, and check resulting dbname.db is created.

Then run docker contrainer:

    cd ..
    make up

Navigate to http://localhost:5000/analyseLog select your database name and
click analyse.