haralyzer 1.0.2

A python framework for getting useful stuff out of HAR files

===========================
Haralyzer version |version|
===========================

.. image:: https://travis-ci.org/mrname/haralyzer.svg?branch=master
:target: https://travis-ci.org/mrname/haralyzer

.. image:: https://readthedocs.org/projects/haralyzer/badge/?version=latest
:target: http://haralyzer.readthedocs.org/en/latest/


A Python Framework For Using HAR Files To Analyze Web Pages.

Overview
--------

The haralyzer module contains two classes for analyzing web pages based
on a HAR file. ``HarParser()`` represents a full file (which might have
multiple pages), and ``HarPage()`` represents a single page from said file.

``HarParser`` has a couple of helpful methods for analyzing single entries
from a HAR file, but most of the pertinent functions are inside of the page.

Quick Intro
-----------

HarParser
+++++++++

The ``HarParser`` takes a single argument of a ``dict`` representing the JSON
of a full HAR file. It has the same properties of the HAR file, EXCEPT that each
page in HarParser.pages is a HarPage object::

import json
from haralyzer import HarParser, HarPage

with open('har_data.har', 'r') as f:
har_parser = HarParser(json.loads(f.read()))

print har_parser.browser
# {u'name': u'Firefox', u'version': u'25.0.1'}

for page in har_parser.pages:
assert isinstance(page, HarPage, None)
# returns True for each

HarPage
+++++++

The ``HarPage`` object contains most of the goods you need to easily analyze a
page. It has helper methods that are accessible, but most of the data you need is
in properties for easy access. You can create a HarPage object directly by giving
it the page ID (yes, I know it is stupid, it's just how HAR is organized), and either
a ``HarParser`` with `har_parser=parser`, or a ``dict`` representing the JSON of a full HAR
file (see example above) with `har_data=har_data`::

import json
from haralyzer import HarPage

with open('har_data.har', 'r') as f:
har_page = HarPage('page_3', har_data=json.loads(f.read()))

### WORK WITH LOAD TIMES (all load times are in ms) ###

# Get image load time in milliseconds as rendered by the browser
har_page.image_load_time
# prints 713

# Get the TOTAL image load time
har_page.total_image_load_time
# prints 2875
# We could do this with 'css', 'js', 'html', 'audio', or 'video'

### WORK WITH SIZES (all sizes are in bytes) ###

# Get the total page size (with all assets)
har_page.total_page_size
# prints 2423765

# Get the size of the actual first page that was not a redirect
# (i.e. - the HTML of the first page we care about)
har_page.page_size
# prints 26951

# Get the total image size
har_page.total_image_size
# prints 733488
# We could do this with 'css', 'js', 'html', 'audio', or 'video'


Advanced Usage
==============

``HarPage`` includes a lot of helpful properties, but they are all
easily produced using the public methods of ``HarParser`` and ``HarPage``::

import json
from haralyzer import HarPage

with open('har_data.har', 'r') as f:
har_page = HarPage('page_3', har_data=json.loads(f.read()))

### ACCESSING FILES ###

# You can get a JSON representation of all assets using HarPage.entries #
for entry in har_page.entries:
if entry['startedDateTime'] == 'whatever I expect':
... do stuff ...

# It also has methods for filtering assets #
# Get a collection of entries that were images in the 2XX status code range #
entries = har_page.filter_entries(content_type='image.*', status_code='2.*')

# Get the size of the collection we just made #
collection_size = har_page.get_total_size(entries)

# We can also access files by type with a property #
for js_file in har_page.js_files:
... do stuff ....

### GETTING LOAD TIMES ###

# Get the BROWSER load time for all images in the 2XX status code range #
load_time = har_page.get_load_time(content_type='image.*', status_code='2.*')

# Get the TOTAL load time for all images in the 2XX status code range #
load_time = har_page.get_load_time(content_type='image.*', status_code='2.*', async=False)

This could potentially be out of date, so please check out the sphinx docs.


More.... Advanced Usage
=======================

All of the HarPage methods above leverage stuff from the HarParser,
some of which can be useful for more complex operations. They either
operate on a single entry (from a HarPage) or a ``list`` of entries::

import json
from haralyzer import HarParser

with open('har_data.har', 'r') as f:
har_parser = HarParser(json.loads(f.read()))

for page in har_parser.pages:
for entry in page.entries:
### MATCH HEADERS ###
if har_parser.match_headers(entry, 'Content-Type', 'image.*'):
print 'This would appear to be an image'
### MATCH REQUEST TYPE ###
if har_parser.match_request_type(entry, 'GET'):
print 'This is a GET request'
### MATCH STATUS CODE ###
if har_parser.match_status_code(entry, '2.*'):
print 'Looks like all is well in the world'


Asset Timelines
+++++++++++++++

The last helper function of ``HarParser`` requires it's own section, because it
is odd, but can be helpful, especially for creating charts and reports.

It can create an asset timeline, which gives you back a ``dict`` where each
key is a ``datetime`` object, and the value is a ``list`` of assets that were
loading at that time. Each value of the ``list`` is a ``dict`` representing
an entry from a page.

It takes a ``list`` of entries to analyze, so it assumes that you have
already filtered the entries you want to know about::

import json
from haralyzer import HarParser

with open('har_data.har', 'r') as f:
har_parser = HarParser(json.loads(f.read()))

### CREATE A TIMELINE OF ALL THE ENTRIES ###
entries = []
for page in har_parser.pages:
for entry in page.entries:
entries.append(entry)

timeline = har_parser.create_asset_timeline(entries)

for key, value in timeline.iteritems():
print type(key)
# <type 'datetime.datetime'>
print key
# 2015-02-21 19:15:41.450000-08:00
print type(value)
# <type 'list'>
print value
# Each entry in the list is an asset from the page
# [{u'serverIPAddress': u'157.166.249.67', u'cache': {}, u'startedDateTime': u'2015-02-21T19:15:40.351-08:00', u'pageref': u'page_3', u'request': {u'cookies':............................


With this, you can examine the timeline for any number of assets. Since the key is a ``datetime``
object, this is a heavy operation. We could always change this in the future, but for now,
limit the assets you give this method to only what you need to examine.



Documentation
-------------

The full documentation is in the repository, run 'make doc' to get it.

.. :changelog

History
-------

1.0.2 (2015-03-01)
++++++++++++++++++

* Tweaking docs for pypi

1.0.1 (2015-03-01)
++++++++++++++++++

* Adding Travis CI integration

1.0 (2015-02-28)
++++++++++++++++

* Initial Beta release