Advanced topics¶
Defining/Running item checkers¶
Django Dynamic Scraper comes with a built-in mechanism to check, if items once scraped are still existing
or if they could be deleted from the database. The entity providing this mechanism in DDS is called a
checker
. A checker
is like a scraper also using the scraping logic from Scrapy. But instead of
building together a new scraped item, it just checks the detail page referenced by a DETAIL_PAGE_URL
of a scraped item. Depending on the checker_type
and the result of the detail page check, the scraped
item is kept or will be deleted from the DB.
Creating a checker class¶
To get a checker up and running you first have to create a checker class for each of your scraped object domain
models. In our open news example, this would be a class called ArticleChecker
in a module called checkers
in our scraper
directory:
from dynamic_scraper.spiders.django_checker import DjangoChecker
from open_news.models import Article
class ArticleChecker(DjangoChecker):
name = 'article_checker'
def __init__(self, *args, **kwargs):
self._set_ref_object(Article, **kwargs)
self.scraper = self.ref_object.news_website.scraper
#self.scrape_url = self.ref_object.url (Not used any more in DDS v.0.8.3+)
self.scheduler_runtime = self.ref_object.checker_runtime
super(ArticleChecker, self).__init__(self, *args, **kwargs)
The checker class inherits from the DjangoChecker class from DDS and mainly gives the checker the information what to check and what parameters to use for checking. Be careful that the reference object is now the scraped object itself, since the checker is scraping from the item page url of this object. Furthermore the checker needs its configuration data from the scraper of the reference object. The scheduler runtime is used to schedule the next check. So if you want to use checkers for your scraped object, you have to provide a foreign key to a SchedulerRuntime object in your model class. The scheduler runtime object also has to be saved manually in your pipeline class (see: Adding the pipeline class).
Checker Configuration¶
You can create one or more checkers per scraper in the Django admin
. A checker is connected to a
DETAIL_PAGE_URL
attribute and has a certain type, defining the checker behaviour. If you define
more than one checker for a scraper an item is deleted when one of the checkers succeed.
There are momentarily the following checker types to choose from:
404 |
Item is deleted after check has returned 404 HTTP status code 2x in a row |
404_OR_X_PATH |
Same as 404 + check for an x_path value in the result |
For selecting a checker type and providing the parameters for an x_path checker you have to look for an example item page url from the website to be scraped which references an item not existing any more. If the urls to your scraped items are build using an item ID you can e.g. try to lower this ID or increase it to a very large number. Be creative! In our Wikinews example it is a bit different, since the news article url there is build using the title of the article. So for the checker we take a random article url to a not existing article: “http://en.wikinews.org/wiki/Random_article_text”.
If your url found is responding with a 404 when invoked, you can simply choose 404
as your checker type.
For a 404_OR_X_PATH
checker you have to provide an XPath
for your chosen url which will extract a string from that url uniquely
indicating, that the content originally expected is not there any more. For our Wikinews example and the url
we choose above there is a text and a url provided suggesting to create the currently not existing wiki page,
so we can use the XPath //a[@href="http://en.wikinews.org/wiki/This_wiki_article_doesnt_exist"]/text()
and the result string “create this page” to uniquely identifying a scraped item not existing any more.
It is also possible to leave out the result string. Then the checker already succeeds when the
given xpath is finding elements on the page.
Note
Be sure to add your new checkers module to the SPIDERS_MODULES list in scrapy’s settings.py.
Note
Attention! Make sure that the XPath/result string combination you choose is NOT succeeding on normal item pages, otherwise the checker will delete all your items!
Note
To make sure your items aren’t deleted accidentally on a 404 response, 404 checks are only deleted on the second try while XPath checks are deleted at once. So to save crawling resources always try to realize your checking with XPath checks, otherwise the crawler need double the amount of checks!
Running your checkers¶
You can test your DDS checkers the same way you would run your scrapers:
scrapy crawl CHECKERNAME -a id=REF_OBJECT_ID
[-a do_action=(yes|no) -a run_type=(TASK|SHELL)
-a output_response_body=(yes|no) ]
As a reference object ID you now have to provide the ID of a scraped item to be checked. With do_action=yes
an item is really deleted, otherwise the checker is only tested without actually manipulating the DB.
Here is an example of a checker run actually keeping the still-existing item:
If you want to test a check on an item scraped in the open news example project, change the url of the item in the DB to the checker reference url, look for the item ID and then run:
scrapy crawl article_checker -a id=ITEM_ID -a do_action=yes
If everything works well, your item should have been deleted.
Run checker tests¶
Django Dynamic Scraper comes with a build-in scraper called checker_test
which can be used to test your checkers
against the defined reference url. You can run this checker on the command line with the following command:
scrapy crawl checker_test -a id=SCRAPER_ID
-a output_response_body=(yes|no) ]
This scraper is useful both to look, if you have chosen a valid checker_x_path_ref_url
and corresponding checker_x_path
and checker_x_path_result
values as well as to see over time if your reference urls stay valid.
For running all checker tests at once there exists a simple Django management command called run_checker_tests
,
which executes the checker_test
scraper for all of your defined scrapers and outputs Scrapy log messages
on WARNING
level and above:
python manage.py run_checker_tests [--only-active --report-only-errors --send-admin-mail]
The option only-active
will limit execution to active scrapers, --report-only-errors
will more
generously pass the test on some not so severe cases (e.g. a checker ref url returning 404
for a
404_OR_X_PATH
checker type).
Executing the command with the --send-admin-mail
flag will send an email to Django admins if checker
configurations are not working which can be useful if you want to run this command as a cronjob.
Scheduling scrapers/checkers¶
Introduction¶
Django Dynamic Scraper comes with a build-in mechanism to schedule the runs of your scrapers as well as your checkers. After each run DDS dynamically calculates the next execution time depending on the success of the run. For a scraper that means, that the time between two scraper runs is shortened when new items could be scraped from a page and is prolonged if not. For a checker, it means that a next check is prolonged if the check was not successful, meaning that the item was not deleted. If it was deleted - well: than it was deleted! No further action! :-) The parameters for this calculation (e.g. a min/max time period between two actions) are defined for each ScrapedObjClass in the DB.
DDS is using django-celery to actually run your scrapers. Celery is a distributed task queue system for Python, which means that you can run a celery daemon which takes task orders from somewhere and then executes the corresponding tasks in a sequential way so that no task is lost, even if the system is under heavy load. In our use case Celery is “just” working as a comfortable cron job replacement, which can be controlled via the Django admin interface. The scheduler of DDS is using the scheduler runtime objects we defined for our example scraper and checker in the sections before. The scheduler runtime objects contain some dynamic information for the calculation of the next execution time of the scraper as well as the next execution time itself. For django-celery a task for each ScrapedObjClass has to be defined, which can than be started and stopped in the Django admin interface. Each task is executed periodically in a configurable time frame (e.g. ever hour). The task is then running all the scrapers associated with its ScrapedObjClass, which next execution time lies in the past. After each run, the next next execution time is calculated by the scraper and saved into its scheduler runtime object. The next time this time lies in the past, the scraper is run again.
Note
The number of spiders/checkers run at each task run is limited by the DSCRAPER_MAX_SPIDER_RUNS_PER_TASK
and DSCRAPER_MAX_CHECKER_RUNS_PER_TASK
settings which can be adopted in your custom settings file (see: Settings).
Installing/configuring django-celery for DDS¶
This paragraph is covering only the specific installation issues with django-celery in regard of installing
it for the use with DDS, so you should be familiar with the basic functionality of Celery and take general
installation infos from the django-celery website. If you have successfully installed and configured
django-celery, you should see the Djcelery
tables in the Django admin interface:
For django-celery
to work, Celery also needs a message broker for the actual message transport. For our
relatively simple use case, kombu is the easiest and recommended choice. Kombu is automatically installed
as a dependency when you install django-celery
and you can add it to your INSTALLED_APPS
:
INSTALLED_APPS = (
...
'kombu.transport.django',
'djcelery',
)
Then we can configure django-celery in combination with kombu in our settings.py
file. A starter
configuration could look similar to this:
# django-celery settings
import djcelery
djcelery.setup_loader()
BROKER_HOST = "localhost"
BROKER_PORT = 5672
BROKER_BACKEND = "django"
BROKER_USER = "guest"
BROKER_PASSWORD = "guest"
BROKER_VHOST = "/"
CELERYBEAT_SCHEDULER = 'djcelery.schedulers.DatabaseScheduler'
Defining your tasks¶
For defining tasks for your scrapers and checkers which can be selected for periodical runs in the Django
admin interface, you have to define two short methods in a Python module in which your tasks are declared and make
sure, that your tasks are found by django-celery. The easiest way to do this is by placing your methods in a
module called tasks.py
in the main directory of your app. The tasks should then be found automatically.
The two methods in our open news example look like this:
from celery.task import task
from django.db.models import Q
from dynamic_scraper.utils.task_utils import TaskUtils
from open_news.models import NewsWebsite, Article
@task()
def run_spiders():
t = TaskUtils()
#Optional: Django field lookup keyword arguments to specify which reference objects (NewsWebsite)
#to use for spider runs, e.g.:
kwargs = {
'scrape_me': True, #imaginary, model NewsWebsite hat no attribute 'scrape_me' in example
}
#Optional as well: For more complex lookups you can pass Q objects vi args argument
args = (Q(name='Wikinews'),)
t.run_spiders(NewsWebsite, 'scraper', 'scraper_runtime', 'article_spider', *args, **kwargs)
@task()
def run_checkers():
t = TaskUtils()
#Optional: Django field lookup keyword arguments to specify which reference objects (Article)
#to use for checker runs, e.g.:
kwargs = {
'check_me': True, #imaginary, model Article hat no attribute 'check_me' in example
}
#Optional as well: For more complex lookups you can pass Q objects vi args argument
args = (Q(id__gt=100),)
t.run_checkers(Article, 'news_website__scraper', 'checker_runtime', 'article_checker', *args, **kwargs)
The two methods are decorated with the Celery task decorator to tell Celery that these methods should be
regarded as tasks. In each task, a method from the TaskUtils
module from DDS is called to run the
spiders/checkers ready for the next execution.
Now you can create a peridoc task both for your scraper and your checker in the Django admin interface:
In the peridoc task form you should be able to select your tasks defined above. Create an interval how often
these tasks are performed. In our open news example, 2 hours should be a good value. Please keep in mind, that
these are not the values how often a scraper/checker is actually run. If you define a two hour timeframe here,
it just means, that ever two hours, the task method executed is checking for scrapers/checkers with a next
execution time (defined by the associated scheduler_runtime
) lying in the past and run these scrapers.
The actual time period between two runs is determined by the next execution time itself which is calculated
dynamically and depending on the scheduling configuration you’ll learn more about below. For the scrapers to
run, remember also that you have to set the scraper active in the associated scraper
object.
Run your tasks¶
To actually run the task (respectively set our scheduling system to work as a whole) we have to run two different
daemon processes. The first one is the celeryd
daemon from django-celery which is responsible for collecting
and executing tasks. We have to run celeryd
with the -B option to also run the celerybeat
task scheduler which executes periodical tasks defined in Celery. Start the daemon with:
python manage.py celeryd -l info -B --settings=example_project.settings
If everything works well, you should now see the following line in your command line output:
[2011-12-12 10:20:01,535: INFO/MainProcess] Celerybeat: Starting...
As a second daemon process we need the server from the separate scrapyd
project
to actually crawl the different websites targeted with our scrapers.
Make sure you have deployed your Scrapy project (see: Scrapy Configuration) and run the
server with:
scrapyd
You should get an output similar to the following:
For testing your scheduling system, you can temporarily set your time interval of your periodic task to a lower interval, e.g. 1 minute. Now you should see a new task coming in and being executed every minute:
Got task from broker: open_news.tasks.run_spiders[5a3fed53-c26a-4f8f-b946-8c4a2c7c5c83]
Task open_news.tasks.run_spiders[5a3fed53-c26a-4f8f-b946-8c4a2c7c5c83] succeeded in 0.052549123764s: None
The executed task should then run the scrapers/checkers which you should see in the output of the Scrapy server:
Process started: project='default' spider='article_spider' job='41f27199259e11e192041093e90a480a' pid=5932...
Process finished: project='default' spider='article_spider' job='41f27199259e11e192041093e90a480a' pid=5932...
Note
Note that you can vary the log level for debugging as well as other run parameters when you start the servers, see the man/help pages of the celery and the Scrapy daemons.
Note
Please see this configuration described here just as a hint to get started. If you want to use this in production you have to provide extra measures to make sure that your servers run constantly and that they are secure. See the specific server documentation for more information.
Note
- There is a known bug causing scheduling not to work when
LOG_STDOUT
is set toTrue
in the scraper - settings. If you know the cause or a fix for this, please report on GitHub!
Scheduling configuration¶
Now coming to the little bit of magic added to all this stuff with dynamic scheduling. The basis for the dynamic scheduling in DDS is layed both for your scrapers and your checkers with the scheduling configuration parameters in your scraped object class definitions in the Django admin interface. The default configuration for a scraper looks like this:
"MIN_TIME": 15,
"MAX_TIME": 10080,
"INITIAL_NEXT_ACTION_FACTOR": 10,
"ZERO_ACTIONS_FACTOR_CHANGE": 20,
"FACTOR_CHANGE_FACTOR": 1.3,
Scheduling now works as follows: the inital time period between two scraper runs is calculated by taking the
product of the MIN_TIME
and the INITIAL_NEXT_ACTION_FACTOR
, with minutes as the basic time unit for
MIN_TIME
and MAX_TIME
:
initial time period := 15 Minutes (MIN_TIME) * 10 (INITIAL_NEXT_ACTION_FACTOR) = 150 Minutes = 2 1/2 Hours
Now, every time a scraper run was successful, the new next action factor is calculated by dividing the actual
next action factor by the FACTOR_CHANGE_FACTOR
. So a successful scraper run would lead to the following new
time period:
new next action factor (NAF) := 10 (INITIAL_NEXT_ACTION_FACTOR) / 1.3 (FACTOR_CHANGE_FACTOR) = 7.69 (rounded)
time period after successful run := 15 Minutes * 7.69 (NAF) = 115 Minutes
So if it turns out that your scraper always find new items the time period between two runs gets smaller and smaller
until the defined MIN_TIME
is reached which is taken as a minimum time period between two scraper runs.
If your scraper was not successful (meaning, that no new items were found) these unsucessful actions (scraper runs)
are counted as ZERO_ACTIONS
. If a number of unsuccessful actions greater than ZERO_ACTIONS_FACTOR_CHANGE
is counted, a new next action factor is calculated, this time by taking the product of the actual action factor
and the FACTOR_CHANGE_FACTOR
(calculation restarting from initial values for the example):
new next action factor (NAF) := 10 (INITIAL_NEXT_ACTION_FACTOR) * 1.3 (FACTOR_CHANGE_FACTOR) = 13
time period after 21 unsuccessful runs := 15 Minutes * 13 (NAF) = 195 Minutes
So the time period between two scraper runs becomes larger. If there is never a new item found for your scraper
this will go on until the calculated time period reaches the MAX_TIME
defined.
In the real world application of this mechanism normally neither the MIN_TIME
nor the MAX_TIME
should be
reached. The normal situation is that your scraper often finds nothing new on the page to be scraped and than
after x executed runs finds new items provided on the website to be scraped. If this x is generally lower than
your defined ZERO_ACTIONS_FACTOR_CHANGE
number, the time period is becoming shorter over time. But since this
means more scraper runs in the same time chances are high that with these narrower scheduled
runs less zero actions occur and leads at some point to an again increased next action factor. So some kind of
(relatively) stable next action factor should be reached over time, representing in the best case a good compromise
between the needs of actuality of your scrapers and not to much resources wasted on running your scraper
on websites not updated in between two runs.
Note
Since this is a relatively complex mechanism also depending on a large part on the update process of your scraped website, it will probably take some time to get a bit a feeling for how the scheduling is developing and to what action factors it tends to, so don’t try to find the perfect solution in the first run. Instead, start with a (maybe rather too conservatively calculated) start configuration and adjust your parameters over time. You can observe the development of your action factors in the scheduler runtime objects.
Note
Please be aware that scraping is a resource consuming task, for your server but as well for the server of the websites you are scraping. Try to find a balanced solution, not just setting your MIN_TIME to 1 minute or similar.
Note
If you don’t need dynamic scheduling, you can also just set the MIN_TIME and the MAX_TIME to the same values and just ignore the rest.
Scheduling of your checkers works very similar to the scraper scheduling, the inital configuration is as follows:
"MIN_TIME": 1440,
"MAX_TIME": 10080,
"INITIAL_NEXT_ACTION_FACTOR": 1,
"ZERO_ACTIONS_FACTOR_CHANGE": 5,
"FACTOR_CHANGE_FACTOR": 1.3,
Since the checker scheduling is terminated with the success of a checker run (meaning the item and the associated
scheduler runtime is deleted), there is only the prolonging time period part of the scheduler actually working.
So scraped items are checked in a (relatively, defined by your configuration) short time period at first.
If the item turns out to be persistently existing, the checks are prolonged till MAX_TIME
is reached.
Advanced Request Options¶
Since DDS v.0.7+
you have more options to fine-tune your scraping requests by e.g. providing additional values for
cookies
or HTTP headers
. These values are internally passed to Scrapy’s Request object. You can find the extended request options in the
Request options
tab in the Scraper form
of your Django project admin
. For the different page types
like the (paginated) main pages and the detail pages following scraped urls you can define different request options.
Note
Parameters for the different options are passed as JSON
dicts. Make sure to use double quotes
for attribute values and to leave the comma
for the last attribute key-value pair.
Request Type and Method¶
The request type - corresponding to Scrapy’s Request classes - and the type of the request being sent as GET
or POST
. Normally you will choose GET
together with a classic Request
and POST
with a FormRequest
but for
special cases you are free too choose here.
HTTP Headers¶
For setting/changing specific HTTP header
fields like the referer URL use the headers
text field in the request options.
You can use attribute placeholders (e.g. “{title}”) to inject results of item attributes scraped from the main page (no escaping of curly brackets inside JSON value strings).
You can also use the {page}
placeholder. This placeholder is replaced for consecutive pages according
to your pagination parameters (see: Pagination).
HTTP Body¶
Setting/changing the HTTP body
. This can be useful for some special-case scenarios, for example if you want
to send a POST
request with content type for the request altered and sending POST
parameters as a JSON
dict.
You can use attribute placeholders (e.g. “{title}”) to inject results of item attributes scraped from the main page.
You can also use the {page}
placeholder. This placeholder is replaced for consecutive pages according
to your pagination parameters (see: Pagination).
Note
Don’t be fooled, especially by the example provided: data for the body attribute is NOT provided as JSON
but
as a string
. While e.g. the Headers
field always has to be in JSON
format, the Body
text is just
randomly JSON
in this example, but it could also be This is my body text.
.
Request Cookies¶
Sometime the output of a website you want to scrape might depend on the values of some cookies sent to the server.
For this occasion you can use the Cookies
form in the request options tab, e.g. for setting the language of a
website to english
.
You can use attribute placeholders (e.g. “{title}”) to inject results of item attributes scraped from the main page (no escaping of curly brackets inside JSON value strings).
You can also use the {page}
placeholder. This placeholder is replaced for consecutive pages according
to your pagination parameters (see: Pagination).
Note
If you want to pass a session ID
for a site as a cookie
, you can open the desired website in your browser
and copy-paste the session ID from the development console for immediately following scraper runs.
Scrapy Meta Options¶
Changing Scrapy meta attributes, see Scrapy docs for reference.
Form Data¶
If you want to scrape data provided on a website via a web form, data is often returned via POST
request after
sending various POST request parameters
for narrowing the results. For this scenario use the FormRequest
request
type and POST
as method in the scraper admin and provide the adequate form data as a JSON dictionary in the request options.
You can use attribute placeholders (e.g. “{title}”) to inject results of item attributes scraped from the main page (no escaping of curly brackets inside JSON value strings).
You can also use the {page}
placeholder. This placeholder is replaced for consecutive pages according
to your pagination parameters (see: Pagination).
Pagination¶
Django Dynamic Scraper supports two layers of pagination for scraping your objects from several overview pages or archives. The following screenshot shows the pagination parameters which can be defined in the Django admin for each scraper:
For using pagination you have to switch the pagination_type
in your scraper definition from NONE
to
your desired type.
First Pagination Layer: Static Pagination¶
The main concept of the static pagination layer is, that you define a pagination_append_str
with a
placeholder {page}
, which is replaced through a list generated by selecting the pagination_type
and
giving a corresponding pagination_page_replace
context. There are the following pagination types to
choose from:
Pagination type: RANGE_FUNCT (+FOLLOW)¶
This pagination type uses the python range function.
As a replace context the same arguments like in the range function are used: range([start], stop[, step])
.
The integer list created by this function will be used as an input to replace the “{page}” template tag in the
append string to form the different urls.
So the parameters in our example above in the screenshot will lead - together with “http://www.urltoscrape.org” as the base scrape url of your scraper runtime - to the following urls to be scraped:
Pagination type: FREE_LIST (+FOLLOW)¶
If the urls from an archive are formed differently you can use this pagination type and just provide a list
with different fitting replacements, the syntax is as follow: 'Replace text 1', 'Some other text 2',
'Maybe a number 3', ...
.
So if you define a list as follows: 'a-d', 'e-h', 'i-n', 'o-z'
, you get the following urls:
Second Pagination Layer: Dynamic Follow Pagination¶
The second pagination layer allows for dynamic following of pages (e.g. from a pagination on the
website to be scraped), by defining an XPath to extract the URL to be followed and an optional additional
XPath for the page number/name which can be used in the {follow_page}
placeholder.
For this follow pagination an extra RequestPageType (RPT)
FOLLOW
can be defined. If there is no such RPT,
the main page RPT is used (in the definition of your elements/attributes to scrape, always assign the main page
RPT though).
The follow pagination can be used as stand-alone pagination or in combination with the static pagination types. In the second case pages are followed starting from all static main pages scraped.
Scraping JSON content¶
Beside creating HTML
or XML
scrapers where you can use classic XPath
notation, DDS
supports also scraping pages encoded in JSON
(v.0.5.0
and above), e.g. for crawling web APIs or ajax call result pages.
For scraping JSON
, JSONPath
is used, an XPath
-like expression language for digging into JSON
.
For reference see expressions as defined here:
Note
Using JSONPath
in DDS
works for standard JSON
page results, but is not as heavily tested as using
XPath
for data extraction. If you are working with more complex JSONPath
queries and run into problems,
please report them on GitHub!
Example¶
Consider the following simple JSON
example:
{
"response": {
"num_results": 3,
"results": [
{
"title": "Example Title",
"description": "Example Description"
},
//...
]
}
}
The title elements of the results can then be scraped by defining response.results
JSONPath
as the
base element and title
as the JSONPath
for the scraped object attribute.
Using the $
for refering to the JSON
root is actually optional, so response.results
is
equivalent to $.response.results
. Sometimes it might be necessary to use the $
though, e.g.
if you directly want to point to the root of the JSON
file, e.g. to reference the objects in
a JSON
array file.
Note
The example project actually contains a working (most of the time :-)) JSON
example scraper!
Scraping images/screenshots¶
Django Dynamic Scraper is providing a custom image pipeline build on Scrapy’s item pipeline for downloading images to scrape and download images associated to your items scraped and and save a reference to each image together with the scraped item in the DB.
Configuration¶
For using image scraping in DDS you have to provide some additional parameters in your Scrapy settings.py file:
import os.path
PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__))
ITEM_PIPELINES = [
'dynamic_scraper.pipelines.DjangoImagesPipeline',
'dynamic_scraper.pipelines.ValidationPipeline',
'open_news.scraper.pipelines.DjangoWriterPipeline',
]
IMAGES_STORE = os.path.join(PROJECT_ROOT, '../thumbnails')
IMAGES_THUMBS = {
'small': (170, 170),
}
In your settings file you have to add the DjangoImagesPipeline
from DDS to your ITEM_PIPELINES
and define
a folder to store images scraped. Don’t forget to create this folder in your file system and give it adequate
permissions. You can also use the thumbnail creation capabilities already build in Scrapy
by defining the thumbnail size via the IMAGES_THUMBS
parameter.
Choosing store format for images¶
Different from Scrapy behaviour DDS is by default storing only one image in a flat store format directly under
the IMAGES_STORE
directory (Scrapy is creating a full/
subdirectory for the original image). If you use the
IMAGES_THUMBS
setting, the scaled down thumbnail image will replace the image with the original size.
Due to this simplification you can only use one entry in your IMAGES_THUMBS
dictionary and the name of the
key there doesn’t matter.
Starting with DDS v.0.3.9
you can change this behaviour with the DSCRAPER_IMAGES_STORE_FORMAT
setting:
DSCRAPER_IMAGES_STORE_FORMAT = 'FLAT' # The original image or - if available - one thumbnail image
DSCRAPER_IMAGES_STORE_FORMAT = 'ALL' # Both the original image and all given thumbnail sizes
DSCRAPER_IMAGES_STORE_FORMAT = 'THUMBS' # Only the thumbnails
FLAT
is the default setting with the behaviour described above. The ALL
setting restores the Scrapy behaviour,
the original images are stored in a full/
directory under IMAGES_STORE
, thumbnail files - if available - in separate
sub directories for different thumbnail sizes (e.g. thumbs/small/
).
Setting DSCRAPER_IMAGES_STORE_FORMAT
to THUMBS
, keeps only the thumbnail files, this setting makes only sense
with setting the IMAGES_THUMBS
setting as well. With ALL
or THUMBS
you can also use different sizes for
thumbnail creation.
Note
Differing from the Scrapy output, an image is stored in the DB just by name, omitting path information like full/
Note
For image scraping to work you need the Pillow Library (PIL fork).
Updating domain model class/scraped obj class definition¶
When Scrapy is downloading images it creates a new unique random file name for each image saved in your image folder defined above. To keep a reference to the image associated with a scraped item DDS will save this filename in a field you have to define in your model class. In our open news example, we use ‘thumbnail’ as a field name:
class Article(models.Model):
title = models.CharField(max_length=200)
news_website = models.ForeignKey(NewsWebsite)
description = models.TextField(blank=True)
thumbnail = models.CharField(max_length=200)
checker_runtime = models.ForeignKey(SchedulerRuntime)
def __unicode__(self):
return self.title
Note, that since there is just the filename of the image saved, you should declare this field as a simple CharField and not using UrlField or ImageField.
Now you have to update your ScrapedObjClass definition in the Django admin interface. Add a new attribute with the same name like in your model class and choose IMAGE as the attribute type. IMAGE is a special type to let your scraper know, that the image pipeline of DDS should be used when scraping this attribute.
Extending/Testing the scraper¶
At last we have to add a new scraper elem to our scraper, again in the Django admin interface, which scrapes and
builds together the url of the image for the image pipeline to download later. Let’s have a look at the Wikinews
website of our open news example. On the news article overview page there is also an image presented with each
article summary, which we want to scrape. div[@class="l_image"]/a/img/@src
should provide us with the url
of that image. Since the image urls we scrape with our XPath are starting with a double slash ‘//’ and not with
‘http://’, we also have to use a pre_url processor with 'pre_url': 'http:'
as the processor context to
complete the url.
That’s it! If you now run your scraper, you should see lines like the following in the output (if you are in debug mode) and you should end up with the images saved in your defined images folder and the names of these images stored in the image field of your domain model in the DB:
DEBUG: Image (downloaded): Downloaded image from <GET http://upload.wikimedia.org/wikipedia/commons/thumb/...
...
u'thumbnail': '1bb3308a4c70b912ba6cf9d67344bb53476d70a2.jpg',
So now you have all these images, but how to rid of them if you don’t need them any more? If you use a checker to delete scraped items not existing any more, your images will be automatically deleted as well. However, if you manually delete scraped items in your database, you have to delete the associated file yourself.
Where to go from here¶
So now that you have got your scraper up and running and maybe even integrated some of the advanced stuff like pagination or scraping images, does that mean that life will become boring because there is nothing to be done left? Definitely not! Here are some ideas about what to do next:
- Contribute to Django Dynamic Scraper through the experiences you made while using it (see How to contribute)
- Make your scraped data searchable with Django Haystack
- Provide an API to your scraped data so that others can use it with Django Tastypie
- Or… just do something no one has ever done before! :-)