# Item Pipeline

After an item has been scraped by a spider, it is sent to the Item Pipeline which processes it through several components that are executed sequentially.

Each item pipeline component (sometimes referred as just “Item Pipeline”) is a Python class that implements a simple method. They receive an item and perform an action over it, also deciding if the item should continue through the pipeline or be dropped and no longer processed.

Typical uses of item pipelines are:

- cleansing HTML data
- validating scraped data (checking that the items contain certain fields)
- checking for duplicates (and dropping them)
- storing the scraped item in a database

## Writing your own item pipeline

Each item pipeline component is a Python class that must implement the following method:

`process_item`(self, item, spider)

  This method is called for every item pipeline component and must either return a dict with data, `Item` (or any descendant class) object or raise a `DropItem` exception. Dropped items are no longer processed by further pipeline components.

  Parameters:

  - item (`Item` object or a dict) – the item scraped
  - spider (`Spider` object) – the spider which scraped the item

Additionally, they may also implement the following methods:

`open_spider`(self, spider)

  This method is called when the spider is opened.

  Parameters: | spider (`Spider` object) – the spider which was opened

`close_spider`(self, spider)

  This method is called when the spider is closed.

  Parameters: | spider (`Spider` object) – the spider which was closed

`from_crawler`(cls, crawler)

  If present, this classmethod is called to create a pipeline instance from a `Crawler`. It must return a new instance of the pipeline. Crawler object provides access to all Scrapy core components like settings and signals; it is a way for pipeline to access them and hook its functionality into Scrapy.

  Parameters: | crawler (`Crawler` object) – crawler that uses this pipeline

## Item pipeline example

### Price validation and dropping items with no prices

Let’s take a look at the following hypothetical pipeline that adjusts the `price` attribute for those items that do not include VAT (`price_excludes_vat` attribute), and drops those items which don’t contain a price:

    from scrapy.exceptions import DropItem

    class PricePipeline(object):

        vat_factor = 1.15

        def process_item(self, item, spider):
            if item['price']:
                if item['price_excludes_vat']:
                    item['price'] = item['price'] * self.vat_factor
                return item
            else:
                raise DropItem("Missing price in %s" % item)

### Write items to a JSON file

The following pipeline stores all scraped items (from all spiders) into a a single `items.jl` file, containing one item per line serialized in JSON format:

    import json

    class JsonWriterPipeline(object):

        def __init__(self):
            self.file = open('items.jl', 'wb')

        def process_item(self, item, spider):
            line = json.dumps(dict(item)) + "\n"
            self.file.write(line)
            return item

Note

The purpose of JsonWriterPipeline is just to introduce how to write item pipelines. If you really want to store all scraped items into a JSON file you should use the Feed exports.

### Write items to MongoDB

In this example we’ll write items to MongoDB using pymongo. MongoDB address and database name are specified in Scrapy settings; MongoDB collection is named after item class.

The main point of this example is to show how to use `from_crawler()` method and how to clean up the resources properly.

Note

Previous example (JsonWriterPipeline) doesn’t clean up resources properly. Fixing it is left as an exercise for the reader.

    import pymongo

    class MongoPipeline(object):

        collection_name = 'scrapy_items'

        def __init__(self, mongo_uri, mongo_db):
            self.mongo_uri = mongo_uri
            self.mongo_db = mongo_db

        @classmethod
        def from_crawler(cls, crawler):
            return cls(
                mongo_uri=crawler.settings.get('MONGO_URI'),
                mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')
            )

        def open_spider(self, spider):
            self.client = pymongo.MongoClient(self.mongo_uri)
            self.db = self.client[self.mongo_db]

        def close_spider(self, spider):
            self.client.close()

        def process_item(self, item, spider):
            self.db[self.collection_name].insert(dict(item))
            return item

### Duplicates filter

A filter that looks for duplicate items, and drops those items that were already processed. Let’s say that our items have a unique id, but our spider returns multiples items with the same id:

    from scrapy.exceptions import DropItem

    class DuplicatesPipeline(object):

        def __init__(self):
            self.ids_seen = set()

        def process_item(self, item, spider):
            if item['id'] in self.ids_seen:
                raise DropItem("Duplicate item found: %s" % item)
            else:
                self.ids_seen.add(item['id'])
                return item

## Activating an Item Pipeline component

To activate an Item Pipeline component you must add its class to the `ITEM_PIPELINES` setting, like in the following example:

    ITEM_PIPELINES = {
        'myproject.pipelines.PricePipeline': 300,
        'myproject.pipelines.JsonWriterPipeline': 800,
    }

The integer values you assign to classes in this setting determine the order in which they run: items go through from lower valued to higher valued classes. It’s customary to define these numbers in the 0-1000 range.