
An Introduction to boto's Cloudsearch interface
***********************************************

This tutorial focuses on the boto interface to AWS' Cloudsearch
(version 2013-01-01). For information about migrating from the
2011-02-01 version please see the migrating document.

This tutorial assumes that you have boto already downloaded and
installed.


Creating a Connection
=====================

The first step in accessing CloudSearch is to create a connection to
the service.

The recommended method of doing this is as follows:

   >>> from boto.cloudsearch2.layer2 import Layer2 as CloudSearch
   >>> conn = CloudSearch(region="us-west-2",
   ...             aws_access_key_id='<aws access key'>,
   ...             aws_secret_access_key='<aws secret key>')

At this point, the variable conn will point to a CloudSearch
connection object in the us-west-2 region. In this example, the AWS
access key and AWS secret key are passed in to the method explicitly.
Alternatively, you can set the environment variables:

* *AWS_ACCESS_KEY_ID* - Your AWS Access Key ID

* *AWS_SECRET_ACCESS_KEY* - Your AWS Secret Access Key

and then simply call:

   >>> from boto.cloudsearch2.layer2 import Layer2 as CloudSearch
   >>> conn = CloudSearch(region="us-west-2")

In either case, conn will point to the high level Connection object
which we will use throughout the remainder of this tutorial.


Creating a Domain
=================

Once you have a connection established with the CloudSearch service,
you will want to create a domain. A domain encapsulates the data that
you wish to index, as well as indexes and metadata relating to it:

   >>> domain = conn.create_domain('demo')

This domain can be used to control access policies, indexes, and the
actual document service, which you will use to index and search.


Setting access policies
=======================

Before you can connect to a document service, you need to set the
correct access properties.  For example, if you were connecting from
192.168.1.0, you could give yourself access as follows:

   >>> our_ip = '192.168.1.0'

   >>> # Allow our IP address to access the document and search services
   >>> policy = domain.get_access_policies()
   >>> policy.allow_search_ip(our_ip)
   >>> policy.allow_doc_ip(our_ip)

You can use the "allow_search_ip" and "allow_doc_ip" methods to give
different CIDR blocks access to searching and the document service
respectively.


Creating index fields
=====================

Each domain can have up to twenty index fields which are indexed by
the CloudSearch service. For each index field, you will need to
specify whether it's a text or integer field, as well as optionaly a
default value:

   >>> # Create an 'text' index field called 'username'
   >>> uname_field = domain.create_index_field('username', 'text')

   >>> # Epoch time of when the user last did something
   >>> time_field = domain.create_index_field('last_activity',
   ...                                        'uint',
   ...                                        default=0)

It is also possible to mark an index field as a facet. Doing so allows
a search query to return categories into which results can be grouped,
or to create drill-down categories:

   >>> # But it would be neat to drill down into different countries
   >>> loc_field = domain.create_index_field('location', 'text', facet=True)

Finally, you can also mark a snippet of text as being able to be
returned directly in your search query by using the results option:

   >>> # Directly insert user snippets in our results
   >>> snippet_field = domain.create_index_field('snippet', 'text', result=True)

You can add up to 20 index fields in this manner:

   >>> follower_field = domain.create_index_field('follower_count',
   ...                                            'uint',
   ...                                            default=0)


Adding Documents to the Index
=============================

Now, we can add some documents to our new search domain. First, you
will need a document service object through which queries are sent:

   >>> doc_service = domain.get_document_service()

For this example, we will use a pre-populated list of sample content
for our import. You would normally pull such data from your database
or another document store:

   >>> users = [
       {
           'id': 1,
           'username': 'dan',
           'last_activity': 1334252740,
           'follower_count': 20,
           'location': 'USA',
           'snippet': 'Dan likes watching sunsets and rock climbing',
       },
       {
           'id': 2,
           'username': 'dankosaur',
           'last_activity': 1334252904,
           'follower_count': 1,
           'location': 'UK',
           'snippet': 'Likes to dress up as a dinosaur.',
       },
       {
           'id': 3,
           'username': 'danielle',
           'last_activity': 1334252969,
           'follower_count': 100,
           'location': 'DE',
           'snippet': 'Just moved to Germany!'
       },
       {
           'id': 4,
           'username': 'daniella',
           'last_activity': 1334253279,
           'follower_count': 7,
           'location': 'USA',
           'snippet': 'Just like Dan, I like to watch a good sunset, but heights scare me.',
       }
   ]

When adding documents to our document service, we will batch them
together. You can schedule a document to be added by using the "add"
method. Whenever you are adding a document, you must provide a unique
ID and the actual document to be indexed. In this case, we are using
the user ID as our unique ID:

   >>> for user in users:
   >>>     doc_service.add(user['id'], user)

When you are ready to send the batched request to the document
service, you can do with the "commit" method. Note that cloudsearch
will charge per 1000 batch uploads. Each batch upload must be under
5MB:

   >>> result = doc_service.commit()

The result is an instance of "CommitResponse" which will make the
plain dictionary response a nice object (ie result.adds,
result.deletes) and raise an exception for us if all of our documents
weren't actually committed.

After you have successfully committed some documents to cloudsearch,
you must use "clear_sdf", if you wish to use the same document service
connection again so that its internal cache is cleared.


Searching Documents
===================

Now, let's try performing a search. First, we will need a
SearchServiceConnection:

   >>> search_service = domain.get_search_service()

A standard search will return documents which contain the exact words
being searched for:

   >>> results = search_service.search(q="dan")
   >>> results.hits
   2
   >>> map(lambda x: x['id'], results)
   [u'1', u'4']

The standard search does not look at word order:

   >>> results = search_service.search(q="dinosaur dress")
   >>> results.hits
   1
   >>> map(lambda x: x['id'], results)
   [u'2']

By default, the search will return 10 terms but it is possible to
adjust this by using the size argument as follows:

   >>> results = search_service.search(q="'dan*'", size=2)
   >>> results.hits
   4
   >>> map(lambda x: x['id'], results)
   [u'1', u'2']

It is also possible to offset the start of the search by using the
start argument as follows:

   >>> results = search_service.search(q="'dan*'", start=2)
   >>> results.hits
   4
   >>> map(lambda x: x['id'], results)
   [u'3', u'4']


Ordering search results and rank expressions
============================================

If your search query is going to return many results, it is good to be
able to sort them. You can order your search results by using the rank
argument. You are able to sort on any fields which have the results
option turned on:

   >>> results = search_service.search(q=query, rank=['-follower_count'])

You can also create your own rank expressions to sort your results
according to other criteria, such as showing most recently active
user, or combining the recency score with the text_relevance:

   >>> domain.create_rank_expression('recently_active', 'last_activity')

   >>> domain.create_rank_expression('activish',
   ...   'text_relevance + ((follower_count/(time() - last_activity))*1000)')

   >>> results = search_service.search(q=query, rank=['-recently_active'])


Viewing and Adjusting Stemming for a Domain
===========================================

A stemming dictionary maps related words to a common stem. A stem is
typically the root or base word from which variants are derived. For
example, run is the stem of running and ran. During indexing, Amazon
CloudSearch uses the stemming dictionary when it performs text-
processing on text fields. At search time, the stemming dictionary is
used to perform text-processing on the search request. This enables
matching on variants of a word. For example, if you map the term
running to the stem run and then search for running, the request
matches documents that contain run as well as running.

To get the current stemming dictionary defined for a domain, use the
"get_stemming" method:

   >>> stems = domain.get_stemming()
   >>> stems
   {u'stems': {}}
   >>>

This returns a dictionary object that can be manipulated directly to
add additional stems for your search domain by adding pairs of
term:stem to the stems dictionary:

   >>> stems['stems']['running'] = 'run'
   >>> stems['stems']['ran'] = 'run'
   >>> stems
   {u'stems': {u'ran': u'run', u'running': u'run'}}
   >>>

This has changed the value locally.  To update the information in
Amazon CloudSearch, you need to save the data:

   >>> stems.save()

You can also access certain CloudSearch-specific attributes related to
the stemming dictionary defined for your domain:

   >>> stems.status
   u'RequiresIndexDocuments'
   >>> stems.creation_date
   u'2012-05-01T12:12:32Z'
   >>> stems.update_date
   u'2012-05-01T12:12:32Z'
   >>> stems.update_version
   19
   >>>

The status indicates that, because you have changed the stems
associated with the domain, you will need to re-index the documents in
the domain before the new stems are used.


Viewing and Adjusting Stopwords for a Domain
============================================

Stopwords are words that should typically be ignored both during
indexing and at search time because they are either insignificant or
so common that including them would result in a massive number of
matches.

To view the stopwords currently defined for your domain, use the
"get_stopwords" method:

   >>> stopwords = domain.get_stopwords()
   >>> stopwords
   {u'stopwords': [u'a',
    u'an',
    u'and',
    u'are',
    u'as',
    u'at',
    u'be',
    u'but',
    u'by',
    u'for',
    u'in',
    u'is',
    u'it',
    u'of',
    u'on',
    u'or',
    u'the',
    u'to',
    u'was']}
   >>>

You can add additional stopwords by simply appending the values to the
list:

   >>> stopwords['stopwords'].append('foo')
   >>> stopwords['stopwords'].append('bar')
   >>> stopwords

Similarly, you could remove currently defined stopwords from the list.
To save the changes, use the "save" method:

   >>> stopwords.save()

The stopwords object has similar attributes defined above for stemming
that provide additional information about the stopwords in your
domain.


Viewing and Adjusting Stopwords for a Domain
============================================

You can configure synonyms for terms that appear in the data you are
searching. That way, if a user searches for the synonym rather than
the indexed term, the results will include documents that contain the
indexed term.

If you want two terms to match the same documents, you must define
them as synonyms of each other. For example:

   cat, feline
   feline, cat

To view the synonyms currently defined for your domain, use the
"get_synonyms" method:

   >>> synonyms = domain.get_synonyms()
   >>> synonyms
   {u'synonyms': {}}
   >>>

You can define new synonyms by adding new term:synonyms entries to the
synonyms dictionary object:

   >>> synonyms['synonyms']['cat'] = ['feline', 'kitten']
   >>> synonyms['synonyms']['dog'] = ['canine', 'puppy']

To save the changes, use the "save" method:

   >>> synonyms.save()

The synonyms object has similar attributes defined above for stemming
that provide additional information about the stopwords in your
domain.


Deleting Documents
==================

It is also possible to delete documents:

   >>> import time
   >>> from datetime import datetime

   >>> doc_service = domain.get_document_service()

   >>> # Again we'll cheat and use the current epoch time as our version number

   >>> doc_service.delete(4, int(time.mktime(datetime.utcnow().timetuple())))
   >>> service.commit()
