esengine - The Elasticsearch Object Document Mapper
esengine is an ODM (Object Document Mapper) it maps Python classes in to Elasticsearch index/doc_type and object instances() in to Elasticsearch documents.
Out of the box ESengine takes care only of the Modeling and CRUD operations including:
- Index, DocType and Mapping specification
- Fields and its types coercion
- basic CRUD operations (Create, Read, Update, Delete)
ESengine does not communicate directly with ElasticSearch, it only creates the basic structure, To communicate it relies on an ES client providing the transport methods (index, delete, update etc).
ESengine does not enforce the use of the official ElasticSearch client, but you are encouraged to use it because it is well maintained and has the support to bulk operations. But you are free to use another client or create your own (useful for tests).
ESengine does not enforce or encourage you to use a DSL language for queries, out of the box you have to write the elasticsearch payload representation as a raw Python dictionary. However ESEngine comes with utils.payload helper module to help you building payloads in a less verbose and Pythonic way.
ElasticSearch DSL is an excellent tool, a very nice effort by the maintainers of the official ES library, it is handy in most of the cases, but because it is built on top of operator overiding, sometimes leads to a confuse query building, sometimes it is better to write raw_queries or use a simpler payload builder having more control and visibility of what os being generated.
ElasticSearch_DSL as a high level abstraction promotes Think only of Python objects, dont't worry about Elastic queries while ESengine promotes Know well the Elastic queries and then write them as Python objects.
ElasticSearch_DSL is more powerful and more complete, tight more with ES specifications while ESEngine is simpler, lightweight shipping only the basics.
It is in beta-Release, working in production, but missing a lot of features, you can help using, testing,, discussing or coding!
ESengine needs a client to communicate with E.S, you can use one of the following:
- ElasticSearch-py (official)
- Py-Elasticsearch (unofficial)
- Create your own implementing the same api-protocol
- Use the MockES provided as py.test fixture (only for tests)
Because of bulk operations you are recommendded to use elasticsearch-py (Official E.S Python library) so the instalation depends on the version of elasticsearch you are using.
Install the client and then install ESEngine
- for 2.0 + use "elasticsearch>=2.0.0,<3.0.0"
- for 1.0 + use "elasticsearch>=1.0.0,<2.0.0"
- under 1.0 use "elasticsearch<1.0.0"
For the latest use:
$ pip install elasticsearch
$ pip install esengine
pip install esengine[es2]pip install esengine[es1]pip install esengine[es0]The above command will install esengine and the elasticsearch library specific for you ES version.
# importing
from elasticsearch import ElasticSearch
from esengine import Document, StringField
# Defining a document
class Person(Document):
# define _meta attributes
_doctype = "person" # optional, it can be set after using "having" method
_index = "universe" # optional, it can be set after using "having" method
_es = ElasticSearch(host='host', port=port) # optional, it can be explicit passed to methods
# define fields
name = StringField()
# Initializing mappings and settings
Person.init()If you do not specify an "id" field, ESEngine will automatically add "id" as StringField. It is recommended that when specifying you use StringField for ids.
A good practice is to import the base module, look the same example
import esengine as ee
class Person(ee.Document):
name = ee.StringField()name = StringField()
age = IntegerField()
weight = FloatField()
factor = LongField()
active = BooleanField()
birthday = DateField()A field to hold GeoPoint with modes dict|array|string and its mappings
class Obj(Document):
location = GeoPointField(mode='dict') # default
# An object representation with lat and lon explicitly named
Obj.init() # important to put the proper mapping for geo location
obj = Obj()
obj.location = {"lat": 40.722, "lon": -73.989}}
class Obj(Document):
location = GeoPointField(mode='string')
# A string representation, with "lat,lon"
obj.location = "40.715, -74.011"
class Obj(Document):
location = GeoPointField(mode='array')
# An array representation with [lon,lat].
obj.location = [-73.983, 40.719]A field to hold nested one-dimension objects, schema-less or with properties validation.
# accepts only dictionaries having strct "street" and "number" keys
address = ObjectField(properties={"street": "string", "number": "integer"})
# Accepts any Python dictionary
extravalues = ObjectField() A Field to hold arrays (python lists)
In the base, any field can accept multi parameter
colors = StringField(multi=True) # accepts ["blue", "green", "yellow", ....]But sometimes (specially for nested objects) it is better to be explicit, and also it generates a better mapping
# accepts an array of strings ["blue", "green", "yellow", ....]
colors = ArrayField(StringField()) It is available for any other field
locations = ArrayField(GeoPointField())
numbers = ArrayField(IntegerField())
fractions = ArrayField(FloatField())
addresses = ArrayField(ObjectField(properties={"street": "string", "number": "integer"}))
list_of_lists_of_strings = ArrayField(ArrayField(StringField()))
person = Person(id=1234, name="Gonzo")
person.save() # or pass .save(es=es_client_instance) if not specified in model Person.get(id=1234)ids = [1234, 5678, 9101]
power_trio = Person.filter(ids=ids)Person.filter(name="Gonzo")ESengine does not try to create abstraction for query building, by default ESengine only implements search transport receiving a raw ES query in form of a Python dictionary.
query = {
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"ids": {
"values": [1, 2]
}
}
}
}
}
Person.search(query, size=10)Person.all()
# with more arguments
Person.all(size=20)Person.count(name='Gonzo')A single document can be updated simply using the .save() method
person = Person.get(id=1234)
person.name = "Another Name"
person.save()The Document methods .get, .filter and .search will return an instance of ResultSet object. This object is an Iterator containing the hits reached by the filtering or search process and exposes some CRUD methods[ update, delete and reload ] to deal with its results.
people = Person.filter(field='value')
people.update(another_field='another_value')When updating documents sometimes you need the changes done in the E.S index reflected in the objects of the ResultSet iterator, so you can use .reload method to perform that action.
people = Person.filter(field='value')
print people
... <Resultset: [{'field': 'value', 'another_field': None},
{'field': 'value', 'another_field': None}]>
# Updating another field on both instances
people.update(another_field='another_value')
print people
... <Resultset: [{'field': 'value', 'another_field': None}, {'field': 'value', 'another_field': None}]>
# Note that in E.S index the values weres changed but the current ResultSet is not updated by defaul
# you have to fire an update
people.reload()
print people
... <Resultset: [{'field': 'value', 'another_field': 'another_value'},
{'field': 'value', 'another_field': 'another_value'}]>
people = Person.all()
people.delete()Person.get(id=123).delete()ESEngine takes advantage of elasticsearch-py helpers for bulk actions, the ResultSet object uses bulk melhod to update and delete documents.
But you can use it in a explicit way using Document's update_all, save__all and delete_all methods.
top_5_racing_bikers = []
for name in ['Eddy Merckx',
'Bernard Hinault',
'Jacques Anquetil',
'Sean Kelly',
'Lance Armstrong']:
top_5_racing_bikers.append(Person(name=name))Person.save_all(top_5_racing_bikers)The above could be achieved using create shortcut
Person.create(name='Eddy Merckx', active=False)Create will return the instance of the indexed Document
top_5_racing_bikers = [
Person.create(name=name, active=False)
for name in ['Eddy Merckx',
'Bernard Hinault',
'Jacques Anquetil',
'Sean Kelly',
'Lance Armstrong']
]NOTE: .create method will automatically save the document to the index, and will not raise an error if there is a document with the same ID (if specified), it will update it acting as upsert.
Turning the field active to True for all documents
Person.update_all(top_5_racing_bikes, active=True)Person.delete_all(top_5_racing_bikes)chunk_size is number of docs in one chunk sent to ES (default: 500) you can change using meta argument.
Person.update_all(
top_5_racing_bikes, # the documents
active=True, # values to be changed
meta={'chunk_size': 200} # meta data passed to **bulk** operation
)ESEngine does not saves mappings automatically, but it offers an utility to generate and save mappings on demand You can create a cron job to refresh mappings once a day or run it every time your model changes
class Person(Document):
# define _meta attributes
_doctype = "person" # optional, it can be set after using "having" method
_index = "universe" # optional, it can be set after using "having" method
_es = ElasticSearch(host='host', port=port) # optional, it can be explicit passed to methods
# define fields
name = StringField()
Person.init() # if not defined in model, pass an **es=es_client** here
Include above in your the last line of your model files or cron jobs or migration scripts
In ESEngine Document all attributes starting with _ is a meta attribute, sometimes you can't define them hardcoded in your models and want them to be dynamic. you can achieve this by subclassing your base document, but sometimes you really need to change at runtime.
Sometimes it is useful for sharding.
from models import Person
BrazilianUsers = Person.having(index='another_index', doctype='brasilian_people', es=Elasticsearch(host='brazil_datacenter'))
AmericanUsers = Person.having(index='another_index', doctype='american_people', es=Elasticsearch(host='us_datacenter'))
brazilian_users = BrasilianUsers.filter(active=True)
american_users = AmericanUsers.search(query=query)To validate each field separately you can set a list of validators, each validator is a callable receiving field_name and value as arguments and should return None to be valid. If raise or return the data will be invalidated
from esengine.exceptions import ValidationError
def category_validator(field_name, value):
# check if value is in valid categories
if value not in ["primary", "secondary", ...]:
raise ValidationError("Invalid category!!!")
class Obj(Document):
category = StringField(validators=[category_validator])
obj = Obj()
obj.category = "another"
obj.save()
Traceback: ValidationError(....)To validate the whole document you can set a list of validators, each validator is a callable receiving the document instance and should return None to be valid. If raise or return the data will be invalidated
from esengine.exceptions import ValidationError
def if_city_state_is_required(obj):
if obj.city and not obj.state:
raise ValidationError("If city is defined you should define state")
class Obj(Document):
_validators = [if_city_state_is_required]
city = StringField()
state = StringField()
obj = Obj()
obj.city = "Sao Paulo"
obj.save()
Traceback: ValidationError(....)Sometimes you need to force indices-shards refresh for testing, you can use
# Will refresh all indices
Document.refresh()Sometimes queries turns in to complex and verbose data structures, to help you (use with moderation) you can use Payload utils to build queries.
query = {
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"ids": {
"values": [1, 2]
}
}
}
}
}
Person.search(query=query, size=10)from esengine import Payload, Query, Filter
payload = Payload(query=Query.filtered(query=Query.match_all(), filter=Filter.ids([1, 2])))
Person.search(payload, size=10)Payload utils exposes Payload, Query, Filter, Aggregate, Suggesters
You can also set model on payload initialization to create a more complete payload definition
from esengine import Payload, Query, Filter
payload = Payload(
model=Person,
query=Query.filtered(query=Query.match_all(), filter=Filter.ids([1, 2]))
sort={"name": {"order": "desc"}},
size=10
)
payload.search()You can use Payload, Query or Filter direct in search
from esengine import Payload, Query, Filter
Person.search(Payload(query=Query.match_all()))
Person.search(Query.bool(must=[Query.match("name", "Gonzo")]))
Person.search(Query.match_all())
Person.search(Filter.ids([1, 2, 3]))Payload object is chainable so you can do:
payload = Payload(query=query).size(10).sort("field", order="desc")
Document.search(payload)
# or the equivalent
payload.search(Document)You can paginate a payload, lets say you have indexed 500 documents under 'test' category and now you need to retrieve 50 per page.
Result will be included in pagination.items
from esengine import Payload, Filter
from models import Doc
payload = Payload(Doc, filter=Filter.term('category', 'test'))
# Total documents
payload.count()
500
# Paginate it
current_page = 1 # you have to increase it on each pagination
pagination = payload.paginate(page=current_page, per_page=50)
pagination.total
500
pagination.pages
10
pagination.has_prev
False
pagination.has_next
True
pagination.next_num
2
len(pagination.items)
50
for item in pagination.items:
# do something with item
# Turn the page
current_page += 1
pagination = payload.paginate(page=current_page, per_page=50)
pagination.page
2
pagination.has_prev
True
# Another option to move pages
pagination = pagination.next_page()
pagination.page
3
pagination = pagination.prev_page()
pagination.page
2
# Turn the page in place
pagination.backward()
pagination.page
1
pagination.forward()
pagination.page
2So you want to create buttons for pagination in your jinja template
{% macro render_pagination(pagination, endpoint) %}
<div class=pagination>
{%- for page in pagination.iter_pages() %}
{% if page %}
{% if page != pagination.page %}
<a href="{{ url_for(endpoint, page=page) }}">{{ page }}</a>
{% else %}
<strong>{{ page }}</strong>
{% endif %}
{% else %}
<span class=ellipsis>…</span>
{% endif %}
{%- endfor %}
</div>
{% endmacro %}ESEngine is OpenSource! join us!
MADE WITH #LOVE AND #PYTHON (which is the same) AT CathoLabs
