class ElasticCatalog(object):
default_indexes = {
'zelastic_doc_id': {
'type': 'string',
'index': 'not_analyzed'
}
}
def __init__(self, connection_string, elastic_name, storage, bulk=False,
bulk_size=400):
self.conn = ES(connection_string, bulk_size=bulk_size)
self.bulk_size = bulk_size
self.name = elastic_name
self.storage = storage
self.bulk = bulk
def update_mapping(self, name):
meta = self.storage.meta(name)
indexes = meta['indexes']
properties = self.default_indexes.copy()
try:
self.conn.create_index(self.name)
except IndexAlreadyExistsException:
pass
for index_name, _type in indexes.items():
index = None
if _type == 'str':
index = {
'type': 'string',
'index': 'not_analyzed',
}
elif _type == 'full':
index = {
'type': 'string',
'index': 'analyzed',
}
elif _type == 'bool':
index = {
'type': 'boolean'
}
elif _type == 'int':
index = {
'type': 'integer',
}
elif _type in ('datetime', 'date'):
index = {
'type': 'date',
}
elif _type == 'float':
index = {
'type': 'float',
}
if index is not None:
properties[index_name] = index
self.conn.indices.put_mapping(
doc_type=name,
mapping={
'ignore_conflicts': True,
'properties': properties
},
indices=[self.name])
def id(self, container_name, key):
return '%s-%s' % (container_name, key)
def index(self, container_name, doc, key):
# need to add data to the index that isn't actually persisted
data = {
'zelastic_doc_id': key
}
meta = self.storage.meta(container_name)
indexes = meta['indexes']
for index in indexes.keys():
if index in doc:
data[index] = doc[index]
self.conn.index(
data,
self.name,
container_name,
self.id(container_name, key),
bulk=self.bulk)
def delete(self, container_name, key):
self.conn.delete(
self.name,
container_name,
self.id(container_name, key),
bulk=self.bulk)
def delete_all(self, container_name):
self.conn.delete_mapping(
self.name,
container_name)
def search(self, container_name, query, **kwargs):
return self.conn.search(
query,
indexes=[self.name],
doc_types=[container_name],
**kwargs)
def getFacets(self, container_name, field, size=100):
return self.conn.search_raw({
"facets": {
field: {
"terms": {
"all_terms": True,
"field": field,
"size": size,
"order": "term"
}
}
}
}, indexes=[self.name], doc_type=container_name)
