class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(
self.indices, doc_type, body, doc_id, **kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(
self.indices, doc_type, body, doc_id, **kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(
self.indices, doc_type, doc_id, body, **kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(
self.indices, doc_type, body, **kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(
self.indices, doc_type, doc_id, body, **kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(
self.indices, doc_type, body, **kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(
self.indices, doc_type, doc_id, body, **kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
def reindex(from_hosts,
from_index,
to_hosts,
to_index,
to_type,
source='{"query":{"match_all":{}}}',
max_docs=0,
page_size=10,
logging_per_docs=1000,
es_scroll='5m',
request_timeout=60):
if from_index is None:
logger.warn('from_index is empty.')
return
from_es = Elasticsearch(hosts=from_hosts)
to_es = Elasticsearch(hosts=to_hosts)
scroll_id = None
counter = 0
running = True
bulk_data = []
while(running):
try:
if scroll_id is None:
response = from_es.search(index=from_index,
body=source,
params={"request_timeout": request_timeout,
"scroll": es_scroll,
"size": page_size})
else:
response = from_es.scroll(scroll_id=scroll_id,
params={"request_timeout": request_timeout,
"scroll": es_scroll})
if len(response['hits']['hits']) == 0:
running = False
break
scroll_id = response['_scroll_id']
for hit in response['hits']['hits']:
if '_source' in hit:
counter += 1
if counter % logging_per_docs == 0:
logger.info(u'Loaded {0} docs.'.format(counter))
if max_docs > 0 and counter >= max_docs:
logger.info(u'{0} docs are loaded, but it exceeded {1} docs.'.format(counter, max_docs))
running = False
break
op_index = to_index if to_index is not None else hit['_index']
op_type = to_type if to_type is not None else hit['_type']
bulk_data.append({"index": {"_index": op_index,
"_type": op_type,
"_id": hit['_id']}
})
bulk_data.append(hit['_source'])
if len(bulk_data) != 0:
to_es.bulk(body=bulk_data, params={"request_timeout": request_timeout})
bulk_data = []
except NotFoundError:
break
except:
logger.exception(u"Failed to load documents from Elasticsearch(Loaded {0} doc).".format(counter))
break
if len(bulk_data) != 0:
to_es.bulk(body=bulk_data, params={"request_timeout": request_timeout})
logger.info('Loaded {0} documents.'.format(counter))
class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(self.indices, doc_type, body, doc_id,
**kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(self.indices, doc_type, body, doc_id,
**kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(self.indices, doc_type, doc_id, body,
**kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(self.indices, doc_type, body,
**kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(self.indices, doc_type, doc_id, body,
**kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(self.indices, doc_type, body,
**kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(self.indices, doc_type, doc_id, body,
**kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(self.indices, doc_type, doc_id,
body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(self.indices, doc_type, doc_id, body,
**kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
