"must_not": {
"exists": {
"field": "processedText"
}
}
}
}
}, index=indexName)
if len(results['hits']['hits']) > 0:
print("Records Found: ", len(results['hits']['hits']) , "Processing Now")
import re
from bs4 import BeautifulSoup
for item in range(len(results['hits']['hits'])):
print("Processing", results['hits']['hits'][item]['_id'])
soup = BeautifulSoup(results['hits']['hits'][item]['_source']['html'], 'html.parser')
for script in soup(["script", "style",""]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = ' '.join(chunk for chunk in chunks if chunk)
body = {
"doc": {
"processedText": text
}
}
_update = es.update(index=results['hits']['hits'][item]['_index'], id=results['hits']['hits'][item]['_id'], body=body)
print("HTML Cleaned, and Converted to Searchable Text, Updating Doc", _update)
print("Quiting Now")
class ELmonocleDB:
log = logging.getLogger("monocle.ELmonocleDB")
def __init__(
self,
elastic_conn="localhost:9200",
index=None,
timeout=10,
prefix=CHANGE_PREFIX,
create=True,
previous_schema=False,
idents_config: Optional[IdentsConfig] = None,
user=None,
password=None,
use_ssl=None,
verify_certs=None,
ssl_show_warn=None,
) -> None:
host, port = elastic_conn.split(":")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
ip = socket.gethostbyname(host)
self.log.info("ES IP is %s" % ip)
self.log.info("ES prefix is %s" % prefix)
elastic_conn = [
{
"host": host,
"port": port,
}
]
if use_ssl:
elastic_conn[0]["use_ssl"] = use_ssl
if not verify_certs:
elastic_conn[0]["verify_certs"] = verify_certs
if not ssl_show_warn:
elastic_conn[0]["ssl_show_warn"] = ssl_show_warn
if user and password:
elastic_conn[0]["http_auth"] = "%s:%s" % (user, password)
while True:
try:
s.connect((ip, int(port)))
s.shutdown(2)
s.close()
break
except Exception as excpt:
self.log.info(
"Unable to connect to %s: %s. Sleeping for %ds."
% (elastic_conn, excpt, timeout)
)
time.sleep(timeout)
self.log.info("Connecting to ES server at %s" % elastic_conn)
self.es = Elasticsearch(elastic_conn)
self.log.info(self.es.info())
if previous_schema:
self.prefix = PREV_CHANGE_PREFIX
else:
self.prefix = prefix
if not index:
self.log.info("No index provided")
return
self.idents_config = idents_config or []
self.index = "{}{}".format(self.prefix, index)
self.log.info("Using ES index %s" % self.index)
self.mapping = {
"properties": {
"id": {"type": "keyword"},
"type": {"type": "keyword"},
"number": {"type": "keyword"},
"change_id": {"type": "keyword"},
"title": {
"type": "text",
"fields": {"keyword": {"type": "keyword", "ignore_above": 8191}},
},
"text": {
"type": "text",
"fields": {"keyword": {"type": "keyword", "ignore_above": 8191}},
},
"url": {"type": "keyword"},
"commit_count": {"type": "integer"},
"additions": {"type": "integer"},
"deletions": {"type": "integer"},
"changed_files_count": {"type": "integer"},
"changed_files": {
"properties": {
"additions": {"type": "integer"},
"deletions": {"type": "integer"},
"path": {"type": "keyword"},
}
},
"commits": {
"properties": {
"sha": {"type": "keyword"},
"author": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"committer": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"authored_at": {
"type": "date",
"format": "date_time_no_millis",
},
"committed_at": {
"type": "date",
"format": "date_time_no_millis",
},
"additions": {"type": "integer"},
"deletions": {"type": "integer"},
"title": {"type": "text"},
}
},
"repository_prefix": {"type": "keyword"},
"repository_fullname": {"type": "keyword"},
"repository_shortname": {"type": "keyword"},
"author": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"on_author": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"committer": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"merged_by": {
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
}
},
"branch": {"type": "keyword"},
"target_branch": {"type": "keyword"},
"created_at": {"type": "date", "format": "date_time_no_millis"},
"on_created_at": {"type": "date", "format": "date_time_no_millis"},
"merged_at": {"type": "date", "format": "date_time_no_millis"},
"updated_at": {"type": "date", "format": "date_time_no_millis"},
"closed_at": {"type": "date", "format": "date_time_no_millis"},
"state": {"type": "keyword"},
"duration": {"type": "integer"},
"mergeable": {"type": "keyword"},
"labels": {"type": "keyword"},
"assignees": {
"type": "nested",
"properties": {
"uid": {"type": "keyword"},
"muid": {"type": "keyword"},
},
},
"approval": {"type": "keyword"},
"draft": {"type": "boolean"},
"self_merged": {"type": "boolean"},
"crawler_metadata": {
"properties": {
"last_commit_at": {
"type": "date",
"format": "date_time_no_millis",
},
"last_post_at": {
"type": "date",
"format": "date_time_no_millis",
},
"total_docs_posted": {"type": "integer"},
"total_changes_updated": {"type": "integer"},
"total_orphans_updated": {"type": "integer"},
}
},
"tasks_data": {
"properties": {
"tid": {"type": "keyword"},
"ttype": {"type": "keyword"},
"crawler_name": {"type": "keyword"},
"updated_at": {"type": "date", "format": "date_time_no_millis"},
"change_url": {"type": "keyword"},
"severity": {"type": "keyword"},
"priority": {"type": "keyword"},
"score": {"type": "integer"},
"url": {"type": "keyword"},
"title": {
"type": "text",
"fields": {
"keyword": {"type": "keyword", "ignore_above": 8191}
},
},
"_adopted": {"type": "boolean"},
}
},
}
}
settings = {"mappings": self.mapping}
self.ic = self.es.indices
if create:
self.ic.create(index=self.index, ignore=400, body=settings)
# The authors_histo is failing on some context with this error when the
# time slice is large: Must be less than or equal to: [10000] but was [10001]. ()This limit can be
# set by changing the [search.max_buckets] cluster level)
# This is an attempt to mitigate the issue
cluster_settings = {"transient": {"search.max_buckets": 100000}}
self.es.cluster.put_settings(body=cluster_settings)
def update(self, source_it: List[Union[Change, Event]]) -> None:
def gen(it):
for _source in it:
source = change_or_event_to_dict(_source)
d = {}
d["_index"] = self.index
d["_op_type"] = "update"
d["_id"] = source["id"]
d["doc"] = source
d["doc_as_upsert"] = True
yield d
bulk(self.es, gen(source_it))
self.es.indices.refresh(index=self.index)
def update_task_data(
self,
source_it: Union[
List[TaskDataForEL],
List[OrphanTaskDataForEL],
List[AdoptedTaskDataForEL],
],
) -> Optional[BulkIndexError]:
def gen(it):
for _source in it:
d = {}
d["_index"] = self.index
d["_op_type"] = "update"
d["_id"] = _source._id
d["doc"] = {}
d["doc"].update({"id": _source._id})
if isinstance(_source, TaskDataForEL):
d["doc"].update(
{"tasks_data": [asdict(td) for td in _source.tasks_data]}
)
if isinstance(_source, OrphanTaskDataForEL):
d["doc"].update({"tasks_data": asdict(_source.task_data)})
d["doc"]["type"] = "OrphanTaskData"
if isinstance(_source, AdoptedTaskDataForEL):
d["doc"].update({"tasks_data": asdict(_source.task_data)})
d["doc_as_upsert"] = True
yield d
ret = None
try:
bulk(self.es, gen(source_it))
except BulkIndexError as err:
ret = err
self.es.indices.refresh(index=self.index)
return ret
def compute_crawler_id_by_name(self, name, _type):
return "crawler/%s/%s" % (_type, name)
def get_task_crawler_metadata(self, name: str) -> Dict:
try:
ret = self.es.get(
self.index, self.compute_crawler_id_by_name(name, "tasks_crawler")
)
return ret["_source"]["crawler_metadata"]
except Exception:
return {}
def set_task_crawler_metadata(
self, name: str, commit_date: datetime = None, push_infos: Dict = None
):
metadata = {}
if commit_date:
metadata.update({"last_commit_at": commit_date})
if push_infos:
prev_metadata = self.get_task_crawler_metadata(name)
metadata.update(
{
"last_post_at": push_infos["last_post_at"],
"total_docs_posted": prev_metadata.get("total_docs_posted", 0)
+ push_infos["total_docs_posted"],
"total_changes_updated": prev_metadata.get(
"total_changes_updated", 0
)
+ push_infos["total_changes_updated"],
"total_orphans_updated": prev_metadata.get(
"total_orphans_updated", 0
)
+ push_infos["total_orphans_updated"],
}
)
body = {
"doc": {"type": "TaskCrawlerDataCommit", "crawler_metadata": metadata},
"doc_as_upsert": True,
}
ret = None
try:
self.es.update(
self.index,
self.compute_crawler_id_by_name(name, "tasks_crawler"),
body=body,
)
self.es.indices.refresh(index=self.index)
except Exception as err:
ret = err
return ret
def delete_index(self):
self.log.info("Deleting index: %s" % self.index)
self.ic.delete(index=self.index)
def delete_repository(self, repository_fullname):
params = {"index": self.index}
body = {
"query": {
"bool": {
"filter": {
"regexp": {
"repository_fullname": {"value": repository_fullname}
}
}
}
}
}
params["body"] = body
self.es.delete_by_query(**params)
self.es.indices.refresh(index=self.index)
def get_last_updated(self, repository_fullname):
params = {"index": self.index}
body = {
"sort": [{"updated_at": {"order": "desc"}}],
"query": {
"bool": {
"filter": [
{"term": {"type": "Change"}},
{
"regexp": {
"repository_fullname": {"value": repository_fullname}
}
},
]
}
},
}
params["body"] = body
try:
res = self.es.search(**params)
except Exception:
return []
ret = [r["_source"] for r in res["hits"]["hits"]]
if not ret:
return []
return ret[0]
def get_changes_by_url(self, change_urls, size):
params = {
"index": self.index,
"body": {
"size": size,
"query": {
"bool": {
"filter": [
{"term": {"type": "Change"}},
{"terms": {"url": change_urls}},
]
}
},
},
}
try:
res = self.es.search(**params)
except Exception:
return []
return [r["_source"] for r in res["hits"]["hits"]]
def get_orphan_tds_by_change_urls(self, change_urls):
assert len(change_urls) <= 50
size = 5000 # Asumming not more that 100 TD data relataed to a change
params = {
"index": self.index,
"body": {
"size": size,
"query": {
"bool": {
"must_not": {"exists": {"field": "tasks_data._adopted"}},
"filter": [
{"term": {"type": "OrphanTaskData"}},
{"terms": {"tasks_data.change_url": change_urls}},
],
}
},
},
}
try:
res = self.es.search(**params)
except Exception:
return []
return [r["_source"] for r in res["hits"]["hits"]]
def get_orphan_tds_and_declare_adpotion(self, changes_url):
assert len(changes_url) <= 50
tds = self.get_orphan_tds_by_change_urls(changes_url)
if tds:
adopted_tds = [
AdoptedTaskDataForEL(
_id=td["id"],
task_data=AdoptedTaskData(_adopted=True),
)
for td in tds
]
self.update_task_data(adopted_tds)
return tds
def update_changes_with_orphan_tds(self, mapping: Dict[str, str]):
change_urls = list(mapping.keys())
while change_urls:
change_urls_to_process = change_urls[:50]
change_urls = change_urls[50:]
tds = self.get_orphan_tds_and_declare_adpotion(change_urls_to_process)
# Group tds in buckets by change_url
_map: Dict[str, List] = dict()
for td in tds:
_map.setdefault(td["tasks_data"]["change_url"], []).append(
td["tasks_data"]
)
# Create update docs to attach tds to matching changes
to_update = []
for change_url, tds in _map.items():
to_update.append(
TaskDataForEL(
_id=mapping[change_url],
tasks_data=createELTaskData(tds),
)
)
self.update_task_data(to_update)
def run_named_query(self, name, *args, **kwargs):
if name not in queries.public_queries:
raise UnknownQueryException("Unknown query: %s" % name)
return getattr(queries, name)(self.es, self.index, *args, **kwargs)
def get_indices(self):
return [
ind.replace(self.prefix, "")
for ind in self.es.indices.get(self.prefix + "*")
]
def iter_index(self):
body = {"query": {"match_all": {}}}
return scan(self.es, query=body, index=self.index, size=5000)
def update_idents(self) -> None:
import json
bulk_size = 7500
def get_obj_hash(obj: Dict) -> int:
obj_json = json.dumps(obj, sort_keys=True)
return hash(obj_json)
def update_ident(dict_ident: Dict) -> Dict:
dict_ident["muid"] = create_muid(dict_ident["uid"], self.idents_config)
return dict_ident
def _update_idents(obj: Dict) -> Tuple[Optional[Union[Change, Event]], bool]:
prev_hash = get_obj_hash(obj)
if obj["type"] == "Change":
obj["author"] = update_ident(obj["author"])
if "committer" in obj:
obj["committer"] = update_ident(obj["committer"])
if "merged_by" in obj:
obj["merged_by"] = update_ident(obj["merged_by"])
if "assignees" in obj:
obj["assignees"] = list(map(update_ident, obj["assignees"]))
if "commits" in obj:
for commit in obj["commits"]:
commit["author"] = update_ident(commit["author"])
commit["committer"] = update_ident(commit["committer"])
if obj["type"] in get_events_list():
if "author" in obj:
obj["author"] = update_ident(obj["author"])
if "on_author" in obj:
obj["on_author"] = update_ident(obj["on_author"])
updated = not prev_hash == get_obj_hash(obj)
if updated:
return dict_to_change_or_event(obj), True
else:
return None, False
def bulk_update(to_update: List) -> List:
print("Updating %s objects ..." % len(to_update))
self.update(to_update)
return []
to_update = []
total_read = 0
for _obj in self.iter_index():
total_read += 1
if total_read % bulk_size == 0:
print("%s objects read from the database" % total_read)
obj = _obj["_source"]
obj, updated = _update_idents(obj)
if updated:
to_update.append(obj)
if len(to_update) == bulk_size:
to_update = bulk_update(to_update)
bulk_update(to_update)
class ESConnector:
"""
as many MS will communicate with ElasticSearch, centralize access
with this library
"""
def __init__(self,
host=None,
port=9200,
timeout=10,
local_env=False):
self.host = host
self.port = port
self.timeout = timeout
self.local_env = local_env
self.es = None
def _connect(self):
"""
connect to a member of the ElasticSearch cluster
"""
try:
if self.local_env:
self.es = Elasticsearch([{'host': self.host,
'port': self.port}])
else:
self.es = Elasticsearch([{'host': self.host,
'port': self.port}],
sniff_on_start=True,
sniff_on_connection_fail=True,
sniffer_timeout=self.timeout)
self.idx = IndicesClient(self.es)
return
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host, self.port)
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchError.unknown_exception(backtrace, str(e))
def _create_index(self, index, doc_type, settings=None, mappings=None):
"""
create a new empty index
mandatory args:
index = index name
doc_type = document type, ie. any valid string
settings = ElasticSearch cluster configuration
mappings = dict of document fields by type and indexing preference
"""
if not settings:
settings = {'index': {'number_of_shards': '1',
'number_of_replicas': '0'}}
if not mappings:
mappings = {'property': {'id': {'type': 'string',
'index': 'not_analyzed'}}}
try:
response = self.es.create(index=index,
doc_type=doc_type,
body=dumps(settings))
self.idx.put_mapping(index=index,
doc_type=doc_type,
body=dumps(mappings))
if not 'created' in response or not response['created']:
return ElasticSearchError.unable_to_create_index(index)
log.info('Index: {} created'.format(index))
log.info('ES create(): response: {}'.format(response))
return
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except NotFoundError as e:
return ElasticSearchError.missing_index(self.index)
except RequestError as e:
return ElasticSearchError.invalid_request(str(e))
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchError.unknown_exception(backtrace,
str(e))
def drop_index(self, index):
try:
if index in self.es.indices.stats()['indices'].keys():
self.es.indices.delete(index=index, ignore=[400, 404])
log.info('Index: {} deleted'.format(index))
return
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except NotFoundError as e:
return ElasticSearcheError.missing_index(self.index)
except RequestError as e:
return ElasticSearcheError.invalid_request(str(e))
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchError.unknown_exception(backtrace,
str(e))
def add_document(self,
index=None,
doc_type=None,
doc_id=0,
settings={},
mappings={},
values={}):
"""
add a new document to an existing index
mandatory args:
index = index name
doc_type = document type, ie. any valid string
settings = ElasticSearch cluster configuration
mappings = dict of document fields by type and indexing preference
values = dictionary of fields and values
"""
try:
err_msg = self._connect()
if err_msg:
return err_msg
if index not in self.es.indices.stats()['indices'].keys():
err_msg = self._create_index(index,
doc_type,
settings,
mappings)
if err_msg:
return err_msg
response = self.es.create(index=index,
doc_type=doc_type,
id=doc_id,
body=dumps(values))
log.info('ES create(): response: {}'.format(response))
return ElasticSearchWrite.object_created(response)
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except RequestError as e:
return ElasticSearchError.invalid_request(str(e))
except NotFoundError as e:
return ElasticSearchError.missing_index(index)
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchWriteError.unknown_exception(doc_id,
values,
backtrace,
str(e))
def update_document(self,
index,
doc_type,
doc_id,
values):
"""
update an existing document in an existing index
mandatory args:
index = index name
doc_type = document type, ie. any valid string
doc_id = document_id
values = dictionary of fields and values
"""
try:
err_msg = self._connect()
if err_msg:
return err_msg
log.info('ES body: {}'.format(values))
response = self.es.update(index=index,
doc_type=doc_type,
id=doc_id,
body=dumps(values))
log.info('ES update(): response: {}'.format(response))
return ElasticSearchWrite.object_updated(response)
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except RequestError as e:
return ElasticSearchError.invalid_request(str(e))
except NotFoundError as e:
return ElasticSearchError.missing_index(index)
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchWriteError.unknown_exception(doc_id,
values,
backtrace,
str(e))
def find_document(self,
index,
doc_type,
dsl=None,
fields=None):
"""
find an existing document in an existing index
mandatory args:
index = index name
doc_type = document type, ie. any valid string
dsl = query parameters in DSL format
fields = list of fields to return
"""
try:
err_msg = self._connect()
if err_msg:
return err_msg
response = self.es.search(index=index,
doc_type=doc_type,
body=dumps(dsl),
_source=fields)
return ElasticSearchRead.object_found(response)
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except RequestError as e:
return ElasticSearchError.invalid_request(str(e))
except NotFoundError as e:
return ElasticSearchError.missing_index(index)
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchReadError.unknown_exception(dsl,
fields,
backtrace,
str(e))
def search_documents(self,
index,
doc_type,
dsl,
fields=None):
"""
find an existing document in an existing index
mandatory args:
index = index name
doc_type = document type, ie. any valid string
dsl = query parameters in DSL format
fields = list of fields to return
"""
try:
err_msg = self._connect()
if err_msg:
return err_msg
response = self.es.search(index=index,
doc_type=doc_type,
body=dumps(dsl),
_source=fields)
return ElasticSearchRead.objects_found(response)
except ConnectionError as e:
return ElasticSearchError.no_host_available(self.host,
self.port)
except RequestError as e:
return ElasticSearchError.invalid_request(str(e))
except NotFoundError as e:
return ElasticSearchError.missing_index(index)
except Exception as e:
(type_e, value, traceback_prev) = exc_info()
backtrace = extract_tb(traceback_prev)
return ElasticSearchReadError.unknown_exception(dsl,
fields,
backtrace,
str(e))
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)
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)