def test_chunked(self):
# chunking nothing yields nothing.
eq_(list(chunked([], 1)), [])
# chunking list where len(list) < n
eq_(list(chunked([1], 10)), [(1,)])
# chunking a list where len(list) == n
eq_(list(chunked([1, 2], 2)), [(1, 2)])
# chunking list where len(list) > n
eq_(list(chunked([1, 2, 3, 4, 5], 2)), [(1, 2), (3, 4), (5,)])
def test_chunked(self):
# chunking nothing yields nothing.
eq_(list(chunked([], 1)), [])
# chunking list where len(list) < n
eq_(list(chunked([1], 10)), [(1, )])
# chunking a list where len(list) == n
eq_(list(chunked([1, 2], 2)), [(1, 2)])
# chunking list where len(list) > n
eq_(list(chunked([1, 2, 3, 4, 5], 2)), [(1, 2), (3, 4), (5, )])
def index_objects(mapping_type, ids, chunk_size=100, es=None, index=None):
"""Index documents of a specified mapping type.
This NOT allows for asynchronous indexing.
If a mapping_type extends Indexable, you can add a ``post_save``
hook for the model that it's based on like this::
@receiver(dbsignals.post_save, sender=MyModel)
def update_in_index(sender, instance, **kw):
# from elasticutils.contrib.django import tasks
index_objects(MyMappingType, [instance.id])
:arg mapping_type: the mapping type for these ids
:arg ids: the list of ids of things to index
:arg chunk_size: the size of the chunk for bulk indexing
.. Note::
The default chunk_size is 100. The number of documents you
can bulk index at once depends on the size of the
documents.
:arg es: The `Elasticsearch` to use. If you don't specify an
`Elasticsearch`, it'll use `mapping_type.get_es()`.
:arg index: The name of the index to use. If you don't specify one
it'll use `mapping_type.get_index()`.
"""
if settings.ES_DISABLED:
return
if len(ids) == 0:
log.warning("0 objects being indexed")
else:
log.debug('Indexing objects {0}-{1}. [{2}]'.format(
ids[0], ids[-1], len(ids)))
# Get the model this mapping type is based on.
model = mapping_type.get_model()
# Retrieve all the objects that we're going to index and do it in
# bulk.
for id_list in chunked(ids, chunk_size):
documents = []
for obj in model.objects.filter(id__in=id_list):
try:
documents.append(mapping_type.extract_document(obj.id, obj))
except StandardError as exc:
log.exception('Unable to extract document {0}: {1}'.format(
obj, repr(exc)))
if documents:
mapping_type.bulk_index(documents,
id_field='id',
es=es,
index=index)
def index_objects(mapping_type, ids, chunk_size=100, es=None, index=None):
"""Index documents of a specified mapping type.
This allows for asynchronous indexing.
If a mapping_type extends Indexable, you can add a ``post_save``
hook for the model that it's based on like this::
@receiver(dbsignals.post_save, sender=MyModel)
def update_in_index(sender, instance, **kw):
from elasticutils.contrib.django import tasks
tasks.index_objects.delay(MyMappingType, [instance.id])
:arg mapping_type: the mapping type for these ids
:arg ids: the list of ids of things to index
:arg chunk_size: the size of the chunk for bulk indexing
.. Note::
The default chunk_size is 100. The number of documents you
can bulk index at once depends on the size of the
documents.
:arg es: The `Elasticsearch` to use. If you don't specify an
`Elasticsearch`, it'll use `mapping_type.get_es()`.
:arg index: The name of the index to use. If you don't specify one
it'll use `mapping_type.get_index()`.
"""
if settings.ES_DISABLED:
return
log.debug('Indexing objects {0}-{1}. [{2}]'.format(
ids[0], ids[-1], len(ids)))
# Get the model this mapping type is based on.
model = mapping_type.get_model()
# Retrieve all the objects that we're going to index and do it in
# bulk.
for id_list in chunked(ids, chunk_size):
documents = []
for obj in model.objects.filter(id__in=id_list):
try:
documents.append(mapping_type.extract_document(obj.id, obj))
except Exception as exc:
log.exception('Unable to extract document {0}: {1}'.format(
obj, repr(exc)))
if documents:
mapping_type.bulk_index(documents, id_field='id', es=es, index=index)
def index_objects(mapping_type, ids, chunk_size=100, public_index=False, **kwargs):
if getattr(settings, 'ES_DISABLED', False):
return
es = get_es()
model = mapping_type.get_model()
for id_list in chunked(ids, chunk_size):
documents = []
qs = model.objects.filter(id__in=id_list)
index = mapping_type.get_index(public_index)
if public_index:
qs = qs.public_indexable().privacy_level(PUBLIC)
for item in qs:
documents.append(mapping_type.extract_document(item.id, item))
mapping_type.bulk_index(documents, id_field='id', es=es, index=index)
mapping_type.refresh_index(es)
def index_objects(mapping_type, ids, chunk_size=100, public_index=False, **kwargs):
if getattr(settings, 'ES_DISABLED', False):
return
es = get_es()
model = mapping_type.get_model()
for id_list in chunked(ids, chunk_size):
documents = []
qs = model.objects.filter(id__in=id_list)
index = mapping_type.get_index(public_index)
if public_index:
qs = qs.public_indexable().privacy_level(PUBLIC)
for item in qs:
documents.append(mapping_type.extract_document(item.id, item))
mapping_type.bulk_index(documents, id_field='id', es=es, index=index)
mapping_type.refresh_index(es)
def index_objects(mapping_type, ids, chunk_size=100, es=None, index=None):
if settings.ES_DISABLED:
return
# create_mapping(mapping_type)
# Get the model this mapping type is based on.
model = mapping_type.get_model()
# Retrieve all the objects that we're going to index and do it in
# bulk.
for id_list in chunked(ids, chunk_size):
documents = []
for obj in model.objects.filter(id__in=id_list):
try:
doc = mapping_type.extract_document(obj.id, obj)
if isinstance(doc, list):
documents.extend(doc)
else:
documents.append(doc)
except StandardError as exc:
print 'Unable to extract document {0}: {1}'.format(obj, repr(exc))
if documents:
mapping_type.bulk_index(documents, id_field='id', es=es, index=index)
