def temporary_search_template(es,
template_file,
template_id_in_file,
size=None,
with_source=False):
"""A context manager that manages a temporary search template."""
def random_string(length=10):
"""Generate a simple random string to use as a temporary template ID"""
return ''.join(
random.choice(string.ascii_letters) for _ in range(length))
# check es argument
if type(es) is str:
es = Elasticsearch(es)
elif type(es) is not Elasticsearch:
raise ValueError(
f"Requires an Elasticsearch client or connection string URL: {type(es)}"
)
# load search template from file
template = load_json(template_file)
# load a single template if multiple are in the file
if type(template) is list:
assert template_id_in_file, "A template ID is required when multiple templates are present"
template = next(
(x for x in template if x["id"] == template_id_in_file), None)
assert template, f"No template found for template ID: {template_id_in_file}"
del template['id']
# set some optional parameters
if size: template['template']['source']['size'] = size
template['template']['source']['_source'] = with_source
# generate a random, temporary template ID
template_id = random_string()
# cleanup to save as script
template['script'] = template['template']
del template['template']
try:
es.put_script(id=template_id, body=template)
yield template_id
finally:
es.delete_script(template_id)
def remove_ml_scripts_pipelines(cls, es_client: Elasticsearch,
ml_type: List[str]) -> dict:
"""Remove all ML script and pipeline files."""
results = dict(script={}, pipeline={})
ingest_client = IngestClient(es_client)
files = cls.get_all_ml_files(es_client=es_client)
for file_type, data in files.items():
for name in list(data):
this_type = name.split('_')[1].lower()
if this_type not in ml_type:
continue
if file_type == 'script':
results[file_type][name] = es_client.delete_script(name)
elif file_type == 'pipeline':
results[file_type][name] = ingest_client.delete_pipeline(
name)
return results
