def add_filters_item(filters, es_url, es_in_index, es_out_index):
elastic_in = ElasticSearch(es_url, es_in_index)
elastic_out = ElasticSearch(es_url, es_out_index)
# Time to just copy from in_index to our_index
total = elastic_out.bulk_upload_sync(fetch(elastic_in, filters), "uuid")
def test_check_instance(self):
"""Test _check_instance function"""
major = ElasticSearch._check_instance(self.url_es5, False)
self.assertEqual(major, '5')
major = ElasticSearch._check_instance(self.url_es6, False)
self.assertEqual(major, '6')
with self.assertRaises(ElasticConnectException):
major = ElasticSearch._check_instance(self.url_es6_err, False)
def create_search(elastic_url, dashboard, index_pattern, es_index=None):
""" Create the base search for vis if used
:param elastic_url: URL for ElasticSearch (ES) server
:param dashboard: kibana dashboard to be used as template
:param enrich_index: ES enriched index used in the new dashboard
"""
search_id = None
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
dash_data = get_dashboard_json(elastic, dashboard)
# First vis
if "panelsJSON" not in dash_data:
logger.error("Can not find vis in dashboard: %s", dashboard)
raise
# Get the search from the first vis in the panel
for panel in json.loads(dash_data["panelsJSON"]):
panel_id = panel["id"]
logger.debug("Checking search in %s vis", panel_id)
search_id = get_search_from_vis(elastic, panel_id)
if search_id:
break
# And now time to create the search found
if not search_id:
logger.info("Can't find search %s", dashboard)
return
logger.debug("Found template search %s", search_id)
search_json = get_search_json(elastic, search_id)
search_source = search_json['kibanaSavedObjectMeta']['searchSourceJSON']
new_search_source = json.loads(search_source)
new_search_source['index'] = index_pattern
new_search_source = json.dumps(new_search_source)
search_json['kibanaSavedObjectMeta']['searchSourceJSON'] = \
new_search_source
search_json['title'] += " " + index_pattern
new_search_id = search_id + "__" + index_pattern
url = elastic.index_url + "/search/" + new_search_id
headers = {"Content-Type": "application/json"}
res = requests_ses.post(url,
data=json.dumps(search_json),
verify=False,
headers=headers)
res.raise_for_status()
logger.debug("New search created: %s", url)
return new_search_id
def exists_dashboard(elastic_url, dash_id, es_index=None):
""" Check if a dashboard exists """
exists = False
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
dash_data = get_dashboard_json(elastic, dash_id)
if 'panelsJSON' in dash_data:
exists = True
return exists
def create_index_pattern(elastic_url, dashboard, enrich_index, es_index=None):
""" Create a index pattern using as template the index pattern
in dashboard template vis
:param elastic_url: URL for ElasticSearch (ES) server
:param dashboard: kibana dashboard to be used as template
:param enrich_index: ES enriched index used in the new dashboard
"""
index_pattern = None
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
dash_data = get_dashboard_json(elastic, dashboard)
# First vis
if "panelsJSON" not in dash_data:
logger.error("Can not find vis in dashboard: %s", dashboard)
raise
# Get the index pattern from the first vis in the panel
# that as index pattern data
for panel in json.loads(dash_data["panelsJSON"]):
panel_id = panel["id"]
logger.debug("Checking index pattern in %s vis", panel_id)
index_pattern = get_index_pattern_from_vis(elastic, panel_id)
if index_pattern:
break
# And now time to create the index pattern found
if not index_pattern:
logger.error("Can't find index pattern for %s", dashboard)
raise
logger.debug("Found %s template index pattern", index_pattern)
new_index_pattern_json = get_index_pattern_json(elastic, index_pattern)
new_index_pattern_json['title'] = enrich_index
url = elastic.index_url + "/index-pattern/" + enrich_index
headers = {"Content-Type": "application/json"}
res = requests_ses.post(url,
data=json.dumps(new_index_pattern_json),
verify=False,
headers=headers)
res.raise_for_status()
logger.debug("New index pattern created: %s", url)
return enrich_index
def get_elastic():
try:
ocean_index = ConfOcean.get_index()
elastic_ocean = ElasticSearch(args.elastic_url, ocean_index)
except ElasticConnectException:
logging.error("Can't connect to Elastic Search. Is it running?")
sys.exit(1)
except ElasticWriteException:
logging.error("Can't write to Elastic Search.")
sys.exit(1)
return elastic_ocean
def feed_dashboard(dashboard,
elastic_url,
es_index=None,
data_sources=None,
add_vis_studies=False):
""" Import a dashboard. If data_sources are defined, just include items
for this data source.
"""
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
import_item_json(elastic, "dashboard", dashboard['dashboard']['id'],
dashboard['dashboard']['value'], data_sources,
add_vis_studies)
if 'searches' in dashboard:
for search in dashboard['searches']:
import_item_json(elastic, "search", search['id'], search['value'],
data_sources)
if 'index_patterns' in dashboard:
for index in dashboard['index_patterns']:
if not data_sources or \
is_index_pattern_from_data_sources(index, data_sources):
import_item_json(elastic, "index-pattern", index['id'],
index['value'])
else:
logger.debug("Index pattern %s not for %s. Not included.",
index['id'], data_sources)
if 'visualizations' in dashboard:
for vis in dashboard['visualizations']:
if not add_vis_studies and is_vis_study(vis):
logger.debug("Vis %s is for an study. Not included.",
vis['id'])
elif not data_sources or is_vis_from_data_sources(
vis, data_sources):
import_item_json(elastic, "visualization", vis['id'],
vis['value'])
else:
logger.debug("Vis %s not for %s. Not included.", vis['id'],
data_sources)
def find_ossmeter_filters(elastic_url, ossmeter_index):
filters_data = {}
elastic = ElasticSearch(elastic_url, ossmeter_index)
def build_query(filter_name):
# ES query
query = '''
{
"size": 0,
"query": {
"bool": {
}
},
"aggs": {
"2": {
"terms": {
"field": "%s.keyword",
"size": 1000,
"order": {
"_count": "desc"
}
}
}
}
} ''' % (filter_name)
return query
for filter_name in OSS_FILTERS:
query = build_query(filter_name)
url = elastic.index_url + "/_search"
res = requests.post(url, data=query, headers=HEADERS_JSON)
res.raise_for_status()
filter_data = [
f['key'] for f in res.json()['aggregations']['2']['buckets']
if not f['key'].find(":") > 0
]
# print(filter_data)
filters_data[filter_name] = filter_data
return filters_data
def search_dashboards(elastic_url, es_index=None):
dashboards = []
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
elastic_ver = find_elasticsearch_version(elastic)
if elastic_ver < 6:
dash_json_url = elastic.index_url + "/dashboard/_search?size=10000"
res = requests_ses.get(dash_json_url, verify=False)
else:
items_json_url = elastic.index_url + "/_search?size=10000"
query = '''
{
"query" : {
"term" : { "type" : "dashboard" }
}
}'''
res = requests_ses.post(items_json_url,
data=query,
verify=False,
headers=HEADERS_JSON)
res.raise_for_status()
res_json = res.json()
if "hits" not in res_json:
logger.error("Can't find dashboards")
raise RuntimeError("Can't find dashboards")
for dash in res_json["hits"]["hits"]:
if elastic_ver < 6:
dash_json = dash["_source"]
else:
dash_json = dash["_source"]["dashboard"]
dashboards.append({"_id": dash["_id"], "title": dash_json["title"]})
return dashboards
if __name__ == '__main__':
args = get_params()
if args.debug:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(message)s')
logging.debug("Debug mode activated")
else:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(message)s')
logging.info("Importing tweets from %s to %s/%s", args.json_dir,
args.elastic_url, args.index)
elastic = ElasticSearch(args.elastic_url, args.index)
total = 0
first_date = None
last_date = None
ids = []
tweets = []
for tweet in fetch_tweets(args.json_dir):
# Check first and last dates
tweet_date = parser.parse(tweet['created_at'])
if not first_date or tweet_date <= first_date:
first_date = tweet_date
if not last_date or tweet_date >= last_date:
if __name__ == '__main__':
ARGS = get_params()
if ARGS.debug:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(message)s')
logging.debug("Debug mode activated")
else:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(message)s')
logging.info("Importing items from %s to %s/%s", ARGS.collection,
ARGS.elastic_url, ARGS.index)
elastic = ElasticSearch(ARGS.elastic_url, ARGS.index)
if ARGS.collection:
mongo_items = fetch_mongodb_collection(ARGS.collection,
ARGS.mongo_host,
ARGS.mongo_port)
elif ARGS.project:
mongo_items = fetch_mongodb_project(ARGS.project, ARGS.mongo_host,
ARGS.mongo_port)
elif ARGS.all_collections:
mongo_items = fetch_mongodb_all(ARGS.mongo_host, ARGS.mongo_port)
else:
raise RuntimeError('Collection to be processed not provided')
if mongo_items:
logging.info("Loading collections in Elasticsearch")
yield tweet
if __name__ == '__main__':
args = get_params()
if args.debug:
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(message)s')
logging.debug("Debug mode activated")
else:
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
logging.info("Importing tweets from %s to %s/%s", args.json_dir, args.elastic_url, args.index)
elastic = ElasticSearch(args.elastic_url, args.index)
total = 0
first_date = None
last_date = None
ids = []
tweets = []
for tweet in fetch_tweets(args.json_dir):
# Check first and last dates
tweet_date = parser.parse(tweet['created_at'])
if not first_date or tweet_date <= first_date:
first_date = tweet_date
if not last_date or tweet_date >= last_date:
connector = get_connector_from_name(backend_name, connectors)
backend = connector[0](**vars(args))
ocean_backend = connector[1](backend, **vars(args))
enrich_backend = connector[2](backend, **vars(args))
es_index = backend.get_name() + "_" + backend.get_id()
clean = args.no_incremental
if args.cache:
clean = True
try:
# Ocean
elastic_state = ElasticSearch(args.elastic_url, es_index,
ocean_backend.get_elastic_mappings(),
clean)
# Enriched ocean
enrich_index = es_index + "_enrich"
elastic = ElasticSearch(args.elastic_url, enrich_index,
enrich_backend.get_elastic_mappings(), clean)
except ElasticConnectException:
logging.error("Can't connect to Elastic Search. Is it running?")
sys.exit(1)
ocean_backend.set_elastic(elastic_state)
enrich_backend.set_elastic(elastic)
try:
def export_items(elastic_url,
in_index,
out_index,
elastic_url_out=None,
search_after=False,
search_after_value=None,
limit=None,
copy=False):
""" Export items from in_index to out_index using the correct mapping """
if not limit:
limit = DEFAULT_LIMIT
if search_after_value:
search_after_value_timestamp = int(search_after_value[0])
search_after_value_uuid = search_after_value[1]
search_after_value = [
search_after_value_timestamp, search_after_value_uuid
]
logging.info("Exporting items from %s/%s to %s", elastic_url, in_index,
out_index)
count_res = requests.get('%s/%s/_count' % (elastic_url, in_index))
try:
count_res.raise_for_status()
except requests.exceptions.HTTPError:
if count_res.status_code == 404:
logging.error("The index does not exists: %s", in_index)
else:
logging.error(count_res.text)
sys.exit(1)
logging.info("Total items to copy: %i", count_res.json()['count'])
# Time to upload the items with the correct mapping
elastic_in = ElasticSearch(elastic_url, in_index)
if not copy:
# Create the correct mapping for the data sources detected from in_index
ds_mapping = find_mapping(elastic_url, in_index)
else:
logging.debug('Using the input index mapping')
ds_mapping = extract_mapping(elastic_url, in_index)
if not elastic_url_out:
elastic_out = ElasticSearch(elastic_url,
out_index,
mappings=ds_mapping)
else:
elastic_out = ElasticSearch(elastic_url_out,
out_index,
mappings=ds_mapping)
# Time to just copy from in_index to our_index
uid_field = find_uuid(elastic_url, in_index)
backend = find_perceval_backend(elastic_url, in_index)
if search_after:
total = elastic_out.bulk_upload_sync(
fetch(elastic_in, backend, limit, search_after_value,
scroll=False), uid_field)
else:
total = elastic_out.bulk_upload_sync(fetch(elastic_in, backend, limit),
uid_field)
logging.info("Total items copied: %i", total)
for dependency in dependencies:
eitem = enrich_item(dependency)
eitem['project'] = project
yield eitem
if __name__ == '__main__':
args = get_params()
if args.debug:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(message)s')
logging.debug("Debug mode activated")
else:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(message)s')
logging.info("Importing items from %s to %s/%s", args.file,
args.elastic_url, args.index)
elastic = ElasticSearch(args.elastic_url, args.index)
items = fetch_dependencies(args.file, args.project)
if items:
logging.info("Loading dependencies in Elasticsearch ...")
elastic.bulk_upload_sync(items, "uuid")
logging.info("Import completed.")
task_init = datetime.now()
arthur_repos = {"repositories": []}
args = get_params()
config_logging(args.debug)
total_repos = 0
# enrich ocean
index_enrich = OCEAN_INDEX + "_" + PERCEVAL_BACKEND + "_enrich"
es_enrich = None
try:
es_enrich = ElasticSearch(args.elastic_url, index_enrich)
except ElasticConnectException:
logging.error("Can't connect to Elastic Search. Is it running?")
# The owner could be an org or an user.
for org in args.org:
owner_url = get_owner_repos_url(org, args.token)
try:
repos = get_repositores(owner_url, args.token, args.nrepos)
except requests.exceptions.HTTPError:
logging.error("Can't get repos for %s" % (owner_url))
continue
if args.db_projects_map:
insert_projects_mapping(args.db_projects_map, org, repos)
for repo in repos:
def execute(self):
cfg = self.config.get_conf()
if 'gerrit' not in cfg or 'git' not in cfg:
logger.error("gerrit and git are needed for track items.")
return
# We need to track the items in all git repositories from OPNFV
git_repos = []
repos_raw = TaskProjects.get_repos_by_backend_section("git")
# git://git.opnfv.org/apex -> https://git.opnfv.org/apex/plain/UPSTREAM
for repo in repos_raw:
repo = repo.replace("git://", "https://")
repo += "/plain/UPSTREAM"
git_repos.append(repo)
project = cfg['track_items']['project']
elastic_url_enrich = cfg['es_enrichment']['url']
# The raw data comes from upstream project
elastic_url_raw = cfg['track_items']['upstream_raw_es_url']
index_gerrit_raw = cfg['track_items']['raw_index_gerrit']
index_git_raw = cfg['track_items']['raw_index_git']
index_gerrit_enrich = cfg['gerrit']['enriched_index']
index_git_enrich = cfg['git']['enriched_index']
db_config = {
"database": cfg['sortinghat']['database'],
"user": cfg['sortinghat']['user'],
"password": cfg['sortinghat']['password'],
"host": cfg['sortinghat']['host']
}
logger.debug("Importing track items from %s ", git_repos)
#
# Gerrit Reviews
#
gerrit_uris = []
for git_repo in git_repos:
gerrit_uris += fetch_track_items(git_repo, self.ITEMS_DATA_SOURCE)
gerrit_numbers = get_gerrit_numbers(gerrit_uris)
logger.info("Total gerrit track items to be imported: %i", len(gerrit_numbers))
enriched_items = enrich_gerrit_items(elastic_url_raw,
index_gerrit_raw, gerrit_numbers,
project, db_config)
logger.info("Total gerrit track items enriched: %i", len(enriched_items))
elastic = ElasticSearch(elastic_url_enrich, index_gerrit_enrich)
total = elastic.bulk_upload(enriched_items, "uuid")
#
# Git Commits
#
commits_sha = get_commits_from_gerrit(elastic_url_raw,
index_gerrit_raw, gerrit_numbers)
logger.info("Total git track items to be checked: %i", len(commits_sha))
enriched_items = enrich_git_items(elastic_url_raw,
index_git_raw, commits_sha,
project, db_config)
logger.info("Total git track items enriched: %i", len(enriched_items))
elastic = ElasticSearch(elastic_url_enrich, index_git_enrich)
total = elastic.bulk_upload(enriched_items, "uuid")
def create_dashboard(elastic_url,
dashboard,
enrich_index,
kibana_host,
es_index=None):
""" Create a new dashboard using dashboard as template
and reading the data from enriched_index """
def new_panels(elastic, panels, search_id):
""" Create the new panels and their vis for the dashboard from the
panels in the template dashboard """
dash_vis_ids = []
new_panels = []
for panel in panels:
if panel['type'] in ['visualization', 'search']:
if panel['type'] == 'visualization':
dash_vis_ids.append(panel['id'])
panel['id'] += "__" + enrich_index
if panel['type'] == 'search':
panel['id'] = search_id
new_panels.append(panel)
create_vis(elastic, dash_vis_ids, search_id)
return new_panels
def create_vis(elastic, dash_vis_ids, search_id):
""" Create new visualizations for the dashboard """
# Create visualizations for the new dashboard
item_template_url = elastic.index_url + "/visualization"
# Hack: Get all vis if they are <10000. Use scroll API to get all.
# Better: use mget to get all vis in dash_vis_ids
item_template_url_search = item_template_url + "/_search?size=10000"
res = requests_ses.get(item_template_url_search, verify=False)
res.raise_for_status()
all_visualizations = res.json()['hits']['hits']
visualizations = []
for vis in all_visualizations:
if vis['_id'] in dash_vis_ids:
visualizations.append(vis)
logger.info("Total template vis found: %i", len(visualizations))
for vis in visualizations:
vis_data = vis['_source']
vis_name = vis['_id'].split("_")[-1]
vis_id = vis_name + "__" + enrich_index
vis_data['title'] = vis_id
vis_meta = json.loads(
vis_data['kibanaSavedObjectMeta']['searchSourceJSON'])
vis_meta['index'] = enrich_index
vis_data['kibanaSavedObjectMeta']['searchSourceJSON'] = \
json.dumps(vis_meta)
if "savedSearchId" in vis_data:
vis_data["savedSearchId"] = search_id
url = item_template_url + "/" + vis_id
headers = {"Content-Type": "application/json"}
res = requests_ses.post(url,
data=json.dumps(vis_data),
verify=False,
headers=headers)
res.raise_for_status()
logger.debug("Created new vis %s", url)
if not es_index:
es_index = ".kibana"
# First create always the index pattern as data source
index_pattern = create_index_pattern(elastic_url, dashboard, enrich_index,
es_index)
# If search is used create a new search with the new index_pàttern
search_id = create_search(elastic_url, dashboard, index_pattern, es_index)
elastic = ElasticSearch(elastic_url, es_index)
# Create the new dashboard from the template
dash_data = get_dashboard_json(elastic, dashboard)
dash_data['title'] = enrich_index
# Load template panels to create the new ones with their new vis
panels = json.loads(dash_data['panelsJSON'])
dash_data['panelsJSON'] = json.dumps(new_panels(elastic, panels,
search_id))
dash_path = "/dashboard/" + dashboard + "__" + enrich_index
url = elastic.index_url + dash_path
res = requests_ses.post(url,
data=json.dumps(dash_data),
verify=False,
headers=HEADERS_JSON)
res.raise_for_status()
dash_url = kibana_host + "/app/kibana#" + dash_path
return dash_url
help="Get metrics for data source")
return parser.parse_args()
if __name__ == '__main__':
args = get_params()
if args.debug:
logging.basicConfig(level=logging.DEBUG,
format='[%(asctime)s] %(message)s')
logging.debug("Debug mode activated")
else:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(message)s')
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("requests").setLevel(logging.WARNING)
data_source = args.data_source
index = "grimoirelab_metrics"
elastic = ElasticSearch(args.elastic_url, index)
if args.elastic_metrics_url:
elastic = ElasticSearch(args.elastic_metrics_url, index)
elastic.bulk_upload_sync(fetch_metric(args.elastic_url, data_source), "id")
# for metric in fetch_metric(es_url, data_source):
# print(metric)
def fetch_dashboard(elastic_url, dash_id, es_index=None):
# Kibana dashboard fields
kibana = {
"dashboard": None,
"visualizations": [],
"index_patterns": [],
"searches": []
}
# Used to avoid having duplicates
search_ids_done = []
index_ids_done = []
logger.debug("Fetching dashboard %s", dash_id)
if not es_index:
es_index = ".kibana"
elastic = ElasticSearch(elastic_url, es_index)
kibana["dashboard"] = {
"id": dash_id,
"value": get_dashboard_json(elastic, dash_id)
}
if "panelsJSON" not in kibana["dashboard"]["value"]:
# The dashboard is empty. No visualizations included.
return kibana
# Export all visualizations and the index patterns and searches in them
for panel in json.loads(kibana["dashboard"]["value"]["panelsJSON"]):
logger.debug("Analyzing panel %s (%s)", panel['id'], panel['type'])
if panel['type'] in ['visualization']:
vis_id = panel['id']
vis_json = get_vis_json(elastic, vis_id)
kibana["visualizations"].append({"id": vis_id, "value": vis_json})
search_id = get_search_from_vis(elastic, vis_id)
if search_id and search_id not in search_ids_done:
search_ids_done.append(search_id)
kibana["searches"].append({
"id":
search_id,
"value":
get_search_json(elastic, search_id)
})
index_pattern_id = get_index_pattern_from_vis(elastic, vis_id)
if index_pattern_id and index_pattern_id not in index_ids_done:
index_ids_done.append(index_pattern_id)
kibana["index_patterns"].append({
"id":
index_pattern_id,
"value":
get_index_pattern_json(elastic, index_pattern_id)
})
elif panel['type'] in ['search']:
# A search could be directly visualized inside a panel
search_id = panel['id']
kibana["searches"].append({
"id":
search_id,
"value":
get_search_json(elastic, search_id)
})
index_pattern_id = get_index_pattern_from_search(
elastic, search_id)
if index_pattern_id and index_pattern_id not in index_ids_done:
index_ids_done.append(index_pattern_id)
kibana["index_patterns"].append({
"id":
index_pattern_id,
"value":
get_index_pattern_json(elastic, index_pattern_id)
})
return kibana
def __create_arthur_json(self, repo, backend_args):
""" Create the JSON for configuring arthur to collect data
https://github.com/grimoirelab/arthur#adding-tasks
Sample for git:
{
"tasks": [
{
"task_id": "arthur.git",
"backend": "git",
"backend_args": {
"gitpath": "/tmp/arthur_git/",
"uri": "https://github.com/grimoirelab/arthur.git"
},
"category": "commit",
"archive_args": {
"archive_path": '/tmp/test_archives',
"fetch_from_archive": false,
"archive_after": None
},
"scheduler_args": {
"delay": 10
}
}
]
}
"""
backend_args = self._compose_arthur_params(self.backend_section, repo)
if self.backend_section == 'git':
backend_args['gitpath'] = os.path.join(self.REPOSITORY_DIR, repo)
backend_args['tag'] = self.backend_tag(repo)
ajson = {"tasks": [{}]}
# This is the perceval tag
ajson["tasks"][0]['task_id'] = self.backend_tag(repo)
ajson["tasks"][0]['backend'] = self.backend_section
ajson["tasks"][0]['backend_args'] = backend_args
ajson["tasks"][0]['category'] = backend_args['category']
ajson["tasks"][0]['archive'] = {}
ajson["tasks"][0]['scheduler'] = {"delay": self.ARTHUR_TASK_DELAY}
# from-date or offset param must be added
es_col_url = self._get_collection_url()
es_index = self.conf[self.backend_section]['raw_index']
# Get the last activity for the data source
es = ElasticSearch(es_col_url, es_index)
connector = get_connector_from_name(self.backend_section)
klass = connector[0] # Backend for the connector
signature = inspect.signature(klass.fetch)
last_activity = None
filter_ = {"name": "tag", "value": backend_args['tag']}
if 'from_date' in signature.parameters:
last_activity = es.get_last_item_field('metadata__updated_on',
[filter_])
if last_activity:
ajson["tasks"][0]['backend_args'][
'from_date'] = last_activity.isoformat()
elif 'offset' in signature.parameters:
last_activity = es.get_last_item_field('offset', [filter_])
if last_activity:
ajson["tasks"][0]['backend_args']['offset'] = last_activity
if last_activity:
logging.info("Getting raw item with arthur since %s",
last_activity)
return (ajson)
