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Example of Elasticsearch scrolling using Python client
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# coding:utf-8 | |
from elasticsearch import Elasticsearch | |
import json | |
# Define config | |
host = "127.0.0.1" | |
port = 9200 | |
timeout = 1000 | |
index = "index" | |
doc_type = "type" | |
size = 1000 | |
body = {} | |
# Init Elasticsearch instance | |
es = Elasticsearch( | |
[ | |
{ | |
'host': host, | |
'port': port | |
} | |
], | |
timeout=timeout | |
) | |
# Process hits here | |
def process_hits(hits): | |
for item in hits: | |
print(json.dumps(item, indent=2)) | |
# Check index exists | |
if not es.indices.exists(index=index): | |
print("Index " + index + " not exists") | |
exit() | |
# Init scroll by search | |
data = es.search( | |
index=index, | |
doc_type=doc_type, | |
scroll='2m', | |
size=size, | |
body=body | |
) | |
# Get the scroll ID | |
sid = data['_scroll_id'] | |
scroll_size = len(data['hits']['hits']) | |
# Before scroll, process current batch of hits | |
process_hits(data['hits']['hits']) | |
while scroll_size > 0: | |
"Scrolling..." | |
data = es.scroll(scroll_id=sid, scroll='2m') | |
# Process current batch of hits | |
process_hits(data['hits']['hits']) | |
# Update the scroll ID | |
sid = data['_scroll_id'] | |
# Get the number of results that returned in the last scroll | |
scroll_size = len(data['hits']['hits']) |
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