Elasticsearch
Elasticsearch knowledge and experiences
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Elasticsearch knowledge and experiences
Last updated
Was this helpful?
Was this helpful?
Inserting to an index
from elasticsearch import Elasticsearch
from elasticsearch.herlpers import bulk
es = Elasticsearch([ENDPOINT])
# ====== Inserting Documents ====== #
# Creating a simple Pandas DataFrame
liste_hello = ['hello1','hello2']
liste_world = ['world1','world2']
df = pd.DataFrame(data = {'hello' : liste_hello, 'world': liste_world})
# Bulk inserting documents. Each row in the DataFrame will be a document in ElasticSearch
documents = df.to_dict(orient='records')
bulk(es, documents, index='helloworld',doc_type='foo', raise_on_error=True)
Searching on an index
# ====== Searching Documents ====== #
# Retrieving all documents in index (no query given)
documents = es.search(index='helloworld',body={})['hits']['hits']
df = pd.DataFrame(documents)
# Retrieving documents in index that match a query
documents2 = es.search(index='helloworld',body={"query":{"term":{"hello" : "hello1" }}})['hits']['hits']
df2 = pd.DataFrame(documents2)
Try this for analyzers
res= es.search(index="test-index", doc_type='content-field',body={"query": {"match": {"text": {"query": "微观文明", "analyzer": "ik_smart"}}}})
Dumping data for a query
elasticdump \
--input=server_url/db_name \
--output=tags_0.json \
--searchBody='{"query":{"term":{"tag": 0}}}'
Dumping data for Mappings
elasticdump \
--input=server_url/apirequests \
--output=/data/apirequests_mapping.json \
--type=mapping
Dumping data for Data
elasticdump \
--input=server_url/apirequests \
--output=apirequests_data.json \
--type=data
Scrolling over cursor
Example with urls as data
filter = {
"query": {
"match": {
"tag": 0
}
}
}
results = es.search(index='index_name', doc_type='doc_type', body=filter,scroll='2m',size=1000)
# Get the scroll ID
sid = results['_scroll_id']
scroll_size = results['hits']['total']
# Before scroll, process current batch of hits
results_dt = pd.concat(map(pd.DataFrame.from_dict, results['hits']['hits']), axis=1)['_source'].T
results_dt = results_dt.reset_index(drop=True)
while scroll_size > 0:
results = es.scroll(scroll_id=sid, scroll='2m')
print(results_dt.shape)
results_dt_temp = pd.concat(map(pd.DataFrame.from_dict, results['hits']['hits']), axis=1)['_source'].T
results_dt_temp = results_dt_temp.reset_index(drop=True)
results_dt = pd.concat([results_dt, results_dt_temp],axis=0)
# Update the scroll ID
sid = results['_scroll_id']
# Get the number of results that returned in the last scroll
scroll_size = len(results['hits']['hits'])