All the queries that we have discussed can be performed with the Elasticsearch Python client using the search function. To do this, first store the query inside a variable that is query in the following example:
query = {
"query": {
"match_all": {}
},
}Call the search function with all the parameters including the index name, document type, and query. The size parameter used in the following search request can also be included inside the query itself:
response = es.search(index='twitter', doc_type='tweets', body=query, size=2, request_timeout=20)
The response data comes in the following format:
{
"hits": {
"hits": [
{
"_score": 1,
"_type": "tweets",
"_id": "649956033515773953",
"_source": {
"contributors": null,
"truncated": false,
"text": "RT @lexcanroar: "No mass shootings in the past 30 years have been stopped by an armed civilian.""
.
.
},
{
...
}
"_index": "twitter"
}
],
"total": 124,
"max_score": 1
},
"_shards": {
"successful": 5,
"failed": 0,
"total": 5
},
"took": 5,
"timed_out": false
}The response contains an object hit that has an array of hits containing all the documents. Further, each hit inside an array of hits contains the following fields in it:
_score: The document score with respect to the query_index: The index name to which the document belongs_type: The document type to which the document belongs_id: The unique ID of the document_source: This contains all the fields and values
The documents inside _source can be accessed with the following code:
for hit in response['hits']['hits']:
print hit.get('_source')..................Content has been hidden....................
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