Elasticsearch长查询无法搜索

Question

Example of my query:

"query" : {
    "bool" : {
        "must" : [
            { "terms" : { "group_id" : ["1","2","3","4","5","6","7","8"]} }
        ]
    }
}

I json encode it and send with curl

http://localhost:9200/document/_search?source=JSON_ENCODED_QUERY

The above works just fine.

But I get the problem if I add few hundred of group IDs to the query.

Server response:

curl: (52) Empty reply from server

How can I solve my problem?

Answer 1

One thing to try is terms lookup . It lets you specify another index as the source of the terms for which you are searching. Here is a simple example.

First, I create an index with two types. "doc" will be the documents we are searching for, and "query_terms" will be used to store the terms we want to query against. Here is the index definition:

PUT /test_index
{
   "mappings": {
      "doc": {
         "properties": {
            "group_id": { "type": "integer" },
            "text": { "type": "string" }
         }
      },
      "query_terms": {
         "properties": {
            "values": { "type": "string" }
         }
      }
   }
}

Then I add some docs:

POST /test_index/doc/_bulk
{"index":{}}
{"group_id":1,"text":"Lorem ipsum"}
{"index":{}}
{"group_id":2,"text":"dolor sit amet"}
{"index":{}}
{"group_id":3,"text":"consectetur adipiscing elit"}
{"index":{}}
{"group_id":4,"text":"Pellentesque eu nisi"}
{"index":{}}
{"group_id":5,"text":"sit amet."}
{"index":{}}
{"group_id":6,"text":"velit pellentesque"}
{"index":{}}
{"group_id":7,"text":"ornare eleifend a leo"}
{"index":{}}
{"group_id":8,"text":"Integer in aliquam turpis"}
{"index":{}}
{"group_id":9,"text":"Pellentesque sed"}
{"index":{}}
{"group_id":10,"text":"quam sit amet"}

Now I'll add a "query_terms" document with a PUT :

PUT /test_index/query_terms/1
{
    "values": [2,4,6,8]
}

And I can use it to query the documents:

POST /test_index/doc/_search
{
   "query": {
      "filtered": {
         "query": {
            "match_all": {}
         },
         "filter": {
            "terms": {
               "group_id": {
                  "index": "test_index",
                  "type": "query_terms",
                  "id": "1",
                  "path": "values"
               }
            }
         }
      }
   }
}
...
{
   "took": 2,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 4,
      "max_score": 1,
      "hits": [
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "d07TqRjjRvWW9MJOH1l13Q",
            "_score": 1,
            "_source": {
               "group_id": 2,
               "text": "dolor sit amet"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "Qqy1idktQBqoKTR279DMXQ",
            "_score": 1,
            "_source": {
               "group_id": 4,
               "text": "Pellentesque eu nisi"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "RoFzGAOhQxmetvbh8-weew",
            "_score": 1,
            "_source": {
               "group_id": 6,
               "text": "velit pellentesque"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "IRH0qS9QQmWJEmgGMIr2SQ",
            "_score": 1,
            "_source": {
               "group_id": 8,
               "text": "Integer in aliquam turpis"
            }
         }
      ]
   }
}

For this example, a simpler approach would have worked just fine, but this approach should scale up to very long lists of terms.

Here is the code I used:

http://sense.qbox.io/gist/cbaab881c5fa5fd7fd6dfc819e2bea82d09495e6