The q parameter allows us to specify the query that we want our documents to match. It allows us to specify the query using the Lucene query syntax described in the The Lucene query syntax section in this chapter. For example, a simple query could look like q=title:elasticsearch.

By using the df parameter, we can specify the default search field that should be used when no field indicator is used in the q parameter. By default, the _all field will be used (the field that Elasticsearch uses to copy the content of all the other fields. We will discuss this in greater depth in the Extending your index structure with additional internal information section in Chapter 2, Indexing Your Data). An example of the df parameter value can be df=title.

The analyzer property allows us to define the name of the analyzer that should be used to analyze our query. By default, our query will be analyzed by the same analyzer that was used to analyze the field contents during indexing.

The default_operator property which can be set to OR or AND allows us to specify the default Boolean operator used for our query. By default, it is set to OR, which means that a single query term match will be enough for a document to be returned. Setting this parameter to AND for a query will result in the returning of documents that match all the query terms.

If we set the explain parameter to true, Elasticsearch will include additional explain information with each document in the result—such as the shard, from which the document was fetched, and detailed information about the scoring calculation (we will talk more about it in the Understanding the explain information section in Chapter 5, Make Your Search Better). Also remember not to fetch the explain information during normal search queries because it requires additional resources and adds performance degradation to the queries. For example, a single result can look like the following code:

{
 "_shard" : 3,
 "_node" : "kyuzK62NQcGJyhc2gI1P2w",
 "_index" : "books",
 "_type" : "es",
 "_id" : "2",
 "_score" : 0.19178301, "_source" : {"title":"Mastering Elasticsearch", "published": 2013},
 "_explanation" : {
 "value" : 0.19178301,
 "description" : "weight(title:elasticsearch in 0) [PerFieldSimilarity], result of:",
 "details" : [ {
 "value" : 0.19178301,
 "description" : "fieldWeight in 0, product of:",
 "details" : [ {
 "value" : 1.0,
 "description" : "tf(freq=1.0), with freq of:",
 "details" : [ {
 "value" : 1.0,
 "description" : "termFreq=1.0"
 } ]
 }, {
 "value" : 0.30685282,
 "description" : "idf(docFreq=1, maxDocs=1)"
 }, {
 "value" : 0.625,
 "description" : "fieldNorm(doc=0)"
 } ]
 } ]
 }
}

By default, for each document returned, Elasticsearch will include the index name, type name, document identifier, score, and the _source field. We can modify this behavior by adding the fields parameter and specifying a comma-separated list of field names. The field will be retrieved from the stored fields (if they exist) or from the internal _source field. By default, the value of the fields parameter is _source. An example can be like this fields=title.

By using the sort parameter, we can specify custom sorting. The default behavior of Elasticsearch is to sort the returned documents by their score in the descending order. If we would like to sort our documents differently, we need to specify the sort parameter. For example, adding sort=published:desc will sort the documents by the published field in the descending order. By adding the sort=published:asc parameter, we will tell Elasticsearch to sort the documents on the basis of the published field in the ascending order.

If we specify custom sorting, Elasticsearch will omit the _score field calculation for documents. This may not be the desired behavior in your case. If you want to still keep a track of the scores for each document when using custom sort, you should add the track_scores=true property to your query. Please note that tracking the scores when doing custom sorting will make the query a little bit slower (you may even not notice it) due to the processing power needed to calculate the score.

By default, Elasticsearch doesn't have timeout for queries, but you may want your queries to timeout after a certain amount of time (for example, 5 seconds). Elasticsearch allows you to do this by exposing the timeout parameter. When the timeout parameter is specified, the query will be executed up to a given timeout value, and the results that were gathered up to that point will be returned. To specify a timeout of 5 seconds, you will have to add the timeout=5s parameter to your query.

Elasticsearch allows you to specify the results window (the range of documents in the results list that should be returned). We have two parameters that allow us to specify the results window size: size and from. The size parameter defaults to 10 and defines the maximum number of results returned. The from parameter defaults to 0 and specifies from which document the results should be returned. In order to return five documents starting from the eleventh one, we will add the following parameters to the query: size=5&from=10.

The URI query allows us to specify the search type by using the search_type parameter, which defaults to query_then_fetch. There are six values that we can use: dfs_query_then_fetch, dfs_query_and_fetch, query_then_fetch, query_and_fetch, count, and scan. We'll learn more about search types in the Understanding the querying process section in Chapter 3, Searching Your Data.

Some of the queries use query expansion, such as the prefix query. We will discuss this in the Query rewrite section of Chapter 3, Searching Your Data. We are allowed to define whether the expanded terms should be lowercased or not by using the lowercase_expanded_terms property. By default, the lowercase_expanded_terms property is set to true, which means that the expanded terms will be lowercased.

By default, the wildcard queries and the prefix queries are not analyzed. If we want to change this behavior, we can set the analyze_wildcard property to true.