Index and query documents

Learn how to use the Redis query engine with JSON and hash documents.

This example shows how to create a search index for JSON documents and run queries against the index. It then goes on to show the slight differences in the equivalent code for hash documents.

Initialize

Make sure that you have Redis Open Source or another Redis server available. Also install the go-redis client library if you haven't already done so.

Add the following dependencies:

Create data

Create some test data to add to your database. The example data shown below is compatible with both JSON and hash objects.

Add the index

Connect to your Redis database. The code below shows the most basic connection but see Connect to the server to learn more about the available connection options.

Note:

The connection options in the example specify RESP2 in the Protocol field. We recommend that you use RESP2 for Redis query engine operations in go-redis because some of the response structures for the default RESP3 are currently incomplete and so you must handle the "raw" responses in your own code.

If you do want to use RESP3, you should set the UnstableResp3 option when you connect:

rdb := redis.NewClient(&redis.Options{
    UnstableResp3: true,
    // Other options...
})

You must also access command results using the RawResult() and RawVal() methods rather than the usual Result() and Val():

res1, err := client.FTSearchWithArgs(
    ctx, "txt", "foo bar", &redis.FTSearchOptions{},
).RawResult()
val1 := client.FTSearchWithArgs(
    ctx, "txt", "foo bar", &redis.FTSearchOptions{},
).RawVal()

Use the code below to create a search index. The FTCreateOptions parameter enables indexing only for JSON objects where the key has a user: prefix. The schema for the index has three fields for the user's name, age, and city. The FieldName field of the FieldSchema struct specifies a JSON path that identifies which data field to index. Use the As struct field to provide an alias for the JSON path expression. You can use the alias in queries as a short and intuitive way to refer to the expression, instead of typing it in full:

Add the data

Add the three sets of user data to the database as JSON objects. If you use keys with the user: prefix then Redis will index the objects automatically as you add them:

Query the data

You can now use the index to search the JSON objects. The query below searches for objects that have the text "Paul" in any field and have an age value in the range 30 to 40:

Specify query options to return only the city field:

You can also use the same query with the CountOnly option enabled to get the number of documents found without returning the documents themselves.

Use an aggregation query to count all users in each city.

Differences with hash documents

Indexing for hash documents is very similar to JSON indexing but you need to specify some slightly different options.

When you create the schema for a hash index, you don't need to add aliases for the fields, since you use the basic names to access the fields anyway. Also, you must set OnHash to true in the FTCreateOptions object when you create the index. The code below shows these changes with a new index called hash-idx:users, which is otherwise the same as the idx:users index used for JSON documents in the previous examples.

You use HSet() to add the hash documents instead of JSONSet(), but the same flat userX maps work equally well with either hash or JSON:

The query commands work the same here for hash as they do for JSON (but the name of the hash index is different). The format of the result is almost the same except that the fields are returned directly in the Document object map of the result (for JSON, the fields are all enclosed in a string under the key "$"):

More information

See the Redis query engine docs for a full description of all query features with examples.

RATE THIS PAGE
Back to top ↑