Index and query documents - Redis

This example shows how to create a [search index]({{< relref "/develop/ai/search-and-query/indexing" >}}) for [JSON]({{< relref "/develop/data-types/json" >}}) documents and run queries against the index. It then goes on to show the slight differences in the equivalent code for [hash]({{< relref "/develop/data-types/hashes" >}}) documents.

{{< note >}}From v9.8.0 onwards, go-redis uses query dialect 2 by default. Redis Search methods such as [FTSearch()]({{< relref "/commands/ft.search" >}}) will explicitly request this dialect, overriding the default set for the server. See [Query dialects]({{< relref "/develop/ai/search-and-query/advanced-concepts/dialects" >}}) for more information. {{< /note >}}

Initialize

Make sure that you have [Redis Open Source]({{< relref "/operate/oss_and_stack/" >}}) or another Redis server available. Also install the [go-redis]({{< relref "/develop/clients/go" >}}) client library if you haven't already done so.

Add the following dependencies:

{{< clients-example set="go_home_json" step="import" description="Foundational: Import go-redis package, which includes support for Redis Search operations" difficulty="beginner" >}} {{< /clients-example >}}

Create data

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

{{< clients-example set="go_home_json" step="create_data" description="Foundational: Create sample user data structures for indexing and querying" difficulty="beginner" >}} {{< /clients-example >}}

Add the index

Connect to your Redis database. The code below shows the most basic connection but see [Connect to the server]({{< relref "/develop/clients/go/connect" >}}) to learn more about the available connection options.

{{< clients-example set="go_home_json" step="connect" description="Foundational: Establish a connection to Redis with RESP2 protocol for Redis Search operations" difficulty="beginner" >}} {{< /clients-example >}}

{{< note >}}The connection options in the example specify [RESP2]({{< relref "/develop/reference/protocol-spec" >}}) in the Protocol field. We recommend that you use RESP2 for Redis Search 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]({{< relref "/develop/ai/search-and-query/indexing" >}}) 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]({{< relref "/develop/data-types/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:

{{< clients-example set="go_home_json" step="make_index" description="Foundational: Create a search index for JSON documents with field definitions and key prefix filtering" difficulty="beginner" >}} {{< /clients-example >}}

Add the data

Add the three sets of user data to the database as [JSON]({{< relref "/develop/data-types/json" >}}) objects. If you use keys with the user: prefix then Redis will index the objects automatically as you add them:

{{< clients-example set="go_home_json" step="add_data" description="Foundational: Store JSON documents with automatic indexing using JSONSet with matching key prefix" difficulty="beginner" >}} {{< /clients-example >}}

Query the data

You can now use the index to search the JSON objects. The [query]({{< relref "/develop/ai/search-and-query/query" >}}) below searches for objects that have the text "Paul" in any field and have an age value in the range 30 to 40:

{{< clients-example set="go_home_json" step="query1" description="Query data: Execute a full-text search with numeric range filtering using FTSearchWithArgs" difficulty="intermediate" >}} {{< /clients-example >}}

Specify query options to return only the city field:

{{< clients-example set="go_home_json" step="query2" description="Restrict query results: Limit query results to specific fields using FTSearchOptions to reduce data transfer" difficulty="intermediate" >}} {{< /clients-example >}}

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

{{< clients-example set="go_home_json" step="query2count_only" description="Performance optimization: Get document count without retrieving results using CountOnly option for better performance" difficulty="intermediate" >}} {{< /clients-example >}}

Use an [aggregation query]({{< relref "/develop/ai/search-and-query/query/aggregation" >}}) to count all users in each city.

{{< clients-example set="go_home_json" step="query3" description="Aggregation: Perform aggregation queries to group and count documents by field values using FTAggregateWithArgs" difficulty="intermediate" >}} {{< /clients-example >}}

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.

{{< clients-example set="go_home_json" step="make_hash_index" description="Foundational: Create a search index for hash documents with OnHash option and simplified field schema" difficulty="intermediate" >}} {{< /clients-example >}}

You use [HSet()]({{< relref "/commands/hset" >}}) to add the hash documents instead of [JSONSet()]({{< relref "/commands/json.set" >}}), but the same flat userX maps work equally well with either hash or JSON:

{{< clients-example set="go_home_json" step="add_hash_data" description="Foundational: Store hash documents with automatic indexing using HSet with matching key prefix" difficulty="beginner" >}} {{< /clients-example >}}

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 "$"):

{{< clients-example set="go_home_json" step="query1_hash" description="Query data: Execute search queries on hash documents with slightly different result structure compared to JSON" difficulty="intermediate" >}} {{< /clients-example >}}

More information

See the [Redis Search]({{< relref "/develop/ai/search-and-query" >}}) docs for a full description of all query features with examples.