--- id: advanced title: Full Text Search & More sidebar_label: More Features --- ## Database Relationships In most cases you don't need this configuration, Super Graph will discover and learn the relationship graph within your database automatically. It does this using `Foreign Key` relationships that you have defined in your database schema. The below configs are only needed in special cases such as when you don't use foreign keys or when you want to create a relationship between two tables where a foreign key is not defined or cannot be defined. For example in the sample below a relationship is defined between the `tags` column on the `posts` table with the `slug` column on the `tags` table. This cannot be defined as using foreign keys since the `tags` column is of type array `text[]` and Postgres for one does not allow foreign keys with array columns. ```yaml tables: - name: posts columns: - name: tags related_to: tags.slug ``` ## Advanced Columns The ablity to have `JSON/JSONB` and `Array` columns is often considered in the top most useful features of Postgres. There are many cases where using an array or a json column saves space and reduces complexity in your app. The only issue with these columns is the really that your SQL queries can get harder to write and maintain. Super Graph steps in here to help you by supporting these columns right out of the box. It allows you to work with these columns just like you would with tables. Joining data against or modifying array columns using the `connect` or `disconnect` keywords in mutations is fully supported. Another very useful feature is the ability to treat `json` or `binary json (jsonb)` columns as seperate tables, even using them in nested queries joining against related tables. To replicate these features on your own will take a lot of complex SQL. Using Super Graph means you don't have to deal with any of this it just works. ### Array Columns Configure a relationship between an array column `tag_ids` which contains integer id's for tags and the column `id` in the table `tags`. ```yaml tables: - name: posts columns: - name: tag_ids related_to: tags.id ``` ```graphql query { posts { title tags { name image } } } ``` ### JSON Column Configure a JSON column called `tag_count` in the table `products` into a seperate table. This JSON column contains a json array of objects each with a tag id and a count of the number of times the tag was used. As a seperate table you can nest it into your GraphQL query and treat it like table using any of the standard features like `order_by`, `limit`, `where clauses`, etc. The configuration below tells Super Graph to create a synthetic table called `tag_count` using the column `tag_count` from the `products` table. And that this new table has two columns `tag_id` and `count` of the listed types and with the defined relationships. ```yaml tables: - name: tag_count table: products columns: - name: tag_id type: bigint related_to: tags.id - name: count type: int ``` ```graphql query { products { name tag_counts { count tag { name } } } } ``` ## Remote Joins It often happens that after fetching some data from the DB we need to call another API to fetch some more data and all this combined into a single JSON response. For example along with a list of users you need their last 5 payments from Stripe. This requires you to query your DB for the users and Stripe for the payments. Super Graph handles all this for you also only the fields you requested from the Stripe API are returned. :::info Is this fast? Super Graph is able fetch remote data and merge it with the DB response in an efficient manner. Several optimizations such as parallel HTTP requests and a zero-allocation JSON merge algorithm makes this very fast. All of this without you having to write a line of code. ::: For example you need to list the last 3 payments made by a user. You will first need to look up the user in the database and then call the Stripe API to fetch his last 3 payments. For this to work your user table in the db has a `customer_id` column that contains his Stripe customer ID. Similiarly you could also fetch the users last tweet, lead info from Salesforce or whatever else you need. It's fine to mix up several different `remote joins` into a single GraphQL query. ### Stripe Example The configuration is self explanatory. A `payments` field has been added under the `customers` table. This field is added to the `remotes` subsection that defines fields associated with `customers` that are remote and not real database columns. The `id` parameter maps a column from the `customers` table to the `$id` variable. In this case it maps `$id` to the `customer_id` column. ```yaml tables: - name: customers remotes: - name: payments id: stripe_id url: http://rails_app:3000/stripe/$id path: data # debug: true # pass_headers: # - cookie # - host set_headers: - name: Authorization value: Bearer ``` #### How do I make use of this? Just include `payments` like you would any other GraphQL selector under the `customers` selector. Super Graph will call the configured API for you and stitch (merge) the JSON the API sends back with the JSON generated from the database query. GraphQL features like aliases and fields all work. ```graphql query { customers { id email payments { customer_id amount billing_details } } } ``` And voila here is the result. You get all of this advanced and honestly complex querying capability without writing a single line of code. ```json "data": { "customers": [ { "id": 1, "email": "linseymertz@reilly.co", "payments": [ { "customer_id": "cus_YCj3ndB5Mz", "amount": 100, "billing_details": { "address": "1 Infinity Drive", "zipcode": "94024" } }, ... ``` Even tracing data is availble in the Super Graph web UI if tracing is enabled in the config. By default it is enabled in development. Additionally there you can set `debug: true` to enable http request / response dumping to help with debugging. ![Query Tracing](/tracing.png "Super Graph Web UI Query Tracing") ## Full text search Every app these days needs search. Enought his often means reaching for something heavy like Solr. While this will work why add complexity to your infrastructure when Postgres has really great and fast full text search built-in. And since it's part of Postgres it's also available in Super Graph. ```graphql query { products( # Search for all products that contain 'ale' or some version of it search: "ale" # Return only matches where the price is less than 10 where: { price: { lt: 10 } } # Use the search_rank to order from the best match to the worst order_by: { search_rank: desc } ) { id name search_rank search_headline_description } } ``` This query will use the `tsvector` column in your database table to search for products that contain the query phrase or some version of it. To get the internal relevance ranking for the search results using the `search_rank` field. And to get the highlighted context within any of the table columns you can use the `search_headline_` field prefix. For example `search_headline_name` will return the contents of the products name column which contains the matching query marked with the `` html tags. ```json { "data": { "products": [ { "id": 11, "name": "Maharaj", "search_rank": 0.243171, "search_headline_description": "Blue Moon, Vegetable Beer, Willamette, 1007 - German Ale, 48 IBU, 7.9%, 11.8°Blg" }, { "id": 12, "name": "Schneider Aventinus", "search_rank": 0.243171, "search_headline_description": "Dos Equis, Wood-aged Beer, Magnum, 1099 - Whitbread Ale, 15 IBU, 9.5%, 13.0°Blg" }, ... ```