super-graph/docs/guide.md

sidebar
auto

Guide to Super Graph

Without writing a line of code get an instant high-performance GraphQL API for your Ruby-on-Rails app. Super Graph will automatically understand your apps database and expose a secure, fast and complete GraphQL API for it. Built in support for Rails authentication and JWT tokens.

Features

• Works with Rails database schemas
• Automatically learns schemas and relationships
• Belongs-To, One-To-Many and Many-To-Many table relationships
• Full text search and Aggregations
• Rails Auth supported (Redis, Memcache, Cookie)
• JWT tokens supported (Auth0, etc)
• Highly optimized and fast Postgres SQL queries
• Configure with a simple config file
• High performance GO codebase
• Tiny docker image and low memory requirements

We currently support the query action which is used for fetching data. Support for mutation and subscriptions is work in progress. For example the below GraphQL query would fetch two products that belong to the current user where the price is greater than 10

GQL Query

query { 
  users {
    id
    email
    picture : avatar
    password
    full_name
    products(limit: 2, where: { price: { gt: 10 } }) {
      id
      name
      description
      price
    }
  }
}

The above GraphQL query returns the JSON result below. It handles all kinds of complexity without you having to writing a line of code.

For example there is a while greater than gt and a limit clause on a child field. And the avatar field is renamed to picture. The password field is blocked and not returned. Finally the relationship between the users table and the products table is auto discovered and used.

JSON Result

{
  "data": {
    "users": [
      {
        "id": 1,
        "email": "odilia@west.info",
        "picture": "https://robohash.org/simur.png?size=300x300",
        "full_name": "Edwin Orn",
        "products": [
          {
            "id": 16,
            "name": "Sierra Nevada Style Ale",
            "description": "Belgian Abbey, 92 IBU, 4.7%, 17.4°Blg",
            "price": 16.47
          },
          ...
        ]
      }
    ]
  }
}

The above command will download the latest docker image for Super Graph and use it to run an example that includes a Postgres DB and a simple Rails ecommerce store app.

If you want to build and run Super Graph from code then the below commands will build the web ui and launch Super Graph in developer mode with a watcher to rebuild on code changes.

# yarn is needed to build the web ui
brew install yarn

# yarn install dependencies and build the web ui
(cd web && yarn install && yarn build)

# generate some stuff the go code needs
go generate ./...

# start super graph in development mode with a change watcher
docker-compose up

Try with an authenticated user

In development mode you can use the X-User-ID: 4 header to set a user id so you don't have to worries about cookies etc. This can be set using the HTTP Headers tab at the bottom of the web UI you'll see when you visit the above link. You can also directly run queries from the commandline like below.

Querying the GQL endpoint

# fetch the response json directly from the endpoint using user id 5
curl 'http://localhost:8080/api/v1/graphql' \
  -H 'content-type: application/json' \
  -H 'X-User-ID: 5' \
  --data-binary '{"query":"{ products { name price users { email }}}"}'

How to GraphQL

GraphQL (GQL) is a simple query syntax that's fast replacing REST APIs. GQL is great since it allows web developers to fetch the exact data that they need without depending on changes to backend code. Also if you squint hard enough it looks a little bit like JSON 😃

The below query will fetch an users name, email and avatar image (renamed as picture). If you also need the users id then just add it to the query.

query {
  user {
    full_name
    email
    picture : avatar
  }
}

Fetching data

To fetch a specific product by it's ID you can use the id argument. The real name id field will be resolved automatically so this query will work even if your id column is named something like product_id.

query {
  products(id: 3) {
    name
  }
}

Postgres also supports full text search using a TSV index. Super Graph makes it easy to use this full text search capability using the search argument.

query {
  products(search "amazing") {
    name
  }
}

Complex queries (Where)

Super Graph support complex queries where you can add filters, ordering,offsets and limits on the query.

Logical Operators

Name	Example	Explained
and	price : { and : { gt: 10.5, lt: 20 }	price > 10.5 AND price < 20
or	or : { price : { greater_than : 20 }, quantity: { gt : 0 } }	price >= 20 OR quantity > 0
not	not: { or : { quantity : { eq: 0 }, price : { eq: 0 } } }	NOT (quantity = 0 OR price = 0)

Other conditions

Name	Example	Explained
eq, equals	id : { eq: 100 }	id = 100
neq, not_equals	id: { not_equals: 100 }	id != 100
gt, greater_than	id: { gt: 100 }	id > 100
lt, lesser_than	id: { gt: 100 }	id < 100
gte, greater_or_equals	id: { gte: 100 }	id >= 100
lte, lesser_or_equals	id: { lesser_or_equals: 100 }	id <= 100
in	status: { in: [ "A", "B", "C" ] }	status IN ('A', 'B', 'C)
nin, not_in	status: { in: [ "A", "B", "C" ] }	status IN ('A', 'B', 'C)
like	name: { like "phil%" }	Names starting with 'phil'
nlike, not_like	name: { nlike "v%m" }	Not names starting with 'v' and ending with 'm'
ilike	name: { ilike "%wOn" }	Names ending with 'won' case-insensitive
nilike, not_ilike	name: { nilike "%wOn" }	Not names ending with 'won' case-insensitive
similar	name: { similar: "%(b|d)%" }	Similar Docs
nsimilar, not_similar	name: { nsimilar: "%(b|d)%" }	Not Similar Docs
has_key	column: { has_key: 'b' }	Does JSON column contain this key
has_key_any	column: { has_key_any: [ a, b ] }	Does JSON column contain any of these keys
has_key_all	column: [ a, b ]	Does JSON column contain all of this keys
contains	column: { contains: [1, 2, 4] }	Is this array/json column a subset of value
contained_in	column: { contains: "{'a':1, 'b':2}" }	Is this array/json column a subset of these value
is_null	column: { is_null: true }	Is column value null or not

Aggregation (Max, Count, etc)

You will often find the need to fetch aggregated values from the database such as count, max, min, etc. This is simple to do with GraphQL, just prefix the aggregation name to the field name that you want to aggregrate like count_id. The below query will group products by name and find the minimum price for each group. Notice the min_price field we're adding min_ to price.

query {
  products {
    name
    min_price
  }
}

Name	Explained
avg	Average value
count	Count the values
max	Maximum value
min	Minimum value
stddev	Standard Deviation
stddev_pop	Population Standard Deviation
stddev_samp	Sample Standard Deviation
variance	Variance
var_pop	Population Standard Variance
var_samp	Sample Standard variance

All kinds of queries are possible with GraphQL. Below is an example that uses a lot of the features available. Comments # hello are also valid within queries.

query {
  products(
    # returns only 30 items
    limit: 30,

    # starts from item 10, commented out for now
    # offset: 10,

    # orders the response items by highest price
    order_by: { price: desc },

    # no duplicate prices returned
    distinct: [ price ]
    
    # only items with an id >= 30 and < 30 are returned
    where: { id: { and: { greater_or_equals: 20, lt: 28 } } }) {
    id
    name
    price
  }
}

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.

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 <b></b> html tags.

{
  "data": {
    "products": [
      {
        "id": 11,
        "name": "Maharaj",
        "search_rank": 0.243171,
        "search_headline_description": "Blue Moon, Vegetable Beer, Willamette, 1007 - German <b>Ale</b>, 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 <b>Ale</b>, 15 IBU, 9.5%, 13.0°Blg"
      },
  ...

Adding search to your Rails app

It's really easy to enable Postgres search on any table within your database schema. All it takes is to create the following migration. In the below example we add a full-text search to the products table.

class AddSearchColumn < ActiveRecord::Migration[5.1]
  def self.up
    add_column :products, :tsv, :tsvector
    add_index :products, :tsv, using: "gin"

    say_with_time("Adding trigger to update the ts_vector column") do
      execute <<-SQL
        CREATE FUNCTION products_tsv_trigger() RETURNS trigger AS $$
        begin
          new.tsv :=
          setweight(to_tsvector('pg_catalog.english', coalesce(new.name,'')), 'A') ||
          setweight(to_tsvector('pg_catalog.english', coalesce(new.description,'')), 'B');
          return new;
        end
        $$ LANGUAGE plpgsql;

        CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE ON products FOR EACH ROW EXECUTE PROCEDURE products_tsv_trigger();
        SQL
      end
  end

  def self.down
    say_with_time("Removing trigger to update the tsv column") do
      execute <<-SQL
        DROP TRIGGER tsvectorupdate
        ON products
        SQL
    end

    remove_index :products, :tsv
    remove_column :products, :tsv
  end
end

Authentication

You can only have one type of auth enabled. You can either pick Rails or JWT. Uncomment the one you use and leave the rest commented out.

Rails Auth (Devise / Warden)

Almost all Rails apps use Devise or Warden for authentication. Once the user is authenticated a session is created with the users ID. The session can either be stored in the users browser as a cookie, memcache or redis. If memcache or redis is used then a cookie is set in the users browser with just the session id.

Super Graph can handle all these variations including the old and new session formats. Just enable the right auth config based on how your rails app is configured.

Cookie session store

auth:
  type: rails
  cookie: _app_session
  store: cookie
  secret_key_base: caf335bfcfdb04e50db5bb0a4d67ab9...

Memcache session store

auth:
  type: rails
  cookie: _app_session
  store: memcache
  host: 127.0.0.1

Redis session store

auth:
  type: rails
  cookie: _app_session
  store: redis
  max_idle: 80,
  max_active: 12000,
  url: redis://127.0.0.1:6379
  password: ""

JWT Token Auth

auth:
  type: jwt
  provider: auth0 #none
  cookie: _app_session
  secret: abc335bfcfdb04e50db5bb0a4d67ab9
  public_key_file: /secrets/public_key.pem
  public_key_type: ecdsa #rsa

For JWT tokens we currently support tokens from a provider like Auth0 or if you have a custom solution then we look for the user_id in the subject claim of of the id token. If you pick Auth0 then we derive two variables from the token user_id and user_id_provider for to use in your filters.

We can get the JWT token either from the authorization header where we expect it to be a bearer token or if cookie is specified then we look there.

For validation a secret or a public key (ecdsa or rsa) is required. When using public keys they have to be in a PEM format file.

Easy to setup

Configuration files can either be in YAML or JSON their names are derived from the GO_ENV variable, for example GO_ENV=prod will cause the prod.yaml config file to be used. or GO_ENV=dev will use the dev.yaml. A path to look for the config files in can be specified using the -path <folder> command line argument.

host_port: 0.0.0.0:8080
web_ui: true
debug_level: 1

# When to throw a 401 on auth failure 
# valid values: always, per_query, never
auth_fail_block: never

# Postgres related environment Variables
# SG_DATABASE_HOST
# SG_DATABASE_PORT
# SG_DATABASE_USER
# SG_DATABASE_PASSWORD

# Auth related environment Variables
# SG_AUTH_SECRET_KEY_BASE
# SG_AUTH_PUBLIC_KEY_FILE
# SG_AUTH_URL
# SG_AUTH_PASSWORD

# inflections:
#   person: people
#   sheep: sheep

auth:
  type: header
  field_name: X-User-ID

# auth:
#   type: rails
#   cookie: _app_session
#   store: cookie
#   secret_key_base: caf335bfcfdb04e50db5bb0a4d67ab9...

# auth:
#   type: rails
#   cookie: _app_session
#   store: memcache
#   host: 127.0.0.1

# auth:
#   type: rails
#   cookie: _app_session
#   store: redis
#   max_idle: 80,
#   max_active: 12000,
#   url: redis://127.0.0.1:6379
#   password: ""

# auth:
#   type: jwt
#   cookie: _app_session
#   secret: abc335bfcfdb04e50db5bb0a4d67ab9
#   public_key_file: /secrets/public_key.pem
#   public_key_type: ecdsa #rsa

database:
  type: postgres
  host: db
  port: 5432
  dbname: app_development
  user: postgres
  password: ''
  #pool_size: 10
  #max_retries: 0
  #log_level: "debug" 

  # Define variables here that you want to use in filters 
  variables:
    account_id: "select account_id from users where id = $user_id"

  # Used to add access to tables 
  filters:
    users: "{ id: { _eq: $user_id } }"
    posts: "{ account_id: { _eq: $account_id } }"

  # Fields and table names that you wish to block
  blacklist:
    - secret
    - password
    - encrypted
    - token

If deploying into environments like Kubernetes it's useful to be able to configure things like secrets and hosts though environment variables therfore we expose the below environment variables. This is escpecially useful for secrets since they are usually injected in via a secrets management framework ie. Kubernetes Secrets

Postgres environment variables

SG_DATABASE_HOST
SG_DATABASE_PORT
SG_DATABASE_USER
SG_DATABASE_PASSWORD

Auth environment variables

SG_AUTH_SECRET_KEY_BASE
SG_AUTH_PUBLIC_KEY_FILE
SG_AUTH_URL
SG_AUTH_PASSWORD

Deploying Super Graph

How do I deploy the Super Graph service with my existing rails app? You have several options here. Esentially you need to ensure your app's session cookie will be passed to this service.

Custom Docker Image

Create a Dockerfile like the one below to roll your own custom Super Graph docker image. And to build it docker build -t my-super-graph .

FROM dosco/super-graph:latest
WORKDIR /app
COPY *.yml ./

Deploy under a subdomain

For this to work you have to ensure that the option :domain => :all is added to your rails app config Application.config.session_store this will cause your rails app to create session cookies that can be shared with sub-domains. More info here http://excid3.com/blog/sharing-a-devise-user-session-across-subdomains-with-rails-3/

With an NGINX loadbalancer

I'm sure you know how to configure it so that the Super Graph endpoint path /api/v1/graphql is routed to wherever you have this service installed within your architecture.

On Kubernetes

If your Rails app runs on Kubernetes then ensure you have an ingress config deployed that points the path to the service that you have deployed Super Graph under.

JWT tokens (Auth0, etc)

In that case deploy under a subdomain and configure this service to use JWT authentication. You will need the public key file or secret key. Ensure your web app passes the JWT token with every GQL request in the Authorize header as a bearer token.

MIT License

MIT Licensed | Copyright © 2018-present Vikram Rangnekar