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)
  • Join with remote REST APIs
  • Highly optimized and fast Postgres SQL queries
  • Configure with a simple config file
  • High performance GO codebase
  • Tiny docker image and low memory requirements

Try it out

# download super graph source
git clone https://github.com/dosco/super-graph.git

# setup the demo rails app & database and run it
./demo start

# signin to the demo app (user1@demo.com / 123456)
open http://localhost:3000

# try the super graph web ui
open http://localhost:8080

DEMO REQUIREMENTS

This demo requires docker you can either install it using brew or from the docker website https://docs.docker.com/docker-for-mac/install/

Trying out GraphQL

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
          },
          ...
        ]
      }
    ]
  }
}

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
  }
}

Using variables

Variables ($product_id) and their values ("product_id": 5) can be passed along side the GraphQL query. Using variables makes for better client side code as well as improved server side SQL query caching. The build-in web-ui also supports setting variables. Not having to manipulate your GraphQL query string to insert values into it makes for cleaner and better client side code.

// Define the request object keeping the query and the variables seperate
var req = { 
  query: '{ product(id: $product_id) { name } }' ,
  variables: { "product_id": 5 }
}

// Use the fetch api to make the query
fetch('http://localhost:8080/api/v1/graphql', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify(req),
})
.then(res => res.json())
.then(res => console.log(res.data));

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

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.

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 API 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.

tables:
  - name: customers
    remotes:
      - name: payments
        id: stripe_id
        url: http://rails_app:3000/stripe/$id
        path: data
        # pass_headers: 
        #   - cookie
        #   - host
        set_headers:
          - name: Authorization
            value: Bearer <stripe_api_key>

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.

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.

"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 for development.

Query Tracing

Authentication

You can only have one type of auth enabled. You can either pick Rails or JWT.

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.

auth:
  type: rails
  cookie: _app_session

  rails:
    # Rails version this is used for reading the
    # various cookies formats.
    version: 5.2

    # Found in 'Rails.application.config.secret_key_base'
    secret_key_base: 0a248500a64c01184edb4d7ad3a805488f8097ac761b76aaa6c17c01dcb7af03a2f18ba61b2868134b9c7b79a122bc0dadff4367414a2d173297bfea92be5566

Memcache session store

auth:
  type: rails
  cookie: _app_session

  rails:
    # Memcache remote cookie store.
    url: memcache://127.0.0.1

Redis session store

auth:
  type: rails
  cookie: _app_session

  rails:
    # Redis remote cookie store
    url: redis://127.0.0.1:6379
    password: ""
    max_idle: 80
    max_active: 12000

JWT Token Auth

auth:
  type: jwt

  jwt:
    # the two providers are 'auth0' and 'none'
    provider: auth0 
    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.

We're tried to ensure that the config file is self documenting and easy to work with.

app_name: "Super Graph Development"
host_port: 0.0.0.0:8080
web_ui: true
debug_level: 1

# enabling tracing also disables query caching
enable_tracing: true

# Throw a 401 on auth failure for queries that need auth
# 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_RAILS_COOKIE_SECRET_KEY_BASE
# SG_AUTH_RAILS_REDIS_URL
# SG_AUTH_RAILS_REDIS_PASSWORD
# SG_AUTH_JWT_PUBLIC_KEY_FILE

# inflections:
#   person: people
#   sheep: sheep

auth:
  # Can be 'rails' or 'jwt'
  type: rails
  cookie: _app_session

  # Comment this out if you want to disable setting
  # the user_id via a header. Good for testing
  header: X-User-ID

  rails:
    # Rails version this is used for reading the
    # various cookies formats.
    version: 5.2

    # Found in 'Rails.application.config.secret_key_base'
    secret_key_base: 0a248500a64c01184edb4d7ad3a805488f8097ac761b76aaa6c17c01dcb7af03a2f18ba61b2868134b9c7b79a122bc0dadff4367414a2d173297bfea92be5566

    # Remote cookie store. (memcache or redis)
    # url: redis://127.0.0.1:6379
    # password: test
    # max_idle: 80,
    # max_active: 12000,

    # In most cases you don't need these
    # salt: "encrypted cookie"
    # sign_salt: "signed encrypted cookie"
    # auth_salt: "authenticated encrypted cookie"

  # jwt:
  #   provider: auth0
  #   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"

  # Define defaults to for the field key and values below
  defaults:
    filter: ["{ user_id: { eq: $user_id } }"]

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

  tables:
    - name: users
      # This filter will overwrite defaults.filter
      filter: ["{ id: { eq: $user_id } }"]

    - name: products
      # Multiple filters are AND'd together
      filter: [
        "{ price: { gt: 0 } }",
        "{ price: { lt: 8 } }"
      ]

    - name: customers
      # No filter is used for this field not
      # even defaults.filter
      filter: none

      remotes:
        - name: payments
          id: stripe_id
          url: http://rails_app:3000/stripe/$id
          path: data
          # pass_headers: 
          #   - cookie
          #   - host
          set_headers:
            - name: Authorization
              value: Bearer <stripe_api_key>

    - # You can create new fields that have a
      # real db table backing them
      name: me
      table: users
      filter: ["{ id: { eq: $user_id } }"]

    # - name: posts
    #   filter: ["{ account_id: { _eq: $account_id } }"]

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

Keep in mind any value can be overwritten using environment variables for example auth.jwt.public_key_type converts to SG_AUTH_JWT_PUBLIC_KEY_TYPE. In short prefix SG_, upper case and all . should changed to _.

Postgres environment variables

SG_DATABASE_HOST
SG_DATABASE_PORT
SG_DATABASE_USER
SG_DATABASE_PASSWORD

Auth environment variables

SG_AUTH_RAILS_COOKIE_SECRET_KEY_BASE
SG_AUTH_RAILS_REDIS_URL
SG_AUTH_RAILS_REDIS_PASSWORD
SG_AUTH_JWT_PUBLIC_KEY_FILE

Developing Super Graph

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. And the demo rails app is also launched to make it essier to test 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 ./...

# do this the only the time to setup the database
docker-compose run rails_app rake db:create db:migrate

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

MIT License

MIT Licensed | Copyright © 2018-present Vikram Rangnekar