@ginger.io/beyonce

Beyonce

A type-safe DynamoDB query builder for TypeScript.

Beyonce's features include:

• Low boilerplate. Define your tables, partitions, indexes and models in YAML and Beyonce codegens TypeScript definitions for you.

• Store heterogeneous models in the same table. Unlike most DynamoDB libraries, Beyonce doesn't force you into a 1 model per table paradigm. It supports storing related models in the same table partition, which allows you to "precompute joins" and retrieve those models with a single roundtrip query to the db.

• Type-safe API. Beyonce's API is type-safe. It's aware of which models live under your partition and sort keys (even for global secondary indexes). When you get, batchGet or query, the result types are automatically inferred. And when you apply filters on your query the attribute names are automatically type-checked.

• Application-level encryption. Beyonce loves Jay-Z and supports him out of the box. Combine them into the power couple they deserve to be, and every non-key, non-index attribute on your models will be automatically encrypted before you send it to Dynamo. This grants an additional layer of security beyond just enabling AWS's DynamoDB server-side-enryption option (which you should do too).

Usage

1. Install

First install beyonce - npm install @ginger.io/beyonce

2. Define your models

Define your tables, partitions and models in YAML:

Tables:
  Library:
    Partitions: # A "partition" is a set of models with the same partition key
      Authors: # e.g. this one contains Authors + Books

        Author:
          partitionKey: [Author, $id]
          sortKey: [Author, $id]
          id: string
          name: string

        Book:
          partitionKey: [Author, $authorId]
          sortKey: [Book, $id]
          id: string
          authorId: string
          name: string

A note on partitionKey and sortKey syntax

Beyonce expects you to specify your partition and sort keys as tuple-2s, e.g. [Author, $id]. The first element is a "key prefix" and the 2nd must be a field on your model. For example we set the primary key of the Author model above to: [Author, $id], would result in the key: Author-$id, where $id is the value of a specific Author's id.

Using the example above, if we wanted to place Books under the same partition key, then we'd need to set the Book model's partitionKey to [Author, $authorId].

Global secondary indexes

If your table(s) have GSI's you can specify them like this:

Tables:
  Library:
    Partitions:
      ...

    GSIs:
      byName: # must match your GSI's name
        partitionKey: $name # name field must exist on at least one model
        sortKey: $id # same here

Note: Beyonce currently assumes that your GSI indexes project all model attributes, which will be reflected in the return types of your queries.

External types

You can specify external types you need to import like so:

Author:
    ...
    address: Address from author/Address

Which transforms into import { Address } from "author/address"

3. Codegen TypeScript classes for your models, partition keys and sort keys

npx beyonce --in src/models.yaml --out src/generated/models.ts

4. Write type-safe queries

Now you can write partition-aware, type safe queries with abandon:

Get yourself a Beyonce

import { Beyonce } from "@ginger.io/beyonce"
import { DynamoDB } from "aws-sdk"
import { LibraryTable } from "generated/models"

const dynamo = new DynamoDB({ endpoint: "...", region: "..."})
const beyonce = new Beyonce(LibraryTable, dynamo)

Then import the generated models

import {
  AuthorModel,
  BookModel,
} from "generated/models"

Queries

Put

const author = AuthorModel.create({
  id: "1",
  name: "Jane Austen"
})

await beyonce.put(author)

Get

const author = await beyonce.get(AuthorModel.key({ id: "1" }))

Note: the key prefix ("Author" from our earlier example) will be automatically appeneded.

Query

Beyoncé supports type-safe query operations that either return a single model type or all model types that live under a given partition key.

Querying for a specific model type

You can query for a single type of model like so:

import { BookModel } from "generated/models"

// Get all Books for an Author
const results = await beyonce
  .query(BookModel.partitionKey({ authorId: "1" }))
  .exec() // returns { Book: Book[] }

To reduce the amount of data retrieved by DynamoDB, Beyoncé automatically applies a KeyConditionExpression that uses the sortKey prefix provided in your model definitions. For example, if the YAML definition for the Book model contains sortKey:[Book, $id] -- then the generated KeyConditionExpression will contain a clause like #partitionKey = :partitionKey AND begins_with(#sortKey, Book).

Query for all models in a partition

You can also query for all models that live in a partition, like so:

import { AuthorPartition } from "generated/models"

// Get an Author + their books
const results = await beyonce
  .query(AuthorPartition.key({ id: "1" }))
  .exec() // returns { Author: Author[], Book: Book[] }

Note that, in this case the generated KeyconditionExpression will not include a clause for the sort key since DynamoDB does not support OR-ing key conditions.

Filtering Queries

You can filter results from a query like so:

// Get an Author + filter on their books
const authorWithFilteredBooks = await beyonce
  .query(AuthorPartition.key({ id: "1" }))
  .attributeNotExists("title") // type-safe fields
  .or("title", "=", "Brave New World") // type safe fields + operators
  .exec()

QueryGSI

import { byNameGSI } from "generated/models"
const prideAndPrejudice = await beyonce
  .queryGSI(byNameGSI.name, byNameGSI.key("Jane Austen"))
  .where("title", "=", "Pride and Prejudice")
  .exec()

BatchGet

// Batch get several items
const batchResults = await beyonce.batchGet({
  keys: [
    // Get 2 authors
    AuthorModel.key({ id: "1" }),
    AuthorModel.key({ id: "2" }),

    // And a specific book from each
    Book.key({ authorId: "1", id: "1" })
    Book.key({ authorId: "2" id: "2" })
  ]
})

// And the return type is:
// { author: Author[], book: Book[] }

BatchPutWithTransaction

// Batch put several items in a transaction
const author1 = AuthorModel.create({
  id: "1",
  name: "Jane Austen"
})

const author2 = AuthorModel.create({
  id: "2",
  name: "Charles Dickens"
})

await beyonce.batchPutWithTransaction({ items: [author1, author2] })

Encryption

Beyonce integrates with Jay-Z to enable transparent application-layer encryption out of the box using KMS with just a few additional lines of code:

import { KMS } from "aws-sdk"
import { KMSDataKeyProvider, JayZ } from "@ginger.io/jay-z"

// Given a dynamo client
const dynamo =  new DynamoDB({endpoint: "...", region: "..."})

// Get yourself a JayZ
const kmsKeyId = "..." // the KMS key id or arn you want to use
const keyProvider = new KMSDataKeyProvider(kmsKeyId, new KMS())
const jayZ = new JayZ({ keyProvider })

// And give him to Beyonce (because she runs this relationship)
const beyonce = new Beyonce(
  LibraryTable,
  dynamo,
  { jayz }
)

Important note on Querying with Jay-Z enabled

Because Jay-Z performs encryption at the application level, DynamoDB query operations occur before decryption. Put plainly, this means you can't filter .query calls using any attribute that isn't a partition or sort key.

Things beyonce should do, but doesn't (yet)

1. Support the full range of Dynamo filter expressions
2. Support partition and sort key names other than pk and sk

An aside on storing heterogenous models in the same table

When using DynamoDB, you often want to "pre-compute" joins by sticking a set of heterogeneous models into the same table, under the same partition key. This allows for retrieving related records using a single query instead of N.

Unfortunately most existing DynamoDB libraries, like DynamoDBMapper, don't support this use case as they follow the SQL convention sticking each model into a separte table.

For example, we might want to fetch an Author + all their Books in a single query. And we'd accomplish that by sticking both models under the same partition key - e.g. author-${id}.

AWS's guidelines, take this to the extreme:

...most well-designed applications require only one table

Keep in mind that the primary reason they recommened this is to avoid forcing the application-layer to perform in-memory joins. Due to Amazon's scale, they are highly motivated to minimize the number of roundtrip db calls.

You are probably not Amazon scale. And thus probably don't need to shove everything into a single table.

But you might want to keep a few related models in the same table, under the same partition key and fetch those models in a type-safe way. Beyonce makes that easy.