electrodb
Advanced tools
A library to more easily create and interact with multiple entities and heretical relationships in dynamodb
ElectroDB is a DynamoDB library that simplifies the process of defining and interacting with DynamoDB tables. It provides a high-level abstraction for defining entities, managing relationships, and performing CRUD operations, making it easier to work with DynamoDB's complex data modeling and querying capabilities.
Entity Definition
This feature allows you to define an entity with its attributes and indexes. The code sample demonstrates how to create a User entity with attributes like userId, name, and email, and a primary index.
const { Entity } = require('electrodb');
const UserEntity = new Entity({
model: {
entity: 'User',
version: '1',
service: 'UserService'
},
attributes: {
userId: { type: 'string', required: true },
name: { type: 'string', required: true },
email: { type: 'string', required: true }
},
indexes: {
primary: {
pk: { field: 'pk', composite: ['userId'] },
sk: { field: 'sk', composite: [] }
}
}
});CRUD Operations
This feature provides methods for performing CRUD operations on the defined entities. The code sample shows how to create, read, update, and delete a user entity.
const user = await UserEntity.put({
userId: '123',
name: 'John Doe',
email: 'john.doe@example.com'
}).go();
const fetchedUser = await UserEntity.get({ userId: '123' }).go();
const updatedUser = await UserEntity.update({ userId: '123' })
.set({ name: 'Jane Doe' })
.go();
const deletedUser = await UserEntity.delete({ userId: '123' }).go();Querying
This feature allows you to perform complex queries on your entities. The code sample demonstrates how to query the User entity using the primary index.
const users = await UserEntity.query.primary({ userId: '123' }).go();Relationships
This feature allows you to define and manage relationships between different entities. The code sample shows how to fetch a user along with their related orders using the Service class.
const { Service } = require('electrodb');
const UserService = new Service({
User: UserEntity,
Order: OrderEntity
});
const userWithOrders = await UserService.User.get({ userId: '123' }).include(OrderEntity).go();DynamoDB Toolbox is a set of tools that makes it easier to work with Amazon DynamoDB. It provides a simple and consistent way to define and interact with DynamoDB tables and items. Compared to ElectroDB, DynamoDB Toolbox offers a more lightweight and flexible approach but may require more manual setup for complex data models.
The AWS SDK for JavaScript provides a comprehensive set of tools for interacting with AWS services, including DynamoDB. While it offers low-level access to DynamoDB's API, it lacks the high-level abstractions and convenience features provided by ElectroDB, making it more suitable for developers who need fine-grained control over their DynamoDB interactions.
Dynogels is a DynamoDB data mapper for Node.js that simplifies the process of defining and interacting with DynamoDB tables. It offers a similar high-level abstraction as ElectroDB but is less actively maintained and may not support some of the latest DynamoDB features.
ElectroDB is a dynamodb library to ease the use of having multiple entities and complex hierarchical relationships in a single dynamodb table.
This library is a work in progress, please submit issues/feedback or reach out on twitter @tinkertamper.
ExpressionAttributeNames, ExpressionAttributeValues..find() method to dynamically and effeciently query based on defined sort key structures..page() to easily iterate through multiquery result sets..d.ts) based on your model.Entities, Services, Models directly from the command line.Entities, Services, Models for easier prototyping.Turn this
StoreLocations.query
.leases({storeId})
.gte({leaseEndDate: "2010"})
.where((attr, op) => `
${op.eq(attr.cityId, "Atlanta1")} AND ${op.contains(attr.category, "food")}
`)
.params()
Into This
{
"TableName": "StoreDirectory",
"ExpressionAttributeNames": {
"#cityId": "cityId",
"#category": "category",
"#pk": "gis2pk",
"#sk1": "gsi2sk"
},
"ExpressionAttributeValues": {
":cityId_w1": "Atlanta1",
":category_w1": "food",
":pk": "$mallstoredirectory#storeid_lattelarrys",
":sk1": "$mallstore_1#leaseenddate_2010"
},
"KeyConditionExpression": "#pk = :pk and #sk1 >= :sk1",
"IndexName": "gis2pk-gsi2sk-index",
"FilterExpression": "#cityId = :cityId_w1 AND contains(#category, :category_w1)"
}
Install from NPM
npm install electrodb --save
Require Entity and/or Service from electrodb:
const {Entity, Service} = require("electrodb");
To see full examples of ElectroDB in action, go to the Examples section.
Entity allows you to create separate and individual business objects in a DynamoDB table. When queried, your results will not include other Entities that also exist the same table. This allows you to easily achieve single table design as recommended by AWS. For more detail, read Entities.
Service allows you to build a relationships across Entities. A service imports Entity Models, builds individual Entities, and creates Collections to allow cross Entity querying. For more detail, read Services.
You can use Entities independent of Services, you do not need to import models into a Service to use them individually. However, If you intend to make queries that join or span multiple Entities you will need to use a Service.
In ElectroDB an Entity is represents a single business object. For example, in a simple task tracking application, one Entity could represent an Employee and or a Task that the employee is assigned to.
Require or import Entity from electrodb:
const {Entity} = require("electrodb");
In ElectroDB a Service represents a collection of Entities and also allows you to build queries span across Entities. Similar to Entities, Services can coexist on a single table without collision. You can use Entities independent of Services, you do not need to import models into a Service to use them individually. However, you do you need to use a Service if you intend make queries that join multiple Entities.
Require electrodb:
const {Service} = require("electrodb");
Create individual (Entities)[#entities] with the (Models)[#models] or join them via a Service.
// Independent Models
let table = "my_table_name";
let employees = new Entity(EmployeesModel, { client, table });
let tasks = new Entity(TasksModel, { client, table });
// Joining Entity instances to a Service
let TaskApp = new Service("TaskApp", { client, table });
TaskApp
.join(employees) // available at TaskApp.entities.employees
.join(tasks); // available at TaskApp.entities.tasks
// Joining models to a Service
let TaskApp = new Service("TaskApp", { client, table });
TaskApp
.join(EmployeesModel) // available at TaskApp.entities.employees
.join(TasksModel); // available at TaskApp.entities.tasks
When joining a Model/Entity to a Service a number of validations are done to ensure that Entity conforms to expectations collectively established by all joined Entities.
Create an Entity's schema. In the below example.
const DynamoDB = require("aws-sdk/clients/dynamodb");
const {Entity, Service} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const EmployeesModel = {
model: {
entity: "employees",
version: "1",
service: "taskapp",
},
attributes: {
employee: {
type: "string",
default: () => uuidv4(),
},
firstName: {
type: "string",
required: true,
},
lastName: {
type: "string",
required: true,
},
office: {
type: "string",
required: true,
},
title: {
type: "string",
required: true,
},
team: {
type: ["development", "marketing", "finance", "product", "cool cats and kittens"],
required: true,
},
salary: {
type: "string",
required: true,
},
manager: {
type: "string",
},
dateHired: {
type: "string",
validate: /^\d{4}-\d{2}-\d{2}$/gi
},
birthday: {
type: "string",
validate: /^\d{4}-\d{2}-\d{2}$/gi
},
},
indexes: {
employee: {
pk: {
field: "pk",
facets: ["employee"],
},
sk: {
field: "sk",
facets: [],
},
},
coworkers: {
index: "gsi1pk-gsi1sk-index",
collection: "workplaces",
pk: {
field: "gsi1pk",
facets: ["office"],
},
sk: {
field: "gsi1sk",
facets: ["team", "title", "employee"],
},
},
teams: {
index: "gsi2pk-gsi2sk-index",
pk: {
field: "gsi2pk",
facets: ["team"],
},
sk: {
field: "gsi2sk",
facets: ["title", "salary", "employee"],
},
},
employeeLookup: {
collection: "assignments",
index: "gsi3pk-gsi3sk-index",
pk: {
field: "gsi3pk",
facets: ["employee"],
},
sk: {
field: "gsi3sk",
facets: [],
},
},
roles: {
index: "gsi4pk-gsi4sk-index",
pk: {
field: "gsi4pk",
facets: ["title"],
},
sk: {
field: "gsi4sk",
facets: ["salary", "employee"],
},
},
directReports: {
index: "gsi5pk-gsi5sk-index",
pk: {
field: "gsi5pk",
facets: ["manager"],
},
sk: {
field: "gsi5sk",
facets: ["team", "office", "employee"],
},
},
},
filters: {
upcomingCelebrations: (attributes, startDate, endDate) => {
let { dateHired, birthday } = attributes;
return `${dateHired.between(startDate, endDate)} OR ${birthday.between(
startDate,
endDate,
)}`;
},
},
};
const TasksModel = {
model: {
entity: "tasks",
version: "1",
service: "taskapp",
},
attributes: {
task: {
type: "string",
default: () => uuidv4(),
},
project: {
type: "string",
},
employee: {
type: "string",
},
description: {
type: "string",
},
},
indexes: {
task: {
pk: {
field: "pk",
facets: ["task"],
},
sk: {
field: "sk",
facets: ["project", "employee"],
},
},
project: {
index: "gsi1pk-gsi1sk-index",
pk: {
field: "gsi1pk",
facets: ["project"],
},
sk: {
field: "gsi1sk",
facets: ["employee", "task"],
},
},
assigned: {
collection: "assignments",
index: "gsi3pk-gsi3sk-index",
pk: {
field: "gsi3pk",
facets: ["employee"],
},
sk: {
field: "gsi3sk",
facets: ["project", "task"],
},
},
},
};
| Property | Description |
|---|---|
| model.service | Name of the application using the entity, used to namespace all entities |
| model.entity | Name of the entity that the schema represents |
| model.version | (optional) The version number of the schema, used to namespace keys |
| attributes | An object containing each attribute that makes up the schema |
| indexes | An object containing table indexes, including the values for the table's default Partition Key and Sort Key |
| filters | An object containing user defined filter template functions |
Optional second parameter
| Property | Description |
|---|---|
| table | Name of the dynamodb table in aws |
| client | (optional) An instance of the docClient from the aws-sdk for use when querying a DynamoDB table. This is optional if you wish to only use the params functionality, but required if you actually need to query against a database. |
Attributes define an Entity record. The AttributeName represents the value your code will use to represent an attribute.
Pro-Tip: Using the
fieldproperty, you can map anAttributeNameto a different field name in your table. This can be useful to utilize existing tables, existing models, or even to reduce record sizes via shorter field names. For example, you may refer to an attribute asorganizationbut want to save the attribute with a field name oforgin DynamoDB.
Assign just the type of the attribute directly to the attribute name. Currently supported options are "string", "number", "boolean", an array of strings representing a fixed set of possible values, or "any" which disables value type checking on that attribute.
attributes: {
<AttributeName>: "string"|"number"|"boolean"|"any"|string[]
}
Use the expanded syntax build out more robust attribute options.
attributes: {
<AttributeName>: {
"type": string|string[],
"required"?: boolean,
"default"?: value|() => value
"validate"?: RegExp|(value: any) => void|string
"field"?: string
"readOnly"?: boolean
"label"?: string
"cast"?: "number"|"string"|"boolean",
"get"?: (attribute, schema) => value,
"set"?: (attribute, schema) => value
}
}
| Property | Type | Required | Description |
|---|---|---|---|
type | string, string[] | yes | Accepts the values: "string", "number" "boolean", an array of strings representing a finite list of acceptable values: ["option1", "option2", "option3"], or "any"which disables value type checking on that attribute. |
required | boolean | no | Whether or not the value is required when creating a new record. |
default | value, () => value | no | Either the default value itself or a synchronous function that returns the desired value. |
validate | RegExp, (value: any) => void, (value: any) => string | no | Either regex or a synchronous callback to return an error string (will result in exception using the string as the error's message), or thrown exception in the event of an error. |
field | string | no | The name of the attribute as it exists dynamo, if named differently in the schema attributes. Defaults to the AttributeName as defined in the schema. |
readOnly | boolean | no | Prevents update of the property after the record has been created. Attributes used in the composition of the table's primary Partition Key and Sort Key are by read-only by default. |
label | string | no | Used in index composition to prefix key facets. By default, the AttributeName is used as the label. |
cast | "number", "string", "boolean" | no | Optionally cast attribute values when interacting with DynamoDB. Current options include: "number", "string", and "boolean". |
set | (attribute, schema) => value | no | A synchronous callback allowing you apply changes to a value before it is set in params or applied to the database. First value represents the value passed to ElectroDB, second value are the attributes passed on that update/put |
get | (attribute, schema) => value | no | A synchronous callback allowing you apply changes to a value after it is retrieved from the database. First value represents the value passed to ElectroDB, second value are the attributes retrieved from the database. |
Using get and set on an attribute can allow you to apply logic before and just after modifying or retrieving a field from DynamoDB. Both methods should be idempotent synchronous functions and may be invoked multiple times during one query.
ElectroDB invokes an Attribute's get method after an Item has been retrieved from DynamoDB, and if that field exists on the item.
ElectroDB invokes an Attribute's set method in the following circumstances:
create or put operationpatch or update operationset method invoked before applying the facet to a keyThe validation property allows for many different function/type signatures. Here the different combinations ElectroDB supports:
| signature | behavior |
|---|---|
Regexp | ElectroDB will call .test(val) on the provided regex with the value passed to this attribute |
(value: T) => string | If a string with length is returned from validate it will be considered the reason an the value is invalid. It will generate an error message with this reason. |
(value: T) => boolean | If a boolean is returned, true or truthy values will signify than a value is invalid while false or falsey will be considered valid |
(value: T) => void | A void/undefined return will be treated as successful, in this scenario you can throw an Error yourself to interrupt the query |
The indexes object requires at least the definition of the table's natural Partition Key and (if applicable) Sort Key.
Indexes are defined, and later referenced by their AccessPatternName. These defined via a facets array that is made up of attributes names as listed the model.
indexes: {
<AccessPatternName>: {
"pk": {
"field": <string>
"facets": <AttributeName[]>
},
"sk"?: {
"field": <string>
"facets": <AttributesName[]>
},
"index"?: string
"collection"?: string
}
}
| Property | Type | Required | Description |
|---|---|---|---|
pk | object | yes | Configuration for the pk of that index or table |
pk.facets | boolean | no | An array that represents the order in which attributes are concatenated to facets the key (see Facets below for more on this functionality). |
pk.field | string | yes | The name of the attribute as it exists dynamo, if named differently in the schema attributes. |
sk | object | no | Configuration for the sk of that index or table |
sk.facets | `array | string` | no |
sk.field | string | yes | The name of the attribute as it exists dynamo, if named differently in the schema attributes. |
index | string | no | Required when the Index defined is a Secondary Index; but is left blank for the table's primary index. |
collection | string | no | Used when models are joined to a Service. When two entities share a collection on the same index, they can be queried with one request to DynamoDB. The name of the collection should represent what the query would return as a pseudo Entity. (see Collections below for more on this functionality). |
When using indexes without Sort Keys, that should be expressed as an index without an sk property at all. Indexes without an sk cannot have a collection, see (Collections)[[#collections] for more detail.
Note: It is generally recommended to have Sort Keys when using ElectroDB as they allow for more advanced query opportunities. Even if your model doesnt need an additional property to define a unique record, having an
skwith no facets still opens the door to many more query opportunities like (collections)[#collections].
// ElectroDB interprets as index *not having* an SK.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
}
}
}
}
When using indexes with Sort Keys, that should be expressed as an index with an sk property. If you don't wish to use the sk in your model, but it does exist on the table, simply use an empty for the facets property. This is still useful as it opens the door to many more query opportunities like (collections)[#collections].
// ElectroDB interprets as index *having* SK, but this model doesnt attach any facets to it.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
},
sk: {
field: "sk",
facets: []
}
}
}
}
A Facet is a segment of a key based on one of the attributes. Facets are concatenated together from either a Partition Key or an Sort Key key, which define an index.
Note: Only attributes with a type of
"string","number", or"boolean"can be used as a facet
There are two ways to provide facets:
For example, in the following Access Pattern, "locations" is made up of the facets storeId, mallId, buildingId and unitId which map to defined attributes in the schema:
// Input
{
storeId: "STOREVALUE",
mallId: "MALLVALUE",
buildingId: "BUILDINGVALUE",
unitId: "UNITVALUE"
};
// Output:
{
pk: '$mallstoredirectory_1#storeId_storevalue',
sk: '$mallstores#mallid_mallvalue#buildingid_buildingvalue#unitid_unitvalue'
}
For PK values, the service and version values from the model are prefixed onto the key.
For SK values, the entity value from the model is prefixed onto the key.
In a Facet Array, each element is the name of the corresponding Attribute defined in the Model. If the Attribute has a label property, that will be used to prefix the facets, otherwise the full Attribute name will be used.
attributes: {
storeId: {
type: "string",
label: "sid",
},
mallId: {
type: "string",
label: "mid",
},
buildingId: {
type: "string",
label: "bid",
},
unitId: {
type: "string",
label: "uid",
}
},
indexes: {
locations: {
pk: {
field: "pk",
facets: ["storeId"]
},
sk: {
field: "sk",
facets: ["mallId", "buildingId", "unitId"]
}
}
}
// Input
{
storeId: "STOREVALUE",
mallId: "MALLVALUE",
buildingId: "BUILDINGVALUE",
unitId: "UNITVALUE"
};
// Output:
{
pk: '$mallstoredirectory_1#sid_storevalue',
sk: '$mallstores#mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}
In a Facet Template, you provide a formatted template for ElectroDB to use when making keys. Facet Templates allow for potential ElectroDB adoption on already established tables and records.
Attributes are identified by a prefixed colon and the attributes name. For example, the syntax :storeId will matches storeId attribute in the model.
Convention for a composing a key use the # symbol to separate attributes, and for labels to attach with underscore. For example, when composing both the mallId and buildingId would be expressed as mid_:mallId#bid_:buildingId.
ElectroDB will not prefix templated keys with the Entity, Project, Version, or Collection. This will give you greater control of your keys but will limit ElectroDB's ability to prevent leaking entities with some queries.
Facet Templates have some "gotchas" to consider:
1. Keys only allow for one instance of an attribute, the template :prop1#:prop1 will be interpreted the same as :prop1#.
2. ElectoDB will continue to always add a trailing delimiter to facets with keys are partially supplied. (More documentation coming on this soon)
attributes: {
storeId: {
type: "string"
},
mallId: {
type: "string"
},
buildingId: {
type: "string"
},
unitId: {
type: "string"
}
},
indexes: {
locations: {
pk: {
field: "pk",
facets: "sid_:storeId"
},
sk: {
field: "sk",
facets: "mid_:mallId#bid_:buildingId#uid_:unitId"
}
}
}
// Input
{
storeId: "STOREVALUE",
mallId: "MALLVALUE",
buildingId: "BUILDINGVALUE",
unitId: "UNITVALUE"
};
// Output:
{
pk: 'sid_storevalue',
sk: 'mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}
A Collection is a grouping of Entities with the same Partition Key and allows you to make efficient query across multiple entities. If you background is SQL, imagine Partition Keys as Foreign Keys, a Collection represents a View with multiple joined Entities.
Collections are defined on an Index, and the name of the collection should represent what the query would return as a pseudo Entity. Additionally Collection names must be unique across a Service.
Note:
collectionshould be unique to a single common index across entities.
Using the TaskApp Models defined in Models, these models share a collection called assignments on the index gsi3pk-gsi3sk-index
let TaskApp = new Service("projectmanagement", { client, table: "projectmanagement" });
TaskApp
.join(EmployeesModel) // TaskApp.entities.employees
.join(TasksModel); // TaskApp.entities.tasks
TaskApp.collections.assignments({employee: "JExotic"}).params();
// Results
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: { ':pk': '$taskapp_1#employee_joeexotic', ':sk1': '$assignments' },
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
IndexName: 'gsi3pk-gsi3sk-index'
}
Filters are no longer the preferred way to add FilterExpressions. Checkout the Where section to find out about how to apply FilterExpressions and ConditionExpressions
Building thoughtful indexes can make queries simple and performant. Sometimes you need to filter results down further. By adding Filters to your model, you can extend your queries with custom filters. Below is the traditional way you would add a filter to Dynamo's DocumentClient directly along side how you would accomplish the same using a Filter function.
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-04-01#rent_',
':sk2': '$mallstore#leaseenddate_2020-07-01#rent_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
FilterExpression: '(#rent between :rent1 and :rent2) AND #discount <= :discount1'
}
Filters can defined on the model and used in your query chain.
/**
* Filter by low rent a specific mall or a leaseEnd withing a specific range
* @param {Object} attributes - All attributes from the model with methods for each filter operation
* @param {...*} values - Values passed when calling the filter in a query chain.
**/
filters: {
rentPromotions: function(attributes, minRent, maxRent, promotion) {
let {rent, discount} = attributes;
return `
${rent.between(minRent, maxRent)} AND ${discount.lte(promotion)}
`
}
}
let StoreLocations = new Entity(model, {table: "StoreDirectory"});
let maxRent = "5000.00";
let minRent = "2000.00";
let promotion = "1000.00";
let stores = MallStores.query
.stores({ mallId: "EastPointe" })
.between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
.rentPromotions(minRent, maxRent, promotion)
.params();
// Results
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-04-01#rent_',
':sk2': '$mallstore#leaseenddate_2020-07-01#rent_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
FilterExpression: '(#rent between :rent1 and :rent2) AND #discount <= :discount1'
}
The easiest way to use filters is to use them inline in your query chain.
let StoreLocations = new Entity(model, {table: "StoreDirectory"});
let maxRent = "5000.00";
let minRent = "2000.00";
let promotion = "1000.00";
let stores = StoreLocations.query
.leases({ mallId: "EastPointe" })
.between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
.filter(({rent, discount}) => `
${rent.between(minRent, maxRent)} AND ${discount.lte(promotion)}
`)
.params();
// Results
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-04-01#rent_',
':sk2': '$mallstore#leaseenddate_2020-07-01#rent_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
FilterExpression: '(#rent between :rent1 and :rent2) AND #discount <= :discount1'
}
Filter functions allow you to write a FilterExpression without having to worry about the complexities of expression attributes. To accomplish this, ElectroDB injects an object attributes as the first parameter to all Filter Functions. This object contains every Attribute defined in the Entity's Model with the following operators as methods:
| operator | example | result |
|---|---|---|
gte | rent.gte(maxRent) | #rent >= :rent1 |
gt | rent.gt(maxRent) | #rent > :rent1 |
lte | rent.lte(maxRent) | #rent <= :rent1 |
lt | rent.lt(maxRent) | #rent < :rent1 |
eq | rent.eq(maxRent) | #rent = :rent1 |
begins | rent.begins(maxRent) | begins_with(#rent, :rent1) |
exists | rent.exists() | attribute_exists(#rent) |
notExists | rent.notExists() | attribute_not_exists(#rent) |
contains | rent.contains(maxRent) | contains(#rent = :rent1) |
notContains | rent.notContains(maxRent) | not contains(#rent = :rent1) |
between | rent.between(minRent, maxRent) | (#rent between :rent1 and :rent2) |
name | rent.name() | #rent |
value | rent.value(maxRent) | :rent1 |
This functionality allows you to write the remaining logic of your FilterExpression with ease. Add complex nested and/or conditions or other FilterExpression logic while ElectroDB handles the ExpressionAttributeNames and ExpressionAttributeValues.
It is possible to include chain multiple filters. The resulting FilterExpressions are concatinated with an implicit AND operator.
let MallStores = new Entity(model, {table: "StoreDirectory"});
let stores = MallStores.query
.leases({ mallId: "EastPointe" })
.between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
.filter(({ rent, discount }) => `
${rent.between("2000.00", "5000.00")} AND ${discount.eq("1000.00")}
`)
.filter(({ category }) => `
${category.eq("food/coffee")}
`)
.params();
// Results
{
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#category': 'category',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':category1': 'food/coffee',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-04-01#storeid_',
':sk2': '$mallstore#leaseenddate_2020-07-01#storeid_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
IndexName: 'idx2',
FilterExpression: '(#rent between :rent1 and :rent2) AND (#discount = :discount1 AND #category = :category1)'
}
The
where()method is an improvement on thefilter()method. Unlikefilter,wherewill be compatible with upcoming features related to complex types.
Building thoughtful indexes can make queries simple and performant. Sometimes you need to filter results down further or add conditions to an update/patch/put/create/delete action.
Below is the traditional way you would add a FilterExpression to Dynamo's DocumentClient directly along side how you would accomplish the same using the where method.
{
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
TableName: 'zoodirectory',
ExpressionAttributeNames: {
'#animal': 'animal',
'#lastFed': 'lastFed',
'#pk': 'pk',
'#sk1': 'sk'
},
ExpressionAttributeValues: {
':animal_w1': 'Warthog',
':lastFed_w1': '2020-09-25',
':lastFed_w2': '2020-09-28',
':pk': '$zoodirectory_1#habitat_africa',
':sk1': '$exibits#enclosure_'
},
FilterExpression: '#animal = :animal_w1 AND (#lastFed between :lastFed_w1 and :lastFed_w2)'
}
animals.query
.farm({habitat: "Africa"})
.where(({animal, dangerous}, {value, name, between}) => `
${name(animal)} = ${value(animal, "Warthog")} AND ${between(dangerous, "2020-09-25", "2020-09-28")}
`)
.params()
Below is the traditional way you would add a ConditionExpression to Dynamo's DocumentClient directly along side how you would accomplish the same using the where method.
{
UpdateExpression: 'SET #dangerous = :dangerous',
ExpressionAttributeNames: { '#animal': 'animal', '#dangerous': 'dangerous' },
ExpressionAttributeValues: {
':animal_w1': 'Zebra',
':dangerous_w1': false,
':dangerous': true
},
TableName: 'zoodirectory',
Key: {
pk: '$zoodirectory_1#habitat_africa',
sk: '$exibits#enclosure_5b'
},
ConditionExpression: '#animal = :animal_w1 AND #dangerous = :dangerous_w1'
}
animals.update({habitat: "Africa", enclosure: "5b"})
.set({dangerous: true})
.where(({animal, dangerous}, {value, name, eq}) => `
${name(animal)} = ${value(animal, "Zebra")} AND ${eq(dangerous)}
`)
.params())
Where functions allow you to write a FilterExpression or ConditionExpression without having to worry about the complexities of expression attributes. To accomplish this, ElectroDB injects an object attributes as the first parameter to all Filter Functions, and an object operations, as the second parameter. Pass the properties from the attributesobject to the methods found on theoperations` object, along with inline values to set filters and conditions:
// A single filter operation
animals.update({habitat: "Africa", enclosure: "5b"})
.set({keeper: "Joe Exotic"})
.where((attr, op) => op.eq(attr.dangerous, true))
.params());
// Multiple conditions
animals.update({habitat: "Africa", enclosure: "5b"})
.set({keeper: "Joe Exotic"})
.where((attr, op) => `
${op.eq(attr.dangerous, true)} AND ${op.contains(attr.diet, "meat")}
`)
.params());
The attributes object contains every Attribute defined in the Entity's Model. The operations object contains the following methods:
| operator | example | result |
|---|---|---|
gte | gte(rent, value) | #rent >= :rent1 |
gt | gt(rent, maxRent) | #rent > :rent1 |
lte | lte(rent, maxRent) | #rent <= :rent1 |
lt | lt(rent, maxRent) | #rent < :rent1 |
eq | eq(rent, maxRent) | #rent = :rent1 |
begins | begins(rent, maxRent) | begins_with(#rent, :rent1) |
exists | exists(rent) | attribute_exists(#rent) |
notExists | notExists(rent) | attribute_not_exists(#rent) |
contains | contains(rent, maxRent) | contains(#rent = :rent1) |
notContains | notContains(rent, maxRent) | not contains(#rent = :rent1) |
between | between(rent, minRent, maxRent) | (#rent between :rent1 and :rent2) |
name | name(rent) | #rent |
value | value(rent, maxRent) | :rent1 |
It is possible to include chain multiple where clauses. The resulting FilterExpressions (or ConditionExpressions) are concatinated with an implicit AND operator.
let MallStores = new Entity(model, {table: "StoreDirectory"});
let stores = MallStores.query
.leases({ mallId: "EastPointe" })
.between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
.where(({ rent, discount }, {between, eq}) => `
${between(rent, "2000.00", "5000.00")} AND ${eq(discount, "1000.00")}
`)
.where(({ category }, {eq}) => `
${eq(category, "food/coffee")}
`)
.params();
// Results
{
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#category': 'category',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':category1': 'food/coffee',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-04-01#storeid_',
':sk2': '$mallstore#leaseenddate_2020-07-01#storeid_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
IndexName: 'idx2',
FilterExpression: '(#rent between :rent1 and :rent2) AND (#discount = :discount1 AND #category = :category1)'
}
For hands-on learners: the following example can be followed along with and executed on runkit: https://runkit.com/tywalch/electrodb-building-queries
Forming a composite Partition Key and Sort Key is a critical step in planning Access Patterns in DynamoDB. When planning composite keys, it is crucial to consider the order in which they are composed. As of the time of writing this documentation, DynamoDB has the following constraints that should be taken into account when planning your Access Patterns:
begins_with, between, >, >=, <, <=, and Equals.Carefully considering your Facet order will allow ElectroDB to express hierarchical relationships and unlock more available Access Patterns for your application.
For example, let's say you have a StoreLocations Entity that represents Store Locations inside Malls:
let schema = {
model: {
service: "MallStoreDirectory",
entity: "MallStore",
version: "1",
},
attributes: {
cityId: {
type: "string",
required: true,
},
mallId: {
type: "string",
required: true,
},
storeId: {
type: "string",
required: true,
},
buildingId: {
type: "string",
required: true,
},
unitId: {
type: "string",
required: true,
},
category: {
type: [
"spite store",
"food/coffee",
"food/meal",
"clothing",
"electronics",
"department",
"misc"
],
required: true
},
leaseEndDate: {
type: "string",
required: true
},
rent: {
type: "string",
required: true,
validate: /^(\d+\.\d{2})$/
},
discount: {
type: "string",
required: false,
default: "0.00",
validate: /^(\d+\.\d{2})$/
}
},
indexes: {
stores: {
pk: {
field: "pk",
facets: ["cityId", "mallId"]
},
sk: {
field: "sk",
facets: ["buildingId", "storeId"]
}
},
units: {
index: "gis1pk-gsi1sk-index",
pk: {
field: "gis1pk",
facets: ["mallId"]
},
sk: {
field: "gsi1sk",
facets: ["buildingId", "unitId"]
}
},
leases: {
index: "gis2pk-gsi2sk-index",
pk: {
field: "gis2pk",
facets: ["storeId"]
},
sk: {
field: "gsi2sk",
facets: ["leaseEndDate"]
}
}
},
filters: {
byCategory: ({category}, name) => category.eq(name),
rentDiscount: (attributes, discount, max, min) => {
return `${attributes.discount.lte(discount)} AND ${attributes.rent.between(max, min)}`
}
}
};
const StoreLocations = new Entity(schema, {table: "StoreDirectory"});
Examples in this section using the
MallStoreschema defined above, and available for interacting with here: https://runkit.com/tywalch/electrodb-building-queries
All queries start from the Access Pattern defined in the schema.
const MallStore = new Entity(schema, {table: "StoreDirectory"});
// Each Access Pattern is available on the Entity instance
// MallStore.query.stores()
// MallStore.query.malls()
All queries require (at minimum) the Facets included in its defined Partition Key, and Facets you have from the start of the Sort Key.
Important: Facets must be supplied in the order they are composed when invoking the Access Pattern
const MallStore = new Entity({
model: {
service: "mallmgmt"
entity: "store",
version: "1"
},
attributes: {
cityId: "string"
mallId: "string",
storeId: "string",
buildingId: "string",
unitId: "string",
name: "string",
description: "string",
category: "string"
},
indexes: {
pk: {
field: "pk",
facets: ["cityId", "mallId"]
},
sk: {
field: "sk",
facets: ["storeId", "unitId"]
}
}
}, {table: "StoreDirectory"});
const cityId = "Atlanta1";
const mallId = "EastPointe";
const storeId = "LatteLarrys";
const unitId = "B24";
const buildingId = "F34";
// Good: Includes at least the PK
StoreLocations.query.stores({cityId, mallId});
// Good: Includes at least the PK, and some of the SK
StoreLocations.query.stores({cityId, mallId, buildingId});
// Bad: No PK facets specified, will throw
StoreLocations.query.stores();
// Bad: Not All PK Facets included (cityId), will throw
StoreLocations.query.stores({mallId});
// Bad: Facets not included in order, will NOT throw but will ignore `storeId`
StoreLocations.query.stores({cityId, mallId, storeId});
| operator | use case |
|---|---|
begins | Keys starting with a particular set of characters. |
between | Keys between a specified range. |
gt | Keys less than some value |
gte | Keys less than or equal to some value |
lt | Keys greater than some value |
lte | Keys greater than or equal to some value |
Each record represents one Store location. All Stores are located in Malls we manage.
To satisfy requirements for searching based on location, you could use the following keys: Each StoreLocations record would have a Partition Key with the store's storeId. This key alone is not enough to identify a particular store. To solve this, compose a Sort Key for the store's location attribute ordered hierarchically (mall/building/unit): ["mallId", "buildingId", "unitId"].
The StoreLocations entity above, using just the stores Index alone enables four Access Patterns:
LatteLarrys locations in all MallsLatteLarrys locations in one MallLatteLarrys locations inside a specific MallLatteLarrys inside of a Mall and BuildingQueries in ElectroDB are built around the Access Patterns defined in the Schema and are capable of using partial key Facets to create performant lookups. To accomplish this, ElectroDB offers a predictable chainable API.
Examples in this section using the
StoreLocationsschema defined above and can be directly experiment with on runkit: https://runkit.com/tywalch/electrodb-building-queries
The methods: Get (get), Create (put), Update (update), and Delete (delete) *require all facets described in the Entities' primary PK and SK.
Provide all Table Index facets in an object to the get method
let results = await StoreLocations.get({
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "Atlanta1"
}).go();
// Equivalent Params:
// {
// Key: {
// pk: "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
// sk: "$mallstore_1#buildingid_f34#storeid_lattelarrys"
// },
// TableName: 'StoreDirectory'
// }
Provide all Table Index facets in an array of objects to the get method to perform a BatchGet query.
Note: Performing a BatchGet will return a response structure unique to BatchGet: a two-dimensional array with the results of the query and any unprocessed records. See the example below.
let [results, unprocessed] = await StoreLocations.get([
{
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "Atlanta1"
},
{
storeId: "MochaJoes",
mallId: "WestEnd",
buildingId: "A21",
cityId: "Madison2"
}
]).go();
// Equivalent Params:
// {
// "RequestItems": {
// "electro": {
// "Keys": [
// {
// "pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
// "sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
// },
// {
// "pk": "$mallstoredirectory#cityid_madison2#mallid_westend",
// "sk": "$mallstore_1#buildingid_a21#storeid_mochajoes"
// }
// ]
// }
// }
// }
The two-dimensional array returned by batch get most easily used when deconstructed into two variables, in the above case: results and unprocessed.
The results array are records that were returned DynamoDB as Responses on the BatchGet query. They will appear in the same format as ElectroDB queries.
Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the facets defined in the Table Index. This is in keeping with DynamoDB's practice of returning back only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as facets but you can pass the query option {lastEvaluatedKeyRaw:true} override this behavior and return the Keys as they came from DynamoDB.
Provide all Table Index facets in an object to the delete method to delete a record.
await StoreLocations.delete({
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "Atlanta1"
}).go();
// Equivalent Params:
// {
// Key: {
// pk: "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
// sk: "$mallstore_1#buildingid_f34#storeid_lattelarrys"
// },
// TableName: 'StoreDirectory'
// }
Provide all table index facets in an array of objects to the delete method to batch delete records.
Note: Performing a Batch Delete will return an array of "unprocessed" records. An empty array signifies all records were processed. If you want the raw DynamoDB response you can always use the option
{raw: true}, more detail found here: Query Options.
let unprocessed = await StoreLocations.delete([
{
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "LosAngeles1"
},
{
storeId: "MochaJoes",
mallId: "EastPointe",
buildingId: "F35",
cityId: "LosAngeles1"
}
]).go();
// Equivalent Params:
{
"RequestItems": {
"StoreDirectory": [
{
"DeleteRequest": {
"Key": {
"pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
}
}
},
{
"DeleteRequest": {
"Key": {
"pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f35#storeid_mochajoes"
}
}
}
]
}
}
Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the facets defined in the Table Index. This is in keeping with DynamoDB's practice of returning back only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as facets but you can pass the query option {lastEvaluatedKeyRaw:true} override this behavior and return the Keys as they came from DynamoDB.
Provide all required Attributes as defined in the model to create a new record. ElectroDB will enforce any defined validations, defaults, casting, and field aliasing.
This example includes an optional conditional expression
await StoreLocations
.put({
cityId: "Atlanta1",
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "BuildingA1",
unitId: "B47",
category: "food/coffee",
leaseEndDate: "2020-03-22",
rent: "4500.00"
})
.where((attr, op) => op.eq(attr.rent, "4500.00"))
.go()
// Equivalent Params:
{
"Item": {
"cityId": "Atlanta1",
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "food/coffee",
"leaseEndDate": "2020-03-22",
"rent": "4500.00",
"discount": "0.00",
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_buildinga1#storeid_lattelarrys",
"gis1pk": "$mallstoredirectory#mallid_eastpointe",
"gsi1sk": "$mallstore_1#buildingid_buildinga1#unitid_b47",
"gis2pk": "$mallstoredirectory#storeid_lattelarrys",
"gsi2sk": "$mallstore_1#leaseenddate_2020-03-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
},
"TableName": "StoreDirectory",
"ConditionExpression": "#rent = :rent_w1",
"ExpressionAttributeNames": {
"#rent": "rent"
},
"ExpressionAttributeValues": {
":rent_w1": "4500.00"
}
}
Provide all required Attributes as defined in the model to create records as an array to .put(). ElectroDB will enforce any defined validations, defaults, casting, and field aliasing.
Note: Performing a Batch Put will return an array of "unprocessed" records. An empty array signifies all records were processed. If you want the raw DynamoDB response you can always use the option
{raw: true}, more detail found here: Query Options.
let unprocessed = await StoreLocations.put([
{
cityId: "LosAngeles1",
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
unitId: "a1",
category: "food/coffee",
leaseEndDate: "2022-03-22",
rent: "4500.00"
},
{
cityId: "LosAngeles1",
storeId: "MochaJoes",
mallId: "EastPointe",
buildingId: "F35",
unitId: "a2",
category: "food/coffee",
leaseEndDate: "2021-01-22",
rent: "1500.00"
}
]).go()
// Equivalent Params:
{
"RequestItems": {
"StoreDirectory": [
{
"PutRequest": {
"Item": {
"cityId": "LosAngeles1",
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "F34",
"unitId": "a1",
"category": "food/coffee",
"leaseEndDate": "2022-03-22",
"rent": "4500.00",
"discount": "0.00",
"pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys",
"gis1pk": "$mallstoredirectory#mallid_eastpointe",
"gsi1sk": "$mallstore_1#buildingid_f34#unitid_a1",
"gis2pk": "$mallstoredirectory#storeid_lattelarrys",
"gsi2sk": "$mallstore_1#leaseenddate_2022-03-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
}
}
},
{
"PutRequest": {
"Item": {
"cityId": "LosAngeles1",
"mallId": "EastPointe",
"storeId": "MochaJoes",
"buildingId": "F35",
"unitId": "a2",
"category": "food/coffee",
"leaseEndDate": "2021-01-22",
"rent": "1500.00",
"discount": "0.00",
"pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f35#storeid_mochajoes",
"gis1pk": "$mallstoredirectory#mallid_eastpointe",
"gsi1sk": "$mallstore_1#buildingid_f35#unitid_a2",
"gis2pk": "$mallstoredirectory#storeid_mochajoes",
"gsi2sk": "$mallstore_1#leaseenddate_2021-01-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
}
}
}
]
}
}
Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the facets defined in the Table Index. This is in keeping with DynamoDB's practice of returning back only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as facets but you can pass the query option {lastEvaluatedKeyRaw:true} override this behavior and return the Keys as they came from DynamoDB.
To update a record, pass all Table index facets to the update method and then pass set attributes that need to be updated. This example contains an optional conditional expression.
Note: If your update includes changes to an attribute that is also a facet for a global secondary index, you must provide all facets for that index.
await StoreLocations
.update({cityId, mallId, storeId, buildingId})
.set({category: "food/meal"})
.where((attr, op) => op.eq(attr.category, "food/coffee"))
.go()
// Equivalent Params:
{
"UpdateExpression": "SET #category = :category",
"ExpressionAttributeNames": {
"#category": "category"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category": "food/meal"
},
"TableName": "StoreDirectory",
"Key": {
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
},
"ConditionExpression": "#category = :category_w1"
}
When scanning for rows, you can use filters the same as you would any query. For more detial on filters, see the Where section.
Note: Scan functionality will be scoped to your Entity. This means your results will only include records that match the Entity defined in the model.
await StoreLocations.scan
.where(({category}, {eq}) => `
${eq(category, "food/coffee")} OR ${eq(category, "spite store")}
`)
.where(({leaseEndDate}, {between}) => `
${between(leaseEndDate, "2020-03", "2020-04")}
`)
.go()
// Equivalent Params:
{
"TableName": "StoreDirectory",
"ExpressionAttributeNames": {
"#category": "category",
"#leaseEndDate": "leaseEndDate",
"#pk": "pk",
"#sk": "sk",
"#__edb_e__": "__edb_e__",
"#__edb_v__": "__edb_v__"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category_w2": "spite store",
":leaseEndDate_w1": "2020-03",
":leaseEndDate_w2": "2020-04",
":pk": "$mallstoredirectory#cityid_",
":sk": "$mallstore_1#buildingid_",
":__edb_e__": "MallStore",
":__edb_v__": "1"
},
"FilterExpression": "begins_with(#pk, :pk) AND #__edb_e__ = :__edb_e__ AND #__edb_v__ = :__edb_v__ AND begins_with(#sk, :sk) AND (#category = :category_w1 OR #category = :category_w2) AND (#leaseEndDate between :leaseEndDate_w1 and :leaseEndDate_w2)"
}
In DynamoDB, update operations by default will insert a record if record being updated does not exist. In ElectroDB, the patch method will utilize the attribute_exists() parameter dynamically to ensure records are only "patched" and not inserted when updating.
await StoreLocations
.patch({cityId, mallId, storeId, buildingId})
.set({category: "food/meal"})
.where((attr, op) => op.eq(attr.category, "food/coffee"))
.go()
// Equivalent Params:
{
"UpdateExpression": "SET #category = :category",
"ExpressionAttributeNames": {
"#category": "category"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category": "food/meal"
},
"TableName": "StoreDirectory",
"Key": {
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
},
"ConditionExpression": "attribute_exists(pk) AND attribute_exists(sk) AND #category = :category_w1"
}
In DynamoDB, put operations by default will overwrite a record if record being updated does not exist. In ElectroDB, the patch method will utilize the attribute_not_exists() parameter dynamically to ensure records are only "created" and not overwriten when inserting new records into the table.
await StoreLocations
.create({
cityId: "Atlanta1",
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "BuildingA1",
unitId: "B47",
category: "food/coffee",
leaseEndDate: "2020-03-22",
rent: "4500.00"
})
.where((attr, op) => op.eq(attr.rent, "4500.00"))
.go()
// Equivalent Params:
{
"Item": {
"cityId": "Atlanta1",
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "food/coffee",
"leaseEndDate": "2020-03-22",
"rent": "4500.00",
"discount": "0.00",
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_buildinga1#storeid_lattelarrys",
"gis1pk": "$mallstoredirectory#mallid_eastpointe",
"gsi1sk": "$mallstore_1#buildingid_buildinga1#unitid_b47",
"gis2pk": "$mallstoredirectory#storeid_lattelarrys",
"gsi2sk": "$mallstore_1#leaseenddate_2020-03-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
},
"TableName": "StoreDirectory",
"ConditionExpression": "attribute_not_exists(pk) AND attribute_not_exists(sk) AND #rent = :rent_w1",
"ExpressionAttributeNames": {
"#rent": "rent"
},
"ExpressionAttributeValues": {
":rent_w1": "4500.00"
}
}
DynamoDB offers three methods to find records: get, query, and scan. In ElectroDB, there is a fourth type: find. Unlike get and query, the find method does not require you to provide keys, but under the covers it will leverage the attributes provided to find the best index to query on. Provide the find method will all properties known to match a record and ElectroDB will generate the most performant query it can to locate the results. This can be helpful with highly dynamic querying needs. If an index cannot be satisfied with the attributes provided, scan will be used as a last resort.
await StoreLocations.find({
mallId: "EastPointe",
buildingId: "BuildingA1",
leaseEndDate: "2020-03-22",
rent: "1500.00"
}).go()
// Equivalent Params:
{
"KeyConditionExpression": "#pk = :pk and begins_with(#sk1, :sk1)",
"TableName": "StoreDirectory",
"ExpressionAttributeNames": {
"#mallId": "mallId",
"#buildingId": "buildingId",
"#leaseEndDate": "leaseEndDate",
"#rent": "rent",
"#pk": "gis1pk",
"#sk1": "gsi1sk"
},
"ExpressionAttributeValues": {
":mallId1": "EastPointe",
":buildingId1": "BuildingA1",
":leaseEndDate1": "2020-03-22",
":rent1": "1500.00",
":pk": "$mallstoredirectory#mallid_eastpointe",
":sk1": "$mallstore_1#buildingid_buildinga1#unitid_"
},
"IndexName": "gis1pk-gsi1sk-index",
"FilterExpression": "#mallId = :mallId1 AND#buildingId = :buildingId1 AND#leaseEndDate = :leaseEndDate1 AND#rent = :rent1"
}
After invoking the Access Pattern with the required Partition Key Facets, you can now choose what Sort Key Facets are applicable to your query. Examine the table in Sort Key Operations for more information on the available operations on a Sort Key.
When you define your indexes in your model, you are defining the access patterns of your entity. The facets you choose, and their order, ultimately define the finite set of index queries that can be made. The more you can leverage these index queries the better from both a cost and performance perspective.
Unlike Partition Keys, Sort Keys can be partially provided. We can leverage this to multiply our available access patterns and use the Sort Key Operations: begins, between, lt, lte, gt, and gte. These queries are more performant and cost effective than filters. The costs associated with DynamoDB directly correlate to how effectively you leverage Sort Key Operations.
For a comprehensive and interactive guide to build queries please visit this runkit: https://runkit.com/tywalch/electrodb-building-queries.
One important consideration when using Sort Key Operations to make is when to use and not to use "begins".
It is possible to supply partially supply Sort Key facets. While they do have to be in order, you can provide only some of the properties to ElectroDB. By default, when you supply a partial Sort Key in the Access Pattern method, ElectroDB will create a beginsWith query. The difference between doing that and using .begins() is that ElectroDB will post-pend the next facet's label onto the query.
The difference is nuanced and makes better sense with an example, but the rule of thumb is that data passed to the Access Pattern method should represent values you know strictly equal the value you want.
The following examples will use the following Access Pattern definition for units:
"units": {
"index": "gis1pk-gsi1sk-index",
"pk": {
"field": "gis1pk",
"facets": ["mallId"]
},
"sk": {
"field": "gsi1sk",
"facets": ["buildingId", "unitId"]
}
}
An Access Pattern method is the method after query you use to query a particular accessType:
// Example #1
StoreLocations.query.units({mallId, buildingId}).go();
// -----------------------^^^^^^^^^^^^^^^^^^^^^^
Data passed to the Access Pattern method is considered to be full, known, data. In the above example, we are saying we know the mallId, buildingId and unitId.
Alternatively, if you only know the start of a piece of data, use .begins():
// Example #2
StoreLocations.query.units({mallId}).begins({buildingId}).go();
// ---------------------------------^^^^^^^^^^^^^^^^^^^^^
Data passed to the .begins() method is considered to be partial data. In the second example, we are saying we know the mallId and buildingId, but only know the beginning of unitId.
For the above queries we see two different sort keys:
"$mallstore_1#buildingid_f34#unitid_""$mallstore_1#buildingid_f34"The first example shows how ElectroDB post-pends the label of the next facet (unitId) on the SortKey to ensure that buildings such as "f340" are not included in the query. This is useful to prevent common issues with multi-facet sort keys like accidental over-querying.
The second example allows you to make queries that do include buildings such as "f340" or "f3409" or "f340356346".
For these reasons it is important to consider that Data passed to the Access Pattern method is considered to be full, known, data.
Collections allow you to query across Entities. To use them you need to join your Models onto a Service instance.
Using the TaskApp Models defined in Models, these models share a
collectioncalledassignmentson the indexgsi3pk-gsi3sk-index
const table = "projectmanagement";
const TaskApp = new Service("projectmanagement", { client, table });
TaskApp
.join(EmployeesModel) // TaskApp.entities.employees
.join(TasksModel); // TaskApp.entities.tasks
Available on your Service are two objects: entites and collections. Entities available on entities have the same capabilities as they would if created individually. When a Model added to a Service with join however, its Collections are automatically added and validated with the other Models joined to that Service. These Collections are available on collections.
TaskApp.collections.assignments({employee: "JExotic"}).params();
// Results
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: { ':pk': '$taskapp_1#employee_joeexotic', ':sk1': '$assignments' },
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
IndexName: 'gsi3pk-gsi3sk-index'
}
Collections do not have the same query functionality and as an Entity, though it does allow for inline filters like an Entity. The attributes available on the filter object include all attributes across entities.
TaskApp.collections
.assignments({employee: "CBaskin"})
.filter((attributes) => `
${attributes.project.notExists()} OR ${attributes.project.contains("murder")}
`)
// Results
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#project': 'project', '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: {
':project1': 'murder',
':pk': '$taskapp_1#employee_carolbaskin',
':sk1': '$assignments'
},
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
IndexName: 'gsi3pk-gsi3sk-index',
FilterExpression: '\n\t\tattribute_not_exists(#project) OR contains(#project, :project1)\n\t'
}
Lastly, all query chains end with either a .go() or a .params() method invocation. These will either execute the query to DynamoDB (.go()) or return formatted parameters for use with the DynamoDB docClient (.params()).
Both .params() and .go() take a query configuration object which is detailed more in the section Query Options.
The params method ends a query chain, and synchronously formats your query into an object ready for the DynamoDB docClient.
For more information on the options available in the
configobject, checkout the section Query Options.
let config = {};
let stores = MallStores.query
.leases({ mallId })
.between(
{ leaseEndDate: "2020-06-01" },
{ leaseEndDate: "2020-07-31" })
.filter(attr) => attr.rent.lte("5000.00"))
.params(config);
// Results:
{
IndexName: 'idx2',
TableName: 'electro',
ExpressionAttributeNames: { '#rent': 'rent', '#pk': 'idx2pk', '#sk1': 'idx2sk' },
ExpressionAttributeValues: {
':rent1': '5000.00',
':pk': '$mallstoredirectory_1#mallid_eastpointe',
':sk1': '$mallstore#leaseenddate_2020-06-01#rent_',
':sk2': '$mallstore#leaseenddate_2020-07-31#rent_'
},
KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
FilterExpression: '#rent <= :rent1'
}
The go method ends a query chain, and asynchronously queries DynamoDB with the client provided in the model.
For more information on the options available in the
configobject, check out the section Query Options.
let config = {};
let stores = MallStores.query
.leases({ mallId })
.between(
{ leaseEndDate: "2020-06-01" },
{ leaseEndDate: "2020-07-31" })
.filter(({rent}) => rent.lte("5000.00"))
.go(config);
As of September 29th 2020 the
.page()now returns the facets that make up theExclusiveStartKeyinstead of theExclusiveStartKeyitself. To get back only theExclusiveStartKey, add the flagexclusiveStartKeyRawto your query options. If you treated this value opaquely no changes are needed, or if you used therawflag.
The page method ends a query chain, and asynchronously queries DynamoDB with the client provided in the model. Unlike the .go(), the .page() method returns a tupple.
The first element for (Entity)[#entity] page query is the "page": an object contains the facets that make up the ExclusiveStartKey that is returned by the DynamoDB client. This is very useful in multi-tenant applications where only some facets are exposed to the client, or there is a need to prevent leaking keys between entities. If there is no ExclusiveStartKey this value will be null. On subsequent calls to .page(), pass the results returned from the previous call to .page() or construct the facets yourself.
The first element for (Collection)[#collections] page query is the ExclusiveStartKey as it was returned by the DynamoDB client.
Note: It is highly recommended to use the
lastEvaluatedKeyRawflag when using.page()in conjunction with scans. This is because when using scan on large tables the docClient may return anExclusiveStartKeyfor a record that does not belong to entity making the query (regardless of the filters set). In these cases ElectroDB will return null (to avoid leaking the keys of other entities) when further pagination may be needed to find your records.
The second element is the results of the query, exactly as it would be returned through a query operation.
Note: When calling
.page()the first argument is reserved for the "page" returned from a previous query, the second parameter is for Query Options. For more information on the options available in theconfigobject, check out the section Query Options.
let [page, stores] = await MallStores.query
.leases({ mallId })
.page();
let [pageTwo, moreStores] = await MallStores.query
.leases({ mallId })
.page(page, {});
// page:
// {
// storeId: "LatteLarrys",
// mallId: "EastPointe",
// buildingId: "BuildingA1",
// unitId: "B47"
// }
// stores
// [{
// mall: '3010aa0d-5591-4664-8385-3503ece58b1c',
// leaseEnd: '2020-01-20',
// sector: '7d0f5c19-ec1d-4c1e-b613-a4cc07eb4db5',
// store: 'MNO',
// unit: 'B5',
// id: 'e0705325-d735-4fe4-906e-74091a551a04',
// building: 'BuildingE',
// category: 'food/coffee',
// rent: '0.00'
// },
// {
// mall: '3010aa0d-5591-4664-8385-3503ece58b1c',
// leaseEnd: '2020-01-20',
// sector: '7d0f5c19-ec1d-4c1e-b613-a4cc07eb4db5',
// store: 'ZYX',
// unit: 'B9',
// id: 'f201a1d3-2126-46a2-aec9-758ade8ab2ab',
// building: 'BuildingI',
// category: 'food/coffee',
// rent: '0.00'
// }]
For a comprehensive and interactive guide to build queries please visit this runkit: https://runkit.com/tywalch/electrodb-building-queries.
const cityId = "Atlanta1";
const mallId = "EastPointe";
const storeId = "LatteLarrys";
const unitId = "B24";
const buildingId = "F34";
const june = "2020-06";
const july = "2020-07";
const discount = "500.00";
const maxRent = "2000.00";
const minRent = "5000.00";
// Lease Agreements by StoreId
await StoreLocations.query.leases({storeId}).go()
// Lease Agreement by StoreId for March 22nd 2020
await StoreLocations.query.leases({storeId, leaseEndDate: "2020-03-22"}).go()
// Lease agreements by StoreId for 2020
await StoreLocations.query.leases({storeId}).begins({leaseEndDate: "2020"}).go()
// Lease Agreements by StoreId after March 2020
await StoreLocations.query.leases({storeId}).gt({leaseEndDate: "2020-03"}).go()
// Lease Agreements by StoreId after, and including, March 2020
await StoreLocations.query.leases({storeId}).gte({leaseEndDate: "2020-03"}).go()
// Lease Agreements by StoreId before 2021
await StoreLocations.query.leases({storeId}).lt({leaseEndDate: "2021-01"}).go()
// Lease Agreements by StoreId before Feburary 2021
await StoreLocations.query.leases({storeId}).lte({leaseEndDate: "2021-02"}).go()
// Lease Agreements by StoreId between 2010 and 2020
await StoreLocations.query
.leases({storeId})
.between(
{leaseEndDate: "2010"},
{leaseEndDate: "2020"})
.go()
// Lease Agreements by StoreId after, and including, 2010 in the city of Atlanta and category containing food
await StoreLocations.query
.leases({storeId})
.gte({leaseEndDate: "2010"})
.where((attr, op) => `
${op.eq(attr.cityId, "Atlanta1")} AND ${op.contains(attr.category, "food")}
`)
.go()
// Rents by City and Store who's rent discounts match a certain rent/discount criteria
await StoreLocations.query
.units({mallId})
.begins({leaseEndDate: june})
.rentDiscount(discount, maxRent, minRent)
.go()
// Stores by Mall matching a specific category
await StoreLocations.query
.units({mallId})
.byCategory("food/coffee")
.go()```
## Query Options
By default **ElectroDB** enables you to work with records as the names and properties defined in the model. Additionally, it removes the need to deal directly with the docClient parameters which can be complex for a team without as much experience with DynamoDB. The Query Options object can be passed to both the `.params()` and `.go()` methods when building you query. Below are the options available:
```typescript
let options = {
params?: object,
table?: string,
raw?: boolean,
includeKeys?: boolean,
originalErr?: boolean,
lastEvaluatedKeyRaw?: boolean,
};
Query options can be added the .params(), .go() and .page() to change query behavior or add customer parameters to a query.
| Option | Description |
|---|---|
| params | Properties added to this object will be merged onto the params sent to the document client. Any conflicts with ElectroDB will favor the params specified here. |
| table | Use a different table than the one defined in the model |
| raw | Returns query results as they were returned by the docClient. |
| includeKeys | By default, ElectroDB does not return partition, sort, or global keys in its response. |
| lastEvaluatedKeyRaw | Used in batch processing and .pages() calls to override ElectroDBs default behaviour to break apart LastEvaluatedKeys or the Unprocessed records into facets. See more in the seconds for (Pages)[#pages], (Batch Get)[#batch-get], (BatchDelete)[#batch-write-delete-records], and (BatchPut)[#batch-write-put-records]. |
| originalErr | By default, ElectroDB alters the stacktrace of any exceptions thrown by the DynamoDB client to give better visibility to the developer. Set this value equal to true to turn off this functionality and return the error unchanged. |
| Error Code | Description |
|---|---|
| 1000s | Configuration Errors |
| 2000s | Invalid Queries |
| 3000s | User Defined Errors |
| 4000s | DynamoDB Errors |
| 5000s | Unexpected Errors |
Code: 1001
Why this occurred:
If a DynamoDB DocClient is not passed to the constructor of an Entity or Service (client), ElectroDB will be unable to query DynamoDB. This error will only appear when a query(using go()) is made because ElectroDB is still useful without a DocClient through the use of it's params() method.
What to do about it: For an Entity be sure to pass the DocClient as the second param to the constructor:
new Entity(schema, {client})
For a Service, the client is passed the same way, as the second param to the constructor:
new Service("", {client});
Code: 1002
Why this occurred: You tried to modify the entity identifier on an Entity.
What to do about it: Make sure the you spelled the identifier correctly or that you actually passed a replacement.
Code: 1003
Why this occurred: You are trying to use the custom Key Facet Template and the format you passed is invalid.
What to do about it: Checkout the section on Facet Templates and verify your template conforms to the rules detailed there.
Code: 1004
Why this occurred: Your model contains duplicate indexes. This could be because you accidentally included an index twice or even forgot to add an index name on a secondary index, which would be interpreted as "duplicate" to the Table's Primary index.
What to do about it: Double check your indexes as theyre defined on the model for duplicate indexes. The error should specify which index has been duplicated.
{
indexes: {
index1: {
index: "idx1", // <-- duplicate "idx1"
pk: {},
sk: {}
},
index2: {
index: "idx1", // <-- duplicate "idx1"
pk: {},
sk: {}
}
}
}
Code: 1005
Why this occurred:
You have added a collection to an index that does not have an SK. Because Collections are used to help query across entities via the Sort Key, not having a Sort Key on an index defeats the purpose of a Collection.
What to do about it: If your index does have an sk but youre unsure of how to inform electro without setting facets to the SK, add the SK object to the index and use an empty array for Facets:
// ElectroDB interprets as index *not having* an SK.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
}
}
}
}
// ElectroDB interprets as index *having* SK, but this model doesnt attach any facets to it.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
},
sk: {
field: "sk",
facets: []
}
}
}
}
Code: 1006
Why this occurred: You have assigned the same collection name to multiple indexes. This is not allowed because collection names must be unique.
What to do about it: Determine a new naming scheme
Code: 1007
Why this occurred:
DynamoDB requires the definition of at least one Primary Index on the table. In Electro this is defined as an Index without an index property. Each model needs at least one, and the facets used for this index must ensure each composite represents a unique record.
What to do about it: Identify the index youre using as the Primary Index and ensure it does not have an index property on it's definition.
// ElectroDB interprets as the Primary Index because it lacks an `index` property.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["org"]
},
sk: {
field: "sk",
facets: ["id"]
}
}
}
}
// ElectroDB interprets as a Global Secondary Index because it has an `index` property.
{
indexes: {
myIndex: {
index: "gsi1"
pk: {
field: "gsipk1",
facets: ["org"]
},
sk: {
field: "gsisk1",
facets: ["id"]
}
}
}
}
Code: 1008
Why this occurred: Some attribute on your model has an invalid configuration.
What to do about it: Use the error to identify which column needs to examined, double check the properties on that attribute. Checkout the section on (Attributes)[#attributes] for more information on how they are structured.
Code: 1009
Why this occurred: Some properties on your model are missing or invalid.
What to do about it: Checkout the section on (Models)[#model] to verify your model against what is expected.
Code: 1010
Why this occurred: Some properties on your options object are missing or invalid.
What to do about it: Checkout the section on (Model/Service Options)[#model-service-options] to verify your model against what is expected.
Code: 1014
Why this occurred:
An Index in your model references the same field twice across indexes. The field property in the definition of an index is a mapping to the name of the field assigned to the the PK or SK of an index.
What to do about it: This is likely a typo, if not double check the names of the fields you assigned to be the PK and SK of your index, these field names must be unique.
Code: 1014
Why this occurred: Within one index you tried to use the same facet in both the PK and SK. A facet may only be used once within an index. With electrodb it is not uncommon to use the same value as both the PK and SK when when a Sort Key exists on a table -- this usually is done because some value is required in that column but for that entity it is not neccessary. If this is your situation remember that ElectroDB does put a value in the SortKey even if does not include a facet, checkout (this seciton)[#collection-without-an-sk] for more information.
What to do about it: Determine how you can change your access pattern to not duplicate the facet. Remember that an empty array for an SK is valid.
Code: 2002
Why this occurred: The current request is missing some facets to complete the query based on the model definition. Facets are used to create the Partition and Sort keys. In DynamoDB Partition keys cannot be partially included, and Sort Keys can be partially include they must be at least passed in the order they are defined on the model.
What to do about it: The error should describe the missing facets, ensure those facets are included in the query or update the model to reflect the needs of the access pattern.
Code: 4002
Why this occurred:
Likely you were were calling .page() on a scan. If you werent please make an issue and include as much detail about your query as possible.
What to do about it: It is highly recommended to use the exclusiveStartKeyRaw flag when using .page() in conjunction with scans. This is because when using scan on large tables the docClient may return an ExclusiveStartKey for a record that does not belong to entity making the query (regardless of the filters set). In these cases ElectroDB will return null (to avoid leaking the keys of other entities) when further pagination may be needed to find your records.
// example
model.scan.page({exclusiveStartKeyRaw: true});
Code: 4001
Why this occurred: DynamoDB didnt like something about your query.
What to do about it:
By default electrodb tries to keep the stack trace close to your code, ideally this can help you identify what might be going on. A tip to help with troubleshooting: use .params() to get insight into how your query is being converted to DocClient params.
Want to just play with ElectroDB instead of read about it? Try it out for yourself! https://runkit.com/tywalch/electrodb-building-queries
For an example, lets look at the needs of application used to manage Employees. The application Looks at employees, offices, tasks, and projects.
const EmployeesModel = {
model: {
entity: "employees",
version: "1",
service: "taskapp",
},
attributes: {
employee: "string",
firstName: "string",
lastName: "string",
office: "string",
title: "string",
team: ["development", "marketing", "finance", "product"],
salary: "string",
manager: "string",
dateHired: "string",
birthday: "string",
},
indexes: {
employee: {
pk: {
field: "pk",
facets: ["employee"],
},
sk: {
field: "sk",
facets: [],
},
},
coworkers: {
index: "gsi1pk-gsi1sk-index",
collection: "workplaces",
pk: {
field: "gsi1pk",
facets: ["office"],
},
sk: {
field: "gsi1sk",
facets: ["team", "title", "employee"],
},
},
teams: {
index: "gsi2pk-gsi2sk-index",
pk: {
field: "gsi2pk",
facets: ["team"],
},
sk: {
field: "gsi2sk",
facets: ["title", "salary", "employee"],
},
},
employeeLookup: {
collection: "assignements",
index: "gsi3pk-gsi3sk-index",
pk: {
field: "gsi3pk",
facets: ["employee"],
},
sk: {
field: "gsi3sk",
facets: [],
},
},
roles: {
index: "gsi4pk-gsi4sk-index",
pk: {
field: "gsi4pk",
facets: ["title"],
},
sk: {
field: "gsi4sk",
facets: ["salary", "employee"],
},
},
directReports: {
index: "gsi5pk-gsi5sk-index",
pk: {
field: "gsi5pk",
facets: ["manager"],
},
sk: {
field: "gsi5sk",
facets: ["team", "office", "employee"],
},
},
},
filters: {
upcomingCelebrations: (attributes, startDate, endDate) => {
let { dateHired, birthday } = attributes;
return `${dateHired.between(startDate, endDate)} OR ${birthday.between(
startDate,
endDate,
)}`;
},
},
};
const TasksModel = {
model: {
entity: "tasks",
version: "1",
service: "taskapp",
},
attributes: {
task: "string",
project: "string",
employee: "string",
description: "string",
},
indexes: {
task: {
pk: {
field: "pk",
facets: ["task"],
},
sk: {
field: "sk",
facets: ["project", "employee"],
},
},
project: {
index: "gsi1pk-gsi1sk-index",
pk: {
field: "gsi1pk",
facets: ["project"],
},
sk: {
field: "gsi1sk",
facets: ["employee", "task"],
},
},
assigned: {
collection: "assignements",
index: "gsi3pk-gsi3sk-index",
pk: {
field: "gsi3pk",
facets: ["employee"],
},
sk: {
field: "gsi3sk",
facets: ["project", "task"],
},
},
},
};
const OfficesModel = {
model: {
entity: "offices",
version: "1",
service: "taskapp",
},
attributes: {
office: "string",
country: "string",
state: "string",
city: "string",
zip: "string",
address: "string",
},
indexes: {
locations: {
pk: {
field: "pk",
facets: ["country", "state"],
},
sk: {
field: "sk",
facets: ["city", "zip", "office"],
},
},
office: {
index: "gsi1pk-gsi1sk-index",
collection: "workplaces",
pk: {
field: "gsi1pk",
facets: ["office"],
},
sk: {
field: "gsi1sk",
facets: [],
},
},
},
};
Join models on a new Service called EmployeeApp
const DynamoDB = require("aws-sdk/clients/dynamodb");
const client = new DynamoDB.DocumentClient({region: "us-east-1"});
const { Service } = require("electrodb");
const table = "projectmanagement";
const EmployeeApp = new Service("EmployeeApp", { client, table });
EmployeeApp
.join(EmployeesModel) // EmployeeApp.entities.employees
.join(TasksModel) // EmployeeApp.entities.tasks
.join(OfficesModel); // EmployeeApp.entities.tasks
Fulfilling Requirement #1.
EmployeeApp.collections.assignements({employee: "CBaskin"}).go();
Returns the following:
{
employees: [{
employee: "cbaskin",
firstName: "carol",
lastName: "baskin",
office: "big cat rescue",
title: "owner",
team: "cool cats and kittens",
salary: "1,000,000",
manager: "",
dateHired: "1992-11-04",
birthday: "1961-06-06",
}].
tasks: [{
task: "Feed tigers",
description: "Prepare food for tigers to eat",
project: "Keep tigers alive",
employee: "cbaskin"
}, {
task: "Fill water bowls",
description: "Ensure the tigers have enough water",
project: "Keep tigers alive",
employee: "cbaskin"
}]
}
Fulfilling Requirement #2.
EmployeeApp.collections.workplaces({office: "big cat rescue"}).go()
Returns the following:
{
employees: [{
employee: "cbaskin",
firstName: "carol",
lastName: "baskin",
office: "big cat rescue",
title: "owner",
team: "cool cats and kittens",
salary: "1,000,000",
manager: "",
dateHired: "1992-11-04",
birthday: "1961-06-06",
}],
offices: [{
office: "big cat rescue",
country: "usa",
state: "florida",
city: "tampa"
zip: "12345"
address: "123 Kitty Cat Lane"
}]
}
Fulfilling Requirement #3.
EmployeeApp.entities.tasks.query.assigned({employee: "cbaskin"}).go();
Returns the following:
[
{
task: "Feed tigers",
description: "Prepare food for tigers to eat",
project: "Keep tigers alive",
employee: "cbaskin"
}, {
task: "Fill water bowls",
description: "Ensure the tigers have enough water",
project: "Keep tigers alive",
employee: "cbaskin"
}
]
Fulfilling Requirement #4.
EmployeeApp.entities.tasks.query.project({project: "Murder Carol"}).go();
Returns the following:
[
{
task: "Hire hitman",
description: "Find someone to murder Carol",
project: "Murder Carol",
employee: "jexotic"
}
];
Fulfilling Requirement #5.
EmployeeApp.entities.office.locations({country: "usa", state: "florida"}).go()
Returns the following:
[
{
office: "big cat rescue",
country: "usa",
state: "florida",
city: "tampa"
zip: "12345"
address: "123 Kitty Cat Lane"
}
]
Fulfilling Requirement #6.
EmployeeApp.entities.employees
.roles({title: "animal wrangler"})
.lte({salary: "150.00"})
.go()
Returns the following:
[
{
employee: "ssaffery",
firstName: "saff",
lastName: "saffery",
office: "gw zoo",
title: "animal wrangler",
team: "keepers",
salary: "105.00",
manager: "jexotic",
dateHired: "1999-02-23",
birthday: "1960-07-11",
}
]
Fulfilling Requirement #7.
EmployeeApp.entities.employees
.workplaces({office: "gw zoo"})
.upcomingCelebrations("2020-05-01", "2020-06-01")
.go()
Returns the following:
[
{
employee: "jexotic",
firstName: "joe",
lastName: "maldonado-passage",
office: "gw zoo",
title: "tiger king",
team: "founders",
salary: "10000.00",
manager: "jlowe",
dateHired: "1999-02-23",
birthday: "1963-03-05",
}
]
Fulfilling Requirement #8.
EmployeeApp.entities.employees
.reports({manager: "jlowe"})
.go()
Returns the following:
[
{
employee: "jexotic",
firstName: "joe",
lastName: "maldonado-passage",
office: "gw zoo",
title: "tiger king",
team: "founders",
salary: "10000.00",
manager: "jlowe",
dateHired: "1999-02-23",
birthday: "1963-03-05",
}
]
For an example, lets look at the needs of application used to manage Shopping Mall properties. The application assists employees in the day-to-day operations of multiple Shopping Malls.
Create a new Entity using the StoreLocations schema defined above
const DynamoDB = require("aws-sdk/clients/dynamodb");
const client = new DynamoDB.DocumentClient();
const StoreLocations = new Entity(model, {client, table: "StoreLocations"});
await StoreLocations.create({
mallId: "EastPointe",
storeId: "LatteLarrys",
buildingId: "BuildingA1",
unitId: "B47",
category: "spite store",
leaseEndDate: "2020-02-29",
rent: "5000.00",
}).go();
Returns the following:
{
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "spite store",
"leaseEndDate": "2020-02-29",
"rent": "5000.00",
"discount": "0.00",
}
When updating a record, you must include all Facets associated with the table's primary PK and SK.
let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
await StoreLocations.update({storeId, mallId, buildingId, unitId}).set({
leaseEndDate: "2021-02-28"
}).go();
Returns the following:
{
"leaseEndDate": "2021-02-28"
}
When retrieving a specific record, you must include all Facets associated with the table's primary PK and SK.
let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
await StoreLocations.get({storeId, mallId, buildingId, unitId}).go();
Returns the following:
{
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "spite store",
"leaseEndDate": "2021-02-28",
"rent": "5000.00",
"discount": "0.00"
}
When removing a specific record, you must include all Facets associated with the table's primary PK and SK.
let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let storeId = "LatteLarrys";
await StoreLocations.delete({storeId, mallId, buildingId, unitId}).go();
Returns the following:
{}
Fulfilling Requirement #1.
let mallId = "EastPointe";
let stores = await StoreLocations.malls({mallId}).query().go();
Fulfilling Requirement #1.
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let stores = await StoreLocations.malls({mallId}).query({buildingId}).go();
Fulfilling Requirement #1.
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let stores = await StoreLocations.malls({mallId}).query({buildingId, unitId}).go();
Fulfilling Requirement #2.
let mallId = "EastPointe";
let category = "food/coffee";
let stores = await StoreLocations.malls({mallId}).byCategory(category).go();
Fulfilling Requirement #3.
let mallId = "EastPointe";
let q2StartDate = "2020-04-01";
let stores = await StoreLocations.leases({mallId}).lt({leaseEndDate: q2StateDate}).go();
Fulfilling Requirement #3.
let mallId = "EastPointe";
let q4StartDate = "2020-10-01";
let q4EndDate = "2020-12-31";
let stores = await StoreLocations.leases(mallId)
.between (
{leaseEndDate: q4StartDate},
{leaseEndDate: q4EndDate})
.go();
Fulfilling Requirement #3.
let mallId = "EastPointe";
let yearStarDate = "2020-01-01";
let yearEndDate = "2020-12-31";
let storeId = "LatteLarrys";
let stores = await StoreLocations.leases(mallId)
.between (
{leaseEndDate: yearStarDate},
{leaseEndDate: yearEndDate})
.filter(attr => attr.category.eq("Spite Store"))
.go();
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let storeId = "LatteLarrys";
let stores = await StoreLocations.malls({mallId}).query({buildingId, storeId}).go();
NOTE: The ElectroCLI is currently in a beta phase and subject to change.
Electro is a CLI utility toolbox for extending the functionality of ElectroDB. Current functionality of the CLI allows you to:
Entities and Services.Entities, Services, Models directly from the command line.Entities, Services, Models.For usage and installation details you can learn more here.
This section is to detail any breaking changes made on the journey to a stable 1.0 product.
It became clear when I added the concept of a Service that the "version" paradigm of having the version in the PK wasnt going to work. This is because collection queries use the same PK for all entities and this would prevent some entities in a Service to change versions without impacting the service as a whole. The better more is the place the version in the SK after the entity name so that all version of an entity can be queried. This will work nicely into the migration feature I have planned that will help migrate between model versions.
To address this change, I decide it would be best to change the structure for defining a model, which is then used as heuristic to determine where to place the version in the key (PK or SK). This has the benefit of not breaking existing models, but does increase some complexity in the underlying code.
Additionally a change was made to the Service class. New Services would take a string of the service name instead of an object as before.
In the old scheme, version came after the service name (see ^).
pk: $mallstoredirectory_1#mall_eastpointe
^
sk: $mallstores#building_buildinga#store_lattelarrys
In the new scheme, version comes after the entity name (see ^).
pk: $mallstoredirectory#mall_eastpointe
sk: $mallstores_1#building_buildinga#store_lattelarrys
^
In practice the change looks like this for use of Entity:
const DynamoDB = require("aws-sdk/clients/dynamodb");
const {Entity} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const table = "dynamodb_table_name";
// old way
let old_schema = {
entity: "model_name",
service: "service_name",
version: "1",
table: table,
attributes: {},
indexes: {}
};
new Entity(old_schema, {client});
// new way
let new_schema = {
model: {
entity: "model_name",
service: "service_name",
version: "1",
},
attributes: {},
indexes: {}
};
new Entity(new_schema, {client, table});
And changes to usage of Service would look like this:
const DynamoDB = require("aws-sdk/clients/dynamodb");
const {Service} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const table = "dynamodb_table_name";
// old way
new Service({
service: "service_name",
version: "1",
table: table,
}, {client});
// new way
new Service("service_name", {client, table});
model to give more general control of interactions with the Entity.FAQs
A library to more easily create and interact with multiple entities and heretical relationships in dynamodb
The npm package electrodb receives a total of 411,753 weekly downloads. As such, electrodb popularity was classified as popular.
We found that electrodb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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