ElectroDB
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.
Features
- Attribute Schema Enforcement - Define a schema for your entities with enforced attribute validation, defaults, types, aliases, and more.
- Easily Compose Hierarchical Access Patterns - Plan and design hierarchical keys for your indexes to multiply your possible access patterns.
- Single Table Entity Segregation - Entities created with ElectroDB will not conflict with other entities when using a single table.
- Simplified Sort Key Condition Querying - Write efficient sort key queries by easily building compose keys.
- Simplified Filter Composition - Easily create complex readable filters for DynamoDB queries without worrying about the implementation of
ExpressionAttributeNames
, ExpressionAttributeValues
. - Easily Query Across Entities - Define "collections" to create powerful/idiomatic queries that return multiple entities in a single request.
- Automatic Index Selection - Use
.find()
method to dynamically and efficiently query based on defined sort key structures. - Simplified Pagination API - Use
.page()
to easily paginate through result sets. - Use With Your Existing Solution - If you are already using DynamoDB, and want to use ElectroDB, use custom Facet Templates to leverage your existing key structures.
- Generate Type Definitions - Generate TypeScript type definition files (
.d.ts
) based on your model. - Query Directly via the Terminal - Execute queries against your
Entities
, Services
, Models
directly from the command line. - Stand Up HTTP Service for Entities - stand up an HTTP Service to interact with your
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)"
}
Table of Contents
Installation
Install from NPM
npm install electrodb --save
Usage
Require Entity
and/or Service
from electrodb
:
const {Entity, Service} = require("electrodb");
Entities and Services
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 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.
Entities
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 is assigned to an employee.
Require or import Entity
from electrodb
:
const {Entity} = require("electrodb");
Services
In ElectroDB a Service
represents a collection of related Entities. Services allow 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");
Join
Create individual Entities with the Models or join
them via a Service.
let table = "my_table_name";
let employees = new Entity(EmployeesModel, { client, table });
let tasks = new Entity(TasksModel, { client, table });
let TaskApp = new Service("TaskApp", { client, table });
TaskApp
.join(employees)
.join(tasks);
let TaskApp = new Service("TaskApp", { client, table });
TaskApp
.join(EmployeesModel)
.join(TasksModel);
When joining a Model/Entity to a Service, ElectroDB will perform a number of validations to ensure that Entity conforms to expectations collectively established by all joined Entities.
- Entity names must be unique across a Service.
- Collection names must be unique across a Service.
- The name of the Service in the Model must match the Name defined on the Service instance.
- Joined instances must be type Model or Entity.
- If the attributes of an Entity have overlapping names with other attributes in that service, they must all have compatible or matching attribute options.
- All primary and global secondary indexes must have the same name field names and be written to assume SortKeys exist/don't exist in the same manor. See Indexes.
- All models conform to the same model format. If you created your model prior to ElectroDB version 0.9.19 see section Version 1 Migration.
Model
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"],
},
},
},
};
Model Properties
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 |
Service Options
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
Attributes define an Entity record. The AttributeName
represents the value your code will use to represent an attribute.
Pro-Tip:
Using the field
property, you can map an AttributeName
to 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 as organization
but want to save the attribute with a field name of org
in DynamoDB.
Simple Syntax
Assign just the type
of the attribute directly to the attribute name. Types 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[]
}
Expanded Syntax
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 | Flag an attribute as required to be present when creating a record. |
default | value , () => value | no | Either the default value itself, as a string literal, 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 in DynamoDB, if named differently in the schema attributes. Defaults to the AttributeName as defined in the schema. |
readOnly | boolean | no | Prevents an attribute from being updated after the record has been created. Attributes used in the composition of the table's primary Partition Key and Sort Key are 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 to 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 to 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. |
Attribute Getters and Setters
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 pure synchronous functions and may be invoked multiple times during one query.
Important Note: Using getters/setters on Facet Attributes is not recommended without considering the consequences of how that will impact your keys. When a Facet Attribute is supplied for a new record via a put
or create
operation, or is changed via a patch
or updated
operation, the Attribute's set
callback will be invoked prior to formatting/building your record's keys.
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:
- Setters for all Attributes will always be invoked when performing a
create
or put
operation. - Setters will only be invoked when an Attribute is modified when performing a
patch
or update
operation.
Attribute Validation
The 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 value with length is returned, the text will be considered the reason the value is invalid. It will generate a new exception this text as the message. |
(value: T) => boolean | If a boolean value 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 or undefined value is returned, will be treated as successful, in this scenario you can throw an Error yourself to interrupt the query |
Indexes
When using ElectroDB, indexes are referenced by their AccessPatternName
. This allows you to maintain generic index names on your DynamoDB table, but reference domain specific names while using your ElectroDB Entity. These will often be referenced as "Access Patterns".
All DynamoDB table start with at least a PartitionKey with an optional SortKey, this can be referred to as the "Table Index". The indexes
object requires at least the definition of this Table Index Partition Key and (if applicable) Sort Key.
In your model, the Table Index this is expressed as an Access Pattern without an index
property. For Secondary Indexes, use the index
property to define the name of the index as defined on your DynamoDB table.
Within these AccessPatterns, you define the PartitionKey and (optionally) SortKeys that are present on your DynamoDB table and map the key's name on the table with the field
property.
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 in DynamoDB, 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 in DynamoDB, 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). |
Indexes Without Sort Keys
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 for more detail.
Note: It is generally recommended to always use Sort Keys when using ElectroDB as they allow for more advanced query opportunities. Even if your model doesn't need an additional property to define a unique record, having an sk
with no facets still opens the door to many more query opportunities like collections.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
}
}
}
}
Indexes With Sort Keys
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.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
},
sk: {
field: "sk",
facets: []
}
}
}
}
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 a 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:
- As a Facet Array
- As a Facet Template
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: '$mallstores#mallid_mallvalue#buildingid_buildingvalue#unitid_unitvalue'
}
For PK
values, the service
and version
values from the model are prefixed onto the key.
For SK
values, the entity
value from the model is prefixed onto the key.
Facet Arrays
In a Facet Array, each element is the name of the corresponding Attribute defined in the Model. If the Attribute has a label
property, that will be used to prefix the facets, otherwise the full Attribute name will be used.
attributes: {
storeId: {
type: "string",
label: "sid",
},
mallId: {
type: "string",
label: "mid",
},
buildingId: {
type: "string",
label: "bid",
},
unitId: {
type: "string",
label: "uid",
}
},
indexes: {
locations: {
pk: {
field: "pk",
facets: ["storeId"]
},
sk: {
field: "sk",
facets: ["mallId", "buildingId", "unitId"]
}
}
}
{
storeId: "STOREVALUE",
mallId: "MALLVALUE",
buildingId: "BUILDINGVALUE",
unitId: "UNITVALUE"
};
{
pk: '$mallstoredirectory_1#sid_storevalue',
sk: '$mallstores#mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}
Facet Templates
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. ElectroDB 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"
}
}
}
{
storeId: "STOREVALUE",
mallId: "MALLVALUE",
buildingId: "BUILDINGVALUE",
unitId: "UNITVALUE"
};
{
pk: 'sid_storevalue',
sk: 'mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}
Collections
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: collection
should 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)
.join(TasksModel);
TaskApp.collections.assignments({employee: "JExotic"}).params();
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: { ':pk': '$taskapp_1#employee_joeexotic', ':sk1': '$assignments' },
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
IndexName: 'gsi3pk-gsi3sk-index'
}
Filters
Filters are no longer the preferred way to add FilterExpressions. Checkout the Where section to find out about how to apply FilterExpressions and ConditionExpressions.
Building thoughtful indexes can make queries simple and performant. Sometimes you need to filter results down further. By adding Filters to your model, you can extend your queries with custom filters. Below is the traditional way you would add a filter to Dynamo's DocumentClient directly alongside how you would accomplish the same using a Filter function.
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$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'
}
Defined on the model
Filters can defined on the model and used in your query chain.
filters: {
rentPromotions: function(attributes, minRent, maxRent, promotion) {
let {rent, discount} = attributes;
return `
${rent.between(minRent, maxRent)} AND ${discount.lte(promotion)}
`
}
}
let StoreLocations = new Entity(model, {table: "StoreDirectory"});
let maxRent = "5000.00";
let minRent = "2000.00";
let promotion = "1000.00";
let stores = MallStores.query
.stores({ mallId: "EastPointe" })
.between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
.rentPromotions(minRent, maxRent, promotion)
.params();
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$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'
}
Defined via Filter method after query operators
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 ${discount.lte(promotion)}
`)
.params();
{
IndexName: 'idx2',
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':pk': '$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
.
Multiple Filters
It is possible to chain together multiple filters. The resulting FilterExpressions are concatenated 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 }) => `
${category.eq("food/coffee")}
`)
.params();
{
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#category': 'category',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':category1': 'food/coffee',
':pk': '$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)'
}
Where
The where()
method is an improvement on the filter()
method. Unlike filter
, where
will 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.
FilterExpressions
Below is the traditional way you would add a FilterExpression
to Dynamo's DocumentClient directly alongside 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 ${between(dangerous, "2020-09-25", "2020-09-28")}
`)
.params()
ConditionExpressions
Below is the traditional way you would add a ConditionExpression
to Dynamo's DocumentClient directly alongside how you would accomplish the same using the where
method.
{
UpdateExpression: 'SET #dangerous = :dangerous',
ExpressionAttributeNames: { '#animal': 'animal', '#dangerous': 'dangerous' },
ExpressionAttributeValues: {
':animal_w1': 'Zebra',
':dangerous_w1': false,
':dangerous': true
},
TableName: 'zoodirectory',
Key: {
pk: '$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()
Attributes and Operations
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 the
operations` object, along with inline values to set filters and conditions:
animals.update({habitat: "Africa", enclosure: "5b"})
.set({keeper: "Joe Exotic"})
.where((attr, op) => op.eq(attr.dangerous, true))
.params();
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 |
Multiple Where Clauses
It is possible to include chain multiple where clauses. The resulting FilterExpressions (or ConditionExpressions) are concatenated 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}) => `
${eq(category, "food/coffee")}
`)
.params();
{
TableName: 'StoreDirectory',
ExpressionAttributeNames: {
'#rent': 'rent',
'#discount': 'discount',
'#category': 'category',
'#pk': 'idx2pk',
'#sk1': 'idx2sk'
},
ExpressionAttributeValues: {
':rent1': '2000.00',
':rent2': '5000.00',
':discount1': '1000.00',
':category1': 'food/coffee',
':pk': '$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)'
}
Building Queries
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:
- You must always supply the Partition Key in full for all queries to DynamoDB.
- You currently only have the following operators available on a Sort Key:
begins_with
, between
, >
, >=
, <
, <=
, and Equals
. - To act on single record, you will need to know the full Partition Key and Sort Key for that record.
Using facets to make hierarchical keys
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:
Shopping Mall Stores
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"});
Query App Records
Examples in this section using the MallStore
schema 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"});
Partition Key Facets
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";
StoreLocations.query.stores({cityId, mallId});
StoreLocations.query.stores({cityId, mallId, buildingId});
StoreLocations.query.stores();
StoreLocations.query.stores({mallId});
StoreLocations.query.stores({cityId, mallId, storeId});
Sort Key Operations
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:
- All
LatteLarrys
locations in all Malls - All
LatteLarrys
locations in one Mall - All
LatteLarrys
locations inside a specific Mall - A specific
LatteLarrys
inside of a Mall and Building
Query Chains
Queries 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 StoreLocations
schema 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
.
Get Method
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();
Batch Get
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.
Additionally, when performing a BatchGet the .params()
method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchGet queries larger than the docClient's limit of 100 records.
If the number of records you are requesting is above the BatchGet threshold of 100 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent
. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.
For example, 150 records (50 records over the DynamoDB maximum):
The default value of concurrent
will be 1
. ElectroDB will execute a BatchGet request of 100, then after that request has responded, make another BatchGet request for 50 records.
If you set the Query Option concurrent
to 2
, ElectroDB will execute a BatchGet request of 100 records, and another BatchGet request for 50 records without waiting for the first request to finish.
It is important to consider your Table's throughput considerations when setting this value.
let [results, unprocessed] = await StoreLocations.get([
{
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "Atlanta1"
},
{
storeId: "MochaJoes",
mallId: "WestEnd",
buildingId: "A21",
cityId: "Madison2"
}
]).go({concurrent: 1});
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 other 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 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.
Delete Method
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();
Batch Write Delete Records
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.
Additionally, when performing a BatchWrite the .params()
method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchWrite queries larger than the docClient's limit of 25 records.
If the number of records you are requesting is above the BatchWrite threshold of 25 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent
. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.
For example, 75 records (50 records over the DynamoDB maximum):
The default value of concurrent
will be 1
. ElectroDB will execute a BatchWrite request of 25, then after that request has responded, make another BatchWrite request for 25 records, and then another.
If you set the Query Option concurrent
to 2
, ElectroDB will execute a BatchWrite request of 25 records, and another BatchGet request for 25 records without waiting for the first request to finish. After those two have finished it will execute another BatchWrite request for 25 records.
It is important to consider your Table's throughput considerations when setting this value.
let unprocessed = await StoreLocations.delete([
{
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "F34",
cityId: "LosAngeles1"
},
{
storeId: "MochaJoes",
mallId: "EastPointe",
buildingId: "F35",
cityId: "LosAngeles1"
}
]).go({concurrent: 1});
{
"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": "$mallstore_1#buildingid_f35#storeid_mochajoes"
}
}
}
]
}
}
Elements of the unprocessed
array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the facets defined in the Table Index. This is in keeping with DynamoDB's practice of returning only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as facets but you can pass the query option {lastEvaluatedKeyRaw:true}
override this behavior and return the Keys as they came from DynamoDB.
Put Record
Provide all required Attributes as defined in the model to create a new record. ElectroDB will enforce any defined validations, defaults, casting, and field aliasing. Another convenience ElectroDB provides, is accepting BatchWrite arrays larger than the 25 record limit. This is achieved making multiple, "parallel", requests to DynamoDB for batches of 25 records at a time. A failure with any of these requests will cause the query to throw, so be mindful of your table's configured throughput.
This example includes an optional conditional expression
await StoreLocations
.put({
cityId: "Atlanta1",
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "BuildingA1",
unitId: "B47",
category: "food/coffee",
leaseEndDate: "2020-03-22",
rent: "4500.00"
})
.where((attr, op) => op.eq(attr.rent, "4500.00"))
.go()
{
"Item": {
"cityId": "Atlanta1",
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "food/coffee",
"leaseEndDate": "2020-03-22",
"rent": "4500.00",
"discount": "0.00",
"pk": "$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"
}
}
Batch Write Put Records
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. Another convenience ElectroDB provides, is accepting BatchWrite arrays larger than the 25 record limit. This is achieved making multiple, "parallel", requests to DynamoDB for batches of 25 records at a time. A failure with any of these requests will cause the query to throw, so be mindful of your table's configured throughput.
Note: Performing a Batch Put will return an array of "unprocessed" records. An empty array signifies all records returned were processed. If you want the raw DynamoDB response you can always use the option {raw: true}
, more detail found here: Query Options.
Additionally, when performing a BatchWrite the .params()
method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchWrite queries larger than the docClient's limit of 25 records.
If the number of records you are requesting is above the BatchWrite threshold of 25 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent
. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.
For example, 75 records (50 records over the DynamoDB maximum):
The default value of concurrent
will be 1
. ElectroDB will execute a BatchWrite request of 25, then after that request has responded, make another BatchWrite request for 25 records, and then another.
If you set the Query Option concurrent
to 2
, ElectroDB will execute a BatchWrite request of 25 records, and another BatchGet request for 25 records without waiting for the first request to finish. After those two have finished it will execute another BatchWrite request for 25 records.
It is important to consider your Table's throughput considerations when setting this value.
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({concurrent: 1});
{
"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": "$mallstore_1#leaseenddate_2021-01-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
}
}
}
]
}
}
Elements of the unprocessed
array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the facets defined in the Table Index. This is in keeping with DynamoDB's practice of returning only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as facets but you can pass the query option {lastEvaluatedKeyRaw:true}
override this behavior and return the Keys as they came from DynamoDB.
Update Record
To update a record, pass all Table index facets to the update method and then pass set
attributes that need to be updated. This example contains an optional conditional expression.
Note: If your update includes changes to an attribute that is also a facet for a global secondary index, you must provide all facets for that index.
await StoreLocations
.update({cityId, mallId, storeId, buildingId})
.set({category: "food/meal"})
.where((attr, op) => op.eq(attr.category, "food/coffee"))
.go()
{
"UpdateExpression": "SET #category = :category",
"ExpressionAttributeNames": {
"#category": "category"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category": "food/meal"
},
"TableName": "StoreDirectory",
"Key": {
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
},
"ConditionExpression": "#category = :category_w1"
}
Scan Records
When scanning for rows, you can use filters the same as you would any query. For more information 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}) => `
${between(leaseEndDate, "2020-03", "2020-04")}
`)
.go()
{
"TableName": "StoreDirectory",
"ExpressionAttributeNames": {
"#category": "category",
"#leaseEndDate": "leaseEndDate",
"#pk": "pk",
"#sk": "sk",
"#__edb_e__": "__edb_e__",
"#__edb_v__": "__edb_v__"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category_w2": "spite store",
":leaseEndDate_w1": "2020-03",
":leaseEndDate_w2": "2020-04",
":pk": "$mallstoredirectory#cityid_",
":sk": "$mallstore_1#buildingid_",
":__edb_e__": "MallStore",
":__edb_v__": "1"
},
"FilterExpression": "begins_with(#pk, :pk) AND #__edb_e__ = :__edb_e__ AND #__edb_v__ = :__edb_v__ AND begins_with(#sk, :sk) AND (#category = :category_w1 OR #category = :category_w2) AND (#leaseEndDate between :leaseEndDate_w1 and :leaseEndDate_w2)"
}
Patch Records
In DynamoDB, update
operations by default will insert a record if record being updated does not exist. In ElectroDB, the patch
method will utilize the attribute_exists()
parameter dynamically to ensure records are only "patched" and not inserted when updating.
await StoreLocations
.patch({cityId, mallId, storeId, buildingId})
.set({category: "food/meal"})
.where((attr, op) => op.eq(attr.category, "food/coffee"))
.go()
{
"UpdateExpression": "SET #category = :category",
"ExpressionAttributeNames": {
"#category": "category"
},
"ExpressionAttributeValues": {
":category_w1": "food/coffee",
":category": "food/meal"
},
"TableName": "StoreDirectory",
"Key": {
"pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
"sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
},
"ConditionExpression": "attribute_exists(pk) AND attribute_exists(sk) AND #category = :category_w1"
}
Create Records
In DynamoDB, put
operations by default will overwrite a record if record being updated does not exist. In ElectroDB, the patch
method will utilize the attribute_not_exists()
parameter dynamically to ensure records are only "created" and not overwritten when inserting new records into the table.
await StoreLocations
.create({
cityId: "Atlanta1",
storeId: "LatteLarrys",
mallId: "EastPointe",
buildingId: "BuildingA1",
unitId: "B47",
category: "food/coffee",
leaseEndDate: "2020-03-22",
rent: "4500.00"
})
.where((attr, op) => op.eq(attr.rent, "4500.00"))
.go()
{
"Item": {
"cityId": "Atlanta1",
"mallId": "EastPointe",
"storeId": "LatteLarrys",
"buildingId": "BuildingA1",
"unitId": "B47",
"category": "food/coffee",
"leaseEndDate": "2020-03-22",
"rent": "4500.00",
"discount": "0.00",
"pk": "$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": "$mallstore_1#leaseenddate_2020-03-22",
"__edb_e__": "MallStore",
"__edb_v__": "1"
},
"TableName": "StoreDirectory",
"ConditionExpression": "attribute_not_exists(pk) AND attribute_not_exists(sk) AND #rent = :rent_w1",
"ExpressionAttributeNames": {
"#rent": "rent"
},
"ExpressionAttributeValues": {
":rent_w1": "4500.00"
}
}
Find Records
DynamoDB offers three methods to find records: get
, query
, and scan
. In ElectroDB, there is a fourth type: find
. Unlike get
and query
, the find
method does not require you to provide keys, but under the covers it will leverage the attributes provided to find the best index to query on. Provide the find
method will all properties known to match a record and ElectroDB will generate the most performant query it can to locate the results. This can be helpful with highly dynamic querying needs. If an index cannot be satisfied with the attributes provided, scan
will be used as a last resort.
await StoreLocations.find({
mallId: "EastPointe",
buildingId: "BuildingA1",
leaseEndDate: "2020-03-22",
rent: "1500.00"
}).go()
{
"KeyConditionExpression": "#pk = :pk and begins_with(#sk1, :sk1)",
"TableName": "StoreDirectory",
"ExpressionAttributeNames": {
"#mallId": "mallId",
"#buildingId": "buildingId",
"#leaseEndDate": "leaseEndDate",
"#rent": "rent",
"#pk": "gis1pk",
"#sk1": "gsi1sk"
},
"ExpressionAttributeValues": {
":mallId1": "EastPointe",
":buildingId1": "BuildingA1",
":leaseEndDate1": "2020-03-22",
":rent1": "1500.00",
":pk": "$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.
Access Pattern Queries
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.
Begins With 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, it's possible to provide only a subset of the Sort Key Facets 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:
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():
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_"
"$mallstore_1#buildingid_f34"
The first example shows how ElectroDB post-pends the label of the next facet (unitId) on the SortKey to ensure that buildings such as "f340"
are not included in the query. This is useful to prevent common issues with multi-facet sort keys like accidental over-querying.
The second example allows you to make queries that do include buildings such as "f340"
or "f3409"
or "f340356346"
.
For these reasons it is important to consider that attributes passed to the Access Pattern method are considered to be full, known, data.
Collection Chains
Collections allow you to query across Entities. To use them you need to join
your Models onto a Service
instance.
Using the TaskApp Models defined in Models, these models share a collection
called assignments
on the index gsi3pk-gsi3sk-index
const table = "projectmanagement";
const TaskApp = new Service("projectmanagement", { client, table });
TaskApp
.join(EmployeesModel)
.join(TasksModel);
Available on your Service are two objects: entites
and collections
. Entities available on entities
have the same capabilities as they would if created individually. When a Model added to a Service with join
however, its Collections are automatically added and validated with the other Models joined to that Service. These Collections are available on collections
.
TaskApp.collections.assignments({employee: "JExotic"}).params();
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: { ':pk': '$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 ${attributes.project.contains("murder")}
`)
{
TableName: 'projectmanagement',
ExpressionAttributeNames: { '#project': 'project', '#pk': 'gsi3pk', '#sk1': 'gsi3sk' },
ExpressionAttributeValues: {
':project1': 'murder',
':pk': '$taskapp_1#employee_carolbaskin',
':sk1': '$assignments'
},
KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
IndexName: 'gsi3pk-gsi3sk-index',
FilterExpression: '\n\t\tattribute_not_exists(#project) OR contains(#project, :project1)\n\t'
}
Execute Queries
Lastly, all query chains end with either a .go()
or a .params()
method invocation. These will either execute the query to DynamoDB (.go()
) or return formatted parameters for use with the DynamoDB docClient (.params()
).
Both .params()
and .go()
take a query configuration object which is detailed more in the section Query Options.
Params
The params
method ends a query chain, and synchronously formats your query into an object ready for the DynamoDB docClient.
For more information on the options available in the config
object, checkout the section Query Options.
let config = {};
let stores = MallStores.query
.leases({ mallId })
.between(
{ leaseEndDate: "2020-06-01" },
{ leaseEndDate: "2020-07-31" })
.filter(attr) => attr.rent.lte("5000.00"))
.params(config);
{
IndexName: 'idx2',
TableName: 'electro',
ExpressionAttributeNames: { '#rent': 'rent', '#pk': 'idx2pk', '#sk1': 'idx2sk' },
ExpressionAttributeValues: {
':rent1': '5000.00',
':pk': '$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'
}
Go
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 config
object, 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);
Page
As of September 29th 2020 the .page()
now returns the facets that make up the ExclusiveStartKey
instead of the ExclusiveStartKey
itself. To get back only the ExclusiveStartKey
, add the flag exclusiveStartKeyRaw
to your query options. If you treated this value opaquely no changes are needed, or if you used the raw
flag.
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 tuple.
The first element for 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 page query is the ExclusiveStartKey
as it was returned by the DynamoDB client.
Note: It is highly recommended to use the lastEvaluatedKeyRaw
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.
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 the config
object, 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, {});
Query Examples
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";
await StoreLocations.query.leases({storeId}).go()
await StoreLocations.query.leases({storeId, leaseEndDate: "2020-03-22"}).go()
await StoreLocations.query.leases({storeId}).begins({leaseEndDate: "2020"}).go()
await StoreLocations.query.leases({storeId}).gt({leaseEndDate: "2020-03"}).go()
await StoreLocations.query.leases({storeId}).gte({leaseEndDate: "2020-03"}).go()
await StoreLocations.query.leases({storeId}).lt({leaseEndDate: "2021-01"}).go()
await StoreLocations.query.leases({storeId}).lte({leaseEndDate: "2021-02"}).go()
await StoreLocations.query
.leases({storeId})
.between(
{leaseEndDate: "2010"},
{leaseEndDate: "2020"})
.go()
await StoreLocations.query
.leases({storeId})
.gte({leaseEndDate: "2010"})
.where((attr, op) => `
${op.eq(attr.cityId, "Atlanta1")} AND ${op.contains(attr.category, "food")}
`)
.go()
await StoreLocations.query
.units({mallId})
.begins({leaseEndDate: june})
.rentDiscount(discount, maxRent, minRent)
.go()
await StoreLocations.query
.units({mallId})
.byCategory("food/coffee")
.go()
Query Options
Query options can be added the .params()
, .go()
and .page()
to change query behavior or add customer parameters to a query.
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:
{
params?: object
table?: string
raw?: boolean
includeKeys?: boolean
lastEvaluatedKeyRaw?: boolean
originalErr?: boolean
concurrency?: number
};
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 Service Options |
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 detail about this in the sections for Pages, BatchGet, BatchDelete, and BatchPut. |
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. |
concurrency | (default: 1) When performing batch operations, how many requests (1 batch operation == 1 request) to DynamoDB should ElectroDB make at one time. Be mindful of your DynamoDB throughput configurations |
Errors:
Error Code | Description |
---|
1000s | Configuration Errors |
2000s | Invalid Queries |
3000s | User Defined Errors |
4000s | DynamoDB Errors |
5000s | Unexpected Errors |
No Client Defined On Model
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});
Invalid Identifier
Code: 1002
Why this occurred:
You tried to modify the entity identifier on an Entity.
What to do about it:
Make sure you have spelled the identifier correctly or that you actually passed a replacement.
Invalid Key Facet Template
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.
Duplicate Indexes
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 the index names on your model for duplicate indexes. The error should specify which index has been duplicated. It is also possible that you have forgotten to include an index name. Each table must have at least one Table Index (which does not include an index
property in ElectroDB), but all Secondary and Local indexes must include an index
property with the name of that index as defined on the table.
{
indexes: {
index1: {
index: "idx1",
pk: {},
sk: {}
},
index2: {
index: "idx1",
pk: {},
sk: {}
}
}
}
Collection Without An 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 a Sort Key but you are 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:
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
}
}
}
}
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["id"]
},
sk: {
field: "sk",
facets: []
}
}
}
}
Duplicate Collections
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
Missing Primary Index
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 you're using as the Primary Index and ensure it does not have an index property on it's definition.
{
indexes: {
myIndex: {
pk: {
field: "pk",
facets: ["org"]
},
sk: {
field: "sk",
facets: ["id"]
}
}
}
}
{
indexes: {
myIndex: {
index: "gsi1"
pk: {
field: "gsipk1",
facets: ["org"]
},
sk: {
field: "gsisk1",
facets: ["id"]
}
}
}
}
Invalid Attribute Definition
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 for more information on how they are structured.
Invalid Model
Code: 1009
Why this occurred:
Some properties on your model are missing or invalid.
What to do about it:
Checkout the section on Models to verify your model against what is expected.
Invalid Options
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 to verify your model against what is expected.
Duplicate Index Fields
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 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.
Duplicate Index Facets
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 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 necessary. If this is your situation remember that ElectroDB does put a value in the SortKey even if does not include a facet, checkout this section 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.
Missing Facets
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.
Missing Table
Code: 2003f
Why this occurred:
You never specified a Table for DynamoDB to use.
What to do about it:
Tables can be defined on the Service Options object when you create a Entity or Service, or if that is not known at the time of creation, it can be supplied as a Query Option and supplied on each query individually. If can be supplied on both, in that case the Query Option will override the Service Option.
Invalid Concurrency Option
Code: 2004
Why this occurred:
When performing a bulk operation (Batch Get, Batch Delete Records, Batch Put Records) you can pass a Query Options called concurrency
, which impacts how many batch requests can occur at the same time. Your value pass the test of both, !isNaN(parseInt(value))
and parseInt(value) > 0
.
What to do about it:
Expect this error only if youre providing a concurrency value. Double check the value you are providing is the value you expect to be passing, and that the value passes the tests listed above.
Invalid Last Evaluated Key
Code: 4002
Why this occurred:
Likely you were were calling .page()
on a scan
. If you weren't 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() 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.
model.scan.page({exclusiveStartKeyRaw: true});
aws-error
Code: 4001
Why this occurred:
DynamoDB did not 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 more insight into how your query is converted to DocClient params.
Unknown Error
Examples
Want to just play with ElectroDB instead of read about it?
Try it out for yourself! https://runkit.com/tywalch/electrodb-building-queries
Employee App
For an example, lets look at the needs of application used to manage Employees. The application Looks at employees, offices, tasks, and projects.
Employee App Requirements
- As a Project Manager, I need to find all tasks and details on a specific employee.
- As a Regional Manager, I need to see all details about an office and its employees
- As an Employee, I need to see all my Tasks.
- As a Product Manager, I need to see all the tasks for a project.
- As a Client, I need to find a physical office close to me.
- As a Hiring manager, I need to find employees with comparable salaries.
- As HR, I need to find upcoming employee birthdays/anniversaries
- As HR, I need to find all the employees that report to a specific manager
App Entities
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)
.join(TasksModel)
.join(OfficesModel);
Query Records
All tasks and employee information for a given employee
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"
}]
}
Find all employees and office details for a given office
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"
}]
}
Tasks for a given employee
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"
}
]
Tasks for a given project
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"
}
];
Find office locations
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"
}
]
Find employee salaries and titles
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",
}
]
Find employee birthdays or anniversaries
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",
}
]
Find direct reports
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",
}
]
Shopping Mall Property Management App
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.
Shopping Mall Requirements
- As a Maintenance Worker I need to know which stores are currently in each Mall down to the Building they are located.
- As a Helpdesk Employee I need to locate related stores in Mall locations by Store Category.
- As a Property Manager I need to identify upcoming leases in need of renewal.
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"});
Access Patterns are accessible on the StoreLocation
PUT Record
Add a new Store to the Mall
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",
}
UPDATE Record
Change the Stores Lease Date
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"
}
GET Record
Retrieve a specific Store in a Mall
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"
}
DELETE Record
Remove a Store location from the Mall
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:
{}
Query Mall Records
All Stores in a particular mall
Fulfilling Requirement #1.
let mallId = "EastPointe";
let stores = await StoreLocations.malls({mallId}).query().go();
All Stores in a particular mall building
Fulfilling Requirement #1.
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let stores = await StoreLocations.malls({mallId}).query({buildingId}).go();
Find the store located in unit B47
Fulfilling Requirement #1.
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let stores = await StoreLocations.malls({mallId}).query({buildingId, unitId}).go();
Stores by Category at Mall
Fulfilling Requirement #2.
let mallId = "EastPointe";
let category = "food/coffee";
let stores = await StoreLocations.malls({mallId}).byCategory(category).go();
Stores by upcoming lease
Fulfilling Requirement #3.
let mallId = "EastPointe";
let q2StartDate = "2020-04-01";
let stores = await StoreLocations.leases({mallId}).lt({leaseEndDate: q2StateDate}).go();
Stores will renewals for Q4
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();
Spite-stores with release renewals this year
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();
All Latte Larrys in a particular mall building
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let storeId = "LatteLarrys";
let stores = await StoreLocations.malls({mallId}).query({buildingId, storeId}).go();
Electro CLI
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:
- Generate TypeScript type definition files for your
Entities
and Services
. - Execute queries against your
Entities
, Services
, Models
directly from the command line. - Dynamically stand up an HTTP Service to interact with your
Entities
, Services
, Models
.
For usage and installation details you can learn more here.
Version 1 Migration
This section is to detail any breaking changes made on the journey to a stable 1.0 product.
New schema format/breaking key format change
It became clear when I added the concept of a Service that the "version" paradigm of having the version in the PK wasn't 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";
let old_schema = {
entity: "model_name",
service: "service_name",
version: "1",
table: table,
attributes: {},
indexes: {}
};
new Entity(old_schema, {client});
let new_schema = {
model: {
entity: "model_name",
service: "service_name",
version: "1",
},
attributes: {},
indexes: {}
};
new Entity(new_schema, {client, table});
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";
new Service({
service: "service_name",
version: "1",
table: table,
}, {client});
new Service("service_name", {client, table});
Coming Soon
- Default query options defined on the
model
to give more general control of interactions with the Entity. - Append/Add/Subtract/Remove updates capabilities
- Complex attributes (list, map, set)