Create and interact with a datastore

Datastores are a Slack-hosted way to store data for your workflow apps. They are available for workflow apps only.

Datastores are backed by DynamoDB, a secure and performant NoSQL database. DynamoDB's data model uses three basic types of data units: tables, items, and attributes. Tables are collections of items, and items are collections of attributes. You will see how a collection of attributes comprises an item when we define a datastore later in this page.

Initializing a datastore

To initialize a datastore:

1. Define the datastore
2. Add it to your manifest

1. Define the datastore

To keep your app tidy, datastores can be defined in their own source files just like custom functions.

If you don't already have one, create a datastores directory in the root of your project, and inside, create a source file to define your datastore.

Throughout this page, we'll use the example of the Announcement bot sample app. First, we'll create a datastore called Drafts and define it in a file named drafts.ts. It will hold information about an announcement the user drafts to send to a channel:

// /datastores/drafts.ts
import { DefineDatastore, Schema } from "deno-slack-sdk/mod.ts";

export default DefineDatastore({
  name: "drafts",
  primary_key: "id",
  attributes: {
    id: {
      type: Schema.types.string,
    },
    created_by: {
      type: Schema.slack.types.user_id,
    },
    message: {
      type: Schema.types.string,
    },
    channels: {
      type: Schema.types.array,
      items: {
        type: Schema.slack.types.channel_id,
      },
    },
    channel: {
      type: Schema.slack.types.channel_id,
    },
    message_ts: {
      type: Schema.types.string,
    },
    icon: {
      type: Schema.types.string,
    },
    username: {
      type: Schema.types.string,
    },
    status: {
      type: Schema.types.string, // possible statuses are draft, sent
    },
  },
});

Datastores can contain three primary properties. The primary_key property is the only one that is required. When using additional optional properties, make sure to handle them properly to avoid running into any TypeScript errors in your code.

Property	Type	Description	Required
name	String	A string to identify your datastore	No
primary_key	String	The attribute to be used as the datastore's unique key; ensure this is an actual attribute that you have defined	Yes
attributes	Object (see below)	Properties to scaffold your datastore's columns	No
time_to_live_attribute	String	An optional attribute used as a Time To Live (TTL) feature to delete datastore items automatically when set to a property of type Schema.slack.types.timestamp, which represents the item's expiration.	No

Attributes can be custom types, Slack types, and the following basic schema types:

• array
• boolean
• int
• number
• object
• string

2. Add the datastore to your app's manifest

The last step in initializing your datastore is to add it to the datastores property in your manifest and include the required datastore bot scopes.

To do that, first add the datastores property to your manifest if it does not exist, then list the datastores you have defined. Second, add the following datastore permission scopes to the botScopes property:

• datastore:read
• datastore:write

Here's an example manifest definition for the above drafts datastore in the Announcement bot sample app:

import { Manifest } from "deno-slack-sdk/mod.ts";
// Import the datastore definition
import AnnouncementDatastore from "./datastores/announcements.ts";
import DraftDatastore from "./datastores/drafts.ts";
import { AnnouncementCustomType } from "./functions/post_summary/types.ts";
import CreateAnnouncementWorkflow from "./workflows/create_announcement.ts";

export default Manifest({
  name: "Announcement Bot",
  description: "Send an announcement to one or more channels",
  icon: "assets/icon.png",
  outgoingDomains: ["cdn.skypack.dev"],
  datastores: [DraftDatastore, AnnouncementDatastore], // Add the datastore to this list
  types: [AnnouncementCustomType],
  workflows: [
    CreateAnnouncementWorkflow,
  ],
  botScopes: [
    "commands",
    "chat:write",
    "chat:write.public",
    "chat:write.customize",
    "datastore:read",
    "datastore:write",
  ],
});

Note that we've also added the required datastore:read and datastore:write bot scopes.

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Importing data to a datastore

You can import data from a JSON Lines file to a datastore using the datastore bulk-put command with the --from-file flag. For example:

slack datastore bulk-put '{"datastore": "running_datastore"}' —-from-file /path/to/file.jsonl

See the Add items to a datastore guide for more information on the API method underlying this command.

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Exporting data from a datastore

You can export data from a datastore to a JSON Lines file using the datastore query command with the --to-file flag. For example:

slack datastore query '{"datastore": "running_datastore"}' —-to-file /path/to/file.jsonl

See the Retrieve items from a datastore guide for more information on the API method underlying this command.

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Counting items in a datastore

You can count the number of items in a datastore that match a query by using the datastore count command. This command handles paginating through an entire datastore to return the number of matched items (rather than the items themselves, as with the datastore query command).

See the Count items in a datastore guide for more information on the API method underlying this command.

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Interacting with a datastore

There are two ways to interact with your app's datastore.

➡️ To interact with your datastore through the command-line tool, see the datastore commands section on the commands page.

⤵️ To interact with your datastore within a custom function, keep reading.

Interacting with your app's datastore requires hitting the SlackAPI. To do this from within your code, we first need to import a mechanism that will allow us to call the SlackAPI. That mechanism is SlackFunction. First we import it into our function file from the deno-slack-sdk package, then we add a SlackFunction into our code. SlackFunction contains a property, client, which allows us to call the datastore.

You can find examples of this in the Slack API methods guide and Add items to a datastore guide.

In all interactions with your datastore, double and triple-check the exact spelling of the fields in the datastore definition match your query, lest you should receive an error.

When interacting with your datastore, it may be helpful to first visualize its structure. In our drafts example, let's say we have stored the following users and their drafted announcements:

id	created_by	message	channels	channel	message_ts	icon	username	status
906dba92-44f5-4680-ada9-065149e4e930	U045A5X302V	This is a test message	["C038M39A2TV"]	C039ARY976C	1691513323.119209		Slackbot	sent
b8457c38-4401-4dd1-b979-a0e56f7c9a3d	BR75C7X4P90	Remember to submit your timesheets	["C038M39A2TV"]	C039ARY976C	1691520476.091369	:robot_face:	The Boss	draft
194a52d8-c75b-4eff-9f8f-4c40292cd9e7	G98I9345NI2	Happy Friday, team!	["D870D2223M23"]	D870D2223M23	2172813323.142610	:t-rex:	Slackasaurus Bot	sent

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Generic types for datastores

You can provide your datastore's definition as a generic type, which will provide some automatic typing on the arguments and response:

import { DefineDatastore, Schema } from "deno-slack-sdk/mod.ts";

export const DraftDatastore = DefineDatastore({
  name: "drafts",
  primary_key: "id",
  attributes: {
    id: {
      type: Schema.types.string,
    },
    created_by: {
      type: Schema.slack.types.user_id,
    },
    message: {
      type: Schema.types.string,
    },
    channels: {
      type: Schema.types.array,
      items: {
        type: Schema.slack.types.channel_id,
      },
    },
    channel: {
      type: Schema.slack.types.channel_id,
    },
    message_ts: {
      type: Schema.types.string,
    },
    icon: {
      type: Schema.types.string,
    },
    username: {
      type: Schema.types.string,
    },
    status: {
      type: Schema.types.string, 
    },
  },
});

You can use the result of your DefineDatastore() call as the type in a function by using its definition property:

import { DraftDatastore } from "../datastores/drafts.ts";
...
    const putResp = await client.apps.datastore.put<
      typeof DraftDatastore.definition
    >({
      datastore: DraftDatastore.name,
      item: {
        id: draftId,
        created_by: inputs.created_by,
        message: inputs.message,
        channels: inputs.channels,
        channel: inputs.channel,
        icon: inputs.icon,
        username: inputs.username,
        status: DraftStatus.Draft,
      },
    });
    ...

By using typed methods, the datastore property (e.g. DraftDatastore.datastore) will enforce that its value matches the datastore definition's name property across methods and the item matches the definition's attributes in arguments and responses. Also, for get() and delete(), a property matching the primary_key will be expected as an argument.

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Onward

Ready to start manipulating data with your workflows? We've got a guide for each type of activity:

• Add items to a datastore
• Retrieve items from a datastore
• Delete items from a datastore
• Delete items from a datastore automatically

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Deleting a datastore

If you need to delete a datastore completely, for instance you've changed the primary key, you have a couple of options. Datastores do support primary key changes, so first try using the --force flag on a datastore CLI operation if the Slack CLI informs you that the datastore has changed. Otherwise, do the following:

Step 1. Remove the datastore definition from the app's manifest.

Step 2. Run slack deploy.

Step 3. Modify the datastore definition to your heart's content and add it back into the app's manifest.

Step 4. Run slack deploy again.

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Troubleshooting

If you're looking to audit or query your datastore from the terminal without having to go through code, see the datastore commands.

If you're getting errors, check the following:

• The primary key is formatted as a string
• The datastore is included in the manifest's datastores property
• The datastore bot scopes are included in the manifest (datastore:read and datastore:write)
• The spelling of the fields in your query match exactly the spelling of the fields in the datastore's definition

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