Transform data

Snaplet supports transforming the data in your database before capturing it as a snapshot. It can do this transformation for you, or let you define how your data should be transformed.

Defining these data transformations is done via JavaScript callbacks. This "Transformation Function" is associated to the structure of your database. As an example if you have a Users table that contains an email column you would create the following:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    public: {
      User: () => {
        return {
          email: 'my-new-email@example.org',
        };
      },
    },
  },
});

When a snapshot is captured the email column is transformed to the value "my-new-email@example.org," which is exactly what we wanted, but you probably want to generate a bunch of emails so the data looks realistic.

That's where @snaplet/copycat comes in! It's a library that generates deterministic fake values: by supplying an input (the original email address), copycat returns a static value (a fake email address). As long as the input remains the same, the output will remain the same.

Example:

import { copycat } from "@snaplet/copycat";
import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    public: {
      User: ({ row }) => {
        return {
          email: copycat.email(row.email), // zakary.block356@gmail.com
        };
      },
    },
  },
});

Each Transformation Function receives a row object that contains the original row's values, this allows you to perform conditional transformations, mutate a JSON object, or create deterministic faker values.

import { copycat } from "@snaplet/copycat";
import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    public: {
      User: ({ row }) => {
        // Transform our user's data, not our developer's data.
        if (row.role !== 'SUPERUSER') {
          return {
            name: copcat.fullName(row.name),
            email: copycat.email(row.email),
            password: copycat.password(row.password),
          };
        }
      },
    },
  },
});

Copycat is open-source and has templates for names, addresses, phone numbers and many other common transformations!

Getting started

We believe that a good developer experience is when developers are in flow, so it's our goal to "work where you work" by providing programatic primitives for transforming the data in your database, and integrating those with your development worflow.

To transform the data in your database Snaplet needs two things:

1. A connection URL to the database that you want to transform (this can be your development or staging database).
2. A snaplet.config.ts file.

Setup: Generating transformation files

Run snaplet config setup at the root of your codebase (in your git repo), so that the configuration and transformations files are shared with the rest of your team.

No configuration found
✔ Create "/Users/avallete/Documents/Programming/Snaplet/docs/.snaplet/config.json"? … yes
✔ Target database connection string … postgresql://postgres@localhost:5432/postgres
📡 Connected to database with "postgresql://postgres@localhost:5432/postgres"
Introspecting database...
📡 Connected to database with "postgresql://postgres@localhost:5432/postgres"
Generated transform type definitions: snaplet.d.ts
⠋ Transform: Detecting PII fields...Smart shape prediciton failed
ℹ Transform: Generate transform config...
Generated transform config: snaplet.config.ts
Generated private key: /Users/avallete/Documents/Programming/Snaplet/docs/.snaplet/id_rsa
Added publicKey to project config: id_rsa
Created .snaplet/snaplet.d.ts
Created snaplet.config.ts

😽 Snaplet has introspected your database structure and generated
suggested transformations for your data in snaplet.config.ts
Please review them.

Updated ".gitignore"

The first thing Snaplet needs is a connection string to your database, we use this to introspect your database and make transformation suggestions for columns that could contain personally identifiable information.

Regenerate these files with snaplet config generate --type=typedefs --type=transform

You can add the .snaplet/snaplet.d.ts and .snaplet/snapshots to your .gitignore configuration.

tip

A user account is not required, you only need a user account if you want to share snapshots with your team.

Choosing the transform mode

The transformations you define in your config tell Snaplet how to transform your database data for the corresponding columns, but you might've noticed that Snaplet doesn't require you to specify each and every column, table and schema that is in your database. What Snaplet does for any values for columns that have not been given in the config depends on the transform mode:

• unsafe (default): The data for columns not specified in the config is simply copied over as is without transformation.
• strict: Fail the capture if any columns, tables or schemas have not been specified in the config.
• auto: Automatically transform the data for any columns, tables or schemas that have not been specified in the config.

Changing the mode

For Snaplet Cloud, you can choose the transform mode within your config by specifying $mode under transform:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "auto",
  },
});

If you're capturing your snapshots locally with snaplet snapshot capture, you can also choose the transform mode using your config (located at snaplet.config.ts) the same way as above. Alternatively, you can use the --transform-mode CLI option:

snaplet snapshot capture --transform-mode=auto

# or the shorthand form:
snaplet snapshot capture -t auto

If the option is given both via the config and with a CLI option, the option given in the config takes precendece.

unsafe mode

unsafe mode copies over values without any transformation. If a transformation is given for a column in the config, the transformation will be used instead.

Lets say you have a User table with the columns id, name and email, with the original data in your production database looking like this:

| id | name             |    email          |
| -- | ---------------- | ------------------|
| 1  | Susan Altenwerth | susan@example.org |
| 2  | Frank Collier    | frank@example.org |

Then lets say your config looked like this:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "unsafe",
    public: {
      User: ({ row }) => {
        email: 'user_' + row.id + '@example.org',
      },
    },
  },
});

In this case, a transformation was given in the config for email, but not for name or id. The chosen mode is unsafe mode, so name and id are copied over as is, and the transformation in the config is used for email. The resulting snapshot would have data that looks something like this:

| id | name             |    email           |
| -- | ---------------- | ------------------ |
| 1  | Susan Altenwerth | user_1@example.org |
| 2  | Frank Collier    | user_2@example.org |

strict mode

In strict mode, Snaplet expects a transformation to be given in the config for every column in the database. If any columns have not been provided in the config, Snaplet will not capture the snapshot, but instead tell you which columns, tables, or schemas have not been given.

Using the same example as above, lets say you have a User table with the columns id, name and email, with the original data in your production database looking like this:

| id | name             |    email          |
| -- | ---------------- | ------------------|
| 1  | Susan Altenwerth | susan@example.org |
| 2  | Frank Collier    | frank@example.org |

Then lets keep the config the same, except now choose strict mode:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "strict",
    public: {
      User: ({ row }) => {
        email: 'user_' + row.id + '@example.org',
      },
    },
  },
});

In this case, a transformation was given in the config for email, but not for name or id. The chosen mode is strict mode, so Snaplet will not capture the snapshot, but instead tell you that name is missing:

The following schemas, tables or columns are missing from your transform config. ...
* Columns: "public"."User"."name"

You'll notice that Snaplet said nothing about id: In strict mode, Snaplet will still copy across data for primary and foreign key columns as is.

To allow a snapshot to be created, lets add a name column to the config:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "strict",
    public: {
      User: ({ row }) => {
        email: 'user_' + row.id + '@example.org',
        name: `User ${row.id}
      },
    },
  },
});

Snaplet now has everything it needs to create a snapshot in strict mode. The resulting data in the snapshot will look something like this:

| id | name             |    email           |
| -- | ---------------- | ------------------ |
| 1  | User 1           | user_1@example.org |
| 2  | User 2           | user_2@example.org |

auto mode

In auto mode, if there are any columns not given in the config, Snaplet will try to automitically transform them.

Using the same example as above, lets say you have a User table with the columns id, name and email, with the original data in your production database looking like this:

| id | name             |    email          |
| -- | ---------------- | ------------------|
| 1  | Susan Altenwerth | susan@example.org |
| 2  | Frank Collier    | frank@example.org |

Then lets keep the config the same, except now choose auto mode:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "auto",
    public: {
      User: ({ row }) => {
        email: 'user_' + row.id + '@example.org',
      },
    },
  },
});

In this case, a transformation was given in the config for email, but not for name or id. The chosen mode is auto mode, so Snaplet will use the transformation given for email, but transform data for name automatically. Data for id will be copied over as is - in auto mode, Snaplet does not do any transformations of its own to primary or foreign key columns.

The resulting data in the snapshot will look something like this:

| id | name             |    email           |
| -- | ---------------- | ------------------ |
| 1  | Oyhbz Bxctktpard | user_1@example.org |
| 2  | Csdif Xloqvvh    | user_2@example.org |

You could even provide no transformations at all for the columns in User:

import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    $mode: "auto"
  },
});

In which case, resulting data in the snapshot will look something like this:

| id | name             |    email           |
| -- | ---------------- | ------------------ |
| 1  | Oyhbz Bxctktpard | ahmai@gfngqph.vcx  |
| 2  | Csdif Xloqvvh    | wjhse@djdegod.kkg  |

How auto transform mode works

Snaplet relies on copycat.scramble for most of auto-transform mode, but also makes use of a few different pieces of information to transform the data more accurately:

• The type of the column: Snaplet accounts for the type of the column as it appears in the database when transforming data. For example, if the column in question is an enum type, the resulting transformations will still be valid enum values for that enum type.

• The shape of the column: Snaplet will try infer the semantic "shape" of the column. For example, the column may be of type text in the database, but if the name of the column is email, Snaplet will infer that that the column represents email addresses, and account for this when transforming. In the email column in the example above, Snaplet made use of copycat.scramble, but made sure to preserve the email address structure.

Limitations of auto transform mode

Application logic

Auto-transform mode is able to transform your database data into data that is still valid according to what your database is expecting, but it may not always be what your application's logic is expecting.

For example, imagine an application for previewing source code. Let's say this application's database has a table called File, with a sourceCode column of type text representing the programming language source code contained in a file. In this case, auto-transform would simply use copycat.scramble() on the data for this sourceCode column, resulting in data that is not actually valid programming language source code. In other words, Snaplet did not enough about the application to know how to automatically transform this value to something that is still what the application is expecting (programming language source code).

Our goal is for auto-transform mode to automatically transform as much of your database data as possible, but it is likely that you'll still need to tell us how to transform some of your fields.

Not all types are supported yet

While we aim to be able to support auto-transforming as many data types as we can, there are types that we're still working on adding support for. These include:

• Composite types
• Geometric types
• Network address types
• Range types
• User-defined types
• Bit-string types

Performance

We're still working on optimising auto-transform to be fast enough to be useful on large datasets and datasets with large values in them. For this same reason, we also currently truncate text values at 1000 characters before transforming them.

This said, auto-transform still may well be efficient enough for use on your own database - it is worth trying it out to see for yourself first.

If you find you are not able to capture snapshots fast enough with auto-transform mode for it to be useful on your own database, we'd like to know about it! Please feel free to reach out to us on Discord about this.

Dealing with character limits

One challenge when it comes to replacing data, is that some character varying (varchar) columns have a defined maximum character length.

For example, lets say you had an address column with the type varchar(16). Simply using copycat.postalAddress() for this column would not always work, since copycat might give back an address that is longer than 16 characters.

In these cases, copycat.scramble might be more helpful for you: it transforms each character in the string, but preserves the string's length. Since the original value is less than the character limit, the transformed result will also be.

import { copycat } from '@snaplet/copycat';
import { defineConfig } from "snaplet";

export default defineConfig({
  transform: {
    public: {
      Users({ row }) {
        return {
          address: copycat.scramble(row.address, {
            preserve: [',', ' '],
          }), // '741 Hazle Forks, Carmel 8164, Dominica' => 'tqynqduk@qjlrftv.fig'
        };
      },
    },
  },
});

Snaplet will also account for character limits this way when generating an example snaplet.config.ts config for you: if Snaplet sees a column containing PII that has a character limit, the example snaplet.config.ts config we generate for you will instead make use of copycat.scramble for that column.