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Saved Objects service

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Saved Objects service

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The Saved Objects service is available both server and client side.

Saved Objects service allows Kibana plugins to use Elasticsearch like a primary database. Think of it as an Object Document Mapper for Elasticsearch. Once a plugin has registered one or more Saved Object types, the Saved Objects client can be used to query or perform create, read, update and delete operations on each type.

By using Saved Objects your plugin can take advantage of the following features:

  • Migrations can evolve your document’s schema by transforming documents and ensuring that the field mappings on the index are always up to date.
  • a HTTP API is automatically exposed for each type (unless hidden=true is specified).
  • a Saved Objects client that can be used from both the server and the browser.
  • Users can import or export Saved Objects using the Saved Objects management UI or the Saved Objects import/export API.
  • By declaring references, an object’s entire reference graph will be exported. This makes it easy for users to export e.g. a dashboard object and have all the visualization objects required to display the dashboard included in the export.
  • When the X-Pack security and spaces plugins are enabled these transparently provide RBAC access control and the ability to organize Saved Objects into spaces.

This document contains developer guidelines and best-practices for plugins wanting to use Saved Objects.

Server side usage

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Registering a Saved Object type

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Saved object type definitions should be defined in their own my_plugin/server/saved_objects directory.

The folder should contain a file per type, named after the snake_case name of the type, and an index.ts file exporting all the types.

src/plugins/my_plugin/server/saved_objects/dashboard_visualization.ts.

import { SavedObjectsType } from 'src/core/server';

export const dashboardVisualization: SavedObjectsType = {
  name: 'dashboard_visualization', 
  hidden: true,
  namespaceType: 'multiple-isolated', 
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: modelVersion1,
    2: modelVersion2,
  },
  mappings: {
    dynamic: false,
    properties: {
      description: {
        type: 'text',
      },
      hits: {
        type: 'integer',
      },
    },
  },
  // ...other mandatory properties
};

Since the name of a Saved Object type may form part of the URL path for the public Saved Objects HTTP API, these should follow our API URL path convention and always be written in snake case.

This field determines "space behavior" — whether these objects can exist in one space, multiple spaces, or all spaces. This value means that objects of this type can only exist in a single space. See Sharing Saved Objects for more information.

src/plugins/my_plugin/server/saved_objects/index.ts.

export { dashboardVisualization } from './dashboard_visualization';
export { dashboard } from './dashboard';

src/plugins/my_plugin/server/plugin.ts.

import { dashboard, dashboardVisualization } from './saved_objects';

export class MyPlugin implements Plugin {
  setup({ savedObjects }) {
    savedObjects.registerType(dashboard);
    savedObjects.registerType(dashboardVisualization);
  }
}

Mappings

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Each Saved Object type can define it’s own Elasticsearch field mappings. Because multiple Saved Object types can share the same index, mappings defined by a type will be nested under a top-level field that matches the type name.

For example, the mappings defined by the search Saved Object type:

.src/plugins/saved_search/server/saved_objects/search.ts

import { SavedObjectsType } from 'src/core/server';
// ... other imports
export function getSavedSearchObjectType: SavedObjectsType = { 
  name: 'search',
  hidden: false,
  namespaceType: 'multiple-isolated',
  mappings: {
    dynamic: false,
    properties: {
      title: { type: 'text' },
      description: { type: 'text' },
    },
  },
  modelVersions: { ... },
  // ...other optional properties
};

Simplification

Will result in the following mappings being applied to the .kibana_analytics index:

{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      ...
      "search": {
        "dynamic": false,
        "properties": {
          "title": {
            "type": "text",
          },
          "description": {
            "type": "text",
          },
        },
      }
    }
  }
}

Do not use field mappings like you would use data types for the columns of a SQL database. Instead, field mappings are analogous to a SQL index. Only specify field mappings for the fields you wish to search on or query. By specifying dynamic: false in any level of your mappings, Elasticsearch will accept and store any other fields even if they are not specified in your mappings.

Since Elasticsearch has a default limit of 1000 fields per index, plugins should carefully consider the fields they add to the mappings. Similarly, Saved Object types should never use dynamic: true as this can cause an arbitrary amount of fields to be added to the .kibana index.

Writing Migrations by defining model versions

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Saved Objects support changes using modelVersions`. The modelVersion API is a new way to define transformations (`‘migrations’') for your savedObject types, and will replace the “legacy” migration API after Kibana version 8.10.0. The legacy migration API has been deprecated, meaning it is no longer possible to register migrations using the legacy system.

Model versions are decoupled from the stack version and satisfy the requirements for zero downtime and backward-compatibility.

Each Saved Object type may define model versions for its schema and are bound to a given savedObject type. Changes to a saved object type are specified by defining a new model.

Defining model versions

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As for old migrations, model versions are bound to a given savedObject type

When registering a SO type, a new modelVersions property is available. This attribute is a map of SavedObjectsModelVersion which is the top-level type/container to define model versions.

This map follows a similar { [version number] => version definition } format as the old migration map, however a given SO type’s model version is now identified by a single integer.

The first version must be numbered as version 1, incrementing by one for each new version.

That way: - SO type versions are decoupled from stack versioning - SO type versions are independent between types

a valid version numbering:

const myType: SavedObjectsType = {
  name: 'test',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: modelVersion1, // valid: start with version 1
    2: modelVersion2, // valid: no gap between versions
  },
  // ...other mandatory properties
};

an invalid version numbering:

const myType: SavedObjectsType = {
  name: 'test',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    2: modelVersion2, // invalid: first version must be 1
    4: modelVersion3, // invalid: skipped version 3
  },
  // ...other mandatory properties
};

Structure of a model version

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Model versions are not just functions as the previous migrations were, but structured objects describing how the version behaves and what changed since the last one.

A base example of what a model version can look like:

const myType: SavedObjectsType = {
  name: 'test',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: {
      changes: [
        {
          type: 'mappings_addition',
          addedMappings: {
            someNewField: { type: 'text' },
          },
        },
        {
          type: 'data_backfill',
          transform: someBackfillFunction,
        },
      ],
      schemas: {
        forwardCompatibility: fcSchema,
        create: createSchema,
      },
    },
  },
  // ...other mandatory properties
};

Note: Having multiple changes of the same type for a given version is supported by design to allow merging different sources (to prepare for an eventual higher-level API)

This definition would be perfectly valid:

const version1: SavedObjectsModelVersion = {
  changes: [
    {
      type: 'mappings_addition',
      addedMappings: {
        someNewField: { type: 'text' },
      },
    },
    {
      type: 'mappings_addition',
      addedMappings: {
        anotherNewField: { type: 'text' },
      },
    },
  ],
};

It’s currently composed of two main properties:

changes

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link to the TS doc for changes

Describes the list of changes applied during this version.

Important: This is the part that replaces the old migration system, and allows defining when a version adds new mapping, mutates the documents, or other type-related changes.

The current types of changes are:

- mappings_addition
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Used to define new mappings introduced in a given version.

Usage example:

const change: SavedObjectsModelMappingsAdditionChange = {
  type: 'mappings_addition',
  addedMappings: {
    newField: { type: 'text' },
    existingNestedField: {
      properties: {
        newNestedProp: { type: 'keyword' },
      },
    },
  },
};

note: When adding mappings, the root type.mappings must also be updated accordingly (as it was done previously).

- mappings_deprecation
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Used to flag mappings as no longer being used and ready to be removed.

Usage example:

let change: SavedObjectsModelMappingsDeprecationChange = {
  type: 'mappings_deprecation',
  deprecatedMappings: ['someDeprecatedField', 'someNested.deprecatedField'],
};

note: It is currently not possible to remove fields from an existing index’s mapping (without reindexing into another index), so the mappings flagged with this change type won’t be deleted for now, but this should still be used to allow our system to clean the mappings once upstream (ES) unblock us.

- data_backfill
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Used to populate fields (indexed or not) added in the same version.

Usage example:

let change: SavedObjectsModelDataBackfillChange = {
  type: 'data_backfill',
  transform: (document) => {
    return { attributes: { someAddedField: 'defaultValue' } };
  },
};

note: Even if no check is performed to ensure it, this type of model change should only be used to backfill newly introduced fields.

- data_removal
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Used to remove data (unset fields) from all documents of the type.

Usage example:

let change: SavedObjectsModelDataRemovalChange = {
  type: 'data_removal',
  attributePaths: ['someRootAttributes', 'some.nested.attribute'],
};

note: Due to backward compatibility, field utilization must be stopped in a prior release before actual data removal (in case of rollback). Please refer to the field removal migration example below in this document

- unsafe_transform
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Used to execute an arbitrary transformation function.

Usage example:

let change: SavedObjectsModelUnsafeTransformChange = {
  type: 'unsafe_transform',
  transformFn: (document) => {
    document.attributes.someAddedField = 'defaultValue';
    return { document };
  },
};

note: Using such transformations is potentially unsafe, given the migration system will have no knowledge of which kind of operations will effectively be executed against the documents. Those should only be used when there’s no other way to cover one’s migration needs. Please reach out to the development team if you think you need to use this, as you theoretically shouldn’t.

schemas

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link to the TS doc for schemas

The schemas associated with this version. Schemas are used to validate or convert SO documents at various stages of their lifecycle.

The currently available schemas are:

forwardCompatibility
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This is a new concept introduced by model versions. This schema is used for inter-version compatibility.

When retrieving a savedObject document from an index, if the version of the document is higher than the latest version known of the Kibana instance, the document will go through the forwardCompatibility schema of the associated model version.

Important: These conversion mechanism shouldn’t assert the data itself, and only strip unknown fields to convert the document to the shape of the document at the given version.

Basically, this schema should keep all the known fields of a given version, and remove all the unknown fields, without throwing.

Forward compatibility schema can be implemented in two different ways.

  1. Using config-schema

Example of schema for a version having two fields: someField and anotherField

const versionSchema = schema.object(
  {
    someField: schema.maybe(schema.string()),
    anotherField: schema.maybe(schema.string()),
  },
  { unknowns: 'ignore' }
);

Important: Note the { unknowns: 'ignore' } in the schema’s options. This is required when using config-schema based schemas, as this what will evict the additional fields without throwing an error.

  1. Using a plain javascript function

Example of schema for a version having two fields: someField and anotherField

const versionSchema: SavedObjectModelVersionEvictionFn = (attributes) => {
  const knownFields = ['someField', 'anotherField'];
  return pick(attributes, knownFields);
}

note: Even if highly recommended, implementing this schema is not strictly required. Type owners can manage unknown fields and inter-version compatibility themselves in their service layer instead.

create
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This is a direct replacement for the old SavedObjectType.schemas definition, now directly included in the model version definition.

As a refresher the create schema is a @kbn/config-schema object-type schema, and is used to validate the properties of the document during create and bulkCreate operations.

note: Implementing this schema is optional, but still recommended, as otherwise there will be no validating when importing objects

For implementation examples, refer to Use case examples.

Use-case examples

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These are example of the migration scenario currently supported (out of the box) by the system.

note: more complex scenarios (e.g field mutation by copy/sync) could already be implemented, but without the proper tooling exposed from Core, most of the work related to sync and compatibility would have to be implemented in the domain layer of the type owners, which is why we’re not documenting them yet.

Adding a non-indexed field without default value
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We are currently in model version 1, and our type has 2 indexed fields defined: foo and bar.

The definition of the type at version 1 would look like:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    // initial (and current) model version
    1: {
      changes: [],
      schemas: {
        // FC schema defining the known fields (indexed or not) for this version
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string() },
          { unknowns: 'ignore' } // note the `unknown: ignore` which is how we're evicting the unknown fields
        ),
        // schema that will be used to validate input during `create` and `bulkCreate`
        create:  schema.object(
          { foo: schema.string(), bar: schema.string() },
        )
      },
    },
  },
  mappings: {
    properties: {
      foo: { type: 'text' },
      bar: { type: 'text' },
    },
  },
};

From here, say we want to introduce a new dolly field that is not indexed, and that we don’t need to populate with a default value.

To achieve that, we need to introduce a new model version, with the only thing to do will be to define the associated schemas to include this new field.

The added model version would look like:

// the new model version adding the `dolly` field
let modelVersion2: SavedObjectsModelVersion = {
  // not an indexed field, no data backfill, so changes are actually empty
  changes: [],
  schemas: {
    // the only addition in this model version: taking the new field into account for the schemas
    forwardCompatibility: schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
      { unknowns: 'ignore' } // note the `unknown: ignore` which is how we're evicting the unknown fields
    ),
    create:  schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
    )
  },
};

The full type definition after the addition of the new model version:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: {
      changes: [],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string() },
        )
      },
    },
    2: {
      changes: [],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
        )
      },
    },
  },
  mappings: {
    properties: {
      foo: { type: 'text' },
      bar: { type: 'text' },
    },
  },
};
Adding an indexed field without default value
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This scenario is fairly close to the previous one. The difference being that working with an indexed field means adding a mappings_addition change and to also update the root mappings accordingly.

To reuse the previous example, let’s say the dolly field we want to add would need to be indexed instead.

In that case, the new version needs to do the following: - add a mappings_addition type change to define the new mappings - update the root mappings accordingly - add the updated schemas as we did for the previous example

The new version definition would look like:

let modelVersion2: SavedObjectsModelVersion = {
  // add a change defining the mapping for the new field
  changes: [
    {
      type: 'mappings_addition',
      addedMappings: {
        dolly: { type: 'text' },
      },
    },
  ],
  schemas: {
    // adding the new field to the forwardCompatibility schema
    forwardCompatibility: schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
      { unknowns: 'ignore' }
    ),
    create:  schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
    )
  },
};

As said, we will also need to update the root mappings definition:

mappings: {
  properties: {
    foo: { type: 'text' },
    bar: { type: 'text' },
    dolly: { type: 'text' },
  },
},

the full type definition after the addition of the model version 2 would be:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: {
      changes: [
        {
          type: 'mappings_addition',
          addedMappings: {
            foo: { type: 'text' },
            bar: { type: 'text' },
          },
        },
      ],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string() },
        )
      },
    },
    2: {
      changes: [
        {
          type: 'mappings_addition',
          addedMappings: {
            dolly: { type: 'text' },
          },
        },
      ],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
        )
      },
    },
  },
  mappings: {
    properties: {
      foo: { type: 'text' },
      bar: { type: 'text' },
      dolly: { type: 'text' },
    },
  },
};
Adding an indexed field with a default value
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Now a slightly different scenario where we’d like to populate the newly introduced field with a default value.

In that case, we’d need to add an additional data_backfill change to populate the new field’s value (in addition to the mappings_addition one):

let modelVersion2: SavedObjectsModelVersion = {
  changes: [
    // setting the `dolly` field's default value.
    {
      type: 'data_backfill',
      transform: (document) => {
        return { attributes: { dolly: 'default_value' } };
      },
    },
    // define the mappings for the new field
    {
      type: 'mappings_addition',
      addedMappings: {
        dolly: { type: 'text' },
      },
    },
  ],
  schemas: {
    // define `dolly` as an know field in the schema
    forwardCompatibility: schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
      { unknowns: 'ignore' }
    ),
    create:  schema.object(
      { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
    )
  },
};

The full type definition would look like:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    1: {
      changes: [
        {
          type: 'mappings_addition',
          addedMappings: {
            foo: { type: 'text' },
            bar: { type: 'text' },
          },
        },
      ],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string() },
        )
      },
    },
    2: {
      changes: [
        {
          type: 'data_backfill',
          transform: (document) => {
            return { attributes: { dolly: 'default_value' } };
          },
        },
        {
          type: 'mappings_addition',
          addedMappings: {
            dolly: { type: 'text' },
          },
        },
      ],
      schemas: {
        forwardCompatibility: schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { foo: schema.string(), bar: schema.string(), dolly: schema.string() },
        )
      },
    },
  },
  mappings: {
    properties: {
      foo: { type: 'text' },
      bar: { type: 'text' },
      dolly: { type: 'text' },
    },
  },
};

Note: if the field was non-indexed, we would just not use the mappings_addition change or update the mappings (as done in example 1)

Removing an existing field
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We are currently in model version 1, and our type has 2 indexed fields defined: kept and removed.

The definition of the type at version 1 would look like:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    // initial (and current) model version
    1: {
      changes: [],
      schemas: {
        // FC schema defining the known fields (indexed or not) for this version
        forwardCompatibility: schema.object(
          { kept: schema.string(), removed: schema.string() },
          { unknowns: 'ignore' } // note the `unknown: ignore` which is how we're evicting the unknown fields
        ),
        // schema that will be used to validate input during `create` and `bulkCreate`
        create:  schema.object(
          { kept: schema.string(), removed: schema.string() },
        )
      },
    },
  },
  mappings: {
    properties: {
      kept: { type: 'text' },
      removed: { type: 'text' },
    },
  },
};

From here, say we want to remove the removed field, as our application doesn’t need it anymore since a recent change.

The first thing to understand here is the impact toward backward compatibility: Say that Kibana version X was still using this field, and that we stopped utilizing the field in version X+1.

We can’t remove the data in version X+1, as we need to be able to rollback to the prior version at any time. If we were to delete the data of this removed field during the upgrade to version X+1, and if then, for any reason, we’d need to rollback to version X, it would cause a data loss, as version X was still using this field, but it would no longer present in our document after the rollback.

Which is why we need to perform any field removal as a 2-step operation: - release X: Kibana still utilize the field - release X+1: Kibana no longer utilize the field, but the data is still present in the documents - release X+2: The data is effectively deleted from the documents.

That way, any prior-version rollback (X+2 to X+1 or X+1 to X is safe in term of data integrity)

The main question then, is what’s the best way of having our application layer simply ignore this removed field during version X+1, as we don’t want this field (now non-utilized) to be returned from the persistence layer, as it could “pollute” the higher-layers where the field is effectively no longer used or even known.

This can easily be done by introducing a new version and using the forwardCompatibility schema to “shallow” the field.

The X+1 model version would look like:

// the new model version ignoring the `removed` field
let modelVersion2: SavedObjectsModelVersion = {
  changes: [],
  schemas: {
    forwardCompatibility: schema.object(
      { kept: schema.string() }, // `removed` is no longer defined here
      { unknowns: 'ignore' }
    ),
    create:  schema.object(
      { kept: schema.string() }, // `removed` is no longer defined here
    )
  },
};

The full type definition after the addition of the new model version:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    // initial (and current) model version
    1: {
      changes: [],
      schemas: {
        // FC schema defining the known fields (indexed or not) for this version
        forwardCompatibility: schema.object(
          { kept: schema.string(), removed: schema.string() },
          { unknowns: 'ignore' } // note the `unknown: ignore` which is how we're evicting the unknown fields
        ),
        // schema that will be used to validate input during `create` and `bulkCreate`
        create:  schema.object(
          { kept: schema.string(), removed: schema.string() },
        )
      },
    },
    2: {
      changes: [],
      schemas: {
        forwardCompatibility: schema.object(
          { kept: schema.string() }, // `removed` is no longer defined here
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { kept: schema.string() }, // `removed` is no longer defined here
        )
      },
    }
  },
  mappings: {
    properties: {
      kept: { type: 'text' },
      removed: { type: 'text' },
    },
  },
};

then, in a later release, we can then deploy the change that will effectively remove the data from the documents:

// the new model version ignoring the `removed` field
let modelVersion3: SavedObjectsModelVersion = {
  changes: [ // define a data_removal change to delete the field
    {
      type: 'data_removal',
      removedAttributePaths: ['removed']
    }
  ],
  schemas: {
    forwardCompatibility: schema.object(
      { kept: schema.string() },
      { unknowns: 'ignore' }
    ),
    create:  schema.object(
      { kept: schema.string() },
    )
  },
};

The full type definition after the data removal would look like:

const myType: SavedObjectsType = {
  name: 'test',
  namespaceType: 'single',
  switchToModelVersionAt: '8.10.0',
  modelVersions: {
    // initial (and current) model version
    1: {
      changes: [],
      schemas: {
        // FC schema defining the known fields (indexed or not) for this version
        forwardCompatibility: schema.object(
          { kept: schema.string(), removed: schema.string() },
          { unknowns: 'ignore' } // note the `unknown: ignore` which is how we're evicting the unknown fields
        ),
        // schema that will be used to validate input during `create` and `bulkCreate`
        create:  schema.object(
          { kept: schema.string(), removed: schema.string() },
        )
      },
    },
    2: {
      changes: [],
      schemas: {
        forwardCompatibility: schema.object(
          { kept: schema.string() }, // `removed` is no longer defined here
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { kept: schema.string() }, // `removed` is no longer defined here
        )
      },
    },
    3: {
      changes: [ // define a data_removal change to delete the field
        {
          type: 'data_removal',
          removedAttributePaths: ['removed']
        }
      ],
      schemas: {
        forwardCompatibility: schema.object(
          { kept: schema.string() },
          { unknowns: 'ignore' }
        ),
        create:  schema.object(
          { kept: schema.string() },
        )
      },
    }
  },
  mappings: {
    properties: {
      kept: { type: 'text' },
      removed: { type: 'text' },
    },
  },
};

Testing model versions

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Model versions definitions are more structured than the legacy migration functions, which makes them harder to test without the proper tooling. This is why a set of testing tools and utilities are exposed from the @kbn/core-test-helpers-model-versions package, to help properly test the logic associated with model version and their associated transformations.

Tooling for unit tests
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For unit tests, the package exposes utilities to easily test the impact of transforming documents from a model version to another one, either upward or backward.

Model version test migrator
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The createModelVersionTestMigrator helper allows to create a test migrator that can be used to test model version changes between versions, by transforming documents the same way the migration algorithm would during an upgrade.

Example:

import {
  createModelVersionTestMigrator,
  type ModelVersionTestMigrator
} from '@kbn/core-test-helpers-model-versions';

const mySoTypeDefinition = someSoType();

describe('mySoTypeDefinition model version transformations', () => {
  let migrator: ModelVersionTestMigrator;

  beforeEach(() => {
    migrator = createModelVersionTestMigrator({ type: mySoTypeDefinition });
  });

  describe('Model version 2', () => {
    it('properly backfill the expected fields when converting from v1 to v2', () => {
      const obj = createSomeSavedObject();

      const migrated = migrator.migrate({
        document: obj,
        fromVersion: 1,
        toVersion: 2,
      });

      expect(migrated.properties).toEqual(expectedV2Properties);
    });

    it('properly removes the expected fields when converting from v2 to v1', () => {
      const obj = createSomeSavedObject();

      const migrated = migrator.migrate({
        document: obj,
        fromVersion: 2,
        toVersion: 1,
      });

      expect(migrated.properties).toEqual(expectedV1Properties);
    });
  });
});
Tooling for integration tests
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During integration tests, we can boot a real Elasticsearch cluster, allowing us to manipulate SO documents in a way almost similar to how it would be done on production runtime. With integration tests, we can even simulate the cohabitation of two Kibana instances with different model versions to assert the behavior of their interactions.

Model version test bed
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The package exposes a createModelVersionTestBed function that can be used to fully setup a test bed for model version integration testing. It can be used to start and stop the ES server, and to initiate the migration between the two versions we’re testing.

Example:

import {
  createModelVersionTestBed,
  type ModelVersionTestKit
} from '@kbn/core-test-helpers-model-versions';

describe('myIntegrationTest', () => {
  const testbed = createModelVersionTestBed();
  let testkit: ModelVersionTestKit;

  beforeAll(async () => {
    await testbed.startES();
  });

  afterAll(async () => {
    await testbed.stopES();
  });

  beforeEach(async () => {
    // prepare the test, preparing the index and performing the SO migration
    testkit = await testbed.prepareTestKit({
      savedObjectDefinitions: [{
        definition: mySoTypeDefinition,
        // the model version that will be used for the "before" version
        modelVersionBefore: 1,
        // the model version that will be used for the "after" version
        modelVersionAfter: 2,
      }]
    })
  });

  afterEach(async () => {
    if(testkit) {
      // delete the indices between each tests to perform a migration again
      await testkit.tearDown();
    }
  });

  it('can be used to test model version cohabitation', async () => {
    // last registered version is `1` (modelVersionBefore)
    const repositoryV1 = testkit.repositoryBefore;
    // last registered version is `2` (modelVersionAfter)
    const repositoryV2 = testkit.repositoryAfter;

    // do something with the two repositories, e.g
    await repositoryV1.create(someAttrs, { id });
    const v2docReadFromV1 = await repositoryV2.get('my-type', id);
    expect(v2docReadFromV1.attributes).toEqual(whatIExpect);
  });
});

Limitations:

Because the test bed is only creating the parts of Core required to instantiate the two SO repositories, and because we’re not able to properly load all plugins (for proper isolation), the integration test bed currently has some limitations:

  • no extensions are enabled

    • no security
    • no encryption
    • no spaces
  • all SO types will be using the same SO index

Limitations and edge cases in serverless environments

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The serverless environment, and the fact that upgrade in such environments are performed in a way where, at some point, the old and new version of the application are living in cohabitation, leads to some particularities regarding the way the SO APIs works, and to some limitations / edge case that we need to document

Using the fields option of the find savedObjects API
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By default, the find API (as any other SO API returning documents) will migrate all documents before returning them, to ensure that documents can be used by both versions during a cohabitation (e.g an old node searching for documents already migrated, or a new node searching for documents not yet migrated).

However, when using the fields option of the find API, the documents can’t be migrated, as some model version changes can’t be applied against a partial set of attributes. For this reason, when the fields option is provided, the documents returned from find will not be migrated.

Which is why, when using this option, the API consumer needs to make sure that all the fields passed to the fields option were already present in the prior model version. Otherwise, it may lead to inconsistencies during upgrades, where newly introduced or backfilled fields may not necessarily appear in the documents returned from the search API when the option is used.

(note: both the previous and next version of Kibana must follow this rule then)

Using bulkUpdate for fields with large json blobs
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The savedObjects bulkUpdate API will update documents client-side and then reindex the updated documents. These update operations are done in-memory, and cause memory constraint issues when updating many objects with large json blobs stored in some fields. As such, we recommend against using bulkUpdate for savedObjects that: - use arrays (as these tend to be large objects) - store large json blobs in some fields

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