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Search Engines

Backstage supports 3 search engines by default, an in-memory engine called Lunr, ElasticSearch and Postgres. You can configure your own search engines by implementing the provided interface as mentioned in the search backend documentation.

Provided search engine implementations have their own way of constructing queries, which may be something you want to modify. Alterations to the querying logic of a search engine can be made by providing your own implementation of a QueryTranslator interface. This modification can be done without touching provided search engines by using the exposed setter to set the modified query translator into the instance.

const searchEngine = new LunrSearchEngine({ logger: env.logger });
searchEngine.setTranslator(new MyNewAndBetterQueryTranslator());

Lunr

Lunr search engine is enabled by default for your backstage instance if you have not done additional changes to the scaffolded app.

Lunr can be instantiated like this:

// app/backend/src/plugins/search.ts
const searchEngine = new LunrSearchEngine({ logger: env.logger });
const indexBuilder = new IndexBuilder({ logger: env.logger, searchEngine });

Postgres

The Postgres based search engine only requires that postgres being configured as the database engine for Backstage. Therefore it targets setups that want to avoid maintaining another external service like elastic search. The search provides decent results and performs well with ten thousands of indexed documents. The connection to postgres is established via the database manager also used by other plugins.

Important: The search plugin requires at least Postgres 12!

To use the PgSearchEngine, make sure that you have a Postgres database configured and make the following changes to your backend:

  1. Add a dependency on @backstage/plugin-search-backend-module-pg to your backend's package.json.
  2. Initialize the search engine. It is recommended to initialize it with a fallback to the lunr search engine if you are running Backstage for development locally with SQLite:
// In packages/backend/src/plugins/search.ts

// Initialize a connection to a search engine.
const searchEngine = (await PgSearchEngine.supported(env.database))
  ? await PgSearchEngine.fromConfig(env.config, { database: env.database })
  : new LunrSearchEngine({ logger: env.logger });

Optional Configuration

The following is an example of the optional configuration that can be applied when using Postgres as the search backend. Currently this is mostly for just the highlight feature:

search:
  pg:
    highlightOptions:
      useHighlight: true # Used to enable to disable the highlight feature. The default value is true
      maxWord: 35 # Used to set the longest headlines to output. The default value is 35.
      minWord: 15 # Used to set the shortest headlines to output. The default value is 15.
      shortWord: 3 # Words of this length or less will be dropped at the start and end of a headline, unless they are query terms. The default value of three (3) eliminates common English articles.
      highlightAll: false # If true the whole document will be used as the headline, ignoring the preceding three parameters. The default is false.
      maxFragments: 0 # Maximum number of text fragments to display. The default value of zero selects a non-fragment-based headline generation method. A value greater than zero selects fragment-based headline generation (see the linked documentation above for more details).
      fragmentDelimiter: ' ... ' # Delimiter string used to concatenate fragments. Defaults to " ... ".

Note: the highlight search term feature uses ts_headline which has been known to potentially impact performance. You only need this minimal config to disable it should you have issues:

search:
  pg:
    highlightOptions:
      useHighlight: false

The Postgres documentation on Highlighting Results has more details.

ElasticSearch

Backstage supports ElasticSearch search engine connections, indexing and querying out of the box. Available configuration options enable usage of either AWS or Elastic.co hosted solutions, or a custom self-hosted solution.

Similarly to Lunr above, ElasticSearch can be set up like this:

// app/backend/src/plugins/search.ts
const searchEngine = await ElasticSearchSearchEngine.initialize({
  logger: env.logger,
  config: env.config,
});
const indexBuilder = new IndexBuilder({ logger: env.logger, searchEngine });

For the engine to be available, your backend package needs a dependency on package @backstage/plugin-search-backend-module-elasticsearch.

ElasticSearch needs some additional configuration before it is ready to use within your instance. The configuration options are documented in the configuration schema definition file.

The underlying functionality uses either the official ElasticSearch client version 7.x (meaning that ElasticSearch version 7 is the only one confirmed to be supported), or the OpenSearch client, when the aws provider is configured.

Should you need to create your own bespoke search experiences that require more than just a query translator (such as faceted search or Relay pagination), you can access the configuration of the search engine in order to create new elastic search clients. The version of the client need not be the same as one used internally by the elastic search engine plugin. For example:

import { isOpenSearchCompatible } from '@backstage/plugin-search-backend-module-elasticsearch';
import { Client as ElasticClient } from '@elastic/elastic-search';
import { Client as OpenSearchClient } from '@opensearch-project/opensearch';

const client = searchEngine.newClient(options => {
  // In reality, you would only import / instantiate one of the following, but
  // for illustrative purposes, here are both:
  if (isOpenSearchCompatible(options)) {
    return new OpenSearchClient(options);
  } else {
    return new ElasticClient(options);
  }
});

Set custom index template

The elasticsearch engine gives you the ability to set a custom index template if needed.

Index templates define settings, mappings, and aliases that can be applied automatically to new indices.

// app/backend/src/plugins/search.ts
const searchEngine = await ElasticSearchSearchEngine.initialize({
  logger: env.logger,
  config: env.config,
});

searchEngine.setIndexTemplate({
  name: '<name-of-your-custom-template>',
  body: {
    index_patterns: ['<your-index-pattern>'],
    template: {
      mappings: {},
      settings: {},
    },
  },
});

Example configurations

AWS

Using AWS hosted ElasticSearch the only configuration option needed is the URL to the ElasticSearch service. The implementation assumes that environment variables for AWS access key id and secret access key are defined in accordance to the default AWS credential chain..

search:
  elasticsearch:
    provider: aws
    node: https://my-backstage-search-asdfqwerty.eu-west-1.es.amazonaws.com

Elastic.co

Elastic Cloud hosted ElasticSearch uses a Cloud ID to determine the instance of hosted ElasticSearch to connect to. Additionally, username and password needs to be provided either directly or using environment variables like defined in Backstage documentation.

search:
  elasticsearch:
    provider: elastic
    cloudId: backstage-elastic:asdfqwertyasdfqwertyasdfqwertyasdfqwerty==
    auth:
      username: elastic
      password: changeme

Others

Other ElasticSearch instances can be connected to by using standard ElasticSearch authentication methods and exposed URL, provided that the cluster supports that. The configuration options needed are the URL to the node and authentication information. Authentication can be handled by either providing username/password or an API key. For more information how to create an API key, see Elastic documentation on API keys.

Configuration examples

With username and password
search:
  elasticsearch:
    node: http://localhost:9200
    auth:
      username: elastic
      password: changeme
With API key
search:
  elasticsearch:
    node: http://localhost:9200
    auth:
      apiKey: base64EncodedKey

Elastic search batch size

Default batch size of the elastic search engine is set to 1000. If you are using a lower spec computing resources (like AWS small instance), you may get an error caused by limited thread_pool configuration. ( 429 Too Many Requests /_bulk )

In this case you need to decrease the batch size to index the resources to prevent this kind of error. You can easily decrease or increase the batch size in your app-config.yaml using the batchSize option provided for elasticsearch configuration.

Configuration example

Set batch size to 100

search:
  elasticsearch:
    batchSize: 100

You can also increase the batch size if you are using a large ES instance.

Last updated on 9/29/2022
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