Important

You are browsing upcoming documentation for version 6.1 of OroCommerce, scheduled for release in 2025. Read the documentation for version 6.0 (the latest LTS version) to get up-to-date information.

See our Release Process documentation for more information on the currently supported and upcoming releases.

Search Index 

The search index component is responsible for interaction with a different search engine and the search index storage inside it.

In the Oro application, the search engine can:

  • perform search quickly (in comparison with the standard ORM search)

  • apply complicated filters to results

  • sort search results by relevancy or a specific field

  • extract additional data from the search index

  • calculate aggregated values

In this section, you will learn about the following concepts related to the search index.

Note

For developer’s documentation, see implementation specifics of the Elasticsearch and ORM-based search index, and the OroSearchBundle and OroWebsiteSearchBundle documentation for search index configuration.

Main Concepts 

Search engine is the specialized software that provides the ability to store data, index data, and perform a quick and relevant search. The biggest difference between a search engine and a conventional data storage is that the search engine performs a search significantly faster and can do an overall full-text search in addition to searching by the specific area. Examples of search engines - Elasticsearch, Lucene, Solr. To use an analogy to relational databases, the search engine is similar to Database Management System (DBMS).

  • Search index (sometimes referred to as just index) is actual storage where the specific scope of search data is stored. One search engine may work with several indexes. The search index is structured, i.e., it provides the ability to organize data in complex multilevel structures; the minimum level required to work with the Oro application is two. A search index can validate the data type of the data stored inside. A search index is document-based storage where each document represents one specific entity from the main relational database. A search index can be considered a specialized reflection of the main relational database. To use an analogy to relational databases, the search index is similar to a database (DB).

  • Entity alias is a text representation of an entity name stored inside the specific search index. Entity alias represents the first level of the search index structure. To use an analogy to relational databases, an entity alias is similar to a table name.

  • Entity field is a text representation of an entity property name assigned to a specific entity alias. Entity alias represents the second level of the search index structure. Entity field has assigned data type (text, integer, decimal, datetime), and search engine uses this information to validate data stored inside the index. To use an analogy to relational databases, entity field is similar to a column name.

  • Entity field value is an actual value of an entity property assigned to a specific entity field. To use an analogy to relational databases, entity field value is similar to a value of a column.

  • All-text field is a particular entity field used to do a comprehensive full-text search. The value of this field is usually calculated automatically based on the text entity field values.

  • Search document is a combination of entity fields and entity field values and represents data of one specific entity from the main relational database. Search document has a simple structure - i.e., field values must not contain other documents. To use an analogy to relational databases, a search document is similar to a table row.

  • Indexation is a process of updating data in a search index - it might be the extraction of the required data from an entity and saving it to the search index or removing required documents from the search index.

  • Search mapping is a combination of entity aliases and entity field definitions. To use an analogy to relational databases, search mapping is similar to a database schema.

  • Search placeholder is a variable part of an entity alias or entity field that can be substituted with an actual value during the indexation or a search request.

The following diagram shows described structure:

../../../../_images/op_search_diag.png

Index Types 

Oro applications use two types of indexes (standard and website) that use standard interfaces.

Common Interfaces 

Standard interfaces are used in all index types to provide a high-level abstraction for functionality that works with any type of search index (e.g., datagrids).

  • Search engine interface Oro\\Bundle\\SearchBundle\\Engine\\EngineInterface is used to perform search requests to a search index.

  • Search indexer interface Oro\\Bundle\\SearchBundle\\Engine\\IndexerInterface is used for indexation, i.e., to change state of a search index (save data, remove data, reset index).

Standard Index Type 

Note

The main logic of this index type is stored inside the OroSearchBundle in the platform package.

The standard index type (sometimes called the default index type or backend index type) is used in all applications based on OroPlatform. Each entity is represented by one plain entity alias and contains plain field names to represent data assigned directly to the primary entity or data from the related entities.

The standard index type triggers the following events:

  • oro_search.mapping_config - during the mapping collection process, used to alter mapping information;

  • oro_search.prepare_entity_map - during the indexation process, triggered for each entity, used to change data stored inside the index;

  • oro_search.before_search - before the search request, used to change search request before its execution;

  • oro_search.prepare_result_item - after the extraction of documents from the search index, used to populate additional information (entity objects, URLs, etc.).

The standard index type performs indexation on an entity level, triggering each entity’s indexation process. Search field values can be calculated automatically based on the defined search mapping. Each search document contains one all-text field value calculated automatically as a concatenation of all text entity field values. Each field might have several values. In this case, it will be represented as an array, and comparison operations will iterate over all of them. During the reindexation, the entities being reindexed are not available for search.

Search mapping can be stored inside any bundle in the Resources/config/oro/search.yml file. Engine-specific services have to be defined in the Resources/config/oro/search_engine/<engine>.yml file.

Website Index Type 

Note

The main logic of this index type is stored inside the OroWebsiteSearchBundle in the commerce package.

The website index type (sometimes called the frontend index type) is used only in the OroCommerce application. The main purpose of the website search is to provide the customer with the ability to use search functionality at the application frontend. Website search should be used only at frontend because of its nature - the data is stored by websites (i.e., each website has its own scope in the storage) and some frontend-specific values (like localization) are necessary for the frontend search use cases - e.g., user should have a possibility to search data only using one specific localization.

Data for the website search index is collected and stored by websites and entity types. It means that each entity for each website has its own scope in the storage, and as a consequence, these scopes are independent and can be handled separately. For example, a developer might ask to reindex only specific entity for a specific website, and this change does not affect any other entity at the specified website or any other website data.

Engine data collection is event based, so any bundle can mix its own information to search index. As a consequence, some entities in the index might contain information that is not related directly, but is still valuable to search by related areas.

Each entity is represented by one alias with an optional search placeholder (e.g., oro_product_WEBSITE_IT), so each website might have its own entity alias (e.g., oro_product_1 and oro_product_2). Entity fields might also contain search placeholders (e.g., name_LOCALIZATION_ID), so each field might have several values depending on the provided placeholders (e.g., name_1, name_2, and name_3).

Website index type triggers the following events:

  • oro_website_search.reindexation_request - triggers the reindexation process for the specified scope of entities;

  • oro_website_search.event.website_search_mapping.configuration - during the mapping collection process, used to alter mapping information;

  • oro_website_search.event.collect_context - before the indexation, used to collect context which will be used during the indexation;

  • oro_website_search.event.restrict_index_entity - during the indexation for all entities, used to decrease the number of entities that have to be indexed;

  • oro_website_search.event.restrict_index_entity.<alias> - during the indexation for the specific entity, used to decrease amount of specific entities that has to be indexed (pay attention that <alias> is a standard entity alias, not search entity alias);

  • oro_website_search.event.index_entity - during the indexation for all entities, used to collect data to put into the search index;

  • oro_website_search.event.index_entity.<alias> - during the indexation for the specific entity, used to collect data for a specific entity to put into search index (pay attention that <alias> is a standard entity alias, not search entity alias);

  • oro_website_search.before_search - before the search request, used to change the request before its execution.

Website index type performs reindexation on an entity batch level, triggering the indexation process for a batch of entities (default batch size is 100). Search field values must be calculated and set manually in a listener to the oro_website_search.event.index_entity event. Each search document contains all-text fields for each available localization (all_text_LOCALIZATION_ID) and one all-text field that includes values from all localizations, and values are calculated automatically based on a flag set during the indexation (i.e., a developer may specify what exact values should be in all-text field value). Each field without a placeholder must have only one value. During reindexation, entities being reindexed are available with the old (outdated) data.

Placeholders are defined in code in classes that implement Oro\Bundle\WebsiteSearchBundle\Placeholder\PlaceholderInterface. Here are the most commonly used placeholders (pay attention that there are more of them in a code):

  • WEBSITE_ID - integer identifier of a current website

  • LOCALIZATION_ID - integer identifier of a current localization

  • CUSTOMER_ID - integer identifier of a current customer user

  • CURRENCY - string identifier of a current currency

  • CPL_ID - integer identifier of a current combined price list

Search mapping can be stored inside any bundle in the Resources/config/oro/website_search.yml file. Engine-specific services have to be defined in the Resources/config/oro/website_search_engine/<engine>.yml file.

By design, website indexation supports both synchronous and asynchronous operation (refer to the Indexation Process section below). When triggering reindexation, you can define whether it should run in the synchronous or asynchronous mode. During the asynchronous reindexation, the appropriate message is put to the message queue and is processed by the consumer later by reindexing the required scope of entities.

WebsiteSearchBundle VS SearchBundle 

The main difference between SearchBundle and WebsiteSearchBundle is the way index is stored. The website (frontend) index storage is separated from the platform index storage and may be moved to a separate server and thus may be properly scaled.

Next important difference is in the information they control. The platform index handles the backend information (e.g., back-office), and the website index contains information about the frontend (e.g., storefront). As a consequence, platform index is usually smaller and the search and indexation speed is well balanced, while frontend index trades off the indexation speed for a faster search.

Though indexation might be a little slower comparing to backend index, frontend index is more flexible in terms of extendability. It is event based, and there are several events that allow to customize different parts of search and indexation.

Supported Search Engines 

Data Structure 

General Information 

Search index stores documents grouped by entity alias. Each document contains scalar fields with values. One of the fields is an all-text field used to perform the overall search. Data from the related entities can also be stored, but in this case, it has to be denormalized to a simple structure. There are four supported entity field value data types:

  • text

  • integer (used to represent boolean values)

  • decimal

  • datetime (used to represent date values)

Note

See more details on a plain data structure with indexed data, localized data, and configuration examples in the Search Index Structure topic.

ORM Search 

ORM search engine stores data using the EAV model to store attributes. One primary entity and four related entities are used to store data for each of the supported field data types. One primary entity contains the key information about the document, such as the entity class, entity ID, entity alias, default title, and flag that indicates whether the entity was changed and createdAt/updatedAt. All four related entities have a similar structure, as they store the name of the entity field and the actual entity field value. The following diagram illustrates this structure:

../../../../_images/op_structure_search_orm.png

Each of the supported index types uses its own set of entities - 5 entities for the standard index type and 5 more entities for the website index type.

Elasticsearch Search 

Elasticsearch search engine is document based from the beginning, so the index data structure is straightforward. The search index is represented by the Elasticsearch index.

Search aliases are represented by the Elasticsearch types. Search field values are stored inside the Elasticsearch document. Search mapping is defined at the Elasticsearch mapping and is generated automatically during index creation. The following diagram illustrates this structure:

../../../../_images/op_structure_search_elastic.png

Each of the supported index types uses its own Elasticsearch index.

Example of Standard Index Type Document

Here is an example of the document from the standard index type under the oro_user entity alias.

{
"username":"admin",
"all_text":"admin admin@example.com example com John Doe",
"email":"admin@example.com",
"firstName":"John",
"lastName":"Doe",
"assigned_organization_id":[
   1
],
"organization":1,
"oro_user_owner":1
}

Pay attention to the following facts:

  • all_text field combines data from all other text fields

  • assigned_organization_id field contains an array of values

Example of Website Index Type Document

Here is example of the document from the website index type under oro_product_WEBSITE_ID (WEBSITE_ID = 1) entity alias.

{
"assigned_to_variant_31":1,
"assigned_to_variant_25":1,
"assigned_to_variant_19":1,
"assigned_to_variant_11":1,
"newArrival":1,
"inventory_status_priority":2,
"product_id":2,
"category_id":8,
"visibility_anonymous":1,
"visibility_new":1,
"is_visible_by_default":1,
"all_text_1":"Retail Supplies Credit Card Pin Pad Reader Choosing the right credit card processing terminal to fit your business needs can help you increase your profits and reduce your processing costs. This credit card reader helps you do just that. Its easy-to-use ATM style interface accepts PIN-based debit card transactions and swipes traditional payment cards. It also accepts chip cards and features a built-in receipt printer Product Information & Features: Catalog Page: 2976 Performs PIN based debit card transactions Accepts chip cards Prints transaction receipts Connectivity: USB (not included) Large, backlit display Supports all magnetic stripe cards Color: Purple Technical Specs: Width: 6\u201d Height: 3\u201d Weight: .6 lb. Acme defaultMetaDescription defaultMetaKeywords 1AB92",
"sku":"1AB92",
"names_1":"Credit Card Pin Pad Reader",
"shortDescriptions_1":"Choosing the right credit card processing terminal to fit your business needs can help you increase your profits and reduce your processing costs. This credit card reader helps you do just that. Its' easy-to-use ATM style interface accepts PIN-based debit card transactions and swipes traditional payment cards. It also accepts chip cards and features a built-in receipt printer",
"inventory_status":"out_of_stock",
"sku_uppercase":"1AB92",
"status":"enabled",
"type":"simple",
"image_product_large":"\/media\/cache\/attachment\/resize\/11\/product_large\/59d20f5ae821f060277950.jpeg",
"image_product_medium":"\/media\/cache\/attachment\/resize\/11\/product_medium\/59d20f5ae821f060277950.jpeg",
"product_units":"a:2:{s:4:\"item\";i:0;s:3:\"set\";i:0;}",
"category_path":"1_8",
"category_title_1":"Retail Supplies",
"minimal_price_3_EUR_item":"60.8000",
"minimal_price_7_EUR_item":"60.8000",
"minimal_price_2_USD_item":"60.8000",
"minimal_price_6_USD_item":"60.8000",
"minimal_price_4_EUR_item":"60.8000",
"minimal_price_1_EUR_item":"76.0000",
"minimal_price_5_EUR_item":"60.8000",
"minimal_price_5_USD_item":"60.8000",
"minimal_price_1_USD_item":"76.0000",
"minimal_price_4_USD_item":"60.8000",
"minimal_price_6_EUR_item":"60.8000",
"minimal_price_2_EUR_item":"60.8000",
"minimal_price_7_USD_item":"60.8000",
"minimal_price_3_USD_item":"60.8000",
"minimal_price_7_EUR":"60.8000",
"minimal_price_3_EUR":"60.8000",
"minimal_price_1_EUR":"76.0000",
"minimal_price_6_USD":"60.8000",
"minimal_price_4_EUR":"60.8000",
"minimal_price_4_USD":"60.8000",
"minimal_price_6_EUR":"60.8000",
"minimal_price_1_USD":"76.0000",
"minimal_price_3_USD":"60.8000",
"minimal_price_7_USD":"60.8000",
"minimal_price_5_EUR":"60.8000",
"minimal_price_2_USD":"60.8000",
"minimal_price_2_EUR":"60.8000",
"minimal_price_5_USD":"60.8000",
"tmp_alias":"oro_product_1_website_search59d7a947de6e69.38892637"
}

Keep in mind that:

  • multiple values are denormalized to a one level structure according to passed placeholder values - assigned_to_ASSIGN_TYPE_ASSIGN_ID, minimal_price_CPL_ID_CURRENCY_UNIT etc

  • localized values are related to specific locale (LOCALIZATION_ID = 1) - names_LOCALIZATION_ID, shortDescriptions_LOCALIZATION_ID etc

  • localized all-text fields (LOCALIZATION_ID = 1) - all_text_LOCALIZATION_ID

  • tmp_alias field is used to maintain outdated data during the reindexation and understand which entities have to be removed after the reindexation

Search Request 

A developer has to build a query for the search index to perform a search request. There are two representations of search query - string representation and object representation.

String Representation 

The string representation is similar to a standard SQL query. This string may contain keywords select, from, where, aggregate, order_by, offset, and max_results. The string representation is used mainly in the API, where users can request specific data with a specific condition. During the processing of string representation, it is converted to object representation.

Object Representation 

Object representation has two levels - low and high. Low-level object (Oro\Bundle\SearchBundle\Query\Query, sometimes called a search query builder) represents a query and has parts similar to the string representation (select, where, etc.). The low-level query is not aware of a specific search engine. All search engines use it as the main query representation. The low-level object is, in fact, a Data transfer object. High-level object (implementation of Oro\Bundle\SearchBundle\Query\SearchQueryInterface) is used to hide search engine specific logic from a developer. It embeds low-level objects and proxies most of the calls. High-level objects are created by the query factory (implementation of Oro\Bundle\SearchBundle\Query\Factory\QueryFactoryInterface). Each index type has its own implementation of the high-level object, encapsulating how this query must be executed and its own implementation of the query factory responsible for creating a high-level object. The high-level object is a Facade. The following diagram demonstrates the connection between the low-level object, the high-level object, the query factory, and the search engine:

../../../../_images/op_structure_search_index_object_representation.png

Triggering Search Request 

The recommended way to trigger a search request is to get an instance of the high-level object, build a query, execute it and get results. We also recommend isolating all search requests in a search repository (see Best Practices section) to separate storage logic from business logic. However, if you need to work on the lower level (e.g., to write a functional test), you can get an instance of an appropriate search engine type. All following engines implement the standard search engine interface Oro\Bundle\SearchBundle\Engine\EngineInterface:

  • standard ORM engine - Oro\Bundle\SearchBundle\Engine\Orm, service ID is oro_search.search.engine;

  • standard Elasticsearch engine - Oro\Bundle\ElasticSearchBundle\Engine\ElasticSearch, service ID is oro_search.search.engine;

  • website ORM engine - Oro\Bundle\WebsiteSearchBundle\Engine\ORM\OrmEngine, service ID is oro_website_search.engine;

  • website Elasticsearch engine - Oro\Bundle\WebsiteElasticSearchBundle\Engine\ElasticSearchEngine, service ID is oro_website_search.engine.

All these engines accept low-level query and execution context as an argument and return an object with a list of found entities, the total number of results, and requested aggregated data.

Indexation Process 

OroCommerce provides two types of data synchronization: scheduled (asynchronous) and immediate (synchronous). Scheduled synchronization is suitable for entities whose changes are not critical, and they can be applied with the acceptable delay period, or there are tons of heavy data that need to be indexed separately to avoid affecting the website performance. Immediate or synchronous data indexation involves immediate updates to the index as soon as the underlying data changes, such as inventory, or payment updates.

Asynchronous Indexation 

Most of the indexation operations are performed asynchronously using a message queue.

The advantage of this approach is that a user does not wait until the indexation is finished to see the response from the application. Asynchronous indexations can also be performed in parallel to speed up the overall indexation process. The disadvantage is that indexation may happen with a delay; the delay time depends on the number of consumers, server hardware, and queue length.

Every time an entity represented by a document in a search index is changed, a new message with the entity class and entity identifier is generated and sent to the message queue. Then, the message queue consumer receives this message and runs an appropriate message processor that performs real indexation and changes in the search index.

Remember that parallel indexation is possible only if several message queue consumers are running; each consumer can run indexation. The higher the number of consumers running, the more indexations can be performed in parallel. All automatically triggered reindexations are processed asynchronously.

Default asynchronous indexer is implemented in the Oro\\Bundle\\WebsiteSearchBundle\\Engine\\AsyncIndexer class and is accessible via the oro_website_search.async.indexer service. To trigger asynchronous indexation, you should trigger ReindexationRequestEvent event and set the $Scheduled parameter to true.

Synchronous Indexation 

Although asynchronous processing is very convenient for a user, sometimes it might be required to track the process manually and ensure that indexation is finished right away. In this case, synchronous indexation should be used instead of asynchronous. The advantage of this approach is that indexation happens right now without any delay. The disadvantage is that it might be slower, and UX is worse than in the case of asynchronous indexations.

Triggering Reindexation 

From the Code 

The website search index type provides an event called oro_website_search.reindexation_request to manually trigger the reindexation process for the specified entities. It uses the event class Oro\Bundle\WebsiteSearchBundle\Event\ReindexationRequestEvent which accepts the $Scheduled boolean parameter to specify whether indexation has to be asynchronous (default behavior, TRUE) or synchronous (FALSE).

Both standard and website search index types have synchronous and asynchronous indexers that trigger the corresponding indexation type. All following indexers implement the same standard indexer interface Oro\Bundle\SearchBundle\Engine\IndexerInterface:

Standard search indexer

  • standard asynchronous indexer - Oro\\Bundle\\SearchBundle\\Async\\Indexer, service ID is oro_search.async.indexer. The indexer is used to redirect indexation request to message queue.

  • standard synchronous ORM indexer - Oro\\Bundle\\SearchBundle\\Engine\\OrmIndexer, service ID is oro_search.search.engine.indexer.

  • standard synchronous Elasticsearch - Oro\\Bundle\\ElasticSearchBundle\\Engine\\ElasticSearchIndexer, service ID is oro_search.search.engine.indexer.

Website search indexer

  • website asynchronous indexer - Oro\\Bundle\\WebsiteSearchBundle\\Engine\\AsyncIndexer, service ID is oro_website_search.async.indexer. The indexer is used to redirect indexation request to message queue.

  • website ORM indexer - Oro\\Bundle\\WebsiteSearchBundle\\Engine\\ORM\\OrmIndexer, service ID is oro_website_search.indexer.

  • website Elasticsearch indexer - Oro\\Bundle\\WebsiteElasticSearchBundle\\Engine\\ElasticSearchIndexer, service ID is oro_website_search.indexer.

All these indexers accept entities of an entity class that has to be reindexed.

Granularized asynchronous indexation

All asynchronous indexers (Oro\\Bundle\\WebsiteSearchBundle\\Engine\\AsyncIndexer) are using the Oro\\Bundle\\WebsiteSearchBundle\\Engine\\AsyncMessaging\\ReindexMessageGranularizer class to automatically split the request message in chunks per entity and websiteId.

The granulizer does the following:

  • To handle the Product entity within all websites [1, 2, 3, 4, 5], the granularizer splits the message into 5 separate groups, that allows handling each Product entity with each websiteId separately.

  • To handle requests that contain large amounts of entityIds, the granularizer splits entityIds table into smaller chunks, for example 1000 entityIds will be split into 10 messages with 100 entityIds each.

Note

For more event triggering examples, see the Website Search Indexation article.

From the CLI 

Both standard and website index types automatically trigger the reindexation process when entity data or related configuration is changed. Reindexation can take a lot of time for a significant amount of data, so running it by schedule (e.g., once a day) is recommended.

Synchronously

Search index type provides CLI command that can be used to manually (synchronously) trigger full reindexation of all entities or only entities of a specific class.

Standard search index command:

php bin/console oro:search:reindex

Website search index command:

php bin/console oro:website-search:reindex

Asynchronously

Reindexation can also be scheduled to be performed in the background by the message queue consumers (asynchronous reindexation). You will need a configured message queue and at least one running consumer worker to use this mode.

You need to use the scheduled parameter to run indexation asynchronously.

Standard search index command:

php bin/console oro:search:reindex --scheduled

Website search index command:

php bin/console oro:website-search:reindex --scheduled

Note

For more console code examples, see the Standard search index console commands and Website search index console commands articles.

Partial Indexation 

Partial indexation is a feature that reduces the load on the engine by updating the index partially. The index is divided into groups ($fieldGroups), each group includes specific fields that should be used in the update process. Partial indexation is a good option both for synchronous and asynchronous indexation when you need to update one, or multiple field groups without affecting others (e.g., product prices).

Supported field groups are: main, collection_sort_order, image, category_sort_order, visibility, pricing, order, customer_recommendation_action, customer_recommendation_revenue, inventory (multiple values allowed).

Note

For more partial indexation code examples, see the Website search partial indexation commands articles.

Schedule or Skip Reindexation 

When you execute oro:platform:update command as part of update process, it performs full reindexation of all the affected entities in the foreground.

To avoid this, you can use the --schedule-search-reindexation and --skip-search-reindexation options that were added to the oro:platform:update command by the SearchBundle and were extended by the WebsiteSearchBundle to also affect the Website search index:

  • --schedule-search-reindexation

This option allows you to postpone full reindexation. In this case, the reindexation command will be added into the message queue and will be executed later, when message queue consumers will be started.

  • --skip-search-reindexation

This option allows to completely skip reindexation during the update process.

Force Reindexation 

If, after customizations, upgrades, and code modifications, the ElasticSearch index structure remains unchanged or only new fields are added, it is sufficient to initiate the indexing process only.

If the changes affect the ElasticSearch index structure (ElasticSearch mapping), you must recreate index and force full indexation. The ES index must be filled with new data from scratch to save them into a new index structure.

For that, you need to:

  1. Recreate the index data to make a new empty structure with no data.

  2. Force full indexation to fill the empty structure with new data and new structure.

You can do it using the following commands for the back-office (standard) index:

php bin/console cache:clear --env=prod
php bin/console oro:elasticsearch:create-standard-indexes --env=prod
php bin/console oro:search:reindex --env=prod --scheduled

The same can be done for the storefront (website) index using commands:

php bin/console cache:clear --env=prod
php bin/console oro:website-elasticsearch:create-website-indexes --env=prod
php bin/console oro:website-search:reindex --env=prod --scheduled

ACL Restrictions 

Standard Index Type 

The standard index type automatically adds owner and organization fields to all entities and fills them with data during the indexation process. Then during the search request, ACL restrictions are automatically applied to a low-level query to show only entities the current user is allowed to see.

Website Index Type 

The website index type doesn’t have common ACL protection like the standard index type. Instead, each entity can be protected by custom-specific conditions. For example, the visibility of a Product entity to a customer user is affected by a product status, product inventory status, and product visibility settings on a customer, customer group, and website levels.

Scalability 

ORM Search 

ORM search engine uses DBMS as the main storage, and its scalability depends on the scalability of DBMS. For example, PostgreSQL supports several clustering solutions so that the ORM search index can be scaled together with the main relational DB. There is another solution. As long as the ORM search engine uses its own connection and its own entity manager, all search-related tables can be moved to a separate DB at the remote server. In this case, the application administrator has to override the configuration for the connection called search and refer to this remote server.

Elasticsearch Search 

Elasticsearch search engine is horizontally scalable out of the box - it can be organized as an Elasticsearch cluster with several nodes inside it. The application administrator can specify how many Elasticsearch shards index will consist of (i.e., how many parts it will be split into); the default number of shards is 5. Then, depending on the number of nodes at the cluster, the search engine can move shards to different nodes and, consequently, allow to perform distributed search.

Unavailability Handling 

General Information 

The search index does not provide any additional logic to recognize that the index is not available. Not availability is processed on a storage level only.

ORM Search 

ORM search engine uses DBMS as the main storage, and the ability to handle the unavilable state of storage depends on how it is organized in the used DBMS. PostgreSQL support replication, which can be used to manage such situation. If you have a search index in a separate DB, you can organize replication exclusively for this DB.

Elasticsearch Search 

Elasticsearch search engine support replications as well. The application administrator can specify how many Elasticsearch replicas index should have; the default number of replicas is 1. After that, the Elasticsearch cluster will create an appropriate number of replica shards and distribute them over the available nodes. For example, by default index is created with 5 shards and 1 replica. This means that Elasticsearch will create 10 shards: 5 primary shards and 5 replica shards.

Logging 

Logging is an essential part of any component, and the search component is no exception. Both standard and website search indexes in the dev mode log all requests to search index storage (DB or Elasticsearch); in the prod mode, only exceptions are logged. In the case of the prod mode, all exceptions are also sent to the emails specified in system configurations in the System Configuration > General setup > Application settings > Error logs notification section. The standard search index can also log all search queries to the database table oro_search_query (the entity name is Oro\Bundle\SearchBundle\Entity\Query); by default, this logging is turned off. Elasticsearch engine impelementations uses their own Monolog logger channels - oro_elastic_search for standard index type and oro_website_elastic_search for website index type.

Use Cases 

Datagrids 

There is a special datagrid search datasource that works with the search index. Search datasource works with the high-level search query object, supporting both index types. Datagrids based on this datasource use configuration similar to ORM configuration - developer can specify shown columns, filters, sorters, properties and actions.

Implemented search datagrids:

  • search results grid (search-grid) - used to show results of the overall search in all Oro applications; a grid can show any entity;

  • products frontend grid (frontend-product-search-grid) - used to represent a list of products and show product search results at OroCommerce frontend part; a grid shows only Product entities.

Autocomplete 

Autocomplete form types, by default, use the standard search index type to find entities and show them to the user (see Oro\Bundle\FormBundle\Autocomplete\SearchHandler). They do not use either a string or an object query representation directly. Instead, it uses the indexer from the standard search index, which uses the low-level query object inside.

Search APIs 

The standard search index provides API resources that can be used to work with this search index.

Simple search API accepts:

  • search request - plain string used to perform the search in the all-text field;

  • entity alias (optional);

  • offset (optional) - used for pagination;

  • max results (optional) - used for pagination.

Advanced search API accepts a string representation of a search request.

In both cases, API returns a list of found entities.

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References 

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