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 Structure
Hint
See the Search Index documentation to get a more high-level understanding of the search index concept in the Oro application.
Indexed Data
Indexed data is stored by entities, i.e. each indexed entity has its own set of related field data in a “key > value” format. There are no field restrictions, entities of the same type might contain various fields.
The following data types are supported in search: text, integer, decimal and datetime. One of these types is assigned to every mapped field. This speeds up the search based on the type of the field.
For a global search by the entity, special fields of all_text_* type store concatenated text value (e.g. name, description and SEO data). More than one special field may exist for the entity in the index, depending on the placeholders used. Typically, all_text_* fields contain localized values; there is a field for every enabled localization and the global field all_text that contains data for all localizations.
Plain Data Structure
Entity data is stored as plain data in “key > value” format. This format is used as the most simple to store and support so that any engine could use the data. Custom search engines and implementations might use more advanced structure, but they are not compatible with other engines.
However, almost any complex structure may be converted to the plain data with minor effort. For example, let’s convert product prices data to plain data.
Let’s assume that price numeric value is defined for a combination of the price list, currency, and unit. We’re going to use the following data:
{
1: {
USD: { item: 12.00, box: 100.00 },
EUR: { item: 10.00, box: 80.00 }
},
2: {
USD: { box: 100.00, container: 1500.00 },
EUR: { box: 80.00, container: 1200.00 }
},
3: {
USD: { container: 1400.00 },
EUR: { container: 1100.00 }
}
}
Where the root nodes define the price lists with IDs 1, 2 and 3, the currencies price list supports are nested inside the price list node (USD and EUR), and, finally, the prices are provided for supported product units ( item, box and container). Basically, denormalized, the data might look like:
{
price_1_USD_item: 12.00,
price_1_USD_box: 100.00,
price_1_EUR_item: 10.00,
price_1_EUR_box: 80.00,
price_2_USD_box: 100.00,
price_2_USD_container: 1500.00,
price_2_EUR_box: 80.00,
price_2_EUR_container: 1200.00,
price_3_USD_container: 1400.00,
price_3_EUR_container: 1100.00
}
Assuming that the price list and currency is fixed for the current session, this structure helps filter by the price defined for a specific unit. To simplify and speed up sorting, we add new maximum and minimum value fields for each combination of price list and currency. The minimum value is used for sorting in ascending order, and the maximum value is used for descending sorting. Note: You may choose to use minimum value only for both ascending and descending sorting, if you are going for consistent and predictable sorting behavior.
So, after the new fields are added, the sample data becomes the following:
{
price_1_USD_item: 12.00,
price_1_USD_box: 100.00,
price_1_EUR_item: 10.00,
price_1_EUR_box: 80.00,
price_2_USD_box: 100.00,
price_2_USD_container: 1500.00,
price_2_EUR_box: 80.00,
price_2_EUR_container: 1200.00,
price_3_USD_container: 1400.00,
price_3_EUR_container: 1100.00,
min_price_1_USD: 12.00,
max_price_1_USD: 100.00,
min_price_1_EUR: 10.00,
max_price_1_EUR: 80.00,
min_price_2_USD: 100.00,
max_price_2_USD: 1500.00,
min_price_2_EUR: 80.00,
max_price_2_EUR: 1200.00,
min_price_3_USD: 1400.00,
max_price_3_USD: 1400.00,
min_price_3_EUR: 1100.00,
max_price_3_EUR: 1100.00
}
Now, you can sort the data using the ORDER BY min_price_PRICE_LIST_ID_CURRENCY ASC and ORDER BY max_price_PRICE_LIST_ID_CURRENCY DESC in the query to get the products sorted by the minimum price in the provided currency (e.g. EUR) listed in the specified price list (e.g. 1). Note: PRICE_LIST_ID and CURRENCY are placeholders and should remain so. Website search substitutes them with actual values from the scope.
Furthermore, to ensure that there is at least one product price in the specific currency (in any price list), you can add a special field (flag) to indicate that the price in this currency exists:
{
price_1_USD_item: 12.00,
price_1_USD_box: 100.00,
price_1_EUR_item: 10.00,
price_1_EUR_box: 80.00,
price_2_USD_box: 100.00,
price_2_USD_container: 1500.00,
price_2_EUR_box: 80.00,
price_2_EUR_container: 1200.00,
price_3_USD_container: 1400.00,
price_3_EUR_container: 1100.00,
price_currency_USD: 1,
price_currency_EUR: 1
}
If you add the flag parameter to indicate that the product price in the CURRENCY is available, you can use WHERE price_currency_CURRENCY EXISTS in your query. Note: CURRENCY is a placeholder and should remain so. Website search substitutes it with an actual value from the scope.
Alternatively, if your implementation adds the boolean flag as a required parameter indicating whether the price is present (1) or absent (0), use the following bit in your query:
WHERE price_currency_CURRENCY = 1
Website Scope
Website search support indexing multiple websites, as every website is an autonomous selling tool with its own search index.
Throughout the bundle, the WEBSITE_ID placeholder (e.g. in the entity alias) helps build unique alias name and set unique storage scope for each website. For example, an alias for product entity may be oro_product_WEBSITE_ID. During the reindexation, each product is saved in several scopes (one per website), and the scopes are named oro_product_1, oro_product_2, etc.
Using this approach, the website search engine automatically gets information about the website the search request came for and knows how to reindex product for all websites when necessary.
The WEBSITE_ID placeholder is automatically substituted with a website current customer uses. During the reindexation, by default, you can specify entity-related data and it will be automatically put into all website scopes. However, you can also set specific data for every website. The method is described in the following section.
Localized Data
Website scope is quite useful when you need to isolate scopes of data for every website, but sometimes you need several variants of the same field (e.g. localized values, like product name and description when several languages are enabled for the website).
Website search bundle provides several parametrized values out-of-the-box:
LOCALIZATION_ID - a special placeholder for multiple localization support in a scope of a website. Similar to the WEBSITE_ID, the LOCALIZATION_ID helps you store several values for the same entity and simplify using them.
all_text_LOCALIZATION_ID - a placeholder for a concatenated information related to the entity per localization. Each entity has dedicated fields that store these data (e.g. all_text_1, all_text_2).
Every new localized field increases the size of the search index and slows down the reindexation. For large volumes of information, real-time updates might be processed with a significant delay. To optimize the process, it is recommended to review the search index data and remove the fields that are not used. Also, moving search index to a separate storage on a separate server might be a good idea.
Examples
Following is the product entity mapping configuration for OroCommerce deployment with one website, one localization, and one currency:
Oro\Bundle\ProductBundle\Entity\Product:
alias: oro_product
fields:
-
name: sku
type: text
-
name: names
type: text
-
name: price
type: decimal
As you can see, no placeholders are used and the search index contains the following information:
oro_product
{
1: {
sku: "PR1",
names: "First product",
price: 12.00,
},
2: {
sku: "PR2",
names: "Second product",
price: 25.00,
}
}
Query for this index is quite simple:
SELECT
text.sku,
text.names,
decimal.price
FROM
oro_product
WHERE
text.names ~ product
ORDER_BY
decimal.price ASC
Now let’s look at the OroCommerce deployment with two websites, two localizations, and three currencies:
Global website (WEBSITE_ID=1) supports two localizations (English LOCALIZATION_ID=1 and Russian LOCALIZATION_ID=2) and two currencies (EUR and GBP);
Russian website (WEBSITE_ID=2) supports one localization (Russian LOCALIZATION_ID=2) and one currency (RUR).
Use placeholders WEBSITE_ID, LOCALIZATION_ID and CURRENCY, like in the mapping configuration of product entity below:
Oro\Bundle\ProductBundle\Entity\Product:
alias: oro_product_WEBSITE_ID
fields:
-
name: sku
type: text
-
name: names_LOCALIZATION_ID
type: text
-
name: price_CURRENCY
type: decimal
-
name: all_text_LOCALIZATION_ID
type: text
Based on this configuration, the data may the following:
oro_product_1
{
1: {
sku: "PR1",
names_1: "First product",
names_2: "Первый продукт",
price_EUR: 12.00,
price_GBP: 9.00,
all_text_1: "PR1 First product",
all_text_2: "PR1 Первый продукт",
},
2: {
sku: "PR2",
names_1: "Second product",
names_2: "Второй продукт",
price_EUR: 25.00,
price_GBP: 20.00,
all_text_1: "PR2 Second product",
all_text_2: "PR2 Второй продукт",
}
}
oro_product_2
{
1: {
sku: "PR1",
names_2: "Первый продукт",
price_RUR: 100.00,
all_text_2: "PR1 Первый продукт",
},
2: {
sku: "PR2",
names_2: "Второй продукт",
price_RUR: 200.00,
all_text_2: "PR2 Второй продукт",
}
}
The following query is automatically modified to substitute placeholders with the appropriate parameters for the current customer based on the scope:
SELECT
text.sku,
text.names_LOCALIZATION_ID AS name,
decimal.price_CURRENCY AS price
FROM
oro_product_WEBSITE_ID
WHERE
text.all_text_LOCALIZATION_ID ~ продукт
ORDER_BY
decimal.price_CURRENCY ASC