Elasticsearch for Bitrix: 10x faster search

When Bitrix built-in search stops working

With 100 thousand products, the built-in MySQL-based search slows down: queries to b_search_content take 300–500 ms, and on a million-record database — timeout. Clients complain about long search times, price sorting doesn't work, facets overload the database. For example, a recent project with a catalog of 250,000 products: standard search returned results in 2–3 seconds, complex property filters (price, brand, size) caused 30-second pauses. Elasticsearch solves this radically: response time 5–30 ms, aggregations on the fly, typos and morphology out of the box. Our experience — 30+ ES integrations with Bitrix, we guarantee a turnkey result. We use licensed components and are 1C-Bitrix certified. To understand if Elasticsearch is right for your project, order a free audit — we will analyze the load and data structure.

Problems that Elasticsearch solves

Elasticsearch replaces the standard Bitrix search engine and solves three key problems:

1. Full-text search speed. MySQL FULLTEXT starts slowing down at 50–100 thousand documents. ES handles millions.
2. Faceted search (aggregations). Bitrix built-in filters are separate queries for each property. ES returns aggregations in one request, giving a 10–20x gain on complex filters.
3. Search with typos and synonyms. Without third-party modules, MySQL does not support fuzzy search. ES has built-in fuzziness and synonyms.

If you face similar issues, contact us for an audit — it will help identify bottlenecks.

Why Elasticsearch is faster than MySQL FULLTEXT?

Elasticsearch is 10–50 times faster than MySQL FULLTEXT on large data.

Integration architecture

The integration consists of three parts:

1. Indexer — a component that reads data from Bitrix (infoblocks, users, pages) and writes documents to Elasticsearch index.
2. Search gateway — replaces standard requests to b_search_content with requests to Elasticsearch API. The gateway is implemented as a PHP proxy: it receives a request from the standard bitrix:search.page component, transforms it into Elasticsearch query DSL, and returns results in the format expected by Bitrix.
3. Event handlers — update the index when entities are modified or deleted.

Index structure for product catalog

The index is created via Elasticsearch Mapping API. Example mapping for products:

PUT /bitrix_catalog { "mappings": { "properties": { "id": { "type": "integer" }, "iblock_id": { "type": "integer" }, "name": { "type": "text", "analyzer": "russian" }, "description": { "type": "text", "analyzer": "russian" }, "sku": { "type": "keyword" }, "price": { "type": "float" }, "active": { "type": "boolean" }, "section_id": { "type": "integer" }, "properties": { "type": "object" }, "updated_at": { "type": "date" } } }, "settings": { "analysis": { "analyzer": { "russian": { "type": "custom", "tokenizer": "standard", "filter": ["lowercase", "russian_stop", "russian_stemmer"] } }, "filter": { "russian_stemmer": { "type": "stemmer", "language": "russian" }, "russian_stop": { "type": "stop", "stopwords": "_russian_" } } } } } 

The russian analyzer with stemmer is a key difference from MySQL FULLTEXT, which without additional dictionaries does not understand morphology.

PUT /bitrix_catalog/_settings { "analysis": { "filter": { "russian_synonyms": { "type": "synonym", "synonyms": [ "брюки, штаны, джинсы => trousers", "смартфон, телефон, мобила => mobile" ] } }, "analyzer": { "russian_with_synonyms": { "tokenizer": "standard", "filter": ["lowercase", "russian_stop", "russian_stemmer", "russian_synonyms"] } } } } 

How to set up automatic index update?

Subscribe to infoblock events:

// local/php_interface/init.php AddEventHandler('iblock', 'OnAfterIBlockElementUpdate', 'esUpdateProduct'); AddEventHandler('iblock', 'OnAfterIBlockElementDelete', 'esDeleteProduct'); function esUpdateProduct(array &$arFields): void { $client = getEsClient(); $productId = (int)$arFields['ID']; // Re-index a single document $client->index([ 'index' => 'bitrix_catalog', 'id' => $productId, 'body' => buildProductDocument($productId), ]); } function esDeleteProduct(int $productId): void { getEsClient()->delete(['index' => 'bitrix_catalog', 'id' => $productId]); } 

The OnAfterIBlockElementUpdate event also triggers on API changes (1C import), which is important for index freshness.

Data indexing

Initial indexing is run via a cron script. Data is read in batches using CIBlockElement::GetList() with nTopCount = 100 and offset to avoid memory overload:

\Bitrix\Main\Loader::includeModule('iblock'); $client = \Elasticsearch\ClientBuilder::create() ->setHosts(['localhost:9200']) ->build(); $offset = 0; $batchSize = 100; do { $res = \CIBlockElement::GetList( [], ['IBLOCK_ID' => CATALOG_IBLOCK_ID, 'ACTIVE' => 'Y'], false, ['nTopCount' => $batchSize, 'nPageSize' => $batchSize, 'iNumPage' => floor($offset / $batchSize) + 1], ['ID', 'NAME', 'DETAIL_TEXT', 'IBLOCK_ID', 'IBLOCK_SECTION_ID'] ); $bulk = []; while ($item = $res->GetNext()) { $bulk[] = ['index' => ['_index' => 'bitrix_catalog', '_id' => $item['ID']]]; $bulk[] = [ 'id' => (int)$item['ID'], 'iblock_id' => (int)$item['IBLOCK_ID'], 'name' => $item['NAME'], 'description'=> strip_tags($item['DETAIL_TEXT']), 'section_id' => (int)$item['IBLOCK_SECTION_ID'], 'active' => true, 'updated_at' => date('c'), ]; $offset++; } if (!empty($bulk)) { $client->bulk(['body' => $bulk]); } } while ($res->SelectedRowsCount() === $batchSize); 

Bulk API allows sending up to 1000 documents per request. Do not use individual index requests for initial indexing — it is 10–50 times slower.

Search query

Replace the standard bitrix:search.page component with a custom one that queries Elasticsearch:

$response = $client->search([ 'index' => 'bitrix_catalog', 'body' => [ 'query' => [ 'multi_match' => [ 'query' => $searchQuery, 'fields' => ['name^3', 'description', 'sku'], 'type' => 'best_fields', 'fuzziness' => 'AUTO', ], ], 'sort' => ['_score' => ['order' => 'desc']], 'from' => ($page - 1) * $pageSize, 'size' => $pageSize, ], ]); 

The fuzziness: AUTO parameter provides typo search: for words up to 5 characters, 1 substitution is allowed; for longer words, 2 substitutions.

What is included in our work?

• Audit of current search and load.
• Setting up Elasticsearch cluster (version, configuration, monitoring).
• Creating mapping according to your data structure.
• Developing indexer and search gateway.
• Configuring events for automatic update.
• Performance and accuracy testing.
• Documentation and training for your team.
• Support during the warranty period.
• Monitoring and alerts for indexing failures.

Implementation timeline

Contact us for a consultation. Get an accurate project estimate and architectural recommendations — with no obligation.