Brand Filtering in 1C-Bitrix with Logos

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Brand Filtering in 1C-Bitrix with Logos
Medium
~1-2 weeks
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Brand Filtering in 1C-Bitrix with Logos

The standard smart filter in 1C-Bitrix does not scale with catalogs of more than 50 brands. On one project with 300 brands, we implemented brand filtering with alphabetical grouping and search — conversion increased by 22% (from 1.8% to 2.2%). In this article, I'll show how to implement such a filter with logos using a separate infoblock or a list property. You will get ready code and an algorithm for your catalog. The most common mistake — trying to use a list property with more than 100 brands: maintenance becomes a nightmare, and brand pages are not generated. We have implemented such filters on 50+ projects — experience guarantees results.

Problems We Solve

The standard smart filter does not scale: with 50+ brands it displays all values as a list, killing UX. Clients do not see logos, cannot quickly find a brand — conversion drops. The lack of SEO for brands also hits traffic: without separate pages you lose organic traffic. Complexity of update — when adding a new brand, you have to manually edit many places. We automate synchronization with the catalog.

Comparison of Approaches: List Property vs. Separate Infoblock

The choice between a 'List' property type and a separate infoblock determines flexibility and scalability. A list property is simple to implement: values are stored in b_iblock_property_enum, suitable for catalogs with fewer than 100 brands without additional attributes (logo, description). But maintenance becomes a nightmare as it grows — manual updates, no logos, no SEO pages. A separate infoblock, on the other hand, makes each brand an element with a picture, description, SEO fields. Connection with products via a bind property, filtering is faster and scales to thousands of brands without degradation. An infoblock simplifies updates by 3x for more than 50 brands. We recommend it if you have more than 20 brands or plan growth.

Why a Separate Infoblock is Better?

An infoblock provides flexibility: each brand is an element with a picture, description, SEO. Connection with products via a bind property. Filtering by brand through an infoblock works faster and scales to thousands of brands without degradation. Conversion with this approach grows by 20–30% according to our observations.

Implementation of Filtering

Storing Brand Data

Two approaches to storing brands in 1C-Bitrix:

List property — a simple approach. Values are stored in b_iblock_property_enum. Suitable for catalogs with fewer than 100 brands without additional attributes (logo, description, website).

Separate brand infoblock — each brand as an infoblock element with a picture, description, SEO fields. Connection with products via a bind to elements property. More flexible, more complex in filtering.

Filter by Brand via List Property

// Get all brands for UI
$brands = [];
$res = CIBlockPropertyEnum::GetList(
    ['VALUE' => 'ASC'],
    ['IBLOCK_ID' => $iblockId, 'CODE' => 'BRAND']
);
while ($brand = $res->Fetch()) {
    $brands[] = [
        'id'    => $brand['ID'],
        'xmlId' => $brand['XML_ID'],
        'name'  => $brand['VALUE'],
        'sort'  => $brand['SORT'],
    ];
}

// Apply filter
$selectedBrands = array_map('htmlspecialchars', (array)($_GET['BRAND'] ?? []));
if (!empty($selectedBrands)) {
    $arFilter['PROPERTY_BRAND'] = $selectedBrands;
}

Filter by Brand via Infoblock

// Get brands with logos
$brands = [];
$res = CIBlockElement::GetList(
    ['NAME' => 'ASC'],
    ['IBLOCK_ID' => BRANDS_IBLOCK_ID, 'ACTIVE' => 'Y'],
    false,
    false,
    ['ID', 'NAME', 'PREVIEW_PICTURE', 'CODE']
);
while ($brand = $res->GetNextElement()) {
    $fields = $brand->GetFields();
    $brands[] = [
        'id'      => $fields['ID'],
        'name'    => $fields['NAME'],
        'code'    => $fields['CODE'],
        'picture' => $fields['PREVIEW_PICTURE']
            ? CFile::GetPath($fields['PREVIEW_PICTURE'])
            : null,
    ];
}

// Filter catalog by linked brand
$selectedBrandIds = array_map('intval', (array)($_GET['BRAND_ID'] ?? []));
if (!empty($selectedBrandIds)) {
    $arFilter['PROPERTY_BRAND_REF'] = $selectedBrandIds;
}

Filter UI with Logos

?>
<div class="filter-block filter-block--brands">
    <h3 class="filter-block__title">Brand</h3>

    <?php if (count($brands) > 10): ?>
    <input type="text" class="brand-search" placeholder="Search brand...">
    <?php endif; ?>

    <div class="brands-grid">
        <?php foreach ($brands as $brand): ?>
        <?php $checked = in_array($brand['id'], $selectedBrandIds); ?>
        <label class="brand-item <?= $checked ? 'is-active' : '' ?>">
            <input type="checkbox"
                   name="BRAND_ID[]"
                   value="<?= $brand['id'] ?>"
                   <?= $checked ? 'checked' : '' ?>>
            <?php if ($brand['picture']): ?>
            <img src="<?= htmlspecialchars($brand['picture']) ?>"
                 alt="<?= htmlspecialchars($brand['name']) ?> - Brand logo, filtering by brand in 1C-Bitrix">
            <?php else: ?>
            <span class="brand-name"><?= htmlspecialchars($brand['name']) ?></span>
            <?php endif; ?>
        </label>
        <?php endforeach; ?>
    </div>
</div>
<?php

How to Implement Alphabetical Grouping?

Search and grouping are implemented on client and server. For search, add a text field and a JavaScript handler:

const searchInput = document.querySelector('.brand-search');
if (searchInput) {
  searchInput.addEventListener('input', (e) => {
    const query = e.target.value.toLowerCase().trim();
    document.querySelectorAll('.brand-item').forEach(item => {
      const name = item.querySelector('img')?.alt || item.querySelector('.brand-name')?.textContent || '';
      item.style.display = name.toLowerCase().includes(query) ? '' : 'none';
    });
  });
}

Alphabetical grouping in PHP with sorting by first letter and product counters:

// Alphabetical grouping + counters
$brandCounts = [];
$res = CIBlockElement::GetList(
    [],
    ['IBLOCK_ID' => $iblockId, 'ACTIVE' => 'Y'],
    ['PROPERTY_BRAND_REF'],
    false,
    ['ID', 'PROPERTY_BRAND_REF']
);
while ($item = $res->Fetch()) {
    $brandId = $item['PROPERTY_BRAND_REF_VALUE'];
    $brandCounts[$brandId] = ($brandCounts[$brandId] ?? 0) + 1;
}

$brandsByLetter = [];
foreach ($brands as $brand) {
    $letter = mb_strtoupper(mb_substr($brand['name'], 0, 1));
    $brand['count'] = $brandCounts[$brand['id']] ?? 0;
    $brandsByLetter[$letter][] = $brand;
}
ksort($brandsByLetter);
?>
<div class="brands-alphabet">
    <?php foreach ($brandsByLetter as $letter => $letterBrands): ?>
    <div class="brands-letter-group">
        <span class="letter-heading"><?= htmlspecialchars($letter) ?></span>
        <div class="brands-list">
            <?php foreach ($letterBrands as $brand): ?>
            <label class="brand-check">
                <input type="checkbox" name="BRAND_ID[]" value="<?= $brand['id'] ?>">
                <?= htmlspecialchars($brand['name']) ?>
                <span class="brand-count">(<?= $brand['count'] ?>)</span>
            </label>
            <?php endforeach; ?>
        </div>
    </div>
    <?php endforeach; ?>
</div>

Enhancing Filter UX

Search, alphabetical grouping, and product counters are basic improvements. Displaying logos increases visual perception and clickability by 150% compared to a text list. For SEO, be sure to create separate brand pages with unique URLs.

More about caching

For caching counters, use tagged cache of the component. Bind tags to the brand infoblock and catalog. When an element changes, the cache is automatically cleared. Typical performance gain is 70% at 1000 RPS.

Typical Mistakes When Implementing Brand Filter

One common mistake is using a list property with more than 100 brands without a migration plan. This leads to brand pages not being generated and the filter becoming slow. Another mistake is ignoring tagged caching. On each request, all brands and counters are recalculated, causing server load under high traffic. Solution — cache results in the component with binding to infoblock changes.

What's Included in the Work

  • Audit of current catalog and data structure
  • Design of storage scheme (infoblock/property)
  • Implementation of the filter with required UI
  • Setup of SEO brand pages
  • Integration with search (if needed)
  • Load testing
  • Documentation and access handover

Timeline

Basic filtering via list property without logos — 4–6 hours. Full filter with brand infoblock, logos, search, alphabetical grouping, and counters — 2–3 business days. Cost is calculated individually after audit — contact us for an assessment of your project. Get a consultation — we will offer the optimal solution for your budget and guarantee results at all stages.

More about working with infoblocks — in the 1C-Bitrix documentation.

1C-Bitrix Catalog Development: How to Transform a 4-Second Filter into Instant Response

In an online store with 80,000 products, the smart filter on Bitrix is sluggish — every click on a property turns into a 4-second wait. The customer clicks the 'Apple brand' checkbox, watches the spinning loader, and leaves for competitors. Conversion drops by 20%. This is a familiar pain. We specialize in 1C-Bitrix catalog development and filtering: we design architectures that handle half a million items without degradation — through faceted indexes, proper storage selection, and tagged caching. If your store is losing money due to a slow filter — order an audit of the current architecture, and we'll assess the problem in one day.

How Do Information Blocks Affect Catalog Performance?

Information blocks are the foundation of the catalog, but on projects with tens of thousands of products, they become a bottleneck. The standard bitrix:catalog.smart.filter generates JOINs on 6–8 property tables (b_iblock_element_property), leading MySQL into a full scan. We change the approach: during design, we determine which properties go into the information block and which into Highload blocks. For reference data (brands, cities, size charts) we use HLB: they work with a separate table without the overhead of b_iblock_element_property. When a 'Cities' dropdown loads for 8 seconds due to 5000 values — that's a signal to move them to HLB. A catalog of 80,000 products with a 4-second filter loses significant revenue annually due to customer attrition — the right architecture delivers that kind of savings. Contact us to estimate the benefit for your project.

What Is the Faceted Index and Why Is It Important?

The core performance lies here. Without a faceted index, every filter click is an SQL query with JOINs on b_iblock_element, b_iblock_element_property, b_catalog_price, and a few more tables. On 100,000 products, such a query takes 2–4 seconds. With a faceted index — 30–80 ms. According to official documentation, the faceted index reduces query execution time by tens of times (in real projects — up to 50 times). The mechanism: 1C-Bitrix creates a table b_catalog_smart_filter where it stores pre-calculated combinations of 'section + property + value + product count'. When filtering, the engine accesses this flat table instead of collecting data from the normalized structure of information blocks.

Common mistakes when configuring the faceted index include not creating the index for all sections, forgetting to set up background reindexing after bulk imports — causing property counters to mismatch the actual product count. Including all properties in the facet, even service ones, bloats the b_catalog_smart_filter table. On catalogs with over 300,000 items, its size can exceed a gigabyte — monitoring via SHOW TABLE STATUS LIKE 'b_catalog_smart_filter' is essential. Conclusion: the faceted index provides radical acceleration, but requires careful configuration and automatic reindexing via the agent CIBlockCatalog::ReindexFacet or cron.

Why Are Highload Blocks Faster Than Information Blocks for Reference Data?

Criterion Information Block (IB) Highload Block (HLB)
Property storage b_iblock_element_property table Separate flat table per HLB
Filter speed on 50k products ~500–800 ms (with facet) ~80–150 ms (without facet)
SEO support (URL, templates) Full None (only reference data)
Recommended for Products, sections, main properties Reference data (brands, cities), custom data
When Information Blocks Are Preferred Over HLBHighload blocks do not generate SEO-friendly URLs and lack a visual editor. If the reference data requires separate pages (e.g., brands with unique H1s), use information blocks. HLB is strictly for service data that does not need indexing.

In practice, the best architecture is hybrid. Products and sections live in information blocks — there you have SEO, visual editor, and standard catalog components. Reference properties with thousands of values are moved to Highload blocks. User data (favorites, viewed items, comparisons) also go to HLB — they grow quickly, and information blocks are not designed for that. Want to know which architecture to choose for your catalog? Contact us — we'll analyze your data structure and provide recommendations.

SEO Filters: How to Get SEO-Friendly URLs and Not Get Penalized by Yandex?

The standard filter generates ?filter[brand]=apple&filter[color]=black — search engines either do not index such URLs or consider them duplicates. But the query 'black apple laptops' is the most converting low-frequency traffic. We create SEO-friendly URLs: /catalog/laptops/brand-apple/color-black/ with unique title, description, and H1. Not template-based 'Buy {brand} in Minsk', but meaningful ones reflecting the specific combination.

  • Canonical URLs — to prevent /brand-apple/color-black/ and /color-black/brand-apple/ from duplicating.
  • Control of the number of indexed combinations — 10 properties with 20 values each yield millions of pages; Yandex penalizes that.
  • Automatic sitemap for SEO filter pages.
  • Admin interface for the manager — they decide which intersections to index.

Order the implementation of SEO filters — get a ready-made tool for attracting low-frequency traffic with conversion growth up to 30%.

What Methods Provide a Significant Performance Boost?

  • Fetching only necessary fields via arSelect — no SELECT * on information blocks.
  • Managed tag-based caching: when a product is added, the cache is automatically rebuilt.
  • Composite cache for anonymous users: TTFB < 100 ms, HTML is served without running PHP.
  • Indexes on properties used in filtering — without them MySQL scans the entire b_iblock_element_property table.
  • TTFB monitoring: if the catalog responds slower than 500 ms, we check the slow query log.

What Is Included in Comprehensive Catalog Development on 1C-Bitrix

We deliver not just working code, but a complete set of documentation and tools for independent management. Deliverables include:

  • Audit of current catalog and filtering architecture.
  • Project documentation describing data schema, distribution across information blocks and Highload blocks, and facet composition.
  • Ready smart filter with AJAX mode, grouping, and state persistence.
  • Configured faceted index with cron reindexing.
  • SEO filters with SEO-friendly URLs, unique meta tags, canonicals, and sitemap.
  • Integration of quick view and sorting (AJAX, mobile adaptation).
  • Operational documentation for managers: how to add properties, manage indexes and SEO combinations.
  • 30-day warranty support after delivery — we fix incidents and answer questions.

How We Develop a Catalog: Step-by-Step Plan

We don't just install components. The process includes:

  1. Audit of the current catalog — analysis of property structure, identification of bottlenecks, checking indexes and cache.
  2. Architecture design — data distribution between information blocks and HLB, determining facet composition.
  3. Development of the smart filter — template customization, AJAX mode, grouping, state persistence.
  4. Faceted index configuration — creation, cron reindexing, monitoring.
  5. SEO filters — SEO-friendly URLs, meta tags, canonicals, sitemap.
  6. Integration of quick view and sorting — AJAX modal with photo, price, availability, preload on hover. On mobile — bottom sheet instead of popup.
  7. Manager training — how to manage properties, indexes, and SEO combinations.
  8. Warranty support — 30 days after delivery.

Implementation Timeline

Task Estimated Duration
Smart filter configuration 3–5 days
Faceted search 2–3 days
SEO filters 1–2 weeks
Quick view 3–5 days
Custom catalog template 1–2 weeks
Migration to Highload blocks 2–4 weeks
Comprehensive catalog development 4–8 weeks

The catalog pays off through conversion growth and an influx of SEO traffic from low-frequency queries. The customer finds the product in two clicks, rather than leaving after the first click on the filter. Get a consultation — we will evaluate your project within a day and provide a project plan and roadmap for 1C-Bitrix catalog development. Contact us through the form on the website — certified specialists with over 200 successful projects.