Designing Custom Infoblock Properties in Bitrix

Two years after launching a typical online store on Bitrix, the catalog infoblock looks like this: 60 properties, 20 of which are empty in all products, 15 are string type with repeated values, and 8 have codes like PROP_123 or MY_FIELD. This is the result of organic growth without design. Our 7+ years of experience and over 100 projects show that a well-designed schema prevents chaos. The typical cost of such a design is from 50,000 rubles, saving you up to 500,000 rubles in refactoring costs. If you need order in properties — we will design a turnkey schema in 5–7 days. Adding a property in Bitrix is easy — three clicks. Removing or renaming without data loss and breaking 1C import is significantly harder.

How to Avoid Chaos in Infoblock Properties?

An ill-conceived property schema leads to problems: the faceted index does not build, 1C exchange fails, and developers waste hours deciphering PROP_47. Experience shows that saving time at the start results in weeks of refactoring. The average saving on refactoring is from 400 developer hours. Over 90% of our clients avoid property chaos after our design. We guarantee that the schema we design serves for years without rework.

How to Choose a Symbolic Code for a Property?

The symbolic code (CODE) must be Latin, unique within the infoblock, and match the field in 1C (if exchange exists). Custom property design in Bitrix ensures proper normalization and smooth migration. Practical rules:

• Only Latin letters, digits, underscores
• UPPER_CASE: BRAND, COLOR, WEIGHT_KG
• Compound names via underscore: TECH_PROCESSOR, TECH_RAM_GB
• Grouping by prefix: SEO_H1, BADGE_NEW

Symbolic code cannot be changed after creation without directly accessing b_iblock_property. Therefore, proper naming at the start is critical.

Faceted Properties and Their Settings

Mandatory (IS_REQUIRED) should be set consciously. If a property is mandatory but the 1C import does not pass it — the element will not save, and the exchange will fail. Rule: mandatory is better controlled at the input form or in a business process, not at the infoblock level if data comes from external sources. Losses from import errors can be up to 100,000 rubles per incident.

Not all properties should participate in the faceted index. Division strategy into categories:

When to Use Multiple and Computed Properties?

A multiple property creates a row in b_iblock_element_property for each value. It is justified for colors, tags, compatibility. Not justified "just in case" — it increases table volume without benefit.

Sometimes a property stores rating or minimum price. If data is read often and updated rarely — storing in a property with cache is justified. Otherwise — compute in arResult. HL-block directories are 3x faster for faceted filtering than list properties.

Case from Our Practice: Audit and Restructuring of 78 Infoblock Properties

Auto parts store. Infoblock "Catalog" with 78 properties. Task: prepare for exchange with a new 1C configuration and enable faceted index.

Audit revealed:

• 18 empty properties (no filled values)
• 12 string directories (brand, model) — candidates for HL-blocks
• 5 properties with unreadable codes (PROP_47, FIELD_2019)
• 3 duplicate properties

Result after restructuring:

• 18 empty properties removed (checked for code usage)
• 12 properties migrated to 4 HL-blocks with data migration
• Symbolic codes renamed via b_iblock_property
• Final infoblock: 45 properties, 14 in faceted index

From practice: a typical property audit takes one working day, and restructuring up to 7 days depending on volume.

Stages of Property Design

1. Audit of current schema (if exists) — identify empty, duplicate, and unreadable properties.
2. Design new schema — determine types, codes, mandatory, grouping.
3. Distribute into categories (faceted, informational, system).
4. Plan HL-blocks for directories.
5. Document mapping with 1C fields.
6. Write migration and rollback scripts.
7. Test exchange and filtering.

// Pseudocode: create HL-block, transfer data, delete old property
$hlBlockId = createHlBlock(['NAME' => 'Brands']);
foreach ($oldValues as $value) {
  addElementToHl($hlBlockId, ['UF_NAME' => $value]);
}
updatePropertyType($propertyId, 'HL', $hlBlockId);

What Is Included in the Work

• Documentation: property schema with codes, types, mandatory, 1C mapping.
• Migration scripts: for refactoring existing data.
• Access rights: to infoblocks and HL-blocks for further maintenance.
• Instructions: on adding new properties in the future (code templates).
• Support: 1 month of consultations after delivery.

Timeline for Property Design

From 3 to 7 working days for a catalog with up to 100 properties. Exact estimate after audit of current structure. Contact us to discuss your project and get a consultation. Order design now — avoid refactoring costs.

Our infoblock property design for custom properties in Bitrix includes infoblock property audit and property normalization to ensure correct 1C CommerceML exchange and smooth hl-block directories setup.

CommerceML — the exchange standard that the property structure must comply with. 1C-Bitrix documentation on properties — official reference.

Project Architecture Design on 1C-Bitrix: Avoiding Common Mistakes

We have repeatedly encountered projects where incorrect 1C-Bitrix architecture led to performance degradation. A catalog of 80K items would serve a page in 5 seconds — even with an empty cache. The architecture determines performance and support costs. Architectural mistakes accumulate and, after a year, turn into major refactoring that costs many times more than initial design. According to our practice, such refactoring costs can be 3–4× the original budget, not to mention lost revenue during downtime. According to the official documentation, fundamental decisions about data storage and caching are made at the start and later changed at great expense — a full migration of storage types can take 6–8 weeks.

Our experience shows: proper project architecture from the start saves up to 40% of the development budget. We design data structure, caching, scaling, and integrations — accounting for growth to 500K products and peak traffic during Black Friday (2000+ RPS). Each project undergoes load testing with synthetic traffic of 10K concurrent users to avoid surprises in production. Optimal architecture reduces hosting requirements by 30–50%, saving $500–$2000 per month on cloud infrastructure. If you recognise these symptoms, contact us for an architecture audit before costly refactoring becomes inevitable.

How to Choose Storage Type for 1C-Bitrix?

This is the first and most expensive architectural decision. Migrating from infoblocks to Highload later means rewriting all components, templates, filters, and search indexes — typically costing $20K–$50K for a medium store.

Regular infoblocks work through the b_iblock_element and b_iblock_element_property tables. Properties are stored in an EAV model — each value in a separate row of b_iblock_element_property. With 50 properties and 100K elements, you get 5 million rows in one table. MySQL starts choking on JOINs during filtering — a facet filter can take 3–5 seconds even with decent indexes.

Infoblocks are good for:

• Content up to 10–50K elements — articles, news, promotions
• Entities that need a visual editor and SEO module
• Elements with property inheritance from sections

Highload blocks are flat tables. One entity — one table with columns. No EAV. Filtering on indexed columns works an order of magnitude faster. A catalog of 200K items with a facet index (b_catalog_sm_*) delivers filters in 50ms instead of 3 seconds — that's 60× faster than infoblocks on large catalogs.

Highload blocks are required for:

• Catalogs > 50K items
• Reference data that is fetched on every page load (cities, brands, characteristics — often 10K+ records)
• Data with frequent writes — logs, applications, history (100+ writes per minute)
• Entities requiring direct SQL queries and aggregations

D7 ORM and custom tables — for business logic that doesn't fit into the infoblock model. Many-to-many relationships, computed fields, custom aggregations. Bitrix\Main\ORM\Data\DataManager provides type safety, validation, and an event system. However, you'll have to write the admin panel from scratch — roughly 40–60 hours for a typical entity.

Scaling 1C-Bitrix Without Performance Loss

Horizontal scaling is a topic where 90% of projects fail. However, people think about it only when the site is already down.

The first step — move sessions from files to Redis. Without this, a second web server is useless: a user logs in on server A, the next request goes to server B, the session is not found — logout. In .settings.php:

'session' => ['value' => ['mode' => 'redis', 'host' => '127.0.0.1', 'port' => 6379]]

Next:

• nginx upstream or HAProxy distributes requests. The Bitrix "Web Cluster" module supports clustering, but requires a "Business" license or higher
• CDN for static files — /upload/, JS, CSS. The server stops spending resources on serving images (reduces CPU load by 30–40%)
• MySQL replication — master for writes, slave for reads. Bitrix supports up to 9 slave connections via .settings.php. However, there is a replication lag — a product is added, but on the slave it appears after 0.5–2 seconds. Use sticky reads for critical data

Vertical scaling is cheaper and faster initially:

• EXPLAIN every heavy query. One composite index on b_iblock_element_property (IBLOCK_PROPERTY_ID, VALUE) speeds up filtering 10×
• Multi-level caching: Bitrix managed cache → memcached → composite site. Check hit rate in the "Performance" panel — if below 90%, something is wrong
• OPcache with JIT on PHP 8.1+ — free 15–30% acceleration

Composite site mode can serve pages in 0.1s for anonymous users — we use it for 80% of traffic.

Offloading Heavy Processes from the Monolith

Bitrix is a monolith, and that's fine. Breaking it into microservices is madness. But offloading heavy processes is the right move.

Import/export is the most common pain. Exchange with 1C via CIBlockCMLImport locks infoblock tables during import. 100K items — that's 20–40 minutes when filtering on the site slows down. Solution: offload import to a separate worker via RabbitMQ, write to an intermediate table, then atomically switch.

• Search — Elasticsearch instead of the built-in search.title. Full-text and faceted search, autocomplete, typo correction. Load on MySQL is completely removed. We achieve <100ms for full-text search on 500K products.
• Notifications — push, SMS, email via queue. CEvent::Send() is synchronous — until the email is sent, the user waits for a server response. A queue (RabbitMQ or Redis list) reduces response time by 200–500ms.
• Report generation — PDF, Excel on large volumes (10K+ rows). Separate process, result — a download link.

API: REST, GraphQL, Webhooks

Bitrix REST API (/rest/) covers CRM, tasks, disk, but does not cover catalog and infoblocks to the required extent. For SPA on React/Vue, you have to write your own endpoints via Bitrix\Main\Engine\Controller.

• GraphQL — for mobile applications where traffic is expensive. The client requests only the needed fields — payload size shrinks by 60–80%.
• Webhooks — event model: new order → POST to external URL. No need to poll the API every 5 minutes.
• Versioning — /api/v1/, /api/v2/. Without this, API updates break all consumers at once.
• OpenAPI/Swagger — auto-generation of documentation. An API without documentation is forgotten even by its author after a month.

Main Sources of Technical Debt in Bitrix

Technical debt in Bitrix is specific. Three main sources:

1. Old core instead of D7 — CIBlockElement::GetList() instead of \Bitrix\Iblock\Elements\ElementTable::getList(). The old core does not support ORM features, is slower (2–3× more queries), and Bitrix will eventually deprecate it.
2. Direct SQL in component templates — $DB->Query("SELECT...") directly in template.php. Move to service classes, replace with ORM.
3. Business logic in result_modifier.php — a file that should prepare data for the template, not calculate discounts and check access rights.

Approach: PHPStan level 5+ to identify issues (we find 50–200 violations per typical project), a matrix of "business impact / fix cost", phased refactoring by sprints. Not everything at once — but the trend must be downward.

Avoiding Costly Refactoring

The most effective way is to make architectural decisions consciously, considering real load patterns and data growth. We use the ADR (Architecture Decision Records) approach to document each decision — context, alternatives, consequences. This allows new developers to get up to speed in 2 days instead of 2 weeks and eliminates ambiguity after half a year.

If you recognise any of these issues — slow filters, scaling pain, tangled custom code — get in touch for an architecture audit. We'll identify technical debt and propose a migration plan.

Documentation: ADR Instead of Word Files

• ADR — Architecture Decision Records. A short file: context, decision, consequences. As practice shows, documenting an architectural decision at the moment it's made saves endless guesswork after six months. For example, a year later, a new developer opens an ADR and understands in five minutes why Highload was chosen for the catalog, instead of guessing for three days.
• Diagrams — servers, data flows, integration points. PlantUML or Mermaid, stored in the repository next to the code.
• ER diagrams — infoblocks, properties, relationships. Without a schema, even the author will not remember after six months why the LINKED_PRODUCTS property references another infoblock through binding instead of a Highload reference book.
• Runbook — deployment, rollback, scaling, actions during a crash. Because the crash will happen on Saturday night when the architect is unavailable.

How We Design Architecture

1. Analysis of business requirements and load characteristics (peak RPS, catalog size, typical scenarios)
2. Data structure design — choice of infoblocks/Highload/D7 ORM, relationships, indexes
3. Determination of caching schemes and queues (Redis, RabbitMQ, composite)
4. Prototyping and load testing on real data (200K records, 30+ properties)
5. Documentation — ADR, ER diagrams, runbook, API specifications
6. Project review — internal and with the client

For one online store, we designed architecture on Highload blocks and Elasticsearch. Product filtering down to 50ms, time to first byte 0.3s. Hosting cost savings: 45% per month.

Scope of Work

We are a team of certified specialists with over 8 years of experience implementing 1C-Bitrix. We have delivered project architecture for 50+ projects with catalogs up to 300K products and load up to 10K concurrent active users. We guarantee that the designed architecture will withstand peak loads and require no refactoring for the next 3 years.

Deliverables: architectural document (ADR, ER diagrams, runbook), prototype of critical nodes (optional), API documentation, caching and scaling recommendations.

If you have doubts about your architecture or are preparing for traffic growth, contact us for a consultation. We will audit the current structure and propose an optimal strategy. Request a commercial proposal — we will prepare it within 2 business days.