Projecting Info Block Structure for an Online Store

Imagine: after launch, the product filter by characteristics times out, and the import from 1C creates duplicates in every element. The reason is incorrectly designed info blocks. Mistakes at this stage are costly: restructuring after launch may require migrating thousands of elements, stopping the catalog for several days, and additional development costs. With us, you will get a structure that will last for years without changes. 10+ years of experience in Bitrix, certified specialists, 1C-Bitrix partner. Proper design of 1C-Bitrix info block structure is an investment in the stability and performance of your catalog.

A typical design mistake: using a single info block for all entities — products, articles, banners, directories. This leads to a bloated b_iblock_element_property table, slower filtering, and caching difficulties. The solution: separate data domains into different info block types.

How to Choose the Right Property Type?

Each info block property has a type, and this decision cannot be changed without data migration. Below is a table with main types and recommendations:

According to 1C-Bitrix documentation, choosing the right property type is the foundation of catalog performance. Strings without indexes and duplicates in b_iblock_element_property are a common cause of slowdowns. The facet index solves the problem, but only for numeric, list, and directory properties.

Info Block Type Structure

An info block type is a grouping without technical significance, but critical for manageability. Rule: one info block type = one data domain. "Catalog", "Articles", "Banners", "Directories" is a correct grouping. "Site" as the only type for everything is an antipattern.

For an online store, a typical type structure:

• catalog — product catalog and SKU info blocks
• content — news, articles, FAQ
• references — directories (used as source for list-type properties, if not HL-blocks)
• landing — landing blocks, banners

How Section Depth Affects Performance

Sections (b_iblock_section) are stored in Bitrix's native tree. Practical limitation: nesting depth of more than 5–6 levels causes problems with breadcrumbs, SEO-friendly URLs, and navigation. If the business requires a deeper hierarchy (e.g., spare parts for equipment: manufacturer → model → series → unit → part), consider replacing the hierarchy with properties using filtering instead of section navigation.

Multiple Properties and Performance

A multiple property stores several values in b_iblock_element_property — one row per value. 10,000 elements × a property with 5 values = 50,000 rows in the table just for this property. For multiple directory properties, the facet index handles them correctly, but the load during index rebuilding is higher.

Rule: if a property is rarely filled (filled in 10% of elements), empty records are not stored, reducing table size. If a property is filled in all elements and rarely changes, consider moving it to a separate HL-table via DataManager.

Why is it Important to Split Data into Different Info Block Types?

Proper separation by types avoids query slowdowns when mixing heterogeneous data. For example, products and banners have different property sets and update frequencies. If combined, when selecting banners, product records are also scanned, increasing load. Agencies that neglect this rule often face increased execution time of the catalog component.

Case: Designing Info Blocks for a Real Estate Aggregator

A platform for sale and rental property listings. Initial solution: one info block "Properties" with 40 properties, including string address, district, metro.

Problems under load:

• Filtering by metro worked as text search (LIKE), not by index
• Duplicate values: "m. Arbatskaya", "Arbatskaya", "arbatskaya" — three different records
• Radius search from metro impossible without geocoordinates

Restructured schema:

• catalog type: info block "Properties" + info block "Residential complexes"
• HL-block hl_metro with fields: UF_NAME, UF_LINE, UF_LAT, UF_LON — 342 records instead of text values
• HL-block hl_district — districts with binding to city
• Properties "Area", "Floor", "Floors" — type "Number" for range filtering
• Property "Metro" — directory (HL), multiple (several stations)

Facet index after restructuring: created in 3 minutes for 85,000 objects, filtering by metro and type — 0.15 seconds. Our client achieved over 50x improvement in filter speed. This saved budget on server resources and reduced user wait time. More about facet index on Wikipedia.

What is Included in Info Block Structure Design

1. Domain analysis — list of entities and relationships (1–2 days).
2. Property schema design — choosing types, HL-blocks, facet index (1–3 days).
3. Section hierarchy design — optimal depth, replacement with properties if necessary (0.5–1 day).
4. Documentation — table schema for each info block (0.5–1 day).
5. Approval — schema approval with the client (0.5 day).
6. Optional: implementation — structure deployment, data migration (from 2 days).

Timeline: from 3 to 10 working days depending on the number of info blocks and domain complexity. Cost is calculated individually after analyzing the technical specifications. Get a consultation — contact us to discuss your project. Ensure reliability: many years of experience, performance guarantee on the designed structure. Order the design — and your system will run without failures for years. A designed structure reduces maintenance and new feature development costs.

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.