Architecture decisions made at the start of a project determine the cost of every subsequent feature. A wrong choice — storing data in infoblock properties instead of separate tables when you have 100,000 items — will make simple queries impossibly slow within two years. Fixing it later costs 10–30 times more than getting it right upfront. Every day we see projects where poor architecture forces the team to spend 70% of their time on maintenance instead of development. In 10+ years of work, we have conducted over 50 architecture consultations and built 30+ projects from scratch.
Typical Architecture Decision Points
Infoblocks vs. ORM Tables: Which to Choose?
Infoblocks are versatile and manageable through the admin interface — great for content. But for complex relationships, high write frequency, or non-standard queries, custom ORM tables (\Bitrix\Main\ORM\Data\DataManager) perform 5–50 times faster.
Rule of thumb: if data is edited through the admin panel by content managers — use infoblocks. If data is handled only by code (logs, queues, events, transactions) — use ORM tables.
Monolith vs. Modular Architecture
At the start, it's convenient to write everything in one custom component. A year later, that 3,000-line component becomes untestable and difficult to hand over. Modular approach: /local/modules/company.module_name/ with a clear public API, events, and Composer dependencies.
AJAX Components vs. SPA Approach
Bitrix supports both. AJAX components (bitrix:main.loader) work natively with the core but have limited capabilities. SPA with React/Vue (via bitrix:ui.sidepanel or a full SPA) provides a better UX but requires a separate API and complicates SSR/SEO.
Why Caching is a Key Performance Factor
Proper caching can speed up a site 10–50 times without changing logic. Bitrix provides several layers:
| Layer |
Mechanism |
Application |
| Managed cache |
\Bitrix\Main\Data\ManagedCache |
Objects with invalidation tags |
| Page cache |
Component settings |
Entire pages/blocks |
| memcache / Redis |
/bitrix/.settings.php |
Sessions, object cache |
| CDN |
External CDN |
Static files, images |
Edition & License Selection
| Task |
Recommendation |
| Corporate portal |
Bitrix24 on-premise, Enterprise |
| Online store with B2B |
1C-Bitrix: Business or Small Business |
| High-load marketplace |
Enterprise + cluster |
| Landing page + CRM |
Bitrix24 cloud |
The edition determines available modules: b2b, catalog (B2B trade catalog), sale.crm (CRM integration in orders).
1C Integration: Architecture Decisions
Classic exchange via CommerceML (file-based) works up to ~50,000 SKUs. For larger volumes or real-time requirements, use REST exchange via 1C API or a message broker (RabbitMQ).
Broker architecture: 1C → publishes event to RabbitMQ → Bitrix worker subscribes and processes → updates data in real time. Latency is seconds instead of hours with file exchange.
How We Consult on Architecture
- Analyze business requirements and constraints (traffic, data volume, budget)
- Compare architectural options with risk and cost assessment
- Select Bitrix edition and module composition
- Design data schema and module structure
- Architecture of 1C and external integrations
- Technology stack recommendations (cache, search, queues)
- Produce an 'Architecture Decision' document for the development team
Case from our practice: B2B platform with 500,000 SKUs
Task: An online store for corporate clients with individual pricing, quotas, and delivery conditions per counterparty.
Problems with the standard approach:
- Storing prices in
b_catalog_price — 500,000 SKUs × 200 buyer groups = 100 million records; any price query >500ms
- Catalog filter via infoblock properties — sequential scan on a table with 5 million property rows
- Standard
sale cart and orders don't support per-counterparty quotas and conditions
Architecture solution:
- Prices moved to a separate ORM table
bl_b2b_price with an index on (user_group_id, product_id) — price query in 5ms
- Filter via ElasticSearch (integrated with Bitrix via custom component)
- Standard
sale extended with module company.b2b_sale — added counterparty fields, quotas, and special conditions
- Prices from 1C transmitted via RabbitMQ → worker updates
bl_b2b_price in real time
Result: catalog page with filter loads in 300ms, price updates from 1C arrive within 30 seconds instead of 4 hours.
| Solution Component |
Technology |
Rationale |
| Price storage |
ORM table + indexes |
b_catalog_price doesn't scale to 100M records |
| Search & filter |
ElasticSearch |
Full-text search + faceted filter in <100ms |
| 1C sync |
RabbitMQ + worker |
Real-time instead of file exchange |
| Cart & orders |
Extension of \Bitrix\Sale |
Maintains compatibility with Bitrix modules |
Get a consultation on your project's architecture — we'll assess risks and propose the optimal solution within 2–3 days. Request an architecture audit to avoid expensive rework down the road.
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.
| Criteria |
Infoblocks |
Highload |
D7 ORM |
| Data volume |
Up to 50K |
50K–10M+ |
Any |
| Filtering speed |
Degrades with growth (2‑5s at 100K) |
Stable (50‑100ms at 200K) |
Maximum (custom indexes) |
| Structure flexibility |
High (EAV) |
Medium (fixed columns) |
Full |
| Admin panel out of the box |
Yes |
Yes |
No |
| SEO module support |
Yes |
Limited |
No |
Real‑world case: catalog migration from infoblocks to Highload
For a client with 250K products and 45 properties, infoblock filters required 4 seconds. We designed Highload blocks with facet indexes, reducing filter time to 60ms. Hosting costs fell by 40% because MySQL IO dropped by 70%.
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:
- 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.
- Direct SQL in component templates —
$DB->Query("SELECT...") directly in template.php. Move to service classes, replace with ORM.
- 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
- Analysis of business requirements and load characteristics (peak RPS, catalog size, typical scenarios)
- Data structure design — choice of infoblocks/Highload/D7 ORM, relationships, indexes
- Determination of caching schemes and queues (Redis, RabbitMQ, composite)
- Prototyping and load testing on real data (200K records, 30+ properties)
- Documentation — ADR, ER diagrams, runbook, API specifications
- 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.
| Stage |
Duration |
Result |
| Requirements gathering |
3–5 days |
Document with load characteristics, user profile, growth plan |
| Design |
1–2 weeks |
Data structure, integration scheme, ADRs for key decisions |
| Prototyping |
1 week |
Load tests on real volumes (Highload block with 200K records and 30 properties — filter performance checked to 50ms) |
| Documentation |
3–5 days |
Diagrams, runbook, API specifications |
| Review |
2–3 days |
Internal review, then with client |
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.