Performance Audit for 1C-Bitrix Sites

A catalog page takes 6 seconds to load, the server is powerful, hosting isn't complaining, but clients are leaving. Without diagnostic tools, people start guessing: "maybe the cache isn't working," "maybe the database is slow." Our performance audit provides a precise answer: exactly where time is lost and how much can be gained. With over 10 years and 500+ audits conducted, typical issues repeat: N+1 queries, disabled cache, missing indexes. The audit identifies them in 3-5 days and delivers a concrete action plan.

Imagine an e-commerce store on Bitrix with 10,000 products; a category page opens in 6 seconds. Clients leave, conversion drops. Internal checks reveal nothing—the server isn't overloaded, the database isn't slow. The only systematic step is to conduct a performance audit. It will reveal the root cause of the slowness and provide numbers: how many seconds can be recovered at each stage. Average load time savings: 40%.

How We Diagnose Slow Queries

BX_DEBUG is a built-in Bitrix tool. In dbconn.php or bitrix/php_interface/init.php:

define('BX_DEBUG', true);

It displays at the bottom of the page: number of SQL queries, PHP execution time, memory usage, cache hits. Norm: < 50 queries, < 500 ms PHP time for a catalog page.

For heavy queries we use EXPLAIN ANALYZE. Slow queries are logged via slow_query_log in MySQL or log_min_duration_statement in PostgreSQL (threshold 200 ms), then the execution plan is analyzed. This is 5 times more effective than guessing without a plan. According to MySQL documentation, using EXPLAIN ANALYZE reduces plan analysis time by half.

Real case: On one project, the catalog was slow due to N+1 queries in a handmade component. After switching to GetList with property selection, the time dropped from 4 s to 1.2 s.

Why Bitrix Slows Down on Large Catalogs

N+1 in components. A product listing makes 1 query for the list and N queries for prices/properties. With 50 products per page, that's 50 extra queries to b_iblock_element_property. Solution: select with required properties or batch fetching.

Disabled cache. A developer turned off caching during development and forgot to re-enable it. Check component settings and global configuration. Enabling the cache can reduce response time by 3-5 times.

Lack of indexes on custom tables. User tables are created without indexes, then queries with WHERE cause full table scans on millions of rows.

Heavy agents in the web thread. CAgent::CheckAgents() is called on every hit if cron is not configured. Agents with heavy logic slow down every page.

How Performance Audit Differs from a Regular Speed Check

A regular check via online services shows only external metrics: TTFB, resource load time. An audit goes inside: profiles PHP code, analyzes SQL queries, checks cache configuration and server settings. The difference in precision is like between a blood pressure monitor and an MRI—the latter sees the problem at the code and data level.

What We Check During the Audit

Here is an example of typical audit results:

According to 1C-Bitrix caching documentation, enabling tagged cache can reduce server load by 5 times.

Work Process

1. Collect metrics — Enable BX_DEBUG, slow_query_log, set up Xdebug. Profile typical pages.
2. Analyze — Examine SQL query plans, look for N+1, check indexes, agents, cache settings.
3. Report — Create a table of bottlenecks with impact assessment (in seconds) and effort to fix.
4. Recommendations — Prioritized plan: what to do first for maximum speedup.
5. Support — If needed, help implement optimizations (cron setup, component fixes, migrations).

Typical Optimization Mistakes

• Cron not configured for agents — every hit triggers CAgent::CheckAgents().
• No indexes on frequently filtered fields (price, status, category).
• Component caching disabled or reset unnecessarily.
• Gzip compression for static files not enabled at the web server level.

Request a consultation to get accurate timelines and cost for your project. Contact us—we are ready to diagnose your site.

80% of Bitrix sites slow down due to one table

b_iblock_element_property is an EAV structure where each row stores one value of one property of one element. A catalog of 50,000 products with 30 properties yields 1.5 million rows. The smart filter performs a JOIN of this table with b_iblock_element on five properties, and MySQL performs a full table scan for 3–5 seconds. Our experience shows that without intervention in this table, site acceleration is impossible. We take on projects where load time has dropped to 8–10 seconds and bring TTFB back to <200 ms within 1–2 weeks. Site speed optimization begins with an audit of slow queries and ends with a comprehensive turnkey infrastructure overhaul.

Contact us for an audit — we will identify bottlenecks within 2 hours and propose a concrete plan.

How to achieve TTFB below 200 ms?

Server optimization is the first step. Nginx configuration goes beyond simple gzip. Specifically:

• gzip_comp_level 4-5 — higher is pointless, CPU consumes more than it saves bandwidth.
• brotli on with brotli_static on for precompressed files.
• HTTP/2 with http2_max_concurrent_streams 128.
• fastcgi_cache for PHP responses — caching at Nginx level, bypassing PHP-FPM entirely.
• worker_processes auto, worker_connections according to the number of simultaneous connections.

PHP-FPM tuning: choose between pm = dynamic and pm = static. Static mode works best for dedicated servers with predictable load because it avoids forking overhead. Dynamic saves RAM under low traffic. Calculate pm.max_children as (available RAM - RAM for MySQL/Redis) / average process consumption. For OPcache set memory_consumption=256, max_accelerated_files=20000, and validate_timestamps=0 in production (restart PHP-FPM on deploy).

MySQL/MariaDB: the main bottleneck is almost always the database. Enable slow_query_log with a threshold of 0.5 sec and analyze every query via EXPLAIN. Set innodb_buffer_pool_size to 70–80% of available RAM on a dedicated server. Create composite indexes for faceted search: (IBLOCK_ID, IBLOCK_PROPERTY_ID, VALUE) on b_iblock_element_property. Run OPTIMIZE TABLE b_iblock_element_property after mass operations.

How to configure three-level caching?

Managed component cache. Set TTL individually for each component. Catalog — 3600 sec, news feed — 300 sec, banners — 86400. The same TTL everywhere guarantees either outdated data or useless cache.

Composite cache. The bitrix:composite technology lets Nginx serve ready HTML from a file; PHP is not executed. Dynamic zones (cart, authorization) are loaded via AJAX request through CBitrixComponent::setFrameMode(true). TTFB drops below 50 ms. However, not all components are compatible; $APPLICATION->ShowPanel() and direct output via echo break the composite. We check every page through the panel 'Performance → Composite Site'. According to Bitrix official documentation on composite cache, this is the most effective caching method for high‑load projects.

Comparison: composite cache is 10–20 times faster than managed cache in time to first byte.

Memcached / Redis. Transfer cache from the file system: sessions go to Redis (session.save_handler = redis) — 10–50 times faster than files, plus cluster support. Component cache goes to Memcached via .settings.php: 'cache' => ['type' => 'memcache']. Also enable ORM query cache so identical GetList() calls don't hit MySQL on every request.

What is the fastest way to optimize Bitrix database?

Default MySQL settings are insufficient. Indexes — composite for faceted search, covering for frequent queries. MySQL responds from the index without accessing the data. Partial indexes (MariaDB) for filtering by ACTIVE = 'Y'. Audit unused indexes — each slows down INSERT/UPDATE.

Partitioning. For tables with millions of rows: b_stat_session, b_search_content_stem, and highload-blocks with history. Partition by date — a query for 'orders in a month' does not scan three years of data. Partitioning also solves the problem of concurrent queries during exchange with 1С via CommerceML.

Real case: a catalog of 200,000 products, 50 properties. Filtering by 10 properties took 12 seconds. After creating composite indexes on (IBLOCK_ID, IBLOCK_PROPERTY_ID, VALUE) and partitioning b_iblock_element_property by IBLOCK_ID, execution time dropped to 0.3 seconds. MySQL load decreased by 40 times.

Cleanup. Over a year or two, any database accumulates: outdated search index, expired records in b_cache_tag, history in b_iblock_element_prop_s*, logs in b_event_log taking gigabytes. We set up regular cleanup via agents.

Frontend and CDN

Images account for 60–80% of page weight. Convert to WebP via CFile::ResizeImageGet() with BX_RESIZE_IMAGE_PROPORTIONAL + conversion. Use srcset + sizes — never load a 3000px image into a 400px block. Add loading="lazy" for everything below the fold. AVIF offers another 20–30% savings vs WebP.

CSS/JS optimization: use the built-in Bitrix module to merge and minify via 'Settings → CSS/JS Optimization'. Apply PurgeCSS / UnCSS — in a typical Bitrix project, 60–70% of CSS is unused. Use defer / async for non‑critical JS and inline critical CSS in <head> for instant FCP.

Fonts: add <link rel="preload" as="font" crossorigin> for the main font. Set font-display: swap — text visible immediately. Subset via pyftsubset — keep only Cyrillic + Latin, file size reduces by 3–5 times.

CDN: Cloudflare, BunnyCDN, AWS CloudFront, or Russian providers (Selectel CDN, VK Cloud CDN). Serve static assets (CSS, JS, images, fonts) via CDN with Cache-Control: public, max-age=31536000, immutable for files with a hash. Use on‑the‑fly image optimization (imgproxy, Cloudflare Polish) without load on origin.

Why is load testing necessary?

Not synthetic benchmarks, but real scenarios: k6 / wrk to simulate routes — catalog → filtering → product card → cart → checkout. Measure RPS, response time (p50, p95, p99), error rate. Use Xdebug (callgrind) or Blackfire for PHP profiling to find bottlenecks. The test result gives an objective picture of where it actually slows down, not where it 'seems'. After optimization, run again to record improvements.

Results

What is included in the work?

1. Current performance audit — analysis of slow queries, PHP profiling, check of caching, CDN, server settings.
2. Server configuration — Nginx, PHP-FPM, MySQL, Redis/Memcached, OPcache.
3. Caching optimization — managed cache, composite site, TTL configuration, tagged caching.
4. Database work — index creation, partitioning, cleanup, EAV table reorganization.
5. Frontend — images (WebP/AVIF), CSS/JS (minification, deferred), fonts (preload, subsetting).
6. CDN — connection, caching rule setup.
7. Load testing — real user scenarios, metric report.
8. Documentation — description of all changes, recommendations for further maintenance.
9. Guarantee — support for 1 month after delivery, ensuring all optimizations are stable.

Monitoring

Without monitoring, everything degrades in six months. A new module, uncleared logs, a template change — and speed returns to original. Use web-vitals API for Real User Monitoring from actual visitors. Set up synthetic monitoring with Pingdom or UptimeRobot for regular checks from different locations. Configure alerts — TTFB > 500 ms or LCP > 3 sec triggers notification.

Timelines and cost

Cost is calculated individually after the audit. Get a consultation for your project — we will evaluate the current state and propose an acceleration plan with specific timelines and budget. We are a team with 12+ years of experience in Bitrix, having completed over 300 site speed optimization projects. Contact us to start the performance audit today.