SQL Query Optimization via EXPLAIN Analysis for 1C-Bitrix

Slow catalog or admin interface performance is a common complaint. After ten years of optimizations, we've learned: the root cause almost always lies in inefficient SQL queries. For example, a product list page loads 8–10 seconds because MySQL scans millions of rows in b_iblock_element, while developers never check the slow query log. Clients lose conversions, and server resources are wasted. Our task is to find bottlenecks through EXPLAIN analysis within 2–5 days and fix them.

EXPLAIN is a MySQL command that shows the query execution plan: which tables are scanned, whether indexes are used, how many rows are processed. It's the primary tool after spotting a slow query in the slow query log. Analysis is performed on a database copy or during low-load hours to avoid impacting the live site.

After a decade working with Bitrix, we see the same pattern: in most projects, blindly adding indexes without analysis yields temporary improvements, and problems return within a week. Only systematic EXPLAIN analysis followed by code and caching corrections provides long-term results. Clients save from $5,500 per year on server resources after such optimization.

How to Read EXPLAIN

EXPLAIN SELECT * FROM b_iblock_element
WHERE IBLOCK_ID = 5 AND ACTIVE = 'Y'
ORDER BY SORT;

Key output columns:

For more precise diagnostics, use EXPLAIN ANALYZE (MySQL 8.0+, MariaDB 10.9+), which executes the query and shows actual time:

EXPLAIN ANALYZE SELECT ...;

How EXPLAIN Reveals Slow Queries

In the slow query log, we look for queries with execution time > 1 second. For each, we run EXPLAIN. If we see type=ALL or rows > 10,000 for a point query, that query is a candidate for optimization. For example, on one project we found a query against b_iblock_element_property with rows=1,200,000. After adding an index, rows dropped to 1,200, and execution time fell from 3 seconds to 0.01 seconds.

Common Bitrix Issues

Using filesort on b_iblock_element is a frequent finding. The query sorts by SORT, but the index doesn't cover the combination (IBLOCK_ID, ACTIVE, SORT). Solution: composite index:

ALTER TABLE b_iblock_element
ADD INDEX ix_iblock_active_sort (IBLOCK_ID, ACTIVE, SORT);

After adding the index, EXPLAIN shows type=ref and Extra without Using filesort. Execution time drops from seconds to milliseconds.

rows = 500,000 on a query to b_iblock_element_property happens when filtering by property value without an index on (IBLOCK_PROPERTY_ID, VALUE). For VARCHAR field VALUE, use a prefix index VALUE(64). This reduces scanned rows to hundreds.

Using temporary with GROUP BY appears in facet filter queries. Bitrix facets build optimized tables b_iblock_find_* — if they are not rebuilt after adding properties, queries bypass them.

Diagnosing ALL Scans

type=ALL is the worst case: MySQL scans the entire table. In Bitrix, this often occurs in queries without conditions or with conditions on non-indexed fields. We immediately add missing indexes. For instance, b_iblock_element should have an index on (IBLOCK_ID, ACTIVE), b_iblock_element_property on (IBLOCK_ELEMENT_ID, IBLOCK_PROPERTY_ID). If the problem is in component code, we rewrite the query using CIBlockElement::GetList with correct parameters.

Why EXPLAIN Analysis Beats Intuitive Approach

Experience shows: developers often add indexes 'by eye' without checking the query plan. EXPLAIN gives an objective picture. Compare for yourself: without index, the query scans 500,000 rows in 2 seconds; with index, 500 rows in 0.002 seconds. A 250x difference. Only this way guarantee results.

What Our Work Includes

1. Audit slow query log — collect all slow queries over a week. We use pt-query-digest or built-in MySQL tools.
2. EXPLAIN analysis of each suspicious query — identify type=ALL, Using filesort, Using temporary.
3. Design indexes — taking into account load and data structure. Sometimes an index can degrade insert performance, so we evaluate trade-offs.
4. Add indexes and rewrite queries — if an index doesn't help, we fix component code or the query (e.g., change ORDER BY or add FORCE INDEX).
5. Re-run EXPLAIN — verify that type changed and rows dropped.
6. Test on production data — measure execution time before and after. Use profiling via EXPLAIN ANALYZE.
7. Documentation — record all changes and recommendations for ongoing monitoring.

Before and After Comparison

Timeframes and Cost

Optimization of one typical query (analysis + fix) takes 2 to 5 hours. Full project audit takes 2 to 5 working days. The cost for optimizing one query ranges from 5,000 to 15,000 rubles. Contact us for a preliminary audit of your slow query log. Order SQL optimization via EXPLAIN analysis and get a free analysis of one query.

Additional information: Wikipedia: EXPLAIN

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.