{"id":8220,"date":"2026-09-06T18:05:07","date_gmt":"2026-09-06T16:05:07","guid":{"rendered":"https:\/\/launix.de\/launix\/?p=8220"},"modified":"2026-09-06T18:05:08","modified_gmt":"2026-09-06T16:05:08","slug":"why-are-my-sql-queries-so-slow","status":"publish","type":"post","link":"https:\/\/launix.de\/launix\/en\/why-are-my-sql-queries-so-slow\/","title":{"rendered":"Why Are My SQL Queries So Slow?"},"content":{"rendered":"<p class=\"wp-block-paragraph\">We all know the problem: Our user base grows and the database queries become slower and slower. This article intends to help you with practical tipps as well as a universal solution that helps you to develop applications faster without them becoming slower over time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">TLDR &#8211; How to get it faster?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To get your SQL queries faster, just follow these steps:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Download <a href=\"https:\/\/memcp.org\">MemCP<\/a> from Github and compile it (maybe even with JIT support)<\/li>\n\n\n\n<li>Start the MemCP service<\/li>\n\n\n\n<li>Import your databases into MemCP<\/li>\n\n\n\n<li>Change MySQL protocol hostname to 127.0.0.1 and port to 3307 instead of 3306<\/li>\n\n\n\n<li>Have fun<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">That&#8217;s the short version. And here&#8217;s the long explaination:<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How query planning, indexing, filtering, and modern columnar execution affect application performance<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Your application was fast when it had a few thousand records. Now the same page takes several seconds\u2014or sometimes minutes\u2014to load. The SQL query has not changed, but the amount of data has.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is one of the most common database problems in growing applications. It often appears first as a slow search page, a delayed admin screen, a sluggish dashboard, or pagination that becomes slower on every subsequent page.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The usual advice is simple:&nbsp;<strong>add an index<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sometimes that is exactly the right solution. But not always. A query may combine several filters, joins, permissions, sorting, text matching, counting, and pagination. In that situation, one additional index may improve one part of the query while leaving the real bottleneck untouched.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This article explains why SQL queries become slow as data grows, what a database has to do before it can return a few rows, and how modern execution techniques such as cost-based query planning, automatic indexing, compact record sets, compressed columns, and late materialization can reduce unnecessary work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The examples focus on large filtered lists, search, permissions, sorting, and analytical reads. They are not a promise that every SQL query will run in milliseconds. Actual performance depends on the query shape, data distribution, hardware, concurrency, cache state, and durability requirements.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The symptom: the application is slow, not just the query<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Application developers usually do not search for \u201clate materialization\u201d when something is wrong. They search for symptoms:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u201cWhy is my website slow with a large database?\u201d<\/li>\n\n\n\n<li>\u201cWhy does my SQL query get slower as the table grows?\u201d<\/li>\n\n\n\n<li>\u201cWhy is database search slow?\u201d<\/li>\n\n\n\n<li>\u201cWhy is pagination slow on a large table?\u201d<\/li>\n\n\n\n<li>\u201cWhy is my filtered list slow?\u201d<\/li>\n\n\n\n<li>\u201cWhy does adding an index not fix my query?\u201d<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These are all different descriptions of a similar problem: the database is doing much more work than the application output suggests.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A page may display only 20 documents. A dashboard may show only five numbers. A search endpoint may return only 100 rows. Nevertheless, the database may have to inspect hundreds of thousands or millions of candidates before it knows which rows are valid, permitted, correctly sorted, and suitable for the requested page.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The important question is therefore not only:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Which index is missing?<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">It is also:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">How many candidates must the database examine, transform, sort, join, and materialize before it can return the requested result?<\/p>\n<\/blockquote>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-1024x576.png\" alt=\"\" class=\"wp-image-8223\" srcset=\"https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-1024x576.png 1024w, https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-300x169.png 300w, https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-768x432.png 768w, https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-1536x864.png 1536w, https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-2048x1152.png 2048w, https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-query-execution-explainer-18x10.png 18w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Why a query gets slower as a table grows<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A database query can become slow for several different reasons.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. The query examines too many rows<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Without a useful access path, the database may scan a large part of a table. Even with an index, the selected condition may match so many rows that the database still has to visit a large candidate set.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A query that is fast with 10,000 rows can become slow with 10 million rows because the amount of work grows with the table, not with the number of rows returned to the user.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. The query uses several conditions at once<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Real application queries rarely filter on one column only. A document list may combine:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the current tenant or customer;<\/li>\n\n\n\n<li>a document state;<\/li>\n\n\n\n<li>a text or prefix search;<\/li>\n\n\n\n<li>user-specific permissions;<\/li>\n\n\n\n<li>a date range;<\/li>\n\n\n\n<li>sorting by a timestamp; and<\/li>\n\n\n\n<li>a\u00a0<code>LIMIT<\/code>\u00a0for pagination.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">An index designed for one of these conditions may not be ideal for the complete query.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Filtering happens too late<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The database may first follow an ordering index, fetch many candidate rows, and only then apply a permission check or another selective filter. If most candidates are rejected, the database has performed a great deal of work to return a small result.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Sorting and pagination create large intermediate results<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><code>ORDER BY ... LIMIT 100<\/code>&nbsp;does not necessarily mean that the database only processes 100 rows. It may need to find, filter, and sort a much larger set before it knows which 100 rows belong on the page.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This becomes especially difficult when the order is different from the most selective filter.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Wide rows are materialized unnecessarily<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A table may contain dozens or hundreds of columns, while the page only needs an ID, a title, and a timestamp. If the execution path fetches complete records before all filters have been applied, it moves and constructs data that will later be discarded.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why \u201cjust add an index\u201d is not always enough<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Indexes are essential database structures. They provide shortcuts for finding rows, but every shortcut is designed for a particular access pattern.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Suppose an application uses these queries:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>WHERE tenant_id = ? AND state = ?\n<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>WHERE tenant_id = ? AND user_id = ? ORDER BY changed_at DESC\n<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>WHERE tenant_id = ? AND state = ? AND title LIKE ?\nORDER BY changed_at DESC LIMIT 100\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">These queries overlap, but they do not have the same optimal access path. The best index order for one query may be less useful for another. Indexes also consume memory and storage, have to be maintained when data changes, and can increase write costs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A growing application creates new combinations over time. Developers may add filters, permissions, sorting, reporting, or tenant boundaries without redesigning every physical access path from scratch.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is why database performance is not only an indexing problem. It is also a&nbsp;<strong>query planning and execution problem<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A realistic slow query<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Consider a document list for a multi-tenant application:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>SELECT d.id, d.title, d.changed_at\nFROM documents AS d\nWHERE d.tenant_id = ?\n  AND d.state IN ('open', 'review')\n  AND d.title LIKE CONCAT('%', ?, '%')\n  AND EXISTS (\n      SELECT 1\n      FROM permissions AS p\n      WHERE p.document_id = d.id\n        AND p.user_id = ?\n  )\nORDER BY d.changed_at DESC\nLIMIT 100;\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The user sees a list of only 100 documents, but the database has to combine several requirements:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Query part<\/th><th>Meaning<\/th><\/tr><\/thead><tbody><tr><td><code>tenant_id<\/code><\/td><td>Only records belonging to the current customer<\/td><\/tr><tr><td><code>state<\/code><\/td><td>Only documents in relevant states<\/td><\/tr><tr><td><code>LIKE<\/code><\/td><td>A text search condition<\/td><\/tr><tr><td><code>permissions<\/code><\/td><td>The current user may see only selected records<\/td><\/tr><tr><td><code>ORDER BY<\/code><\/td><td>Newest or recently changed records first<\/td><\/tr><tr><td><code>LIMIT 100<\/code><\/td><td>Only the first page is returned<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">A conventional execution path may inspect many records in the requested order and reject them one by one when the permission or text condition does not match. Alternatively, it may materialize a large intermediate result and sort it before applying the limit.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The difficulty is not the&nbsp;<code>LIMIT<\/code>. The difficulty is finding 100 valid rows without doing the work for every possible candidate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Query planning: choosing a route instead of following the table<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">SQL describes the result that an application wants. It does not prescribe the physical route used to produce that result.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A query planner is like a route planner. It decides which table or filter to access first, which joins to reorder, which conditions to apply early, whether to use an index or a scan, when to sort, and how to combine partial results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A simple planner question is:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Which column has an index?<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">A more useful question is:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">What is the least expensive way to reduce the candidate set and produce the requested result?<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">For complex application queries, the planner may need to consider several possible strategies. It may be better to start with the permission domain, the tenant boundary, an ordered scan, a compressed column scan, a cached grouping, or an automatically derived access structure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MemCP separates logical query optimization from physical execution decisions. Its planner can decorrelate supported subqueries, reorder joins, and lower the logical query into physical scans, indexes, RecSets, caches, and execution pipelines&nbsp;<a href=\"https:\/\/github.com\/launix-de\/memcp\">1<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Automatic indexing: fewer predictions for the application developer<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A manually created index is a shortcut designed in advance. The developer has to predict which columns will be filtered, in which order, and together with which sorting or joining conditions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That is difficult because applications evolve. A list that initially filters by status may later add tenant boundaries, permissions, date ordering, and text filters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MemCP can derive useful compound indexes from real access, filter, and ordering patterns. In simple terms, it can help organize data around the ways the application actually accesses it instead of requiring the developer to predict every future query shape&nbsp;<a href=\"https:\/\/github.com\/launix-de\/memcp\">1<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An analogy is a large warehouse. A traditional index is a shelf or shortcut that a worker labels manually for one expected route. Automatic indexing observes which routes are used repeatedly and helps create useful shortcuts for those routes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This does not mean that every query becomes fast automatically. Automatic indexing does not replace correct SQL, data modeling, measurement, or workload analysis. Its purpose is to reduce the amount of manual physical-design work required to keep changing application access patterns efficient.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">RecSets: compact candidate lists instead of wide temporary rows<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A&nbsp;<strong>RecSet<\/strong>&nbsp;is a compact set of records that currently satisfies part of a query.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine a database with one million documents. A first filter may identify the documents belonging to one tenant. A second filter may select documents in an open state. A permission check may identify the documents visible to a particular user. A text condition may reduce the set again.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The database does not need to copy every complete document after every step. It can carry compact membership information between operators and intersect candidate sets as the query progresses.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The analogy is a guest list. If one million people are registered for an event, the next processing step does not need a copy of every person\u2019s complete profile. It may only need a compact list of the people who are still eligible. That list can be intersected with other lists until only the final candidates remain.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">RecSets are useful for queries that combine filters, joins, permissions, ordering, and pagination. They help avoid repeatedly materializing wide result rows before the database knows whether those rows will be returned&nbsp;<a href=\"https:\/\/memcp.org\/wiki\/MySQL_is_too_slow\">1<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Compressed columns: read what the query actually needs<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A row-oriented table stores the fields of a record together. That is convenient for many point accesses, but it can be wasteful when a query touches only a small subset of a wide table.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A document table might contain 80 columns, while a list page needs only:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>id, title, changed_at\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">A compressed columnar execution path can read only the referenced columns. Suitable values can remain compressed while they are scanned, reducing the amount of data moved through memory and the cache hierarchy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MemCP stores stable data in compressed columns and supports compact representations such as bit-packed values, dictionaries, ranges, and sparse forms&nbsp;<a href=\"https:\/\/github.com\/launix-de\/memcp\">1<\/a>. The actual benefit depends on the data distribution and must be measured with representative data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Late materialization: build complete rows at the end<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Late materialization<\/strong>&nbsp;means delaying the construction of complete result rows until the database knows which records will actually be returned.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A useful execution order is:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>identify the relevant candidate domain;<\/li>\n\n\n\n<li>apply tenant, state, permission, and search filters;<\/li>\n\n\n\n<li>select candidates in the requested order;<\/li>\n\n\n\n<li>stop once enough valid records have been found; and<\/li>\n\n\n\n<li>fetch the output columns needed for those final records.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">This avoids building complete output rows for candidates that will later be rejected by a permission check, a search condition, a sort, or the page limit.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The principle is simple:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><strong>Do not move and materialize data before you know that the user will see it.<\/strong><\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\">Ordered execution: stop when the page is complete<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Suppose a user requests the newest 100 documents they are allowed to see. If the database can inspect candidates in the correct order, apply filters in batches, and maintain a compact candidate domain, it may stop after finding 100 valid records.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is different from fully processing every document and sorting the complete result. The database still has to preserve correct SQL semantics, but it can avoid unnecessary work when the query shape allows an ordered top-k strategy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This type of execution is especially relevant for search and filtered lists where the user sees a small page but the underlying candidate domain is large.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Parallel execution: use the available CPU cores<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Large scans, joins, and aggregations often contain independent work. A modern execution engine can divide that work across shards or batches and combine partial results afterward.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MemCP is designed around parallel, batch-oriented execution for multicore CPUs. This is relevant not only for analytical queries, but also for operational pages that scan and filter large amounts of current data&nbsp;<a href=\"https:\/\/github.com\/launix-de\/memcp\">1<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Parallelism alone does not fix a poor access path. Running unnecessary work on more CPU cores is still unnecessary work. The strongest results usually come from combining a smaller candidate domain, compressed data movement, late materialization, and parallel execution.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What this means for application developers<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">If an application is slow, the first step should still be measurement. Check the actual query plan, row counts, rejected candidates, sort operations, I\/O, locking, and application-side time. A missing conventional index may be the complete solution.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But when a query combines several conditions and becomes slower as the data grows, ask broader questions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Is the database examining far more candidates than the page returns?<\/li>\n\n\n\n<li>Are permission checks applied only after a large number of rows have been fetched?<\/li>\n\n\n\n<li>Is a large intermediate result being sorted before\u00a0<code>LIMIT<\/code>\u00a0can help?<\/li>\n\n\n\n<li>Are wide records being materialized even though only a few columns are displayed?<\/li>\n\n\n\n<li>Do several different filter and ordering combinations need to be supported?<\/li>\n\n\n\n<li>Would a separate search or analytical copy create more operational complexity than it removes?<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These questions identify cases where physical execution design matters more than adding another single-column index.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How to evaluate a different database honestly<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A benchmark should use the query that users actually wait for, not a synthetic&nbsp;<code>SELECT 1<\/code>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A useful evaluation should preserve:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the same logical query and result requirements;<\/li>\n\n\n\n<li>representative data volume and data distribution;<\/li>\n\n\n\n<li>the same filters, permissions, sorting, and pagination;<\/li>\n\n\n\n<li>the same concurrency assumptions;<\/li>\n\n\n\n<li>comparable hardware and storage;<\/li>\n\n\n\n<li>warm and cold cache conditions;<\/li>\n\n\n\n<li>the required durability mode; and<\/li>\n\n\n\n<li>result validation, not only elapsed time.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">MemCP provides MySQL protocol compatibility and SQL-over-HTTP interfaces, but it is a beta project and does not claim complete equivalence with every MySQL feature. Application-critical queries, transactions, metadata calls, backups, recovery, and connector behavior should be tested before a production migration&nbsp;<a href=\"https:\/\/memcp.org\/wiki\/Performance_Measurement\">1<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The most practical migration is often partial. Search, filtered lists, reports, dashboards, and analytical reads can be evaluated first while the existing database remains the system of record. If the result is correct, the durability requirements are satisfied, and the complete application workflow improves, the evaluation can be expanded gradually.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Slow SQL is not always caused by one missing index. As data grows, the database may spend most of its time examining candidates that will never be returned, fetching columns that are not needed, sorting intermediate results, or repeating permission and join work in an inefficient order.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Modern query execution addresses the amount of unnecessary work:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Query planning<\/strong>\u00a0chooses a physical route instead of blindly scanning tables.<\/li>\n\n\n\n<li><strong>Automatic indexing<\/strong>\u00a0reduces the need to predict every future access pattern manually.<\/li>\n\n\n\n<li><strong>RecSets<\/strong>\u00a0carry compact candidate membership between filters and joins.<\/li>\n\n\n\n<li><strong>Compressed columns<\/strong>\u00a0reduce the amount of data that has to be read and moved.<\/li>\n\n\n\n<li><strong>Late materialization<\/strong>\u00a0delays complete row construction until the final candidates are known.<\/li>\n\n\n\n<li><strong>Ordered execution<\/strong>\u00a0can stop when enough valid results have been found.<\/li>\n\n\n\n<li><strong>Parallel processing<\/strong>\u00a0uses multiple CPU cores for independent batches and shards.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The goal is not to claim that every query will run in milliseconds. The goal is to make the database do less unnecessary work before returning the rows the application actually needs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If a real application is slow because it combines large tables with search, filtering, permissions, sorting, pagination, counts, or analytics, the correct next step is not another generic benchmark. Test the real query, on representative data, with result validation and the required durability guarantees.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Your database should help organize the work around the queries your application actually runs\u2014not require you to predict every future access pattern by hand.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Further reading<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/memcp.org\/wiki\/Main_Page\">MemCP Database: Main Page<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/memcp.org\/wiki\/MySQL_is_too_slow\">MySQL is too slow? Evaluate MemCP Database<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/memcp.org\/wiki\/Performance_Measurement\">Performance Measurement<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/github.com\/launix-de\/memcp\">MemCP on GitHub<\/a><\/li>\n\n\n\n<li><\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>We all know the problem: Our user base grows and the database queries become slower and slower. This article intends to help you with practical tipps as well as a universal solution that helps you to develop applications faster without them becoming slower over time. TLDR &#8211; How to get it faster? To get your&#8230;<\/p>","protected":false},"author":2,"featured_media":8222,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-8220","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-allgemein","single-item"],"featured_image_urls_v2":{"full":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero.png",2560,1440,false],"thumbnail":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-150x150.png",150,150,true],"medium":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-300x169.png",300,169,true],"medium_large":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-768x432.png",751,422,true],"large":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-1024x576.png",751,422,true],"1536x1536":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-1536x864.png",1536,864,true],"2048x2048":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-2048x1152.png",2048,1152,true],"trp-custom-language-flag":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-18x10.png",18,10,true],"xs-thumb":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-64x64.png",64,64,true],"appku-shop-single":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-620x500.png",620,500,true]},"post_excerpt_stackable_v2":"<p>We all know the problem: Our user base grows and the database queries become slower and slower. This article intends to help you with practical tipps as well as a universal solution that helps you to develop applications faster without them becoming slower over time. TLDR &#8211; How to get it faster? To get your SQL queries faster, just follow these steps: Download MemCP from Github and compile it (maybe even with JIT support) Start the MemCP service Import your databases into MemCP Change MySQL protocol hostname to 127.0.0.1 and port to 3307 instead of 3306 Have fun That&#8217;s the&hellip;<\/p>\n","category_list_v2":"<a href=\"https:\/\/launix.de\/launix\/en\/category\/allgemein\/\" rel=\"category tag\">Allgemein<\/a>","author_info_v2":{"name":"Carl-Philip H\u00e4nsch","url":"https:\/\/launix.de\/launix\/en\/author\/carli\/"},"comments_num_v2":"0 comments","uagb_featured_image_src":{"full":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero.png",2560,1440,false],"thumbnail":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-150x150.png",150,150,true],"medium":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-300x169.png",300,169,true],"medium_large":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-768x432.png",751,422,true],"large":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-1024x576.png",751,422,true],"1536x1536":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-1536x864.png",1536,864,true],"2048x2048":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-2048x1152.png",2048,1152,true],"trp-custom-language-flag":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-18x10.png",18,10,true],"xs-thumb":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-64x64.png",64,64,true],"appku-shop-single":["https:\/\/launix.de\/launix\/wp-content\/uploads\/2026\/09\/sql-slow-queries-hero-620x500.png",620,500,true]},"uagb_author_info":{"display_name":"Carl-Philip H\u00e4nsch","author_link":"https:\/\/launix.de\/launix\/en\/author\/carli\/"},"uagb_comment_info":0,"uagb_excerpt":"We all know the problem: Our user base grows and the database queries become slower and slower. This article intends to help you with practical tipps as well as a universal solution that helps you to develop applications faster without them becoming slower over time. TLDR &#8211; How to get it faster? To get your...","_links":{"self":[{"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/posts\/8220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/comments?post=8220"}],"version-history":[{"count":4,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/posts\/8220\/revisions"}],"predecessor-version":[{"id":8226,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/posts\/8220\/revisions\/8226"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/media\/8222"}],"wp:attachment":[{"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/media?parent=8220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/categories?post=8220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/launix.de\/launix\/en\/wp-json\/wp\/v2\/tags?post=8220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}