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ProxySQL can answer a repeated MySQL read from its own query cache, avoiding a backend connection and SQL execution on a cache hit. The safe way to use it is to cache only shared, repeatable reads, give each query class a deliberate millisecond TTL, and verify the result with cache and query-digest counters. ProxySQL caching is not a general-purpose invalidation system: a changed row does not remove an old result, so stale data can remain until the hard TTL expires.
What ProxySQL query caching actually does
ProxySQL sits between clients and MySQL. Query rules are evaluated before a statement is sent to a backend server. When a rule enables caching and a matching result is still valid, ProxySQL returns the stored result set directly. The request does not need to acquire a backend connection or execute the SQL on MySQL.
The cache stores complete result sets, not individual rows or application objects. A cache key is therefore tied to the query shape and the request context ProxySQL uses when matching it. Two statements that produce different text, parameters, or relevant context may not share an entry even if they are logically similar. Conversely, a broad rule can make unrelated reads share a policy they should not share. Query-rule scope is the main safety control.
cache_ttl is the hard freshness boundary for a rule and is measured in milliseconds. When that period ends, the entry is no longer served as a valid cached result and a backend request is needed to repopulate it. ProxySQL documents no invalidation mechanism beyond TTL expiration. A write to MySQL therefore does not automatically purge a result that was cached earlier.
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Find the right queries before changing configuration
Start with workload evidence rather than with a blanket rule such as “cache every SELECT.” Inspect ProxySQL query-digest data and identify statements that are both expensive and repeated. A useful prioritization is execution cost multiplied by repeat frequency: a moderately expensive query requested thousands of times can be a better target than a very expensive report requested once a day.
For every candidate, record the query shape, whether the result is shared by many users, how quickly the underlying data changes, whether an empty result is meaningful, and whether the client uses a prepared statement. This inventory prevents a high hit rate from masking incorrect results.
- Read-only: the statement must not modify data or depend on side effects.
- Shared result: the same result can safely be returned to different callers.
- Repeatable: the query occurs often enough that a cache entry will be reused.
- Bounded staleness: the owner of the data accepts the selected TTL as the maximum age of a served result.
- Protocol compatibility: the request path must not rely on a prepared statement, which ProxySQL documents as incompatible with query caching.
Choose data that is safe to serve slightly stale
The strongest candidates are shared reads whose value changes infrequently or whose consumers can tolerate a short delay. Reference tables, feature or configuration data, country and currency lists, and other lookup results often fit. Expensive aggregates and dashboard summaries can also benefit when a boundedly stale number is preferable to repeatedly consuming backend CPU.
Exclude statements whose result depends on a user, session, temporary state, or nondeterministic evaluation unless the deployment has specifically verified the semantics. Examples include per-user account pages, authorization-sensitive queries, statements influenced by session variables, temporary tables, and reads that use current time or another changing value. A result that is safe for one identity may be wrong or confidential when returned to another.
| Query class | Typical policy | Why |
|---|---|---|
| Reference and configuration reads | Long TTL, narrow rule | Values are shared and change relatively rarely. |
| Expensive shared aggregates | Medium TTL, with an agreed freshness limit | Repeated computation can consume substantial backend CPU. |
| Frequently changing shared lists | Short TTL or no cache | A long window would expose noticeable staleness. |
| User-specific or authorization-sensitive reads | Exclude | A cached result may cross identity or policy boundaries. |
| Session-, temporary-table-, or nondeterministic reads | Exclude unless semantics are proven | The result depends on context not represented safely by a shared entry. |
| Writes and statements with side effects | Exclude | Query caching is for repeatable reads, not mutations. |
Set TTL from the freshness requirement
TTL should be chosen by the maximum age a consumer can accept, not by an arbitrary performance target. Treat it as a contract: if a five-second TTL is selected, a caller may receive a result that is nearly five seconds old even when the source row changed immediately after the cache was filled.
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- Long-TTL class: use for stable reference or configuration data after confirming how changes are deployed and communicated.
- Medium-TTL class: use for shared aggregates where a few minutes of staleness is acceptable and backend work is significant.
- Short-TTL class: use for data that changes regularly but still benefits from coalescing bursts of identical reads.
- Excluded class: use when any stale result is unacceptable, when the result is private, or when context makes a shared entry unsafe.
Because TTL is the only documented invalidation path, coordinate it with operational changes. If an administrator changes configuration data and the application must see the change immediately, waiting for the existing hard TTL may violate that requirement. In that situation, shorten the policy, avoid caching the query, or use an application-level design with explicit invalidation rather than assuming a MySQL write will clear ProxySQL’s entry.
Write a narrow query rule
ProxySQL’s mysql_query_rules table supports matching with match_digest or match_pattern. Prefer the smallest matcher that identifies the intended query class. A digest-based rule is useful when normalized query shapes are stable; a tightly scoped pattern can be appropriate when you need to distinguish a particular form. Avoid matching every SELECT in production unless you have proved that every selected result is safe to share and tolerate the same TTL.
The official documentation shows this basic configuration:
INSERT INTO mysql_query_rules
(rule_id, active, match_digest, cache_ttl, cache_empty_result, apply)
VALUES
(1, 1, '^SELECT', 5000, 1, 1);
LOAD MYSQL QUERY RULES TO RUNTIME;
SAVE MYSQL QUERY RULES TO DISK;This rule gives matching statements a 5,000-millisecond TTL and allows empty result sets to be cached. It is a demonstration, not a production matcher. Replace ^SELECT with a digest or pattern that identifies one approved query class, and give each class the TTL that its data owner accepts.
Decide whether empty results belong in the cache
cache_empty_result controls whether a query returning no rows can create a cache entry. Caching an empty result can protect the backend from repeated misses when a key is genuinely absent. It can also prolong the appearance of “not found” after a row is inserted. Make the choice deliberately, and check the related global behavior controlled by mysql-query_cache_stores_empty_result. The rule-level setting and global policy must agree with the application’s expectations.
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Account for rule ordering
When a broad inclusion rule and a safety exclusion could both match, put the exclusion ahead of the inclusion according to the rule-ordering behavior of your deployment. This is especially important during a gradual rollout: an explicitly excluded query should not fall through to a later catch-all cache rule.
Load and persist changes
Editing mysql_query_rules does not by itself make the new policy active everywhere. Load the rules to runtime, test the target query, and save the working configuration to disk so a restart does not discard it. Roll out one query class at a time rather than changing every read simultaneously; that makes hit-rate, latency, and correctness changes attributable.
Use soft TTL when refresh should happen before expiry
mysql-query_cache_soft_ttl_pct provides an optional refresh window. It accepts a percentage from 0 to 100, and the documented default is 0 percent. With a nonzero value, an entry can reach the soft threshold while it is still inside its hard cache_ttl. The next requester can trigger a backend refresh, while concurrent requests continue receiving the still-valid cached result. The hard TTL remains the final expiry boundary.
Soft TTL is useful when you want to avoid a synchronized burst of misses at hard expiry. It does not create immediate invalidation and does not make stale data impossible. Choose the percentage only after deciding how much of the TTL may be used for refresh, and monitor backend load during that window. A refresh that is too aggressive can erase the CPU savings you expected from caching.
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Size and monitor the cache
Set mysql-query_cache_size_MB with the proxy’s available memory in mind. The documented limit is soft: a background purging thread manages pressure rather than enforcing a strict hard cap at the instant an entry is added. ProxySQL also documents that there is no LRU eviction mechanism when limits are reached. This makes entry selection and TTL discipline important; a large collection of low-value entries can consume memory without producing useful hits.
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Inspect the stats_mysql_query_cache counters and fields together with query-digest data. The following values show whether the cache is doing useful work:
| Metric | What it tells you | How to use it |
|---|---|---|
Query_Cache_count_GET |
Number of cache lookup attempts | Confirms that requests are reaching cache evaluation. |
Query_Cache_count_GET_OK |
Successful cache lookups | Compare with GET to calculate an observed hit ratio. |
Query_Cache_count_SET |
Entries written after backend execution | Shows how often misses populate the cache. |
Query_Cache_Memory_bytes |
Memory currently used by cached results | Relate memory growth to the value of the hits. |
Query_Cache_Purged |
Entries removed by purging activity | Investigate churn, pressure, and short-lived entries. |
Query_Cache_Entries |
Number of stored entries | Compare entry count with memory and query classes. |
You can inspect the cache statistics from the ProxySQL administration interface, for example with:
SELECT * FROM stats_mysql_query_cache;Do not judge success from Query_Cache_count_GET_OK alone. Compare backend latency, CPU consumption, and query volume before and after enabling each rule. A high hit ratio on a trivial query may save little, while a smaller number of hits on an expensive aggregate can materially reduce backend work. Also check that response correctness and freshness remain within the agreed limits.
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Lookups are zero
- Confirm the rule is active and was loaded to runtime after editing.
- Check that the query actually matches
match_digestormatch_pattern; whitespace, comments, and normalization can produce a different shape than expected. - Verify that another rule is not excluding the statement or stopping evaluation before the cache rule.
- Confirm that the request path is not using a prepared statement, which the query cache does not support.
- Run the same eligible read more than once. The first request normally populates the entry; later identical requests are the ones that can hit.
Lookups occur but successful hits stay low
Low hit rates usually mean the result is being requested with many distinct shapes or parameters, the TTL is too short for reuse, or entries are being purged before another request arrives. Use query-digest data to see whether the supposedly repeated statement is actually fragmented into many forms. Tighten the matcher only after confirming that the forms are semantically interchangeable, and remove cache rules that create memory usage without reuse.
Results are stale
Staleness is expected within the selected TTL because writes do not invalidate entries. Reduce the hard TTL, exclude the query, or redesign the read path if the freshness requirement has changed. Do not attempt to solve a correctness problem by merely increasing memory or soft-TTL refresh; neither provides write-triggered invalidation.
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Memory rises or purging is frequent
Review Query_Cache_Memory_bytes, Query_Cache_Entries, and Query_Cache_Purged together. Broad rules, large result sets, and long TTLs can create many low-value entries. Narrow the matcher, shorten TTLs for volatile classes, exclude large or private responses, and set mysql-query_cache_size_MB in line with the proxy’s available memory. Because the limit is soft and eviction is not LRU-based, prevention is more predictable than relying on emergency purging.
Empty-result behavior is surprising
Check both the rule’s cache_empty_result value and the global mysql-query_cache_stores_empty_result setting. If a missing row may be created shortly afterward, caching an empty result can extend the period during which callers see “not found.” If repeated misses are expensive and absence is stable, caching the empty result may be worthwhile.
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ProxySQL documents query caching as incompatible with prepared statements. If an application depends on prepared statements for a candidate query, do not assume that adding a cache rule will produce hits. Either leave that statement uncached or verify a different, supported request path before changing application behavior.
Query annotations can request a per-query cache_ttl. An annotation is evaluated before query rules, and a matching rule may supersede it. Treat annotations as an additional control, not as proof that a statement will bypass all policy. Test the effective behavior through the cache counters and the actual response path.
Compare ProxySQL caching with other cache layers
ProxySQL is strongest when many callers issue the same safe read and the desired unit is the complete SQL result set. It requires no application-side object serialization and can prevent backend connection and execution work on a hit. Its trade-off is that freshness is time-based and invalidation is limited to TTL expiration.
An application cache or a dedicated key-value cache may be a better architectural fit when the application needs explicit invalidation after a write, object-level keys, user-aware authorization, or a data model different from SQL result sets. That choice is workload-specific; compare the alternatives on the following axes before implementation:
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| Decision axis | ProxySQL query cache | What to verify in an alternative |
|---|---|---|
| Freshness | Hard TTL; no documented write invalidation | Whether changes can invalidate exactly the affected object or key |
| Hit potential | Highest for identical shared query results | Whether requests can share a normalized object or key |
| Backend work saved | Can avoid backend connection acquisition and SQL execution on a hit | Whether the layer prevents the same database work |
| Memory footprint | Complete result sets stored at the proxy; soft memory limit | Serialization size, eviction policy, and ownership of memory |
| Prepared statements | Documented incompatibility | Compatibility with the application’s protocol |
| Operational complexity | Query rules, TTL classes, counters, and purge behavior | Invalidation, deployment, monitoring, and failure handling |
A safe rollout plan
- Baseline the workload. Capture query-digest frequency, execution latency, backend CPU, and query volume for the candidate statement before caching.
- Define the freshness contract. Write down the maximum acceptable age and whether an empty result may be cached.
- Classify the query. Confirm that it is read-only, shared, deterministic enough for a shared result, and not prepared-statement based.
- Create the narrowest rule. Use
match_digestor a tightly scopedmatch_pattern; setcache_ttl,cache_empty_result, andapplyexplicitly. - Protect exclusions. Place any exclusion ahead of a broader inclusion rule where ordering matters.
- Load and save. Run
LOAD MYSQL QUERY RULES TO RUNTIME;, test the request, then runSAVE MYSQL QUERY RULES TO DISK;after validation. - Observe one class. Watch GET, GET_OK, SET, memory, entries, purges, backend latency, CPU, and query volume before adding another rule.
- Adjust or revert. Shorten TTL, narrow the matcher, disable empty-result caching, or remove the rule if correctness, memory, or backend metrics do not improve.
Practical decision checklist
- Can the same result safely be returned to every caller who matches this query?
- What is the maximum acceptable age of that result?
- Will a write need to be visible before the TTL expires?
- Does the statement depend on identity, session variables, temporary state, or nondeterministic values?
- Is the client using a prepared statement?
- Should an empty result be cached?
- Is the matcher narrow enough to avoid unrelated SELECT statements?
- Do cache hits save meaningful backend CPU or latency compared with the memory consumed?
- Are cache statistics and query-digest data included in ongoing monitoring?
Final guidance
Use ProxySQL query caching as a selective acceleration layer, not as a universal MySQL cache. Profile first, cache shared reads with a clear freshness contract, keep matchers narrow, and treat the hard TTL as the only guaranteed invalidation boundary. A rollout is successful only when cache counters show real reuse and backend work falls without exposing stale, private, or context-dependent results.
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