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Start by measuring the whole VPS, not just the mysqld resident-set size. MySQL deliberately keeps data in global caches, allocates buffers per connection and statement, and can retain Performance Schema memory while the server runs. High RAM use is a problem when it leaves the operating system or neighboring services without headroom, causes swap activity, triggers the OOM killer, or produces errors and latency—not simply because the mysqld number looks large.
Use the sequence below: establish host pressure, capture MySQL configuration and concurrency, attribute memory, then make one controlled change at a time. On a small VPS, the practical fix is often a smaller InnoDB buffer pool combined with a realistic connection limit and tighter per-session or temporary-table buffers. If those settings still leave no safe headroom, the correct fix is more RAM or a separate managed database, not increasingly aggressive tuning.
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MySQL memory has several layers. InnoDB’s buffer pool caches table and index pages so frequently used data can be served from memory. The server also reserves global areas such as the InnoDB log buffer and, for MyISAM workloads, the key buffer. These allocations form much of the baseline resident set.
Other allocations scale with activity. Every established connection can consume session memory. Network and result buffers begin at net_buffer_length and can grow as high as max_allowed_packet. A session also receives max_digest_length bytes for statement-digest storage. Sort, join, read, and read-rnd buffers are additional per-session or per-operation consumers; many simultaneous sessions can multiply them.
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Internal temporary tables can consume RAM when a query needs intermediate results. Performance Schema may allocate memory at startup and while observing workload. Its instrumented memory is not freed during normal operation; it is released when the server shuts down. Therefore, a resident set that grows during a busy period is not automatically a leak. The important question is which category is growing and whether the VPS is under pressure.
1. Confirm that the VPS, not only MySQL, is short of memory
Take a baseline during normal traffic and again during the period when memory use is reported as high. Run:
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ps -o pid,ppid,%mem,rss,vsz,cmd -C mysqldfree -h shows total, available, and swap memory. The ps output shows the MySQL process resident set (RSS). RSS includes allocator overhead and memory that may not be represented by a MySQL status table, so do not expect the numbers to match exactly.
Record whether the same VPS runs Apache, PHP-FPM, Redis, backup jobs, monitoring agents, containers, or another database. A buffer pool that is reasonable on a database-only host can be unsafe when a web stack needs several gigabytes at the same time. If your provider exposes systemd or cgroup metrics, use those as well; a container or service limit can be lower than the VPS’s advertised RAM.
Check the kernel and service logs for out-of-memory events. Typical commands are:
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dmesg -T | grep -i -E 'out of memory|oom|killed process'
journalctl -k --since '24 hours ago' | grep -i -E 'out of memory|oom|killed process'
journalctl -u mysql --since '24 hours ago'Use the command that your distribution permits; some VPS kernels restrict dmesg. An OOM-killer event, active swap-in or swap-out, failed allocations, or simultaneous failures in PHP and MySQL confirms host-level pressure. If available memory remains healthy and there are no symptoms, a large RSS may simply be MySQL’s intended cache.
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2. Capture configuration and concurrency before changing anything
Save the current values so every change is reversible and so you can compare a quiet period with a peak. Run:
SHOW VARIABLES WHERE Variable_name IN (
'innodb_buffer_pool_size',
'innodb_log_buffer_size',
'max_connections',
'max_allowed_packet',
'net_buffer_length',
'sort_buffer_size',
'join_buffer_size',
'read_buffer_size',
'read_rnd_buffer_size',
'tmp_table_size',
'max_heap_table_size',
'performance_schema'
);Then capture connection demand:
SHOW GLOBAL STATUS WHERE Variable_name IN
('Threads_connected','Threads_running','Max_used_connections');| Metric | What it tells you | How to use it |
|---|---|---|
Threads_connected |
Connections currently open | Shows the present session-memory multiplier. |
Threads_running |
Connections actively executing | Useful for identifying a peak that can multiply operation buffers. |
Max_used_connections |
Highest simultaneous connection count since the last reset | Compare with max_connections and the application pool’s configured limit. |
max_connections is a capacity ceiling, not a performance target. A value far above observed demand reserves the possibility of a large memory spike. Conversely, lowering it below the application’s legitimate peak will cause connection failures, so check web-server and application pool settings before editing it.
3. Attribute the resident set with Performance Schema
When performance_schema is enabled, use its memory instruments instead of guessing. First inspect which memory instruments exist and whether they are enabled:
SELECT NAME, ENABLED, TIMED
FROM performance_schema.setup_instruments
WHERE NAME LIKE 'memory/%'
ORDER BY NAME;Enable only the instruments needed for diagnosis, following the syntax supported by your installed MySQL release. Then query global totals:
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SELECT EVENT_NAME, CURRENT_NUMBER_OF_BYTES_USED,
CURRENT_COUNT_USED, HIGH_NUMBER_OF_BYTES_USED
FROM performance_schema.memory_summary_global_by_event_name
WHERE CURRENT_NUMBER_OF_BYTES_USED > 0
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FROM performance_schema.memory_summary_by_account_by_event_name
ORDER BY CURRENT_NUMBER_OF_BYTES_USED DESC
LIMIT 25;Column names and available summary tables vary by release. If a query fails, inspect information_schema.tables for the memory-summary tables present on your server and use the corresponding columns documented for that release.
Look for code areas such as memory/innodb and memory/sql. Compare the instrumented total with the process RSS from the operating system. The difference includes allocator overhead and uninstrumented areas, so a gap is expected; a rapidly growing gap is a reason to investigate the allocator, plugins, connectors, or a possible leak rather than blindly increasing limits.
4. Check the InnoDB buffer pool before shrinking it
The buffer pool is often the largest intentional allocation. Oracle’s MySQL Reference Manual says that a typical recommended innodb_buffer_pool_size is 50 to 75 percent of system memory. That is a starting range for a host dedicated largely to MySQL, not a promise for a small VPS running a complete application stack. Reserve memory for the kernel, filesystem cache, web workers, agents, replication, and temporary peaks.
Inspect InnoDB’s view of the pool:
SHOW ENGINE INNODB STATUSGUse the buffer-pool sections and your operating-system baseline to determine whether the pool consumes most of the available RAM. A pool that is too small can cause page churn and more storage reads; a pool that is too large can force swapping and make the entire VPS unstable.
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Stage a buffer-pool reduction
- Choose a modest reduction based on the memory headroom you actually need; do not jump directly to a generic percentage.
- Confirm whether your installed MySQL version supports dynamic changes to
innodb_buffer_pool_sizeand what granularity or restart behavior it requires. Variable behavior differs by release and distribution. - Apply the change through the supported administrative method, record the old and new values, and watch available memory, swap activity, query latency, and errors.
- If the release requires a restart, schedule it, verify that the new value loaded, and recheck the application immediately afterward.
Make one buffer-pool change at a time. If latency rises because the working set no longer fits, restore part of the reduction or plan additional RAM rather than allowing the host to swap.
5. Control connection and per-session memory
Each established connection can consume memory even when it is idle, and active statements can allocate additional buffers. Calculate pressure from the observed peak, not from an average request rate. A high max_connections combined with large session buffers creates a worst-case multiplication that may never appear in a quiet test.
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Set a defensible connection ceiling
- Compare
Max_used_connectionswithmax_connectionsover a representative busy period. - Check the connection-pool limits in the application, PHP-FPM, job workers, and proxy layer.
- Lower
max_connectionsonly when the new ceiling exceeds the real peak plus a small operational margin. - Monitor rejected connections and application errors after the change; raise the ceiling or fix pooling if legitimate traffic is being refused.
Do not treat a lower ceiling as a substitute for fixing a connection leak. Idle sessions that never return to the pool still consume resources and should be closed or recycled by the application.
Keep packet and operation buffers tied to real workloads
net_buffer_length is the initial network buffer, while a session can grow its connection and result buffers up to max_allowed_packet. Set the packet limit high enough for the largest legitimate statement or row, but do not use an enormous value as a general memory fix. Verify import, replication, and application requirements before reducing it.
Review sort_buffer_size, join_buffer_size, read_buffer_size, and read_rnd_buffer_size. These are not free global reservations: concurrent statements can allocate them simultaneously. Prefer query and index improvements over large buffers. Apply session-level values for an exceptional report or maintenance job instead of raising global defaults for every connection.
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6. Limit internal temporary-table memory
When a query cannot complete an operation in its normal execution structures, MySQL may create an internal temporary table. Large in-memory temporary tables can produce short, sharp spikes that a baseline measurement misses. If memory summaries, status counters, or a workload trace points to this category, review tmp_table_size and max_heap_table_size.
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- Reduce
tmp_table_sizeandmax_heap_table_sizein a controlled step so oversized temporary results spill to disk rather than exhausting RAM. - Fix the underlying queries and indexes: reduce unnecessary grouping or sorting, select only required columns, and add indexes that match the access pattern.
- Measure storage I/O and latency after the change.
Lower limits protect the VPS but can increase disk I/O and response time because more work is performed on on-disk temporary tables. Faster storage does not remove the need to fix inefficient queries; it only reduces the cost of the spill.
7. Investigate Performance Schema growth without disabling it blindly
Performance Schema can allocate memory at startup and while it observes new workload patterns. It does not free that memory during normal operation, although a shutdown releases it. Autoscaled buffers have sizing parameters that can cap growth. Use the memory instruments and summary tables to identify the category before changing instrumentation.
If a particular instrument expands with a predictable workload, set an appropriate cap supported by your release and recheck whether the workload still has the observability it needs. Disabling all instrumentation may hide the evidence needed to find the real source of growth. A restart can be useful as containment when memory must be returned immediately, but recurring growth after restart calls for workload, configuration, plugin, or leak investigation.
8. Choose the least risky remedy
| Remedy | Memory effect | Trade-offs | Restart usually required? | Protects |
|---|---|---|---|---|
| Reduce InnoDB buffer pool | Lowers baseline RSS | Less cache capacity, more page reads, possible latency increase | Depends on MySQL release and change method | Whole VPS, when the pool is the dominant consumer |
Lower max_connections |
Reduces worst-case session multiplier | Can reject legitimate clients if set below the real peak | Often dynamic, but verify your release | Whole VPS during connection spikes |
| Tighten per-session buffers | Reduces memory per active statement | Sorts, joins, or reads may become slower; query-specific exceptions may be needed | Usually variable-dependent | MySQL during concurrent workloads |
| Lower temporary-table limits | Caps in-memory intermediate results | More disk I/O and potentially higher latency | Verify variable behavior | Whole VPS during query spikes |
| Cap Performance Schema autoscaling | Limits instrumentation growth | Less retained observability or lower diagnostic capacity | Some sizing changes require restart | MySQL’s instrumentation footprint |
| Increase VPS RAM or move the database | Adds headroom without shrinking caches | Higher recurring cost and possible migration or network changes | Migration planning may be required | Entire stack when safe tuning is insufficient |
9. Validate every change under representative load
After each change, repeat the same measurements rather than relying on a single ps snapshot. Check:
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mysqldRSS and, where applicable, service or cgroup limits;- swap-in and swap-out activity and new OOM-killer messages;
Threads_connected,Threads_running, and rejected-connection errors;- query latency, throughput, deadlocks, and application error rates;
- disk I/O and temporary-table spill behavior after changing temporary limits;
- Performance Schema memory summaries and the gap between instrumented bytes and RSS.
Test during the workload that caused the incident: batch imports, traffic bursts, reporting queries, backups, or deployment jobs can have very different memory profiles. Keep a rollback value for every variable and change only one category at a time so the effect is attributable.
When tuning is not enough
If the VPS still swaps or reaches the OOM threshold after the buffer pool, connection ceiling, session buffers, temporary tables, and instrumentation have been measured and right-sized, there may simply be insufficient capacity. Increase RAM when the application and database must share the host and the working set legitimately needs more memory. Consider a managed MySQL service or a separate database host when backups, failover, maintenance, and isolation matter as much as raw capacity.
A persistent, unexplained increase after attribution deserves a focused investigation of the workload, plugins, connectors, and MySQL release. A controlled restart can reclaim memory as short-term containment, but it does not prove that a leak is fixed. Keep the before-and-after measurements so the next decision is based on evidence rather than a single headline memory number.
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