High mysqld CPU is a symptom, not a diagnosis. The safest fix is to identify the statement or workload consuming the processor, capture its evidence, and then make one reversible query, index, or application change. Do not begin by changing global buffers or restarting the server.
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Start with the live process list, use the slow query log to find recurring work, inspect the exact statement with EXPLAIN, and use Performance Schema when the expensive activity is intermittent. Validate CPU, latency, execution rate, rows examined, lock waits, and the execution plan after each change.
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What high mysqld CPU usually means
The mysqld process can consume a core or several cores while it scans rows, joins tables, sorts results, evaluates expressions, processes a large report, repeatedly executes a moderately expensive query, or spends CPU on an unexpected application workload. The percentage shown by Linux depends on the number of logical CPUs and the monitoring tool, so there is no universal “bad” percentage or duration. Hardware, MySQL version, schema, indexes, and traffic pattern determine what is abnormal for your server.
CPU usage alone does not identify the cause. A full table scan may be correct when a table is small or a query returns most of its rows. Conversely, a query that takes only a fraction of a second can create a sustained incident if an application executes it thousands of times per second. Treat the CPU graph as an alarm that starts an attribution process.
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First response: capture the incident without restarting MySQL
Collect evidence while the problem is occurring. A restart destroys the active statement, connection context, and useful timing information, and it does not correct the query or workload that caused the spike.
1. Confirm which process and threads are busy
At the Linux layer, record the start time, whether the load is sustained or periodic, and which mysqld process or threads are using CPU. Capture a short sample rather than relying on one instantaneous reading. If your operating-system tooling can display per-thread CPU, note the thread IDs and correlate them with MySQL connections when possible.
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2. Capture the MySQL process list
Run the full process list from an administrative session:
SHOW FULL PROCESSLIST;FULL is important because it shows the complete statement text rather than a truncated version. The list exposes each server thread’s connection ID, user, host, database, command, elapsed time, state, and current statement. Seeing other users’ threads requires the MySQL PROCESS privilege.
MySQL’s own debugging guidance recommends the equivalent administrative command when a server “starts to eat up CPU or memory” or appears to hang:
mysqladmin processlist statusFor a queryable view, use the Performance Schema process-list table when it is available and populated:
SELECT *
FROM performance_schema.processlist
ORDER BY TIME DESC;Save the output with a timestamp and repeat it after a short interval. A statement that remains active, accumulates elapsed time, or appears in many connections is a stronger lead than an old connection that is idle.
3. Interpret states before taking action
Look for statements in active execution states, repeated scans, long-running joins or sorts, and clients that are issuing the same work over and over. A thread in Sleep is idle; its age alone is not evidence that it is consuming CPU. A thread waiting for a lock may have a long elapsed time but little CPU, while the transaction holding the lock may be doing the expensive work.
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Do not kill sessions merely because they are old or have a large elapsed time. First identify the statement, its owner, whether it is part of a report or maintenance task, and whether it is blocking more important work. If one connection is clearly harmful—such as an accidental unbounded report—record its connection ID, notify the owner when practical, and terminate only that session:
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KILL <connection_id>;Keep the captured process-list row and the reason for termination for post-incident review. Killing a query is an emergency containment step, not a performance fix.
Find recurring expensive statements with the slow query log
The process list shows what is running now. The slow query log shows statements that repeatedly cross a chosen execution-time threshold, including work that finished before you inspected the server. Set a threshold appropriate to your normal workload; a value useful for an interactive application may be too noisy for a data warehouse.
Enable targeted logging
Check the current settings first:
SHOW VARIABLES LIKE 'slow_query%';
SHOW VARIABLES LIKE 'long_query_time';
SHOW VARIABLES LIKE 'log_queries_not_using_indexes';On servers where dynamic changes are permitted, enable the log and choose a measured threshold:
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 1;The value 1 is an example of one second, not a universal recommendation. Choose a threshold that captures problematic work without filling the disk. Configuration methods and persistence differ by MySQL edition and deployment, so verify the active configuration after a restart or configuration-management run.
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SET GLOBAL log_queries_not_using_indexes = 'ON';Use this option temporarily or with a retention plan. It can produce a large volume of entries on workloads where scans are legitimate. “No index” is a lead for investigation, not proof of a defect.
Summarize the log with mysqldumpslow
Raw slow-log files contain many individual executions. mysqldumpslow groups similar statements so you can rank recurring patterns:
mysqldumpslow -s t -t 20 /path/to/slow-query.logThe -s t sort focuses on query time and -t 20 limits the output to the top 20 groups. Other useful views include total time, average time, lock time, and rows examined; select the sort that matches the symptom. Compare each candidate’s execution time, rows examined, call frequency, and business necessity. A frequently called query with modest latency can consume more CPU in aggregate than a single dramatic report.
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Use EXPLAIN to connect a statement to its access path
Take the exact normalized statement from the process list or slow log, substitute representative parameter values, and inspect its plan:
EXPLAIN SELECT ...;Read the plan as a sequence of access decisions rather than as a single “good” or “bad” label.
- Access type: Identify whether the table is reached through a selective index lookup, a range, an index scan, or a full table scan.
- Possible and chosen keys: Check which indexes the optimizer considered and which one it selected. An index being listed as possible does not mean it was used.
- Estimated rows: Compare the estimated row count with the number of rows the query logically needs. Large estimates can explain CPU-heavy joins and filters.
- Join order: Confirm that selective tables are not being joined after a much larger intermediate result has been created.
- Filtering: Look for predicates that eliminate most rows only after they have already been read.
- Temporary tables and filesorts: These can add CPU and memory work for grouping, ordering, or joining.
If the server supports an execution-analysis form of EXPLAIN, use it only when its runtime behavior and overhead are acceptable for your version and environment. Otherwise, compare the estimated plan with slow-log timings and row counts.
Common query and schema fixes
Build a composite index around the predicate
For a query with selective equality predicates followed by a range or ordering condition, a composite index often works best when its column order matches that access pattern. For example, a workload filtering by tenant_id and status, then limiting by a time range, may benefit from an index beginning with those equality columns and continuing with the time column. Confirm the design with the actual query and data distribution rather than copying a generic index recipe.
Before adding an index, check existing definitions and duplicate prefixes:
SHOW INDEX FROM your_table;An index consumes storage, increases write and update work, and can make optimizer choices more complex. Test that it improves the target statement and does not impose unacceptable write amplification.
Keep indexed columns searchable
Functions, expressions, and implicit type conversions applied to an indexed column can prevent an efficient lookup. Prefer comparing a column to a value in the column’s native type. Move transformations to the parameter side where semantics allow, and verify the resulting plan with EXPLAIN.
Reduce rows before joins and sorts
Apply selective filters as early as the query’s semantics permit. Return only required columns, limit result sets for interactive screens, and avoid sorting or grouping a large intermediate result when the application needs only a small page. These changes reduce the rows that later joins, aggregations, and sorts must process.
Remove N+1 request patterns
An application that fetches one parent row and then issues a separate query for every child can create high CPU even when each query looks harmless. Replace the loop with a set-based query, a carefully bounded batch, or a prefetch strategy. Verify that the replacement does not return an unexpectedly large result.
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Use Performance Schema for intermittent CPU spikes
When the expensive statement finishes before a process-list snapshot or does not consistently exceed the slow-log threshold, Performance Schema can attribute work during a spike. Its statement and thread instrumentation records low-level execution activity without changing query execution plans, and it is separate from binary logging.
Use it to compare a busy interval with a quiet interval. The exact enabled instruments and consumers vary by MySQL version and configuration, so inspect what is active before interpreting an empty result:
SELECT *
FROM performance_schema.setup_instruments
WHERE NAME LIKE 'statement/%';
SELECT *
FROM performance_schema.setup_consumers
WHERE NAME LIKE '%events%';Current thread activity can be viewed through the process-list table:
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FROM performance_schema.processlist
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Choose between a query rewrite and an index change
Both approaches can reduce CPU, but they carry different risks. Make the choice against the measured statement and its plan.
| Criterion | Query rewrite | Index change |
|---|---|---|
| Read latency | Can reduce rows, joins, or repeated calls without adding storage. | Can make selective lookups much faster when the access path is the bottleneck. |
| Write amplification | Usually unchanged, unless the rewrite changes write behavior. | Every insert, update, and delete may maintain the new index. |
| Storage footprint | Usually unchanged. | Requires additional index pages and buffer-pool space. |
| Plan stability | Depends on the new SQL shape and data distribution. | Can introduce another optimizer choice and may change as statistics change. |
| Deployment risk | Requires application or migration testing and coordinated rollout. | Requires an index operation and validation of write impact. |
| Rollback ease | Revert the application or query definition. | Drop the added index after confirming no other workload depends on it. |
Do not apply both changes at once unless an incident demands it. A single change preserves causal evidence and makes rollback straightforward.
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Most attribution and query fixes can be performed while the server remains online. A practical sequence is:
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- Contain an accidental source: Stop or throttle the report, job, polling loop, or client that is demonstrably generating the load.
- Preserve evidence: Save the process-list output, original SQL, slow-log sample,
EXPLAINoutput, relevant configuration values, and existing index definitions. - Make one change: Rewrite the worst statement, add one justified index, correct a connection pattern, or adjust a narrowly scoped logging setting.
- Observe representative traffic: Allow enough normal workload to exercise the statement instead of judging from a single test execution.
- Record the result: Note CPU utilization, query latency, executions per second, rows examined, lock waits, slow-log volume, and the before/after plan.
- Roll back if needed: Restore the original query or configuration, or remove the new index, if latency, write load, storage use, or plan stability worsens.
Dynamic settings such as slow-log controls do not necessarily persist across restarts. If a setting is part of the long-term fix, update the managed configuration only after validating it and documenting the intended scope.
Validation checklist
- CPU is lower under comparable traffic, not merely during a quiet period.
- The target statement’s latency and execution rate have improved.
- Rows examined and temporary work have fallen where the fix was intended to help.
- Lock waits, queueing, and application response time have not regressed.
- The new
EXPLAINplan uses the intended access path and remains sensible for representative parameter values. - Write latency, storage consumption, and maintenance cost are acceptable after an index change.
- Logging and instrumentation are bounded so they do not create a second incident.
Pitfalls that make CPU incidents worse
Changing global settings before finding the workload
Increasing buffers or changing thread-related settings may move the bottleneck without reducing the work. Configuration changes also make it harder to tell which action helped. Attribution should come first.
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Assuming every scan is a missing index
A scan can be the cheapest plan for a small table or a query that returns most rows. Judge it with execution time, rows examined, selectivity, and call frequency.
Using only one process-list snapshot
Short queries can disappear between snapshots. Combine repeated snapshots with slow-log history, Performance Schema summaries, and application metrics.
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Logging every non-indexed query or every instrumented event can consume CPU and disk. Set a retention period, estimate growth, and turn off diagnostic options that are no longer needed.
Killing idle sessions indiscriminately
Sleeping connections are not automatically harmful. Connection-pool sizing, transaction state, and application behavior matter more than age alone.
When the evidence remains inconclusive
If CPU is still high after the first change, do not stack unrelated indexes and configuration edits. Return to attribution: capture another process-list sample, compare active and quiet Performance Schema intervals, inspect slow-log frequency, and correlate the database timeline with application deployments or scheduled jobs.
If no single statement dominates, the workload may be many small calls, parallel reporting, inefficient connection behavior, or CPU spent outside statement execution. In that case, compare executions per second and aggregate statement time rather than searching only for one long query. Keep the incident record so recurring patterns can be recognized during the next spike.
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Frequently asked questions
Is there a fixed CPU percentage at which mysqld is unhealthy?
No. The consulted MySQL guidance does not publish a universal CPU threshold or benchmark for this situation. Interpret utilization alongside latency, throughput, lock waits, and the server’s normal baseline.
Can I fix high mysqld CPU without restarting the server?
Often yes. Capturing process lists, enabling diagnostic logging where supported, terminating one clearly harmful session, rewriting a query, and adding a tested index can all be performed online. Persistence and locking behavior must still be checked for your MySQL version and workload.
Which tool should I use first: the slow log or Performance Schema?
Use the process list first for an active incident. The slow log is better for recurring statements and historical review; Performance Schema is useful for intermittent activity and aggregate attribution. They answer different time-window questions and are most effective together.
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