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Use SHOW FULL PROCESSLIST or query information_schema.PROCESSLIST to identify long-running queries by examining the Time and Info columns. 2. Filter active queries with conditions like COMMAND != 'Sleep' and TIME > threshold to isolate problematic ones. 3. Kill the identified query using KILL [connection_id] to terminate the thread or KILL QUERY [connection_id] to cancel only the current query. 4. Automate detection with scripts and cron jobs to proactively manage long-running queries. 5. Optionally, use Performance Schema for deeper insights into query execution and timing. The key is to monitor, identify, and terminate long-running queries promptly to maintain database performance and responsiveness.
Long-running queries in MySQL can slow down your database, consume resources, and even block other operations. Finding and killing them is a common task for DBAs and developers managing production systems. Here’s how to identify and terminate long-running queries effectively.
1. Find Long-Running Queries Using SHOW PROCESSLIST
The easiest way to see active queries is by using the SHOW PROCESSLIST command:
SHOW FULL PROCESSLIST;
This displays:
- Id: The connection/thread ID (important for killing)
- User: Who ran the query
- Host: Where the connection came from
- db: Database in use
- Command: Type of command (e.g., Query, Sleep)
- Time: How long the query has been running (in seconds)
- State: Current state (e.g., Sending data, Locked)
- Info: The actual SQL query (only visible if
FULLis used)
Look for rows where Time is high and Info shows a running query.
Tip: Run this periodically or use monitoring tools to catch long queries early.
2. Use information_schema.PROCESSLIST for Advanced Filtering
You can query the information_schema.PROCESSLIST table for more control, especially when writing scripts:
SELECT * FROM information_schema.PROCESSLIST WHERE COMMAND != 'Sleep' AND TIME > 60 AND INFO IS NOT NULL;
This finds all non-sleeping queries running longer than 60 seconds that have an SQL statement.
You can also order by time to see the longest-running first:
SELECT ID, USER, HOST, DB, COMMAND, TIME, STATE, INFO FROM information_schema.PROCESSLIST WHERE TIME > 120 ORDER BY TIME DESC;
3. Kill the Offending Query
Once you identify a problematic query, note its Id (thread ID). Then use the KILL command:
KILL [connection_id];
For example:
KILL 1234;
This terminates the connection or query associated with thread ID 1234.
Note:
KILLstops the query and rolls back any uncommitted transaction. Large transactions may take time to roll back.
Optional: Use KILL QUERY 1234 to kill only the current query but keep the connection alive (if supported and needed).
4. Automate Detection with Monitoring or Scripts
For proactive management, automate detection using a script. Example bash script:
#!/bin/bash
THRESHOLD=120
mysql -e "SHOW FULL PROCESSLIST" | \
awk -v threshold=$THRESHOLD '$0 ~ /[^Sleep]/ && $6 ~ /^[0-9] $/ && $6 > threshold {print $1}' | \
while read -r pid; do
echo "Killing long-running query: $pid"
mysql -e "KILL $pid"
doneSchedule this via cron to run every few minutes if needed.
Warning: Be cautious with automation—killing the wrong query can affect applications.
Bonus: Use Performance Schema for Deeper Insights
For more detailed analysis, enable and use the Performance Schema:
-- Check if enabled
SELECT * FROM performance_schema.setup_instruments WHERE NAME = 'statement/sql/select';
-- Query current long-running statements
SELECT
thread_id,
sql_text,
timer_wait / 1000000000000 AS duration_sec
FROM performance_schema.events_statements_current
JOIN performance_schema.threads USING(thread_id)
WHERE TYPE = 'FOREGROUND'
ORDER BY duration_sec DESC;This gives more granular timing and context, especially useful in complex environments.
Basically, the key steps are: monitor with PROCESSLIST, filter by execution time, identify harmful queries, and kill them using the thread ID. It’s simple but powerful—and essential for keeping MySQL responsive under load.
The above is the detailed content of How to Find and Kill Long-Running Queries in MySQL?. For more information, please follow other related articles on the PHP Chinese website!
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