CPU
You can run the top command to check the CPU usage of each node in openGauss and analyze whether performance bottleneck caused by heavy CPU load exists. The top command is used to monitor the Linux OS status. It is a common performance analysis tool and can display the resource usage of each process in the system in real time.
Description
- -d: number of seconds, indicating the interval for updating the page displayed by running the top command. The default value is 5 seconds.
- -b: executes the top command in batches.
- -n: This parameter is used together with -b to indicate the number of times that the top command is executed.
- -p: specifies a PID for observation.
Checking CPU Usage
You can query the CPU usage of the server in the following ways:
On each storage node, run the top command to check the CPU usage. Then, press 1 to view the usage of each CPU core.
top - 17:05:04 up 32 days, 20:34, 5 users, load average: 0.02, 0.02, 0.00
Tasks: 124 total, 1 running, 123 sleeping, 0 stopped, 0 zombie
Cpu0 : 0.0%us, 0.3%sy, 0.0%ni, 69.7%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu1 : 0.3%us, 0.3%sy, 0.0%ni, 69.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu2 : 0.3%us, 0.3%sy, 0.0%ni, 69.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu3 : 0.3%us, 0.3%sy, 0.0%ni, 69.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Mem: 8038844k total, 7165272k used, 873572k free, 530444k buffers
Swap: 4192924k total, 4920k used, 4188004k free, 4742904k cached
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
35184 omm 20 0 822m 421m 128m S 0 5.4 5:28.15 gaussdb
1 root 20 0 13592 820 784 S 0 0.0 1:16.62 init
In the command output, focus on the CPU usage occupied by each process.
us indicates the CPU percentage occupied by the user space, sy indicates the CPU percentage occupied by the kernel space, and id indicates the idle CPU percentage. If id is less than 10%, the CPU load is high. In this case, you can reduce the CPU load by reducing the number of tasks on nodes.
Analyzing Performance Parameters
Run the top-H command to check the CPU usage. The following is displayed:
14 root 20 0 0 0 0 S 0 0.0 0:16.41 events/3 top - 14:22:49 up 5 days, 21:51, 2 users, load average: 0.08, 0.08, 0.06 Tasks: 312 total, 1 running, 311 sleeping, 0 stopped, 0 zombie Cpu(s): 1.3%us, 0.7%sy, 0.0%ni, 95.0%id, 2.4%wa, 0.5%hi, 0.2%si, 0.0%st Mem: 8038844k total, 5317668k used, 2721176k free, 180268k buffers Swap: 4192924k total, 0k used, 4192924k free, 2886860k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 3105 root 20 0 50492 11m 2708 S 3 0.1 22:22.56 acc-snf 4015 gdm 20 0 232m 23m 11m S 0 0.3 11:34.70 gdm-simple-gree 51001 omm 20 0 12140 1484 948 R 0 0.0 0:00.94 top 54885 omm 20 0 615m 396m 116m S 0 5.1 0:09.44 gaussdb 1 root 20 0 13592 944 792 S 0 0.0 0:08.54 initIn the query result for Cpu(s), check whether the system CPU (sy) or user CPU (us) usage is high.
- If the system CPU usage is too high, you need to identify the abnormal system processes and handle them.
- If the CPU usage of the openGauss process whose USER is omm is too high, optimize the service-related SQL statements based on the running services queries. Based on the features of the currently running service, perform the following operations to check whether this process containing infinite loop logics.
Run the top -H -p pid command to identify the threads that use much CPU in the process.
top -H -p 54952The threads causing high CPU usage are displayed in the top column of the command output. In this section, thread 54775 is used as an example for analyzing the causes of the high CPU usage.
top - 14:23:27 up 5 days, 21:52, 2 users, load average: 0.04, 0.07, 0.05 Tasks: 13 total, 0 running, 13 sleeping, 0 stopped, 0 zombie Cpu(s): 0.9%us, 0.4%sy, 0.0%ni, 97.3%id, 1.1%wa, 0.2%hi, 0.1%si, 0.0%st Mem: 8038844k total, 5322180k used, 2716664k free, 180316k buffers Swap: 4192924k total, 0k used, 4192924k free, 2889860k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 54775 omm 20 0 684m 424m 131m S 0 5.4 0:00.32 gaussdb 54951 omm 20 0 684m 424m 131m S 0 5.4 0:00.84 gaussdb 54732 omm 20 0 684m 424m 131m S 0 5.4 0:00.24 gaussdb 54758 omm 20 0 684m 424m 131m S 0 5.4 0:00.00 gaussdb 54759 omm 20 0 684m 424m 131m S 0 5.4 0:00.02 gaussdb 54773 omm 20 0 684m 424m 131m S 0 5.4 0:02.79 gaussdb 54780 omm 20 0 684m 424m 131m S 0 5.4 0:00.04 gaussdb 54781 omm 20 0 684m 424m 131m S 0 5.4 0:00.21 gaussdb 54782 omm 20 0 684m 424m 131m S 0 5.4 0:00.02 gaussdb 54798 omm 20 0 684m 424m 131m S 0 5.4 0:16.70 gaussdb 54952 omm 20 0 684m 424m 131m S 0 5.4 0:07.51 gaussdb 54953 omm 20 0 684m 424m 131m S 0 5.4 0:00.81 gaussdb 54954 omm 20 0 684m 424m 131m S 0 5.4 0:06.54 gaussdbRun the following command to view the function invocation stack for each thread in the process. Check the thread number for the ID of the thread that occupies high CPU usage in the last step.
gstack 54954The query result is as follows. The thread number for the thread ID 54775 is 10.
192.168.0.11:~ # gstack 54954 Thread 10 (Thread 0x7f95a5fff710 (LWP 54775)): #0 0x00007f95c41d63c6 in poll () from /lib64/libc.so.6 #1 0x0000000000d3d2d3 in WaitLatchOrSocket(Latch volatile*, int, int, long) () #2 0x000000000095ed25 in XLogPageRead(XLogRecPtr*, int, bool, bool) () #3 0x000000000095f6dd in ReadRecord(XLogRecPtr*, int, bool) () #4 0x000000000096aef0 in StartupXLOG() () #5 0x0000000000d5607a in StartupProcessMain() () #6 0x00000000009e19f9 in AuxiliaryProcessMain(int, char**) () #7 0x0000000000d50135 in SubPostmasterMain(int, char**) () #8 0x0000000000d504ec in MainStarterThreadFunc(void*) () #9 0x00007f95c79b85f0 in start_thread () from /lib64/libpthread.so.0 #10 0x00007f95c41df84d in clone () from /lib64/libc.so.6 #11 0x0000000000000000 in ?? ()