openGauss 3.1.0 版本gs_stack功能解密
openGauss 2022-11-14 18:01 发表于广东
不管是测试还是研发,工作中总有遇到各种各样的问题。比如,你有没有遇到过在数据库中执行某个SQL,却一直不返回结果,这时候的你是不是非常想看一下代码执行到了哪个函数?或者是数据库不响应连接,需要查看数据库当前线程的执行情况呢?而在实际生产中,获取生产系统进程堆栈比较麻烦,需要在服务端后台执行gstack命令。本期为大家介绍的openGauss 3.1.0版本中内置gs_stack工具,则可以通过函数调用的方式输出指定线程的堆栈,用于解决现网环境缺少gs_stack工具无法获取调用栈的问题。
内置gs_stack工具介绍
在openGauss的很多客户场景中,会出现gdb、gstack等工具无法使用或当系统出现hang、慢等问题时,无法通过调用栈进行进一步的定位;还有一种情况是登录客户数据库的流程非常繁杂,需要经过层层审批,这时通过gsql等工具连接数据库就相对容易一些。针对以上痛点,通过复用openGauss未使用操作系统信号,并在信号处理函数中获取调用栈的方式开发了调用栈工具,以获得服务端openGauss的调用栈。
获取调用栈主要包含两种方式,一种是通过执行SQL语句获取,另一种是通过gs_ctl工具执行命令获取。
1
在客户端工具执行gs_stack([tid])函数
使用具有monadmin或者sysadmin用户权限的用户,通过gsql或者其他工具连接数据库;
执行命令:
openGuass=# select * from gs_stack();
返回当前openGauss所有线程的调用栈:
tid | lwtid | stack ---------------+------ +------------------------------------------------------------------ 14026731434848 | 2626 | _poll + 0x2d + | | WaitLatch0rSocket(Latch volatile*,int,int,long) + 0x29f + | | WaitLatch(Latch voatile*,int,long) + 0x2e + | | start_thread +oxc5 + | | clone + OXC5 + 140116075071232| 23864 |__poll + 0x2d + | | poll + 0x81 + | | WaitLatchOrSocket(Latch volatile*, int, int, long) + 0x6af + | | WaitLatch(Latch volatile*, int, long) + 0x2e + | | ckpt_pagewriter_sub_thread_loop() + 0x284 + | | ckpt_pagewriter_main() + 0x92e + | | int GaussDbAuxiliaryThreadMain<(knl_thread_role)46>(knl_thread_arg*) + 0x482 + | | int GaussDbThreadMain<(knl_thread_role)46>(knl_thread_arg*) + 0x854 + | | InternalThreadFunc(void*) + 0x5c + | | ThreadStarterFunc(void*) + 0xa4 + | | start_thread + 0xc5 + | | clone + 0x6d +
只需要查看某一个线程的调用栈时,执行命令:
openGuass=# select gs_stack(xxx);
说明
xxx为某个线程的thread_id,能够返回thread_id为xxx的线程的调用栈:
gs_stack
------------------------------------------------------------------------------------------
pthread_sigmask + 0x2a +
gs_signal_recover_mask(__sigset_t) + 0x17 +
gs_signal_send(unsigned long, int, int) + 0x2f9 +
signal_child(unsigned long, int, int) + 0x36 +
get_stack_according_to_tid(unsigned long, StringInfoData*) + 0x191 +
gs_stack(FunctionCallInfoData*) + 0xcb +
unsigned long ExecMakeFunctionResult<false, false, true>(FuncExprState*, ExprContext*, bool*, ExprDoneCond*) + 0x554 +
ExecEvalFunc(FuncExprState*, ExprContext*, bool*, ExprDoneCond*) + 0x147 +
ExecTargetList(List*, ExprContext*, unsigned long*, bool*, ExprDoneCond*, ExprDoneCond*) + 0x15d +
ExecProject(ProjectionInfo*, ExprDoneCond*) + 0x40f +
ExecResult(ResultState*) + 0x1da +
ExecResultWrap(PlanState*) + 0x18 +
ExecProcNode(PlanState*) + 0xde +
ExecutePlan(EState*, PlanState*, CmdType, bool, long, ScanDirection, _DestReceiver*) + 0x1a6 +
standard_ExecutorRun(QueryDesc*, ScanDirection, long) + 0x3d9 +
explain_ExecutorRun(QueryDesc*, ScanDirection, long) + 0x109 +
ExecutorRun(QueryDesc*, ScanDirection, long) + 0x1ad +
PortalRunSelect(PortalData*, bool, long, _DestReceiver*) + 0x294 +
PortalRun(PortalData*, long, bool, _DestReceiver*, _DestReceiver*, char*) + 0x62e +
exec_simple_query(char const*, MessageType, StringInfoData*) + 0x12b0 +
PostgresMain(int, char**, char const*, char const*) + 0x2e10 +
BackendRun(Port*) + 0x327 +
int GaussDbThreadMain<(knl_thread_role)1>(knl_thread_arg*) + 0x5a8 +
InternalThreadFunc(void*) + 0x2d +
ThreadStarterFunc(void*) + 0xa4 +
start_thread + 0xc5 +
clone + 0x6d +
openGauss=# select gs_stack(140115727259392);
gs_stack
--------------------------------------------------------------------------------------------
__select + 0x33 +
pg_usleep(long) + 0xa1 +
pg_sleep(FunctionCallInfoData*) + 0xeb +
unsigned long ExecMakeFunctionResultNoSets<false, false>(FuncExprState*, ExprContext*, bool*, ExprDoneCond*) + 0x206f +
ExecEvalFunc(FuncExprState*, ExprContext*, bool*, ExprDoneCond*) + 0x622 +
ExecTargetList(List*, ExprContext*, unsigned long*, bool*, ExprDoneCond*, ExprDoneCond*) + 0x45d +
ExecProject(ProjectionInfo*, ExprDoneCond*) + 0xc2d +
ExecResult(ResultState*) + 0x79b +
ExecResultWrap(PlanState*) + 0x18 +
ExecProcNode(PlanState*) + 0x2db +
ExecutePlan(EState*, PlanState*, CmdType, bool, long, ScanDirection, _DestReceiver*) + 0x765 +
standard_ExecutorRun(QueryDesc*, ScanDirection, long) + 0xbb5 +
explain_ExecutorRun(QueryDesc*, ScanDirection, long) + 0x1f7 +
ExecutorRun(QueryDesc*, ScanDirection, long) + 0x947 +
PortalRunSelect(PortalData*, bool, long, _DestReceiver*) + 0x7d2 +
PortalRun(PortalData*, long, bool, _DestReceiver*, _DestReceiver*, char*) + 0xe11 +
exec_simple_query(char const*, MessageType, StringInfoData*) + 0x3929 +
PostgresMain(int, char**, char const*, char const*) + 0x61f8 +
BackendRun(Port*) + 0x64d +
int GaussDbThreadMain<(knl_thread_role)1>(knl_thread_arg*) + 0x9c7 +
InternalThreadFunc(void*) + 0x5c +
ThreadStarterFunc(void*) + 0xa4 +
start_thread + 0xc5 +
clone + 0x6d
2 在服务器端使用gs_ctl stack –D data_dir命令
当线程池满,无法通过gsql连接数据库的时候,可以使用gs_ctl工具执行命令获取线程调用栈:
使用集群用户登录服务器,执行命令gs_ctl stack –D data_dir,data_dir是指定gaussdb的数据目录的绝对路径:
gs_ctl stack –D /path/to/install/data/
可以取gaussdb所有线程的调用栈。
[user@euler omm]$ gs_ctl stack -D /path/to/install/data/opengauss [2022-11-03 20:17:59.288][19256][][gs_ctl]: gs_stack start: Thread 0 tid<140120252633600> lwtid<23675> __poll + 0x2d poll + 0x81 CommWaitPollParam::caller(int (*)(pollfd*, unsigned long, int), unsigned long) + 0xb1 int comm_socket_call<CommWaitPollParam, int (*)(pollfd*, unsigned long, int)>(CommWaitPollParam*, int (*)(pollfd*, unsigned long, int)) + 0x28 comm_poll(pollfd*, unsigned long, int) + 0x388 ServerLoop() + 0xb77 PostmasterMain(int, char**) + 0x612e main + 0xaeb __libc_start_main + 0xf5 0x55feac9a9907 Thread 1 tid<140116236076800> lwtid<23848> __poll + 0x2d poll + 0x81 WaitLatchOrSocket(Latch volatile*, int, int, long) + 0x6af SysLoggerMain(int) + 0x17c9 int GaussDbThreadMain<(knl_thread_role)17>(knl_thread_arg*) + 0x860 InternalThreadFunc(void*) + 0x5c ThreadStarterFunc(void*) + 0xa4 start_thread + 0xc5 clone + 0x6d
只需要查看某一个线程的调用栈时,执行命令:
gs_ctl stack –D data_dir –I xx
说明
data_dir是指定gaussdb的数据目录的绝对路径,xxx指的是线程的lwpid(taskid),可以通过top –Hp的方式获取线程的lwpid, 也可以通过cat /proc/yyyy/task获取线程的lwpid 。yyyy指的是进程id,可以通过ps –ux | grep gaussdb获取。
[uesr@euler omm]$ gs_ctl stack -D /path/to/install/data -I 23860 [2022-11-03 20:22:01.327][40608][][gs_ctl]: gs_stack start: tid<140116142843648> lwtid<23860> __poll + 0x2d poll + 0x81 WaitLatchOrSocket(Latch volatile*, int, int, long) + 0x6af WaitLatch(Latch volatile*, int, long) + 0x2e ckpt_pagewriter_sub_thread_loop() + 0x284 ckpt_pagewriter_main() + 0x92e int GaussDbAuxiliaryThreadMain<(knl_thread_role)46>(knl_thread_arg*) + 0x482 int GaussDbThreadMain<(knl_thread_role)46>(knl_thread_arg*) + 0x854 InternalThreadFunc(void*) + 0x5c ThreadStarterFunc(void*) + 0xa4 start_thread + 0xc5 clone + 0x6d
总结
通过以上我们介绍的openGauss的gs_stack功能,我们可以很方便地定位某个openGauss线程正在做的事情,并可以根据这些函数调用情况判断当前openGauss任务是否出现了问题,以及发现性能瓶颈。后续,我们将会进一步在这个功能上进行演进,不断增强openGauss的核心竞争力。
本文介绍了openGauss3.1.0版本中的gs_stack工具,该工具允许在没有外部工具的情况下获取数据库线程的堆栈信息,有助于问题定位和性能优化。通过SQL查询或gs_ctl命令,开发者能快速查看线程调用链,增强系统的可维护性。
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