Recently, I resolved a performance issue with one of our esteemed clients. I found the problem interesting and worth blogging about.
An application makes 300 static connections to database DB1 in the database server, say CLNTDB server. The application relies on database link and over a period of time, each session executes SQL through a database link, creating a connection in central database PROD1. So, there are 300 connections in the PROD1 database coming from the DB1 database through database links. Performance is fine during normal operation.
The problem starts when the application is shutdown. Shutting down the application in DB1 creates massive CPU consumption on the PROD1DB server. Unfortunately, this spike in CPU usage lasts for five to ten seconds and causes the ASM heartbeat to fail. Considering that PROD1 is a central database, this has a global effect on applications’ functionality. See the presentation below for graphical representation of this problem.
Looking at the symptoms in detail, we can see the CPU usage in sys mode. It is not uncommon to see high CPU usage during a storm of disconnects. But, this specific CPU usage is much higher and uses all CPUs in kernel-mode. If this is a problem due to process destruction, then this will show up on the CLNTDB server too, as it too faces 300 disconnects. But, this problem manifests only on the PROD1DB server.
mpstat output from the PROD1DB server shows the CPU usage in %sys mode. Notice the numbers below, under
sys column—almost all CPUs are used in sys mode. We need to drill down further to understand why this many CPUs are used in sys mode. It can also be seen that no other columns have any abnormally higher values:
CPU ..intr ithr csw icsw migr smtx srw syscl usr sys wt idl 0 554 237 651 219 87 491 0 4349 9 91 0 0 1 911 0 2412 591 353 630 0 15210 30 63 0 7 2 313 0 613 106 190 105 0 3562 8 90 0 2 3 255 0 492 92 161 530 0 2914 6 92 0 2 4 86 1 2 3 1 63 0 8 0 100 0 0 5 662 0 1269 153 326 211 0 6753 13 77 0 10 6 349 0 589 54 170 1534 0 3002 7 88 0 5 7 317 81 411 105 51 61 0 2484 4 93 0 3 8 6423 6321 697 36 148 546 0 3663 6 86 0 9 9 363 0 639 41 154 1893 0 3214 9 85 0 6 10 456 0 964 194 193 77 0 5797 10 81 0 8 11 104 0 42 3 9 183 0 207 0 95 0 5 12 195 0 279 110 31 80 0 1590 3 97 0 0 13 449 0 844 117 158 127 0 4486 7 85 0 8
Let’s analyze this issue for a single connection to simplify the problem. I created a test user in DB1 database, created a private database link from DB1 to PROD1, and then executed a select statement over that database link.
create user test1 identified by test1; grant connect, resource to test1; conn test1/test1 create database link proddb connect to test1 identified by test1 using 'proddb'; select * from dual@proddb;
At this point, a database link connection has been created on the PROD1 database. Let’s query
v$session in PROD1 database to find the session created for database link. SID 4306 is the session created for our test connection from DB1 database.
select sid, serial#, LOGON_TIME,LAST_CALL_ET from v$session where logon_time > sysdate-(1/24)*(1/60) and machine='machine_name_here' / SID SERIAL# LOGON_TIME LAST_CALL_ET ---------- ---------- -------------------- ------------ 4306 51273 12-SEP-2008 20:47:29 1
Associating this session to
v$process, we find the UNIX process ID for this session. Now, we will start
truss on this UNIX process( Solaris platform):
truss -p -d -o /tmp/truss.log
In DB1, we execute
exit. This will disconnect the session in the PROD1 database.
The output of
truss shows activity generated for the disconnect from the PROD1 server. Reading through the truss output, we can see that system call (
shmdt) consumed CPU time. Calculating from the output below (18.5053-18.4807=0.0242), we see that each
shmdt call consumes approximately 24ms. We are using the
-d flag as above to print the time spent in that call. Of course, this is a system call, and so this CPU usage will be in kernel-mode.
18.4630 close(10) = 0 18.4807 shmdt(0x380000000) = 0 18.5053 shmdt(0x440000000) = 0 18.5295 shmdt(0x640000000) = 0 18.5541 shmdt(0x840000000) = 0 18.5784 shmdt(0xA40000000) = 0 18.6026 shmdt(0xC40000000) = 0 18.6273 shmdt(0xE40000000) = 0 18.6512 shmdt(0x1040000000) = 0 18.6752 shmdt(0x1240000000) = 0 18.6753 shmdt(0x1440000000) = 0
So, one disconnect executes 10 system calls and consumes approximately 0.24 CPU seconds in kernel-mode.
shmdt calls are used to detach from a shared memory segment. Since there are 10 shared memory segments ( as visible in ipcs -ma), 10 shmdt calls are executed per session disconnect.
Projecting this calculation for 300 connections, CPU consumption will last approximately 72 seconds in total. With 12 CPUs, at least 6 seconds will be used in kernel-mode, assuming linear projections. (But in practice, due to mutex calls and such, this may not be linear and will be over 72 seconds.) This matches our observation: 5-10 seconds of CPU consumption in kernel-mode. The first thing we need to do is reduce number of
shmdt calls. One way is to reduce the number of shared memory segments.
Shared memory segments
I thought that the
SHMMAX kernel parameter (see also Paul Moen’s The mysterious world of shmmax and shmall) was limiting as the
SGA size was bigger than
SHMMAX size. After changing the
SHMMAX parameter, restarting the server and so on, many shared memory segments were still being created.
That’s interesting. I tried
truss-ing the instance startup to understand why multiple shared memory segments are created. A few lines from the
truss output shows calls to system calls
4.5957 munmap(0xFFFFFD7FFDAE0000, 32768) = 0 4.5958 lgrp_version(1, ) = 1 4.5958 _lgrpsys(1, 0, ) = 42 4.5958 _lgrpsys(3, 0x00000000, 0x00000000) = 19108 4.5959 _lgrpsys(3, 0x00004AA4, 0x06399D60) = 19108 4.5959 _lgrpsys(1, 0, ) = 42 4.5960 pset_bind(PS_QUERY, P_LWPID, 4294967295, 0xFFFFFD7FFFDFB11C) = 0 4.5960 pset_info(PS_MYID, 0x00000000, 0xFFFFFD7FFFDFB0D4, 0x00000000) = 0 4.5961 pset_info(PS_MYID, 0x00000000, 0xFFFFFD7FFFDFB0D4, 0x061AA2B0) = 0
_lgrpsys calls indicate that there is some form of NUMA activity going on here.
pset_bind is used to bind a thread or process to a specific processor set.
NUMA or Locality groups
NUMA stands for Non Uniform Memory Access. In high-end SMP systems, the backplane bus can become a bottleneck. In non-NUMA platforms, all memory access must go through the system bus and that can reduce scalability.
NUMA technology (specifically, cache-coherent NUMA or ccNUMA) was introduced to relieve symptoms of this bottleneck. Every processor has its own local memory connected by some form of hardware interconnect. A differentiation is also made between local and remote memory in NUMA-based servers. Compared to access to remote memory from a processor set, access to local memory has less latency. There’s an enormous amount of literature available about NUMA. Refer to the Wikipedia entry for NUMA, and to Kevin Closson’s blog on Oracle on Opteron, K8L, NUMA, etc. And also to my presentation below.
On Solaris, NUMA is implemented as locality groups. To oversimplify, a process running in a locality group will have much less latency accessing memory local to that group. As the “remoteness” of memory increases, latency also increases. In future releases of Solaris, this locality might be applied to other resources such as I/O.
To optimally use NUMA technology, Oracle code spreads SGA in to all locality groups. Then a DBWR is assigned to that locality group.
pset_bind calls binds that DBWR to a specific locality group or processor set. (I believe that an LGWR is also assigned and bound to a processor set, but I cannot confirm that.)
Now we understand that why there are 10 shared memory segments for this SGA.
I was curious—with 12 CPUs in the server, do we really have this many locality groups defined? Or are we hitting some kind of bug?
lgrpinfo can be used to get locality group details. We found identical machine architecture, and installed observability tools from OpenSolaris.org’s Observability tools for NUMA.
The output of
lgrpinfo shows locality groups defined in this server:
#/usr/local/bin/lgrpinfo lgroup 0 (root): Children: 10 12 14 15 17 19 21 23 CPUs: 0-15 Memory: installed 65024 Mb, allocated 2548 Mb, free 62476 Mb Lgroup resources: 1-8 (CPU); 1-8 (memory)? Latency: 146 lgroup 1 (leaf): Children: none, Parent: 9 <-- leaf node CPUs: 0 1 Memory: installed 7680 Mb, allocated 1964 Mb, free 5716 Mb Lgroup resources: 1 (CPU); 1 (memory)? Load: 0.105 Latency: 51 ... lgroup 9 (intermediate): Children: 1, Parent: 10 CPUs: 0-5 Memory: installed 24064 Mb, allocated 2187 Mb, free 21877 Mb Lgroup resources: 1-3 (CPU); 1-3 (memory) Latency: 81 ... lgroup 10 (intermediate): Children: 9, Parent: 0 <-- intermediate group CPUs: 0-13 Memory: installed 56832 Mb, allocated 2491 Mb, free 54341 Mb Lgroup resources: 1-7 (CPU); 1-7 (memory) Latency: 113 ...
There are many locality groups defined in Solaris. Local groups are defined as a hierarchy, and seven of them are leaf-node local groups. In the output above,
lgroup 0 is the
root group. Intermediate groups are
For example, for
lgroup 1, the hierarchy is:
1 -> 9 -> 10 -> 0.
Locality group latencies can be easily seen with
lgrpinfo -l. From the output below, we can see that access to local memory has lower latency. For example, accessing memory area
1 from locality group
51), whereas access to remote memory is
146 for accessing locality group
24 (these numbers are not actual latency, merely a relative representation of latency):
The high CPU usage in kernel-mode was caused by an excessive number of calls to
shmdt. Since there are 10 shared memory segments, there are 10
shmdt calls per disconnect. SGA is spread across NUMA nodes creating these many segments.
Ten shared memory segments were created in order to exploit NUMA technology. NUMA is an excellent technology and it is a pity that we are suffering a side-effect of NUMA. We need to resolve this issue and we have a handful of options at this point. We can:
- disable NUMA completely, by setting the
- reduce NUMA nodes using
_db_block_numa. (Interestingly, this throws an ORA-600 error during startup.)
- completely eliminate db link-based architecture with streams or materialized views.
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