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This chapter is from the book


Application data access through a filesystem is much more common than access through raw storage. Filesystem meta structures are maintained by the filesystem's driver, removing the overhead from the application. For this reason, very few applications are written to perform raw I/O, except for a few database systems whose creators believe they can maintain the integrity or performance better than a standardized filesystem. This section addresses performance characteristics with regards to multiple filesystems and draws a comparison to the previous section on raw device access.

The configuration for this section is identical to the previous section. We use the IA64 host atlorca2. Filesystem types used for comparisons are xfs and ext3. To begin, we define a filesystem using the same disks from previous examples. By creating a filesystem, we simply add an additional layer to the data management overhead.

Journaling to a Separate Disk

The following fdisk output shows device sdj with one primary partition of type 83 known as Linux native. The objective is to compare performance of a large sequential read. We compare performance with raw to performance with XFS, illustrating performance overhead.

The first step in comparing performance between raw and filesystem is to set a baseline. Here we set a performance baseline for a single threaded read on a raw device named /dev/sdj.

atlorca2:~ # fdisk -l /dev/sdj

Disk /dev/sdj: 250.2 GB, 250219069440 bytes
255 heads, 63 sectors/track, 30420 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes

   Device Boot      Start         End      Blocks    Id   System
/dev/sdj1               1       30420   244348618+   83   Linux

The fdisk output shows that partition 1 is active, with 250GB of capacity.

The XFS filesystem type offers the performance feature of creating a journal log on a disk separate from the filesystem data. Next we demonstrate this feature and test it. In this example, device sdk is the journal log device, and sdj is used for the metadata device.

atlorca2:~ # mkfs.xfs -f -l logdev=/dev/sdk1,size=10000b /dev/sdj1

meta-data=/dev/sdj1              isize=256    agcount=16, agsize=3817947 blks
         =                       sectsz=512
data     =                       bsize=4096   blocks=61087152, imaxpct=25
         =                       sunit=0      swidth=0 blks, unwritten=1
naming   =version 2              bsize=4096
log      =/dev/sdk1              bsize=4096   blocks=10000, version=1
         =                       sectsz=512   sunit=0 blks
realtime =none                   extsz=65536  blocks=0, rtextents=0

The following demonstrates a large block write and the performance boost that an external logger offers.

atlorca2:/xfs.test # dd if=/dev/zero of=/xfs.test/zero.out bs=512k

atlorca2:~ # iostat -x 1 100|egrep "sdj|sdk"

Device:    rrqm/s wrqm/s   r/s   w/s  rsec/s   wsec/s   rkB/s   wkB/s avgrq-sz avgqu-sz   await  svctm  %util

sdj         0.00 32512.00  0.00 281.00    0.00 262144.00     0.00 131072.00   932.90   141.99  394.24   3.56 100.00
sdk         0.00   0.00  0.00  0.00    0.00    0.00     0.00    0.00   0.00     0.00    0.00   0.00   0.00
sdj         0.00 36576.00  0.00 277.00    0.00 294929.00    0.00 147464.50  1064.73   142.75  511.21   3.61 100.00
sdk         0.00   0.00  0.00  1.00    0.00    5.00     0.00    2.50 5.00     0.10  105.00 105.00  10.50
sdj         0.00 35560.00  0.00 279.00    0.00 286736.00    0.00 143368.00  1027.73   141.26  380.54   3.58 100.00
sdk         0.00   0.00  0.00  0.00    0.00    0.00     0.00    0.00 0.00     0.00    0.00   0.00   0.00
sdj         0.00 36576.00  0.00 277.00    0.00 294912.00    0.00 147456.00  1064.66   142.35  204.65   3.61 100.00
sdk         0.00   0.00  0.00  0.00    0.00    0.00     0.00    0.00 0.00     0.00    0.00   0.00   0.00

The journal log device provides little to no added performance. There is almost no I/O to disk sdk, which contains the log. Putting the log on a separate disk won't help performance because we are not diverting a meaningful amount of I/O. The journal log device provides no added benefit because the intent to modify/write is established at the beginning of the file access. From this point forward, the I/O is completed on the meta device, as depicted in the previous iostat.

Determining I/O Size for Filesystem Requests

As shown previously, the dd command bs option sets the block size to 512k for each I/O transaction. We can see this in iostat. To calculate this value, find the average request size (avgrq-sz) column from iostat. In this example, we find that avgrq-sz has a value of 1024 sectors. To calculate the block size, multiply avgrq-sz by the sector size (512 bytes). In this example:

1024 (sectors) x 512 (bytes/sector) = 524288 (bytes) / 1024 (KB/bytes) = 512KB

However, the same dd command using the XFS filesystem reveals that the largest avgrq-sz value set forth by a sequential read is equal to 256 (sectors) regardless of block size set by dd. Following the same calculations, we determine that an XFS sequential read has a block size set to 128KB. The block size of any I/O is an important item to understand because not all programs control the block size. A program can request a given block size; however, a lower-layer driver can require that the I/O request be broken into smaller requests as illustrated in the previous iostat output.

Thus we have demonstrated that using a remote journal provides no performance improvements for large data access on sequential reads and writes. However, when writing to our XFS filesystem with a large number of small files, the opposite becomes true: The remote journal does in fact help.

Loading a Filesystem with Small Block I/O Transfers

In the next test, we write 512KB files as fast as the system allows while watching the load on the journal device and the filesystem meta device. To run this test, we must write a short, simple program to control the number of files to create during our test phase. The program is as follows:

while [ $total -ne $* ]
total='expr $total + $count'
  touch $total
        dd if=/xfs.test/zero.out of=/xfs.test/$total bs=512k >         /dev/null 2>&1  #Using dd to control the BS.

This program uses the same dd command used throughout our testing. It is important to always keep the control in any test constant. By running the previous program, thousands of 512KB files are created, causing an increased load on the journal log device (sdk), as depicted in the following listing:

atlorca2:/xfs.test # ./count_greg.sh 10000
atlorca2:~ # iostat -x 1 100|egrep "sdj|sdk"

Device:    rrqm/s wrqm/s   r/s   w/s  rsec/s  wsec/s    rkB/s    wkB/s avgrq-sz avgqu-sz   await  svctm  %util

sdj          0.00 16219.00  0.00 181.00    0.00 131169.00     0.00 65584.50   724.69     9.31   54.90   2.77  50.20
sdk          0.00   0.00  0.00 53.00    0.00 3200.00     0.00   1600.00    60.38     1.43   28.96   9.40  49.80
sdj          0.00 20274.00  0.00 201.00    0.00 163936.00      0.00 81968.00   815.60    11.90   54.74   2.83  56.90
sdk          0.00   0.00  0.00 39.00    0.00 2752.00     0.00   1376.00    70.56     1.19   26.26  14.00  54.60
sdj          0.00 20273.00  0.00 198.00    0.00 163936.00      0.00 81968.00   827.96    10.96   54.34   2.77  54.80
sdk          0.00   0.00  0.00 50.00    0.00 3072.00     0.00  1536.00    61.44     1.43   30.48  10.90  54.50
sdj          0.00 16217.00  0.00 200.00    0.00 131138.00      0.00 65569.00   655.69    10.22   56.56   2.71  54.10
sdk          0.00   0.00  0.00 50.00    0.00 2982.00     0.00   1491.00    59.64     1.37   28.78  10.92  54.60

By creating thousands of files or modifying the same file thousands of times a minute, we can see the added load on the journal device sdk as well as the filesystem device sdj. By understanding the end goal, we can make better decisions about how to size and lay out a filesystem.

In addition, notice the block size of the I/O request submitted to the filesystem and how the filesystem responded. In the previous example on a sequential read, the block size is restricted to 128K. However, on a sequential write, the blocking structure on the XFS filesystem is that which is set forth by the command calling the SCSI write, setting the average request size to 512k as shown in the previous iostat illustration. However, will the same results be found using a completely different filesystem?

Let's repeat our test on ext3, also known as ext2 with journaling, to depict I/O latency and blocking factors.

atlorca2:/ext3.test # dd if=/ext3.test/usr.tar of=/dev/null bs=512k
atlorca2:/ # iostat -x 1 100| grep sdj

Device:    rrqm/s wrqm/s   r/s   w/s  rsec/s wsec/s    rkB/s wkB/s avgrq-sz avgqu-sz   await  svctm  %util

sdj         60.00   0.00 898.00  0.00 229856.00   0.00 114928.00 0.00   255.96     0.98    1.10   1.09  98.20
sdj         58.00   0.00 939.00  0.00 240360.00   0.00 120180.00 0.00   255.97     0.98    1.04   1.04  97.50
sdj         62.00   1.00 918.00  3.00 234984.00  32.00 117492.00 16.00  255.17     0.97    1.06   1.05  97.00
sdj         62.00   0.00 913.00  0.00 233704.00   0.00 116852.00 0.00   255.97     0.98    1.07   1.07  97.80
sdj         58.00   0.00 948.00  0.00 242664.00   0.00 121332.00 0.00   255.97     0.96    1.01   1.01  96.20
sdj         62.00   0.00 933.00  0.00 238824.00   0.00 119412.00 0.00   255.97     0.97    1.04   1.04  97.30

The sequential read test holds the same blocking factor as previously seen on XFS, with a little savings on overhead with respect to average wait time and service time. To continue our example, let's proceed with the file create script discussed previously.

while [ $total -ne $* ]
total='expr $total + $count'
  touch $total
        dd if=/ext3.test/zero.out of=/ext3.test/$total bs=512k >         /dev/null 2>&1 #Using dd to control the BS.
atlorca2:/ext3.test # ./count_greg.sh 10000

atlorca2:/ # iostat -x 1 100| grep sdj

Device:    rrqm/s wrqm/s   r/s   w/s  rsec/s  wsec/s    rkB/s    wkB/s avgrq-sz avgqu-sz   await  svctm  %util

sdj          0.00 51687.00  0.00 268.00    0.00 416864.00     0.00 208432.00  1555.46   128.62  346.74   3.45  92.40
sdj          0.00 27164.00  1.00 264.00    8.00 219040.00     4.00 109520.00   826.60   139.42  521.48   3.77 100.00
sdj          0.00 656.00  1.00 113.00    8.00 5312.00   4.00  2656.00   46.67    21.95  516.42   3.85  43.90
sdj          0.00   0.00  1.00  0.00    8.00    0.00    4.00     0.00   8.00     0.00    1.00   1.00   0.10
sdj          0.00   0.00  0.00  0.00    0.00    0.00    0.00     0.00   0.00     0.00    0.00   0.00   0.00
sdj          0.00 52070.00  1.00 268.00    8.00 419936.00     4.00 209968.00  1561.13   128.44  346.09   3.43  92.30
sdj          0.00 27800.00  0.00 271.00    0.00 224160.00     0.00 112080.00   827.16   139.27  513.93   3.69 100.00
sdj          0.00 662.00  2.00 112.00   16.00 5368.00   8.00  2684.00   47.23    20.45  489.31   3.91  44.60
sdj          0.00   0.00  0.00  0.00    0.00    0.00    0.00     0.00   0.00     0.00    0.00   0.00   0.00
sdj          0.00   0.00  1.00  0.00    8.00    0.00     4.00     0.00
8.00     0.00    0.00   0.00   0.00
sdj          0.00 51176.00  0.00 273.00    0.00 412792.00     0.00 206396.00  1512.06   128.12  336.55   3.37  92.00
sdj          0.00 27927.00  0.00 274.00    0.00 225184.00     0.00 112592.00   821.84   138.00  510.66   3.65 100.00
sdj          0.00 658.00  0.00 105.00    0.00 5328.00   0.00  2664.00    50.74    17.44  492.92   3.57  37.50
sdj          0.00   0.00  1.00 128.00    8.00 1024.00   4.00   512.00    8.00     2.88   22.32   0.35   4.50

Notice how the filesystem buffers the outbound I/O, submitting them to the SCSI layer in a burst pattern. Though nothing is wrong with this I/O pattern, you must understand that the larger the burst, the larger the strain on the storage array. For example, exchange servers save up and de-stage out a burst of I/O operations, which can flood an array's write pending cache, so you should monitor for excessive write burst. However, the write block size maintains a 512k average block, which is similar to the XFS on writes with large block requests.

Utilizing Key Benefits of a Filesystem

As we've seen, I/O block sizes, stripe size, and filesystem layouts have unique benefits that aid I/O performance. In addition to these items, most filesystems have unique characteristics that are designed to guess the next request, trying to save resources by anticipating requests. This is accomplished by read-ahead algorithms. Ext2, Ext3, JFS, and XFS all have the capability to perform read-ahead, as shown with XFS in the following XFS source code for mounting /usr/src/linux/fs/xfs/xfs_mount.c:

 * Set the number of readahead buffers to use based on
 * physical memory size.
if (xfs_physmem <= 4096)                /* <= 16MB */
        mp->m_nreadaheads = XFS_RW_NREADAHEAD_16MB;
else if (xfs_physmem <= 8192)   /* <= 32MB */
        mp->m_nreadaheads = XFS_RW_NREADAHEAD_32MB;
        mp->m_nreadaheads = XFS_RW_NREADAHEAD_K32;
if (sbp->sb_blocklog > readio_log) {
        mp->m_readio_log = sbp->sb_blocklog;
} else {
        mp->m_readio_log = readio_log;
mp->m_readio_blocks = 1 << (mp->m_readio_log - sbp->sb_blocklog);
if (sbp->sb_blocklog > writeio_log) {
        mp->m_writeio_log = sbp->sb_blocklog;
} else {
        mp->m_writeio_log = writeio_log;
mp->m_writeio_blocks = 1 << (mp->m_writeio_log - sbp->sb_blocklog);

Although read-ahead is a powerful attribute, a concern exists. It is not fair to say that read-ahead causes these drawbacks, as a true increase in read performance can be seen on any filesystem that uses read-ahead functionality. When using read-ahead, filesystem block size is an important factor. For example, if filesystem block size is 8k and sequential read pattern exist where an application is reading 1K sequential blocks (index), readahead kicks in and pulls an extra predefined number of blocks, where each block is equal to the filesystem block size. To sum up the concern with read-ahead, one must be careful not to read in more data than is needed. Another performance boost can be found by utilizing buffer cache.

Filesystems such as XFS, Reiser, Ext2, and Ext3 use the buffer cache and reduce the amount of memory for an application to process data in the buffer cache, forcing more physical I/O (PIO). Later in this chapter we discuss the difference between raw, Oracle Cluster File System (OCFS), and XFS in an example with buffer cache and readahead. Before we jump too far ahead, though, we need to cover one last topic with respect to disk performance.

Linux and Windows Performance and Tuning Sector Alignments

We have covered some in-depth I/O troubleshooting tactics for character and block devices. Now we need to address the rumor mill about disk geometry alignment. Geometry alignment, also known as sector alignment, is the new craze in Windows performance tweaking. Cylinders lie in a small band, like a ring on a platter. The cylinders are then divided into tracks (wedges), which contain sectors, which are described in great detail in Chapter 6, "Disk Partitions and File Systems." However, to discuss performance concerns with sector alignment, we would like to first depict sector locations (see Figure 4-1).


Figure 4-1 Cylinders, tracks, and sectors

Sector alignment provides little to no performance boost in Linux. To date, no issues exist with regards to how partitions and filesystems interact with sectors alignment for a given platter, regardless of whether the platter is logical or physical within Linux. However, for those who are interested, a performance boost has been documented with respect to DOS 6.X, Windows 2000, and greater. See http://www.microsoft.com/resources/documentation/Windows/2000/server/reskit/en-us/Default.asp?url=/resources/documentation/Windows/2000/server/reskit/en-us/prork/pree_exa_oori.asp and http://www.microsoft.com/resources/documentation/windows/2000/professional/reskit/en-us/part6/proch30.mspx for more information.

Performance Tuning and Benchmarking Using bonnie++

Now that we have covered some basics guidelines about I/O performance metrics, we need to revisit our primary goal. As already mentioned, our primary goal is to deliver methods for finding performance problems. In all circumstances, a good performance snapshot should be taken at every data center before and after changes to firmware roles, fabric changes, host changes, and so on.

The following is generalized performance data from a single LUN RAID 5 7d+1p, and it is provided to demonstrate the performance benchmark tool called bonnie. The following test does not depict the limit of the array used for this test. However, the following example enables a brief demonstration of a single LUN performance characteristic between three filesystems. The following bonnie++ benchmark reflects the results of the equipment used throughout this chapter with XFS and Ext3 filesystems.

atlorca2:/ext3.test # bonnie++ -u root:root -d /ext3.test/bonnie.scratch/ -s 8064m -n 16:262144:8:128

Version  1.03 ------Sequential Output------ --Sequential Input- --Random-
              -Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --Seeks--
Machine  Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP  /sec %CP
atlorca2  8064M 15029  99 144685 44 52197  8 14819 99 124046   7 893.3 1
              ------Sequential Create------ --------Random Create--------
                   -Create-- --Read--- -Delete-- -Create-- --Read--- -
files:max:min       /sec %CP  /sec %CP  /sec %CP  /sec %CP  /sec %CP /sec %CP
  16:262144:8/128   721  27 14745  99  4572  37   765  28  3885  28 6288  58

Testing with XFS and journal on the same device yields the following:

atlorca2:/ # mkfs.xfs -f -l size=10000b /dev/sdj1
meta-data=/dev/sdj1              isize=256    agcount=16, agsize=3817947 blks
         =                       sectsz=512
data     =                       bsize=4096   blocks=61087152, imaxpct=25
         =                       sunit=0      swidth=0 blks, unwritten=1
naming   =version 2              bsize=4096
log      =internal log           bsize=4096   blocks=10000, version=1
         =                       sectsz=512   sunit=0 blks
realtime =none                   extsz=65536  blocks=0, rtextents=0

atlorca2:/ # mount -t xfs -o logbufs=8,logbsize=32768 /dev/sdj1 /xfs.test
atlorca2:/ # mkdir /xfs.test/bonnie.scratch/
atlorca2:/ # mount -t xfs -o logbufs=8,logbsize=32768 /dev/sdj1 /xfs.test

atlorca2:/xfs.test # bonnie++ -u root:root -d /xfs.test/bonnie.scratch/ -s 8064m -n 16:262144:8:128

Version  1.03   ------Sequential Output------ --Sequential Input- --
              -Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --Seeks--
Machine  Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP  /sec %CP
atlorca2      8064M 15474  99 161153  21 56513   8 14836  99 125513    9 938.8   1
                    ------Sequential Create------ -------- Random Create--
                    -Create-- --Read--- -Delete-- -Create-- --Read---  -
files:max:min        /sec %CP  /sec %CP  /sec %CP  /sec %CP  /sec %CP /sec %CP
    16:262144:8/128  1151  24 12654 100  9705  89  1093  22 12327  99 6018  71

Testing with XFS without remote journal provides these results:

atlorca2:~ # mount -t xfs -o logbufs=8,logbsize=32768,logdev=/dev /sdk1/dev/sdj1 /xfs.test

atlorca2:/xfs.test # mkdir bonnie.scratch/
atlorca2:/xfs.test # bonnie++ -u root:root -d /xfs.test/bonnie.scratch/ -s 8064m -n 16:262144:8:128

Version  1.03       ------Sequential Output------  --Sequential Input- --
                    -Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --

Machine        Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP  /sec %CP

atlorca2      8064M 15385  99 146197  20 58263   8 14833  99 126001   9 924.6   1

              ------Sequential Create------ --------Random Create--------
                    -Create-- --Read--- -Delete-- -Create-- --Read--- -
files:max:min       /sec %CP  /sec %CP  /sec %CP  /sec %CP  /sec %CP /sec %CP
    16:262144:8/128  1175  24 12785 100 10236  95  1097  22 12280  99 6060  72

Just by changing the filesystem and journal log location, we pick up some nice performance on sequential I/O access with respect to XFS. The point of the previous demonstration is to identify factors other than hardware that increase performance. As we have seen, simply changing the filesystem layout or type can increase performance greatly. One other performance tool we enjoy using for SCSI measurements is IOzone, found at www.iozone.org.

Assessing Application CPU Utilization Issues

As with any performance problem, usually more than one factor exists. Our troubleshooting performance journey continues with coverage of application CPU usage and how to monitor it. In this section, CPU usage and application-specific topics are covered, focusing on process threads.

Determining What Processes Are Causing High CPU Utilization

To begin, we want to demonstrate how a few lines of code can load a CPU to a 100% busy state. The C code "using SLES 9 with long integer" illustrates a simple count program, which stresses the CPU in user space. It is important to understand that our application is not in system space, also called kernel mode, because we are not focusing on any I/O as previously discussed in this chapter.

Example 1 goes like this:

        #include <stdio.h>
        #include <sched.h>
        #include <pthread.h> /* POSIX threads */
        #include <stdlib.h>

        #define num_threads 1

        void *print_func(void *);

        int main ()
        int x;
        printf("main() process has PID= %d PPID= %d\n", getpid(),

        pthread_t tid[num_threads];
        /* Now to create pthreads */
        for (x=0; x <= num_threads;x++)
        pthread_create(tid + x, NULL, print_func, NULL );

        /*wait for termination of threads before main continues*/
        for (x=0; x < num_threads;x++)

        pthread_join(tid[x], NULL);
        printf("Main() PID %d joined with thread %d\n", getpid(),

void *print_func (void *arg)
        long int y;
        printf("PID %d PPID = %d TID = %d\n",getpid(), getppid(),
        /* creating a very large loop for the CPU to chew on :) */

/* Note, the following line may be changed to: for (y=1;y>0;y++) */

        for (y=1; y<10000000000000;y++)
        printf ("%d\n", y);
        return 0;

Note that instead of just counting to a large integer, you may want to create an infinite loop. The C code in Example 2 (also shown in Chapter 8, "Linux Processes: Structures, Hangs, and Core Dumps") generates an infinite loop with a signal handler used to kill the threads.

#include <pthread.h> /* POSIX threads */
#include <signal.h>
#include <stdlib.h>
#include <linux/unistd.h>
#include <errno.h>

#define num_threads  8

void *print_func(void *);
void threadid(int);
void stop_thread(int sig);

int main ()

        int x;
        pid_t tid;
        pthread_t threadid[num_threads];

       (void) signal(SIGALRM,stop_thread); /*signal handler */

        printf("Main process has PID= %d PPID= %d and TID= %d\n",
        getpid(), getppid(), gettid());

        /* Now to create pthreads */
        for (x=1; x <= num_threads;++x)
        pthread_create(&threadid[x], NULL, print_func, NULL );

        sleep(60); /* Let the threads warm the cpus up!!! :) */
        for (x=1; x < num_threads;++x)
                pthread_kill(threadid[x], SIGALRM);

        /*wait for termination of threads before main continues*/
        for (x=1; x < num_threads;++x)
        pthread_join(threadid[x], NULL);
        printf("Main() PID %d joined with thread %d\n", getpid(),
void *print_func (void *arg)
        printf("PID %d PPID = %d Thread value of pthread_self = %d and
        TID= %d\n",getpid(), getppid(), pthread_self(),gettid());
        while(1);  /* nothing but spinning */

void stop_thread(int sig) {

To continue with Example 1, compile the code and run the application as follows:

atlorca2:/home/greg # cc -o CPU_load_count CPU_Load_count.c -lpthread
atlorca2:/home/greg # ./CPU_load_count | grep -i pid
main() process has PID= 5970 PPID= 3791
PID 5970  PPID = 3791  TID = 36354240
PID 5970  PPID = 3791  TID = 69908672

When running top, shown next, we find that our CPUs have a load, yet the PID reports zero percent usage for the CPU_load_count program. So where is the load coming from? To answer this question, we must look at the threads spawned by the parent process.

atlorca2:~ # top
top - 20:27:15 up 36 min, 3 users, load average: 1.24, 1.22, 1.14
Tasks:  79 total,   1 running, 78 sleeping,  0 stopped,   0 zombie
 Cpu0 :  0.1% us, 0.6% sy,  0.0% ni, 99.1% id, 0.2% wa,  0.0% hi, 0.0% si
 Cpu1 :  1.7% us,  1.6% sy,  0.0% ni, 96.7% id,  0.0% wa,  0.0% hi, 0.0% si
 Cpu2 : 44.1% us, 19.0% sy,  0.0% ni, 36.9% id,  0.0% wa,  0.0% hi, 0.0% si
 Cpu3 : 43.3% us, 21.0% sy,  0.0% ni, 35.7% id,  0.0% wa,  0.0% hi, 0.0% si
Mem:   4142992k total,   792624k used,  3350368k free,   176352k buffers
Swap:  1049568k total,        0k used,  1049568k free,   308864k cached

 5974 root      16   0  3632 2112 3184 R  0.7  0.1   0:00.13 3874 root        0 -         top
 5971 root      15   0  3168 1648 2848 S  3.3  0.0   0:02.16 3791 root        0 pipe_wait grep
 5970 root      17   0 68448 1168 2560 S  0.0  0.0   0:00.00 3791 root        0 schedule_ CPU_load_count
 5966 root      16   0  7376 4624 6128 S  0.0  0.1   0:00.01 5601 root        0 schedule_ vi
 5601 root      15   0  5744 3920 4912 S  0.0  0.1   0:00.11 5598 root        0 wait4     bash
 5598 root      16   0 15264 6288  13m S  0.0  0.2   0:00.04 3746 root        0 schedule_ sshd

You can see top shows that the CPU_Load_count program is running, yet it reflects zero load on the CPU. This is because top is not thread-aware in SLES 9 by default.

A simple way to determine a thread's impact on a CPU is by using ps with certain flags, as demonstrated in the following. This is because in Linux, threads are separate processes (tasks).

atlorca2:/proc # ps -elfm >/tmp/ps.elfm.out

vi the file, find the PID, and focus on the threads in running (R) state.


4 - root     5970  3791   0    -   - -  4276 -      20:26 pts/0   00:00:00 ./CPU_load_count
4 S root         -     -  0   77   0 -     - schedu 20:26 - 00:00:00 -
1 R root         -     - 64   77   0 -     - schedu 20:26 - 00:02:41 -
1 S root         -     - 64   79   0 -     - schedu 20:26 - 00:02:40 -

Though top provides a great cursory view of your system, other performance tools are sometimes required to find the smoking gun, such as the ps command in the preceding example. Other times, performance trends are required to isolate a problem area, and products such as HP Insight Manager with performance plug-ins may be more suited.

Using Oracle statspak

In the following example, we use Oracle's statistics package, called statspak, to focus on an Oracle performance concern.

DB Name      DB Id  Instance  Inst Num  Release      Cluster Host
------------ ----------- ---------- -------- --------- ------- ----------
DB_NAME      1797322438 DB_NAME      3     YES     Server_name

            Snap Id     Snap Time      Sessions Curs/Sess Comment
        ------- ------------------ -------- --------- -------------------
Begin Snap:                        23942 Date 11:39:12      144       3.0

  End Snap:                        23958 Date 11:49:17      148       3.1

   Elapsed:                               10.08 (mins)

Cache Sizes (end)
               Buffer Cache:      6,144M    Std Block Size:      8K
           Shared Pool Size:      1,024M        Log Buffer: 10,240K
Load Profile
~~~~~~~~~~~~                  Per Second    Per Transaction
                         ---------------    ---------------
            Redo size:      6,535,063.00           6,362.58
        Logical reads:         30,501.36              29.70 <-Cache Reads LIO
        Block changes:         15,479.36              15.07
       Physical reads:          2,878.69               2.80 <-Disk reads PIO
   Physical writes:         3,674.53               3.58 <-Disk Writes PIO
            User calls:         5,760.67               5.61
                Parses:             1.51               0.00
           Hard parses:             0.01               0.00
                 Sorts:           492.41               0.48
                Logons:             0.09               0.00
              Executes:         2,680.27               2.61
          Transactions:         1,027.11

 % Blocks changed per Read:      50.75    Recursive Call %:       15.27
Rollback per transaction %:       0.01       Rows per Sort:        2.29

Instance Efficiency Percentages (Target 100%)
            Buffer Nowait %:   99.98       Redo NoWait %:          100.00
            Buffer  Hit   %:   90.57    In-memory Sort %:          100.00
            Library Hit   %:  100.00        Soft Parse %:           99.45
         Execute to Parse %:   99.94         Latch Hit %:           98.37
Parse CPU to Parse Elapsed %:  96.00     % Non-Parse CPU:           99.99

 Shared Pool Statistics        Begin     End
                               ------   ------
             Memory Usage %:   28.16    28.17
    % SQL with executions>1:   82.91    81.88
  % Memory for SQL w/exec>1:   90.94    90.49
Top 5 Timed Events
~~~~~~~~~~~~~~~~~~                                                 % Total
Event                                         Waits    Time (s)   Ela Time
---------------------------------------- ------------ ----------- --------
db file sequential read                   1,735,573     17,690      51.88
log file sync                               664,956      8,315      24.38
CPU time                                                 3,172       9.32
global cache open x                       1,556,450      1,136       3.36
log file sequential read                      3,652        811       2.35

-> s  - second
-> cs - centisecond -     100th of a second
-> ms - millisecond -    1000th of a second
-> us - microsecond - 1000000th of a second
                                               Total Wait   Wait    Waits
Event                         Waits   Timeouts   Time (s)   (ms)    /txn
---------------------- ------------ ---------- ---------- ------ --------
db file sequential read   1,738,577          0     17,690    10      2.8

This statspak output is truncated to conserve space. Some of the key points of interest are highlighted in bold: Logical reads (LIO), Physical reads/writes (PIO), Latches, Buffered I/O, and db file sequential read. Please understand that many hundred parameters exist, and Oracle has published many performance documents and created many classes that we recommend reading and taking. However, this chapter's main goal is performance troubleshooting from a wide view, or in other words, a quick reference guide to performance troubleshooting. The bold areas in the previous listing are some critical places to focus, especially with LIO and PIO.

To elaborate on the bold elements in the previous listing, we begin with LIO and PIO. LIO is the access of a memory register, residing in the database buffer cache, and PIO is an I/O operation request from Oracle to the system for a data block fetch from spindle. In short, LIO is faster than PIO, but PIO is not always the bottleneck. A critical piece of the puzzle with regards to performance problems on any database connected to any storage device is understanding that a small percentage of PIOs (from Oracle's viewpoint) are actually a read from the host cache, storage cache, or both. Thus, a PIO from the database's perspective may in fact still be a read from cache, so having a high number of PIOs is not always a bad omen. In the previous example, the PIO reads were around 1.7 million with an average latency of 10ms, which, by the way, is not bad. However, although the I/O is fine, a memory control performance problem may still linger in the background.

Thus, while I/O may in fact be fine, memory lock control also must be addressed for smooth, fast database operation. Note that in this example, the Latch hit percentage is around 98%, which raises a red flag. A latch is basically a protection agent for access control with regards to shared memory within the system's global area (SGA). In short, the goal is to keep memory available so that the latch-free count remains high, keeping the percentage of Latch hit percentage around 99.5%. Latch info can be viewed by reviewing both the willing-to-wait and not-willing-to-wait latches found in the immediate_gets and immediate_misses columns by using V$LATCH or by looking at Oracle's statspack. In addition to waiting on free memory segments with regards to latches, we need to touch on buffer waits.

When a database starts to show an increase in buffer waits, the objective is to focus on the two main issues. The first issue is that memory is running low, which is impacting the second issue, that of physical I/O read/writes. A buffer wait is logged when the database must flush a write to spindle to clear up some available cache for data processing. The quick solution to this problem is to run raw (or other proprietary filesystem such as OCFS) to bypass host buffer cache so that buffer cache is used for data processing only. However, the huge drawback to using a non-buffer cache filesystem is the loss of performance with respect to read-ahead as discussed earlier in this chapter.

Now that we have covered some I/O basics, both physical and logical, and memory control with respect to latches, we present an example of an application failing to initialize due to lack of shared memory space. We demonstrate a lack of shared memory without going into detail about system V message queues, semaphores, or shared memory. As with all applications, more memory equals faster performance, and without enough memory, system failure is imminent.

Troubleshooting "No Space Left on Device" Errors When Allocating Shared Memory

Our example shows a common error 28 example using a 64-bit kernel, with a 64-bit application failing to initialize due to a memory address problem. Our focus is on interprocess communication (IPCS), as with any large application that spawns multiple threads/processes. Using our 64-bit machine, we bring a 64-bit Oracle 10g instance online, which fails with the following error to demonstrate a failed IPCS.

ORA-27102: out of memory
Linux-x86_64 Error: 28: No space left on device

System parameters are as follows:

# ipcs
------ Shared Memory Segments --------

   key shmid owner perms bytes nattch status

   0x00000000 1245184 gdm 600 393216 2 dest

   0x852af124 127926273 oracle 640 8558477312 15
------ Semaphore Arrays --------
key semid owner perms nsems
0x3fbfeb1c 1933312 oracle 640 154

==== kernel parameters ======
# sysctl -a
kernel.sem = 250 32000 100 128
kernel.msgmnb = 16384
kernel.msgmni = 16
kernel.msgmax = 8192
kernel.shmmni = 4096
kernel.shmall = 2097152
kernel.shmmax = 34359738368
==== process ulimits (bash shell)
$ ulimit -a
core file size (blocks, -c) 0
data seg size (kbytes, -d) unlimited
file size (blocks, -f) unlimited
max locked memory (kbytes, -l) 4
max memory size (kbytes, -m) unlimited
open files (-n) 65536
pipe size (512 bytes, -p) 8
stack size (kbytes, -s) 10240
cpu time (seconds, -t) unlimited
max user processes (-u) 16384
virtual memory (kbytes, -v) unlimited

This failure is a result of the kernel not being able to fulfill the shared memory request. Not enough space is a condition explained in /usr/src/linux/ipc/shm.c, which reads:

if (shm_tot + numpages >= shm_ctlall)
        return -ENOSPC;

The program we tried to start previously required more shared memory than we had allocated, which in turn caused the Oracle application to fail on initialization. The solution is to increase shared memory by the kernel parameter. In this example, we simply increase it to shmall=8388608.

Additional Performance Tools

As we conclude this chapter, we cover some uncommon tools that can be used to monitor performance characteristics and build charts in most cases. isag, RRDtool, Ganglia (which uses RRDtool to monitor grid computing and clustering), and Nagios are great performance tools. More monitoring tools exist, but for the most part, they are common tools used every day such as sar, iostat, top, and netstat. Due to space limitations, we only cover isag in this chapter. However, the other tools are easy to find and configure if one so desires. isag, found at http://www.volny.cz/linux_monitor/isag/index.html, provides a nice graphical front end to sar. After systat tools have been loaded, isag should be included, as depicted here:

atlorca2:/tmp # rpm -qf /usr/bin/isag


Most engineers who work with sar, iostat, and other performance tools will enjoy using isag, the GUI front end to sar. To give a quick demonstration of how the tool works, we must enable sar to collect some data. To achieve a real-world demonstration, we repeat our previous bonnie++ test while running sar -A to collect as much detail as possible and display it through isag.

To demonstrate, we mount an XFS filesystem with a local journal and use an NFS mount point to a HPUX server to demonstrate both disk and network load through bonnie++ while monitoring through sar and isag.

atlorca2:/var/log/sa # mount -t xfs -o logbufs=8,logbsize=32768 /dev/sdj1 /xfs.test

atlorca2:/var/log/sa # df
Filesystem           1K-blocks      Used Available Use% Mounted on


                     284470272  50955984 218940848 19% /scratch/customers
/dev/sdj1            244308608      4608 244304000  1% /xfs.test

atlorca2:/var/log/sa #  sar -A -o 1 100 #This will build a fine in /var/log/sa that isag will use.

atlorca2:/var/log/sa # isag

The resulting screenshots provide information on CPU utilization (see Figure 4-2) and swap utilization (see Figure 4-3).

Remember, if you are swapping, you need more memory.


Figure 4-2 CPU utilization


Figure 4-3 Swap utilization

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