DBA Kevlar

Tips, tricks, (and maybe a few rants) so more DBAs become bulletproof!

Solid Choices for Oracle Tuning on Solid State Disk

As I continue to work on very large databases, (VLDB), I am exposed to more  opportunities to speed up IO.  This can involve Oracle’s solution of Exadata or stand alone improvements with options such as SSD, (Solid State Disk) which can offer faster IO performance at a fraction of the price.  When this option becomes a reality, there will always be non-DBA’s that advise what would best benefit from the hardware, but to take the time to research what would truly benefit is important for the DBA to perform.

The Just the Facts on Solid State Disk:

There are several types of SSD available:

  • Flash memory-based
  • DRAM-based
  • Cache or Buffer

The SSD can have different types of host interfaces, depending on the main hardware you are interfacing with and/or vendor choices:

  • PCI
  • Fibre Channel
  • ATA, (Serial or Parallel)
  • SCSI, (Serial or Parallel)
  • USB

Rarely do we get a chance to move entire Terabytes of data onto fast disk, but rather are offered limited, faster disk to utilize for crucial objects that can give us the “most bang for the buck.”   Commonly this is due to the price of these specialized and impressive IO read/write drives, but it can also be due to limitations on the hardware they are interfacing with.

As I started working on databases that utilized faster disk, with or without ASM, it became apparent that what these speedy disks were allocated to wasn’t always what SHOULD have been placed in the new location.  Where indexes, look up tables and temp tablespace experienced impressive gains vs. the standard disk drives they had formerly resided on, I have been quick to dissuade anyone from placing redo logs on SSD.

I’m going to go through what data, reports and queries that I utilize to decide what should be on fast disk, along with my benchmark findings when I did have the opportunity to create an entire database on Fusion Octal fast disk.

Getting the most out of SSD is all about getting what won’t fit in memory, (SGA and PGA) onto a faster disk.  All consistently large, [consistent] read tasks that the database must direct to disk for,  but doesn’t write as often to disk, (visualizing batch loads vs. heavy transactional) and ONCE TUNING OPPORTUNITIES HAVE BEEN EXHAUSTED, are excellent choices for research when deciding what should be placed on SSD. This information can be achieved multiple ways as a DBA.  AWR/ADDM and ASH reports can provide solid, high level data to direct you in the right direction if you are not as familiar with your data or wish to validate some of what you already know.  For those of you that do not have the tuning pack license, then Statspack can do the same.  Tracing can offer a detailed output that will tell you about objects that you are often going to slower disk for.  OEM can provide graphs that will show IO demands on a heavily “weighted” system, as can other GUI tools in the market.

 

AWR/Statspack and I/O Wait Indicators

Your group has already decided that IO is an issue and should have verified this in the top 5 wait events that can be seen through AWR or statspack.  The snapshots utilized for this examination should be times of heavy IO in the database environment as can be seen in the example Table 1.

 

Table 1

Top 5 Timed Events                                         Avg %Total
~~~~~~~~~~~~~~~~~~                                     wait   Call
Event Waits Time (s) (ms) Time Wait Class
—————————— ———— ———– —— —— ———-
db file sequential read

979,382

36,066

37

45.1

User I/O
db file scattered read

5,083,058

22,401

4

28

User I/O
Direct path write temp

13,577

17

User I/O
db file parallel write

464,287

5,136

11

6.4

System I/O
direct path read temp

366,956

2,671

7

3.3

User I/O

From here, we inspect our AWR or statspack reports, there is a section that should be inspected first and foremost, referred to as Segments by Physical Reads the output from this section can be seen in Table 2.

Table 2

Tablespace Obj. Physical
Owner Name Object Name Type Reads %Total
———- ———- ——————– —– ———— ——-
SCHM_OWNR TBLSPC1_DATA TBL1_FILE_1 TABLE

86,788,592

47.87

SCHM_OWNR TBLSPC2_DATA TBL1_FILE_PK INDEX

80,544,192

46.59

SCHM_OWNR TBLSPC1_IDX TBL2_MR_PK INDEX

74,742,752

45.39

SCHM_OWNR TBLSPC1_IDX TBL3_M_PK INDEX

40,924,576

28.43

SCHM_OWNR TBLSPC2_DATA TBL4 TABLE

26,790,464

15.52

Tuning, Always the First Step

The first step in the process is to inspect I/O issues with large objects. Is there a partitioning strategy that can take the physical reads and IO down for the objects in question? If there is not or there is still a requirement for full scans or large index or partition scans, then you need to look and see what tuning options there are for the code involved.  If there is already partitioning in place, is it the right partitioning key and/or is sub-partitioning in order.

Once this process has completed, then inspect performance for physical reads again and verify the objects in question are still a bottleneck for IO.  If so, then they may be a valid choice to relocate to a new ASM diskgroup residing on SSD.

Creating a specific ASM disk group for the SSD disk is the obvious choice, as the SSD will not be part of the standard disk groups without performance and rebalance challenges.  Once complete, you will then have the new SSD diskgroup available for use.

Inspect the sizes of the objects in left in your “top 5 physical IO objects” and decide what you move over for initial testing.  I commonly make a copy and test a copy of the objects against the code to test true performance gains, ensuring that there are no required physical storage required changes as well.  ***over what you need for capacity growth estimates.  What should you bring over next?  Now if we are still using the same reports that are showing above, I would look carefully at what I have available and would start to inspect temp usage as a possible next candidate.

It is important that if you consider temp, that it is in a “controlled” state for your environment.  It is not uncommon for many DBA’s to set TEMP to autoextend and not pay attention to temp tablespace usage.  I fully advocate the opposite and track temp usage, along with monitor alerts with scripts for anytime any user or process consumes a certain threshold per process on any of my production systems.

Considering the amount of waits on temp read and writes, tuning opportunities may be boundless on hash joins and sorting.  Low hanging fruit in these categories will involve looking for “order by’s” that have been left in for insert statements, (not sure how often I’ve seen this, but it’s a very common and an unfortunate occurrence…)  In regards to hash joins, there can be examples of wide reporting tables only one or two columns are actually required for the results and the join.  A choice of CTAS, (create table as select) of only the columns required for the process, dropping post the join to the second table, can drastically trim time and temp usage for a hash of tables that involve only a few columns on a wide table where an index is a less than efficient answer.  This choice allows the performance gain of the hash without the performance hit of swapping to temp when wide tables cause PGA to never be enough.

After tuning temp usage due to large hash joins and sorting outside of PGA, inspect the max temp tablespace required.  If this will now fit without impacting capacity planning requirements for the SSD, move the temp tablespace onto the SSD ASM disk group.

Scripts to Inspect IO Usage

There are many scripts that can be written or available on the web and in reports to inspect IO usage.  The following is a good example of one:

select
io.cnt Count,
io.event Event,
substr(io.obj,1,20) Object_Name,
io.p1 P1_Value,
f.tablespace_name Tablespace_Name
from
(
select
count(*) cnt,
round(count(*)/(60*60),2) aas,
substr(event,0,15) event,
nvl(o.object_name,decode(CURRENT_OBJ#,-1,0,CURRENT_OBJ#)) obj,
ash.p1,
o.object_type otype
from v$active_session_history ash,
all_objects o
where ( event like 'db file s%' or event like 'direct%' )
and o.object_id (+)= ash.CURRENT_OBJ#
and sample_time > sysdate - 7/(60*24)
group by
substr(event,0,15) ,
CURRENT_OBJ#, o.object_name ,
o.object_type ,
ash.p1
) io,
dba_data_files f
where
f.file_id = io.p1
and f.tablespace_name not like '%RAM%' –-exclude SSD objects
Order by io.cnt desc
/

 

COUNT EVENT OBJECT_NAME P1_Value TABLESPACE_NAME
122 db file sequent TBL1_CHAIN 102 N_DATA
33 db file sequent HH_TBL1_FDX01 161 H_INDX1
28 db file sequent CA_TBL2_PK 270 C_INDX
25 db file sequent I_TBL3_IDX02 225 I_INDX2
21 db file sequent E_TBL4 43 E_DATA
20 direct path rea I_MRG_TBL 75 M_DATA
23 db file scatter C_TBL3 50 C_DATA

 

The above script gives you clear examples of what objects you should point your research to, first indexes, (sequential) and in this case, a look up table, (direct path read).

Building a Database Entirely on SSD

We were given this opportunity recently to test performance gains and decide if budget should be set aside for investing in the hardware to build entire databases on SSD vs. strategic objects within a database.  We have a process that takes approximately five days to aggregate a snapshot in time, up to 12TB of data.  The goal was to see, could we accomplish this in two days if given all SSD for the database vs. a combination of standard disks on a disk array and SSD for high read/write data.

This sounds like a slam dunk, but it is more challenging than one might think.  There are small things to that have to be updated in the database, such as system statistics in 10g to ensure the database knows fully the gift you have granted it, but then you may also need to make significant logical changes to take advantage of the hardware due to limitations in CPU and memory per process.  The build was on a server that utilized hyper-threading and some of the “performance settings” actually appeared to work against the database vs. the lesser setting that might stripe the CPU usage more efficiently.  The graph below show the hits against the first 32 of “hyper-threaded” 64 CPU’s:

Figure 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This graph in Figure 1 only shows CPU usage over a small snapshot of time, but over long intervals, it showed the same differing data vs. SAR or other reports from the Admin side-  the database continued to hit the same CPU’s over and over, leaving other CPU’s untouched for extended periods of time.  This did not bode well for the database performance, high read/write capability or not.

 

The build time improvements were impressive, but the one thing that must be included is that the improvement in performance was not just a hardware improvement step.  There was first the additional hardware and then a tuning process at the database level to ensure the processes were able to achieve the best performance the solid state disk offered it, (comparison of columns New Run Time against the Final Run Time in Figure 2.)

Process Step Standard Disk/SSD Total Min. SSD Without Tuning New Run Time Initial Perf. Gain SSD With Tuning Final Run Time Total Perf. Gain
DIM Table 1 CTAS 4 HOURS 20 MINUTES 27 SECS 260 min 3 HOURS 38 MINUTES 24 SECS 218 min

19%

2 HOURS 43 MINUTES 45 SECS 164 min

58%

CTAS Table 2 4 HOURS 23 MINUTES 11 SECS 263 min 0 HOURS 16 MINUTES 2 SECS 16 min 16 Times Perf 0 HOURS 19 MINUTES 8 SECS No Tuning N/A
CTAS Table 3 1 HOURS 29 MINUTES 21 SECS 89 min 0 HOURS 44 MINUTES 27 SECS 44 min 2 Times Perf. 0 HOURS 57 MINUTES 19 SECS No Tuning N/A
CTAS Table 4 2 HOURS 55 MINUTES 58 SECS 175 min 0 HOURS 42 MINUTES 16 SECS 42 min 4 Times Perf. 0 HOURS 47 MINUTES 55 SECS No Tuning N/A
CTAS Table 5 10 HOURS 7 MINUTES 41 SECS 607 min 1 HOURS 50 MINUTES 7 SECS 110 min 6 Times Perf. 1 HOURS 42 MINUTES 6 SECS No Tuning N/A
CTAS Table 6 11 HOURS 32 MINUTES 40 SECS 692 min 4 HOURS 51 MINUTES 17 SECS 291 min 2 Times Perf. 5 HOURS 9 MINUTES 26 SECS No Tuning N/A
Multiple Table Aggregation 25 HOURS 15 MINUTES 3 SECS 1515 min 9 HOURS 58 MINUTES 1 SECS 598 min 3 Times Perf. 5 HOURS 16 MINUTES 31 SECS 316 min 5 Times Perf.
Summary Table 1 Agg. 25 HOURS 24 MINUTES 35 SECS 1524 min 10 HOURS 0 MINUTES 20 SECS 600 min 3 Times Perf. 5 HOURS 18 MINUTES 14 SECS 318 min 5 Times Perf.
Summary Table 2 Agg. 25 HOURS 23 MINUTES 56 SECS 1523 min 10 HOURS 7 MINUTES 22 SECS 607 min 3 Times Perf. 5 HOURS 25 MINUTES 54 SECS 325 min 5 Times Perf.
Index Creation Table 1 1 HOURS 16 MINUTES 33 SECS 76 min 0 HOURS 53 MINUTES 42 SECS 54 min

39%

0 HOURS 53 MINUTES 14 SECS No Tuning N/A
Index Creation Table 2 1 HOURS 22 MINUTES 55 SECS 82 min 0 HOURS 59 MINUTES 55 SECS 60 min

28%

0 HOURS 59 MINUTES 6 SECS No Tuning N/A
CTAS Aggr Table 3 6 HOURS 36 MINUTES 20 SECS 396 min 3 HOURS 21 MINUTES 18 SECS 201 min

50%

3 HOURS 13 MINUTES 38 SECS No Tuning N/A
Index Creation Table 3 0 HOURS 52 MINUTES 2 SECS 52 min 0 HOURS 40 MINUTES 3 SECS 40 min

24%

0 HOURS 48 MINUTES 15 SECS No Tuning N/A
CTAS Aggr. Table 4 2 HOURS 41 MINUTES 13 SECS 161 min 1 HOURS 32 MINUTES 8 SECS 92 min

43%

1 HOURS 28 MINUTES 25 SECS No Tuning N/A
CTAS Aggr Table 5 3 HOURS 46 MINUTES 59 SECS 226 min 2 HOURS 58 MINUTES 29 SECS 179 min

21%

2 HOURS 55 MINUTES 20 SECS No Tuning N/A
CTAS Aggr. Table 6 0 HOURS 51 MINUTES 27 SECS 51 min 0 HOURS 36 MINUTES 46 SECS 37 min

28%

0 HOURS 34 MINUTES 33 SECS No Tuning N/A
Insert to Table 6 0 HOURS 5 MINUTES 24 SECS 5 min 0 HOURS 5 MINUTES 6 SECS 5 min NONE 0 HOURS 4 MINUTES 52 SECS 5 min NONE
Update to Table 6 26 HOURS 40 MINUTES 41 SECS 1640 min 25 HOURS 9 MINUTES 52 SECS 1510 min

8%

17 HOURS 44 MINUTES 2 SECS 1084 min

44%

CTAS Table 7 1 HOURS 1 MINUTES 48 SECS 61 min 0 HOURS 7 MINUTES 43 SECS 8 min 13 Times Perf. 0 HOURS 6 MINUTES 37 SECS No Tuning N/A
CTAS Aggr Table 8 0 HOURS 28 MINUTES 31 SECS 28 min 0 HOURS 22 MINUTES 12 SECS 22 min

22%

0 HOURS 19 MINUTES 25 SECS No Tuning N/A
CTAS Mod TBLS 9/10 1 HOURS 42 MINUTES 36 SECS 102 min 1 HOURS 42 MINUTES 22 SECS 102 min NONE 1 HOURS 39 MINUTES 25 SECS No Tuning N/A
CTAS Table Aggr. 11 2 HOURS 26 MINUTES 58 SECS 147 min 1 HOURS 29 MINUTES 53 SECS 90 min

49%

1 HOURS 24 MINUTES 42 SECS No Tuning N/A
CTAS Aggr. Table 12 7 HOURS 24 MINUTES 44 SECS 445 min 6 HOURS 7 MINUTES 48 SECS 368 min

18%

6 HOURS 6 MINUTES 40 SECS No Tuning N/A
CTAS Aggr. Table 13 6 HOURS 47 MINUTES 31 SECS 408 min 4 HOURS 38 MINUTES 1 SECS 278 min

32%

5 HOURS 5 MINUTES 32 SECS No Tuning N/A
CTAS Aggr. Table 14 25 HOURS 23 MINUTES 32 SECS 1524 min 10 HOURS 9 MINUTES 51 SECS 610 min 3 Times Perf. 5 HOURS 27 MINUTES 17 SECS 327 min 5 Times Perf.
CTAS Aggr. Table 15 1 HOURS 21 MINUTES 59 SECS 82 min 0 HOURS 22 MINUTES 49 SECS 23 min

65%

0 HOURS 4 MINUTES 33 SECS 4 min 20 Times Perf.
Update to Table 13 0 HOURS 12 MINUTES 45 SECS 13 min 0 HOURS 49 MINUTES 58 SECS 50 min 3 Times LOSS!! 0 HOURS 1 MINUTES 22 SECS 1 min 9 Times Perf.

Figure 2

I must note that what challenged us in unresolved issues were waits on CPU due to hyper-threaded CPU issues. 

Tuning involved for the third columns time elapsed involved the following:

  • Bind variable additions
  • Literal additions where bind peeking was an issue.
  • A change from ASSM, (Automatic Segment Space Management) to manual segment space management where freelists could be set at the object level, (dynamically allocated freelists were not able to adjust quickly enough for some of the load processes…)
  • Changes to initial transactions, percent free and parallel that made sense, (upping it for some, downgrading it for others that did not work with the partitioning or a need for partitioning…)

Inspecting I/O by SQL_ID

This script, (adopted from Tim Gorman’s sqlhistory.sql from, www.evdbt.com)  does a wonderful job of pulling a clean, clear picture of what physical and logical I/O is occurring in a single SQL_ID, seen here in Table 3 :

Table 3

+————————————————————————————————–+
Plan HV     Min Snap  Max Snap  Execs       LIO            PIO            CPU         Elapsed    
+————————————————————————————————–+
1766271350  659       659       1           593,134,283    12,961,814     14,657.45   15,067.05
+————————————————————————————————–+
========== PHV = 1766271350==========
First seen from “07/15/11 13:00:31″ (snap #659)
Last seen from  “07/15/11 13:00:31″ (snap #659)
Execs          LIO            PIO            CPU            Elapsed
=====          ===            ===            ===            =======
1              593,134,283    12,961,814     14,657.45      15,067.05
Plan hash value: 1766271350

 

    TQ  IN-OUT  PQ Distrib            

0

 CREATE TABLE STATEMENT   1543M(100)

1

  PX COORDINATOR

2

   PX SEND QC (RANDOM)  :TQ10001    464M    397G   4128K  (7)
  Q1,01  P->S  QC (RAND)

3

    LOAD AS SELECT
  Q1,01  PCWP

4

     PX RECEIVE    464M    397G   4128K  (7)
  Q1,01  PCWP

5

      PX SEND RANDOM LOCAL  :TQ10000    464M    397G   4128K  (7)
  Q1,00  P->P  RANDOM LOCA

6

       PX PARTITION LIST ALL    464M    397G   4128K  (7)

1

1000

  Q1,00  PCWC

7

        HASH JOIN RIGHT OUTER    464M    397G     14G   4128K  (7)
  Q1,00  PCWP

8

         TABLE ACCESS FULL HDN_TBL    231M    112G    576K (22)

1

1000

  Q1,00  PCWP

9

         HASH JOIN RIGHT OUTER    464M    171G   6967M   1551K  (7)
  Q1,00  PCWP

10

          TABLE ACCESS FULL HD_TBL    310M     50G    144K (34)

1

1000

  Q1,00  PCWP

11

          TABLE ACCESS FULL H_TBL    464M     95G    339K (13)

1

1000

  Q1,00  PCWP

 

 

                                              Summary Execution Statistics Over Time
                                                                              Avg                 Avg
Snapshot                          Avg LIO             Avg PIO          CPU (secs)      Elapsed (secs)
Time                 Execs            Per Exec            Per Exec            Per Exec            Per Exec
———— ——– ——————- ——————- ——————- ——————-
15-JUL 13:00        1      593,134,283.00       12,961,814.00           14,657.45           15,067.05
             ——– ——————- ——————- ——————- ——————-
avg                                 593,134,283.00       12,961,814.00           14,657.45           15,067.05
sum                        1
                                              Per-Plan Execution Statistics Over Time
                                                                                         Avg                 Avg
      Plan Snapshot                          Avg LIO             Avg PIO          CPU (secs)      Elapsed (secs)
Hash Value Time            Execs            Per Exec            Per Exec            Per Exec            Per Exec
———- ———— ——– ——————- ——————- ——————- ——————-
1766271350 15-JUL 13:00        1      593,134,283.00       12,961,814.00           14,657.45           15,067.05
**********              ——– ——————- ——————- ——————- ——————-
avg                                                        593,134,283.00       12,961,814.00           14,657.45           15,067.05
sum                                               1

+—————————————————————————————————————————

This report clearly shows the amount of logical vs. physical I/O coming from the statement in question.  This gives the DBA a clear indicator if any object in the poor performing process would benefit a move to SSD or if tuning is in order to eliminate the I/O performance challenge.  A combination of both may be chosen, as there are multiple right outer hash-joins which clearly show as the performance hit in the time elapsed and in the temp tablespace usage/significant I/O categories, (note that the process needs to scan ALL the partitions for the objects in question…)

SSD and Forced Hash Joins on Indexes

When a database design is impacted by the front-end tool required to present data in a proper format, such as Business Analytics Software, the price can be high to the DBA who has to manage resource usage.  Many times the data must be presented in a very flat, wide format and requires a large amount of data pulled across a network interface.  This can be in anywhere from a couple 100GB’s to multiple Terabytes.  When you are the DBA looking at ways to increase performance when logical performance tuning is limited, solid state disk can offer you gains not offered anywhere else.

Business Analytics Software often will query a few 100GB to 1TB objects, hash join and then perform an order by.  For the DBA, to create an index, then using a hint to force a hash join between an index and the large table can improve performance greatly, but to move the index onto SSD can increase the hash and limit the requirements for SSD at the same time.

create table    new_ordertmp_tbl  compress pctfree 0 tablespace data_1 as
SELECT /*+ USE_HASH(t,i) INDEX_FFS(i,I_TBL2_IDX) INDEX(t,CT1) */
cast(MOD(t.i_id, 1000) as number(3)) im_key
, LEAST(ROUND(MONTHS_BETWEEN(:b1,  t.t_dt) + .4999 ), 48) AS r_key , t.i_id AS ib_id
, t.m_id, t.t_dt, cast(:b5 as varchar2(5)) m_cd, FIRST_VALUE(i.ib_id) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
) AS ibcid, t.t_nbr, cast(TO_NUMBER(TO_CHAR(FIRST_VALUE(t.t_dt) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
), ''YYYYMMDD'')) as number(8)) AS d_id,
FIRST_VALUE(DECODE(t.oct_cd, NULL, 'O','W', 'O', 'E', 'O', 'R', 'R', 'F')
) OVER(PARTITION BY t.d_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
) AS tct_cd, SUM(t.ot_amt) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt) AS ot_amt
, FIRST_VALUE(NVL(t.pmt_cd, ''U'')) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
) AS pmt_cd, SUM(t.i_cnt) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt) AS i_cnt
, FIRST_VALUE(t.cs_cd IGNORE NULLS) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
) AS cs_cd, FIRST_VALUE(t.cc_cd IGNORE NULLS) OVER(
PARTITION BY t.i_id, t.m_id, t.t_nbr,t.t_dt ORDER BY t.t_dt ASC
) AS cc_cd, t.oct_cd
FROM CT_TBL1 t, I_TBL2 i
WHERE t.m_id = :b5
AND t.t_dt BETWEEN  :b1  AND  :b2 AND i.ibid = t.i_id
order by i.i_id; 

Object Sizes:

CT_TBL1, partition 7= 800GB

I_TBL2=1.2TB

While the I_TBL2_IDX, the index created on the I_TBL2 and possessing only the columns required for this routinely run query and leading with the I_ID column, is only 200GB.

Execution Plan for Query:

Table 4

Description Object Cost Cardinality Bytes PartitionID
SELECT STATEMENT, GOAL = ALL_ROWS

107587

16356015

10079496

 WINDOW SORT

107587

16356015

10079496

  WINDOW BUFFER

107587

16356015

10079496

   WINDOW SORT

107587

16356015

10079496

    WINDOW SORT

107587

16356015

10079496

     FILTER
      HASH JOIN

107371

16356015

10079496

       PARTITION LIST SINGLE

330

16356015

8166868

7

        TABLE ACCESS FULL CT_TBL1

330

16356015

8166868

7

       INDEX FAST FULL SCAN I_TBL2_IDX

23597

6399400008

2120000

The hash join is thus, decreased to a total size of 1TB, vs. the much larger size it would have been if the hash join would have been run against the table.  By running it with the index residing on solid state disks, the actual performance to create the table from the CTAS in question was increased by 12 fold.

What does the IO look like on the solid state disk vs. the old standard disk?  The differences are startling when viewed through iostat, (table 5).

Table 5

Device: rsec/s wsec/s avgqu-sz %util
Raid 5 Disk

55200

 30224

215.72

84.03

SSD

52394.67

   41306

223.74

7.49

As you can see, the IO is much less impacting on the SSD than the standard disk.

Via graphs, such as from Cacti, the differences in IO throughput can be seen for standard disk, (figure 3) and solid state disk, (figure 4.)

Figure 3

 

 

 

 

 

Figure 4

 

 

 

 

 

 

 

Summary

Solid state disk is here to stay and often will be seen as a “silver bullet” for production I/O issues.  The goal of the DBA is to utilize this technology in a way that does not replace logical tuning and focus instead, in ways that may actually support positive changes enforcing both physical and logical tuning to get the most out of the new hardware available on the market today.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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