2. Who am I?
Frits Hoogland
– Working with Oracle products since 1996
– Working with VX Company since 2009
Interests
– Databases, Operating Systems, Application Servers
– Web techniques, TCP/IP, network security
– Technical security, performance
Twitter: @fritshoogland
Blog: http://fritshoogland.wordpress.com
Email: fhoogland@vxcompany.com
Oracle ACE Director
OakTable member
3. What is exadata
– Engineered system specifically for oracle database.
– Ability to reach high number of read IOPS and huge
bandwidth.
– Has it‟s own patchbundles.
– Validated versions and patch versions across database,
clusterware, o/s and storage, firmware.
– Dedicated, private storage for databases.
– ASM.
– Recent hardware & recent CPU.
– No virtualisation.
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4. Exadata versions
– Oracle database 64 bit version >= 11
– ASM 64 bit version >= 11
- Exadata communication is layer in skgxp code
– Linux OL5 x64
- No UEK kernel used (except X2-8)
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5. Exadata hardware
– Intel Xeon server hardware
– Infiniband 40Gb/s
– Oracle cell (storage) server
- Flash to mimic SAN cache
- High performance disks or high capacity disks
- 600GB 15k RPM / ~ 5ms latency
- 2/3TB 7.2k RPM / ~ 8ms latency
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6. Flash
– Flashcards are in every storage server
– Total of 384GB per storage server
– Do not confuse exadata STORAGE server flashcache
with oracle database flashcache
– Flash can be configured either as cache (flash cache
and flash log) or as diskgroup or both
– When flash is used as diskgroup latency is ~ 1 ms
- Much faster than disk
- My guess was < 400µs
- 1µs infiniband
- 200µs flash IO time
- some time for storage server
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7. Flash
– Flash is restricted to 4x96GB = 384GB per storage
server.
- Totals:
- Q:1152GB, H:2688GB, F:5376GB
- Net (ASM Normal redundancy):
- Q: 576GB, H:1344GB, F:2688GB
– That is a very limited amount of storage.
– But with flash as diskgroup there‟s no cache for PIO‟s!
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8. Exadata specific features
– The secret sauce of exadata: the storage server
- smartscan
- storage indexes
- EHCC *
- IO Resource manager
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9. OLTP
– How does OLTP look like (in general | simplistic)
– Fetch small amount of data
- Invoice numbers, client id, product id
- select single values or small ranges via index
– Create or update rows
- Sold items on invoice, payments, order status
- insert or update values
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10. SLOB
– A great way to mimic or measure OLTP performance is
SLOB
– Silly Little Oracle Benchmark
– Author: Kevin Closson
– http://oaktable.net/articles/slob-silly-little-oracle-
benchmark
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11. SLOB
–It can do reading:
FOR i IN 1..5000 LOOP
v_r := dbms_random.value(257, 10000) ;
SELECT COUNT(c2) into x
FROM cf1 where custid > v_r - 256 AND custid < v_r;
END LOOP;
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12. SLOB
–And writing:
FOR i IN 1..500 LOOP
v_r := dbms_random.value(257, 10000) ;
UPDATE cf1 set
c2 =
'AAAAAAAABBBBBBBBAAAAAAAABBBBBBBBAAAAAAAABBBBBBBBAAAAAAAA
BBBBBBBBAAAAAAAABBBBBBBBAAAAAAAABBBBBBBBAAAAAAAABBBBBBBBA
AAAAAAABBBBBBBB',
....up to column 20 (c20)....
where custid > v_r - 256 AND custid < v_r;
COMMIT;
END LOOP;
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19. 1 reader conclusion
- The time spend on PIO is 15% - 45%
- Majority of time spend on LIO/CPU time
- Because main portion is CPU, fastest CPU “wins”
- Actually: fastest CPU, memory bus and memory.
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20. LIO benchmark
– Let‟s do a pre-warmed cache run
- Pre-warmed means: no PIO, data already in BC
- This means ONLY LIO speed is measured
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26. LIO benchmark
Core difference and slower memory shows when # readers
exceeds core count.
Same memory speed: CPU speed matters less with
more concurrency.
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28. LIO benchmark
Lesser core’s and slower memory make LIO processing
increasingly slower with more concurrency
For LIO processing ODA (non-Exadata) and Exadata
does not matter.
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29. – Conclusion:
- LIO performance is impacted by:
- CPU speed
- Number of sockets and core‟s
- L1/2/3 cache sizes
- Memory speed
- Exadata does not matter here!
- When comparing entirely different systems also consider:
- Oracle version
- O/S and version (scheduling)
- Hyper threading / CPU architecture
- NUMA (Exadata/ODA: no NUMA!)
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30. – But how about physical IO?
- Lower the buffercache to 4M
- sga_max_size to 1g
- cpu_count to 1
- db_cache_size to 1M (results in 4M)
- Slob run with 1 reader
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31. The V2 is the slowest with 106 seconds.
The X2 is only a little slower with 76 seconds.
Surprise! ODA is the fastest here with 73 seconds.
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32. Total time (s) CPU time (s) IO time (s)
ODA 73 17 60
X2 76 33 55
V2 106 52 52
– IO ODA:
- 60/1264355= 0.047 ms
– IO X2:
- 55/1265602= 0.043 ms
– IO V2:
- 52/1240941= 0.042 ms
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33. – This is not random disk IO!
- Average latency of random IO 15k rpm disk ~ 5ms
- Average latency of random IO 7.2k rpm disk ~ 8ms
– So this must come from a cache or is not random disk IO
- Exadata has flashcache.
- On ODA, data probably very nearby on disk.
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34. Total time (s) CPU time (s) IO time (s)
ODA 73 17 60
X2 76 33 55
V2 106 52 52
- Exadata IO takes (way) more CPU.
- Roughly the same time is spend on doing IO‟s.
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36. Now IO responsetime on ODA is way higher than
Exadata (3008s)
Both Exadata’s perform alike: X2 581s, V2 588s.
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37. Total time (s) CPU time (s) IO time (s)
ODA 3008 600 29428
X2 581 848 5213
V2 588 1388 4866
– IO ODA:
- 29428/13879603 = 2.120 ms
– IO X2:
- 5213/14045812 = 0.371 ms
– IO V2:
- 4866/14170303 = 0.343 ms
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40. Total time (s) CPU time (s) IO time (s)
ODA 4503 1377 88756
X2 721 2069 13010
V2 747 3373 12405
– IO ODA:
- 88756/28246604 =0.003142183039 = 3.142 ms
– IO X2:
- 13010/28789330=0.0004519035351 = 0.452 ms
– IO V2:
- 12405/28766804=0.0004312262148 = 0.431 ms
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42. ODA (20 x 15krpm HDD) disk capacity is saturated so
response time increases with more readers.
Flashcache is not saturated, so response time of IO
of 10-20-30 readers increases very little.
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44. ODA response time more or less increases linear.
The V2 response time (with more flashcache!) starts
increasing at 70 readers. A bottleneck is showing up!
(7x384GB!!)
X2 flashcache (3x384GB) is not saturated, so little
increase in response time.
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45. IOPS view instead of responsetime
3x384GB Flashcache and IB can serve
> 115’851 read IOPS!
This V2 has more flashcache, so
decline in read IOPS probably due to
something else!
ODA maxed out at ~ 11’200 read IOPS
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46. - V2 top 5 timed events with 80 readers:
Event Waits Time(s) (ms) time Wait Class
------------------------------ ------------ ----------- ------ ------ ----------
cell single block physical rea 102,345,354 56,614 1 47.1 User I/O
latch: cache buffers lru chain 27,187,317 33,471 1 27.8 Other 44.1%
latch: cache buffers chains 14,736,819 19,594 1 16.3 Concurrenc
DB CPU 13,427 11.2
wait list latch free 932,930 553 1 .5 Other
- X2 top 5 timed events with 80 readers:
Event Waits Time(s) (ms) time Wait Class
------------------------------ ------------ ----------- ------ ------ ----------
cell single block physical rea 102,899,953 68,209 1 87.9 User I/O
DB CPU 9,297 12.0
latch: cache buffers lru chain 10,917,303 1,585 0 2.0 Other 2.9%
latch: cache buffers chains 2,048,395 698 0 .9 Concurrenc
cell list of blocks physical r 368,795 522 1 .7 User I/O
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48. 1 LIO 80 LIO 1 PIO 80 PIO
ODA 1 11 73 9795
X2 (HC disks) 2 11 76 976
V2 (HP disks) 3 22 106 1518
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49. 1 LIO 80 LIO 1 PIO 80 PIO
ODA 1 11 73 9795
X2 2 11 76 976
V2 3 22 106 1518
1 PIO w/o flashcache 80 PIO w/o flashcache
ODA 73 9795
X2 167 ?
V2 118 5098
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50. - For scalability, OLTP needs buffered IO (LIO)
- Flashcache is EXTREMELY important physial IO scalability
- Never, ever, let flash be used for something else
- Unless you can always keep all your small reads in cache
- Flash mimics a SAN/NAS cache
- So nothing groundbreaking here, it does what current, normal infra should
do too...
- The bandwidth needed to deliver the data to the database is
provided by Infiniband
- 1 Gb ethernet = 120MB/s, 4 Gb fiber = 400MB/s
- Infiniband is generally available.
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51. – How much IOPS can a single cell do?
- According to
https://blogs.oracle.com/mrbenchmark/entry/inside_the_sun_oracl
e_database
- A single cell can do 75‟000 IOPS from flash (8kB)
- Personal calculation: 60‟000 IOPS with 8kB
– Flashcache cache
- Caches small reads & writes (8kB and less) mostly
- Large multiblock reads are not cached, unless segment property
„cell_flash_cache‟ is set to „keep‟.
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52. – Is Exadata a good idea for OLTP?
- From a strictly technical point of view, there is no benefit.
– But...
– Exadata gives you IORM
– Exadata gives you reasonably up to date hardware
– Exadata gives a system engineered for performance
– Exadata gives you dedicated disks
– Exadata gives a validated combination of database,
clusterware, operating system, hardware, firmware.
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53. – Exadata storage servers provide NO redundancy for
data
- That‟s a function of ASM
– Exadata is configured with either
- Normal redundancy (mirroring) or
- High redundancy (triple mirroring)
– to provide data redundancy.
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54. – Reading has no problem with normal/high redundancy.
– During writes, all two or three AU‟s need to be written.
– This means when you calculate write throughput, you
need to double all physical writes if using normal
redundancy.
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55. – But we got flash! Right?
– Yes, you got flash. But it probably doesn‟t do what you
think it does:
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58. – Please mind these are cumulative numbers!
– The half-rack is a POC machine, no heavy usage
between POC‟s.
– The quarter-rack has had some load, but definately not
heavy OLTP.
– I can imagine flashlog can prevent long write times if disk
IO‟s queue.
- A normal configured database on Exadata has online redo in
DATA and in RECO diskgroup
- Normal redundancy means every log write must be done 4 times
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60. – Log file write response time on Exadata is not in the
same range as reads.
– There‟s the flashlog feature, but it does not work as the
whitepaper explains
– Be careful with heavy writing on Exadata.
- There‟s no Exadata specific improvement for writes.
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