Archive for the 'Oracle performance' Category

SLOB Chewed Up All My File System Space and Spit It Out. But, Why?

This is a quick blog post in response to a recent interaction with a SLOB user. The user reached out to me to lament that all her file system space was consumed as the result of a SLOB execution (runit.sh). I reminded her that runit.sh will alert to possible derelict mpstat/iostat/vmstat processes from an aborted SLOB test. If these processes exist they will be spooling their output to unlinked files.

The following screen shot shows what to expect if a SLOB test detects potential “deadwood” processes. If you see this sort of output from runit.sh, it’s best to investigate whether in fact they remain from an aborted test or whether there are other users on the system that left these processes behind.

 

The next screenshot shows how to take action on the runit.sh output:

Summary

If SLOB is consuming all your file system space, it’s probably already telling you why–all you need to do is take action. I hope this is helpful for some wayward Googler someday.

EMC XtremIO – The Full-Featured All-Flash Array. Interested In Oracle Performance? See The Whitepaper.

NOTE: There’s a link to the full article at the end of this post.

I recently submitted a manuscript to the EMC XtremIO Business Unit covering some compelling lab results from testing I concluded earlier this year. I hope you’ll find the paper interesting.

There is a link to the full paper at the bottom of this block post. I’ve pasted the executive summary here:

Executive Summary

Physical I/O patterns generated by Oracle Database workloads are well understood. The predictable nature of these I/O characteristics have historically enabled platform vendors to implement widely varying I/O acceleration technologies including prefetching, coalescing transfers, tiering, caching and even I/O elimination. However, the key presumption central to all of these acceleration technologies is that there is an identifiable active data set. While it is true that Oracle Database workloads generally settle on an active data set, the active data set for a workload is seldom static—it tends to move based on easily understood factors such as data aging or business workflow (e.g., “month-end processing”) and even the data source itself. Identifying the current active data set and keeping up with movement of the active data set is complex and time consuming due to variability in workloads, workload types, and number of workloads. Storage administrators constantly chase the performance hotspots caused by the active dataset.

All-Flash Arrays (AFAs) can completely eliminate the need to identify the active dataset because of the ability of flash to service any part of a larger data set equally. But not all AFAs are created equal.

Even though numerous AFAs have come to market, obtaining the best performance required by databases is challenging. The challenge isn’t just limited to performance. Modern storage arrays offer a wide variety of features such as deduplication, snapshots, clones, thin provisioning, and replication. These features are built on top of the underlying disk management engine, and are based on the same rules and limitations favoring sequential I/O. Simply substituting flash for hard drives won’t break these features, but neither will it enhance them.

EMC has developed a new class of enterprise data storage system, XtremIO flash array, which is based entirely on flash media. XtremIO’s approach was not simply to substitute flash in an existing storage controller design or software stack, but rather to engineer an entirely new array from the ground-up to unlock flash’s full performance potential and deliver array-based capabilities that are unprecedented in the context of current storage systems.

This paper will help the reader understand Oracle Database performance bottlenecks and how XtremIO AFAs can help address such bottlenecks with its unique capability to deal with constant variance in the I/O profile and load levels. We demonstrate that it takes a highly flash-optimized architecture to ensure the best Oracle Database user experience. Please read more:  Link to full paper from emc.com.

Oracle’s Timeline, Copious Benchmarks And Internal Deployments Prove Exadata Is The Worlds First (Best?) OLTP Machine – Part 1.5

In Part I of this series about Oracle OLTP/ERP on Exadata versus non-Exadata, I took a moment to point out the inaccuracies of a particular piece of Oracle marketing literature. In a piece aimed at chronicling Oracle Corporation history, the marketing department went way out of line by making the following claim regarding Exadata:

[…] wins benchmarks against key competitors [..]

Please don’t get me wrong, those five words appearing in any random sentence wouldn’t pose any sort of  a problem. However, situated as they are in this particular sentence does create a  problem because the statement is utterly false.  Exadata has not won a single benchmark against any competitor–“key” or otherwise.

Along For The Ride
In Part I of this series I pointed out the fact HP Oracle Exadata Storage Server cells (a.k.a., V1 Exadata) were used in this June 2009 HP BladeSystem 1-TB Scale TPC-H. However, merely involving Exadata hardware can hardly support Oracle’s marketing claim vis a vis winning benchmarks against key competitors.

There is a big difference between being involved in a benchmark and being the technology that contributes to the result.

I made it clear, in Part I, that Exadata storage was used in that 2009 HP TPC-H result but none of the Exadata features contributed to the result. I clarified that assertion by pointing out that the particular benchmark in question was an In-memory Parallel Query result. Since the result establish Oracle database performance achieved through in-memory database processing I didn’t feel compelled to shore up my assertion. I didn’t think anyone would be confused over the fact that in-memory database processing is not improved by storage technology.

I was wrong.

In the comment section of Part I a comment by a blog reader took offense at my audacious claim. Indeed, how could I assert that storage is not a relevant component in achieving good in-memory database processing benchmark results. The reader stated:

You give reference to a TPHC that used Exadata and then say no Exadata features were used. [..] You obviously don’t know what you are talking about

Having seen that I began to suspect there may be other readers confused on the matter so I let the comment through moderation and decided to address the confusion it in this post.

So now it’s time to address the reader’s comment. If Exadata is used in a benchmark, but Exadata Storage Server offload processing is disabled, would one consider that an Exadata benchmark or was Exadata merely along for the ride?

Here is a screenshot of the full disclosure report that shows Exadata storage intelligence (offload processing) features were disabled. For this reason I assert that Exadata has never won a benchmark against “competitors”, neither “key” nor otherwise.

The screenshot:

Oracle’s Timeline, Copious Benchmarks And Internal Deployments Prove Exadata Is The Worlds First (Best?) OLTP Machine – Part I

I recently took a peek at this online, interactive history of Oracle Corporation. When I got to the year 2008, I was surprised to see no mention of the production release of Exadata–the HP Oracle Database Machine. The first release of Exadata occurred in September 2008.

Once I advanced to 2009, however, I found mention of Exadata but I also found a couple of errors:

  • The text says “Sun Oracle Database Machine” yet the photograph is that of an HP Oracle Database Machine (minor, if not comical, error)
  • The text says Exadata “wins benchmarks against key competitors” (major error, unbelievably so)

What’s First, Bad News or Good News?

Bad News

The only benchmark Exadata has ever been used in was this 1TB scale TPC-H in 2009 with HP Blades.  Be aware, as I pointed out in this blog post, that particular TPC-H was an in-memory Parallel Query benchmark. Exadata features were not used. Exadata was a simple block storage device. The table and index scans were conducted against cached blocks in the Oracle SGAs configured in the 64 nodes of the cluster. Exadata served as nothing more than persistent storage for the benchmark. Don’t get me wrong. I’m not saying there was no physical I/O. The database was loaded as a timed test (per TPC-H spec) which took 142 minutes and the first few moments of query processing required physical I/O so the data could be pulled up into the aggregate SGAs. The benchmark also requires updates. However, these ancillary I/O operations did not lean on Exadata feature nor are they comparable to a TPC-H that centers on physical I/O.  So could using Exadata in an in-memory Parallel Query benchmark be classified as winning “benchmarks against key competitors?” Surely not but I’m willing to hear from dissenters on that. Now that the bad news is out of the way I’ll get to what I’m actually blogging about. I’m blogging about the good news.

Good News

The good news I’d like to point out from screenshot (below) of Oracle’s interactive history is that it spares us the torment of referring to the Sun Oracle Exadata Machine as the First Database Machine for OLTP as touted in this press release from that time frame.  A system that offers 60-fold more capacity for random reads than random writes cannot possibly be mistaken as a special-built OLTP machine.  I’m delighted that the screen shot below honestly represents the design center for Exadata which is DW/BI. For that reason, Exadata features have nothing at all to do with OLTP. That’s a good readon the term OLTP is not seen in that screen shot. That is good news.

OLTP does not trigger Smart Scans thus no offload processing (filtration,projection, storage index, etc). Moreover, Hybrid Columnar Compression has nothing to do with OLTP, except perhaps, in an information life cycle management hierarchy. So, there’s the good news. Exadata wasn’t an OLTP machine in Oracle’s timeline and it still is not an OLTP machine. No, Oracle was quite right for not putting the “First OLTP Machine” lark into that timeline. After all, 2009 is quite close to 40 years after the true first OLTP Machine which was CICS/IMS.  I don’t understand the compulsion to make outlandish claims.

Bad News

Yes, more bad news. Oracle has never published an Exadata benchmark result even with their own benchmarks. That’s right. Oracle has a long history of publishing Oracle Application Standard benchmarks–but no Exadata results.

I’ve gone on the record as siding with Oracle for not publishing TPC benchmarks with Exadata for many reasons.  However, I cannot think of any acceptable excuse for why Oracle would pitch Exadata to you as best for Oracle Applications when a) there are no OLTP features in Exadata*, b) Oracle Corporation does not use Exadata for their ERP and c) there is no benchmark proof for Exadata OLTP/ERP capabilities.

Closing Thoughts

Given all I’ve just said, why is it that (common knowledge) the majority of Exadata units shipping to customers are quarter-rack for non-DW/BI use cases? Has Exadata just become the modern replacement for “[…] nobody ever got fired for buying […]?” Is that how platforms are chosen these days? How did we get to that point of lowered-expectations?

Enjoy the screen shot of memory lane, wrong photo, bad, good and all:

* I am aware of the Exadata Smart Flash Log feature.

Oracle Exadata Storage Server: A Black Box with No Statistics.

A question came in about whether it is possible to measure how much data is filtered out when running a query serviced by a Smart Scan in the Exadata Storage Server grid. The following is the long answer.

An Example of Offload Processing Effectiveness Accounting

I’d like to answer this question by taking real information from a test configuration consisting of 4 Real Application Clusters nodes attached to 6 Exadata Storage Server cells with the SAS disk option (12 x 300GB 3.5″ 15K RPM).

Test Workload – The Affinity Card Program Test Database

The Affinity Card Program Test Database (ACPTD) is a test schema and set of queries that mimics the type of queries performed by a marketing group responsible for promoting the use of a retail club card and affinity credit card for a fictitious company. In this example deployment of the ACPTD, the group responsible for promoting club card and affinity credit card activity for Acme Inc has built a data mart with data from several sources, including the main corporate ERP and CRM systems; partner merchant ERP systems; and, outside syndicated data.

For this blog post I’ll focus on the main syndicated card transaction table called all_card_trans. However, there are other tables in the schema and for reference sake the following text box shows the table sizes in my current deployment of the test kit. As the query shows, the all_card_trans table is 613 GB. Yes, I know that’s small, but this is a test system and I don’t like watching 6 Exadata cells hammering 1 GB/s each for tens of minutes when a couple of minutes will do. I hope you’ll understand.

SQL> set echo on
SQL> @tab_sizes
SQL> col segment_name for a32
SQL> select segment_name, sum(bytes)/1024/1024 mb from user_segments
  2  where segment_name not like 'BIN%' and segment_name not like 'SYS%'
  3  group by segment_name;

SEGMENT_NAME                             MB
-------------------------------- ----------
PARTNER_MERCHANTS                         8
PARTNER_MERCHANT_SALES                67780
ZIPCODES                                  8
OUR_SALES                             21956
ALL_CARD_TRANS                       629032
MCC                                       8
OUR_STORES                                8
CUST_SERVICE                             76
TM                                   .03125
CUST_FACT                              4708

10 rows selected.

The table definition for the all_card_trans table is shown in the following text box:

SQL> desc all_card_trans
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 CARD_NO                                   NOT NULL VARCHAR2(20)
 CARD_TYPE                                          CHAR(20)
 MCC                                       NOT NULL NUMBER(6)
 PURCHASE_AMT                              NOT NULL NUMBER(6)
 PURCHASE_DT                               NOT NULL DATE
 MERCHANT_CODE                             NOT NULL NUMBER(7)
 MERCHANT_CITY                             NOT NULL VARCHAR2(40)
 MERCHANT_STATE                            NOT NULL CHAR(3)
 MERCHANT_ZIP                              NOT NULL NUMBER(6)

The following queries show the total row count in the all_card_trans table as well as the number of rows representing transactions with credit cards with leading 4 digits of 3333 and 4447, which represent the card block granted to our fictitious company, Acme Inc, for their affinity card program. This is a needle in a haystack query since only .3% of the data matches the where predicate. Now, before you look at the text box please note that this is a single instance of Real Application Clusters driving this query. And, yes, the RAC node is a small 8-core DL360. Both scans are driving storage at 6 GB/s (613GB/102s). Folks, remember that to do what I just did in the following text box with Fibre Channel I’d have to have a system with 15 FC HBAs attached to several huge SAN arrays. Oh, and I should point out that it would also require about 85 Netezza Snippet Processing Units to match this throughput.

SQL> select count(*) from all_card_trans where  card_no like '4777%' or card_no like '3333%’;

  COUNT(*)
----------
  22465830

Elapsed: 00:01:42.71
SQL> select count(*) from all_card_trans;

  COUNT(*)
----------
7897611641

Elapsed: 00:01:42.47

I Can’t Believe I Ate the Whole Thing

The following query uses a method for measuring cell activity by forcing the database to ingest all rows and all columns. Don’t try to make sense of the query because it isn’t supposed to make sense. It is only supposed to drive Exadata to return all rows and all columns, which it does.

$ more demo_offload_bytes.sql

drop view ingest;
create view ingest as
select avg(length(CARD_NO)) c1,
avg(length(CARD_TYPE)) c2,
max(MCC + PURCHASE_AMT) c3,
max(length(PURCHASE_DT)) c4,
max(MERCHANT_CODE) c5,
avg(length(MERCHANT_CITY)) c6,
avg(length(MERCHANT_STATE)) c7,
max(MERCHANT_ZIP) c8 from all_card_trans;

col MB format 99999999.9
select NAME,VALUE/(1024*1024*1024) GB from gv$sysstat where STATISTIC# in (196, 44, 43 );

select c1+c2+c3+c4+c5+c6+c7 from ingest;

select NAME,VALUE/(1024*1024*1024) GB from gv$sysstat where STATISTIC# in (196, 44, 43);

The following text box shows that when I ran the query Exadata (in aggregate) scanned 613 GB of disk and returned all but 6% to the RDBMS instance (delta cell physical IO interconnect bytes). Also recorded by v$sysstat (bytes eligible) is the fact that there was nothing peculiar about the data being scanned-peculiar in any such fashion that would have interfered with offload processing (e.g., chained rows, etc). Since I asked for all the data, that is what I got. It is nice to know, however, that the entirety of the data was a candidate for offload processing.

Notice I didn’t time the query. I’ll offer more on that later in this blog post.

SQL> @demo_offload_bytes

View dropped.

View created.

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           1227.80814
cell physical IO interconnect bytes                              1151.52453
cell physical IO bytes eligible for predicate offload            1227.31451

C1+C2+C3+C4+C5+C6+C7
--------------------
          9010106.59

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                            1841.5774
cell physical IO interconnect bytes                              1727.15227
cell physical IO bytes eligible for predicate offload            1840.97177

So let’s take a look at some offload processing. The following change to the “ingest” query reduces the amount of data ingested by the database tier by only selecting rows where the credit card number started with 4777 or 3333. We know from the scan of the table that this whittles down the dataset by 99.7%.

$ more demo_offload_bytes1.sql

create view ingest_less as
select avg(length(CARD_NO)) c1,
avg(length(CARD_TYPE)) c2,
max(MCC + PURCHASE_AMT) c3,
max(length(PURCHASE_DT)) c4,
max(MERCHANT_CODE) c5,
avg(length(MERCHANT_CITY)) c6,
avg(length(MERCHANT_STATE)) c7,
max(MERCHANT_ZIP) c8 from all_card_trans act
where card_no like '4777%' or card_no like '3333%';

col MB format 99999999.9
select NAME,VALUE/(1024*1024*1024) GB from gv$sysstat where STATISTIC# in (196, 44, 43 );

set timing on
select c1+c2+c3+c4+c5+c6+c7 from ingest_less;

select NAME,VALUE/(1024*1024*1024) GB from gv$sysstat where STATISTIC# in (196, 44, 43);

The following text box shows that that Exadata scanned and filtered out the uninteresting rows. The projected columns accounted for only 1.7 GB across the iDB intelligent I/O fabric. That is, the database tier only had to ingest 1.7 GB, or 17 MB/s since the query completed in 103 seconds.

SQL> @demo_offload_bytes_1

View dropped.

Elapsed: 00:00:00.21

View created.

Elapsed: 00:00:00.00

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           1841.13743
cell physical IO interconnect bytes                              43.1782733
cell physical IO bytes eligible for predicate offload            1840.97177

Elapsed: 00:00:00.01

C1+C2+C3+C4+C5+C6+C7
--------------------
           9010106.9

Elapsed: 00:01:43.31

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           2454.80203
cell physical IO interconnect bytes                              44.8776852
cell physical IO bytes eligible for predicate offload            2454.62903

Elapsed: 00:00:00.00
SQL>

So, yes, Exadata does let you see how effectively Smart Scans are working. In this example we saw a single-table scan on a small Oracle Exadata Storage Server grid weeding out 99.7% of the data.

Sneaky, Sneaky.

You’ll notice when I executed demo_offload_bytes.sql (the non-filtering example) I was not tracking query completion time. That’s because up to this point I’ve only been showing examples driven by a single Real Application Clusters node. Now, let’s think about this. I have 8 Intel processor cores and a single inbound Infiniband path on the database server. From an inbound I/O bandwidth perspective the host can “only” ingest 1.6GB/s, but the CPUs may also further throttle that since the database is doing its own projection in this case.

I’ve shown, in this post, that this test Exadata Storage grid configuration can scan disk at 6 GB/s. The question you might ask-and I’m about to answer-is how much does this single database host throttle storage and why does that matter? Well, it matters because having ample storage bandwidth with limited database server bandwidth is the classic imbalance addressed by the HP Oracle Database Machine. So, let me run it again-with timing turned on. As you’ll see, bottlenecks are bottlenecks. This single database host cannot keep up with storage, so storage is throttled and the result is 18X increase in query completion time when compared to the heavily filtered case. Both queries had to read the same amount of data but in this case there was an imbalance in the upstream ability to ingest the data both from an I/O bandwidth and CPU perspective.

SQL> @demo_offload_bytes

View dropped.

Elapsed: 00:00:00.02

View created.

Elapsed: 00:00:00.01

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           2454.81084
cell physical IO interconnect bytes                               44.886501
cell physical IO bytes eligible for predicate offload            2454.62903

Elapsed: 00:00:00.00

C1+C2+C3+C4+C5+C6+C7
--------------------
          9010106.59

Elapsed: 00:29:26.85

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           3068.55792
cell physical IO interconnect bytes                              620.492259
cell physical IO bytes eligible for predicate offload            3068.28629

Elapsed: 00:00:00.00

Need More RDBMS Bandwidth? OK, Use It.

So, let’s see what the full-ingestion case looks like with 4 Real Application Clusters nodes. And please forgive that I forgot to aggregate the v$sysstat output. I’ve added 4-fold database grid resources so I should complete the full-ingestion query in 75% less time. The text box starts out showing that the number of RAC instances was increased from 1 to 4.

SQL> host date
Fri Oct  3 16:27:52 PDT 2008

SQL> select instance_name from gv$instance;

INSTANCE_NAME
----------------
test1

Elapsed: 00:00:00.02
SQL> select instance_name from gv$instance;

INSTANCE_NAME
----------------
test1
test4
test3
test2

Elapsed: 00:00:00.08
SQL> host date
Fri Oct  3 16:32:06 PDT 2008
SQL>
SQL> @demo_offload_bytes

View dropped.

Elapsed: 00:00:00.03

View created.

Elapsed: 00:00:00.01

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           3068.69843
cell physical IO interconnect bytes                              620.632769
cell physical IO bytes eligible for predicate offload            3068.28629
physical IO disk bytes                                           .013385296
cell physical IO interconnect bytes                              .013385296
cell physical IO bytes eligible for predicate offload                     0
physical IO disk bytes                                           .010355473
cell physical IO interconnect bytes                              .010355473
cell physical IO bytes eligible for predicate offload                     0
physical IO disk bytes                                           .031275272
cell physical IO interconnect bytes                              .031275272

NAME                                                                     GB
---------------------------------------------------------------- ----------
cell physical IO bytes eligible for predicate offload                     0

12 rows selected.

Elapsed: 00:00:00.02

C1+C2+C3+C4+C5+C6+C7
--------------------
          9010106.59
Elapsed: 00:07:25.01

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           3222.13893
cell physical IO interconnect bytes                              764.535856
cell physical IO bytes eligible for predicate offload            3221.70425
physical IO disk bytes                                            154.31714
cell physical IO interconnect bytes                              144.732708
cell physical IO bytes eligible for predicate offload            154.280945
physical IO disk bytes                                           152.860648
cell physical IO interconnect bytes                              143.367385
cell physical IO bytes eligible for predicate offload            152.828033
physical IO disk bytes                                           153.195674
cell physical IO interconnect bytes                                143.6831

NAME                                                                     GB
---------------------------------------------------------------- ----------
cell physical IO bytes eligible for predicate offload             153.13031

12 rows selected.

Elapsed: 00:00:00.02
SQL>

Well, that was predictable, but cool nonetheless. What about the filtered ingest-query? Should there be much of a speedup given the fact that storage bandwidth remains constant and the ingest rate of the filtered query was only roughly 17 MB/s with a single Real Application Clusters node? I could save the cut and paste effort and just tell you that adding Real Application Clusters nodes in this case will, of course, not reduce the query time, but since I’ve bored you this long, here it is:

SQL> @demo_offload_bytes_1

View dropped.

Elapsed: 00:00:00.01

View created.

Elapsed: 00:00:00.01

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           3222.15758
cell physical IO interconnect bytes                              764.554509
cell physical IO bytes eligible for predicate offload            3221.70425
physical IO disk bytes                                           154.334342
cell physical IO interconnect bytes                               144.74991
cell physical IO bytes eligible for predicate offload            154.280945
physical IO disk bytes                                           153.213285
cell physical IO interconnect bytes                               143.70071
cell physical IO bytes eligible for predicate offload             153.13031
physical IO disk bytes                                           152.878347
cell physical IO interconnect bytes                              143.385084

NAME                                                                     GB
---------------------------------------------------------------- ----------
cell physical IO bytes eligible for predicate offload            152.828033

12 rows selected.

Elapsed: 00:00:00.01

C1+C2+C3+C4+C5+C6+C7
--------------------
           9010106.9

Elapsed: 00:01:42.79

NAME                                                                     GB
---------------------------------------------------------------- ----------
physical IO disk bytes                                           3375.27835
cell physical IO interconnect bytes                              764.974159
cell physical IO bytes eligible for predicate offload            3374.81995
physical IO disk bytes                                             308.0746
cell physical IO interconnect bytes                              144.133529
cell physical IO bytes eligible for predicate offload            307.986572
physical IO disk bytes                                           310.634206
cell physical IO interconnect bytes                              145.189768
cell physical IO bytes eligible for predicate offload              310.5755
physical IO disk bytes                                           302.274069
cell physical IO interconnect bytes                              143.805239

NAME                                                                     GB
---------------------------------------------------------------- ----------
cell physical IO bytes eligible for predicate offload            302.218781

12 rows selected.

Elapsed: 00:00:00.01
SQL> host date
Fri Oct  3 16:49:32 PDT 2008

Summary

Exadata works and has statistics so that the solution doesn’t feel so much like a black box. In the end, however, none of the statistcs stuff really matters. What matters is whether your queries complete in the desired service times.

Databases are the Contents of Storage. Future Oracle DBAs Can Administer More. Why Would They Want To?

I’ve taken the following quote from this Oracle whitepaper about low cost storage:

A Database Storage Grid does not depend on flawless execution from its component storage arrays. Instead, it is designed to tolerate the failure of individual storage arrays.

In spite of the fact that the Resilient Low-Cost Storage Initiative program was decommissioned along with the Oracle Storage Compatability Program, the concepts discussed in that paper should be treated as a barometer of the future of storage for Oracle databases-with two exceptions: 1) Fibre Channel is not the future and 2) there’s more to “the database” than just the database. What do I mean by point 2? Well, with features like SecureFiles, we aren’t just talking rows and columns any more and I doubt (but I don’t know) that SecureFiles is the end of that trend.

Future Oracle DBAs
Oracle DBAs of the future become even more critical to the enterprise since the current “stove-pipe” style IT organization will invariably change. In today’s IT shop, the application team talks to the DBA team who talks to the Sys Admin team who tlks to the Storage Admin team. All this to get an application to store data on disk through a Oracle database. I think that will be the model that remains for lightly-featured products like MySQL and SQL Server, but Oracle aims for more. Yes, I’m only whetting your appetite but I will flesh out this topic over time. Here’s food for thought: Oracle DBAs should stop thinking their role in the model stops at the contents of the storage.

So while Chen Shapira may be worried that DBAs will get obviated, I’d predict instead that Oracle technology will become more full-featured at the storage level. Unlike the stock market where past performance is no indicator of future performance, Oracle has consistently brought to market features that were once considered too “low-level” to be in the domain of a Database vendor.

The IT industry is going through consolidation. I think we’ll see Enterprise-level IT roles go through some consolidation over time as well. DBAs who can wear more than “one hat” will be more valuable to the enterprise. Instead of thinking about “encroachment” from the low-end database products, think about your increased value proposition with Oracle features that enable this consolidation of IT roles-that is, if I’m reading the tea leaves correctly.

How to Win Friends and Influence People
Believe me, my positions on Fibre Channel have prompted some fairly vile emails in my inbox-especially the posts in my Manly Man SAN series. Folks, I don’t “have it out”, as they say, for the role of Storage Administrators. I just believe that the Oracle DBAs of today are on the cusp of being in control of more of the stack. Like I said, it seems today’s DBA responsibilities stop at the contents of the storage-a role that fits the Fibre Channel paradigm quite well, but a role that makes little sense to me. I think Oracle DBAs are capable of more and will have more success when they have more control. Having said that, I encourage any of you DBAs who would love to be in more control of the storage to look at my my post about the recent SAN-free Oracle Data Warehouse. Read that post and give considerable thought to the model it discusses. And give even more consideration to the cost savings it yields.

The Voices in My Head
Now my alter ego (who is a DBA, whereas I’m not) is asking, “Why would I want more control at the storage level?” I’ll try to answer him in blog posts, but perhaps some of you DBAs can share experiences where performance or availability problems were further exacerbated by finger pointing between you and the Storage Administration group.

Note to Storage Administrators
Please, please, do not fill my email box with vitriolic messages about the harmony today’s typical stove-pipe IT organization creates. I’m not here to start battles.

Let me share a thought that might help this whole thread make more sense. Let’s recall the days when an Oracle DBA and a System Administrator together (yet alone) were able to provide Oracle Database connectivity and processing for thousands of users without ever talking to a “Storage Group.” Do you folks remember when that was? I do. It was the days of Direct Attach Storage (DAS). The problem with that model was that it only took until about the late 1990s to run out of connectivity-enter the Fibre Channel SAN. And since SANs are spokes attached to hubs of storage systems (SAN arrays), we wound up with a level of indirection between the Oracle server and its blocks on disk. Perhaps there are still some power DBAs that remember how life was with large numbers of DAS drives (hundreds). Perhaps they’ll recall the level of control they had back then. On the other hand, perhaps I’m going insane, but riddle me this (and feel free to quote me elsewhere):

Why is it that the industry needed SANs to get more than a few hundred disks attached to a high-end Oracle system in the late 1990s and yet today’s Oracle databases often reside on LUNs comprised of a handful of drives in a SAN?

The very thought of that twist of fate makes me feel like a fish flopping around on a hot sidewalk. Do you remember my post about capacity versus spindles? Oh, right, SAN cache makes that all better. Uh huh.

Am I saying the future is DAS? No. Can I tell you now exactly what model I’m alluding to? Not yet, but I enjoy putting out a little food for thought.

What Oracle Topic Is On Your Coffee Table?

I’ve received a couple of emails over the last few weeks in which readers asked me what my favorite blog posts are on my own blog. I’d have to sift through old posts to answer that definitively, but one recent post comes to mind. I think my recent post about why Oracle shops generally aren’t realizing the full potential of their hard drives is one of my favorites.


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I work for Amazon Web Services. The opinions I share in this blog are my own. I'm *not* communicating as a spokesperson for Amazon. In other words, I work at Amazon, but this is my own opinion.

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All content is © Kevin Closson and "Kevin Closson's Blog: Platforms, Databases, and Storage", 2006-2015. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Kevin Closson and Kevin Closson's Blog: Platforms, Databases, and Storage with appropriate and specific direction to the original content.

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