Search Engine Optimizers sau Search Engine Pirates?

În mod normal, când discutăm despre pirați ai internetului, ne referim  la cei care uploadează diferite materiale ce nu le aparțin. Definiția poate fi lărgită, cu siguranță, dar voi presupune că majoritatea publicului cititor este deja familiarizat cât de cât cu termenul. Totuși, nu vreau să mă axez numai pe acest tip de pirat, căci metode de furt prin internet mai sunt.

Am avut norocul de a lucra anul trecut într-un domeniu relativ nou – SEO (adică Search Engine Optimization). Spun ”norocul” pentru că ce am învățat mi-a schimbat percepția asupra internetului. Exact ce am făcut nu poate fi explicat cu ușurință. În mare, optimizerii sunt cei care știu cum să-și promoveze site-ul astfel încât să ajungă printre primele pagini ale Google-ului (dacă nu chiar pe prima) la diferite search-uri. Și, desigur, ca orice metodă de promovare a unui produs (căci majoritatea site-urilor asta sunt/vor să fie), există și mijloace nu tocmai cinstite de a-ți face treaba.

Pe înțelesul tuturor, webmasterii se fură și între ei! Internetul este un monopson, unde site-urile se întrec pentru a-i face pe plac singurului client, Google. Recomand următorul filmuleț pentru a înțelege mai bine termenii anterior menționați:  http://www.youtube.com/watch?v=jq4rUcmALhY&feature=related

Desigur, într-o lume perfectă sau cel puțin într-un monopson perfect, cererea stabilește mai toate regulile și, într-adevăr, Google este șeful aici, dar oferta este într-atât de numeroasă încât uneori nu poate fi controlată.

O tehnică de a face SEO este Link Building-ul, menționat și în filmuleț – adică să mărești numărul de linkuri externe ce duc către site-ul tău. Metoda normală este cea de afiliere, adică X pune link-ul meu, iar eu pe-al său. Dar dacă eu văd că X este mai nepriceput ca webmaster/optimizer, eu pot să-i dau linkului său un tag numit ”no follow”, iar Google pur și simplu nu îi va indexa site-ul, trecând printr-al meu.

Evident, acest lucru este din toate punctele de vedere condamnabil, dar când cel care face legea, adică unicul client, este ocupat cu milioane de alte site-uri, mai are el timp să mă verifice pe mine?

Este concurența neloială furt când nu există legislație să te condamne?

This blog featured on my previous blog URL (still unaccessible) and was published on 7th September 2008. Thought of reposting this, as a similar performance issue was recently reported by one of my customer, where a query had to be fixed by way of an Index Hint. This blog would also help you understanding the way optimizer_index_cost_adj is to be calculated for optimizing a query performance. This calculation was requested by one of a regular reader of my blogs.

Modifying an existing index to optimize multiple queries is a common practice, as this eliminates the overheads of maintaning muliple indexes, by way of INSERTS / UPDATES / DELETES. This modification of an existing index includes, re-arranging column ordering, adding a new column etc. This blog focuses on an issue that may trigger and impact the performance of your production system due to addition of a new column in an existing index.

The scenario that I am going to discuss in this blog is, “Assuming one day you are at a customer site investigating a performance issue or doing a proactive health check of a production system. During investigation, you come across two queries that are almost similar and use same Index. The only difference between the two queries is an additional Filter Predicate.” The two queries may look like :

select * from emp where sal=:b2;
select * from emp where sal=:b2 and deptno=:b1;

Both the queries use an Index on SAL column, which is a Single Column Index. We all know that the logical I/O’s of a query can be reduced if a query can make best use of an Index and scans the table, only when required. Which means, a composite index on (SAL, DEPTNO) would be efficient and should reduce the Logical I/O’s for the second query, without impacting the first query. Therefore, instead of creating a new composite index, the suggestion is put forward to modify an existing index on SAL and add DEPTNO as a second column. This will ensure that both the queries use the same index. After the alteration of this Index on production, won’t you be stunned if the customer comes back and says that the performance is still not OK, and infact, is worst than it was a day before ? This is because, while the alteration improved the performance of the second query, it impacted the performance of the first with the change in optimizer plan as it calculates the cost of an Altered Index to be costlier than Full Table Scan. Let us walk through a test case to understand the issue and the rationale behind optimizer estimation.

TEST CASE

drop table vivek_test;

exec dbms_random.seed(0);

create table vivek_test as
with test as
(select * from all_objects where rownum between 1 and 10000)
select	trunc((rownum-1)/1000) object_id,
	round(dbms_random.value(1,100),0) bad_clustered,
	a.object_name,
	a.temporary,
	a.created
from	test a,
	test b
where  rownum between 1 and 100000; 

create index vivek_test_idx on vivek_test(object_id);

exec dbms_stats.gather_table_stats(user,'VIVEK_TEST',cascade=>true);

Below are the statistics for the table and its index (in my test database) :

select owner, table_name, num_rows, blocks from dba_tables where table_name='VIVEK_TEST';

OWNER                          TABLE_NAME                       NUM_ROWS     BLOCKS
------------------------------ ------------------------------ ---------- ----------
VIVEK                          VIVEK_TEST                          99889        420

select index_name, num_rows, blevel, leaf_blocks, distinct_keys, clustering_factor
from	dba_indexes
where	table_name='VIVEK_TEST';

INDEX_NAME                       NUM_ROWS     BLEVEL LEAF_BLOCKS DISTINCT_KEYS CLUSTERING_FACTOR
------------------------------ ---------- ---------- ----------- ------------- -----------------
VIVEK_TEST_IDX                     100000          1         195           100               420

Now, I will run two queries (assume these to be application queries) against the table and both of these queries use the Index created on Object_ID. The queries and their Runtime Execution Plans are pasted below :

variable b1 number;
variable b2 number;
exec :b1:=0;
exec :b2:=57;
select /*+ QUERY1 */ * from vivek_test where object_id=:b1;

SQL_ID  99ss8hxuax3q2, child number 0
-------------------------------------
select /*+ QUERY1 */ * from vivek_test where object_id=:b1

Plan hash value: 3696163677

----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                |       |       |     7 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| VIVEK_TEST     |   981 | 24525 |     7   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | VIVEK_TEST_IDX |   990 |       |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("OBJECT_ID"=:B1)

select /*+ QUERY2 */ * from vivek_test where object_id=:b1 and bad_clustered=:b2;

Plan hash value: 3696163677

----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                |       |       |     7 (100)|          |
|*  1 |  TABLE ACCESS BY INDEX ROWID| VIVEK_TEST     |    10 |   250 |     7   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | VIVEK_TEST_IDX |   990 |       |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   1 - filter("BAD_CLUSTERED"=:B2)
   2 - access("OBJECT_ID"=:B1)

Based on the queries executed against this table, it is decided to drop the exisiting index on OBJECT_ID and recreate it on OBJECT_ID, BAD_CLUSTERED. This is to optimize the second query (eliminate unwanted logical I/O’s) and with an assumption that this change will not impact the first query, with QUERY1 hint.

drop index vivek_test_idx;
create index vivek_test_idx on vivek_test(object_id, bad_clustered);
exec dbms_stats.gather_table_stats(user,'VIVEK_TEST',cascade=>true);
SQL> select owner, table_name, num_rows, blocks from dba_tables where table_name='VIVEK_TEST';

OWNER                          TABLE_NAME                       NUM_ROWS     BLOCKS
------------------------------ ------------------------------ ---------- ----------
VIVEK                          VIVEK_TEST                         102477        420

Elapsed: 00:00:00.11
SQL> select index_name, num_rows, blevel, leaf_blocks, distinct_keys, clustering_factor
  2  from dba_indexes
  3  where table_name='VIVEK_TEST';

INDEX_NAME                       NUM_ROWS     BLEVEL LEAF_BLOCKS DISTINCT_KEYS CLUSTERING_FACTOR
------------------------------ ---------- ---------- ----------- ------------- -----------------
VIVEK_TEST_IDX                     100000          1         237          9998             41509

On a production system, these changes are normally done during the downtime and the system is released for the users. The impact is usually visible when the users start executing these queries concurrently. Let us re-execute the queries with the change in the index implemented. We will execute the second query first (with QUERY2 hint) and then the first query and compare the runtime execution plan.

variable b1 number;
variable b2 number;
exec :b1:=0;
exec :b2:=57;

select /*+ QUERY2 */ * from vivek_test where object_id=:b1 and bad_clustered=:b2;

Plan hash value: 3696163677

----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                |       |       |     6 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| VIVEK_TEST     |    10 |   250 |     6   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | VIVEK_TEST_IDX |    10 |       |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("OBJECT_ID"=:B1 AND "BAD_CLUSTERED"=:B2)

select /*+ QUERY1 */ * from vivek_test where object_id=:b1;

Plan hash value: 3275957777

--------------------------------------------------------------------------------
| Id  | Operation         | Name       | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |            |       |       |    97 (100)|          |
|*  1 |  TABLE ACCESS FULL| VIVEK_TEST |  1015 | 25375 |    97   (4)| 00:00:02 |
--------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   1 - filter("OBJECT_ID"=:B1)

While the cost of the second query, and the logical I/O’s as well, has gone down, the change has impacted the first query, that was working absolutely fine before the change. This can lead to a greater impact when these are run concurrently. To check for the impact, I simulated concurrency by way of spawning 20 sessions, each running the impacted query. With these 20 concurrent sessions, my 2 CPU laptop choked up with 100% utilization and 95% waits on CBC Latches. The code to simulate the concurrency is pasted below with an explanation :

1. Create a Staging table and populate the object id’s in random order to be queried.
2. The staging table holds 20000 object id’s with 10 parallel streams (PSTREAMS).
3. The procedure will be scheduled from dbms_job, with pstreams as an Input parameter (10 Streams).

drop table staging;

create table staging as
select object_id, mod(rownum,10) pstreams
from vivek_test where rownum between 1 and 20000
order by dbms_random.random;

create or replace procedure process_job(ps in number) as
l_cnt	number;
begin
for i in (select object_id from staging where pstreams=ps)
loop
select count(distinct temporary) into l_cnt from vivek_test
where	object_id=i.object_id;
end loop;
end;
/

variable jobno number;
variable hrs number;
variable mns number;
exec :hrs:=14;
exec :mns:=35;

begin
for i in 1..10
loop
BEGIN
     DBMS_JOB.SUBMIT(:jobno,'process_job('||i||');',
         trunc(sysdate)+:hrs/24+:mns/(24*60));
     COMMIT;
END;
end loop;
end;
/

What could have triggered the change in plan ?

Before getting into the cause of this change, let us execute the query with an Index Hint. This will help us investigate the issue.

select /*+ INDEX(VIVEK_TEST,VIVEK_TEST_IDX) */ * from vivek_test where object_id=:b1;

Plan hash value: 3696163677

----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                |       |       |   420 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| VIVEK_TEST     |  1015 | 25375 |   420   (0)| 00:00:06 |
|*  2 |   INDEX RANGE SCAN          | VIVEK_TEST_IDX |  1015 |       |     4   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("OBJECT_ID"=:B1)

With an Index Hint, the cost of step TABLE ACCESS BY INDEX ROWID step is 420 as against 97 for the unhinted query (TABLE SCAN FULL). Optimizer, while generating an optimal execution plan, compares cost of each available access path and selects the one with least cost. In this case, the cost of a TABLE SCAN FULL was assumed to be cheaper than the cost of an INDEX SCAN. The Index can be considered costlier due to two reasons : by way of Cost and / or, due to huge number of Logical I/O’s. TABLE ACCESS BY INDEX ROWID is always followed by INDEX RANGE SCAN, and as can be seen from the plan, the cost of an INDEX SCAN is mere 4, while the cost of TABLE ACCESS step is 420. While the former is calculated from BLEVEL and LEAF_BLOCKS, the later is calculated based on CLUSTERING_FACTOR (CF).

Compare the CF of the Index, before and after change. Before the change, CF was 420 (near to the num_blocks of the table), after the change it changed drastically to 41509. This was due to the bad clustering of the additional column bad_clustered and this change was enough to blow up the cost. The cost calculation before and after the change is as under :

BEFORE THE CHANGE
Index Cost = BLEVEL + CEIL(LEAF_BLOCKS * SELECTIVITY)
Table Scan = CEIL(CF * SELECTIVITY)
Total Cost of TABLE ACCESS BY INDEX ROWID = Index Cost + Table Scan

While BLEVEL is considered for Index Cost Calculation, optimizer ignores this if the BLEVEL of an Index is 1 and all the columns of an Index are used as an EQUALITY Predicate in the query. Therefore, in our case too, since the columns are used as an Equality predicate and the blevel is 1, this value will be ignored.

SQL> select ceil(195*1/100), ceil(420*1/100) from dual;

CEIL(195*1/100) CEIL(420*1/100)
--------------- ---------------
              2               5

The cost of the first query, before the change is 7. Now, after the change :

SQL> select 1+ceil(237*1/100), ceil(41509*1/100) from dual;

1+CEIL(237*1/100) CEIL(41509*1/100)
----------------- -----------------
                4               416

With Index Hint, the cost is 420 and this is costlier than 97. There are two solutions for this issue. These are :

1. Chnage the value for OPTIMIZER_INDEX_COST_ADJ (OICA) and set this globally.
2. Fix the Query with an Index Hint.

The first change (i.e.OICA), would require application testing as setting this globally can impact other queries. The second change was a feasible solution and could be applied without impacting other queries. Let us visit the formula to calculate the value of OICA.

OPTIMIZER_INDEX_COST_ADJ

This is calculated as COST OF A FTS divided by TABLE ACCESS BY INDEX ROWID. In our case, the cost of FTS is 97 and Table Access by Index Rowid is 420. Therefore 97/420*100 is 23.09. This means, if the value of OICA is set to less than 23, the query should use an Index, without modification.

SQL> alter session set optimizer_index_cost_adj=22;

Session altered.
select /*+ OICA_22 */ * from vivek_test where object_id=:b1

Plan hash value: 3696163677

----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                |       |       |    92 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| VIVEK_TEST     |  1015 | 25375 |    92   (0)| 00:00:02 |
|*  2 |   INDEX RANGE SCAN          | VIVEK_TEST_IDX |  1015 |       |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("OBJECT_ID"=:B1)

As mentioned earlier, a better option would be to hint the query. Once the query is modified with an Index Hint, I spawned the Concurrency jobs and could see that gain, with less than 5% waits on CBC latches, as against, more than 95% and 100% CPU Utilization.

How do we predict the impact of this change before implementing it ?

Before making these changes, the impact can be predicated. This prediction should help estimate the cost change, before and after. Following query can be modified and run (for a bigger table, should be run in parallel during less load period). The only change in the query will be the ordering of the columns in an Index. For this, I will recreate the Index on a Single Column and then execute the query to check for the Change in the CF after adding another column.

SQL> drop index vivek_test_idx;

Index dropped.

Elapsed: 00:00:00.07
SQL> create index vivek_test_idx on vivek_test(object_id);

SQL> select  sum(block_change) from (
  2  select  block_fno, object_id, bad_clustered, prev_bfno,
  3   (case when nvl(prev_bfno,0)block_fno then 1 else 0 end) block_change from (
  4  select  block_fno, object_id, bad_clustered, lag(block_fno) over (order by object_id) prev_bfno from (
  5  select    dbms_rowid.rowid_block_number(rowid)||'.'||
  6    dbms_rowid.ROWID_TO_ABSOLUTE_FNO(rowid,'VIVEK','VIVEK_TEST') block_fno,
  7    object_id, bad_clustered
  8  from  VIVEK_TEST
  9  order by object_id, bad_clustered, block_fno)));

SUM(BLOCK_CHANGE)
-----------------
            41509

The output of this query should approximately match the CF calculated by the Optimizer and should be enough to proactively arrive at the impact due to the change before making the change.

ppi.sql

Article Source: http://EzineArticles.com/?expert=Johnson_Smith_Woods

It seems as though everyone has a website these days. Along with social media applications, websites can be a way for anyone to become famous overnight. Notwithstanding, a great many of them don’t tally a good deal of web presence on the Internet. There’s a reason for this.

Many sites lack Search Engine Optimization.  What is Search Engine Optimization (SEO), you ask? According to Wikipedia, SEO is the process of improving the volume or quality of traffic to a website from the respective search engines via “natural” or un-paid (“organic” or “algorithmic”) search results. This is a major requirement for your site to come up in a search within Google’s first five pages; prime real estate for websites.  Sounds a little technical, doesn’t it? In essence, SEO is the strategic placement of keywords within your site’s content, be it articles, HTML, back end coding, image placement and so forth.

The objective of any site is to be seen by millions, which in some cases can equate to dollars and cents. Typically, with SEO and the right keywords, the earlier (or higher) a site appears in the search results list, the more visitors it will receive from the respective search engine. For example, if a health guru just launched a site designed to educate people on losing weight and I typed the following search parameters “how to lose weight” into the Google search bar, it would yeild all the top sites within the first few pages. With SEO and the appropriate placement of keywords, a site owner can be the top listed site in a search engine and this should be the objective of all new websites.

There are many parameters to why sites become visible on the web. One sure fire way is registering a unique domain name. Before choosing your site’s domain name, you should always do an initial search to guarantee your name is unique or at the very least, not overused. Another way to be visible, though this is a more passive method in search engine terms, is through word of mouth. SEO works to get those visitors that you can’t reach through contemporary means, while word of mouth promotes your site through your own initiative and ingenuity. Rule of thumb, word of mouth generally garners three to five persons more for every person you tell about your site; not a bad deal if you’re a social butterfly with a lot of friends.

While many of site owners won’t have a mastery of SEO or keyword placement overnight, utilization of your connections, contacts and social media resources can help promote your site to a large audience. There are lots of free and cost effective ways of promoting your site. All that’s left is taking your passion and drive and applying it to learning and mastering your craft. However, if the resources [money] are available, you can hire a Search Engine Optimizer to help get your site to the top of the search engine list. For the right price, I’m available!

Download

http://hotfile.com/dl/17136290/72423c7/Sybex.Always.Be.Testing.The.Complete.Guide.to.Google.Website.Optimizer.Aug.2008.pdf.html

http://kewlshare.com/dl/fa7bc41b0362/Sybex.Always.Be.Testing.The.Complete.Guide.to.Google.Website.Optimizer.Aug.2008.pdf.html

opis:
Ashampoo Photo Optimizer niezbędny dla każdego, kto posiada aparat cyfrowy. Jedno kliknięcie myszką wystarczy do pełnej optymalizacji zdjęcia. Przy pomocy Ashampoo Photo Optimizer zdjęcia stają się jaskrawsze, jaśniejsze, uzyskują większą wyraźność i kontrast.
Aparat cyfrowy teraz ma już prawie każdy. Problem w tym, że fotografie bywają dalekie od tego, jakimi chciałaby je widzieć osoba wykonująca zdjęcie. Są one zupełnie ciemne, ze złym kontrastem i niejaskrawymi kolorami. Często wychodzą rozmyte i wyblakłe. Każdą fotografię można poddać obróbce przy pomocy profesjonalnego edytora obrazów i doprowadzić ją do doskonałości. Ale czy każdy potrafi to zrobić? Ci którzy potrafią, często nie mają na to czasu – obróbka fotografii może go wymagać dużo.

http://rapidshare.com/files/291978810/POPTIMIZER_up_by_Lamp5.rar

SQLPass unfortunately can’t last forever, but happily it’s still going strong. Here’s some highlights from my Day #2.

Paul Randal Knows Exactly What’s Going on in Your Transaction Log…

A definite highlight of day 2 was attending Paul Randal’s session on Logging and Recovery in SQL Server. I’ve read Pauls’ blog posts on this topic and attended his classes before, but even being familiar with the material I find I always learn something from his talks. You just can’t beat being strong on the basics!

I took a lot of notes in the session, this is my favorite excerpt from my notes:

• SQL Server must reserve space in the TLOG so that it can roll back the active transactions, if needed.
• Once a VLF no longer contains log records that are required, it can be cleared
• This is done by a log backup in full or bulk_logged recovery models, or by checkpiont in simple
• All that happens when a VLF is “cleared” is that it is marked as inactive
  • Nothing is cleared at that time
  • Nothing is truncated
  • Nothing is overwritten
  • The log file size does not change
  • The only thing that happens is that whole VLFs are marked inactive if possible (no active transactions)

Ben Nevarz asks, “How You Doing, Optimizer?”

One of my favorite pieces of information on day 2 was in Ben Nevarez’s talk on how the query optimizer works. He mentioned this DMV, which I hadn’t used before yesterday:

Sys.dm_exec_query_optimizer_info ←Check me out!

The other useful bit of info is that the timeout flag is recorded in the xml for the sql plans, so plans which the optimizer finds so complicated that it times out on compilation can be queried from the cache!

SQLPASS homework assignment: Write and test this query, determine how to automate running it and collecting the information.

Sample Queries

This sample from BOL  to find excessive compiles/recompiles:

select *
from sys.dm_exec_query_optimizer_info
where counter = 'optimizations'
or counter = 'elapsed time'

See Also…

Ben Nevarez on the Phases of Query Optimization

Conor Cunningham’s Blog on sys.dm_exec_query_optimizer_info– excerpt:

“The other use of the DMV is to get a good statistical picture of a running system.  Say that I’m a DBA and I want to know how many queries in my application have hints or _need_ hints to work well.  Well, this will tell you.  Granted, it doesn’t separate recompiles from compiles, and if you have a system where plans are getting kicked out of the cache things may be a bit skewed, but I can tell you that this is far better than simply guessing.  Often the DB application developer doesn’t realize that they’ve built an application that requires a lot of hinting or a lot of compilations, and you can see this in more detail than you get with the performance counters”

I spend most of my family-blogging words on my sons. There are several reasons for this. For one thing, they tend to say and do funnier things than, say, the cats. Also, they will not correct me if I apply selective editing to tighten up the tales of their goofballery. Finally, they do not claim that just because I was an English major, I am not permitted to use words in whatever way I deem fit. Like goofballery. So generally they make easier targets for writing about than, say, my husband.

However, my SONS are not turning 33 today, so I will risk grammatical corrections and the fact my that my target will actually read what I write to tell you about my husband.

First if all, he’s 33 today.

Which is divisible by 11, in case you’re curious.

Give me the camera

My husband claims that before he met me, he was innocent of sin. He did not know what the “Snooze” button on the alarm clock did. Long before he met me, however, he was deeply immersed in the world of the RPG – Role Playing Games. He spent his childhood reading supplements and devising fantastic adventures with intricate maps and completely consistent world-views. When he grew to adulthood, he put aside such childish things in order to focus on more mature pursuits: rules systems. He wrote several of his own and has an entire bookcase of rules systems, which he’s generally read cover to cover. I remember he once turned to me and said, “Brenda, I think I’ve actually read everything on the internet about these games.” Granted, that was when the internet was a smaller infinity, but still.

I mention role-playing first because when I think about what makes my husband who he is, it’s right up there. But that’s hardly comprehensive.

Adam loves delving deeply into arcane problems. He’s currently being tempted, non-ironically, by a book called “Growing Object-Oriented Software, Guided By Tests”. He used to complain that no one would ever play obscure gaming systems with him. Now he complains that he doesn’t get to do test-driven agile programming. Previous deep-drinking has included medieval sociology (where he’s more knowledgeable than I) and aikido texts.

His #1 repetitive complaint is that he doesn’t have a photographic memory and can’t remember everything he’s read.

Adam and sons

If you’re met Adam, you were probably dazzled by his smile. He has a great smile, which always includes his eyes. I don’t think he knows how to fake-smile.

His dazzling smile

Adam is an optimizer. He’s always looking for ways to make things better; for the most efficient and most logical way of doing things. Once he arrives at what he thinks is an optimal solution, he’s happy to stick with it until and unless data presents itself that there is a better solution. I love variety. I’ll go out one way and come back another only because they are different. This boggles his mind.

He listens to techno when he programs.

His body is composed of 60% pretzels, 30% iced tea and 10% trace elements.

He mixes three cereals in the morning in order to arrive at the optimum combination of texture and taste.

Adam loves songs. He has a beautiful warm tenor and he’s not afraid to use it. He specializes in Celtic/Irish songs and folk ballads. When we were in Saudi Arabia and Washington State (you do not want to KNOW about our phone bills that summer), he used to sing me “Road Go Ever Ever On”. I thought he was an English major when I met him, because he quoted Kipling and Byron at me until I was bedazzled. I never stopped being bedazzled.

His actual degree was in Biochemistry, Cellular and Molecular Biology — but he hated Chemistry.

He has an astonishing ability to fall without hurting himself. Usually he hurts himself doing things that you couldn’t possibly think you could hurt yourself doing, like walking down the hall.

He reminds me to pray.

He is a remarkable husband. He supports me when I choose to do something, advocates for my needs when I subsume them, compliments me even when I feel unlovely, never fails to look me in the eyes and tell me he loves me, and holds my hand as often as he can.

This is the 14th time we’ve celebrated together on his birthday. The first time I got him a wax dragon candle thingy.

I could run through a thousand more bullet points and still fail to capture just who he is. He is my husband and I love him.

How I will always see my husband

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So, here’s my rash statement from Twitter last night: “If FAST isn’t free, I don’t want it!  All it’s doing is automating process I could script/do manually”.  It’s a bold statement, I know, so is FAST really offering something better than what could be achieved today using EMC’s Symmetrix Optimizer?

Hot Spots

EMC’s Symmetrix architecture (18 years old and counting, I believe) uses the concept of disk hypers to present LUNs.  Each physical disk is carved into a number of slices, which are then recombined to create LUNs to present to a host.  A mirrored (RAID-1) LUN uses two hypers, a RAID-5 (3+1) LUN uses 4.  EMC ensure general performance by setting standards on how LUNs are created from hypers and that’s reflected in a “binfile” layout.  However despite this sensible planning, it is possible (especially as hard drives are now much larger and contain many more hypers) that two hypers on a single physical disk could be highly active and so contend against each other – in other words “hot spots” on disk.

Optimizer helps alleviate the issue of hot spots by exchanging the high I/O hypers with low I/O ones, distributing busy LUNs across more physical spindles.  This is classic load balancing where resources are distributed across the available infrastructure in order to obtain better overall generic performance.  EMC have now rebranded Optimizer as part of Ionix for Storage Resource Managment, but it’s still effectively the same product.  Hyper swaps can be managed automatically, based on historical performance data.  They can also be user-defined – a manual swap at the users request.

Although tedious (and not as well automated as Hitachi’s HiCommand Tiered Storage Manager), in theory Optimizer could be used to manually move workload between storage tiers.  In fact, Optimizer is already aware of a tiered storage infrastructure.  Here’s a quote directly from the ControlCenter 6.1 manual:

“Optimizer is also aware of physical drives that operate at different speeds, as well as location of the data on the physical media, which influences the I/O rate. This information is used when determining which logical devices to move.”

So with a little bit of knowledge on the layout of data on a Symmetrix array, it would be possible today to use Optimizer to perform LUN-based FAST.

Load-Balancing Versus Policy

Unfortunately, simple load-balancing of I/O across a storage array doesn’t offer what should be seen as the next generation of storage tiering.  Where Storage Tiering 1.0 was about offering multiple layers of storage within the same physical infrastructure and manually placing or moving LUNs to the appropriate tier, Storage Tiering 2.0 will be about establishing policies that determine more service-based measurements of the performance and availability customers receive. 

A policy-based approach would allow rules to be established on how data at the application layer moves between tiers.  This is a critical distinction from the load-balancing  methodology earlier described.  As an example, where an application was known to require higher performance at a certain time of day or day of the week, data could be moved proactively to a faster tier of storage, returning later once the high I/O workload had completed.  Whilst achievable using Optimizer, there’s no doubt the process of application migration would be tedious and time consuming.  I expect the v1.0 implementation of FAST will simply package up Optimizer into a tool that automates the migration of related data between tiers.  Don’t forget, other vendors have been offering this feature for some time – for example Hitachi and Tiered Storage Manager.

Increasing Granularity

Now LUN-based migration has its benefits.  Where large numbers of disks exist in an infrastructure, application data can be placed or moved to the most appropriate location as required.  However with the introduction of solid state disks (SSDs), a more granular approach is needed as the number of SSDs deployed in an array is likely to be low due to their excessive cost.  Moving an entire application (or even LUN) to SSD will be undesirable unless that application can take full use of the SSD hardware.  There are very few, if any, applications that require high-intensity read/write activity from every piece of application data all the time.

Block-level tiering offers a higher level of granularity to the placement of data.  A LUN can be split into blocks and placed across multiple layers of storage technology including traditional HDDs and faster SSDs.  Selective placement will ensure the more efficient use of expensive SSD media by placing only the highly active data onto it.

All of a sudden with increased granularity we’re back to Storage Tiering 1.0 where data is being placed on faster technology purely based on increasing overall system performance.  This is a feature Compellent have been offering for some time.  Data is migrated up or down the tier hierarchy on a daily basis, subject to performance figures over a 12-day period.  This level of granular performance management is possible because data is stored in a block-based structure.  Unfortunately for EMC, the hyper design legacy represents a technical challenge in making FAST version 2 a reality. 

Patent Rights

As just mentioned, Compellent already offer block-based data migration in their products.  At a recent dinner in London with the Compellent team, they highlighted their strong position in the market, protected by patents covering block-level data migration between tiers.  You can find the filed patent here.  Compellent use the term “Data Progression” to describe how blocks are moved between tiers based on I/O activity.  As I/O activity is monitored over time, it is possible to determine the most appropriate tier of storage to use when expanding capacity.  Typically these are lower tier SATA drives, as initial performance requirements are usually over-estimated.  This metholodogy is very much Storage Tiering 1.0 discussed earlier.

Compellent aren’t the only people claiming rights to block-level tiering within a storage array.  I’ve also found the following patent application from IBM, filed by Barry Whyte, Steve Legg and others.  If IBM and Compellent both claim to have invented the FAST concept, how does that position EMC?  Do they have an earlier patent which trumps these two?

Summary

Storage Tiering 1.0 provides performance management of storage arrays.  Storage Tiering 2.0 extends this to offer policy-driven optimisation offerings.  Both of these technologies are available today from existing vendors in one format or another.  EMC will simply be playing catchup with these vendors once FAST 1 & FAST 2 are released.  I’d like to be surprised and see EMC offer something the competition currently don’t.  I’m not holding my breath…

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Pason Systems Inc. has acquired Petron Industries, provider of a comprehensive suite of equipment, software and sensors to acquire, manage, control and communicate all aspects of drilling operations both onshore and offshore.

            Petron’s current product offering includes:

• Rig monitoring and data acquisition equipment;
• Control and safety systems;
• Drilling application software;
• Sensors and instrumentation; and
• Communication and remote data access services.

Founded in 1983, Petron is based in Houston, TX, and has offices in Texas, Louisiana and Arkansas.

            Pason Systems Inc., an oil services company, provides rental oilfield instrumentation designed and manufactured for use on land-based drilling and service rigs. Pason offers a tightly integrated package of complex services including data acquisition, well site reporting software, remote communications and Internet information management tools to oil and gas companies and drilling contractors in Canada, the US and internationally. Founded in 1978, Pason is headquartered in Calgary, with additional offices in the US, Mexico, Argentina and Australia.

            Pason recently unveiled two new products:

• Remote Directional System – a cost-reduction solution for directional drilling companies; and
• Optimizer – a standalone drilling simulation tool to improve the drilling process.

www.pason.com

www.petronworld.com

Advanced System Optimizer é uma suite de otimização e melhoria do desempenho do sistema poderoso e fácil de usar. Possui mais de 30 ferramentas que permitem melhorar o desempenho de seu computador. O usuário pode melhorar a segurança, deixar mais bonito e otimizar o Windows, melhorar as configurações do Windows, configurar as telas e deixar o Windows do jeito que você quiser.

Estilo: Otimizadores
Fabricante: Systweak Inc.
Tamanho: 12.6 Mb
Formato: Rar
Idioma: Inglês

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