cost based optimizer - Page 159

Why is there no way to do introspection in Objective C with Objects?

- by user346741

I have seen examples (on here especially) of calling hideous C functions and getting structures back that have to be iterated, replete with reams of underbars. Why can't I do this (pseudo to follow): Money *cost = [[Money alloc] init]; for (Property *property in [[cost class] properties]){ .. } for (Method *method in [[cost class] methods]){ .. } ?

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How to generate a monotone MART ROC in R?

- by user1521587

I am using R and applying MART (Alg. for multiple additive regression trees) on a training set to build prediction models. When I look at the ROC curve, it is not monotone. I would be grateful if someone can help me with how I should fix this. I am guessing the issue is that initially, MART generates n trees and if these trees are not the same for all the models I am building, the results will not be comparable. Here are the steps I take: 1) Fix the false-negative cost, c_fn. Let cost = c(0, 1, c_fn, 0). 2) use the following line to build the mart model: mart(x, y, lx, martmode='class', niter=2000, cost.mtx=cost) where x is the matrix of training set variables, y is the observation matrix, lx is the matrix which specifies which of the variables in x is numerical, which one categorical. 3) I predict the test set observations using the mart model found in step 2 using this line: y_pred = martpred(x_test, probs=T) 4) I compute the false-positive and false-negative errors as follows: t = 1/(1+c_fn) %threshold based on Bayes optimal rule where c_fp=1 and c_fn. p_0 = length(which(y_test==1))/dim(y_test)[1] p_01 = sum(1*(y_pred[,2]t & y_test==0))/dim(y_test)[1] p_11 = sum(1*(y_pred[,2]t & y_test==1))/dim(y_test)[1] p_fp = p_01/(1-p_0) p_tp = p_11/p_0 5) repeat step 1-4 for a new false-negative cost.

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Algorithm for generating an array of non-equal costs for a transport problem optimization

- by Carlos

I have an optimizer that solves a transportation problem, using a cost matrix of all the possible paths. The optimiser works fine, but if two of the costs are equal, the solution contains one more path that the minimum number of paths. (Think of it as load balancing routers; if two routes are same cost, you'll use them both.) I would like the minimum number of routes, and to do that I need a cost matrix that doesn't have two costs that are equal within a certain tolerance. At the moment, I'm passing the cost matrix through a baking function which tests every entry for equality to each of the other entries, and moves it a fixed percentage if it matches. However, this approach seems to require N^2 comparisons, and if the starting values are all the same, the last cost will be r^N bigger. (r is the arbitrary fixed percentage). Also there is the problem that by multiplying by the percentage, you end up on top of another value. So the problem seems to have an element of recursion, or at least repeated checking, which bloats the code. The current implementation is basically not very good (I won't paste my GOTO-using code here for you all to mock), and I'd like to improve it. Is there a name for what I'm after, and is there a standard implementation? Example: {1,1,2,3,4,5} (tol = 0.05) becomes {1,1.05,2,3,4,5}

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How to optimize shopping carts for minimal prices?

- by tangens

I have a list of items I want to buy. The items are offered by different shops and different prices. The shops have individual delivery costs. I'm looking for an optimal shopping strategy (and a java library supporting it) to purchase all of the items with a minimal total price. Example: Item1 is offered at Shop1 for $100, at Shop2 for $111. Item2 is offered at Shop1 for $90, at Shop2 for $85. Delivery cost of Shop1: $10 if total order < $150; $0 otherwise Delivery cost of Shop2: $5 if total order < $50; $0 otherwise If I buy Item1 and Item2 at Shop1 the total cost is $100 + $90 +$0 = $190. If I buy Item1 and Item2 at Shop2 the total cost is $111 + $85 +$0 = $196. If I buy Item1 at Shop1 and Item2 at Shop2 the total cost is $100 + $10 + $85 + $0 = 195. I get the minimal price if I order Item1 at Shop1 and Item2 at Shop2: $195 Question I need some hints which algorithms may help me to solve optimization problems of this kind for number of items about 100 and number of shops about 20. I already looked at apache-math and its optimization package, but I have no idea what algorithm to look for.

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how to show all added items into another activity, like: AddtoCart and ViewCart Functionality

- by Stanley

i am trying to make a shopping cart app, allowing user to choose category then select item to purchase, once user will click on any item to purchase, then showing that selected item into another activity with item image, name, cost, qty (to accept by user) and also providing add to cart functionality, now i want whenever user will click on Add to Cart button, then selected item need to show in ViewCart Activity, so here i am placing my AddtoCart Activity code, please tell me what i need to write to show added item(s) into ViewCart Category just like in shopping cart, In ViewCart activity i just want to show item title, cost and qty (entered by user):- public class AddtoCart extends Activity{ static final String KEY_TITLE = "title"; static final String KEY_COST = "cost"; static final String KEY_THUMB_URL = "imageUri"; @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.single); Intent in = getIntent(); String title = in.getStringExtra(KEY_TITLE); String thumb_url = in.getStringExtra(KEY_THUMB_URL); String cost = in.getStringExtra(KEY_COST); ImageLoader imageLoader = new ImageLoader(getApplicationContext()); ImageView imgv = (ImageView) findViewById(R.id.single_thumb); TextView txttitle = (TextView) findViewById(R.id.single_title); TextView txtcost = (TextView) findViewById(R.id.single_cost); txttitle.setText(title); txtcost.setText(cost); imageLoader.DisplayImage(thumb_url, imgv); // Save a reference to the quantity edit text final EditText editTextQuantity = (EditText) findViewById(R.id.edit_qty); ImageButton addToCartButton = (ImageButton) findViewById(R.id.img_add); addToCartButton.setOnClickListener(new OnClickListener() { public void onClick(View v) { // Check to see that a valid quantity was entered int quantity = 0; try { quantity = Integer.parseInt(editTextQuantity.getText() .toString()); if (quantity <= 0) { Toast.makeText(getBaseContext(), "Please enter a quantity of 1 or higher", Toast.LENGTH_SHORT).show(); return; } } catch (Exception e) { Toast.makeText(getBaseContext(), "Please enter a numeric quantity", Toast.LENGTH_SHORT).show(); return; } // Close the activity finish(); } }); }}

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Simple aggregating query very slow in PostgreSql, any way to improve?

- by Ash

HI I have a table which holds files and their types such as CREATE TABLE files ( id SERIAL PRIMARY KEY, name VARCHAR(255), filetype VARCHAR(255), ... ); and another table for holding file properties such as CREATE TABLE properties ( id SERIAL PRIMARY KEY, file_id INTEGER CONSTRAINT fk_files REFERENCES files(id), size INTEGER, ... // other property fields ); The file_id field has an index. The file table has around 800k lines, and the properties table around 200k (not all files necessarily have/need a properties). I want to do aggregating queries, for example find the average size and standard deviation for all file types. But it's very slow - around 70 seconds for the latter query. I understand it needs a sequential scan, but still it seems too much. Here's the query SELECT f.filetype, avg(size), stddev(size) FROM files as f, properties as pr WHERE f.id = pr.file_id GROUP BY f.filetype; and the explain HashAggregate (cost=140292.20..140293.94 rows=116 width=13) (actual time=74013.621..74013.954 rows=110 loops=1) -> Hash Join (cost=6780.19..138945.47 rows=179564 width=13) (actual time=1520.104..73156.531 rows=179499 loops=1) Hash Cond: (f.id = pr.file_id) -> Seq Scan on files f (cost=0.00..108365.41 rows=1140941 width=9) (actual time=0.998..62569.628 rows=805270 loops=1) -> Hash (cost=3658.64..3658.64 rows=179564 width=12) (actual time=1131.053..1131.053 rows=179499 loops=1) -> Seq Scan on properties pr (cost=0.00..3658.64 rows=179564 width=12) (actual time=0.753..557.171 rows=179574 loops=1) Total runtime: 74014.520 ms Any ideas why it is so slow/how to make it faster?

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Speeding up a group by date query on a big table in postgres

- by zaius

I've got a table with around 20 million rows. For arguments sake, lets say there are two columns in the table - an id and a timestamp. I'm trying to get a count of the number of items per day. Here's what I have at the moment. SELECT DATE(timestamp) AS day, COUNT(*) FROM actions WHERE DATE(timestamp) >= '20100101' AND DATE(timestamp) < '20110101' GROUP BY day; Without any indices, this takes about a 30s to run on my machine. Here's the explain analyze output: GroupAggregate (cost=675462.78..676813.42 rows=46532 width=8) (actual time=24467.404..32417.643 rows=346 loops=1) -> Sort (cost=675462.78..675680.34 rows=87021 width=8) (actual time=24466.730..29071.438 rows=17321121 loops=1) Sort Key: (date("timestamp")) Sort Method: external merge Disk: 372496kB -> Seq Scan on actions (cost=0.00..667133.11 rows=87021 width=8) (actual time=1.981..12368.186 rows=17321121 loops=1) Filter: ((date("timestamp") >= '2010-01-01'::date) AND (date("timestamp") < '2011-01-01'::date)) Total runtime: 32447.762 ms Since I'm seeing a sequential scan, I tried to index on the date aggregate CREATE INDEX ON actions (DATE(timestamp)); Which cuts the speed by about 50%. HashAggregate (cost=796710.64..796716.19 rows=370 width=8) (actual time=17038.503..17038.590 rows=346 loops=1) -> Seq Scan on actions (cost=0.00..710202.27 rows=17301674 width=8) (actual time=1.745..12080.877 rows=17321121 loops=1) Filter: ((date("timestamp") >= '2010-01-01'::date) AND (date("timestamp") < '2011-01-01'::date)) Total runtime: 17038.663 ms I'm new to this whole query-optimization business, and I have no idea what to do next. Any clues how I could get this query running faster?

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Getting confused why i dont get expected amount ?

- by Stackfan

I have 1 result and which i will receive in Bank account, Based on that account i have to Put a balance to user account. How can you find the Handling cost from total tried 491.50 / 0.95 = 517.36 which is wrong ? It should be 500.00 (to my expectation) User balance requires 500.00 When 500.00 selected he gets 5% discount There is a handling cost for this ex: 1) Discount: 500.00 - 5% = 475.00 2) Handling cost: (475.00 x 0.034) + 0.35 = 16.50 3) Total: 475.00 + 16.50 = 491.50 So problem is from 491.50, i have to find atleast handling cost to get promised Balance. Any solution ? Cant figure it out myself...

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multiple join query in entity framework

- by gvLearner

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Using SQL Execution Plans to discover the Swedish alphabet

- by Rob Farley

SQL Server is quite remarkable in a bunch of ways. In this post, I’m using the way that the Query Optimizer handles LIKE to keep it SARGable, the Execution Plans that result, Collations, and PowerShell to come up with the Swedish alphabet. SARGability is the ability to seek for items in an index according to a particular set of criteria. If you don’t have SARGability in play, you need to scan the whole index (or table if you don’t have an index). For example, I can find myself in the phonebook easily, because it’s sorted by LastName and I can find Farley in there by moving to the Fs, and so on. I can’t find everyone in my suburb easily, because the phonebook isn’t sorted that way. I can’t even find people who have six letters in their last name, because also the book is sorted by LastName, it’s not sorted by LEN(LastName). This is all stuff I’ve looked at before, including in the talk I gave at SQLBits in October 2010. If I try to find everyone who’s names start with F, I can do that using a query a bit like: SELECT LastName FROM dbo.PhoneBook WHERE LEFT(LastName,1) = 'F'; Unfortunately, the Query Optimizer doesn’t realise that all the entries that satisfy LEFT(LastName,1) = 'F' will be together, and it has to scan the whole table to find them. But if I write: SELECT LastName FROM dbo.PhoneBook WHERE LastName LIKE 'F%'; then SQL is smart enough to understand this, and performs an Index Seek instead. To see why, I look further into the plan, in particular, the properties of the Index Seek operator. The ToolTip shows me what I’m after: You’ll see that it does a Seek to find any entries that are at least F, but not yet G. There’s an extra Predicate in there (a Residual Predicate if you like), which checks that each LastName is really LIKE F% – I suppose it doesn’t consider that the Seek Predicate is quite enough – but most of the benefit is seen by its working out the Seek Predicate, filtering to just the “at least F but not yet G” section of the data. This got me curious though, particularly about where the G comes from, and whether I could leverage it to create the Swedish alphabet. I know that in the Swedish language, there are three extra letters that appear at the end of the alphabet. One of them is ä that appears in the word Västerås. It turns out that Västerås is quite hard to find in an index when you’re looking it up in a Swedish map. I talked about this briefly in my five-minute talk on Collation from SQLPASS (the one which was slightly less than serious). So by looking at the plan, I can work out what the next letter is in the alphabet of the collation used by the column. In other words, if my alphabet were Swedish, I’d be able to tell what the next letter after F is – just in case it’s not G. It turns out it is… Yes, the Swedish letter after F is G. But I worked this out by using a copy of my PhoneBook table that used the Finnish_Swedish_CI_AI collation. I couldn’t find how the Query Optimizer calculates the G, and my friend Paul White (@SQL_Kiwi) tells me that it’s frustratingly internal to the QO. He’s particularly smart, even if he is from New Zealand. To investigate further, I decided to do some PowerShell, leveraging the Get-SqlPlan function that I blogged about recently (make sure you also have the SqlServerCmdletSnapin100 snap-in added). I started by indicating that I was going to use Finnish_Swedish_CI_AI as my collation of choice, and that I’d start whichever letter cam straight after the number 9. I figure that this is a cheat’s way of guessing the first letter of the alphabet (but it doesn’t actually work in Unicode – luckily I’m using varchar not nvarchar. Actually, there are a few aspects of this code that only work using ASCII, so apologies if you were wanting to apply it to Greek, Japanese, etc). I also initialised my $alphabet variable. $collation = 'Finnish_Swedish_CI_AI'; $firstletter = '9'; $alphabet = ''; Now I created the table for my test. A single field would do, and putting a Clustered Index on it would suffice for the Seeks. Invoke-Sqlcmd -server . -data tempdb -query "create table dbo.collation_test (col varchar(10) collate $collation primary key);" Now I get into the looping. $c = $firstletter; $stillgoing = $true; while ($stillgoing) { I construct the query I want, seeking for entries which start with whatever $c has reached, and get the plan for it: $query = "select col from dbo.collation_test where col like '$($c)%';"; [xml] $pl = get-sqlplan $query "." "tempdb"; At this point, my $pl variable is a scary piece of XML, representing the execution plan. A bit of hunting through it showed me that the EndRange element contained what I was after, and that if it contained NULL, then I was done. $stillgoing = ($pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange -ne $null); Now I could grab the value out of it (which came with apostrophes that needed stripping), and append that to my $alphabet variable. if ($stillgoing) { $c=$pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange.RangeExpressions.ScalarOperator.ScalarString.Replace("'",""); $alphabet += $c; } Finally, finishing the loop, dropping the table, and showing my alphabet! } Invoke-Sqlcmd -server . -data tempdb -query "drop table dbo.collation_test;"; $alphabet; When I run all this, I see that the Swedish alphabet is ABCDEFGHIJKLMNOPQRSTUVXYZÅÄÖ, which matches what I see at Wikipedia. Interesting to see that the letters on the end are still there, even with Case Insensitivity. Turns out they’re not just “letters with accents”, they’re letters in their own right. I’m sure you gave up reading long ago, and really aren’t that fazed about the idea of doing this using PowerShell. I chose PowerShell because I’d already come up with an easy way of grabbing the estimated plan for a query, and PowerShell does allow for easy navigation of XML. I find the most interesting aspect of this as the fact that the Query Optimizer uses the next letter of the alphabet to maintain the SARGability of LIKE. I’m hoping they do something similar for a whole bunch of operations. Oh, and the fact that you know how to find stuff in the IKEA catalogue. Footnote: If you are interested in whether this works in other languages, you might want to consider the following screenshot, which shows that in principle, it should work with Japanese. It might be a bit harder to run this in PowerShell though, as I’m not sure how it translates. In Hiragana, the Japanese alphabet starts ?, ?, ?, ?, ?, ...

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The SSIS tuning tip that everyone misses

- by Rob Farley

I know that everyone misses this, because I’m yet to find someone who doesn’t have a bit of an epiphany when I describe this. When tuning Data Flows in SQL Server Integration Services, people see the Data Flow as moving from the Source to the Destination, passing through a number of transformations. What people don’t consider is the Source, getting the data out of a database. Remember, the source of data for your Data Flow is not your Source Component. It’s wherever the data is, within your database, probably on a disk somewhere. You need to tune your query to optimise it for SSIS, and this is what most people fail to do. I’m not suggesting that people don’t tune their queries – there’s plenty of information out there about making sure that your queries run as fast as possible. But for SSIS, it’s not about how fast your query runs. Let me say that again, but in bolder text: The speed of an SSIS Source is not about how fast your query runs. If your query is used in a Source component for SSIS, the thing that matters is how fast it starts returning data. In particular, those first 10,000 rows to populate that first buffer, ready to pass down the rest of the transformations on its way to the Destination. Let’s look at a very simple query as an example, using the AdventureWorks database: We’re picking the different Weight values out of the Product table, and it’s doing this by scanning the table and doing a Sort. It’s a Distinct Sort, which means that the duplicates are discarded. It'll be no surprise to see that the data produced is sorted. Obvious, I know, but I'm making a comparison to what I'll do later. Before I explain the problem here, let me jump back into the SSIS world... If you’ve investigated how to tune an SSIS flow, then you’ll know that some SSIS Data Flow Transformations are known to be Blocking, some are Partially Blocking, and some are simply Row transformations. Take the SSIS Sort transformation, for example. I’m using a larger data set for this, because my small list of Weights won’t demonstrate it well enough. Seven buffers of data came out of the source, but none of them could be pushed past the Sort operator, just in case the last buffer contained the data that would be sorted into the first buffer. This is a blocking operation. Back in the land of T-SQL, we consider our Distinct Sort operator. It’s also blocking. It won’t let data through until it’s seen all of it. If you weren’t okay with blocking operations in SSIS, why would you be happy with them in an execution plan? The source of your data is not your OLE DB Source. Remember this. The source of your data is the NCIX/CIX/Heap from which it’s being pulled. Picture it like this... the data flowing from the Clustered Index, through the Distinct Sort operator, into the SELECT operator, where a series of SSIS Buffers are populated, flowing (as they get full) down through the SSIS transformations. Alright, I know that I’m taking some liberties here, because the two queries aren’t the same, but consider the visual. The data is flowing from your disk and through your execution plan before it reaches SSIS, so you could easily find that a blocking operation in your plan is just as painful as a blocking operation in your SSIS Data Flow. Luckily, T-SQL gives us a brilliant query hint to help avoid this. OPTION (FAST 10000) This hint means that it will choose a query which will optimise for the first 10,000 rows – the default SSIS buffer size. And the effect can be quite significant. First let’s consider a simple example, then we’ll look at a larger one. Consider our weights. We don’t have 10,000, so I’m going to use OPTION (FAST 1) instead. You’ll notice that the query is more expensive, using a Flow Distinct operator instead of the Distinct Sort. This operator is consuming 84% of the query, instead of the 59% we saw from the Distinct Sort. But the first row could be returned quicker – a Flow Distinct operator is non-blocking. The data here isn’t sorted, of course. It’s in the same order that it came out of the index, just with duplicates removed. As soon as a Flow Distinct sees a value that it hasn’t come across before, it pushes it out to the operator on its left. It still has to maintain the list of what it’s seen so far, but by handling it one row at a time, it can push rows through quicker. Overall, it’s a lot more work than the Distinct Sort, but if the priority is the first few rows, then perhaps that’s exactly what we want. The Query Optimizer seems to do this by optimising the query as if there were only one row coming through: This 1 row estimation is caused by the Query Optimizer imagining the SELECT operation saying “Give me one row” first, and this message being passed all the way along. The request might not make it all the way back to the source, but in my simple example, it does. I hope this simple example has helped you understand the significance of the blocking operator. Now I’m going to show you an example on a much larger data set. This data was fetching about 780,000 rows, and these are the Estimated Plans. The data needed to be Sorted, to support further SSIS operations that needed that. First, without the hint. ...and now with OPTION (FAST 10000): A very different plan, I’m sure you’ll agree. In case you’re curious, those arrows in the top one are 780,000 rows in size. In the second, they’re estimated to be 10,000, although the Actual figures end up being 780,000. The top one definitely runs faster. It finished several times faster than the second one. With the amount of data being considered, these numbers were in minutes. Look at the second one – it’s doing Nested Loops, across 780,000 rows! That’s not generally recommended at all. That’s “Go and make yourself a coffee” time. In this case, it was about six or seven minutes. The faster one finished in about a minute. But in SSIS-land, things are different. The particular data flow that was consuming this data was significant. It was being pumped into a Script Component to process each row based on previous rows, creating about a dozen different flows. The data flow would take roughly ten minutes to run – ten minutes from when the data first appeared. The query that completes faster – chosen by the Query Optimizer with no hints, based on accurate statistics (rather than pretending the numbers are smaller) – would take a minute to start getting the data into SSIS, at which point the ten-minute flow would start, taking eleven minutes to complete. The query that took longer – chosen by the Query Optimizer pretending it only wanted the first 10,000 rows – would take only ten seconds to fill the first buffer. Despite the fact that it might have taken the database another six or seven minutes to get the data out, SSIS didn’t care. Every time it wanted the next buffer of data, it was already available, and the whole process finished in about ten minutes and ten seconds. When debugging SSIS, you run the package, and sit there waiting to see the Debug information start appearing. You look for the numbers on the data flow, and seeing operators going Yellow and Green. Without the hint, I’d sit there for a minute. With the hint, just ten seconds. You can imagine which one I preferred. By adding this hint, it felt like a magic wand had been waved across the query, to make it run several times faster. It wasn’t the case at all – but it felt like it to SSIS.

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Upgrading PEAR from 1.9.0 to 1.9.1 fails

- by Skelton

Hi All, I'm willing to install phpunit 5.3 with MAMP 1.9 and there for I need to upgrade PEAR to version 1.9.1. The current version installed is 1.9.0. When I try the to upgrade I get the following: sudo pear channel-update pear.php.net sudo pear upgrade pear Could not get contents of package "/Applications/MAMP/bin/php5.3/bin/pear". Invalid tgz file. upgrade failed When I force the upgrade It still doesn't work: sudo pear upgrade --force PEAR downloading PEAR-1.9.1.tgz ... Starting to download PEAR-1.9.1.tgz (293,587 bytes) .............................................................done: 293,587 bytes upgrade ok: channel://pear.php.net/PEAR-1.9.1 PEAR: Optional feature webinstaller available (PEAR's web-based installer) PEAR: Optional feature gtkinstaller available (PEAR's PHP-GTK-based installer) PEAR: Optional feature gtk2installer available (PEAR's PHP-GTK2-based installer) PEAR: To install optional features use "pear install pear/PEAR#featurename" sudo pear -V PEAR Version: 1.9.0 As bindbn suggested: sudo pear install --offline /Users/tom/Downloads/PEAR-1.9.1.tgz Ignoring installed package pear/PEAR Nothing to install sudo pear upgrade --force --alldeps PEAR downloading PEAR-1.9.1.tgz ... Starting to download PEAR-1.9.1.tgz (293,587 bytes) .............................................................done: 293,587 bytes upgrade ok: channel://pear.php.net/PEAR-1.9.1 PEAR: Optional feature webinstaller available (PEAR's web-based installer) PEAR: Optional feature gtkinstaller available (PEAR's PHP-GTK-based installer) PEAR: Optional feature gtk2installer available (PEAR's PHP-GTK2-based installer) PEAR: To install optional features use "pear install pear/PEAR#featurename" pear -V PEAR Version: 1.9.0 I hope someone can figure this out! Thanks!

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How can I update generic non-pnp monitor?

- by njk

Background I've been running a KVM switch with my monitor at 1920 x 1080 over VGA for over a year. Did a Windows Update on 12/11/12 which did the following: Update for Windows 7 for x64-based Systems (KB2779562) Security Update for Windows 7 for x64-based Systems (KB2779030) Cumulative Security Update for Internet Explorer 8 for Windows 7 for x64-based Systems (KB2761465) Windows Malicious Software Removal Tool x64 - December 2012 (KB890830) Security Update for Windows 7 for x64-based Systems (KB2753842) Security Update for Windows 7 for x64-based Systems (KB2758857) Security Update for Windows 7 for x64-based Systems (KB2770660) After a restart, my extended monitor was dark. I attempted to reset the extended display configuration, and noticed my monitor was being detected as a Generic Non-PnP Monitor: I uninstalled, downloaded new, and re-installed display drivers. Nothing. I attempted to unplug my monitor from the power for 15 minutes. Nothing. I followed some of the suggestions on this thread; specifically DanM's which suggested to create a new *.inf file and replace that in Device Manager. Device Manager said the "best driver software for your device is already installed". The only thing that works is when the monitor is directly attached to the laptop. This obviously is not what I want. My thought is to somehow remove the Generic Non-PnP Monitor from registry. How would I accomplish this and would this help? Any other suggestions? Relevant Hardware ASUS VE276 Monitor TRENDnet 2-Port USB KVM Switch (TK-207K) HP Laptop w/ ATI Radeon HD 4200 Screens

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SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28

- by pinaldave

Earlier we discussed about the what is the common solution to solve the issue with CXPACKET wait time. Today I am going to talk about few of the other suggestions which can help to reduce the CXPACKET wait. If you are going to suggest that I should focus on MAXDOP and COST THRESHOLD – I totally agree. I have covered them in details in yesterday’s blog post. Today we are going to discuss few other way CXPACKET can be reduced. Potential Reasons: If data is heavily skewed, there are chances that query optimizer may estimate the correct amount of the data leading to assign fewer thread to query. This can easily lead to uneven workload on threads and may create CXPAKCET wait. While retrieving the data one of the thread face IO, Memory or CPU bottleneck and have to wait to get those resources to execute its tasks, may create CXPACKET wait as well. Data which is retrieved is on different speed IO Subsystem. (This is not common and hardly possible but there are chances). Higher fragmentations in some area of the table can lead less data per page. This may lead to CXPACKET wait. As I said the reasons here mentioned are not the major cause of the CXPACKET wait but any kind of scenario can create the probable wait time. Best Practices to Reduce CXPACKET wait: Refer earlier article regarding MAXDOP and Cost Threshold. De-fragmentation of Index can help as more data can be obtained per page. (Assuming close to 100 fill-factor) If data is on multiple files which are on multiple similar speed physical drive, the CXPACKET wait may reduce. Keep the statistics updated, as this will give better estimate to query optimizer when assigning threads and dividing the data among available threads. Updating statistics can significantly improve the strength of the query optimizer to render proper execution plan. This may overall affect the parallelism process in positive way. Bad Practice: In one of the recent consultancy project, when I was called in I noticed that one of the ‘experienced’ DBA noticed higher CXPACKET wait and to reduce them, he has increased the worker threads. The reality was increasing worker thread has lead to many other issues. With more number of the threads, more amount of memory was used leading memory pressure. As there were more threads CPU scheduler faced higher ‘Context Switching’ leading further degrading performance. When I explained all these to ‘experienced’ DBA he suggested that now we should reduce the number of threads. Not really! Lower number of the threads may create heavy stalling for parallel queries. I suggest NOT to touch the setting of number of the threads when dealing with CXPACKET wait. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest reading book on-line for further clarification. All the discussion of Wait Stats over here is generic and it varies by system to system. You are recommended to test this on development server before implementing to production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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Speed up SQL Server queries with PREFETCH

- by Akshay Deep Lamba

Problem The SAN data volume has a throughput capacity of 400MB/sec; however my query is still running slow and it is waiting on I/O (PAGEIOLATCH_SH). Windows Performance Monitor shows data volume speed of 4MB/sec. Where is the problem and how can I find the problem? Solution This is another summary of a great article published by R. Meyyappan at www.sqlworkshops.com. In my opinion, this is the first article that highlights and explains with working examples how PREFETCH determines the performance of a Nested Loop join. First of all, I just want to recall that Prefetch is a mechanism with which SQL Server can fire up many I/O requests in parallel for a Nested Loop join. When SQL Server executes a Nested Loop join, it may or may not enable Prefetch accordingly to the number of rows in the outer table. If the number of rows in the outer table is greater than 25 then SQL will enable and use Prefetch to speed up query performance, but it will not if it is less than 25 rows. In this section we are going to see different scenarios where prefetch is automatically enabled or disabled. These examples only use two tables RegionalOrder and Orders. If you want to create the sample tables and sample data, please visit this site www.sqlworkshops.com. The breakdown of the data in the RegionalOrders table is shown below and the Orders table contains about 6 million rows. In this first example, I am creating a stored procedure against two tables and then execute the stored procedure. Before running the stored proceudre, I am going to include the actual execution plan. --Example provided by www.sqlworkshops.com --Create procedure that pulls orders based on City --Do not forget to include the actual execution plan CREATE PROC RegionalOrdersProc @City CHAR(20) AS BEGIN DECLARE @OrderID INT, @OrderDetails CHAR(200) SELECT @OrderID = o.OrderID, @OrderDetails = o.OrderDetails FROM RegionalOrders ao INNER JOIN Orders o ON (o.OrderID = ao.OrderID) WHERE City = @City END GO SET STATISTICS time ON GO --Example provided by www.sqlworkshops.com --Execute the procedure with parameter SmallCity1 EXEC RegionalOrdersProc 'SmallCity1' GO After running the stored procedure, if we right click on the Clustered Index Scan and click Properties we can see the Estimated Numbers of Rows is 24. If we right click on Nested Loops and click Properties we do not see Prefetch, because it is disabled. This behavior was expected, because the number of rows containing the value ‘SmallCity1’ in the outer table is less than 25. Now, if I run the same procedure with parameter ‘BigCity’ will Prefetch be enabled? --Example provided by www.sqlworkshops.com --Execute the procedure with parameter BigCity --We are using cached plan EXEC RegionalOrdersProc 'BigCity' GO As we can see from the below screenshot, prefetch is not enabled and the query takes around 7 seconds to execute. This is because the query used the cached plan from ‘SmallCity1’ that had prefetch disabled. Please note that even if we have 999 rows for ‘BigCity’ the Estimated Numbers of Rows is still 24. Finally, let’s clear the procedure cache to trigger a new optimization and execute the procedure again. DBCC freeproccache GO EXEC RegionalOrdersProc 'BigCity' GO This time, our procedure runs under a second, Prefetch is enabled and the Estimated Number of Rows is 999. The RegionalOrdersProc can be optimized by using the below example where we are using an optimizer hint. I have also shown some other hints that could be used as well. --Example provided by www.sqlworkshops.com --You can fix the issue by using any of the following --hints --Create procedure that pulls orders based on City DROP PROC RegionalOrdersProc GO CREATE PROC RegionalOrdersProc @City CHAR(20) AS BEGIN DECLARE @OrderID INT, @OrderDetails CHAR(200) SELECT @OrderID = o.OrderID, @OrderDetails = o.OrderDetails FROM RegionalOrders ao INNER JOIN Orders o ON (o.OrderID = ao.OrderID) WHERE City = @City --Hinting optimizer to use SmallCity2 for estimation OPTION (optimize FOR (@City = 'SmallCity2')) --Hinting optimizer to estimate for the currnet parameters --option (recompile) --Hinting optimize not to use histogram rather --density for estimation (average of all 3 cities) --option (optimize for (@City UNKNOWN)) --option (optimize for UNKNOWN) END GO Conclusion, this tip was mainly aimed at illustrating how Prefetch can speed up query execution and how the different number of rows can trigger this.

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How do I deal with a third party application that has embedded hints that result in a sub-optimal execution plan in my environment?

- by Maria Colgan

I have gotten many variations on this question recently as folks begin to upgrade to Oracle Database 11g and there have been several posts on this blog and on others describing how to use SQL Plan Management (SPM) so that a non-hinted SQL statement can use a plan generated with hints. But what if the hint is supplied in the third party application and is causing performance regressions on your system? You can actually use a very similar technique to the ones shown before but this time capture the un-hinted plan and have the hinted SQL statement use that plan instead. Below is an example that demonstrates the necessary steps. 1. We will begin by running the hinted statement 2. After examining the execution plan we can see it is suboptimal because of a bad join order. 3. In order to use SPM to correct the problem we must create a SQL plan baseline for the statement. In order to create a baseline we will need the SQL_ID for the hinted statement. Easy place to get it is in V$SQL. 4. A SQL plan baseline can be created using a SQL_ID and DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE. This will capture the existing plan for this SQL_ID from the shared pool and store in the SQL plan baseline. 5. We can check the SQL plan baseline got created successfully by querying DBA_SQL_PLAN_BASELINES. 6. When you manually create a SQL plan baseline the first plan added is automatically accepted and enabled. We know that the hinted plan is poorly performing plan so we will disable it using DBMS_SPM.ALTER_SQL_PLAN_BASELINE. Disabling the plan tells the optimizer that this plan not a good plan, however since there is no alternative plan at this point the optimizer will still continue to use this plan until we provide a better one. 7. Now let's run the statement without the hint. 8. Looking at the execution plan we can see that the join order is different. The plan without the hint also has a lower cost (3X lower), which indicates it should perform better. 9. In order to map the un-hinted plan to the hinted SQL statement we need to add the plan to the SQL plan baseline for the hinted statement. We can do this using DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE but we will need the SQL_ID and PLAN_HASH_VALUE for the non-hinted statement, which we can find in V$SQL. 10. Now we can add the non-hinted plan to the SQL plan baseline of the hinted SQL statement using DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE. This time we need to pass a few more arguments. We will use the SQL_ID and PLAN_HASH_VALUE of the non-hinted statement but the SQL_HANDLE of the hinted statement. 11. The SQL plan baseline for our statement now has two plans. But only the newly added plan (SQL_PLAN_gbpcg3f67pc788a6d8911) is enabled and accepted. This tells the Optimizer that this is the plan it should use for this statement. We can confirm that the correct plan (non-hinted) will be selected for the statement from now on by re-executing the hinted statement and checking its execution plan.

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SQL Constraints – CHECK and NOCHECK

- by David Turner

One performance issue i faced at a recent project was with the way that our constraints were being managed, we were using Subsonic as our ORM, and it has a useful tool for generating your ORM code called SubStage – once configured, you can regenerate your DAL code easily based on your database schema, and it can even be integrated into your build as a pre-build event if you want to do this. SubStage also offers the useful feature of being able to generate DDL scripts for your entire database, and can script your data for you too. The problem came when we decided to use the generate scripts feature to migrate the database onto a test database instance – it turns out that the DDL scripts that it generates include the WITH NOCHECK option, so when we executed them on the test instance, and performed some testing, we found that performance wasn’t as expected. A constraint can be disabled, enabled but not trusted, or enabled and trusted. When it is disabled, data can be inserted that violates the constraint because it is not being enforced, this is useful for bulk load scenarios where performance is important. So what does it mean to say that a constraint is trusted or not trusted? Well this refers to the SQL Server Query Optimizer, and whether it trusts that the constraint is valid. If it trusts the constraint then it doesn’t check it is valid when executing a query, so the query can be executed much faster. Here is an example base in this article on TechNet, here we create two tables with a Foreign Key constraint between them, and add a single row to each. We then query the tables: 1 DROP TABLE t2 2 DROP TABLE t1 3 GO 4 5 CREATE TABLE t1(col1 int NOT NULL PRIMARY KEY) 6 CREATE TABLE t2(col1 int NOT NULL) 7 8 ALTER TABLE t2 WITH CHECK ADD CONSTRAINT fk_t2_t1 FOREIGN KEY(col1) 9 REFERENCES t1(col1) 10 11 INSERT INTO t1 VALUES(1) 12 INSERT INTO t2 VALUES(1) 13 GO14 15 SELECT COUNT(*) FROM t2 16 WHERE EXISTS17 (SELECT *18 FROM t1 19 WHERE t1.col1 = t2.col1) This all works fine, and in this scenario the constraint is enabled and trusted. We can verify this by executing the following SQL to query the ‘is_disabled’ and ‘is_not_trusted’ properties: 1 select name, is_disabled, is_not_trusted from sys.foreign_keys This gives the following result: We can disable the constraint using this SQL: 1 alter table t2 NOCHECK CONSTRAINT fk_t2_t1 And when we query the constraints again, we see that the constraint is disabled and not trusted: So the constraint won’t be enforced and we can insert data into the table t2 that doesn’t match the data in t1, but we don’t want to do this, so we can enable the constraint again using this SQL: 1 alter table t2 CHECK CONSTRAINT fk_t2_t1 But when we query the constraints again, we see that the constraint is enabled, but it is still not trusted: This means that the optimizer will check the constraint each time a query is executed over it, which will impact the performance of the query, and this is definitely not what we want, so we need to make the constraint trusted by the optimizer again. First we should check that our constraints haven’t been violated, which we can do by running DBCC: 1 DBCC CHECKCONSTRAINTS (t2) Hopefully you see the following message indicating that DBCC completed without finding any violations of your constraint: Having verified that the constraint was not violated while it was disabled, we can simply execute the following SQL: 1 alter table t2 WITH CHECK CHECK CONSTRAINT fk_t2_t1 At first glance this looks like it must be a typo to have the keyword CHECK repeated twice in succession, but it is the correct syntax and when we query the constraints properties, we find that it is now trusted again: To fix our specific problem, we created a script that checked all constraints on our tables, using the following syntax: 1 ALTER TABLE t2 WITH CHECK CHECK CONSTRAINT ALL

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Non use of persisted data

- by Dave Ballantyne

Working at a client site, that in itself is good to say, I ran into a set of circumstances that made me ponder, and appreciate, the optimizer engine a bit more. Working on optimizing a stored procedure, I found a piece of code similar to : select BillToAddressID, Rowguid, dbo.udfCleanGuid(rowguid) from sales.salesorderheaderwhere BillToAddressID = 985 A lovely scalar UDF was being used, in actuality it was used as part of the WHERE clause but simplified here. Normally I would use an inline table valued function here, but in this case it wasn't a good option. So this seemed like a pretty good case to use a persisted column to improve performance. The supporting index was already defined as create index idxBill on sales.salesorderheader(BillToAddressID) include (rowguid) and the function code is Create Function udfCleanGuid(@GUID uniqueidentifier)returns varchar(255)with schemabindingasbegin Declare @RetStr varchar(255) Select @RetStr=CAST(@Guid as varchar(255)) Select @RetStr=REPLACE(@Retstr,'-','') return @RetStrend Executing the Select statement produced a plan of : Nothing surprising, a seek to find the data and compute scalar to execute the UDF. Lets get optimizing and remove the UDF with a persisted column Alter table sales.salesorderheaderadd CleanedGuid as dbo.udfCleanGuid(rowguid)PERSISTED A subtle change to the SELECT statement… select BillToAddressID,CleanedGuid from sales.salesorderheaderwhere BillToAddressID = 985 and our new optimized plan looks like… Not a lot different from before! We are using persisted data on our table, where is the lookup to fetch it ? It didnt happen, it was recalculated. Looking at the properties of the relevant Compute Scalar would confirm this , but a more graphic example would be shown in the profiler SP:StatementCompleted event. Why did the lookup happen ? Remember the index definition, it has included the original guid to avoid the lookup. The optimizer knows this column will be passed into the UDF, run through its logic and decided that to recalculate is cheaper than the lookup. That may or may not be the case in actuality, the optimizer has no idea of the real cost of a scalar udf. IMO the default cost of a scalar UDF should be seen as a lot higher than it is, since they are invariably higher. Knowing this, how do we avoid the function call? Dropping the guid from the index is not an option, there may be other code reliant on it. We are left with only one real option, add the persisted column into the index. drop index Sales.SalesOrderHeader.idxBillgocreate index idxBill on sales.salesorderheader(BillToAddressID) include (rowguid,cleanedguid) Now if we repeat the statement select BillToAddressID,CleanedGuid from sales.salesorderheaderwhere BillToAddressID = 985 We still have a compute scalar operator, but this time it wasnt used to recalculate the persisted data. This can be confirmed with profiler again. The takeaway here is, just because you have persisted data dont automatically assumed that it is being used.

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?12c database ????Adaptive Execution Plans ????????

- by Liu Maclean(???)

12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ??????? ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only. The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | | | 7 (100)| | |* 1 | HASH JOIN | | 4 | 128 | 7 (0)| 00:00:01 | |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???; ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization??????????????? ? ??????????????? ???????????????????????????????? ???????????,??????????????, ???????????????????? ??????????? Re-optimization??, ????????????????????? Re-optimization????dynamic plan?????????? dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are: Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 269 |00:00:00.02 | 1336 | 18 | | | | | 1 | NESTED LOOPS | | 1 | 1 | 269 |00:00:00.02 | 1336 | 18 | | | | | 2 | MERGE JOIN CARTESIAN| | 1 | 4 | 9135 |00:00:00.02 | 34 | 15 | | | | |* 3 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 1 | 87 |00:00:00.01 | 33 | 14 | | | | | 4 | BUFFER SORT | | 87 | 105 | 9135 |00:00:00.01 | 1 | 1 | 4096 | 4096 | 4096 (0)| | 5 | INDEX FULL SCAN | ORDER_PK | 1 | 105 | 105 |00:00:00.01 | 1 | 1 | | | | |* 6 | INDEX UNIQUE SCAN | ORDER_ITEMS_UK | 9135 | 1 | 269 |00:00:00.01 | 1302 | 3 | | | | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 269 |00:00:00.01 | 63 | 3 | | | | | 1 | NESTED LOOPS | | 1 | 269 | 269 |00:00:00.01 | 63 | 3 | | | | |* 2 | HASH JOIN | | 1 | 313 | 269 |00:00:00.01 | 42 | 3 | 1321K| 1321K| 1234K (0)| |* 3 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 87 | 87 |00:00:00.01 | 16 | 0 | | | | | 4 | INDEX FAST FULL SCAN| ORDER_ITEMS_UK | 1 | 665 | 665 |00:00:00.01 | 26 | 3 | | | | |* 5 | INDEX UNIQUE SCAN | ORDER_PK | 269 | 1 | 269 |00:00:00.01 | 21 | 0 | | | | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 5 - access("O"."ORDER_ID"="ORDER_ID") Note ----- - statistics feedback used for this statement SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y 0 N 1 1* select child_number,other_xml From v$SQL_PLAN where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ -------------------------------------------------------------------------------- 1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1 2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_ hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></ hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]> </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7 4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[ LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$ F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml> 0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]> </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]> </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[ INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1 " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@" SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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What development technologies or technology stack is typically used in the security industry?

- by vfilby

In this case security means building security (access control, alarm systems, etc). And I am not talking about working directly with the hardware, more focused on web based applications/api's that clients or companies can use? Are there technologies that are commonly used? Are there technologies that shouldn't be used? Are there any real benefits to a linux based stack as opposed to a windows based stack for exposing web based applications?

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what should I take into consideration when choosing a session implementation?

- by Satoru.Logic

Hi, all. Yesterday, my supervisor told me that tmp-file-based session should be THE answer to session implementation, and I should abandon any idea of making sessions persistent. He argues that file-based session is much faster and eaiser to use than other choices like db-based or memcached-based implementations. What he said was really a shock to my past learning experience, so please tell me whether he was wrong and why? Thanks in advance.

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links for 2010-03-15

- by Bob Rhubart

ComputerworldUK: Morrison boosts IT investment by £200 million "[I]mproving efficiencies in areas such as manufacturing and distribution...helped the company make total savings of £526 million, surpassing its expected cost savings of £460 million. A total £43 million in cost savings was due to the IT investment." -- Anh Nguyen, ComputerworldUK (h/t to Brian Dayton for the link) (tags: oracle investment informationtechnology soasuite fusionmiddleware)

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HP ProLiant DL980-Oracle TPC-C Benchmark spat

- by jchang

The Register reported a spat between HP and Oracle on the TPC-C benchmark. Per above, HP submitted a TPC-C result of 3,388,535 tpm-C for their ProLiant DL980 G7 (8 Xeon X7560 processors), with a cost of $0.63 per tpm-C. Oracle has refused permission to publish. Late last year (2010) Oracle published a result of 30M tpm-C for a 108 processors (sockets) SPARC cluster ($30M complete system cost). Oracle is now comparing this to the HP Superdome result from 2007 of 4M tpm-C at $2.93 per tpm-C, calling...(read more)

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Is Financial Inclusion an Obligation or an Opportunity for Banks?

- by tushar.chitra

Why should banks care about financial inclusion? First, the statistics, I think this will set the tone for this blog post. There are close to 2.5 billion people who are excluded from the banking stream and out of this, 2.2 billion people are from the continents of Africa, Latin America and Asia (McKinsey on Society: Global Financial Inclusion). However, this is not just a third-world phenomenon. According to Federal Deposit Insurance Corp (FDIC), in the US, post 2008 financial crisis, one family out of five has either opted out of the banking system or has been moved out (American Banker). Moving this huge unbanked population into mainstream banking is both an opportunity and a challenge for banks. An obvious opportunity is the significant untapped customer base that banks can target, so is the positive brand equity a bank can build by fulfilling its social responsibilities. Also, as banks target the cost-conscious unbanked customer, they will be forced to look at ways to offer cost-effective products and services, necessitating technology upgrades and innovations. However, cost is not the only hurdle in increasing the adoption of banking services. The potential users need to be convinced of the benefits of banking and banks will also face stiff competition from unorganized players. Finally, the banks will have to believe in the viability of this business opportunity, and not treat financial inclusion as an obligation. In what ways can banks target the unbanked For financial inclusion to be a success, banks should adopt innovative business models to develop products that address the stated and unstated needs of the unbanked population and also design delivery channels that are cost effective and viable in the long run. Through business correspondents and facilitators In rural and remote areas, one of the major hurdles in increasing banking penetration is connectivity and accessibility to banking services, which makes last mile inclusion a daunting challenge. To address this, banks can avail the services of business correspondents or facilitators. This model allows banks to establish greater connectivity through a trusted and reliable intermediary. In India, for instance, banks can leverage the local Kirana stores (the mom & pop stores) to service rural and remote areas. With a supportive nudge from the central bank, the commercial banks can enlist these shop owners as business correspondents to increase their reach. Since these neighborhood stores are acquainted with the local population, they can help banks manage the KYC norms, besides serving as a conduit for remittance. Banks also have an opportunity over a period of time to cross-sell other financial products such as micro insurance, mutual funds and pension products through these correspondents. To exercise greater operational control over the business correspondents, banks can also adopt a combination of branch and business correspondent models to deliver financial inclusion. Through mobile devices According to a 2012 world bank report on financial inclusion, out of a world population of 7 billion, over 5 billion or 70% have mobile phones and only 2 billion or 30% have a bank account. What this means for banks is that there is scope for them to leverage this phenomenal growth in mobile usage to serve the unbanked population. Banks can use mobile technology to service the basic banking requirements of their customers with no frills accounts, effectively bringing down the cost per transaction. As I had discussed in my earlier post on mobile payments, though non-traditional players have taken the lead in P2P mobile payments, banks still hold an edge in terms of infrastructure and reliability. Through crowd-funding According to the Crowdfunding Industry Report by Massolution, the global crowdfunding industry raised $2.7 billion in 2012, and is projected to grow to $5.1 billion in 2013. With credit policies becoming tighter and banks becoming more circumspect in terms of loan disbursals, crowdfunding has emerged as an alternative channel for lending. Typically, these initiatives target the unbanked population by offering small loans that are unviable for larger banks. Though a significant proportion of crowdfunding initiatives globally are run by non-banking institutions, banks are also venturing into this space. The next step towards inclusive finance Banks by themselves cannot make financial inclusion a success. There is a need for a whole ecosystem that is supportive of this mission. The policy makers, that include the regulators and government bodies, must be in sync, the IT solution providers must put on their thinking caps to come out with innovative products and solutions, communication channels such as internet and mobile need to expand their reach, and the media and the public need to play an active part. The other challenge for financial inclusion is from the banks themselves. While it is true that financial inclusion will unleash a hitherto hugely untapped market, the normal banking model may be found wanting because of issues such as flexibility, convenience and reliability. The business will be viable only when there is a focus on increasing the usage of existing infrastructure and that is possible when the banks can offer the entire range of products and services to the large number of users of essential banking services. Apart from these challenges, banks will also have to quickly master and replicate the business model to extend their reach to the remotest regions in their respective geographies. They will need to ensure that the transactions deliver a viable business benefit to the bank. For tapping cross-sell opportunities, banks will have to quickly roll-out customized and segment-specific products. The bank staff should be brought in sync with the business plan by convincing them of the viability of the business model and the need for a business correspondent delivery model. Banks, in collaboration with the government and NGOs, will have to run an extensive financial literacy program to educate the unbanked about the benefits of banking. Finally, with the growing importance of retail banking and with many unconventional players eyeing the opportunity in payments and other lucrative areas of banking, banks need to understand the importance of micro and small branches. These micro and small branches can help banks increase their presence without a huge cost burden, provide bankers an opportunity to cross sell micro products and offer a window of opportunity for the large non-banked population to transact without any interference from intermediaries. These branches can also help diminish the role of the unorganized financial sector, such as local moneylenders and unregistered credit societies. This will also help banks build a brand awareness and loyalty among the users, which by itself has a cascading effect on the business operations, especially among the rural and un-banked centers. In conclusion, with the increasingly competitive banking sector facing frequent slowdowns and downturns, the unbanked population presents a huge opportunity for banks to enhance their customer base and fulfill their social responsibility.

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Building Dynamic Websites With XML, XSLT, and ASP

While online businesses are expanding rapidly in this day and age and searching for a way to reduce website cost, it is imperative for the internet business executive to understand and utilize the technical tools available on the internet to build a dynamic website. XML, XSLT, and ASP are internet website building tools that operate effectively to help sites survive in the booming online business market as well as reduce website cost.

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