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  • Pay For Performance of Your SEO Experts

    Search Engine Optimization, or SEO as it is popularly known the world over, is now amply discussed topic. What is SEO, is it useful, how is it useful, what are the advantages of SEO, how SEO is done, who does the SEO, what sources are required for SEO, what not to do in SEO - all these questions about SEO have been discussed in detail and supposedly detailed answers have been obtained about each of them.

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  • ??????????????????????????????????????????????????Oracle Database 11g Enterprise Edition??????Oracle Data Guard?

    - by Yusuke.Yamamoto
    ????? ??:2011/05/25 ??:???? ??3??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Oracle Database 11g Enterprise Edition ?????????????????????????·???????????Oracle Data Guard????????????????? Oracle Data Guard ??????????????????????????????? ????????????????????????? Oracle Data Guard???????"???"????????????????????????????????????WAN????????????????????????Oracle Data Guard ?????? ????????? ????????????????? http://oracletech.jp/products/pickup/000298.html

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  • ????????SQL Developer?Data Modeler?????????????

    - by Yusuke.Yamamoto
    ????? ??:2010/05/18 ??:?????? Oracle ?GUI?????????···??????????????????SQL Developer ? Data Modeler ???????GUI???????????????????????????SQL Developer ? Data Modeler ?????????????????? ????Oracle SQL Developer Data Modeler ??Oracle SQL Developer Data Modeler ????Oracle SQL Developer ???Oracle SQL Developer ???????? ????????? ????????????????? http://www.oracle.com/technology/global/jp/ondemand/otn-seminar/pdf/100518_sqldeveloper_evening.pdf

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  • Good strategy for copying a "sliding window" of data from a table?

    - by chiborg
    I have a MySQL table from a third-party application that has millions of rows and only one index - the timestamp of each entry. Now I want to do some heavy self-joins and queries on the data using fields other than the timestamp. Doing the query on the original table would bring the database to a crawl, adding indexes to the table is not an option. Additionally, I only need entries that are newer than one week. My current strategy for doing the queries efficiently is to use a separate table (aux_table) that has the necessary indexes. My questions are: Is there another way to do the queries? and if not, How do I update the data in the indexed table efficiently? So far I have found two approaches for updating aux_table: Truncate aux_table and insert the desired data from the original table. Not very efficient because all the indexes must be re-crated. Check for the biggest timestamp in aux_table and insert all entries with a greater or equal timestamp from the original table. Occasionally drop older entries. Only copying entries with greater timestamp leads to dropped entries (because of entries with same timestamp that were inserted into the original table after the last update).

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  • Syncing Data to Remote Services, Best Practices for Caching?

    - by viatropos
    I want to be able to publish events to Eventbrite, Eventful, and Google Calendar for my Google Apps. Each service has slightly different properties for events... I will be syncing many other things too, such as users with Google Contacts and MailChimp, Documents with Google Docs and some other services, etc... So I'm wondering, what is the recommended way of retrieving the data for the end user so that it's reasonably maintainable and optimized? Here are the things I'm thinking that I'm having trouble with: My App keeps a central database of all the models (Event, Document, User, Form, etc.), and whenever Admin creates an object (e.g. create through Eventbrite or through our Admin panel), we sync them and store a copy in our local database. When User goes to the site /events, App retrieves the events from the database. Read Events from a target feed, such as the Eventbrite or Eventful feed, and scrap the local database. Basically, I'm wondering, if we're storing all of the data on a remote service, do we really need to have a local database copy of the data? When would we need to have a local database, when wouldn't we?

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  • Why is processing a sorted array faster than an unsorted array?

    - by GManNickG
    Here is a piece of code that shows some very peculiar performance. For some strange reason, sorting the data miraculously speeds up the code by almost 6x: #include <algorithm> #include <ctime> #include <iostream> int main() { // generate data const unsigned arraySize = 32768; int data[arraySize]; for (unsigned c = 0; c < arraySize; ++c) data[c] = std::rand() % 256; // !!! with this, the next loop runs faster std::sort(data, data + arraySize); // test clock_t start = clock(); long long sum = 0; for (unsigned i = 0; i < 100000; ++i) { // primary loop for (unsigned c = 0; c < arraySize; ++c) { if (data[c] >= 128) sum += data[c]; } } double elapsedTime = static_cast<double>(clock() - start) / CLOCKS_PER_SEC; std::cout << elapsedTime << std::endl; std::cout << "sum = " << sum << std::endl; } Without std::sort(data, data + arraySize);, the code runs in 11.54 seconds. With the sorted data, the code runs in 1.93 seconds. Initially I thought this might be just a language or compiler anomaly. So I tried it Java... import java.util.Arrays; import java.util.Random; public class Main { public static void main(String[] args) { // generate data int arraySize = 32768; int data[] = new int[arraySize]; Random rnd = new Random(0); for (int c = 0; c < arraySize; ++c) data[c] = rnd.nextInt() % 256; // !!! with this, the next loop runs faster Arrays.sort(data); // test long start = System.nanoTime(); long sum = 0; for (int i = 0; i < 100000; ++i) { // primary loop for (int c = 0; c < arraySize; ++c) { if (data[c] >= 128) sum += data[c]; } } System.out.println((System.nanoTime() - start) / 1000000000.0); System.out.println("sum = " + sum); } } with a similar but less extreme result. My first thought was that sorting brings the data into cache, but my next thought was how silly that is because the array was just generated. What is going on? Why is a sorted array faster than an unsorted array? The code is summing up some independent terms, the order should not matter.

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  • What's the fastest lookup algorithm for a pair data structure (i.e, a map)?

    - by truncheon
    In the following example a std::map structure is filled with 26 values from A - Z (for key) and 0 – 26 for value. The time taken (on my system) to lookup the last entry (10000000 times) is roughly 250 ms for the vector, and 125 ms for the map. (I compiled using release mode, with O3 option turned on for g++ 4.4) But if for some odd reason I wanted better performance than the std::map, what data structures and functions would I need to consider using? I apologize if the answer seems obvious to you, but I haven't had much experience in the performance critical aspects of C++ programming. UPDATE: This example is rather trivial and hides the true complexity of what I'm trying to achieve. My real world project is a simple scripting language that uses a parser, data tree, and interpreter (instead of a VM stack system). I need to use some kind of data structure (perhaps map) to store the variables names created by script programmers. These are likely to be pretty randomly named, so I need a lookup method that can quickly find a particular key within a (probably) fairly large list of names. #include <ctime> #include <map> #include <vector> #include <iostream> struct mystruct { char key; int value; mystruct(char k = 0, int v = 0) : key(k), value(v) { } }; int find(const std::vector<mystruct>& ref, char key) { for (std::vector<mystruct>::const_iterator i = ref.begin(); i != ref.end(); ++i) if (i->key == key) return i->value; return -1; } int main() { std::map<char, int> mymap; std::vector<mystruct> myvec; for (int i = 'a'; i < 'a' + 26; ++i) { mymap[i] = i - 'a'; myvec.push_back(mystruct(i, i - 'a')); } int pre = clock(); for (int i = 0; i < 10000000; ++i) { find(myvec, 'z'); } std::cout << "linear scan: milli " << clock() - pre << "\n"; pre = clock(); for (int i = 0; i < 10000000; ++i) { mymap['z']; } std::cout << "map scan: milli " << clock() - pre << "\n"; return 0; }

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  • flex4: swfloader doesn't properly load games.

    - by ufk
    Hiya. I have several flash games that when i load them in my firefox browser they load fine, but when i load them with swfloader using flex 4 (flash builder) they load until 100% and don't show the start menu. any ideas how to resolve the issue ?

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  • Frame load interrupted error while loading a word document in UIWebView

    - by Mugunth Kumar
    I want to load a word document using UIWebView. I used the code provided in http://developer.apple.com/iphone/library/qa/qa2008/qa1630.html to load the document. But not all the documents load successfully. Sometimes I get an error Error Domain=WebKitErrorDomain Code=102 UserInfo=0x145bc10 "Frame load interrupted" The error seems to be very sporadic and I get this error only for some documents. However the same document loads correctly in mail.app. What am I missing?

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  • UITableView rows load asynchronously

    - by Mohan
    On launch of app, i want to show 10 rows, at the bottom "Load 15 more", on click on "Load 15 more" the next 15 rows should add to the bottom of the table and again show "Load 15 more", until no rows found. note: I am using php to load table data as an xml feed. If anyone has any tutorial/sample code pl send me the link

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  • SQL SERVER – Puzzle – Statistics are not Updated but are Created Once

    - by pinaldave
    After having excellent response to my quiz – Why SELECT * throws an error but SELECT COUNT(*) does not?I have decided to ask another puzzling question to all of you. I am running this test on SQL Server 2008 R2. Here is the quick scenario about my setup. Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Note: Auto Update Statistics and Auto Create Statistics for database is TRUE Expected Result – Statistics should be updated – SQL SERVER – When are Statistics Updated – What triggers Statistics to Update Now the question is why the statistics are not updated? The common answer is – we can update the statistics ourselves using UPDATE STATISTICS TableName WITH FULLSCAN, ALL However, the solution I am looking is where statistics should be updated automatically based on algorithm mentioned here. Now the solution is to ____________________. Vinod Kumar is not allowed to take participate over here as he is the one who has helped me to build this puzzle. I will publish the solution on next week. Please leave a comment and if your comment consist valid answer, I will publish with due credit. Here is the script to reproduce the scenario which I mentioned. -- Execution Plans Difference -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table - none listed sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here -- NOTE: Replace your _WA_Sys with stats from above query DBCC SHOW_STATISTICS('ExecTable', _WA_Sys_00000004_7D78A4E7); GO -------------------------------------------------------------- -- Round 2 -- Insert Ten Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 10000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here -- NOTE: Replace your _WA_Sys with stats from above query DBCC SHOW_STATISTICS('ExecTable', _WA_Sys_00000004_7D78A4E7); GO -- You will notice that Statistics are still updated with 1000 rows -- Clean up Database DROP TABLE ExecTable GO USE MASTER GO ALTER DATABASE SampleDB SET SINGLE_USER WITH ROLLBACK IMMEDIATE; GO DROP DATABASE SampleDB GO Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: SQL Statistics, Statistics

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  • SQL SERVER – How to Ignore Columnstore Index Usage in Query

    - by pinaldave
    Earlier I wrote about SQL SERVER – Fundamentals of Columnstore Index and very first question I received in email was as following. “We are using SQL Server 2012 CTP3 and so far so good. In our data warehouse solution we have created 1 non-clustered columnstore index on our large fact table. We have very unique situation but your article did not cover it. We are running few queries on our fact table which is working very efficiently but there is one query which earlier was running very fine but after creating this non-clustered columnstore index this query is running very slow. We dropped the columnstore index and suddenly this one query is running fast but other queries which were benefited by this columnstore index it is running slow. Any workaround in this situation?” In summary the question in simple words “How can we ignore using columnstore index in selective queries?” Very interesting question – you can use I can understand there may be the cases when columnstore index is not ideal and needs to be ignored the same. You can use the query hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX to ignore the columnstore index. SQL Server Engine will use any other index which is best after ignoring the columnstore index. Here is the quick script to prove the same. We will first create sample database and then create columnstore index on the same. Once columnstore index is created we will write simple query. This query will use columnstore index. We will then show the usage of the query hint. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO Now we have created columnstore index so if we run following query it will use for sure the same index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO We can specify Query Hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX as described in following query and it will not use columnstore index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID OPTION (IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX) GO Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO Again, make sure that you use hint sparingly and understanding the proper implication of the same. Make sure that you test it with and without hint and select the best option after review of your administrator. Here is the question for you – have you started to use SQL Server 2012 for your validation and development (not on production)? It will be interesting to know the answer. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • What’s the Difference Between Succession Management and Talent Reviews?

    - by HCM-Oracle
    By Marcie Van Houten Is there a difference or are they pieces of one holistic strategic talent process? And can you have one without the other?  First, let me give a quick definition of each.  Succession planning (or management) is about creating succession slates or talent pools in support of a critical job or position or sets thereof. And then using those plans to help mitigate risk and plan talent needs for the organization.  Talent reviews (known by other names often) are sets of meetings where managers and executives come together to review, discuss and often heatedly debate the merits and potential of their employees, and then place and sometimes calibrate that talent on a performance to potential matrix.  These are some of the most strategic conversations happening in conference rooms across the globe. I speak with a lot of organizations about their practices in this area and the answers to these questions are as varied and nuanced as there are organizations thinking about them.  Some are passionate about their talent review processes and have a very evolved and thoughtful approach.  They really know their people, where their talent is, and the opportunities they plan to offer them.  And to them that is their succession process.  They may never create a slate of named candidates for a job or assign employees to formal talent pools.   On the flip side there are other organizations that create slates and slates and often multiple talent pools to support their strategic positions.  Through these, they are able to mitigate the risk associated with having a key player leave their organization.  And for them, that is their succession process.  Some will start from the lower levels of their organization and roll up their succession plans, while other organizations only cover their top 200 executives and key positions with plans.  And then there are organizations that leverage some of all of these.  Ultimately, the goals are to increase employee engagement, reduce talent-related risk, ensure the right talent is aligned to the strategic initiatives and to drive business value.  The approaches are as unique as the organizations they represent and the business opportunities they are looking to seize upon.   And that's ok.  It's great in fact. Because one thing that is common is the recognition that the need to know your people and align your top talent to the future needs of the organization is mission critical. Sure, there are a set of commonly recognized best practices and guiding principles for all of this.  There is no one right or perfect answer.  And that is what makes this all so much darn fun.  With Talent Review and Succession Management from Oracle HCM Cloud, we’ve blended the ability to support your strategic talent review conversations with both succession plans and talent pools allowing for one very seamless and interactive process. So whether you create a lot of succession plans, only focus on talent pools, have a robust talent review process, or all of the above, Oracle has you covered. I’m looking forward to spending time with our customers at the upcoming OHUG Global Conference 2014 happening June 9-13 in Las Vegas.  It’s an opportunity for me to talk to customers about their business and how they are doing strategic talent processes like talent reviews and succession.  I hope to see you there. Marcie Van Houten brings over 20 years of management consulting, information systems and human capital management experience to her role as director of product strategy at Oracle. Ms. Van Houten has spent the past several years at Oracle working closely with customers to help drive the direction of the company's talent and succession management applications. Additionally, she spent nine years at PeopleSoft as Director of Information Systems leading human capital management implementation projects. Marcie Van Houten lives in Walnut Creek, California, and holds a MBA from Southern Methodist University in Dallas, Texas.  You can follow her on Twitter: @MarcieVH

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  • Filtering data in LINQ with the help of where clause

    - by vik20000in
     LINQ has bought with itself a super power of querying Objects, Database, XML, SharePoint and nearly any other data structure. The power of LINQ lies in the fact that it is managed code that lets you write SQL type code to fetch data.  Whenever working with data we always need a way to filter out the data based on different condition. In this post we will look at some of the different ways in which we can filter data in LINQ with the help of where clause. Simple Filter for an array. Let’s say we have an array of number and we want to filter out data based on some condition. Below is an example int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 }; var lowNums =                 from num in numbers                 where num < 5                 select num;   Filter based on one of the property in the class. With the help of LINQ we can also filer out data from a list based on value of some property. var soldOutProducts =                 from prod in products                 where prod.UnitsInStock == 0                 select prod; Filter based on Multiple of the property in the class. var expensiveInStockProducts =         from prod in products         where prod.UnitsInStock > 0 && prod.UnitPrice > 3.00M         select prod; Filter based on the index of the Item in the list.In the below example we can see that we are able to filter data based on the index of the item in the list. string[] digits = { "zero", "one", "two", "three", "four", "five", "six"}; var shortDigits = digits.Where((digit, index) => digit.Length < index); There are many other way in which we can filter out data in LINQ. In the above post I have tried and shown few ways using the LINQ. Vikram

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  • SQL Server IO handling mechanism can be severely affected by high CPU usage

    - by sqlworkshops
    Are you using SSD or SAN / NAS based storage solution and sporadically observe SQL Server experiencing high IO wait times or from time to time your DAS / HDD becomes very slow according to SQL Server statistics? Read on… I need your help to up vote my connect item – https://connect.microsoft.com/SQLServer/feedback/details/744650/sql-server-io-handling-mechanism-can-be-severely-affected-by-high-cpu-usage. Instead of taking few seconds, queries could take minutes/hours to complete when CPU is busy.In SQL Server when a query / request needs to read data that is not in data cache or when the request has to write to disk, like transaction log records, the request / task will queue up the IO operation and wait for it to complete (task in suspended state, this wait time is the resource wait time). When the IO operation is complete, the task will be queued to run on the CPU. If the CPU is busy executing other tasks, this task will wait (task in runnable state) until other tasks in the queue either complete or get suspended due to waits or exhaust their quantum of 4ms (this is the signal wait time, which along with resource wait time will increase the overall wait time). When the CPU becomes free, the task will finally be run on the CPU (task in running state).The signal wait time can be up to 4ms per runnable task, this is by design. So if a CPU has 5 runnable tasks in the queue, then this query after the resource becomes available might wait up to a maximum of 5 X 4ms = 20ms in the runnable state (normally less as other tasks might not use the full quantum).In case the CPU usage is high, let’s say many CPU intensive queries are running on the instance, there is a possibility that the IO operations that are completed at the Hardware and Operating System level are not yet processed by SQL Server, keeping the task in the resource wait state for longer than necessary. In case of an SSD, the IO operation might even complete in less than a millisecond, but it might take SQL Server 100s of milliseconds, for instance, to process the completed IO operation. For example, let’s say you have a user inserting 500 rows in individual transactions. When the transaction log is on an SSD or battery backed up controller that has write cache enabled, all of these inserts will complete in 100 to 200ms. With a CPU intensive parallel query executing across all CPU cores, the same inserts might take minutes to complete. WRITELOG wait time will be very high in this case (both under sys.dm_io_virtual_file_stats and sys.dm_os_wait_stats). In addition you will notice a large number of WAITELOG waits since log records are written by LOG WRITER and hence very high signal_wait_time_ms leading to more query delays. However, Performance Monitor Counter, PhysicalDisk, Avg. Disk sec/Write will report very low latency times.Such delayed IO handling also occurs to read operations with artificially very high PAGEIOLATCH_SH wait time (with number of PAGEIOLATCH_SH waits remaining the same). This problem will manifest more and more as customers start using SSD based storage for SQL Server, since they drive the CPU usage to the limits with faster IOs. We have a few workarounds for specific scenarios, but we think Microsoft should resolve this issue at the product level. We have a connect item open – https://connect.microsoft.com/SQLServer/feedback/details/744650/sql-server-io-handling-mechanism-can-be-severely-affected-by-high-cpu-usage - (with example scripts) to reproduce this behavior, please up vote the item so the issue will be addressed by the SQL Server product team soon.Thanks for your help and best regards,Ramesh MeyyappanHome: www.sqlworkshops.comLinkedIn: http://at.linkedin.com/in/rmeyyappan

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  • RPi and Java Embedded GPIO: Big Data and Java Technology

    - by hinkmond
    Java Embedded and Big Data go hand-in-hand, especially as demonstrated by prototyping on a Raspberry Pi to show how well the Java Embedded platform can perform on a small embedded device which then becomes the proof-of-concept for industrial controllers, medical equipment, networking gear or any type of sensor-connected device generating large amounts of data. The key is a fast and reliable way to access that data using Java technology. In the previous blog posts you've seen the integration of a static electricity sensor and the Raspberry Pi through the GPIO port, then accessing that data through Java Embedded code. It's important to point out how this works and why it works well with Java code. First, the version of Linux (Debian Wheezy/Raspian) that is found on the RPi has a very convenient way to access the GPIO ports through the use of Linux OS managed file handles. This is key in avoiding terrible and complex coding using register manipulation in C code, or having to program in a less elegant and clumsy procedural scripting language such as python. Instead, using Java Embedded, allows a fast way to access those GPIO ports through those same Linux file handles. Java already has a very easy to program way to access file handles with a high degree of performance that matches direct access of those file handles with the Linux OS. Using the Java API java.io.FileWriter lets us open the same file handles that the Linux OS has for accessing the GPIO ports. Then, by first resetting the ports using the unexport and export file handles, we can initialize them for easy use in a Java app. // Open file handles to GPIO port unexport and export controls FileWriter unexportFile = new FileWriter("/sys/class/gpio/unexport"); FileWriter exportFile = new FileWriter("/sys/class/gpio/export"); ... // Reset the port unexportFile.write(gpioChannel); unexportFile.flush(); // Set the port for use exportFile.write(gpioChannel); exportFile.flush(); Then, another set of file handles can be used by the Java app to control the direction of the GPIO port by writing either "in" or "out" to the direction file handle. // Open file handle to input/output direction control of port FileWriter directionFile = new FileWriter("/sys/class/gpio/gpio" + gpioChannel + "/direction"); // Set port for input directionFile.write("in"); // Or, use "out" for output directionFile.flush(); And, finally, a RandomAccessFile handle can be used with a high degree of performance on par with native C code (only milliseconds to read in data and write out data) with low overhead (unlike python) to manipulate the data going in and out on the GPIO port, while the object-oriented nature of Java programming allows for an easy way to construct complex analytic software around that data access functionality to the external world. RandomAccessFile[] raf = new RandomAccessFile[GpioChannels.length]; ... // Reset file seek pointer to read latest value of GPIO port raf[channum].seek(0); raf[channum].read(inBytes); inLine = new String(inBytes); It's Big Data from sensors and industrial/medical/networking equipment meeting complex analytical software on a small constraint device (like a Linux/ARM RPi) where Java Embedded allows you to shine as an Embedded Device Software Designer. Hinkmond

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  • SSIS Debugging Tip: Using Data Viewers

    - by Jim Giercyk
    When you have an SSIS package error, it is often very helpful to see the data records that are causing the problem.  After all, if your input has 50,000 records and 1 of them has corrupt data, it can be a chore.  Your execution results will tell you which column contains the bad data, but not which record…..enter the Data Viewer. In this scenario I have created a truncation error.  The input length of [lastname] is 50, but the output table has a length of 15.  When it runs, at least one of the records causes the package to fail.     Now what?  We can tell from our execution results that there is a problem with [lastname], but we have no idea WHICH record?     Let’s identify the row that is actually causing the problem.  First, we grab the oft’ forgotten Row Count shape from our toolbar and connect it to the error output from our input query.  Remember that in order to intercept errors with the error output, you must redirect them.     The Row Count shape requires 1 integer variable.  For our purposes, we will not reference the variable, but it is still required in order for the package to run.  Typically we would use the variable to hold the number of rows in the table and refer back to it later in our process.  We are simply using the Row Count as a “Dead End” for errors.  I called my variable RowCounter.  To create a variable, with no shapes selected, right-click on the background and choose Variable.     Once we have setup the Row Count shape, we can right-click on the red line (error output) from the query, and select Data Viewers.  In the popup, we click the add button and we will see this:     There are other fancier options we can play with, but for now we just want to view the output in a grid.  WE select Grid, then click OK on all of the popup windows to shut them down.  We should now see a grid with a pair of glasses on the error output line.     So, we are ready to catch the error output in a grid and see that is causing the problem!  This time when we run the package, it does not fail because we directed the error to the Row Count.  We also get a popup window showing the error record in a grid.  If there were multiple errors we would see them all.     Indeed, the [lastname] column is longer than 15 characters.  Notice the last column in the grid, [Error Code – Description].  We knew this was a truncation error before we added the grid, but if you have worked with SSIS for any length of time, you know that some errors are much more obscure.  The description column can be very useful under those circumstances! Data viewers can be used any time we want to see the data that is actually in the pipeline;  they stop the package temporarily until we shut them.  Also remember that the Row Count shape can be used as a “Dead End”.  It is useful during development when we want to see the output from a dataflow, but don’t want to update a table or file with the data.  Data viewers are an invaluable tool for both development and debugging.  Just remember to REMOVE THEM before putting your package into production

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  • Reducing Deadlocks - not a DBA issue ?

    - by steveh99999
     As a DBA, I'm involved on an almost daily basis troubleshooting 'SQL Server' performance issues. Often, this troubleshooting soon veers away from a 'its a SQL Server issue' to instead become a wider application/database design/coding issue.One common perception with SQL Server is that deadlocking is an application design issue - and is fixed by recoding...  I see this reinforced by MCP-type questions/scenarios where the answer to prevent deadlocking is simply to change the order in code in which tables are accessed....Whilst this is correct, I do think this has led to a situation where many 'operational' or 'production support' DBAs, when faced with a deadlock, are happy to throw the issue over to developers without analysing the issue further....A couple of 'war stories' on deadlocks which I think are interesting :- Case One , I had an issue recently on a third-party application that I support on SQL 2008.  This particular third-party application has an unusual support agreement where the customer is allowed to change the index design on the third-party provided database.  However, we are not allowed to alter application code or modify table structure..This third-party application is also known to encounter occasional deadlocks – indeed, I have documentation from the vendor that up to 50 deadlocks per day is not unusual !So, as a DBA I have to support an application which in my opinion has too many deadlocks - but, I cannot influence the design of the tables or stored procedures for the application. This should be the classic - blame the third-party developers scenario, and hope this issue gets addressed in a future application release - ie we could wait years for this to be resolved and implemented in our production environment...But, as DBAs  can change the index layout, is there anything I could do still to reduce the deadlocks in the application ?I initially used SQL traceflag 1222 to write deadlock detection output to the SQL Errorlog – using this I was able to identify one table heavily involved in the deadlocks.When I examined the table definition, I was surprised to see it was a heap – ie no clustered index existed on the table.Using SQL profiler to see locking behaviour and plan for the query involved in the deadlock, I was able to confirm a table scan was being performed.By creating an appropriate clustered index - it was possible to produce a more efficient plan and locking behaviour.So, less locks, held for less time = less possibility of deadlocks. I'm still unhappy about the overall number of deadlocks on this system - but that's something to be discussed further with the vendor.Case Two,  a system which hadn't changed for months suddenly started seeing deadlocks on a regular basis. I love the 'nothing's changed' scenario, as it gives me the opportunity to appear wise and say 'nothings changed on this system, except the data'.. This particular deadlock occurred on a table which had been growing rapidly. By using DBCC SHOW_STATISTICS - the DBA team were able to see that the deadlocks seemed to be occurring shortly after auto-update stats had regenerated the table statistics using it's default sampling behaviour.As a quick fix, we were able to schedule a nightly UPDATE STATISTICS WITH FULLSCAN on the table involved in the deadlock - thus, greatly reducing the potential for stats to be updated via auto_update_stats, consequently reducing the potential for a bad plan to be generated based on an unrepresentative sample of the data. This reduced the possibility of a deadlock occurring.  Not a perfect solution by any means, but quick, easy to implement, and needed no application code changes. This fix gave us some 'breathing space'  to properly fix the code during the next scheduled application release.   The moral of this post - don't dismiss deadlocks as issues that can only be fixed by developers...

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  • Why is my CPU being used while doing nothing?

    - by Jop
    I have installed Ubuntu GNOME in BIOS mode on my MacBook (BIOS mode so that the proprietary NVIDIA drivers work. I need them for gaming.). For some reason, a lot of CPU is being used while not really doing anything. It swings between 20-30% on both cores, usually. But when I look at the list of processes and sort by CPU usage, I do not see anything special. No processes intensively doing anything. How can I fix this? EDIT: Output of top command. jop@jop-MacBook:~$ top top - 17:08:02 up 41 min, 2 users, load average: 0,51, 0,69, 0,95 Tasks: 202 total, 2 running, 200 sleeping, 0 stopped, 0 zombie %Cpu(s): 11,9 us, 5,8 sy, 0,0 ni, 80,3 id, 0,5 wa, 0,0 hi, 1,5 si, 0,0 st KiB Mem: 7908316 total, 2919940 used, 4988376 free, 153248 buffers KiB Swap: 3906244 total, 0 used, 3906244 free, 1326544 cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 3785 root 20 0 195m 82m 26m S 22,9 1,1 2:43.77 Xorg 4429 jop 20 0 1543m 150m 60m S 7,3 1,9 1:26.26 compiz 4198 jop 20 0 633m 21m 11m S 1,7 0,3 0:04.96 unity-panel-ser 7425 jop 20 0 564m 18m 12m S 1,7 0,2 0:00.84 gnome-terminal 7019 jop 20 0 806m 89m 46m S 1,0 1,2 0:10.01 chrome 7323 jop 20 0 966m 93m 23m S 1,0 1,2 0:06.85 chrome 6742 root 20 0 0 0 0 S 0,7 0,0 0:00.43 kworker/0:3 3 root 20 0 0 0 0 S 0,3 0,0 0:06.01 ksoftirqd/0 7008 root 20 0 0 0 0 S 0,3 0,0 0:00.27 kworker/1:3 7302 jop 20 0 972m 96m 28m S 0,3 1,2 0:06.32 chrome 7310 jop 20 0 382m 63m 39m S 0,3 0,8 0:00.34 chrome 7498 jop 20 0 24840 1600 1120 R 0,3 0,0 0:00.22 top 1 root 20 0 27176 2944 1412 S 0,0 0,0 0:01.58 init 2 root 20 0 0 0 0 S 0,0 0,0 0:00.00 kthreadd 5 root 0 -20 0 0 0 S 0,0 0,0 0:00.00 kworker/0:0H 6 root 20 0 0 0 0 S 0,0 0,0 0:00.00 kworker/u4:0 7 root rt 0 0 0 0 S 0,0 0,0 0:02.04 migration/0 Even when xorg isn't so busy like when I copied, CPU usage is higher then what the processes use.

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