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  • Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • July, the 31 Days of SQL Server DMO’s – Day 22 (sys.dm_db_index_physical_stats)

    - by Tamarick Hill
    The sys.dm_db_index_physical_stats Dynamic Management Function is used to return information about the fragmentation levels, page counts, depth, number of levels, record counts, etc. about the indexes on your database instance. One row is returned for each level in a given index, which we will discuss more later. The function takes a total of 5 input parameters which are (1) database_id, (2) object_id, (3) index_id, (4) partition_number, and (5) the mode of the scan level that you would like to run. Let’s use this function with our AdventureWorks2012 database to better illustrate the information it provides. SELECT * FROM sys.dm_db_index_physical_stats(db_id('AdventureWorks2012'), NULL, NULL, NULL, NULL) As you can see from the result set, there is a lot of beneficial information returned from this DMF. The first couple of columns in the result set (database_id, object_id, index_id, partition_number, index_type_desc, alloc_unit_type_desc) are either self-explanatory or have been explained in our previous blog sessions so I will not go into detail about these at this time. The next column in the result set is the index_depth which represents how deep the index goes. For example, If we have a large index that contains 1 root page, 3 intermediate levels, and 1 leaf level, our index depth would be 5. The next column is the index_level which refers to what level (of the depth) a particular row is referring to. Next is probably one of the most beneficial columns in this result set, which is the avg_fragmentation_in_percent. This column shows you how fragmented a particular level of an index may be. Many people use this column within their index maintenance jobs to dynamically determine whether they should do REORG’s or full REBUILD’s of a given index. The fragment count represents the number of fragments in a leaf level while the avg_fragment_size_in_pages represents the number of pages in a fragment. The page_count column tells you how many pages are in a particular index level. From my result set above, you see the the remaining columns all have NULL values. This is because I did not specify a ‘mode’ in my query and as a result it used the ‘LIMITED’ mode by default. The LIMITED mode is meant to be lightweight so it does collect information for every column in the result set. I will re-run my query again using the ‘DETAILED’ mode and you will see we now have results for these rows. SELECT * FROM sys.dm_db_index_physical_stats(db_id('AdventureWorks2012'), NULL, NULL, NULL, ‘DETAILED’)   From the remaining columns, you see we get even more detailed information such as how many records are in a particular index level (record_count). We have a column for ghost_record_count which represents the number of records that have been marked for deletion, but have not physically been removed by the background ghost cleanup process. We later see information on the MIN, MAX, and AVG record size in bytes. The forwarded_record_count column refers to records that have been updated and now no longer fit within the row on the page anymore and thus have to be moved. A forwarded record is left in the original location with a pointer to the new location. The last column in the result set is the compressed_page_count column which tells you how many pages in your index have been compressed. This is a very powerful DMF that returns good information about the current indexes in your system. However, based on the mode you select, it could be a very resource intensive function so be careful with how you use it. For more information on this Dynamic Management Function, please see the below Books Online link: http://msdn.microsoft.com/en-us/library/ms188917.aspx Follow me on Twitter @PrimeTimeDBA

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  • SQL SERVER – DMV – sys.dm_os_waiting_tasks and sys.dm_exec_requests – Wait Type – Day 4 of 28

    - by pinaldave
    Previously, we covered the DMV sys.dm_os_wait_stats, and also saw how it can be useful to identify the major resource bottleneck. However, at the same time, we discussed that this is only useful when we are looking at an instance-level picture. Quite often we want to know about the processes going in our server at the given instant. Here is the query for the same. This DMV is written taking the following into consideration: we want to analyze the queries that are currently running or which have recently ran and their plan is still in the cache. SELECT dm_ws.wait_duration_ms, dm_ws.wait_type, dm_es.status, dm_t.TEXT, dm_qp.query_plan, dm_ws.session_ID, dm_es.cpu_time, dm_es.memory_usage, dm_es.logical_reads, dm_es.total_elapsed_time, dm_es.program_name, DB_NAME(dm_r.database_id) DatabaseName, -- Optional columns dm_ws.blocking_session_id, dm_r.wait_resource, dm_es.login_name, dm_r.command, dm_r.last_wait_type FROM sys.dm_os_waiting_tasks dm_ws INNER JOIN sys.dm_exec_requests dm_r ON dm_ws.session_id = dm_r.session_id INNER JOIN sys.dm_exec_sessions dm_es ON dm_es.session_id = dm_r.session_id CROSS APPLY sys.dm_exec_sql_text (dm_r.sql_handle) dm_t CROSS APPLY sys.dm_exec_query_plan (dm_r.plan_handle) dm_qp WHERE dm_es.is_user_process = 1 GO You can change CROSS APPLY to OUTER APPLY if you want to see all the details which are omitted because of the plan cache. Let us analyze the result of the above query and see how it can be helpful to identify the query and the kind of wait type it creates. Click to Enlarage The above query will return various columns. There are various columns that provide very important details. e.g. wait_duration_ms – it indicates current wait for the query that executes at that point of time. wait_type – it indicates the current wait type for the query text – indicates the query text query_plan – when clicked on the same, it will display the query plans There are many other important information like CPU_time, memory_usage, and logical_reads, which can be read from the query as well. In future posts on this series, we will see how once identified wait type we can attempt to reduce the same. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQLBits VI session submission - Today is your last day

    - by simonsabin
    We will be deciding on the sessions tomorrow (Tuesday 8th March) so make sure you get your session in for SQLBits quick. Don't forget we are focussing on performance an scalability so make sure your session covers one or both of these. To submit your session Step 1 - Complete your Speaker Profile Step 2 - Submit My Sessions

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  • SQLBits VI session submission - Today is your last day

    - by simonsabin
    We will be deciding on the sessions tomorrow (Tuesday 8th March) so make sure you get your session in for SQLBits quick. Don't forget we are focussing on performance an scalability so make sure your session covers one or both of these. To submit your session Step 1 - Complete your Speaker Profile Step 2 - Submit My Sessions

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  • Mario Warfare Episode 1: Invasion Day [Video]

    - by Jason Fitzpatrick
    Back in September we shared the trailer for Mario Warfare with you–a clever live-action take on the battle in the Mushroom Kingdom. The team behind it just released the first full length episode, check it out here. We loved the trailer and the first episode is just as awesome; clearly combining the landscape and politics of Super Mario Bros. with highly stylized fighting and CGI was the right choice. If you’re interested in following the project check out their YouTube Channel and Kickstarter. Mario Warfare Episode 1 Secure Yourself by Using Two-Step Verification on These 16 Web Services How to Fix a Stuck Pixel on an LCD Monitor How to Factory Reset Your Android Phone or Tablet When It Won’t Boot

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  • Day 1: iPhone Web Development

    - by BizTalk Visionary
    Preamble: In an attempt to keep an ageing mind alive I have decided to write an iPhone web app. The app will pull together a whole host of disciplines and should be a challenge. The App: Over the last month or so I have been playing around with Google maps and Instead (see InstaMapper.com) so decided the app must build on this learning. Looking to create an Uber-where’s Tigger! Framework: After an exhaustive research period I’m going with JQTouch. tPhone Simulator:   MobiOne More to follow later……

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  • Tip of the day: Don’t misuse the Link button control

    - by anas
    Misuse ? Yes it is ! I have seen a lot of developers who are using the LinkButton to do redirection only ! They are handling it’s click event to just write Response.Redirect ("url”) like this: protected void LinkButton1_Click( object sender, EventArgs e) { Response.Redirect( "~/ForgotPassword.aspx" ); } Ok so to understand why it’s not a good practice let’s discuss the redirection steps involved when using the mentioned method: User submits the page by clicking on the LinkButton control...(read more)

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  • July, the 31 Days of SQL Server DMO’s – Day 23 (sys.dm_db_index_usage_stats)

    - by Tamarick Hill
    The sys.dm_db_index_usage_stats Dynamic Management View is used to return usage information about the various indexes on your SQL Server instance. Let’s have a look at this DMV against our AdventureWorks2012 database so we can examine the information returned. SELECT * FROM sys.dm_db_index_usage_stats WHERE database_id = db_id('AdventureWorks2012') The first three columns in the result set represent the database_id, object_id, and index_id of a given row. You can join these columns back to other system tables to extract the actual database, object, and index names. The next four columns are probably the most beneficial columns within this DMV. First, the user_seeks column represents the number of times that a user query caused a seek operation against a particular index. The user_scans column represents how many times a user query caused a scan operation on a particular index. The user_lookups column represents how many times an index was used to perform a lookup operation. The user_updates column refers to how many times an index had to be updated due to a write operation that effected a particular index. The last_user_seek, last_user_scan, last_user_lookup, and last_user_update columns provide you with DATETIME information about when the last user scan, seek, lookup, or update operation was performed. The remaining columns in the result set are the same as the ones we previously discussed, except instead of the various operations being generated from user requests, they are generated from system background requests. This is an extremely useful DMV and one of my favorites when it comes to Index Maintenance. As we all know, indexes are extremely beneficial with improving the performance of your read operations. But indexes do have a downside as well. Indexes slow down the performance of your write operations, and they also require additional resources for storage. For this reason, in my opinion, it is important to regularly analyze the indexes on your system to make sure the indexes you have are being used efficiently. My AdventureWorks2012 database is only used for demonstrating or testing things, so I dont have a lot of meaningful information here, but for a Production system, if you see an index that is never getting any seeks, scans, or lookups, but is constantly getting a ton of updates, it more than likely would be a good candidate for you to consider removing. You would not be getting much benefit from the index, but yet it is incurring a cost on your system due to it constantly having to be updated for your write operations, not to mention the additional storage it is consuming. You should regularly analyze your indexes to ensure you keep your database systems as efficient and lean as possible. One thing to note is that these DMV statistics are reset every time SQL Server is restarted. Therefore it would not be a wise idea to make decisions about removing indexes after a Server Reboot or a cluster roll. If you restart your SQL Server instances frequently, for example if you schedule weekly/monthly cluster rolls, then you may not capture indexes that are being used for weekly/monthly reports that run for business users. And if you remove them, you may have some upset people at your desk on Monday morning. If you would like to begin analyzing your indexes to possibly remove the ones that your system is not using, I would recommend building a process to load this DMV information into a table on scheduled basis, depending on how frequently you perform an operation that would reset these statistics, then you can analyze the data over a period of time to get a more accurate view of what indexes are really being used and which ones or not. For more information about this DMV, please see the below Books Online link: http://msdn.microsoft.com/en-us/library/ms188755.aspx Follow me on Twitter @PrimeTimeDBA

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  • Big Data – Buzz Words: What is NoSQL – Day 5 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored the basic architecture of Big Data . In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – NoSQL. What is NoSQL? NoSQL stands for Not Relational SQL or Not Only SQL. Lots of people think that NoSQL means there is No SQL, which is not true – they both sound same but the meaning is totally different. NoSQL does use SQL but it uses more than SQL to achieve its goal. As per Wikipedia’s NoSQL Database Definition – “A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases.“ Why use NoSQL? A traditional relation database usually deals with predictable structured data. Whereas as the world has moved forward with unstructured data we often see the limitations of the traditional relational database in dealing with them. For example, nowadays we have data in format of SMS, wave files, photos and video format. It is a bit difficult to manage them by using a traditional relational database. I often see people using BLOB filed to store such a data. BLOB can store the data but when we have to retrieve them or even process them the same BLOB is extremely slow in processing the unstructured data. A NoSQL database is the type of database that can handle unstructured, unorganized and unpredictable data that our business needs it. Along with the support to unstructured data, the other advantage of NoSQL Database is high performance and high availability. Eventual Consistency Additionally to note that NoSQL Database may not provided 100% ACID (Atomicity, Consistency, Isolation, Durability) compliance.  Though, NoSQL Database does not support ACID they provide eventual consistency. That means over the long period of time all updates can be expected to propagate eventually through the system and data will be consistent. Taxonomy Taxonomy is the practice of classification of things or concepts and the principles. The NoSQL taxonomy supports column store, document store, key-value stores, and graph databases. We will discuss the taxonomy in detail in later blog posts. Here are few of the examples of the each of the No SQL Category. Column: Hbase, Cassandra, Accumulo Document: MongoDB, Couchbase, Raven Key-value : Dynamo, Riak, Azure, Redis, Cache, GT.m Graph: Neo4J, Allegro, Virtuoso, Bigdata As of now there are over 150 NoSQL Database and you can read everything about them in this single link. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • July, the 31 Days of SQL Server DMO’s – Day 1 (sys.dm_exec_requests)

    - by Tamarick Hill
    The first DMO that I would like to introduce you to is one of the most common and basic DMV’s out there. I use the term DMV because this DMO is actually a view as opposed to a function. This DMV is server-scoped and it returns information about all requests that are currently executing on your SQL Server instance. To illustrate what this DMV returns, lets take a look at the results. As you can see, this DMV returns a wealth of information about requests occurring on your server. You are able to see the SPID, the start time of a request, current status, and the command the SPID is executing. In addition to this you see columns for sql_handle and plan_handle. These columns (when combined with other DMO’s we will discuss later) can return the actual sql text that is being executed on your server as well as the actual execution plan that is cached and being used. This DMV also returns information about various wait types that may be occurring for your spid. The percent_complete column displays a percentage to completion for certain database actions such as DBCC CheckDB, Database Restores, Rollback’s, etc. In addition to these, you are also able to see the amount of reads, writes, and cpu that the SPID has consumed. You will find this DMV to be one of the primary DMV’s that you use when looking for information about what is occurring on your server.

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  • SQL SERVER – LOGBUFFER – Wait Type – Day 18 of 28

    - by pinaldave
    At first, I was not planning to write about this wait type. The reason was simple- I have faced this only once in my lifetime so far maybe because it is one of the top 5 wait types. I am not sure if it is a common wait type or not, but in the samples I had it really looks rare to me. From Book On-Line: LOGBUFFER Occurs when a task is waiting for space in the log buffer to store a log record. Consistently high values may indicate that the log devices cannot keep up with the amount of log being generated by the server. LOGBUFFER Explanation: The book online definition of the LOGBUFFER seems to be very accurate. On the system where I faced this wait type, the log file (LDF) was put on the local disk, and the data files (MDF, NDF) were put on SanDrives. My client then was not familiar about how the file distribution was supposed to be. Once we moved the LDF to a faster drive, this wait type disappeared. Reducing LOGBUFFER wait: There are several suggestions to reduce this wait stats: Move Transaction Log to Separate Disk from mdf and other files. (Make sure your drive where your LDF is has no IO bottleneck issues). Avoid cursor-like coding methodology and frequent commit statements. Find the most-active file based on IO stall time, as shown in the script written over here. You can also use fn_virtualfilestats to find IO-related issues using the script mentioned over here. Check the IO-related counters (PhysicalDisk:Avg.Disk Queue Length, PhysicalDisk:Disk Read Bytes/sec and PhysicalDisk :Disk Write Bytes/sec) for additional details. Read about them over here. If you have noticed, my suggestions for reducing the LOGBUFFER is very similar to WRITELOG. Although the procedures on reducing them are alike, I am not suggesting that LOGBUFFER and WRITELOG are same wait types. From the definition of the two, you will find their difference. However, they are both related to LOG and both of them can severely degrade the performance. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussion of Wait Stats in this blog is generic and varies from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: 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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  • 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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  • SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28

    - by pinaldave
    CXPACKET has to be most popular one of all wait stats. I have commonly seen this wait stat as one of the top 5 wait stats in most of the systems with more than one CPU. Books On-Line: Occurs when trying to synchronize the query processor exchange iterator. You may consider lowering the degree of parallelism if contention on this wait type becomes a problem. CXPACKET Explanation: When a parallel operation is created for SQL Query, there are multiple threads for a single query. Each query deals with a different set of the data (or rows). Due to some reasons, one or more of the threads lag behind, creating the CXPACKET Wait Stat. There is an organizer/coordinator thread (thread 0), which takes waits for all the threads to complete and gathers result together to present on the client’s side. The organizer thread has to wait for the all the threads to finish before it can move ahead. The Wait by this organizer thread for slow threads to complete is called CXPACKET wait. Note that not all the CXPACKET wait types are bad. You might experience a case when it totally makes sense. There might also be cases when this is unavoidable. If you remove this particular wait type for any query, then that query may run slower because the parallel operations are disabled for the query. Reducing CXPACKET wait: We cannot discuss about reducing the CXPACKET wait without talking about the server workload type. OLTP: On Pure OLTP system, where the transactions are smaller and queries are not long but very quick usually, set the “Maximum Degree of Parallelism” to 1 (one). This way it makes sure that the query never goes for parallelism and does not incur more engine overhead. EXEC sys.sp_configure N'cost threshold for parallelism', N'1' GO RECONFIGURE WITH OVERRIDE GO Data-warehousing / Reporting server: As queries will be running for long time, it is advised to set the “Maximum Degree of Parallelism” to 0 (zero). This way most of the queries will utilize the parallel processor, and long running queries get a boost in their performance due to multiple processors. EXEC sys.sp_configure N'cost threshold for parallelism', N'0' GO RECONFIGURE WITH OVERRIDE GO Mixed System (OLTP & OLAP): Here is the challenge. The right balance has to be found. I have taken a very simple approach. I set the “Maximum Degree of Parallelism” to 2, which means the query still uses parallelism but only on 2 CPUs. However, I keep the “Cost Threshold for Parallelism” very high. This way, not all the queries will qualify for parallelism but only the query with higher cost will go for parallelism. I have found this to work best for a system that has OLTP queries and also where the reporting server is set up. Here, I am setting ‘Cost Threshold for Parallelism’ to 25 values (which is just for illustration); you can choose any value, and you can find it out by experimenting with the system only. In the following script, I am setting the ‘Max Degree of Parallelism’ to 2, which indicates that the query that will have a higher cost (here, more than 25) will qualify for parallel query to run on 2 CPUs. This implies that regardless of the number of CPUs, the query will select any two CPUs to execute itself. EXEC sys.sp_configure N'cost threshold for parallelism', N'25' GO EXEC sys.sp_configure N'max degree of parallelism', N'2' GO RECONFIGURE WITH OVERRIDE GO Read all the post in the Wait Types and Queue series. Additionally a must read comment of Jonathan Kehayias. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest you all to read the online book for further clarification. All the discussion of Wait Stats over here is generic and it varies from system to system. It is recommended that you test this on the development server before implementing on the 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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  • TechDays 2010 Portugal - The Day After

    - by Ricardo Peres
    Well, TechDays 2010 Portugal is over, time for a balance. I really enjoyed being a speaker, although my presentation took a lot more time than it should, it was gratifying to see so many people staying until the end. Lots of subjects were left behind, though. My presentation is available at my SkyDrive, here. Soon I will place there the source code, too. I would like to know if you've been there, and, if so, what do you think of my presentation! Feel free to send your thoughts, whatever they are. On the other hand, I saw some really interesting presentations, to name a few, from Nuno Antunes, Nuno Godinho, Filipe Prezado, Nuno Silva and my friend André Lage. I also had the chance to finally meet Caio Proiete and Pedro Perfeito. Perhaps we'll meet again at TechDays Remix, who knows.

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  • OTN Database Developer Day in LA/OC

    - by shay.shmeltzer
    We are taking a little break from the Fusion OTN Developer Days, and instead we'll be taking part in several OTN Developer Days ran by the database team. The aim is to show what Oracle has to offer to various developer groups. As you might guess we specifically are going to be in the Java track. Specifically we are running a lab that will get you to experience Oracle JDeveloper (or OEPE) and will show you how to build an application based on EJB/JSF with Ajax UI. I'm going to be in the upcoming event on May 5th - if you are in the LA area and haven't experienced JDeveloper yet - come in and see what it is all about. Details here.

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  • SQL SERVER – Guest Post – Glenn Berry – Wait Type – Day 26 of 28

    - by pinaldave
    Glenn Berry works as a Database Architect at NewsGator Technologies in Denver, CO. He is a SQL Server MVP, and has a whole collection of Microsoft certifications, including MCITP, MCDBA, MCSE, MCSD, MCAD, and MCTS. He is also an Adjunct Faculty member at University College – University of Denver, where he has been teaching since 2000. He is one wonderful blogger and often blogs at here. I am big fan of the Dynamic Management Views (DMV) scripts of Glenn. His script are extremely popular and the reality is that he has inspired me to start this series with his famous DMV which I have mentioned in very first  wait stats blog post (I had forgot to request his permission to re-use the script but when asked later on his whole hearty approved it). Here is is his excellent blog post on this subject of wait stats: Analyzing cumulative wait stats in SQL Server 2005 and above has become a popular and effective technique for diagnosing performance issues and further focusing your troubleshooting and diagnostic  efforts.  Rather than just guessing about what resource(s) that SQL Server is waiting on, you can actually find out by running a relatively simple DMV query. Once you know what resources that SQL Server is spending the most time waiting on, you can run more specific queries that focus on that resource to get a better idea what is causing the problem. I do want to throw out a few caveats about using wait stats as a diagnostic tool. First, they are most useful when your SQL Server instance is experiencing performance problems. If your instance is running well, with no indication of any resource pressure from other sources, then you should not worry that much about what the top wait types are. SQL Server will always be waiting on some resource, but many wait types are quite benign, and can be safely ignored. In spite of this, I quite often see experienced DBAs obsessing over the top wait type, even when their SQL Server instance is running extremely well. Second, I often see DBAs jump to the wrong conclusion based on seeing a particular well-known wait type. A good example is CXPACKET waits. People typically jump to the conclusion that high CXPACKET waits means that they should immediately change their instance-level MADOP setting to 1. This is not always the best solution. You need to consider your workload type, and look carefully for any important “missing” indexes that might be causing the query optimizer to use a parallel plan to compensate for the missing index. In this case, correcting the index problem is usually a better solution than changing MAXDOP, since you are curing the disease rather than just treating the symptom. Finally, you should get in the habit of clearing out your cumulative wait stats with the  DBCC SQLPERF(‘sys.dm_os_wait_stats’, CLEAR); command. This is especially important if you have made an configuration or index changes, or if your workload has changed recently. Otherwise, your cumulative wait stats will be polluted with the old stats from weeks or months ago (since the last time SQL Server was started or the stats were cleared).  If you make a change to your SQL Server instance, or add an index, you should clear out your wait stats, and then wait a while to see what your new top wait stats are. At any rate, enjoy Pinal Dave’s series on Wait Stats. This blog post has been written by Glenn Berry (Twitter | Blog) Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Watching Green Day and discovering Sitecore, priceless.

    - by jonel
    I’m feeling inspired and I’d like to share a technique we’ve implemented in Sitecore to address a URL mapping from our legacy site that we wanted to carry over to the new beautiful Littelfuse.com. The challenge is to carry over all of our series URLs that have been published in our datasheets, we currently have a lot of series and having to create a manual mapping for those could be really tedious. It has the format of http://www.littelfuse.com/series/series-name.html, for instance, http://www.littelfuse.com/series/flnr.html. It would have been easier if we have our information architecture defined like this but that would have been too easy. I took a solution that is 2-fold. First, I need to create a URL rewrite rule using the IIS URL Rewrite Module 2.0. Secondly, we need to implement a handler that will take care of the actual lookup of the actual series. It will be amazing after we’ve gone over the details. Let’s start with the URL rewrite. Create a new blank rule, you can name it with anything you wish. The key part here to talk about is the Pattern and the Action groups. The Pattern is nothing but regex. Basically, I’m telling it to match the regex I have defined. In the Action group, I am telling it what to do, in this case, rewrite to the redirect.aspx webform. In this implementation, I will be using Rewrite instead of redirect so the URL sticks in the browser. If you opt to use Redirect, then the URL bar will display the new URL our webform will redirect to. Let me explain one small thing, the (\w+) in my Pattern group’s regex, will actually translate to {R:1} in my Action’s group. This is where the magic begins. Now let’s see what our Redirect.aspx contains. Remember our {R:1} above which becomes the query string variable s? This are basic .Net code. The only thing that you will probably ask is the LFSearch class. It’s our own implementation of addressing finding items by using a field search, we supply the fieldname, the value of the field, the template name of the item we are after, and the value of true or false if we want to do an exact search, or not. If eureka, then redirect to that item’s Path (Url). If not, tell the user tough luck, here’s the 404 page as a consolation. Amazing, ain’t it?

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  • Apress Deal of the Day - 10/Mar/2011 - Beginning Microsoft Word 2010

    - by TATWORTH
    Today's $10 deal at http://www.apress.com/info/dailydeal is Beginning Microsoft Word 2010 This has been on before, I bought a copy and have found it useful. Beginning Microsoft Word 2010 Beginning Word 2010 is a visually stimulating introductory guide that teaches the complete Word newbie (as well as slightly experienced yet equally baffled users) what they need to know to write that thesis or proposal tonight.

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  • AZGroups May 10 2010 Day of Net

    WOW. Another event behind us. What a speaker line up this year huh? Scott Guthrie Scott Guthrie Scott Hanselman Jeffrey Palermo Tim Heuer Scott Guthrie Why is ScottGu listed 3 times? Because he gave us 4 hours of content. Amazing that hes got so much energy, coding talent, stage presence, and community concern to still donate this much of this time. I cant say how grateful we are as a community that ScottGu will agrees to come do our event. We also have to take a moment and...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • July, the 31 Days of SQL Server DMO’s – Day 20 (sys.dm_tran_locks)

    - by Tamarick Hill
    The sys.dm_tran_locks DMV is used to return active lock resources on your server. Locking is a mechanism used by SQL Server to protect the integrity of data when you have multiple users that may potentially access the same data at the same time. Let’s run a query against this DMV so we can analyze the results. SELECT * FROM sys.dm_tran_locks As we can see, its a lot of lock information returned from this DMV. I will not go into detail about each of the columns returned, but I will touch on the ones that I feel are the most important. The first column in the output is the resource_type column which tells you the type of lock a particular row represents. It could be a PAGE lock, RID, OBJECT, DATABASE, or several other lock types. The resource_database_id represents the id of the database for a particular lock resource. The resource_lock_partition column represents the ID of a lock partition. When you have a table that is partitioned, locks can be escalated to the partition level before going to a table level lock. The request_mode column gives us information about the type of lock that is being requested. From the screenshots above we see RangeS-S locks which represent a share range lock and IS locks which represent Intent Shared locks. The request_status column displays whether the lock has been granted or whether the lock is waiting to be acquired. The request_session_id  shows the session_id that is requesting the lock. This DMV is the best place to go when you need to identify the exact locks that are being held or pending for individual requests. You might need this information when you are troubleshooting severe blocking or deadlocking problems on your server. For more information on this DMV, please see the below Books Online link: http://msdn.microsoft.com/en-us/library/ms190345.aspx Follow me on Twitter @PrimeTimeDBA

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  • Big Data – Evolution of Big Data – Day 3 of 21

    - by Pinal Dave
    In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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