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• Cisco Catalyst 3550 + Alteon 184 Load-Balancing Issues...

  - by upkels

  I have just deployed a couple Cisco Catalyst 3550 switches, and a couple Alteon 184 Web Switches for load-balancing. I can ping all RIPs and VIPs to/from the Alteon. Topology Before: (server) <- (Alteon) <- (Internet) Topology Now: (server) <- (3550) <- Alteon <- (Internet) Cisco Port Configuration (Alteon Uplink Port): description LB_1_PORT_9_PRIMARY switchport access vlan 10 switchport mode access switchport nonegotiate speed 100 duplex full Alteon Port 9 Configuration (VLAN 10 WAN): >> Main# /c/port 9/cur Current Port 9 configuration: enabled pref fast, backup gig, PVID 10, BW Contract 1024 name UPLINK >> Main# /c/port 9/fast/cur Current Port 9 Fast link configuration: speed 100, mode full duplex, fctl none, auto off Cisco Configuration (Load-Balanced Servers Port): description LB_1_PORT_1_PRIMARY switchport access vlan 30 switchport mode access switchport nonegotiate speed 100 duplex full Alteon Port 1 Configuration (VLAN 30 LOAD-BALANCED LAN): >> Main# /c/port 1/cur Current Port 1 configuration: enabled pref fast, backup gig, PVID 30, BW Contract 1024 name LB_PORT_1 >> Main# /c/port 1/fast/cur Current Port 1 Fast link configuration: speed 100, mode full duplex, fctl both, auto on Each of my servers are on vlan 10 and 30, properly communicating. I have tried to turn on VLAN tagging on the Alteon, however it seems to cause all communications to stop working. When I tcpdump -i vlan30 on any of the webservers, I see normal ARP communications, and some STP communications, which may or may not be part of the problem: ... 15:00:51.035882 STP 802.1d, Config, Flags [none], bridge-id 801e.00:11:5c:62:fe:80.8041, length 42 15:00:51.493154 IP 10.1.1.254.33923 > 10.1.1.1.http: Flags [S], seq 707324510, win 8760, options [mss 1460], length 0 15:00:51.493336 IP 10.1.1.1.http > 10.1.1.254.33923: Flags [S.], seq 3981707623, ack 707324511, win 65535, options [mss 1460], len gth 0 15:00:51.493778 ARP, Request who-has 10.1.3.1 tell 10.1.3.254, length 46 etc... I'm not sure if I've provided enough information, so please let me know if any more is necessary. Thank you!

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• Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned how to become a Data Scientist for Big Data. In this article we will go over various learning resources related to Big Data. In this series we have covered many of the most essential details about Big Data. At the beginning of this series, I have encouraged readers to send me questions. One of the most popular questions is - “I want to learn more about Big Data. Where can I learn it?” This is indeed a great question as there are plenty of resources out to learn about Big Data and it is indeed difficult to select on one resource to learn Big Data. Hence I decided to write here a few of the very important resources which are related to Big Data. Learn from Pluralsight Pluralsight is a global leader in high-quality online training for hardcore developers.  It has fantastic Big Data Courses and I started to learn about Big Data with the help of Pluralsight. Here are few of the courses which are directly related to Big Data. Big Data: The Big Picture Big Data Analytics with Tableau NoSQL: The Big Picture Understanding NoSQL Data Analysis Fundamentals with Tableau I encourage all of you start with this video course as they are fantastic fundamentals to learn Big Data. Learn from Apache Resources at Apache are single point the most authentic learning resources. If you want to learn fundamentals and go deep about every aspect of the Big Data, I believe you must understand various concepts in Apache’s library. I am pretty impressed with the documentation and I am personally referencing it every single day when I work with Big Data. I strongly encourage all of you to bookmark following all the links for authentic big data learning. Haddop - The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing. Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which include support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heat maps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in a user-friendly manner. Avro: A data serialization system. Cassandra: A scalable multi-master database with no single points of failure. Chukwa: A data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports structured data storage for large tables. Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying. Mahout: A Scalable machine learning and data mining library. Pig: A high-level data-flow language and execution framework for parallel computation. ZooKeeper: A high-performance coordination service for distributed applications. Learn from Vendors One of the biggest issues with about learning Big Data is setting up the environment. Every Big Data vendor has different environment request and there are lots of things require to set up Big Data framework. Many of the users do not start with Big Data as they are afraid about the resources required to set up framework as well as a time commitment. Here Hortonworks have created fantastic learning environment. They have created Sandbox with everything one person needs to learn Big Data and also have provided excellent tutoring along with it. Sandbox comes with a dozen hands-on tutorial that will guide you through the basics of Hadoop as well it contains the Hortonworks Data Platform. I think Hortonworks did a fantastic job building this Sandbox and Tutorial. Though there are plenty of different Big Data Vendors I have decided to list only Hortonworks due to their unique setup. Please leave a comment if there are any other such platform to learn Big Data. I will include them over here as well. Learn from Books There are indeed few good books out there which one can refer to learn Big Data. Here are few good books which I have read. I will update the list as I will learn more. Ethics of Big Data Balancing Risk and Innovation Big Data for Dummies Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions If you search on Amazon there are millions of the books but I think above three books are a great set of books and it will give you great ideas about Big Data. Once you go through above books, you will have a clear idea about what is the next step you should follow in this series. You will be capable enough to make the right decision for yourself. Tomorrow In tomorrow’s blog post we will wrap up this series of 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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• SQL SERVER – Iridium I/O – SQL Server Deduplication that Shrinks Databases and Improves Performance

  - by Pinal Dave

  Database performance is a common problem for SQL Server DBA’s.  It seems like we spend more time on performance than just about anything else.  In many cases, we use scripts or tools that point out performance bottlenecks but we don’t have any way to fix them.  For example, what do you do when you need to speed up a query that is already tuned as well as possible?  Or what do you do when you aren’t allowed to make changes for a database supporting a purchased application? Iridium I/O for SQL Server was originally built at Confio software (makers of Ignite) because DBA’s kept asking for a way to actually fix performance instead of just pointing out performance problems. The technology is certified by Microsoft and was so promising that it was spun out into a separate company that is now run by the Confio Founder/CEO and technology management team. Iridium uses deduplication technology to both shrink the databases as well as boost IO performance.  It is intriguing to see it work.  It will deduplicate a live database as it is running transactions.  You can watch the database get smaller while user queries are running. Iridium is a simple tool to use. After installing the software, you click an “Analyze” button which will spend a minute or two on each database and estimate both your storage and performance savings.  Next, you click an “Activate” button to turn on Iridium I/O for your selected databases.  You don’t need to reboot the operating system or restart the database during any part of the process. As part of my test, I also wanted to see if there would be an impact on my databases when Iridium was removed.  The ‘revert’ process (bringing the files back to their SQL Server native format) was executed by a simple click of a button, and completed while the databases were available for normal processing. I was impressed and enjoyed playing with the software and encourage all of you to try it out.  Here is the link to the website to download Iridium for free. . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Data Storage Options

  - by Kenneth

  When I was working as a website designer/engineer I primarily used databases for storage of much of my dynamic data. It was very easy and convenient to use this method and seemed like a standard practice from my research on the matter. I'm now working on shifting away from websites and into desktop applications. What are the best practices for data storage for desktop applications? I ask because I have noticed that most programs I use on a personal level don't appear to use a database for data storage unless its embedded in the program. (I'm not thinking of an application like a word processor where it makes sense to have data stored in individual files as defined by the user. Rather I'm thinking of something more along the lines of a calendar application which would need to store dates and event info and such where accessing that information would be much easier if stored in a database... at least as far as my experience would indicate.) Thanks for the input!

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• What is a Data Warehouse?

  Typically Data Warehouses are considered to be non-volatile in comparison to traditional databasesdue to the fact that data within the warehouse does not change that often.  In addition, Data Warehouses typically represent data through the use of Multidimensional Conceptual Views that allow data to be extracted based on the view and the current position within the view. Common Data Warehouse Traits Relatively Non-volatile Data Supports Data Extraction and Analysis Optimized for Data Retrieval and Analysis Multidimensional Views of Data Flexible Reporting Multi User Support Generic Dimensionality Transparent Accessible Unlimited Dimensions of Data Unlimited Aggregation levels of Data Normally, Data Warehouses are much larger then there traditional database counterparts due to the fact that they store the basis data along with derived data via Multidimensional Conceptual Views. As companies store larger and larger amounts of data, they will need a way to effectively and accurately extract analysis information that can be used to aide in formulating current and future business decisions. This process can be done currently through data mining within a Data Warehouse. Data Warehouses provide access to data derived through complex analysis, knowledge discovery and decision making. Secondly, they support the demands for high performance in regards to analyzing an organization’s existing and current data. Data Warehouses provide support for an organization’s data and acquired business knowledge.  Within a Data Warehouse multiple types of operations/sub systems are supported. Common Data Warehouse Sub Systems Online Analytical Processing (OLAP) Decision –Support Systems (DSS) Online Transaction Processing (OLTP)

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• JMeter: how to asign a single distinct value from CSV Data Set Config to each thread in thread group?

  - by JohnnyM

  I have to make a load test for a relatively large number of users so I cant realy use User Parameters pre-processor to parametrize each thread with custom user data. I've read that I should use CSV Data Set Config instead. However I run into a problem with how JMeter interprets the input of this Config. Example: I have a thread group of 3 threads and Loop Count:10 with one HTTP request sampler with server www.example.com and path: \${user}. The csv file (bullet is a single line in file) for CSV Data Set Config to extract the user parameter: 1 2 3 4 5 Expected output is that for thread 1-x the path of the request should be: \x. So the output file should consist of 10 samples per thread namely: for thread 1-1 : 10 requests to www.example.com\1 for thread 1-2 : 10 requests to www.example.com\2 for thread 1-3 : 10 requests to www.example.com\3 but instead i get requests to each \1 - \5 and then to EOF. Does anyone know how to achieve the expected effect with CSV Data Set Config in jmeter 2.9?

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• Postfix performance

  - by Brian G

  Running postfix on ubuntu, sending alot of mail ( ~ 1 million messages ) per day. loads are extremly high but not much in terms of cpu and memory load. Anyone in a similiar situation and know how to remove the bottleneck? All mail on this server is outbound. I would have to assume the bottleneck is disk. Just an update, here is what iostat looks like: avg-cpu: %user %nice %system %iowait %steal %idle 0.00 0.00 0.12 99.88 0.00 0.00 Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util sda 0.00 12.38 0.00 2.48 0.00 118.81 48.00 0.00 0.00 0.00 0.00 sdb 1.49 22.28 72.28 42.57 629.70 1041.58 14.55 135.56 834.31 8.71 100.00 Are these numbers in line with the performance you would expect from a single disk? sdb is dedicated to postfix. I think it is queue shuffling, from incoming-active-deferred More details from questions: Server: Quad core Xeon(R) CPU E5405 @ 2.00GH with 4 GB ram Load average: 464.88, 489.11, 483.91, 4 cores. but the memory utilization and cpu is minimal Postfix instances between 16 - 32

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• How do I objectively measure an application's load on a server

  - by Joe

  All, I'm not even sure where to begin looking for resources to answer my question, and I realize that speculation about this kind of thing is highly subjective. I need help determining what class of server I should purchase to host a MS Silverlight application with a MSSQL server back-end on a Windows Server 2008 platform. It's an interactive program, so I can't simply generate a list of URLs to test against, and run it with 1000 simultaneous users. What tools are out there to help me determine what kind of load the application will put on a server at varying levels of concurrent users? Would you all suggest separating the SQL server form the web server, to better differentiate the generated load on the different parts of the stack?

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• How Do You Actually Model Data?

  Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols.  But what is data modeling?  The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago.  I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects.  Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines.  These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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• EPM 11.1.2 - In WebLogic Server, Enable Native IO Performance Pack

  - by Ahmed Awan

  Performance can be improved by enabling native IO in production mode. WebLogic Server benchmarks show major performance improvements when native performance packs are used on machines that host Oracle WebLogic Server instances. Important Note:  Always enable native I/O, if available, and check for errors at startup to make sure it is being initialed properly. Tip: The use of NATIVE performance packs are enabled by default in the configuration shipped with your distribution. You can use the Administration Console to verify that performance packs are enabled by clicking on each managed server and click on Tuning tab.

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• Data Structures usage and motivational aspects

  - by Aubergine

  For long student life I was always wondering why there are so many of them yet there seems to be lack of usage at all in many of them. The opinion didn't really change when I got a job. We have brilliant books on what they are and their complexities, but I never encounter resources which would actually give a good hint of practical usage. I perfectly understand that I have to look at problem , analyse required operations, look for data structure that does them efficiently. However in practice I never do that, not because of human laziness syndrome, but because when it comes to work I acknowledge time priority over self-development. Over time I thought that when I would be better developer I will automatically use more of them - that didn't happen at all or maybe I just didn't. Then I found that the colleagues usually in the same plate as me - knowing more or less some three of data structures and being totally happy about it and refusing to discuss this matter further with me, coming back to conversations about 'cool new languages' 'libraries that do jobs for you' and the joy to work under scrumban etc. I am stuck with ArrayLists, Arrays and SortedMap , which no matter what I do always suffice or either I tweak them to be capable of fulfilling my task. Yes, it might be inefficient but do we really have to care if Intel increases performance over years no matter if we improve our skills? Does new Xeon or IBM machines really care what we use? What if I like build things, but I am not particularly excited whether it is n log(n) or just n? Over twenty years the processing power increased enormously, which gives us freedom of not being critical about which one to use? On top of that new more optimized languages appear which support multiple cores more efficiently. To be more specific: I would like to find motivational material on complex real areas/cases of possible effective usages of data structures. I would be really grateful if you would provide relevant resources. There is similar question ,but in the end the links again mostly describe or do dumb example(vehicles, students or holy grail quest - yes, very relevant) them and people keep referring to the "scenario decides the data structure to use". I want to know these complex scenarios to be able to identify similarities to my scenario and then use them. The complex scenarios where it really matters and not necessarily of quantitive nature. It seems that data structures only concern is efficiency and nothing else? There seems to be no particular convenience for developer in use one over another. (only when I found scientific resources on why exactly simple carbohydrates are evil I stopped eating sugar and candies completely replacing it with less harmful fruits - I hope you can see the analogy)

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• Load and performance testing for webapps with JavaScript support

  - by MrG

  Years ago I used OpenSTA to perform load and performance tests. Unfortunately it doesn't support JavaScript, which is a requirement this time. But I remember that it offered great recording possibilities which enabled us to quickly create new test scripts. Please let me which tools you recommend. Free tools are clearly preferred ;)

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• High load average due to high system cpu load (%sys)

  - by Nick

  We have server with high traffic website. Recently we moved from 2 x 4 core server (8 cores in /proc/cpuinfo), 32 GB RAM, running CentOS 5.x, to 2 x 4 core server (16 cores in /proc/cpuinfo), 32 GB RAM, running CentOS 6.3 Server running nginx as a proxy, mysql server and sphinx-search. Traffic is high, but mysql and sphinx-search databases are relatively small, and usually everything works blazing fast. Today server experienced load average of 100++. Looking at top and sar, we noticed that (%sys) is very high - 50 to 70%. Disk utilization was less 1%. We tried to reboot, but problem existed after the reboot. At any moment server had at least 3-4 GB free RAM. Only message shown by dmesg was "possible SYN flooding on port 80. Sending cookies.". Here is snippet of sar 11:00:01 CPU %user %nice %system %iowait %steal %idle 11:10:01 all 21.60 0.00 66.38 0.03 0.00 11.99 We know that this is traffic issue, but we do not know how to proceed future and where to check for solution. Is there a way we can find where exactly those "66.38%" are used. Any suggestions would be appreciated.

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• Unleash AutoVue on Your Unmanaged Data

  - by [email protected]

  Over the years, I've spoken to hundreds of customers who use AutoVue to collaborate on their "managed" data stored in content management systems, product lifecycle management systems, etc. via our many integrations. Through these conversations I've also learned a harsh reality - we will never fully move away from unmanaged data (desktops, file servers, emails, etc). If you use AutoVue today you already know that even if your primary use is viewing content stored in a content management system, you can still open files stored locally on your computer. But did you know that AutoVue actually has - built-in - a great solution for viewing, printing and redlining your data stored on file servers? Using the 'Server protocol' you can point AutoVue directly to a top-level location on any networked file server and provide your users with a link or shortcut to access an interface similar to the sample page shown below. Many customers link to pages just like this one from their internal company intranets. Through this webpage, users can easily search and browse through file server data with a 'click-and-view' interface to find the specific image, document, drawing or model they're looking for. Any markups created on a document will be accessible to everyone else viewing that document and of course real-time collaboration is supported as well. Customers on maintenance can consult the AutoVue Admin guide or My Oracle Support Doc ID 753018.1 for an introduction to the server protocol. Contact your local AutoVue Solutions Consultant for help setting up the sample shown above.

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• SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

  - by pinaldave

  Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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• Consolidate Data in Private Clouds, But Consider Security and Regulatory Issues

  - by Troy Kitch

  The January 13 webcast Security and Compliance for Private Cloud Consolidation will provide attendees with an overview of private cloud computing based on Oracle's Maximum Availability Architecture and how security and regulatory compliance affects implementations. Many organizations are taking advantage of Oracle's Maximum Availability Architecture to drive down the cost of IT by deploying private cloud computing environments that can support downtime and utilization spikes without idle redundancy. With two-thirds of sensitive and regulated data in organizations' databases private cloud database consolidation means organizations must be more concerned than ever about protecting their information and addressing new regulatory challenges. Join us for this webcast to learn about greater risks and increased threats to private cloud data and how Oracle Database Security Solutions can assist in securely consolidating data and meet compliance requirements. Register Now.

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• Validating Data Using Data Annotation Attributes in ASP.NET MVC

  - by bipinjoshi

  The data entered by the end user in various form fields must be validated before it is saved in the database. Developers often use validation HTML helpers provided by ASP.NET MVC to perform the input validations. Additionally, you can also use data annotation attributes from the System.ComponentModel.DataAnnotations namespace to perform validations at the model level. Data annotation attributes are attached to the properties of the model class and enforce some validation criteria. They are capable of performing validation on the server side as well as on the client side. This article discusses the basics of using these attributes in an ASP.NET MVC application.http://www.bipinjoshi.net/articles/0a53f05f-b58c-47b1-a544-f032f5cfca58.aspx       

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• High Server Load cannot figure out why

  - by Tim Bolton

  My server is currently running CentOS 5.2, with WHM 11.34. Currently, we're at 6.43 to 12 for a load average. The sites that we're hosting are taking a lot time to respond and resolve. top doesn't show anything out of the ordinary and iftop doesn't show a lot of traffic. We have many resellers, and some not so good at writing code, how can we find the culprit? vmstat output: vmstat procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu------ r b swpd free buff cache si so bi bo in cs us sy id wa st 0 2 84 78684 154916 1021080 0 0 72 274 0 14 6 3 80 12 0 top output (ordered by %CPU) top - 21:44:43 up 5 days, 10:39, 3 users, load average: 3.36, 4.18, 4.73 Tasks: 222 total, 3 running, 219 sleeping, 0 stopped, 0 zombie Cpu(s): 5.8%us, 2.3%sy, 0.2%ni, 79.6%id, 11.8%wa, 0.0%hi, 0.2%si, 0.0%st Mem: 2074580k total, 1863044k used, 211536k free, 174828k buffers Swap: 2040212k total, 84k used, 2040128k free, 987604k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 15930 mysql 15 0 138m 46m 4380 S 4 2.3 1:45.87 mysqld 21772 igniteth 17 0 23200 7152 3932 R 4 0.3 0:00.02 php 1586 root 10 -5 0 0 0 S 2 0.0 11:45.19 kjournald 21759 root 15 0 2416 1024 732 R 2 0.0 0:00.01 top 1 root 15 0 2156 648 560 S 0 0.0 0:26.31 init 2 root RT 0 0 0 0 S 0 0.0 0:00.35 migration/0 3 root 34 19 0 0 0 S 0 0.0 0:00.32 ksoftirqd/0 4 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/0 5 root RT 0 0 0 0 S 0 0.0 0:02.00 migration/1 6 root 34 19 0 0 0 S 0 0.0 0:00.11 ksoftirqd/1 7 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/1 8 root RT 0 0 0 0 S 0 0.0 0:01.29 migration/2 9 root 34 19 0 0 0 S 0 0.0 0:00.26 ksoftirqd/2 10 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/2 11 root RT 0 0 0 0 S 0 0.0 0:00.90 migration/3 12 root 34 19 0 0 0 R 0 0.0 0:00.20 ksoftirqd/3 13 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/3 top output (ordered by CPU time) top - 21:46:12 up 5 days, 10:41, 3 users, load average: 2.88, 3.82, 4.55 Tasks: 217 total, 1 running, 216 sleeping, 0 stopped, 0 zombie Cpu(s): 3.7%us, 2.0%sy, 2.0%ni, 67.2%id, 25.0%wa, 0.0%hi, 0.1%si, 0.0%st Mem: 2074580k total, 1959516k used, 115064k free, 183116k buffers Swap: 2040212k total, 84k used, 2040128k free, 1090308k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ TIME COMMAND 32367 root 16 0 215m 212m 1548 S 0 10.5 62:03.63 62:03 tailwatchd 1586 root 10 -5 0 0 0 S 0 0.0 11:45.27 11:45 kjournald 1576 root 10 -5 0 0 0 S 0 0.0 2:37.86 2:37 kjournald 27722 root 16 0 2556 1184 800 S 0 0.1 1:48.94 1:48 top 15930 mysql 15 0 138m 46m 4380 S 4 2.3 1:48.63 1:48 mysqld 2932 root 34 19 0 0 0 S 0 0.0 1:41.05 1:41 kipmi0 226 root 10 -5 0 0 0 S 0 0.0 1:34.33 1:34 kswapd0 2671 named 25 0 74688 7400 2116 S 0 0.4 1:23.58 1:23 named 3229 root 15 0 10300 3348 2724 S 0 0.2 0:40.85 0:40 sshd 1580 root 10 -5 0 0 0 S 0 0.0 0:30.62 0:30 kjournald 1 root 17 0 2156 648 560 S 0 0.0 0:26.32 0:26 init 2616 root 15 0 1816 576 480 S 0 0.0 0:23.50 0:23 syslogd 1584 root 10 -5 0 0 0 S 0 0.0 0:18.67 0:18 kjournald 4342 root 34 19 27692 11m 2116 S 0 0.5 0:18.23 0:18 yum-updatesd 8044 bollingp 15 0 3456 2036 740 S 1 0.1 0:15.56 0:15 imapd 26 root 10 -5 0 0 0 S 0 0.0 0:14.18 0:14 kblockd/1 7989 gmailsit 16 0 3196 1748 736 S 0 0.1 0:10.43 0:10 imapd iostat -xtk 1 10 output [root@server1 tmp]# iostat -xtk 1 10 Linux 2.6.18-53.el5 12/18/2012 Time: 09:51:06 PM avg-cpu: %user %nice %system %iowait %steal %idle 5.83 0.19 2.53 11.85 0.00 79.60 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 1.37 118.83 18.70 54.27 131.47 692.72 22.59 4.90 67.19 3.10 22.59 sdb 0.35 39.33 20.33 61.43 158.79 403.22 13.75 5.23 63.93 3.77 30.80 Time: 09:51:07 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.50 0.00 0.50 24.00 0.00 74.00 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 25.00 2.00 2.00 128.00 108.00 118.00 0.03 7.25 4.00 1.60 sdb 0.00 16.00 41.00 145.00 200.00 668.00 9.33 107.92 272.72 5.38 100.10 Time: 09:51:08 PM avg-cpu: %user %nice %system %iowait %steal %idle 2.00 0.00 1.50 29.50 0.00 67.00 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 95.00 3.00 33.00 12.00 480.00 27.33 0.07 1.72 1.31 4.70 sdb 0.00 14.00 1.00 228.00 4.00 960.00 8.42 143.49 568.01 4.37 100.10 Time: 09:51:09 PM avg-cpu: %user %nice %system %iowait %steal %idle 13.28 0.00 2.76 21.30 0.00 62.66 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 21.00 1.00 19.00 16.00 192.00 20.80 0.06 3.55 1.30 2.60 sdb 0.00 36.00 28.00 181.00 124.00 884.00 9.65 121.16 617.31 4.79 100.10 Time: 09:51:10 PM avg-cpu: %user %nice %system %iowait %steal %idle 4.74 0.00 1.50 25.19 0.00 68.58 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 20.00 3.00 15.00 12.00 136.00 16.44 0.17 7.11 3.11 5.60 sdb 0.00 0.00 103.00 60.00 544.00 248.00 9.72 52.35 545.23 6.14 100.10 Time: 09:51:11 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.24 0.00 1.24 25.31 0.00 72.21 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 75.00 4.00 28.00 16.00 416.00 27.00 0.08 3.72 2.03 6.50 sdb 2.00 9.00 124.00 17.00 616.00 104.00 10.21 3.73 213.73 7.10 100.10 Time: 09:51:12 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.00 0.00 0.75 24.31 0.00 73.93 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 24.00 1.00 9.00 4.00 132.00 27.20 0.01 1.20 1.10 1.10 sdb 4.00 40.00 103.00 48.00 528.00 212.00 9.80 105.21 104.32 6.64 100.20 Time: 09:51:13 PM avg-cpu: %user %nice %system %iowait %steal %idle 2.50 0.00 1.75 23.25 0.00 72.50 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 125.74 3.96 46.53 15.84 689.11 27.92 0.20 4.06 2.41 12.18 sdb 2.97 0.00 91.09 84.16 419.80 471.29 10.17 85.85 590.78 5.66 99.11 Time: 09:51:14 PM avg-cpu: %user %nice %system %iowait %steal %idle 0.75 0.00 0.50 24.94 0.00 73.82 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 88.00 1.00 7.00 4.00 380.00 96.00 0.04 4.38 3.00 2.40 sdb 3.00 7.00 111.00 44.00 540.00 208.00 9.65 18.58 581.79 6.46 100.10 Time: 09:51:15 PM avg-cpu: %user %nice %system %iowait %steal %idle 11.03 0.00 3.26 26.57 0.00 59.15 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 145.00 7.00 53.00 28.00 792.00 27.33 0.15 2.50 1.55 9.30 sdb 1.00 0.00 155.00 0.00 800.00 0.00 10.32 2.85 18.63 6.46 100.10 [root@server1 tmp]# MySQL Show Full Processlist mysql> show full processlist; +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Id | User | Host | db | Command | Time | State | Info | +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | DB_USER_ONE | localhost | DB_ONE | Query | 3 | waiting for handler insert | INSERT DELAYED INTO defers (mailtime,msgid,email,transport_method,message,host,ip,router,deliveryuser,deliverydomain) VALUES(FROM_UNIXTIME('1355879748'),'1TivwL-0003y8-8l','[email protected]','remote_smtp','SMTP error from remote mail server after initial connection: host mx1.mail.tw.yahoo.com [203.188.197.119]: 421 4.7.0 [TS01] Messages from 75.125.90.146 temporarily deferred due to user complaints - 4.16.55.1; see http://postmaster.yahoo.com/421-ts01.html','mx1.mail.tw.yahoo.com','203.188.197.119','lookuphost','','') | | 2 | DELAYED | localhost | DB_ONE | Delayed insert | 52 | insert | | | 3 | DELAYED | localhost | DB_ONE | Delayed insert | 68 | insert | | | 911 | DELAYED | localhost | DB_ONE | Delayed insert | 99 | Waiting for INSERT | | | 993 | DB_USER_TWO | localhost | DB_TWO | Sleep | 832 | | NULL | | 994 | DB_USER_ONE | localhost | DB_ONE | Query | 185 | Locked | delete from failures where FROM_UNIXTIME(UNIX_TIMESTAMP(NOW())-1296000) > mailtime | | 1102 | DB_USER_THREE | localhost | DB_THREE | Query | 29 | NULL | commit | | 1249 | DB_USER_FOUR | localhost | DB_FOUR | Query | 13 | NULL | commit | | 1263 | root | localhost | DB_FIVE | Query | 0 | NULL | show full processlist | | 1264 | DB_USER_SIX | localhost | DB_SIX | Query | 3 | NULL | commit | +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 10 rows in set (0.00 sec)

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• DNS servers and load balancing

  - by RadiantHex

  Hi there! I'm wondering if a simple DNS server could offer, even a limited amount, of load balancing capability. I have a couple of servers and I've been told that multiple IPAddresses can be associated with one domain. Help would be very much appreciated!

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• NGINX load balancer DOS itself

  - by cjaredrun

  I have been running a load balancing machine for a number of months now which has had no problems in the past. I got woken up to some downtime and I am seeing this a lot in syslog: TCP: Possible SYN flooding on port 80. Sending cookies. At which point Nginx takes up 100% of the cpu and doesn't come back down to normal for several minutes. I have it running on Ubuntu currently but I also was able to replicate on Debian 6.

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• How to collect the performance data of a server during an unreachable/down period using Nagios?

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• Server Performance

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• More Value From Data Using Data Mining Presentation

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