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  • Timeout ssh sessions after inactivity?

    - by Insyte
    PCI requirement 8.5.15 states: "If a session has been idle for more than 15 minutes, require the user to re-enter the password to re-activate the terminal." The first, and most obvious, way to deal with ssh sessions that are idling at the bash prompt is by enforcing a read-only, global $TMOUT of 900. Unfortunately, that only covers sessions sitting at the bash prompt. The spirit of the PCI spec would also require killing sessions running top/vim/etc. I've considered writing a */1 cron job that parses the output of "/usr/bin/w" and kills the associated shell, but that seems like a blunt instrument. Any ideas for something that would actually do what the spec requires and just lock the terminal? I've looked at away and vlock; they both seem great for voluntarily locking your terminal, but I need a cron/daemon task that will enforce locking.

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  • MSSQL 2005 Snapshot Agent Uses 100% CPU

    - by matth1jd
    When setting up a new subscription to a publication (transactional replication) from 64-bit SQL Server 2005 to 64-bit SQL Server 2005 the Snapshot Agent on the publisher consumes 100% of the CPU. I am using SSMS to create the new subscription. My initial impression is that this could be from row locking occurring during the generation of the snapshot but I have read that a concurrent snapshot is generated by default in SQL Server 2005, and that row locking shouldn't be a concern. As this is a production server I would like to be able to initialize replication without bringing the box to it's knees. Any suggestions would be helpful and appreciated.

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  • "FOR UPDATE" v/s "LOCK IN SHARE MODE" : Allow concurrent threads to read updated "state" value of locked row

    - by shadesco
    I have the following scenario: User X logs in to the application from location lc1: call it Ulc1 User X (has been hacked, or some friend of his knows his login credential, or he just logs in from a different browser on his machine,etc.. u got the point) logs in at the same time from location lc2: call it Ulc2 I am using a main servlet which : - gets a connection from database pooling - sets autocommit to false - executes a command that goes through app layers: if all successful, set autocommit to true in a "finally" statement, and closes connection. Else if an exception happens, rollback(). In my database (mysql/innoDb) i have a "history" table, with row columns: id(primary key) |username | date | topic | locked The column "locked" has by default value "false" and it serves as a flag that marks if a specific row is locked or not. Each row is specific to a user (as u can see from the username column) So back to the scenario: --Ulc1 sends the command to update his history from the db for date "D" and topic "T". --Ulc2 sends the same command to update history from the db for the same date "D" and same topic "T" at the exact same time. I want to implement an mysql/innoDB locking system that will enable whichever thread arriving to do the following check: Is column "locked" for this row true or not? if true, return a message to the user that " he is already updating the same data from another location" if not true (ie not locked) : flag it as locked and update then reset locked to false once finished. Which of these two mysql locking techniques, will actually allow the 2nd arriving thread from reading the "updated" value of the locked column to decide wt action to take?Should i use "FOR UPDATE" or "LOCK IN SHARE MODE"? This scenario explains what i want to accomplish: - Ulc1 thread arrives first: column "locked" is false, set it to true and continue updating process - Ulc2 thread arrives while Ulc1's transaction is still in process, and even though the row is locked through innoDb functionalities, it doesn't have to wait but in fact reads the "new" value of column locked which is "true", and so doesn't in fact have to wait till Ulc1 transaction commits to read the value of the "locked" column(anyway by that time the value of this column will already have been reset to false). I am not very experienced with the 2 types of locking mechanisms, what i understand so far is that LOCK IN SHARE MODE allow other transaction to read the locked row while FOR UPDATE doesn't even allow reading. But does this read gets on the updated value? or the 2nd arriving thread has to wait the first thread to commit to then read the value? Any recommendations about which locking mechanism to use for this scenario is appreciated. Also if there's a better way to "check" if the row has been locked (other than using a true/false column flag) please let me know about it. thank you SOLUTION (Jdbc pseudocode example based on @Darhazer's answer) Table : [ id(primary key) |username | date | topic | locked ] connection.setautocommit(false); //transaction-1 PreparedStatement ps1 = "Select locked from tableName for update where id="key" and locked=false); ps1.executeQuery(); //transaction 2 PreparedStatement ps2 = "Update tableName set locked=true where id="key"; ps2.executeUpdate(); connection.setautocommit(true);// here we allow other transactions threads to see the new value connection.setautocommit(false); //transaction 3 PreparedStatement ps3 = "Update tableName set aField="Sthg" where id="key" And date="D" and topic="T"; ps3.executeUpdate(); // reset locked to false PreparedStatement ps4 = "Update tableName set locked=false where id="key"; ps4.executeUpdate(); //commit connection.setautocommit(true);

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  • Thread Synchronization and Synchronization Primitives

    When considering synchronization in an application, the decision truly depends on what the application and its worker threads are going to do. I would use synchronization if two or more threads could possibly manipulate the same instance of an object at the same time. An example of this in C# can be demonstrated through the use of storing data in a static object. A static object is initialized once per application and the data within the object can be accessed by all threads. I would use the synchronization primitives to prevent any data from being manipulated by multiple threads simultaneously. This would reduce any data corruption from occurring within the object. On the other hand if all the threads used non static objects and were independent of the other tasks there would be no need to use synchronization. Synchronization Primitives in C#: Basic Blocking Locking Signaling Non-Blocking Synchronization Constructs The Basic Blocking methods include Sleep, Join, and Task.Wait.  These methods force threads to wait until other threads have completed. In addition, these methods can also force a thread to wait a set amount of time before continuing to work.   The Locking primitive prevents a thread from entering a critical section of code while another thread is in the same critical section.  If another thread attempts to enter a locked code, it will wait, until the code block is released. The Signaling primitive allows a thread to temporarily pause work until receiving a notification from another thread that it is ok to continue working. The Signaling primitive removes the need for polling.The Non-Blocking Synchronization Constructs protect access to a common field by calling upon processor primitives.

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  • JMS : Specifying Message Paging Directory on Weblogic server.

    - by adejuanc
    Two ways to configure or modify Paging directory, here the examples : 1.- Via config.xml file. <paging-directory>C:\temp</paging-directory> <jms-server> <name>JMSServerMS1</name> <target>MS1</target> <persistent-store xsi:nil="true"></persistent-store> <hosting-temporary-destinations>true</hosting-temporary-destinations> <temporary-template-resource xsi:nil="true"></temporary-template-resource> <temporary-template-name xsi:nil="true"></temporary-template-name> <message-buffer-size>-1</message-buffer-size> <paging-directory>C:\temp</paging-directory> <paging-file-locking-enabled>true</paging-file-locking-enabled> <expiration-scan-interval>30</expiration-scan-interval> </jms-server> ------------------------------------------------------- 2 .- Via WLST (Weblogic scripting tool) startEdit() cd('/Deployments/JMSServerMS1') cmo.setPagingDirectory('C:\\temp') activate()

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  • JUnit Testing in Multithread Application

    - by e2bady
    This is a problem me and my team faces in almost all of the projects. Testing certain parts of the application with JUnit is not easy and you need to start early and to stick to it, but that's not the question I'm asking. The actual problem is that with n-Threads, locking, possible exceptions within the threads and shared objects the task of testing is not as simple as testing the class, but testing them under endless possible situations within threading. To be more precise, let me tell you about the design of one of our applications: When a user makes a request several threads are started that each analyse a part of the data to complete the analysis, these threads run a certain time depending on the size of the chunk of data (which are endless and of uncertain quality) to analyse, or they may fail if the data was insufficient/lacking quality. After each completed its analysis they call upon a handler which decides after each thread terminates if the collected analysis-data is sufficient to deliver an answer to the request. All of these analysers share certain parts of the applications (some parts because the instances are very big and only a certain number can be loaded into memory and those instances are reusable, some parts because they have a standing connection, where connecting takes time, ex.gr. sql connections) so locking is very common (done with reentrant-locks). While the applications runs very efficient and fast, it's not very easy to test it under real-world conditions. What we do right now is test each class and it's predefined conditions, but there are no automated tests for interlocking and synchronization, which in my opionion is not very good for quality insurances. Given this example how would you handle testing the threading, interlocking and synchronization?

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  • Does immutability entirely eliminate the need for locks in multi-processor programming?

    - by GlenPeterson
    Part 1 Clearly Immutability minimizes the need for locks in multi-processor programming, but does it eliminate that need, or are there instances where immutability alone is not enough? It seems to me that you can only defer processing and encapsulate state so long before most programs have to actually DO something. If a program performs actions on multiple processors, something needs to collect and aggregate the results. All this involves multi-process communication before, after, and possibly during some transformations. The start and end state of the machines are different. Can this always be done with no locks just by throwing out each object and creating a new one instead of changing the original (a crude view of immutability)? What cases still require locking? I'm interested in both the theoretical/academic answer and the practical/real-world answer. I know a lot of functional programmers like to talk about "no side effect" but in the "real world" everything has a side effect. Every processor click takes time and electricity and machine resources away from other processes. So I understand that there may be more than one perspective to answer this question from. If immutability is safe, given certain bounds or assumptions, I want to know what the borders of the "safety zone" are exactly. Some examples of possible boundaries: I/O Exceptions/errors Interfaces with programs written in other languages Interfaces with other machines (physical, virtual, or theoretical) Special thanks to @JimmaHoffa for his comment which started this question! Part 2 Multi-processor programming is often used as an optimization technique - to make some code run faster. When is it faster to use locks vs. immutable objects? Given the limits set out in Amdahl's Law, when can you achieve better over-all performance (with or without the garbage collector taken into account) with immutable objects vs. locking mutable ones? Summary I'm combining these two questions into one to try to get at where the bounding box is for Immutability as a solution to threading problems.

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  • What level of detail to use in an interface members descriptions?

    - by famousgarkin
    I am extracting interfaces from some classes in .NET, and I am not completely sure about what level of detail of description to use for some of the interface members (properties, methods). An example: interface ISomeInterface { /// <summary> /// Checks if the object is checked out. /// </summary> /// <returns> /// Returns true if the object is checked out, or if the object locking is not enabled, /// otherwise returns false. /// </returns> bool IsObjectCheckedOut(); } class SomeImplementation : ISomeInterface { public bool IsObjectCheckedOut() { // An implementation of the method that returns true if the object is checked out, // or if the object locking is not enabled } } The part in question is the <returns>...</returns> section of the IsObjectCheckedOut description in the interface. Is it ok to include such a detail about return value in the interface itself, as the code that will work with the interface should know exactly what that method will do? All the current implementations of the method will do just that. But is it ok to limit the possible other/future implementations by description this way? Or should this not be included in the interface description, as there is no way to actually ensure that other/future implementations will do exactly this? Is it better to be as general as possible regarding the interface in such circumstances? I am currently inclined to the latter option.

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  • Google search does not show sub-pages from my website

    - by Chang
    My website appears in Google search, but only the first page. Of course I have sub-pages linked from the first page, but the sub-pages do not show in Google search. Not in Yahoo, not in Bing. What should I do? It has been three years that sub-pages do not show. (I tried searching site:mydomain.com and pressed 'repeat the search with the omitted results included' link) What would you suspect the reason? My website addresses were like xxx.php?yy=zzz etc, etc, so I changed it to /yy/zzz using mod_rewrite. I thought it might be (X)HTML standard violations, so now I changed it. I hope Google will soon have my entire website, but I am a little bit pessimistic. Do you have any thought?

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  • Google search does not show sub-pages from my website

    - by user5679
    My website appears in Google search, but only the first page. Of course I have sub-pages linked from the first page, but the sub-pages do not show in Google search. Not in Yahoo, not in Bing. What should I do? It has been three years that sub-pages do not show. (I tried searching site:mydomain.com and pressed 'repeat the search with the omitted results included' link) What would you suspect the reason? My website addresses were like xxx.php?yy=zzz etc, etc, so I changed it to /yy/zzz using mod_rewrite. I thought it might be (X)HTML standard violations, so now I changed it. I hope Google will soon have my entire website, but I am a little bit pessimistic. Do you have any thought?

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  • Coherence Data Guarantees for Data Reads - Basic Terminology

    - by jpurdy
    When integrating Coherence into applications, each application has its own set of requirements with respect to data integrity guarantees. Developers often describe these requirements using expressions like "avoiding dirty reads" or "making sure that updates are transactional", but we often find that even in a small group of people, there may be a wide range of opinions as to what these terms mean. This may simply be due to a lack of familiarity, but given that Coherence sits at an intersection of several (mostly) unrelated fields, it may be a matter of conflicting vocabularies (e.g. "consistency" is similar but different in transaction processing versus multi-threaded programming). Since almost all data read consistency issues are related to the concept of concurrency, it is helpful to start with a definition of that, or rather what it means for two operations to be concurrent. Rather than implying that they occur "at the same time", concurrency is a slightly weaker statement -- it simply means that it can't be proven that one event precedes (or follows) the other. As an example, in a Coherence application, if two client members mutate two different cache entries sitting on two different cache servers at roughly the same time, it is likely that one update will precede the other by a significant amount of time (say 0.1ms). However, since there is no guarantee that all four members have their clocks perfectly synchronized, and there is no way to precisely measure the time it takes to send a given message between any two members (that have differing clocks), we consider these to be concurrent operations since we can not (easily) prove otherwise. So this leads to a question that we hear quite frequently: "Are the contents of the near cache always synchronized with the underlying distributed cache?". It's easy to see that if an update on a cache server results in a message being sent to each near cache, and then that near cache being updated that there is a window where the contents are different. However, this is irrelevant, since even if the application reads directly from the distributed cache, another thread update the cache before the read is returned to the application. Even if no other member modifies a cache entry prior to the local near cache entry being updated (and subsequently read), the purpose of reading a cache entry is to do something with the result, usually either displaying for consumption by a human, or by updating the entry based on the current state of the entry. In the former case, it's clear that if the data is updated faster than a human can perceive, then there is no problem (and in many cases this can be relaxed even further). For the latter case, the application must assume that the value might potentially be updated before it has a chance to update it. This almost aways the case with read-only caches, and the solution is the traditional optimistic transaction pattern, which requires the application to explicitly state what assumptions it made about the old value of the cache entry. If the application doesn't want to bother stating those assumptions, it is free to lock the cache entry prior to reading it, ensuring that no other threads will mutate the entry, a pessimistic approach. The optimistic approach relies on what is sometimes called a "fuzzy read". In other words, the application assumes that the read should be correct, but it also acknowledges that it might not be. (I use the qualifier "sometimes" because in some writings, "fuzzy read" indicates the situation where the application actually sees an original value and then later sees an updated value within the same transaction -- however, both definitions are roughly equivalent from an application design perspective). If the read is not correct it is called a "stale read". Going back to the definition of concurrency, it may seem difficult to precisely define a stale read, but the practical way of detecting a stale read is that is will cause the encompassing transaction to roll back if it tries to update that value. The pessimistic approach relies on a "coherent read", a guarantee that the value returned is not only the same as the primary copy of that value, but also that it will remain that way. In most cases this can be used interchangeably with "repeatable read" (though that term has additional implications when used in the context of a database system). In none of cases above is it possible for the application to perform a "dirty read". A dirty read occurs when the application reads a piece of data that was never committed. In practice the only way this can occur is with multi-phase updates such as transactions, where a value may be temporarily update but then withdrawn when a transaction is rolled back. If another thread sees that value prior to the rollback, it is a dirty read. If an application uses optimistic transactions, dirty reads will merely result in a lack of forward progress (this is actually one of the main risks of dirty reads -- they can be chained and potentially cause cascading rollbacks). The concepts of dirty reads, fuzzy reads, stale reads and coherent reads are able to describe the vast majority of requirements that we see in the field. However, the important thing is to define the terms used to define requirements. A quick web search for each of the terms in this article will show multiple meanings, so I've selected what are generally the most common variations, but it never hurts to state each definition explicitly if they are critical to the success of a project (many applications have sufficiently loose requirements that precise terminology can be avoided).

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  • Reliable Storage Systems for SQL Server

    By validating the IO path before commissioning the production database system, and performing ongoing validation through page checksums and DBCC checks, you can hopefully avoid data corruption altogether, or at least nip it in the bud. If corruption occurs, then you have to take the right decisions fast to deal with it. Rod Colledge explains how a pessimistic mindset can be an advantage...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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  • 6 Reasons Why You Can’t Move Your Cell Phone To Any Carrier You Want

    - by Chris Hoffman
    You can buy a laptop or Wi-Fi tablet and use it on Wi-Fi anywhere in the world, so why are cell phones and devices with mobile data not portable between different cellular networks in the same country? Unlike with Wi-Fi, there are many different competing cellular network standards — both around the world and within countries. Cellular carriers also like locking you to their specific network and making it difficult to move. That’s what contracts are for. Phone Locking Many phones are sold locked to a specific network. When you buy a phone from a cellular carrier, they often lock that phone to their network so you can’t take it to a competitor’s network. That’s why you’ll often need to unlock a phone before you can move it to a different cellular provider or take it to a different country and use it on a local provider instead of roaming. Cellular carriers will generally unlock your phone for you as long as you’re no longer in a contract with them. However, unlocking a cell phone you’ve paid for without your carrier’s permission is currently a crime in the USA. GSM vs. CDMA Some cellular networks use the GSM (Global System for Mobile Communications) standard, while some use CDMA (Code-division multiple access). Worldwide, most cellular networks use GSM. In the USA, both GSM and CDMA are popular. Verizon, Sprint, and other carriers that use their networks use CDMA. AT&T, T-Mobile, and other carriers that use their networks are use GSM. These are two competing standards and are not interoperable. This means you can’t simply take a phone from Verizon to T-Mobile, or from AT&T to Sprint. These carriers have incompatible phones. CDMA Restrictions CDMA is more restricted than GSM. GSM phones have SIM cards. Simply open the phone, pop out the SIM card, and pop in a new SIM card to switch carriers. (In reality, it’s more complicated thanks to phone locking and other factors here.) CDMA phones don’t have removable modules like this. All CDMA phones ship locked to a specific network and you’d have to get both your old carrier and your new carrier to cooperate to switch phones between them. In reality, many people just consider CDMA phones eternally locked to a specific carrier. Frequencies Different cellular networks throughout the USA and the rest of the world use different frequencies. These radio frequencies have to be supported by your phone’s hardware or your phone simply can’t work on a network using those frequencies. Many GSM phones support three or four bands of frequencies — 900/1800/1900 MHz, 850/1800/1900 MHz, or 850/900/1800/1900 MHz. These are sometimes called “world phones” because they allow easier roaming. This allows the manufacturer to produce a phone that will support all GSM networks in the world and allows their customers to travel with those phones. If your phone doesn’t support the appropriate frequencies, it won’t work on certain networks. LTE Bands When it comes to newer, faster LTE networks, different frequencies are still a concern. LTE frequencies are generally known as “LTE bands.” To use a smartphone on a certain LTE network, that smartphone will have to support that LTE network’s frequency. Different models of phones are often created to work on different LTE networks around the world. However, phones are generally supporting more and more LTE networks and becoming more and more interoperable over time. SIM Card Sizes The SIM cards used in GSM phones come in different sizes. Newer phones use smaller SIM cards to save space and be more compact. This isn’t a big obstacle, as the different sizes of SIM cards — full-size SIM, mini-SIM, micro-SIM, and nano-SIM are actually compatible. The only difference between them is the size of the plastic card surrounding the SIM’s chip. The actual chip is the same size between all the SIM cards. This means you can take an old SIM card and cut the plastic off until it becomes a smaller-size SIM card that fits in a modern phone. Or, you can take a smaller-size SIM card and insert it into a tray so that it becomes a larger-size SIM card that fits in an older phone. Be aware that it’s very possible to damage your SIM card and make it not work properly by cutting it to the wrong dimensions. Your cellular carrier will often be able to cut your SIM card for you or give you a new one if you want to use an old SIM card in a new phone. Hopefully they won’t overcharge you for this service, too. Be sure to check what types of networks, frequencies, and LTE bands your phone supports before trying to move it between networks. You may have to buy a new phone when moving between certain cellular carriers. Image Credit: Morgan on Flickr, 22n on Flickr

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  • PASS Summit 2011 &ndash; Part III

    - by Tara Kizer
    Well we’re about a month past PASS Summit 2011, and yet I haven’t finished blogging my notes! Between work and home life, I haven’t been able to come up for air in a bit.  Now on to my notes… On Thursday of the PASS Summit 2011, I attended Klaus Aschenbrenner’s (blog|twitter) “Advanced SQL Server 2008 Troubleshooting”, Joe Webb’s (blog|twitter) “SQL Server Locking & Blocking Made Simple”, Kalen Delaney’s (blog|twitter) “What Happened? Exploring the Plan Cache”, and Paul Randal’s (blog|twitter) “More DBA Mythbusters”.  I think my head grew two times in size from the Thursday sessions.  Just WOW! I took a ton of notes in Klaus' session.  He took a deep dive into how to troubleshoot performance problems.  Here is how he goes about solving a performance problem: Start by checking the wait stats DMV System health Memory issues I/O issues I normally start with blocking and then hit the wait stats.  Here’s the wait stat query (Paul Randal’s) that I use when working on a performance problem.  He highlighted a few waits to be aware of such as WRITELOG (indicates IO subsystem problem), SOS_SCHEDULER_YIELD (indicates CPU problem), and PAGEIOLATCH_XX (indicates an IO subsystem problem or a buffer pool problem).  Regarding memory issues, Klaus recommended that as a bare minimum, one should set the “max server memory (MB)” in sp_configure to 2GB or 10% reserved for the OS (whichever comes first).  This is just a starting point though! Regarding I/O issues, Klaus talked about disk partition alignment, which can improve SQL I/O performance by up to 100%.  You should use 64kb for NTFS cluster, and it’s automatic in Windows 2008 R2. Joe’s locking and blocking presentation was a good session to really clear up the fog in my mind about locking.  One takeaway that I had no idea could be done was that you can set a timeout in T-SQL code view LOCK_TIMEOUT.  If you do this via the application, you should trap error 1222. Kalen’s session went into execution plans.  The minimum size of a plan is 24k.  This adds up fast especially if you have a lot of plans that don’t get reused much.  You can use sys.dm_exec_cached_plans to check how often a plan is being reused by checking the usecounts column.  She said that we can use DBCC FLUSHPROCINDB to clear out the stored procedure cache for a specific database.  I didn’t know we had this available, so this was great to hear.  This will be less intrusive when an emergency comes up where I’ve needed to run DBCC FREEPROCCACHE. Kalen said one should enable “optimize for ad hoc workloads” if you have an adhoc loc.  This stores only a 300-byte stub of the first plan, and if it gets run again, it’ll store the whole thing.  This helps with plan cache bloat.  I have a lot of systems that use prepared statements, and Kalen says we simulate those calls by using sp_executesql.  Cool! Paul did a series of posts last year to debunk various myths and misconceptions around SQL Server.  He continues to debunk things via “DBA Mythbusters”.  You can get a PDF of a bunch of these here.  One of the myths he went over is the number of tempdb data files that you should have.  Back in 2000, the recommendation was to have as many tempdb data files as there are CPU cores on your server.  This no longer holds true due to the numerous cores we have on our servers.  Paul says you should start out with 1/4 to 1/2 the number of cores and work your way up from there.  BUT!  Paul likes what Bob Ward (twitter) says on this topic: 8 or less cores –> set number of files equal to the number of cores Greater than 8 cores –> start with 8 files and increase in blocks of 4 One common myth out there is to set your MAXDOP to 1 for an OLTP workload with high CXPACKET waits.  Instead of that, dig deeper first.  Look for missing indexes, out-of-date statistics, increase the “cost threshold for parallelism” setting, and perhaps set MAXDOP at the query level.  Paul stressed that you should not plan a backup strategy but instead plan a restore strategy.  What are your recoverability requirements?  Once you know that, now plan out your backups. As Paul always does, he talked about DBCC CHECKDB.  He said how fabulous it is.  I didn’t want to interrupt the presentation, so after his session had ended, I asked Paul about the need to run DBCC CHECKDB on your mirror systems.  You could have data corruption occur at the mirror and not at the principal server.  If you aren’t checking for data corruption on your mirror systems, you could be failing over to a corrupt database in the case of a disaster or even a planned failover.  You can’t run DBCC CHECKDB against the mirrored database, but you can run it against a snapshot off the mirrored database.

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  • TCPlistener.BeginAcceptSocket - async question

    - by Mirek
    Hi, Some time ago I have payed to a programmer for doing multithread server. In the meantime I have learned C# a bit and now I think I can see the slowndown problem - I was told by that guy that nothing is processed on the main thread (Form) so it cannot be frozen..but it is. But I think that altough BeginAcceptSocket is async operation, but its callback runs on the main thread and if there is locking, thats the reason why the app freezes. Am I right? Thanks this.mTcpListener.BeginAcceptSocket(this.AcceptClient, null); protected void AcceptClient(IAsyncResult ar) { //some locking stuff }

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  • Long-running transactions structured approach

    - by disown
    I'm looking for a structured approach to long-running (hours or more) transactions. As mentioned here, these type of interactions are usually handled by optimistic locking and manual merge strategies. It would be very handy to have some more structured approach to this type of problem using standard transactions. Various long-running interactions such as user registration, order confirmation etc. all have transaction-like semantics, and it is both error-prone and tedious to invent your own fragile manual roll-back and/or time-out/clean-up strategies. Taking a RDBMS as an example, I realize that it would be a major performance cost associated with keeping all the transactions open. As an alternative, I could imagine having a database supporting two isolation levels/strategies simultaneously, one for short-running and one for long-running conversations. Long-running conversations could then for instance have more strict limitations on data access to facilitate them taking more time (read-only semantics on some data, optimistic locking semantics etc). Are there any solutions which could do something similar?

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  • iPhone Orientation Relayout From Single Column to Double Column

    - by kkrizka
    I am trying to create a UIView in Interface Builder that shows to the user two boxes containing some text. This UIView should support both landscape and portrait modes. When in portrait orientation, the two boxes should be centered horizontally and be under each other. Like in the picture below: But when in landscape orientation, it should show the two boxes centered vertically and by side by side. Like in the picture below: Is this possible using only the autosizing options (or any other IB options), or do I have to relayout the view in code on orientation change events? I would prefer using only IB. I tried locking the top and left margins of the top box and locking the bottom and right margins of the bottom box. But the problem is that for it to work I also need to shrink the two boxes as one changes from portrait to landscape, because otherwise they would overlap.

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  • Is there any benefit to encrypting twice using pgp?

    - by ojblass
    I am asking from a "more secure" perspective. I can imagine a scenario with two required private keys needed for decryption scenarios that may make this an attractive model. This is to settle an argument. My vote is that it is not adding any additional security other than having to compromise two different private keys. I think that if it was any more secure than encrypting it one million times would be the best way to secure informaiton and I don't buy it. So I guess my question becomes is a two locking mechanism equivalent to another one locking mechanism with a single key? Update: Forgive me if the answer is obvious but my bread goes dead as I read books on the topic.

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  • What's the performance penalty of weak_ptr?

    - by Kornel Kisielewicz
    I'm currently designing a object structure for a game, and the most natural organization in my case became a tree. Being a great fan of smart pointers I use shared_ptr's exclusively. However, in this case, the children in the tree will need access to it's parent (example -- beings on map need to be able to access map data -- ergo the data of their parents. The direction of owning is of course that a map owns it's beings, so holds shared pointers to them. To access the map data from within a being we however need a pointer to the parent -- the smart pointer way is to use a reference, ergo a weak_ptr. However, I once read that locking a weak_ptr is a expensive operation -- maybe that's not true anymore -- but considering that the weak_ptr will be locked very often, I'm concerned that this design is doomed with poor performance. Hence the question: What is the performance penalty of locking a weak_ptr? How significant is it?

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  • how to "lock" live site when doing (phing) deployment

    - by Jorre
    On http://www.slideshare.net/eljefe/automated-deployment-with-phing in slide 15 they are talking about "locking the live site" when doing deployment. We are running multiple webshops in a SAAS application where it is possible that users are adding products, buying products and paying for products online, and so on... When deploying we want to do this as clean as possible, so that not payments or orders or other critical data will be lost. We have a deployment scenario set up using phing (amazing tool!) but we are missing one crucial step being the "locking of the live site" while deploying. What is a possible way to lock a live site and bring it back online after deploying?

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  • Why boost::recursive_mutex is not working as expected?

    - by Kjir
    I have a custom class that uses boost mutexes and locks like this (only relevant parts): template<class T> class FFTBuf { public: FFTBuf(); [...] void lock(); void unlock(); private: T *_dst; int _siglen; int _processed_sums; int _expected_sums; int _assigned_sources; bool _written; boost::recursive_mutex _mut; boost::unique_lock<boost::recursive_mutex> _lock; }; template<class T> FFTBuf<T>::FFTBuf() : _dst(NULL), _siglen(0), _expected_sums(1), _processed_sums(0), _assigned_sources(0), _written(false), _lock(_mut, boost::defer_lock_t()) { } template<class T> void FFTBuf<T>::lock() { std::cerr << "Locking" << std::endl; _lock.lock(); std::cerr << "Locked" << std::endl; } template<class T> void FFTBuf<T>::unlock() { std::cerr << "Unlocking" << std::endl; _lock.unlock(); } If I try to lock more than once the object from the same thread, I get an exception (lock_error): #include "fft_buf.hpp" int main( void ) { FFTBuf<int> b( 256 ); b.lock(); b.lock(); b.unlock(); b.unlock(); return 0; } This is the output: sb@dex $ ./src/test Locking Locked Locking terminate called after throwing an instance of 'boost::lock_error' what(): boost::lock_error zsh: abort ./src/test Why is this happening? Am I understanding some concept incorrectly?

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  • Why timed lock doesnt throws a timeout exception in C++0x?

    - by Vicente Botet Escriba
    C++0x allows to lock on a mutex until a given time is reached, and return a boolean stating if the mutex has been locked or not. template <class Clock, class Duration> bool try_lock_until(const chrono::time_point<Clock, Duration>& abs_time); In some contexts, I consider an exceptional situation that the locking fails because of timeout. In this case an exception should be more appropriated. To make the difference a function lock_until could be used to get a timeout exception when the time is reached before locking. template <class Clock, class Duration> void lock_until(const chrono::time_point<Clock, Duration>& abs_time); Do you think that lock_until should be more adequate in some contexts? if yes, on which ones? If no, why try_lock_until will always be a better choice?

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  • Will lock() statement block all threads in the proccess/appdomain?

    - by MikeJ
    Maybe the question sounds silly, but I don't understand 'something about threads and locking and I would like to get a confirmation (here's why I ask). So, if I have 10 servers and 10 request in the same time come to each server, that's 100 request across the farm. Without locking, thats 100 request to the database. If I do something like this: private static readonly object myLockHolder = new object(); if (Cache[key] == null) { lock(myLockHolder) { if (Cache[key] == null) { Cache[key] = LengthyDatabaseCall(); } } } How many database requests will I do? 10? 100? Or as much as I have threads?

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  • SQL Server concurrency and generated sequence

    - by Goyuix
    I need a sequence of numbers for an application, and I am hoping to leverage the abilities of SQL Server to do it. I have created the following table and procedure (in SQL Server 2005): CREATE TABLE sequences ( seq_name varchar(50) NOT NULL, seq_value int NOT NULL ) CREATE PROCEDURE nextval @seq_name varchar(50) AS BEGIN DECLARE @seq_value INT SET @seq_value = -1 UPDATE sequences SET @seq_value = seq_value = seq_value + 1 WHERE seq_name = @seq_name RETURN @seq_value END I am a little concerned that without locking the table/row another request could happen concurrently and end up returning the same number to another thread or client. This would be very bad obviously. Is this design safe in this regard? Is there something I can add that would add the necessary locking to make it safe? Note: I am aware of IDENTITY inserts in SQL Server - and that is not what I am looking for this in particular case. Specifically, I don't want to be inserting/deleting rows. This is basically to have a central table that manages the sequential number generator for a bunch of sequences.

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