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  • How can I implement "real time" messaging on Google AppEngine?

    - by Freed
    I'm creating a web application on Google AppEngine where I want the user to be notified a quickly as possible after certain events occour. The problem is similar to say a chat server in that I need something happening on one connection (someone is writing a message in a chat room) to propagate to a number of other connections (other people in that chat room gets the message). To get speedy updates from the server to the client I'm planning on using long polling with XmlHttpRequest, hoping that AppEngine won't interfere other than possibly restriing the timeout. The real problem however is efficient notification between connections on AppEngine. Is there any support for this type of cross connection notification on AppEngine that does not involve busy-waiting? The only tools I can think of to do this at all is either using the data storage (slow) or memcache (unreliable), and none of them would let me avoid busy-waiting. Note: I know about XMPP support on AppEngine. It's related, but I want a browser based solution, sending messages to the users by XMPP is not an option.

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  • Is there a way to determine the current Outlook activity level?

    - by dlittau
    I am working on an Outlook 2007 Add-in in C# (VS2008) and we want to send some background email items only when Outlook is not busy doing something else. Is there a .net way to see if Outlook is busy doing other things (perhaps due to other add-ins or the like taking up CPU cycles)? Alternately, is there a way to send emails such that they never appear in the Outbox? We need a method that would not require any additional software be installed. Thanks for any input.

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  • Distributed and/or Parallel SSIS processing

    - by Jeff
    Background: Our company hosts SaaS DSS applications, where clients provide us data Daily and/or Weekly, which we process & merge into their existing database. During business hours, load in the servers are pretty minimal as it's mostly users running simple pre-defined queries via the website, or running drill-through reports that mostly hit the SSAS OLAP cube. I manage the IT Operations Team, and so far this has presented an interesting "scaling" issue for us. For our daily-refreshed clients, the server is only "busy" for about 4-6 hrs at night. For our weekly-refresh clients, the server is only "busy" for maybe 8-10 hrs per week! We've done our best to use some simple methods of distributing the load by spreading the daily clients evenly among the servers such that we're not trying to process daily clients back-to-back over night. But long-term this scaling strategy creates two notable issues. First, it's going to consume a pretty immense amount of hardware that sits idle for large periods of time. Second, it takes significant Production Support over-head to basically "schedule" the ETL such that they don't over-lap, and move clients/schedules around if they out-grow the resources on a particular server or allocated time-slot. As the title would imply, one option we've tried is running multiple SSIS packages in parallel, but in most cases this has yielded VERY inconsistent results. The most common failures are DTExec, SQL, and SSAS fighting for physical memory and throwing out-of-memory errors, and ETLs running 3,4,5x longer than expected. So from my practical experience thus far, it seems like running multiple ETL packages on the same hardware isn't a good idea, but I can't be the first person that doesn't want to scale multiple ETLs around manual scheduling, and sequential processing. One option we've considered is virtualizing the servers, which obviously doesn't give you any additional resources, but moves the resource contention onto the hypervisor, which (from my experience) seems to manage simultaneous CPU/RAM/Disk I/O a little more gracefully than letting DTExec, SQL, and SSAS battle it out within Windows. Question to the forum: So my question to the forum is, are we missing something obvious here? Are there tools out there that can help manage running multiple SSIS packages on the same hardware? Would it be more "efficient" in terms of parallel execution if instead of running DTExec, SQL, and SSAS same machine (with every machine running that configuration), we run in pairs of three machines with SSIS running on one machine, SQL on another, and SSAS on a third? Obviously that would only make sense if we could process more than the three ETL we were able to process on the machine independently. Another option we've considered is completely re-architecting our SSIS package to have one "master" package for all clients that attempts to intelligently chose a server based off how "busy" it already is in terms of CPU/Memory/Disk utilization, but that would be a herculean effort, and seems like we're trying to reinvent something that you would think someone would sell (although I haven't had any luck finding it). So in summary, are we missing an obvious solution for this, and does anyone know if any tools (for free or for purchase, doesn't matter) that facilitate running multiple SSIS ETL packages in parallel and on multiple servers? (What I would call a "queue & node based" system, but that's not an official term). Ultimately VMWare's Distributed Resource Scheduler addresses this as you simply run a consistent number of clients per VM that you know will never conflict scheduleing-wise, then leave it up to VMWare to move the VMs around to balance out hardware usage. I'm definitely not against using VMWare to do this, but since we're a 100% Microsoft app stack, it seems like -someone- out there would have solved this problem at the application layer instead of the hypervisor layer by checking on resource utilization at the OS, SQL, SSAS levels. I'm open to ANY discussion on this, and remember no suggestion is too crazy or radical! :-) Right now, VMWare is the only option we've found to get away from "manually" balancing our resources, so any suggestions that leave us on a pure Microsoft stack would be great. Thanks guys, Jeff

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  • SQL SERVER – Remove Debug Button in SSMS – SQL in Sixty Seconds #020 – Video

    - by pinaldave
    SQL in Sixty Seconds is indeed tremendous fun to do. Every week, we try to come up with some new learning which we can share in Sixty Seconds. In this busy world, we all have sixty seconds to learn something new – no matter how much busy we are. In this episode of the series, we talk about another interesting feature of SQL Server Management Studio. In SQL Server Management Studio (SSMS) we have two button side by side. 1) Execute (!) and 2) Debug (>). It is quite confusing to a few developers. The debug button which looks like a play button encourages developers to click on the same thinking it will execute the code. Also developer with a Visual Studio background often click it because of their habit. However, Debug button is not the same as Execute button. In most of the cases developers want to click on Execute to run the query but by mistake they click on Debug and it wastes their valuable time. It is very easy to fix this. If developers are not frequently using a debug feature in SQL Server they should hide it from the toolbar itself. This will reduce the chances to incorrectly click on the debug button greatly as well save lots of time for developer as invoking debug processes and turning it off takes a few extra moments. In this Sixty second video we will discuss how one can hide the debug button and avoid confusion regarding execution button. I personally use function key F5 to execute the T-SQL code so I do not face this problem that often. More on Removing Debug Button in SSMS: SQL SERVER – Read Only Files and SQL Server Management Studio (SSMS) SQL SERVER – Standard Reports from SQL Server Management Studio – SQL in Sixty Seconds #016 – Video SQL SERVER – Discard Results After Query Execution – SSMS SQL SERVER – Tricks to Comment T-SQL in SSMS – SQL in Sixty Seconds #019 – Video SQL SERVER – Right Aligning Numerics in SQL Server Management Studio (SSMS) I encourage you to submit your ideas for SQL in Sixty Seconds. We will try to accommodate as many as we can. If we like your idea we promise to share with you educational material. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video

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  • My Speaking Engagements in the Last Two Months

    - by gsusx
    I’ve been so busy lately with the activities around Moesion that I haven’t had time to blog about a couple of great conferences I had the opportunity to speak at in the last two months. Software Architect Conference, UK ( http://www.software-architect.co.uk/ ) This conference is becoming one of my favorite events of the year. As always Nick Payne and his team did a remarkable job lining up an all-star group of speakers that covered some of the hottest topics in today’s software industry. The first...(read more)

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  • Python PyBluez loses Bluetooth connection after a while

    - by Travis G.
    I am using Python to write a simple serial Bluetooth script that sends information about my computer stats periodically. The receiving device is a Sparkfun BlueSmirf Silver. The problem is that, after the script runs for a few minutes, it stops sending packets to the receiver and fails with the error: (11, 'Resource temporarily unavailable') Noticing that this inevitably happens, I added some code to automatically try to reopen the connection. However, then I get: Could not connect: (16, 'Device or resource busy') Am I doing something wrong with the connection? Do I need to occasionally reopen the socket? I'm not sure how to recover from this type of error. I understand that sometimes the port will be busy and a write operation is deferred to avoid blocking other processes, but I wouldn't expect the connection to fail so regularly. Any thoughts? Here is the script: import psutil import serial import string import time import bluetooth sampleTime = 1 numSamples = 5 lastTemp = 0 TEMP_CHAR = 't' USAGE_CHAR = 'u' SENSOR_NAME = 'TC0D' #gauges = serial.Serial() #gauges.port = '/dev/rfcomm0' #gauges.baudrate = 9600 #gauges.parity = 'N' #gauges.writeTimeout = 0 #gauges.open() filename = '/sys/bus/platform/devices/applesmc.768/temp2_input' def parseSensorsOutputLinux(output): return int(round(float(output) / 1000)) def connect(): while(True): try: gaugeSocket = bluetooth.BluetoothSocket(bluetooth.RFCOMM) gaugeSocket.connect(('00:06:66:42:22:96', 1)) break; except bluetooth.btcommon.BluetoothError as error: print "Could not connect: ", error, "; Retrying in 5s..." time.sleep(5) return gaugeSocket; gaugeSocket = connect() while(1): usage = psutil.cpu_percent(interval=sampleTime) sensorFile = open(filename) temp = parseSensorsOutputLinux(sensorFile.read()) try: #gauges.write(USAGE_CHAR) gaugeSocket.send(USAGE_CHAR) #gauges.write(chr(int(usage))) #write the first byte gaugeSocket.send(chr(int(usage))) #print("Wrote usage: " + str(int(usage))) #gauges.write(TEMP_CHAR) gaugeSocket.send(TEMP_CHAR) #gauges.write(chr(temp)) gaugeSocket.send(chr(temp)) #print("Wrote temp: " + str(temp)) except bluetooth.btcommon.BluetoothError as error: print "Caught BluetoothError: ", error time.sleep(5) gaugeSocket = connect() pass gaugeSocket.close() EDIT: I should add that this code connects fine after I power-cycle the receiver and start the script. However, it fails after the first exception until I restart the receiver. P.S. This is related to my recent question, Why is /dev/rfcomm0 giving PySerial problems?, but that was more about PySerial specifically with rfcomm0. Here I am asking about general rfcomm etiquette.

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  • GlassFish Back from Devoxx 2011 Mature Java EE 6 and EE 7 well on its way

    - by alexismp
    I'm back from my 8th (!) Devoxx conference (I don't think I've missed one since 2004) and this conference keeps delivering on the promise of a Java developer paradise week. GlassFish was covered in many different ways and I was not involved in a good number of them which can only be a good sign! Several folks asked me when my Java EE 6 session with Antonio Goncalves was scheduled (we've been covering this for the past two years in University sessions, hands-on labs and regular sessions). It turns out we didn't team up this year (Antonio was crazy busy preparing for Devoxx France) and I had a regular GlassFish session. Instead, this year, Bert Ertman and Paul Bakker covered the 3-hour Java EE 6 University session ("Duke’s Duct Tape Adventures") on the very first day (using GlassFish) with great success it seems. The Java EE 6 lab was also a hit with a full room of folks covering a lot of technical ground in 2.5 hours (with GlassFish of course). GlassFish was also mentioned during Cameron Purdy's keynote (pretty natural even if that surprised a number of folks that had not been closely following GlassFish) but also in Stephan Janssen's Keynote as the engine powering Parleys.com. In fact Stephan was a speaker in the GlassFish session describing how they went from a single-instance Tomcat setup to a clustered GlassFish + MQ environment. Also in the session was Johan Vos (of Mollom fame, along other things). Both of these customer testimonials were made possible because GlassFish has been delivering full Java EE 6 implementations for almost two years now which is plenty of time to see serious production deployments on it. The Java EE Gathering (BOF) was very well attended and very lively with many spec leads participating and discussing progress and also pain points with folks in the room. Thanks to all those attending this session, a good number of RFE's, and priority points came out of this. While this wasn't a GlassFish session by any means, it's great to have the current RESTful Admin and upcoming Java EE 7 planned features be a satisfactory answer to some of the requests from the attendance. Last but certainly not least, the GlassFish team is busy with Java EE 7 and version 4 of the product. This was discussed and shown during the Java EE keynote and in greater details in Jerome Dochez' session. If any indication, the tweets on his demo (virtualization, provisioning, etc...) were very encouraging. Java EE 6 adoption is doing great and GlassFish, being a production-quality reference implementation, is one of the first to benefit from this. And with GlassFish 4.0, we're looking at increasing the product and community adoption by offering a pragmatic technical solution to Java EE PaaS deployments. Stay tuned ! (the impatient in you is encouraged to grab a 4.0 build and provide feedback).

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  • PeopleSoft HCM @ OHUG 11: Enter the Matrix

    - by Jay Zuckert
    The PeopleSoft HCM team is back from a very busy and exciting OHUG conference in Orlando. The packed, standing-room only PeopleSoft HCM Roadmap keynote was the highlight of the conference for many attendees and the reviews are in : PeopleSoft rocked the house ! Great demonstration of products in the keynote. Best keynote in a long time, and fun. Engaging and entertaining, great demonstration of capabilities. Message received loud and clear, PeopleSoft applications are here to stay.  PeopleSoft has a real vision moving forward. Real-time polls using mobile texting were cutting edge.                          Tracy Martin (as Trinity) and other members of the PeopleSoft HCM team presented a ‘must-see’ Matrix-themed session while dressed as movie characters. The keynote highlighted planned HCM capabilities for Matrix administration and future organization visualization enhancements. The team also previewed the planned Manager Dashboard and Talent Summary.                           Following the keynote, some of the cast posed for photo opportunities at the OHUG booth in the exhibition hall. As you can imagine, they received some interesting looks walking by the other vendor booths. The PeopleSoft HCM team also presented numerous other OHUG sessions covering PeopleSoft Talent Management, Compensation, HR HelpDesk, Payroll, Global HCM Practices, Time & Labor, Absence Management, and Benefits. All of those presentations are available from the OHUG site at www.ohug.org. When not in one of the well-attended PeopleSoft HCM sessions, conference attendees filled the Oracle booth in the exhibition hall to see live product demonstrations. True to their PeopleSoft roots, some of the PeopleSoft HCM team played as hard as they worked in Orlando and enjoyed the OHUG Appreciation event along with customers at the Hard Rock. We are already busy planning for Oracle OpenWorld 2011 and prepping sessions our PeopleSoft HCM customers are sure to like. We hope to see you there in San Francisco from Oct. 2-6. To learn more about OpenWorld or to register, click here.

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  • SQL SERVER – Inviting Ideas for SQL in Sixty Seconds – 12/12/12

    - by pinaldave
    Today is 12/12/12 – I am not sure when will I write this kind of date again – maybe never. This opportunity comes once in a lifetime when we have the same date, month and year all have same digit. December 12th is one of the most fantastic day in my personal life. Four years ago, this day I got married to my wife – Nupur Dave.  Here are photos of our wedding (Dec 12, 2008). Here is a very interesting photo of myself earlier this year. It is not photoshoped or modified photo. The only modification I have done here is to add arrow and speech bubble. Every Wednesday I tried to put one SQL in Sixty Seconds Video. The journey has been fantastic and so far I have put a total of 35 SQL in Sixty Seconds Video. The goal of the video is to learn something in 1 minute. In our daily life we are all very busy and hardly have time for anything. No matter how much we are busy – we all have one minute of time. Sometime we wait for a minute in elevators, at the escalator, at a coffee shop, or just waiting for our phone reboot. Today is a fantastic day – 12/12/12. Let me invite all of you submits SQL in Sixty Seconds idea. If I like your idea and create a sixty second video over it – you will win surprise learning material from me. There are two very simple rules of the contest: - I should have not have already recorded the tip. The tip should be descriptive. Do not just suggest to cover “Performance Tuning” or “How to Create Index” or “More of reporting services”. The tip should have around 100 words of description explaining SQL Tip. The contest is open forever. The winner will be announced whenever I use the tip to convert to video. If I use your tip, I will for sure mention in the blog post that it is inspired from your suggestion. Meanwhile, do not forget to subscribe YouTube Channel. Here are my latest three videos from SQL in Sixty Seconds. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: About Me, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video

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  • Friday Fun: Shape Fold

    - by Asian Angel
    This week’s game comes with lots of puzzle solving, brain-teasing goodness to keep you busy. On each level you will need to rotate, twist, and/or move the hinged puzzle pieces into their proper shape. Do you have the patience and skill to solve all the puzzles or will you be forced to admit defeat? 7 Ways To Free Up Hard Disk Space On Windows HTG Explains: How System Restore Works in Windows HTG Explains: How Antivirus Software Works

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • SQL Server v.Next ("Denali") : How a columnstore index is not like a normal index

    - by AaronBertrand
    At the end of my Denali presentation at SQL Saturday #65 in Vancouver, a member of the audience asked, "What makes a columnstore index different from a regular nonclustered index?" At the end of a busy day, I was at a loss for an answer, and I'll explain why. First, I'll briefly explain the basic, core, high-level functionality of a columnstore index (you can read a lot more details in this white paper ). Basically, instead of storing index data together on a page, it divvies up the data from each...(read more)

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  • Welcome Back !!!

    - by sanket
    Well, its been quite sometime since I have been able to post anything significant.Have been quite busy with some personal stuff, which required more immediate attention. Finally, got the time today to log about something. I have switched companies, I have been cussed about and world seems to have gone awry to awesome for me in the meanwhile. Any ways, back to my blogging ways again and soon I would be starting a series of blogs about WCF, and Networking stuff. Till Then, - Happy Coding!

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  • Upcoming Upgrade Workshops in the US

    - by Mike Dietrich
    As Roy is really busy in traveling the whole North American continent I would like to highlight a few of Roy's upcoming workshops with registration links - so simply "click" and register :-) March 23, 2011: Philadelphia, PA March 24, 2011: Reston, VA April 07, 2011: Dallas, TX April 13, 2011: Birmingham, AL April 14, 2011: Minneapolis, MN Roy is looking forward to meet you in one of the above or the upcoming events in California and Oregon. Mike

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  • Catch AutoVue at the COE 2010 PLM Conference

    - by [email protected]
    It's a busy tradeshow season! The AutoVue team will be exhibiting at next week's COE 2010 PLM Conference and Technifair in Las Vegas, NV. This will be a unique opportunity to meet with AutoVue visualization experts and discuss how to leverage visualization throughout your engineering organization to capitalize on product and engineering information to improve business processes, such as design reviews, change management and design revisions. If you plan on attending, be sure to stop by Oracle's AutoVue booth (#508). Click here for more details about the show.

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  • Has the SQL Community Lost its Focus?

    - by Jonathan Kehayias
    Yesterday, Thomas LaRock’s blog post, WMI Code Creator , was brought to my attention by a member of the SQL Community.  I subscribe to Tom’s blog in my blog reader so eventually I’d like to think that his post would have come to my attention, but to be perfectly honest, I have been to busy with other obligations lately that have made reading blog posts almost impossible.  When I looked at Tom’s post, I was kind of put off when I did a copy paste of the Code from it and got the following:...(read more)

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  • Tuxedo Load Balancing

    - by Todd Little
    A question I often receive is how does Tuxedo perform load balancing.  This is often asked by customers that see an imbalance in the number of requests handled by servers offering a specific service. First of all let me say that Tuxedo really does load or request optimization instead of load balancing.  What I mean by that is that Tuxedo doesn't attempt to ensure that all servers offering a specific service get the same number of requests, but instead attempts to ensure that requests are processed in the least amount of time.   Simple round robin "load balancing" can be employed to ensure that all servers for a particular service are given the same number of requests.  But the question I ask is, "to what benefit"?  Instead Tuxedo scans the queues (which may or may not correspond to servers based upon SSSQ - Single Server Single Queue or MSSQ - Multiple Server Single Queue) to determine on which queue a request should be placed.  The scan is always performed in the same order and during the scan if a queue is empty the request is immediately placed on that queue and request routing is done.  However, should all the queues be busy, meaning that requests are currently being processed, Tuxedo chooses the queue with the least amount of "work" queued to it where work is the sum of all the requests queued weighted by their "load" value as defined in the UBBCONFIG file.  What this means is that under light loads, only the first few queues (servers) process all the requests as an empty queue is often found before reaching the end of the scan.  Thus the first few servers in the queue handle most of the requests.  While this sounds non-optimal, in fact it capitalizes on the underlying operating systems and hardware behavior to produce the best possible performance.  Round Robin scheduling would spread the requests across all the available servers and thus require all of them to be in memory, and likely not share much in the way of hardware or memory caches.  Tuxedo's system maximizes the various caches and thus optimizes overall performance.  Hopefully this makes sense and now explains why you may see a few servers handling most of the requests.  Under heavy load, meaning enough load to keep all servers that can handle a request busy, you should see a relatively equal number of requests processed.  Next post I'll try and cover how this applies to servers in a clustered (MP) environment because the load balancing there is a little more complicated. Regards,Todd LittleOracle Tuxedo Chief Architect

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  • Secret of SQL Trace Duration Column

    - by Dan Guzman
    Why would a trace of long-running queries not show all queries that exceeded the specified duration filter?  We have a server-side SQL Trace that includes RPC:Completed and SQL:BatchCompleted events with a filter on Duration >= 100000.  Nearly all of the queries on this busy OLTP server run in under this 100 millisecond threshold so any that appear in the trace are candidates for root cause analysis and/or performance tuning opportunities. After an application experienced query timeouts, the DBA looked at the trace data to corroborate the problem.  Surprisingly, he found no long-running queries in the trace from the application that experienced the timeouts even though the application’s error log clearly showed detail of the problem (query text, duration, start time, etc.).  The trace did show, however, that there were hundreds of other long-running queries from different applications during the problem timeframe.  We later determined those queries were blocked by a large UPDATE query against a critical table that was inadvertently run during this busy period. So why didn’t the trace include all of the long-running queries?  The reason is because the SQL Trace event duration doesn’t include the time a request was queued while awaiting a worker thread.  Remember that the server was under considerable stress at the time due to the severe blocking episode.  Most of the worker threads were in use by blocked queries and new requests were queued awaiting a worker to free up (a DMV query on the DAC connection will show this queuing: “SELECT scheduler_id, work_queue_count FROM sys.dm_os_schedulers;”).  Technically, those queued requests had not started.  As worker threads became available, queries were dequeued and completed quickly.  These weren’t included in the trace because the duration was under the 100ms duration filter.  The duration reflected the time it took to actually run the query but didn’t include the time queued waiting for a worker thread. The important point here is that duration is not end-to-end response time.  Duration of RPC:Completed and SQL:BatchCompleted events doesn’t include time before a worker thread is assigned nor does it include the time required to return the last result buffer to the client.  In other words, duration only includes time after the worker thread is assigned until the last buffer is filled.  But be aware that duration does include the time need to return intermediate result set buffers back to the client, which is a factor when large query results are returned.  Clients that are slow in consuming results sets can increase the duration value reported by the trace “completed” events.

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  • What do you think of the EntLib 5.0 configuration tool?

    Hello again! Its been a while, I know. Ive been busy over the last few months with several projects, some of them software related, and one of them human my son Jesse was born on 26 February 2010. Fun times! Meanwhile, back in Redmond, the p&p team has been busy working on Enterprise Library 5.0 see Grigoris announcement for details on the beta. Theres a ton of new stuff in this release, but theres one big new feature that hasnt received a lot of attention that Im keen to hear your perspectives on. The change is the biggest overhaul to the configuration tool since Enterprise Library was launched. If you havent yet grabbed the EntLib 5.0 beta, heres a before and after shot of the config tool: Enterprise Library 4.1 config tool Enterprise Library 5.0 (beta 1) config tool The tool has been rebuilt from the ground up in response to some feedback and usability studies from the previous version of the tool. But is this a step in the right direction? Id love to hear what you think. If youve downloaded EntLib 5.0 and tried out the tool, please share your thoughts on: First impressions. Is the tool easy to understand? Easy to find what youre looking for? Easy to read existing configuration? Pretty? Ease of use for real life tasks. Rather than make up your own tasks, here are a few sample scenarios you might want to try: Configure the data access block with a SQL Server connection called Audit that points to a database called Audit on a server called DB Configure the logging block so that any log entries in the Audit category are written to both the Event Log and the Audit database (see above) Configure the validation block with a ruleset called Email Address that uses an appropriate regular expression for e-mail addresses Configure the policy injection block such that any calls to classes in the MyCompany.Security namespace are logged before and after the call using the Audit category (see above) Comparison with the old config tool. What do you like better in the new tool? What did you like better in the old tool? How do you rate your level of expertise using the old tool? Keep in mind that I no longer work in the p&p team, so I cant say how any of this feedback will be used (although Im sure the team is listening!). However since Ive invested so much time in Enterprise Library, both in leading the team and using the product on real projects Im very interested to hear what you all think of the tools new direction.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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  • 7 Things that High Availability is Not

    Wesley has heard High Availablity touted as all sorts of technological cure-all for busy SysAdmins and DBAs, and now he's taking a stand against it. There are a range of things that High Availability is regularly confused with (either deliberately or innocently), and Wesley's clearing it all up The Future of SQL Server Monitoring "Being web-based, SQL Monitor 2.0 enables you to check on your servers from almost any location" Jonathan Allen.Try SQL Monitor now.

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