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  • Manage dirty rect efficiently

    - by Tianzhou Chen
    Hi all, I am implementing a view system and I want to keep track of all the dirty rects. It seems my dirty rect management is a bottleneck for the whole system. On one hand, invalidating the bounding box of the dirty region seems to be an easy approach. But in the situation like this: Say I have a client area of 100x100; I have a dirty rect with (0, 0, 1, 1) and another dirty rect with (99, 99, 1, 1). Invalidating the bounding box which turns out to be 100x100 is not efficient at all. So I want to ask if someone can give any hint or give me a link of the related literatures. Thanks in advance!

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  • Why don't scripting languages output Unicode to the Windows console?

    - by hippietrail
    The Windows console has been Unicode aware for at least a decade and perhaps as far back as Windows NT. However for some reason the major cross-platform scripting languages including Perl and Python only ever output various 8-bit encodings, requiring much trouble to work around. Perl gives a "wide character in print" warning, Pythong gives a charmap error and quits. Why on earth after all these years do they not just simply call the Win32 -W APIs that output UTF-16 Unicode instead of forcing everything through the ANSI/codepage bottleneck? Is it just that cross-platform performance is low priority? Is it that the languages use UTF-8 internally and find it too much bother to output UTF-16? Or are the -W APIs inherently broken to such a degree that they can't be used as-is?

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  • Performance problem loading lots of user controls

    - by codymanix
    My application is loading a bunch of the same user control into a ScrollPanel. The problem is, this is very slow. The profiler show that the method Application.LoadComponent(), which is called internally by in the designer code in the constructor of my user control, is the bottleneck. The documentation of this method says, that this method load XAML files. I alway though the compiler compiles XAML to BAML and embedds it into the assembly. So the question is, how can I use BAML instead of XAML? Is there another way to make loading my user controls faster?

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  • Sorting 1000-2000 elements with many cache misses

    - by Soylent Graham
    I have an array of 1000-2000 elements which are pointers to objects. I want to keep my array sorted and obviously I want to do this as quick as possible. They are sorted by a member and not allocated contiguously so assume a cache miss whenever I access the sort-by member. Currently I'm sorting on-demand rather than on-add, but because of the cache misses and [presumably] non-inlining of the member access the inner loop of my quick sort is slow. I'm doing tests and trying things now, (and see what the actual bottleneck is) but can anyone recommend a good alternative to speeding this up? Should I do an insert-sort instead of quicksorting on-demand, or should I try and change my model to make the elements contigious and reduce cache misses? OR, is there a sort algorithm I've not come accross which is good for data that is going to cache miss?

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  • How can I speed up line by line reading of an ASCII file? (C++)

    - by Jon
    Here's a bit of code that is a considerable bottleneck after doing some measuring: //----------------------------------------------------------------------------- // Construct dictionary hash set from dictionary file //----------------------------------------------------------------------------- void constructDictionary(unordered_set<string> &dict) { ifstream wordListFile; wordListFile.open("dictionary.txt"); string word; while( wordListFile >> word ) { if( !word.empty() ) { dict.insert(word); } } wordListFile.close(); } I'm reading in ~200,000 words and this takes about 240 ms on my machine. Is the use of ifstream here efficient? Can I do better? I'm reading about mmap() implementations but I'm not understanding them 100%. The input file is simply text strings with *nix line terminations.

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  • How to find root cause for "too many connections" error in MySQL/PHP

    - by Nir
    I'm running a web service which runs algorithms that serve millions of calls daily and run some background processing as well. Every now and than I see "Too many connections" error in attempts to connect to the MySQL box" for a few seconds. However this is not necessarily attributed to high traffic times or anything I can put my finger on. I want to find the bottleneck causing it. Other than in the specific times this happens the server isn't too loaded in terms of CPU and Memory, and has 2-3 connections (threads) open and everything works smoothly. (I use Zabbix for monitoring) Any creative ideas on how to trace it?

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  • Stored procedure called from C# executes 6 times longer than from SQL Management studio

    - by Sergey Osypchuk
    I have search stored procedure which is my performance bottleneck. In order to get control about what is happened, I added logging for all parameters and also execution time in SP. I noticed, that when I call SP from MIcrosoft SQL server management Studio execution time is 1.3-1.6 seconds, but when i call it from C#, it takes 6-8 secods (!!!) Parameters | Time (ms) "tb *"TreeType:259Parents:212fL:13;14fV:0;lcid:2057min:0max:10sort:-1 | 6406 "tb *"TreeType:259Parents:212fL:13;14fV:0;lcid:2057min:0max:10sort:-1 | 1346 SP is called with LINQ. Login settings are same. SP uses full text search What could cause this?

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  • How do I ensure data consistency in this concurrent situation?

    - by MalcomTucker
    The problem is this: I have multiple competing threads (100+) that need to access one database table Each thread will pass a String name - where that name exists in the table, the database should return the id for the row, where the name doesn't already exist, the name should be inserted and the id returned. There can only ever be one instance of name in the database - ie. name must be unique How do I ensure that thread one doesn't insert name1 at the same time as thread two also tries to insert name1? In other words, how do I guarantee the uniqueness of name in a concurrent environment? This also needs to be as efficient as possible - this has the potential to be a serious bottleneck. I am using MySQL and Java. Thanks

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  • Enumerating combinations in a distributed manner

    - by Reyzooti
    I have a problem where I must analyse 500C5 combinations (255244687600) of something. Distributing it over a 10 node cluster where each cluster processes roughly 10^6 combinations per second means the job will be complete in about 7hours. The problem I have is distributing the 255244687600 combinations over the 10 nodes. I'd like to present each node with 25524468760, however the algorithms I'm using can only produce the combinations sequentially, I'd like to be able to pass the set of elements and a range of combination indicies eg: [0-10^7) or [10^7,2.0 10^7) etc and have the nodes themselves figure out the combinations. The algorithms I'm using at the moment are from the following: http://home.roadrunner.com/~hinnant/combinations.html A logical question I've considered using a master node, that enumerates each of the combinations and sends work to each of the nodes, however the overhead incurred in iterating the combinations from a single node and communicating back and forth work is enormous, and will subsequently lead to the master node becoming the bottleneck. Are there any good combination iterating algorithms geared up for efficient/optimal distributed enumeration?

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  • How can I measure file access performance (and volume) of a (Java) application

    - by stmoebius
    Given an application, how can I measure the amount of data read and written by that application? the time spent reading/writing to disk? The specific application is Java-based (JBoss), and multi-threaded, and running as a service on Windows 7/2008 x64. The overall goal I have is determining whether and why file access is a bottleneck in my application. Therefore, running the application in a defined and repeatable scenario is a given. File access may be local as well as on network shares. Windows performance monitor appears to be too hard to use (unless someone can point me to a helpful explanation). Any ideas?

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  • Fastest way for inserting very large number of records into a Table in SQL

    - by Irchi
    The problem is, we have a huge number of records (more than a million) to be inserted into a single table from a Java application. The records are created by the Java code, it's not a move from another table, so INSERT/SELECT won't help. Currently, my bottleneck is the INSERT statements. I'm using PreparedStatement to speed-up the process, but I can't get more than 50 recods per second on a normal server. The table is not complicated at all, and there are no indexes defined on it. The process takes too long, and the time it takes will make problems. What can I do to get the maximum speed (INSERT per second) possible? Database: MS SQL 2008. Application: Java-based, using Microsoft JDBC driver.

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  • Speedup C++ code

    - by Werner
    Hi, I am writing a C++ number crunching application, where the bottleneck is a function that has to calculate for double: template<class T> inline T sqr(const T& x){return x*x;} and another one that calculates Base dist2(const Point& p) const { return sqr(x-p.x) + sqr(y-p.y) + sqr(z-p.z); } These operations take 80% of the computation time. I wonder if you can suggest approaches to make it faster, even if there is some sort of accuracy loss Thanks

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  • best method in jquery for replacing rows in a table after server side processing such as mysql sorti

    - by Kevin J
    What is the 'best practice' when returning dynamic data for a table (server side sorting, filtering etc from a db) ? Do you return just the data in json, and repeatedly clone a row element, replacing the values in each row (thus decreasing the size of the ajax call, but increasing the client side processing), or return the full html, and replace with .html or .append? Or is there another method I'm missing? This is a frequent situation in my app, and in some cases a bottleneck, and I am unsure if what I am doing is the best solution. Currently, I return the row html and use a single .append call, after emptying all the rows except the header.

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  • Does beginTransaction in Hibernate allocate a new DB connection?

    - by illscience
    Hi folks - Just wondering if beginning a new transaction in Hibernate actually allocates a connection to the DB? I'm concerned b/c our server begins a new transaction for each request received, even if that request doesn't interact with the DB. We're seeing DB connections as a major bottleneck, so I'm wondering if I should take the time narrow the scope of my transactions. Searched everywhere and haven't been able to find a good answer. The very simple code is here: SessionFactory sessionFactory = (SessionFactory) Context.getContext().getBean("sessionFactory"); sessionFactory.getCurrentSession().beginTransaction(); sessionFactory.getCurrentSession().setFlushMode(FlushMode.AUTO); thanks very much! a

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  • Beginners PHP / mySQL question

    - by Reg H
    I'm brand new to PHP & MySQL, and one function I'm creating needs to access a large table or database. I've created the database and it's currently in a MySQL table, which I'm accessing with no problem. The table is 11,000 rows in length, with 8 columns (all text less than 8 characters long) - it's static, and will never change. Without getting too particular, my users will hit a button which will trigger scripts to access the data, say 500 times or more. So in general would it be better practice to include all of this data in a big 'switch' or 'if... then' conditional right in my scripts, rather than opening and accessing the database connection hundreds, or maybe even thousands of times? It just seems like that might be a bottleneck waiting to happen. Thanks!

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  • sql query is too slow, how to improve speed

    - by user1289282
    I have run into a bottleneck when trying to update one of my tables. The player table has, among other things, id, skill, school, weight. What I am trying to do is: SELECT id, skill FROM player WHERE player.school = (current school of 4500) AND player.weight = (current weight of 14) to find the highest skill of all players returned from the query UPDATE player SET starter = 'TRUE' WHERE id = (highest skill) move to next weight and repeat when all weights have been completed move to next school and start over all schools completed, done I have this code implemented and it works, but I have approximately 4500 schools totaling 172000 players and the way I have it now, it would take probably a half hour or more to complete (did not wait it out), which is way too slow. How to speed this up? Short of reducing the scale of the system, I am willing to do anything that gets the intended result. Thanks! *the weights are the standard folk style wrestling weights ie, 103, 113, 120, 126, 132, 138, 145, 152, 160, 170, 182, 195, 220, 285 pounds

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  • Older SAS1 hardware Vs. newer SAS2 hardware

    - by user12620172
    I got a question today from someone asking about the older SAS1 hardware from over a year ago that we had on the older 7x10 series. They didn't leave an email so I couldn't respond directly, but I said this blog would be blunt, frank, and open so I have no problem addressing it publicly. A quick history lesson here: When Sun first put out the 7x10 family hardware, the 7410 and 7310 used a SAS1 backend connection to a JBOD that had SATA drives in it. This JBOD was not manufactured by Sun nor did Sun own the IP for it. Now, when Oracle took over, they had a problem with that, and I really can’t blame them. The decision was made to cut off that JBOD and it’s manufacturer completely and use our own where Oracle controlled both the IP and the manufacturing. So in the summer of 2010, the cut was made, and the 7410 and 7310 had a hardware refresh and now had a SAS2 backend going to a SAS2 JBOD with SAS2 drives instead of SATA. This new hardware had two big advantages. First, there was a nice performance increase, mostly due to the faster backend. Even better, the SAS2 interface on the drives allowed for a MUCH faster failover between cluster heads, as the SATA drives were the bottleneck on the older hardware. In September of 2010 there was a major refresh of the rest of the 7000 hardware, the controllers and the other family members, and that’s where we got today’s current line-up of the 7x20 series. So the 7x20 has always used the new trays, and the 7410 and 7310 have used the new SAS2 trays since last July of 2010. Now for the bad news. People who have the 7410 and 7310 from BEFORE the July 2010 cutoff have the models with SAS1 HBAs in them to connect to the older SAS1 trays. Remember, that manufacturer cut all ties with us and stopped making the JBOD, so there’s just no way to get more of them, as they don’t exist. There are some options, however. Oracle support does support taking out the SAS1 HBAs in the old 7410 and 7310 and put in newer SAS2 HBAs which can talk to the new trays. Hey, I didn’t say it was a great option, I just said it’s an option. I fully realize that you would then have a SAS1 JBOD full of SATA drives that you could no longer connect. I do know a client that did this, and took the SAS1 JBOD and connected it to another server and formatted the drives and is using it as a plain, non-7000 JBOD. This is not supported by Oracle support. The other option is to just keep it as-is, as it works just fine, but you just can’t expand it. Then you can get a newer 7x20 series, and use the built-in ZFSSA replication feature to move the data over. Now you can use the newer one for your production data and use the older one for DR, snaps and clones.

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  • Using BPEL Performance Statistics to Diagnose Performance Bottlenecks

    - by fip
    Tuning performance of Oracle SOA 11G applications could be challenging. Because SOA is a platform for you to build composite applications that connect many applications and "services", when the overall performance is slow, the bottlenecks could be anywhere in the system: the applications/services that SOA connects to, the infrastructure database, or the SOA server itself.How to quickly identify the bottleneck becomes crucial in tuning the overall performance. Fortunately, the BPEL engine in Oracle SOA 11G (and 10G, for that matter) collects BPEL Engine Performance Statistics, which show the latencies of low level BPEL engine activities. The BPEL engine performance statistics can make it a bit easier for you to identify the performance bottleneck. Although the BPEL engine performance statistics are always available, the access to and interpretation of them are somewhat obscure in the early and current (PS5) 11G versions. This blog attempts to offer instructions that help you to enable, retrieve and interpret the performance statistics, before the future versions provides a more pleasant user experience. Overview of BPEL Engine Performance Statistics  SOA BPEL has a feature of collecting some performance statistics and store them in memory. One MBean attribute, StatLastN, configures the size of the memory buffer to store the statistics. This memory buffer is a "moving window", in a way that old statistics will be flushed out by the new if the amount of data exceeds the buffer size. Since the buffer size is limited by StatLastN, impacts of statistics collection on performance is minimal. By default StatLastN=-1, which means no collection of performance data. Once the statistics are collected in the memory buffer, they can be retrieved via another MBean oracle.as.soainfra.bpel:Location=[Server Name],name=BPELEngine,type=BPELEngine.> My friend in Oracle SOA development wrote this simple 'bpelstat' web app that looks up and retrieves the performance data from the MBean and displays it in a human readable form. It does not have beautiful UI but it is fairly useful. Although in Oracle SOA 11.1.1.5 onwards the same statistics can be viewed via a more elegant UI under "request break down" at EM -> SOA Infrastructure -> Service Engines -> BPEL -> Statistics, some unsophisticated minds like mine may still prefer the simplicity of the 'bpelstat' JSP. One thing that simple JSP does do well is that you can save the page and send it to someone to further analyze Follows are the instructions of how to install and invoke the BPEL statistic JSP. My friend in SOA Development will soon blog about interpreting the statistics. Stay tuned. Step1: Enable BPEL Engine Statistics for Each SOA Servers via Enterprise Manager First st you need to set the StatLastN to some number as a way to enable the collection of BPEL Engine Performance Statistics EM Console -> soa-infra(Server Name) -> SOA Infrastructure -> SOA Administration -> BPEL Properties Click on "More BPEL Configuration Properties" Click on attribute "StatLastN", set its value to some integer number. Typically you want to set it 1000 or more. Step 2: Download and Deploy bpelstat.war File to Admin Server, Note: the WAR file contains a JSP that does NOT have any security restriction. You do NOT want to keep in your production server for a long time as it is a security hazard. Deactivate the war once you are done. Download the bpelstat.war to your local PC At WebLogic Console, Go to Deployments -> Install Click on the "upload your file(s)" Click the "Browse" button to upload the deployment to Admin Server Accept the uploaded file as the path, click next Check the default option "Install this deployment as an application" Check "AdminServer" as the target server Finish the rest of the deployment with default settings Console -> Deployments Check the box next to "bpelstat" application Click on the "Start" button. It will change the state of the app from "prepared" to "active" Step 3: Invoke the BPEL Statistic Tool The BPELStat tool merely call the MBean of BPEL server and collects and display the in-memory performance statics. You usually want to do that after some peak loads. Go to http://<admin-server-host>:<admin-server-port>/bpelstat Enter the correct admin hostname, port, username and password Enter the SOA Server Name from which you want to collect the performance statistics. For example, SOA_MS1, etc. Click Submit Keep doing the same for all SOA servers. Step 3: Interpret the BPEL Engine Statistics You will see a few categories of BPEL Statistics from the JSP Page. First it starts with the overall latency of BPEL processes, grouped by synchronous and asynchronous processes. Then it provides the further break down of the measurements through the life time of a BPEL request, which is called the "request break down". 1. Overall latency of BPEL processes The top of the page shows that the elapse time of executing the synchronous process TestSyncBPELProcess from the composite TestComposite averages at about 1543.21ms, while the elapse time of executing the asynchronous process TestAsyncBPELProcess from the composite TestComposite2 averages at about 1765.43ms. The maximum and minimum latency were also shown. Synchronous process statistics <statistics>     <stats key="default/TestComposite!2.0.2-ScopedJMSOSB*soa_bfba2527-a9ba-41a7-95c5-87e49c32f4ff/TestSyncBPELProcess" min="1234" max="4567" average="1543.21" count="1000">     </stats> </statistics> Asynchronous process statistics <statistics>     <stats key="default/TestComposite2!2.0.2-ScopedJMSOSB*soa_bfba2527-a9ba-41a7-95c5-87e49c32f4ff/TestAsyncBPELProcess" min="2234" max="3234" average="1765.43" count="1000">     </stats> </statistics> 2. Request break down Under the overall latency categorized by synchronous and asynchronous processes is the "Request breakdown". Organized by statistic keys, the Request breakdown gives finer grain performance statistics through the life time of the BPEL requests.It uses indention to show the hierarchy of the statistics. Request breakdown <statistics>     <stats key="eng-composite-request" min="0" max="0" average="0.0" count="0">         <stats key="eng-single-request" min="22" max="606" average="258.43" count="277">             <stats key="populate-context" min="0" max="0" average="0.0" count="248"> Please note that in SOA 11.1.1.6, the statistics under Request breakdown is aggregated together cross all the BPEL processes based on statistic keys. It does not differentiate between BPEL processes. If two BPEL processes happen to have the statistic that share same statistic key, the statistics from two BPEL processes will be aggregated together. Keep this in mind when we go through more details below. 2.1 BPEL process activity latencies A very useful measurement in the Request Breakdown is the performance statistics of the BPEL activities you put in your BPEL processes: Assign, Invoke, Receive, etc. The names of the measurement in the JSP page directly come from the names to assign to each BPEL activity. These measurements are under the statistic key "actual-perform" Example 1:  Follows is the measurement for BPEL activity "AssignInvokeCreditProvider_Input", which looks like the Assign activity in a BPEL process that assign an input variable before passing it to the invocation:                                <stats key="AssignInvokeCreditProvider_Input" min="1" max="8" average="1.9" count="153">                                     <stats key="sensor-send-activity-data" min="0" max="1" average="0.0" count="306">                                     </stats>                                     <stats key="sensor-send-variable-data" min="0" max="0" average="0.0" count="153">                                     </stats>                                     <stats key="monitor-send-activity-data" min="0" max="0" average="0.0" count="306">                                     </stats>                                 </stats> Note: because as previously mentioned that the statistics cross all BPEL processes are aggregated together based on statistic keys, if two BPEL processes happen to name their Invoke activity the same name, they will show up at one measurement (i.e. statistic key). Example 2: Follows is the measurement of BPEL activity called "InvokeCreditProvider". You can not only see that by average it takes 3.31ms to finish this call (pretty fast) but also you can see from the further break down that most of this 3.31 ms was spent on the "invoke-service".                                  <stats key="InvokeCreditProvider" min="1" max="13" average="3.31" count="153">                                     <stats key="initiate-correlation-set-again" min="0" max="0" average="0.0" count="153">                                     </stats>                                     <stats key="invoke-service" min="1" max="13" average="3.08" count="153">                                         <stats key="prep-call" min="0" max="1" average="0.04" count="153">                                         </stats>                                     </stats>                                     <stats key="initiate-correlation-set" min="0" max="0" average="0.0" count="153">                                     </stats>                                     <stats key="sensor-send-activity-data" min="0" max="0" average="0.0" count="306">                                     </stats>                                     <stats key="sensor-send-variable-data" min="0" max="0" average="0.0" count="153">                                     </stats>                                     <stats key="monitor-send-activity-data" min="0" max="0" average="0.0" count="306">                                     </stats>                                     <stats key="update-audit-trail" min="0" max="2" average="0.03" count="153">                                     </stats>                                 </stats> 2.2 BPEL engine activity latency Another type of measurements under Request breakdown are the latencies of underlying system level engine activities. These activities are not directly tied to a particular BPEL process or process activity, but they are critical factors in the overall engine performance. These activities include the latency of saving asynchronous requests to database, and latency of process dehydration. My friend Malkit Bhasin is working on providing more information on interpreting the statistics on engine activities on his blog (https://blogs.oracle.com/malkit/). I will update this blog once the information becomes available. Update on 2012-10-02: My friend Malkit Bhasin has published the detail interpretation of the BPEL service engine statistics at his blog http://malkit.blogspot.com/2012/09/oracle-bpel-engine-soa-suite.html.

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

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

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  • SQL SERVER – Maximize Database Performance with DB Optimizer – SQL in Sixty Seconds #054

    - by Pinal Dave
    Performance tuning is an interesting concept and everybody evaluates it differently. Every developer and DBA have different opinion about how one can do performance tuning. I personally believe performance tuning is a three step process Understanding the Query Identifying the Bottleneck Implementing the Fix While, we are working with large database application and it suddenly starts to slow down. We are all under stress about how we can get back the database back to normal speed. Most of the time we do not have enough time to do deep analysis of what is going wrong as well what will fix the problem. Our primary goal at that time is to just fix the database problem as fast as we can. However, here is one very important thing which we need to keep in our mind is that when we do quick fix, it should not create any further issue with other parts of the system. When time is essence and we want to do deep analysis of our system to give us the best solution we often tend to make mistakes. Sometimes we make mistakes as we do not have proper time to analysis the entire system. Here is what I do when I face such a situation – I take the help of DB Optimizer. It is a fantastic tool and does superlative performance tuning of the system. Everytime when I talk about performance tuning tool, the initial reaction of the people is that they do not want to try this as they believe it requires lots of the learning of the tool before they use it. It is absolutely not true with the case of the DB optimizer. It is a very easy to use and self intuitive tool. Once can get going with the product, in no time. Here is a quick video I have build where I demonstrate how we can identify what index is missing for query and how we can quickly create the index. Entire three steps of the query tuning are completed in less than 60 seconds. If you are into performance tuning and query optimization you should download DB Optimizer and give it a go. Let us see the same concept in following SQL in Sixty Seconds Video: You can Download DB Optimizer and reproduce the same Sixty Seconds experience. Related Tips in SQL in Sixty Seconds: Performance Tuning – Part 1 of 2 – Getting Started and Configuration Performance Tuning – Part 2 of 2 – Analysis, Detection, Tuning and Optimizing What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Interview Questions and Answers, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Identity

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

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

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  • Scaling-out Your Services by Message Bus based WCF Transport Extension &ndash; Part 1 &ndash; Background

    - by Shaun
    Cloud computing gives us more flexibility on the computing resource, we can provision and deploy an application or service with multiple instances over multiple machines. With the increment of the service instances, how to balance the incoming message and workload would become a new challenge. Currently there are two approaches we can use to pass the incoming messages to the service instances, I would like call them dispatcher mode and pulling mode.   Dispatcher Mode The dispatcher mode introduces a role which takes the responsible to find the best service instance to process the request. The image below describes the sharp of this mode. There are four clients communicate with the service through the underlying transportation. For example, if we are using HTTP the clients might be connecting to the same service URL. On the server side there’s a dispatcher listening on this URL and try to retrieve all messages. When a message came in, the dispatcher will find a proper service instance to process it. There are three mechanism to find the instance: Round-robin: Dispatcher will always send the message to the next instance. For example, if the dispatcher sent the message to instance 2, then the next message will be sent to instance 3, regardless if instance 3 is busy or not at that moment. Random: Dispatcher will find a service instance randomly, and same as the round-robin mode it regardless if the instance is busy or not. Sticky: Dispatcher will send all related messages to the same service instance. This approach always being used if the service methods are state-ful or session-ful. But as you can see, all of these approaches are not really load balanced. The clients will send messages at any time, and each message might take different process duration on the server side. This means in some cases, some of the service instances are very busy while others are almost idle. For example, if we were using round-robin mode, it could be happened that most of the simple task messages were passed to instance 1 while the complex ones were sent to instance 3, even though instance 1 should be idle. This brings some problem in our architecture. The first one is that, the response to the clients might be longer than it should be. As it’s shown in the figure above, message 6 and 9 can be processed by instance 1 or instance 2, but in reality they were dispatched to the busy instance 3 since the dispatcher and round-robin mode. Secondly, if there are many requests came from the clients in a very short period, service instances might be filled by tons of pending tasks and some instances might be crashed. Third, if we are using some cloud platform to host our service instances, for example the Windows Azure, the computing resource is billed by service deployment period instead of the actual CPU usage. This means if any service instance is idle it is wasting our money! Last one, the dispatcher would be the bottleneck of our system since all incoming messages must be routed by the dispatcher. If we are using HTTP or TCP as the transport, the dispatcher would be a network load balance. If we wants more capacity, we have to scale-up, or buy a hardware load balance which is very expensive, as well as scaling-out the service instances. Pulling Mode Pulling mode doesn’t need a dispatcher to route the messages. All service instances are listening to the same transport and try to retrieve the next proper message to process if they are idle. Since there is no dispatcher in pulling mode, it requires some features on the transportation. The transportation must support multiple client connection and server listening. HTTP and TCP doesn’t allow multiple clients are listening on the same address and port, so it cannot be used in pulling mode directly. All messages in the transportation must be FIFO, which means the old message must be received before the new one. Message selection would be a plus on the transportation. This means both service and client can specify some selection criteria and just receive some specified kinds of messages. This feature is not mandatory but would be very useful when implementing the request reply and duplex WCF channel modes. Otherwise we must have a memory dictionary to store the reply messages. I will explain more about this in the following articles. Message bus, or the message queue would be best candidate as the transportation when using the pulling mode. First, it allows multiple application to listen on the same queue, and it’s FIFO. Some of the message bus also support the message selection, such as TIBCO EMS, RabbitMQ. Some others provide in memory dictionary which can store the reply messages, for example the Redis. The principle of pulling mode is to let the service instances self-managed. This means each instance will try to retrieve the next pending incoming message if they finished the current task. This gives us more benefit and can solve the problems we met with in the dispatcher mode. The incoming message will be received to the best instance to process, which means this will be very balanced. And it will not happen that some instances are busy while other are idle, since the idle one will retrieve more tasks to make them busy. Since all instances are try their best to be busy we can use less instances than dispatcher mode, which more cost effective. Since there’s no dispatcher in the system, there is no bottleneck. When we introduced more service instances, in dispatcher mode we have to change something to let the dispatcher know the new instances. But in pulling mode since all service instance are self-managed, there no extra change at all. If there are many incoming messages, since the message bus can queue them in the transportation, service instances would not be crashed. All above are the benefits using the pulling mode, but it will introduce some problem as well. The process tracking and debugging become more difficult. Since the service instances are self-managed, we cannot know which instance will process the message. So we need more information to support debug and track. Real-time response may not be supported. All service instances will process the next message after the current one has done, if we have some real-time request this may not be a good solution. Compare with the Pros and Cons above, the pulling mode would a better solution for the distributed system architecture. Because what we need more is the scalability, cost-effect and the self-management.   WCF and WCF Transport Extensibility Windows Communication Foundation (WCF) is a framework for building service-oriented applications. In the .NET world WCF is the best way to implement the service. In this series I’m going to demonstrate how to implement the pulling mode on top of a message bus by extending the WCF. I don’t want to deep into every related field in WCF but will highlight its transport extensibility. When we implemented an RPC foundation there are many aspects we need to deal with, for example the message encoding, encryption, authentication and message sending and receiving. In WCF, each aspect is represented by a channel. A message will be passed through all necessary channels and finally send to the underlying transportation. And on the other side the message will be received from the transport and though the same channels until the business logic. This mode is called “Channel Stack” in WCF, and the last channel in the channel stack must always be a transport channel, which takes the responsible for sending and receiving the messages. As we are going to implement the WCF over message bus and implement the pulling mode scaling-out solution, we need to create our own transport channel so that the client and service can exchange messages over our bus. Before we deep into the transport channel, let’s have a look on the message exchange patterns that WCF defines. Message exchange pattern (MEP) defines how client and service exchange the messages over the transportation. WCF defines 3 basic MEPs which are datagram, Request-Reply and Duplex. Datagram: Also known as one-way, or fire-forgot mode. The message sent from the client to the service, and no need any reply from the service. The client doesn’t care about the message result at all. Request-Reply: Very common used pattern. The client send the request message to the service and wait until the reply message comes from the service. Duplex: The client sent message to the service, when the service processing the message it can callback to the client. When callback the service would be like a client while the client would be like a service. In WCF, each MEP represent some channels associated. MEP Channels Datagram IInputChannel, IOutputChannel Request-Reply IRequestChannel, IReplyChannel Duplex IDuplexChannel And the channels are created by ChannelListener on the server side, and ChannelFactory on the client side. The ChannelListener and ChannelFactory are created by the TransportBindingElement. The TransportBindingElement is created by the Binding, which can be defined as a new binding or from a custom binding. For more information about the transport channel mode, please refer to the MSDN document. The figure below shows the transport channel objects when using the request-reply MEP. And this is the datagram MEP. And this is the duplex MEP. After investigated the WCF transport architecture, channel mode and MEP, we finally identified what we should do to extend our message bus based transport layer. They are: Binding: (Optional) Defines the channel elements in the channel stack and added our transport binding element at the bottom of the stack. But we can use the build-in CustomBinding as well. TransportBindingElement: Defines which MEP is supported in our transport and create the related ChannelListener and ChannelFactory. This also defines the scheme of the endpoint if using this transport. ChannelListener: Create the server side channel based on the MEP it’s. We can have one ChannelListener to create channels for all supported MEPs, or we can have ChannelListener for each MEP. In this series I will use the second approach. ChannelFactory: Create the client side channel based on the MEP it’s. We can have one ChannelFactory to create channels for all supported MEPs, or we can have ChannelFactory for each MEP. In this series I will use the second approach. Channels: Based on the MEPs we want to support, we need to implement the channels accordingly. For example, if we want our transport support Request-Reply mode we should implement IRequestChannel and IReplyChannel. In this series I will implement all 3 MEPs listed above one by one. Scaffold: In order to make our transport extension works we also need to implement some scaffold stuff. For example we need some classes to send and receive message though out message bus. We also need some codes to read and write the WCF message, etc.. These are not necessary but would be very useful in our example.   Message Bus There is only one thing remained before we can begin to implement our scaling-out support WCF transport, which is the message bus. As I mentioned above, the message bus must have some features to fulfill all the WCF MEPs. In my company we will be using TIBCO EMS, which is an enterprise message bus product. And I have said before we can use any message bus production if it’s satisfied with our requests. Here I would like to introduce an interface to separate the message bus from the WCF. This allows us to implement the bus operations by any kinds bus we are going to use. The interface would be like this. 1: public interface IBus : IDisposable 2: { 3: string SendRequest(string message, bool fromClient, string from, string to = null); 4:  5: void SendReply(string message, bool fromClient, string replyTo); 6:  7: BusMessage Receive(bool fromClient, string replyTo); 8: } There are only three methods for the bus interface. Let me explain one by one. The SendRequest method takes the responsible for sending the request message into the bus. The parameters description are: message: The WCF message content. fromClient: Indicates if this message was came from the client. from: The channel ID that this message was sent from. The channel ID will be generated when any kinds of channel was created, which will be explained in the following articles. to: The channel ID that this message should be received. In Request-Reply and Duplex MEP this is necessary since the reply message must be received by the channel which sent the related request message. The SendReply method takes the responsible for sending the reply message. It’s very similar as the previous one but no “from” parameter. This is because it’s no need to reply a reply message again in any MEPs. The Receive method takes the responsible for waiting for a incoming message, includes the request message and specified reply message. It returned a BusMessage object, which contains some information about the channel information. The code of the BusMessage class is 1: public class BusMessage 2: { 3: public string MessageID { get; private set; } 4: public string From { get; private set; } 5: public string ReplyTo { get; private set; } 6: public string Content { get; private set; } 7:  8: public BusMessage(string messageId, string fromChannelId, string replyToChannelId, string content) 9: { 10: MessageID = messageId; 11: From = fromChannelId; 12: ReplyTo = replyToChannelId; 13: Content = content; 14: } 15: } Now let’s implement a message bus based on the IBus interface. Since I don’t want you to buy and install the TIBCO EMS or any other message bus products, I will implement an in process memory bus. This bus is only for test and sample purpose. It can only be used if the service and client are in the same process. Very straightforward. 1: public class InProcMessageBus : IBus 2: { 3: private readonly ConcurrentDictionary<Guid, InProcMessageEntity> _queue; 4: private readonly object _lock; 5:  6: public InProcMessageBus() 7: { 8: _queue = new ConcurrentDictionary<Guid, InProcMessageEntity>(); 9: _lock = new object(); 10: } 11:  12: public string SendRequest(string message, bool fromClient, string from, string to = null) 13: { 14: var entity = new InProcMessageEntity(message, fromClient, from, to); 15: _queue.TryAdd(entity.ID, entity); 16: return entity.ID.ToString(); 17: } 18:  19: public void SendReply(string message, bool fromClient, string replyTo) 20: { 21: var entity = new InProcMessageEntity(message, fromClient, null, replyTo); 22: _queue.TryAdd(entity.ID, entity); 23: } 24:  25: public BusMessage Receive(bool fromClient, string replyTo) 26: { 27: InProcMessageEntity e = null; 28: while (true) 29: { 30: lock (_lock) 31: { 32: var entity = _queue 33: .Where(kvp => kvp.Value.FromClient == fromClient && (kvp.Value.To == replyTo || string.IsNullOrWhiteSpace(kvp.Value.To))) 34: .FirstOrDefault(); 35: if (entity.Key != Guid.Empty && entity.Value != null) 36: { 37: _queue.TryRemove(entity.Key, out e); 38: } 39: } 40: if (e == null) 41: { 42: Thread.Sleep(100); 43: } 44: else 45: { 46: return new BusMessage(e.ID.ToString(), e.From, e.To, e.Content); 47: } 48: } 49: } 50:  51: public void Dispose() 52: { 53: } 54: } The InProcMessageBus stores the messages in the objects of InProcMessageEntity, which can take some extra information beside the WCF message itself. 1: public class InProcMessageEntity 2: { 3: public Guid ID { get; set; } 4: public string Content { get; set; } 5: public bool FromClient { get; set; } 6: public string From { get; set; } 7: public string To { get; set; } 8:  9: public InProcMessageEntity() 10: : this(string.Empty, false, string.Empty, string.Empty) 11: { 12: } 13:  14: public InProcMessageEntity(string content, bool fromClient, string from, string to) 15: { 16: ID = Guid.NewGuid(); 17: Content = content; 18: FromClient = fromClient; 19: From = from; 20: To = to; 21: } 22: }   Summary OK, now I have all necessary stuff ready. The next step would be implementing our WCF message bus transport extension. In this post I described two scaling-out approaches on the service side especially if we are using the cloud platform: dispatcher mode and pulling mode. And I compared the Pros and Cons of them. Then I introduced the WCF channel stack, channel mode and the transport extension part, and identified what we should do to create our own WCF transport extension, to let our WCF services using pulling mode based on a message bus. And finally I provided some classes that need to be used in the future posts that working against an in process memory message bus, for the demonstration purpose only. In the next post I will begin to implement the transport extension step by step.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Keep taking the tablets

    - by Roger Hart
    A guest editorial for the SimpleTalk newsletter. So why would Red Gate build an Ipad Game? Is it just because tablet devices are exciting and cool? Ok, maybe a little. Mostly, it was seeing that the best existing tablet and smartphone apps do simple, intuitive things, using simple intuitive interfaces to solve single problems. That's pretty close to what we call our own "intuitively simple" approach to software. Tablets and mobile could be fantastic for us, if we can identify those problems that a tablet device can solve. How do you create THE next tool for a completely new technology? We're glad we don't face that problem every day, but it's pretty exciting when we do. We figure we should learn by doing. We created "MobileFoo" (a Red Gate Company) , we picked up some shiny Apple tech, and got to grips with Objective C, and life in the App Store ecosystem. The result so far is an iPad game: Stacks and Heaps It's Rob and Marine's spin on Snakes and Ladders. Instead of snakes we have unhandled exceptions, a blue screen of death, and other hazards. We wanted something compellingly geeky on mobile, and we're pretty sure we've got it. It's trudging through App Store approval as we speak. but if you want to get an idea of what it is like to switch from .net to Objective C, take a look at Rob's post Android and iOS is quite a culture-change for Windows developers. So to give them a feel for the problems real users might have, we needed some real users - we offered our colleagues subsidised tablets. The only conditions were that they get used at work, and we get the feedback. Seeing tablets around the office is starting to give us some data points: Is typing the bottleneck? Will tablets ever cut it as text-entry devices, and could we fix it? Is mobile working held up by the pain of connecting to work LANs? How about security? Multi-tasking will let tablets do more. They're small, easy to use, almost instant to switch on, and connect by Wi Fi. There's plenty on that list to make a sysadmin twitchy. We'll find out as people spend more time working with these devices, and we'd love to hear what you think about tablet devices too. (comments are filtered, what with the spam)

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  • Kauffman Foundation Selects Stackify to Present at Startup@Kauffman Demo Day

    - by Matt Watson
    Stackify will join fellow Kansas City startups to kick off Global Entrepreneurship WeekOn Monday, November 12, Stackify, a provider of tools that improve developers’ ability to support, manage and monitor their enterprise applications, will pitch its technology at the Startup@Kauffman Demo Day in Kansas City, Mo. Hosted by the Ewing Marion Kauffman Foundation, the event will mark the start of Global Entrepreneurship Week, the world’s largest celebration of innovators and job creators who launch startups.Stackify was selected through a competitive process for a six-minute opportunity to pitch its new technology to investors at Demo Day. In his pitch, Stackify’s founder, Matt Watson, will discuss the current challenges DevOps teams face and reveal how Stackify is reinventing the way software developers provide application support.In October, Stackify had successful appearances at two similar startup events. At Tech Cocktail’s Kansas City Mixer, the company was named “Hottest Kansas City Startup,” and it won free hosting service after pitching its solution at St. Louis, Mo.’s Startup Connection.“With less than a month until our public launch, events like Demo Day are giving Stackify the support and positioning we need to change the development community,” said Watson. “As a serial technology entrepreneur, I appreciate the Kauffman Foundation’s support of startup companies like Stackify. We’re thrilled to participate in Demo Day and Global Entrepreneurship Week activities.”Scheduled to publicly launch in early December 2012, Stackify’s platform gives developers insights into their production applications, servers and databases. Stackify finally provides agile developers safe and secure remote access to look at log files, config files, server health and databases. This solution removes the bottleneck from managers and system administrators who, until now, are the only team members with access. Essentially, Stackify enables development teams to spend less time fixing bugs and more time creating products.Currently in beta, Stackify has already been named a “Company to Watch” by Software Development Times, which called the startup “the next big thing.” Developers can register for a free Stackify account on Stackify.com.###Stackify Founded in 2012, Stackify is a Kansas City-based software service provider that helps development teams troubleshoot application problems. Currently in beta, Stackify will be publicly available in December 2012, when agile developers will finally be able to provide agile support. The startup has already been recognized by Tech Cocktail as “Hottest Kansas City Startup” and was named a “Company to Watch” by Software Development Times. To learn more, visit http://www.stackify.com and follow @stackify on Twitter.

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  • ArchBeat Link-o-Rama for 11/17/2011

    - by Bob Rhubart
    Building an Infrastructure Cloud with Oracle VM for x86 + Enterprise Manager 12c | Richard Rotter Richard Rotter demonstrates "how easy it could be to build a cloud infrastructure with Oracle's solution for cloud computing." Article: Social + Lean = Agile | Dave Duggal In today’s increasingly dynamic business environment, organizations must continuously adapt to survive. Change management has become a major bottleneck. Organizations’ need a practical mechanism for managing controlled variance and change in-flight to break the logjam. This paper provides a foundation for applying lean and agile principles to achieve Enterprise Agility through social collaboration. Stress Testing Java EE 6 Applications - Free Article In Free Java Magazine : Adam Bien "It is strange," says Adam Bien, "everyone is obsessed about green bars and code coverage, but testing of multi threaded behavior is widely ignored - until the applications run into massive problems." Using Access Manager to Secure Applications Deployed on WebLogic | Rene van Wijk Another great how-to post from Oracle ACE Rene van Wijk, this time involving JBoss RichFaces, Facelets, Oracle Coherence, and Oracle WebLogic Server. DOAG 2011 vs. Devoxx - Value and Attraction | Markus Eisele Oracle ACE Director Markus Eisele compares and contrasts these popular conferences with the aim of helping others decide which to attend. SOA All the Time; Architects in AZ; Clearing Info Integration hurdles SOA all the Time; Architects in AZ; Clearing Info Integration Hurdles This week on the Architect Home Page on OTN. Webcast: Oracle Business Intelligence Mobile Event Date: Wednesday, December 7, 2011 Time: 10 a.m. PT/1 p.m. ET Featuring Manan Goel (Director BI Product Marketing, Oracle) and Shailesh Shedge (Director BI and Analytics Practice, Ascentt). Webcast: Maximum Availability on Private Clouds A discussion of Oracle’s Maximum Availability Architecture, Oracle Database 11g, Oracle Exadata Database Machine, and Oracle Database appliance, featuring Margaret Hamburger (Director, Product Marketing, Oracle) and Joe Meeks (Director, Product Management, Oracle). November 30, 2011 at 10:00am PT / 1:00pm ET. Oracle Technology Network Architect Day - Phoenix, AZ Wednesday December 14, 2011, 8:30am - 5:00pm. The Ritz-Carlton, Phoenix, 2401 East Camelback Road, Phoenix, AZ 85016. Registration is free, but seating is limited.

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