Search Results

Search found 1657 results on 67 pages for 'writes on'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 20/67 | < Previous Page | 16 17 18 19 20 21 22 23 24 25 26 27  | Next Page >

  • Installing 2 versions of app on one device

    - by dl
    I've got two branches of an iPhone app going. I would like to load them both onto my provisioned iPad at the same time. The iPad sees them as the same app though and writes over whichever one is currently installed. Does anyone have good system for loading two versions concurrently. Thanks!

    Read the article

  • input box gets cleared on refresh page : javascript

    - by p p
    Hi, I have a javascript function that writes the value of input box to a cookie, then it tells the page to refresh. The page must be refreshed to the server side can reconstruct the page based on the values of the cookies. Output the correct data on the page. However I do not want the user to lose the value they type on the input box unless they erase it. what can i do?

    Read the article

  • SSIS - user variable used in derived column transform is not available - in some cases

    - by soo
    Unfortunately I don't have a repro for my issue, but I thought I would try to describe it in case it sounds familiar to someone... I am using SSIS 2005, SP2. My package has a package-scope user variable - let's call it user_var first step in the control flow is an Execute SQL task which runs a stored procedure. All that SP does is insert a record in a SQL table (with an identity column) and then go back and get the max ID value. The Execute SQL task saves this output into user_var the control flow then has a Data Flow Task - it goes and gets some source data, has a derived column which sets a column called run_id to user_var - and saves the data to a SQL destination In most cases (this template is used for many packages, running every day) this all works great. All of the destination records created get set with a correct run_id. However, in some cases, there is a set of the destination data that does not get run_id equal to user_var, but instead gets a value of 0 (0 is the default value for user_var). I have 2 instances where this has happened, but I can't make it happen. In both cases, it was just less that 10,000 records that have run_id = 0. Since SSIS writes data out in 10,000 record blocks, this really makes me think that, for the first set of data written out, user_var was not yet set. Then, after that first block, for the rest of the data, run_id is set to a correct value. But control passed on to my data flow from the Execute SQL task - it would have seemed reasonable to me that it wouldn't go on until the SP has completed and user_var is set. Maybe it just runs the SP, but doesn't wait for it to complete? In both cases where this has happened there seemed to be a few packages hitting the table to get a new user_var at about the same time. And in both cases lots of data was written (40 million rows, 60 million rows) - my thinking is that that means the writes were happening for a while. Sorry to be both long-winded AND vague. A winning combination! Does this sound familiar to anyone? Thanks.

    Read the article

  • What's wrong with this "if"?

    - by Gurzo
    Hello, I'm currently trying to make an AppleScript which should recognize the selected file in Finder and do the relative command in Terminal. Everything was going fine since I reached the part where it should define the language of the selected file: it just doesn't check the ifs. I checked if it writes fileExtension correctly (via return) and it does. Here is the Gist with the code, any help would be appreciated. Thanks

    Read the article

  • Using C# to read/write from excel spreadsheet.

    - by Aaron
    Hi there, I need to make a program that writes some data to an excel spreadsheet. Something basic along the lines of First name, last name, phone number, e-mail per row with each category in its own column. I don't even know where to start. If someone could tell me which assemblies to reference and maybe point me to a website or a book that covers writing/reading data from an excel spreadsheet via a c# program that would be great. Many thanks.

    Read the article

  • android communication between two applications

    - by androidTesting
    Hello, i need some help in how to start developing two android applications (on one phone) which communicate with each other. 1. Application A sends a string to application B 2. Application B receives the string for example "startClassOne", app B using a method starts classOne and gets the result. The result is send back (again as string!) to Application A. 3. Application A writes in console the received string from B.

    Read the article

  • Take user input and write to file from javascript inside a pdf?

    - by dazedandconfused
    Is it possible to have a pdf file that asks a user a set of questions and then writes their answers to a file, then next time it is viewed loads those answers as default values? I know pdfs can include javascript and have figured out how to add javascript to a pdf with iText (http://itextpdf.com/) but don't know how to prompt for user input or write to a file from inside the pdf. Any help would be appreciated.

    Read the article

  • Why do I get a Null Pointer Exception?

    - by Roman
    I have this code: Manager manager = new Manager("Name"); MyWindowListener windowListener = new MyWindowListener(); manager.addWindowListener(windowListener); Eclipse writes that I have a NullPointerException in the last line. What can be the reason for that. I do have constructors in the Manager and MyWindowListener. If it's important MyWindowListener implements WindowListener.

    Read the article

  • How to block writing in pipes, until the read has taken place ? (in C)

    - by user492194
    Hi everyone :) I'm currently working on some C program, and I'd like to know if there's any chance to block writing in the writer process (until the read is done) ? i.e. I have 3 pipes between the parent process and the children processes (the parent writes and the children read), I'd like to let the parent to write only to the process that finishes its reading :) I hope it's clear.. Thanks in advance.

    Read the article

  • Cannot Start MySQL Server on Fresh MAMP Install

    - by alexpelan
    I'm using Mac OS X 10.6.2 on my Macbook Pro. I can get the apache server to start, but not the mysql server, on both the default apache and default MAMP ports. When I try to go to my start page, I get the message "Error: Could not connect to MySQL server!" . Here's what's in my mysql error log: 00513 02:00:07 mysqld_safe mysqld from pid file /Applications/MAMP/tmp/mysql/mysql.pid ended 100513 02:00:16 mysqld_safe Starting mysqld daemon with databases from /Applications/MAMP/db/mysql 100513 2:00:16 [Warning] The syntax '--log_slow_queries' is deprecated and will be removed in a future release. Please use '--slow_query_log'/'--slow_query_log_file' instead. 100513 2:00:16 [Warning] You have forced lower_case_table_names to 0 through a command-line option, even though your file system '/Applications/MAMP/db/mysql/' is case insensitive. This means that you can corrupt a MyISAM table by accessing it with different cases. You should consider changing lower_case_table_names to 1 or 2 100513 2:00:16 [Warning] One can only use the --user switch if running as root 100513 2:00:16 [Note] Plugin 'FEDERATED' is disabled. 100513 2:00:16 [Note] Plugin 'ndbcluster' is disabled. InnoDB: Error: log file /usr/local/mysql/data/ib_logfile0 is of different size 0 5242880 bytes InnoDB: than specified in the .cnf file 0 16777216 bytes! 100513 2:00:16 [ERROR] Plugin 'InnoDB' init function returned error. 100513 2:00:16 [ERROR] Plugin 'InnoDB' registration as a STORAGE ENGINE failed. 100513 2:00:16 [ERROR] /Applications/MAMP/Library/libexec/mysqld: unknown option '--skip-bdb' 100513 2:00:16 [ERROR] Aborting 100513 2:00:16 [Note] /Applications/MAMP/Library/libexec/mysqld: Shutdown complete 100513 02:00:16 mysqld_safe mysqld from pid file /Applications/MAMP/tmp/mysql/mysql.pid ended A couple of things: 1) There are a bunch of different .cnf files that come with MAMP (my-huge, my-medium, etc.)...how can I tell which one is actually being used? 2) I deleted the ib_logfile0 and ib_logfile1 as recommended by another post on serverfault, and then ended up with more errors: 100519 16:01:30 InnoDB: Log file /usr/local/mysql/data/ib_logfile0 did not exist: new to be created InnoDB: Setting log file /usr/local/mysql/data/ib_logfile0 size to 16 MB InnoDB: Database physically writes the file full: wait... 100519 16:01:30 InnoDB: Log file /usr/local/mysql/data/ib_logfile1 did not exist: new to be created InnoDB: Setting log file /usr/local/mysql/data/ib_logfile1 size to 16 MB InnoDB: Database physically writes the file full: wait... InnoDB: The log sequence number in ibdata files does not match InnoDB: the log sequence number in the ib_logfiles! 100519 16:01:31 InnoDB: Database was not shut down normally! InnoDB: Starting crash recovery. InnoDB: Reading tablespace information from the .ibd files... InnoDB: Restoring possible half-written data pages from the doublewrite InnoDB: buffer... 100519 16:01:31 InnoDB: Started; log sequence number 0 44556 100519 16:01:31 [ERROR] /Applications/MAMP/Library/libexec/mysqld: unknown option '--skip-bdb' 100519 16:01:31 [ERROR] Aborting And then I got this the next time I tried to run it: InnoDB: Unable to lock /usr/local/mysql/data/ibdata1, error: 35 InnoDB: Check that you do not already have another mysqld process InnoDB: using the same InnoDB data or log files. Sorry that this is a lot of information, but I don't want to leave anything out. Thanks.

    Read the article

  • Motion - can't get streaming working from a webcam

    - by Emmanuel Brunet
    I'm trying to record a video stream from my Tenvis IP camera with motion 3.2.12 on Debian 7.5. I used the standard debian package with sudo apt-get install motion Assume my DNS IP cam is webcam, user : admin, password : password /etc/motion/motion.conf Bellow are my configuration file settings : netcam_url http://webcam/videostream.cgi netcam_userpass admin:password target_dir /media/videos/log/motion # The mini-http server listens to this port for requests (default: 0 = disabled) webcam_port 1234 ffmpeg_cap_new on ffmpeg_video_codec mpeg4 output_motion off snapshot_interval 0 # Quality of the jpeg (in percent) images produced (default: 50) webcam_quality 50 # Output frames at 1 fps when no motion is detected and increase to the # rate given by webcam_maxrate when motion is detected (default: off) webcam_motion on # Maximum framerate for webcam streams (default: 1) webcam_maxrate 15 # Restrict webcam connections to localhost only (default: on) webcam_localhost on # Limits the number of images per connection (default: 0 = unlimited) # Number can be defined by multiplying actual webcam rate by desired number of seconds # Actual webcam rate is the smallest of the numbers framerate and webcam_maxrate webcam_limit 0 control_port 8080 control_authentication admin:password Issue #1 when I try display http:/localhost:1234 the browser looks frozen, no HTTP 404 received but it stills waiting for data it seems .. Issue #2 in the output directory motion writes a lot of jpeg snapshots althought I just want to have several video sequenced files. Log I run motion in interactive mode in a terminal, here is the ouput root@mercure:/etc/motion# motion -c motion-1.0.conf [0] Processing thread 0 - config file motion-1.0.conf [0] Motion 3.2.12 Started [0] ffmpeg LIBAVCODEC_BUILD 3482368 LIBAVFORMAT_BUILD 3478785 [0] Thread 1 is from motion-1.0.conf [0] motion-httpd/3.2.12 running, accepting connections [0] motion-httpd: waiting for data on port TCP 8080 [1] Thread 1 started [1] Resizing pre_capture buffer to 1 items [1] Started stream webcam server in port 1234 [1] avcodec_open - could not open codec: Operation now in progress [1] ffopen_open error creating (new) file [~/tmp/motion/01-20140603165303.avi]: Operation now in progress [1] File of type 1 saved to: ~/tmp/motion/01-20140603165303-01.jpg [1] Thread exiting [1] Calling vid_close() from motion_cleanup [1] vid_close: calling netcam_cleanup [1] netcam camera handler: finish set, exiting [0] Motion thread 1 restart [1] Thread 1 started [1] Resizing pre_capture buffer to 1 items [1] Started stream webcam server in port 1234 [1] avcodec_open - could not open codec: Resource temporarily unavailable [1] ffopen_open error creating (new) file [~/tmp/motion/01-20140603165329.avi]: Resource temporarily unavailable [1] File of type 1 saved to: ~/tmp/motion/01-20140603165329-00.jpg [1] Thread exiting [1] Calling vid_close() from motion_cleanup [1] vid_close: calling netcam_cleanup [1] netcam camera handler: finish set, exiting [0] Motion thread 1 restart [1] Thread 1 started [1] Resizing pre_capture buffer to 1 items [1] Started stream webcam server in port 1234 [1] avcodec_open - could not open codec: Connection reset by peer [1] ffopen_open error creating (new) file [~/tmp/motion/01-20140603165355.avi]: Connection reset by peer [1] File of type 1 saved to: ~/tmp/motion/01-20140603165355-06.jpg [1] Thread exiting [1] Calling vid_close() from motion_cleanup [1] vid_close: calling netcam_cleanup [0] httpd - Finishing [0] httpd Closing [0] httpd thread exit [1] netcam camera handler: finish set, exiting [0] Motion thread 1 restart [1] Thread 1 started [1] Resizing pre_capture buffer to 1 items [1] Started stream webcam server in port 1234 It doesn't find the codec ... avcodec_open - could not open codec: Operation now in progress I've changed fmpeg_video_codec from mpeg4 to swf the result is the same. When using flv format motion writes a lot of .jpg image but I can't see anything at http://localhost:1234 [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-00.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-01.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-02.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-03.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-04.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-05.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171035-06.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171036-00.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171036-01.jpg [1] File of type 1 saved to: ~/tmp/motion/01-20140603171036-02.jpg Any idea just to get the video stream recoded on my local disk ?

    Read the article

  • C# 4: The Curious ConcurrentDictionary

    - by James Michael Hare
    In my previous post (here) I did a comparison of the new ConcurrentQueue versus the old standard of a System.Collections.Generic Queue with simple locking.  The results were exactly what I would have hoped, that the ConcurrentQueue was faster with multi-threading for most all situations.  In addition, concurrent collections have the added benefit that you can enumerate them even if they're being modified. So I set out to see what the improvements would be for the ConcurrentDictionary, would it have the same performance benefits as the ConcurrentQueue did?  Well, after running some tests and multiple tweaks and tunes, I have good and bad news. But first, let's look at the tests.  Obviously there's many things we can do with a dictionary.  One of the most notable uses, of course, in a multi-threaded environment is for a small, local in-memory cache.  So I set about to do a very simple simulation of a cache where I would create a test class that I'll just call an Accessor.  This accessor will attempt to look up a key in the dictionary, and if the key exists, it stops (i.e. a cache "hit").  However, if the lookup fails, it will then try to add the key and value to the dictionary (i.e. a cache "miss").  So here's the Accessor that will run the tests: 1: internal class Accessor 2: { 3: public int Hits { get; set; } 4: public int Misses { get; set; } 5: public Func<int, string> GetDelegate { get; set; } 6: public Action<int, string> AddDelegate { get; set; } 7: public int Iterations { get; set; } 8: public int MaxRange { get; set; } 9: public int Seed { get; set; } 10:  11: public void Access() 12: { 13: var randomGenerator = new Random(Seed); 14:  15: for (int i=0; i<Iterations; i++) 16: { 17: // give a wide spread so will have some duplicates and some unique 18: var target = randomGenerator.Next(1, MaxRange); 19:  20: // attempt to grab the item from the cache 21: var result = GetDelegate(target); 22:  23: // if the item doesn't exist, add it 24: if(result == null) 25: { 26: AddDelegate(target, target.ToString()); 27: Misses++; 28: } 29: else 30: { 31: Hits++; 32: } 33: } 34: } 35: } Note that so I could test different implementations, I defined a GetDelegate and AddDelegate that will call the appropriate dictionary methods to add or retrieve items in the cache using various techniques. So let's examine the three techniques I decided to test: Dictionary with mutex - Just your standard generic Dictionary with a simple lock construct on an internal object. Dictionary with ReaderWriterLockSlim - Same Dictionary, but now using a lock designed to let multiple readers access simultaneously and then locked when a writer needs access. ConcurrentDictionary - The new ConcurrentDictionary from System.Collections.Concurrent that is supposed to be optimized to allow multiple threads to access safely. So the approach to each of these is also fairly straight-forward.  Let's look at the GetDelegate and AddDelegate implementations for the Dictionary with mutex lock: 1: var addDelegate = (key,val) => 2: { 3: lock (_mutex) 4: { 5: _dictionary[key] = val; 6: } 7: }; 8: var getDelegate = (key) => 9: { 10: lock (_mutex) 11: { 12: string val; 13: return _dictionary.TryGetValue(key, out val) ? val : null; 14: } 15: }; Nothing new or fancy here, just your basic lock on a private object and then query/insert into the Dictionary. Now, for the Dictionary with ReadWriteLockSlim it's a little more complex: 1: var addDelegate = (key,val) => 2: { 3: _readerWriterLock.EnterWriteLock(); 4: _dictionary[key] = val; 5: _readerWriterLock.ExitWriteLock(); 6: }; 7: var getDelegate = (key) => 8: { 9: string val; 10: _readerWriterLock.EnterReadLock(); 11: if(!_dictionary.TryGetValue(key, out val)) 12: { 13: val = null; 14: } 15: _readerWriterLock.ExitReadLock(); 16: return val; 17: }; And finally, the ConcurrentDictionary, which since it does all it's own concurrency control, is remarkably elegant and simple: 1: var addDelegate = (key,val) => 2: { 3: _concurrentDictionary[key] = val; 4: }; 5: var getDelegate = (key) => 6: { 7: string s; 8: return _concurrentDictionary.TryGetValue(key, out s) ? s : null; 9: };                    Then, I set up a test harness that would simply ask the user for the number of concurrent Accessors to attempt to Access the cache (as specified in Accessor.Access() above) and then let them fly and see how long it took them all to complete.  Each of these tests was run with 10,000,000 cache accesses divided among the available Accessor instances.  All times are in milliseconds. 1: Dictionary with Mutex Locking 2: --------------------------------------------------- 3: Accessors Mostly Misses Mostly Hits 4: 1 7916 3285 5: 10 8293 3481 6: 100 8799 3532 7: 1000 8815 3584 8:  9:  10: Dictionary with ReaderWriterLockSlim Locking 11: --------------------------------------------------- 12: Accessors Mostly Misses Mostly Hits 13: 1 8445 3624 14: 10 11002 4119 15: 100 11076 3992 16: 1000 14794 4861 17:  18:  19: Concurrent Dictionary 20: --------------------------------------------------- 21: Accessors Mostly Misses Mostly Hits 22: 1 17443 3726 23: 10 14181 1897 24: 100 15141 1994 25: 1000 17209 2128 The first test I did across the board is the Mostly Misses category.  The mostly misses (more adds because data requested was not in the dictionary) shows an interesting trend.  In both cases the Dictionary with the simple mutex lock is much faster, and the ConcurrentDictionary is the slowest solution.  But this got me thinking, and a little research seemed to confirm it, maybe the ConcurrentDictionary is more optimized to concurrent "gets" than "adds".  So since the ratio of misses to hits were 2 to 1, I decided to reverse that and see the results. So I tweaked the data so that the number of keys were much smaller than the number of iterations to give me about a 2 to 1 ration of hits to misses (twice as likely to already find the item in the cache than to need to add it).  And yes, indeed here we see that the ConcurrentDictionary is indeed faster than the standard Dictionary here.  I have a strong feeling that as the ration of hits-to-misses gets higher and higher these number gets even better as well.  This makes sense since the ConcurrentDictionary is read-optimized. Also note that I tried the tests with capacity and concurrency hints on the ConcurrentDictionary but saw very little improvement, I think this is largely because on the 10,000,000 hit test it quickly ramped up to the correct capacity and concurrency and thus the impact was limited to the first few milliseconds of the run. So what does this tell us?  Well, as in all things, ConcurrentDictionary is not a panacea.  It won't solve all your woes and it shouldn't be the only Dictionary you ever use.  So when should we use each? Use System.Collections.Generic.Dictionary when: You need a single-threaded Dictionary (no locking needed). You need a multi-threaded Dictionary that is loaded only once at creation and never modified (no locking needed). You need a multi-threaded Dictionary to store items where writes are far more prevalent than reads (locking needed). And use System.Collections.Concurrent.ConcurrentDictionary when: You need a multi-threaded Dictionary where the writes are far more prevalent than reads. You need to be able to iterate over the collection without locking it even if its being modified. Both Dictionaries have their strong suits, I have a feeling this is just one where you need to know from design what you hope to use it for and make your decision based on that criteria.

    Read the article

  • Is Berkeley DB a NoSQL solution?

    - by Gregory Burd
    Berkeley DB is a library. To use it to store data you must link the library into your application. You can use most programming languages to access the API, the calls across these APIs generally mimic the Berkeley DB C-API which makes perfect sense because Berkeley DB is written in C. The inspiration for Berkeley DB was the DBM library, a part of the earliest versions of UNIX written by AT&T's Ken Thompson in 1979. DBM was a simple key/value hashtable-based storage library. In the early 1990s as BSD UNIX was transitioning from version 4.3 to 4.4 and retrofitting commercial code owned by AT&T with unencumbered code, it was the future founders of Sleepycat Software who wrote libdb (aka Berkeley DB) as the replacement for DBM. The problem it addressed was fast, reliable local key/value storage. At that time databases almost always lived on a single node, even the most sophisticated databases only had simple fail-over two node solutions. If you had a lot of data to store you would choose between the few commercial RDBMS solutions or to write your own custom solution. Berkeley DB took the headache out of the custom approach. These basic market forces inspired other DBM implementations. There was the "New DBM" (ndbm) and the "GNU DBM" (GDBM) and a few others, but the theme was the same. Even today TokyoCabinet calls itself "a modern implementation of DBM" mimicking, and improving on, something first created over thirty years ago. In the mid-1990s, DBM was the name for what you needed if you were looking for fast, reliable local storage. Fast forward to today. What's changed? Systems are connected over fast, very reliable networks. Disks are cheep, fast, and capable of storing huge amounts of data. CPUs continued to follow Moore's Law, processing power that filled a room in 1990 now fits in your pocket. PCs, servers, and other computers proliferated both in business and the personal markets. In addition to the new hardware entire markets, social systems, and new modes of interpersonal communication moved onto the web and started evolving rapidly. These changes cause a massive explosion of data and a need to analyze and understand that data. Taken together this resulted in an entirely different landscape for database storage, new solutions were needed. A number of novel solutions stepped up and eventually a category called NoSQL emerged. The new market forces inspired the CAP theorem and the heated debate of BASE vs. ACID. But in essence this was simply the market looking at what to trade off to meet these new demands. These new database systems shared many qualities in common. There were designed to address massive amounts of data, millions of requests per second, and scale out across multiple systems. The first large-scale and successful solution was Dynamo, Amazon's distributed key/value database. Dynamo essentially took the next logical step and added a twist. Dynamo was to be the database of record, it would be distributed, data would be partitioned across many nodes, and it would tolerate failure by avoiding single points of failure. Amazon did this because they recognized that the majority of the dynamic content they provided to customers visiting their web store front didn't require the services of an RDBMS. The queries were simple, key/value look-ups or simple range queries with only a few queries that required more complex joins. They set about to use relational technology only in places where it was the best solution for the task, places like accounting and order fulfillment, but not in the myriad of other situations. The success of Dynamo, and it's design, inspired the next generation of Non-SQL, distributed database solutions including Cassandra, Riak and Voldemort. The problem their designers set out to solve was, "reliability at massive scale" so the first focal point was distributed database algorithms. Underneath Dynamo there is a local transactional database; either Berkeley DB, Berkeley DB Java Edition, MySQL or an in-memory key/value data structure. Dynamo was an evolution of local key/value storage onto networks. Cassandra, Riak, and Voldemort all faced similar design decisions and one, Voldemort, choose Berkeley DB Java Edition for it's node-local storage. Riak at first was entirely in-memory, but has recently added write-once, append-only log-based on-disk storage similar type of storage as Berkeley DB except that it is based on a hash table which must reside entirely in-memory rather than a btree which can live in-memory or on disk. Berkeley DB evolved too, we added high availability (HA) and a replication manager that makes it easy to setup replica groups. Berkeley DB's replication doesn't partitioned the data, every node keeps an entire copy of the database. For consistency, there is a single node where writes are committed first - a master - then those changes are delivered to the replica nodes as log records. Applications can choose to wait until all nodes are consistent, or fire and forget allowing Berkeley DB to eventually become consistent. Berkeley DB's HA scales-out quite well for read-intensive applications and also effectively eliminates the central point of failure by allowing replica nodes to be elected (using a PAXOS algorithm) to mastership if the master should fail. This implementation covers a wide variety of use cases. MemcacheDB is a server that implements the Memcache network protocol but uses Berkeley DB for storage and HA to replicate the cache state across all the nodes in the cache group. Google Accounts, the user authentication layer for all Google properties, was until recently running Berkeley DB HA. That scaled to a globally distributed system. That said, most NoSQL solutions try to partition (shard) data across nodes in the replication group and some allow writes as well as reads at any node, Berkeley DB HA does not. So, is Berkeley DB a "NoSQL" solution? Not really, but it certainly is a component of many of the existing NoSQL solutions out there. Forgetting all the noise about how NoSQL solutions are complex distributed databases when you boil them down to a single node you still have to store the data to some form of stable local storage. DBMs solved that problem a long time ago. NoSQL has more to do with the layers on top of the DBM; the distributed, sometimes-consistent, partitioned, scale-out storage that manage key/value or document sets and generally have some form of simple HTTP/REST-style network API. Does Berkeley DB do that? Not really. Is Berkeley DB a "NoSQL" solution today? Nope, but it's the most robust solution on which to build such a system. Re-inventing the node-local data storage isn't easy. A lot of people are starting to come to appreciate the sophisticated features found in Berkeley DB, even mimic them in some cases. Could Berkeley DB grow into a NoSQL solution? Absolutely. Our key/value API could be extended over the net using any of a number of existing network protocols such as memcache or HTTP/REST. We could adapt our node-local data partitioning out over replicated nodes. We even have a nice query language and cost-based query optimizer in our BDB XML product that we could reuse were we to build out a document-based NoSQL-style product. XML and JSON are not so different that we couldn't adapt one to work with the other interchangeably. Without too much effort we could add what's missing, we could jump into this No SQL market withing a single product development cycle. Why isn't Berkeley DB already a NoSQL solution? Why aren't we working on it? Why indeed...

    Read the article

  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

    Read the article

  • Anti-Forgery Request Helpers for ASP.NET MVC and jQuery AJAX

    - by Dixin
    Background To secure websites from cross-site request forgery (CSRF, or XSRF) attack, ASP.NET MVC provides an excellent mechanism: The server prints tokens to cookie and inside the form; When the form is submitted to server, token in cookie and token inside the form are sent in the HTTP request; Server validates the tokens. To print tokens to browser, just invoke HtmlHelper.AntiForgeryToken():<% using (Html.BeginForm()) { %> <%: this.Html.AntiForgeryToken(Constants.AntiForgeryTokenSalt)%> <%-- Other fields. --%> <input type="submit" value="Submit" /> <% } %> This invocation generates a token then writes inside the form:<form action="..." method="post"> <input name="__RequestVerificationToken" type="hidden" value="J56khgCvbE3bVcsCSZkNVuH9Cclm9SSIT/ywruFsXEgmV8CL2eW5C/gGsQUf/YuP" /> <!-- Other fields. --> <input type="submit" value="Submit" /> </form> and also writes into the cookie: __RequestVerificationToken_Lw__= J56khgCvbE3bVcsCSZkNVuH9Cclm9SSIT/ywruFsXEgmV8CL2eW5C/gGsQUf/YuP When the above form is submitted, they are both sent to server. In the server side, [ValidateAntiForgeryToken] attribute is used to specify the controllers or actions to validate them:[HttpPost] [ValidateAntiForgeryToken(Salt = Constants.AntiForgeryTokenSalt)] public ActionResult Action(/* ... */) { // ... } This is very productive for form scenarios. But recently, when resolving security vulnerabilities for Web products, some problems are encountered. Specify validation on controller (not on each action) The server side problem is, It is expected to declare [ValidateAntiForgeryToken] on controller, but actually it has be to declared on each POST actions. Because POST actions are usually much more then controllers, this is a little crazy Problem Usually a controller contains actions for HTTP GET and actions for HTTP POST requests, and usually validations are expected for HTTP POST requests. So, if the [ValidateAntiForgeryToken] is declared on the controller, the HTTP GET requests become invalid:[ValidateAntiForgeryToken(Salt = Constants.AntiForgeryTokenSalt)] public class SomeController : Controller // One [ValidateAntiForgeryToken] attribute. { [HttpGet] public ActionResult Index() // Index() cannot work. { // ... } [HttpPost] public ActionResult PostAction1(/* ... */) { // ... } [HttpPost] public ActionResult PostAction2(/* ... */) { // ... } // ... } If browser sends an HTTP GET request by clicking a link: http://Site/Some/Index, validation definitely fails, because no token is provided. So the result is, [ValidateAntiForgeryToken] attribute must be distributed to each POST action:public class SomeController : Controller // Many [ValidateAntiForgeryToken] attributes. { [HttpGet] public ActionResult Index() // Works. { // ... } [HttpPost] [ValidateAntiForgeryToken(Salt = Constants.AntiForgeryTokenSalt)] public ActionResult PostAction1(/* ... */) { // ... } [HttpPost] [ValidateAntiForgeryToken(Salt = Constants.AntiForgeryTokenSalt)] public ActionResult PostAction2(/* ... */) { // ... } // ... } This is a little bit crazy, because one application can have a lot of POST actions. Solution To avoid a large number of [ValidateAntiForgeryToken] attributes (one for each POST action), the following ValidateAntiForgeryTokenAttribute wrapper class can be helpful, where HTTP verbs can be specified:[AttributeUsage(AttributeTargets.Class | AttributeTargets.Method, AllowMultiple = false, Inherited = true)] public class ValidateAntiForgeryTokenWrapperAttribute : FilterAttribute, IAuthorizationFilter { private readonly ValidateAntiForgeryTokenAttribute _validator; private readonly AcceptVerbsAttribute _verbs; public ValidateAntiForgeryTokenWrapperAttribute(HttpVerbs verbs) : this(verbs, null) { } public ValidateAntiForgeryTokenWrapperAttribute(HttpVerbs verbs, string salt) { this._verbs = new AcceptVerbsAttribute(verbs); this._validator = new ValidateAntiForgeryTokenAttribute() { Salt = salt }; } public void OnAuthorization(AuthorizationContext filterContext) { string httpMethodOverride = filterContext.HttpContext.Request.GetHttpMethodOverride(); if (this._verbs.Verbs.Contains(httpMethodOverride, StringComparer.OrdinalIgnoreCase)) { this._validator.OnAuthorization(filterContext); } } } When this attribute is declared on controller, only HTTP requests with the specified verbs are validated:[ValidateAntiForgeryTokenWrapper(HttpVerbs.Post, Constants.AntiForgeryTokenSalt)] public class SomeController : Controller { // GET actions are not affected. // Only HTTP POST requests are validated. } Now one single attribute on controller turns on validation for all POST actions. Maybe it would be nice if HTTP verbs can be specified on the built-in [ValidateAntiForgeryToken] attribute, which is easy to implemented. Submit token via AJAX The browser side problem is, if server side turns on anti-forgery validation for POST, then AJAX POST requests will fail be default. Problem For AJAX scenarios, when request is sent by jQuery instead of form:$.post(url, { productName: "Tofu", categoryId: 1 // Token is not posted. }, callback); This kind of AJAX POST requests will always be invalid, because server side code cannot see the token in the posted data. Solution The tokens are printed to browser then sent back to server. So first of all, HtmlHelper.AntiForgeryToken() must be called somewhere. Now the browser has token in HTML and cookie. Then jQuery must find the printed token in the HTML, and append token to the data before sending:$.post(url, { productName: "Tofu", categoryId: 1, __RequestVerificationToken: getToken() // Token is posted. }, callback); To be reusable, this can be encapsulated into a tiny jQuery plugin:/// <reference path="jquery-1.4.2.js" /> (function ($) { $.getAntiForgeryToken = function (tokenWindow, appPath) { // HtmlHelper.AntiForgeryToken() must be invoked to print the token. tokenWindow = tokenWindow && typeof tokenWindow === typeof window ? tokenWindow : window; appPath = appPath && typeof appPath === "string" ? "_" + appPath.toString() : ""; // The name attribute is either __RequestVerificationToken, // or __RequestVerificationToken_{appPath}. tokenName = "__RequestVerificationToken" + appPath; // Finds the <input type="hidden" name={tokenName} value="..." /> from the specified. // var inputElements = $("input[type='hidden'][name='__RequestVerificationToken" + appPath + "']"); var inputElements = tokenWindow.document.getElementsByTagName("input"); for (var i = 0; i < inputElements.length; i++) { var inputElement = inputElements[i]; if (inputElement.type === "hidden" && inputElement.name === tokenName) { return { name: tokenName, value: inputElement.value }; } } return null; }; $.appendAntiForgeryToken = function (data, token) { // Converts data if not already a string. if (data && typeof data !== "string") { data = $.param(data); } // Gets token from current window by default. token = token ? token : $.getAntiForgeryToken(); // $.getAntiForgeryToken(window). data = data ? data + "&" : ""; // If token exists, appends {token.name}={token.value} to data. return token ? data + encodeURIComponent(token.name) + "=" + encodeURIComponent(token.value) : data; }; // Wraps $.post(url, data, callback, type). $.postAntiForgery = function (url, data, callback, type) { return $.post(url, $.appendAntiForgeryToken(data), callback, type); }; // Wraps $.ajax(settings). $.ajaxAntiForgery = function (settings) { settings.data = $.appendAntiForgeryToken(settings.data); return $.ajax(settings); }; })(jQuery); In most of the scenarios, it is Ok to just replace $.post() invocation with $.postAntiForgery(), and replace $.ajax() with $.ajaxAntiForgery():$.postAntiForgery(url, { productName: "Tofu", categoryId: 1 }, callback); // Token is posted. There might be some scenarios of custom token. Here $.appendAntiForgeryToken() is provided:data = $.appendAntiForgeryToken(data, token); // Token is already in data. No need to invoke $.postAntiForgery(). $.post(url, data, callback); And there are scenarios that the token is not in the current window. For example, an HTTP POST request can be sent by iframe, while the token is in the parent window. Here window can be specified for $.getAntiForgeryToken():data = $.appendAntiForgeryToken(data, $.getAntiForgeryToken(window.parent)); // Token is already in data. No need to invoke $.postAntiForgery(). $.post(url, data, callback); If you have better solution, please do tell me.

    Read the article

  • Scripting Language Sessions at Oracle OpenWorld and MySQL Connect, 2012

    - by cj
    This posts highlights some great scripting language sessions coming up at the Oracle OpenWorld and MySQL Connect conferences. These events are happening in San Francisco from the end of September. You can search for other interesting conference sessions in the Content Catalog. Also check out what is happening at JavaOne in that event's Content Catalog (I haven't included sessions from it in this post.) To find the timeslots and locations of each session, click their respective link and check the "Session Schedule" box on the top right. GEN8431 - General Session: What’s New in Oracle Database Application Development This general session takes a look at what’s been new in the last year in Oracle Database application development tools using the latest generation of database technology. Topics range from Oracle SQL Developer and Oracle Application Express to Java and PHP. (Thomas Kyte - Architect, Oracle) BOF9858 - Meet the Developers of Database Access Services (OCI, ODBC, DRCP, PHP, Python) This session is your opportunity to meet in person the Oracle developers who have built Oracle Database access tools and products such as the Oracle Call Interface (OCI), Oracle C++ Call Interface (OCCI), and Open Database Connectivity (ODBC) drivers; Transparent Application Failover (TAF); Oracle Database Instant Client; Database Resident Connection Pool (DRCP); Oracle Net Services, and so on. The team also works with those who develop the PHP, Ruby, Python, and Perl adapters for Oracle Database. Come discuss with them the features you like, your pains, and new product enhancements in the latest database technology. CON8506 - Syndication and Consolidation: Oracle Database Driver for MySQL Applications This technical session presents a new Oracle Database driver that enables you to run MySQL applications (written in PHP, Perl, C, C++, and so on) against Oracle Database with almost no code change. Use cases for such a driver include application syndication such as interoperability across a relationship database management system, application migration, and database consolidation. In addition, the session covers enhancements in database technology that enable and simplify the migration of third-party databases and applications to and consolidation with Oracle Database. Attend this session to learn more and see a live demo. (Srinath Krishnaswamy - Director, Software Development, Oracle. Kuassi Mensah - Director Product Management, Oracle. Mohammad Lari - Principal Technical Staff, Oracle ) CON9167 - Current State of PHP and MySQL Together, PHP and MySQL power large parts of the Web. The developers of both technologies continue to enhance their software to ensure that developers can be satisfied despite all their changing and growing needs. This session presents an overview of changes in PHP 5.4, which was released earlier this year and shows you various new MySQL-related features available for PHP, from transparent client-side caching to direct support for scaling and high-availability needs. (Johannes Schlüter - SoftwareDeveloper, Oracle) CON8983 - Sharding with PHP and MySQL In deploying MySQL, scale-out techniques can be used to scale out reads, but for scaling out writes, other techniques have to be used. To distribute writes over a cluster, it is necessary to shard the database and store the shards on separate servers. This session provides a brief introduction to traditional MySQL scale-out techniques in preparation for a discussion on the different sharding techniques that can be used with MySQL server and how they can be implemented with PHP. You will learn about static and dynamic sharding schemes, their advantages and drawbacks, techniques for locating and moving shards, and techniques for resharding. (Mats Kindahl - Senior Principal Software Developer, Oracle) CON9268 - Developing Python Applications with MySQL Utilities and MySQL Connector/Python This session discusses MySQL Connector/Python and the MySQL Utilities component of MySQL Workbench and explains how to write MySQL applications in Python. It includes in-depth explanations of the features of MySQL Connector/Python and the MySQL Utilities library, along with example code to illustrate the concepts. Those interested in learning how to expand or build their own utilities and connector features will benefit from the tips and tricks from the experts. This session also provides an opportunity to meet directly with the engineers and provide feedback on your issues and priorities. You can learn what exists today and influence future developments. (Geert Vanderkelen - Software Developer, Oracle) BOF9141 - MySQL Utilities and MySQL Connector/Python: Python Developers, Unite! Come to this lively discussion of the MySQL Utilities component of MySQL Workbench and MySQL Connector/Python. It includes in-depth explanations of the features and dives into the code for those interested in learning how to expand or build their own utilities and connector features. This is an audience-driven session, so put on your best Python shirt and let’s talk about MySQL Utilities and MySQL Connector/Python. (Geert Vanderkelen - Software Developer, Oracle. Charles Bell - Senior Software Developer, Oracle) CON3290 - Integrating Oracle Database with a Social Network Facebook, Flickr, YouTube, Google Maps. There are many social network sites, each with their own APIs for sharing data with them. Most developers do not realize that Oracle Database has base tools for communicating with these sites, enabling all manner of information, including multimedia, to be passed back and forth between the sites. This technical presentation goes through the methods in PL/SQL for connecting to, and then sending and retrieving, all types of data between these sites. (Marcelle Kratochvil - CTO, Piction) CON3291 - Storing and Tuning Unstructured Data and Multimedia in Oracle Database Database administrators need to learn new skills and techniques when the decision is made in their organization to let Oracle Database manage its unstructured data. They will face new scalability challenges. A single row in a table can become larger than a whole database. This presentation covers the techniques a DBA needs for managing the large volume of data in a standard Oracle Database instance. (Marcelle Kratochvil - CTO, Piction) CON3292 - Using PHP, Perl, Visual Basic, Ruby, and Python for Multimedia in Oracle Database These five programming languages are just some of the most popular ones in use at the moment in the marketplace. This presentation details how you can use them to access and retrieve multimedia from Oracle Database. It covers programming techniques and methods for achieving faster development against Oracle Database. (Marcelle Kratochvil - CTO, Piction) UGF5181 - Building Real-World Oracle DBA Tools in Perl Perl is not normally associated with building mission-critical application or DBA tools. Learn why Perl could be a good choice for building your next killer DBA app. This session draws on real-world experience of building DBA tools in Perl, showing the framework and architecture needed to deal with portability, efficiency, and maintainability. Topics include Perl frameworks; Which Comprehensive Perl Archive Network (CPAN) modules are good to use; Perl and CPAN module licensing; Perl and Oracle connectivity; Compiling and deploying your app; An example of what is possible with Perl. (Arjen Visser - CEO & CTO, Dbvisit Software Limited) CON3153 - Perl: A DBA’s and Developer’s Best (Forgotten) Friend This session reintroduces Perl as a language of choice for many solutions for DBAs and developers. Discover what makes Perl so successful and why it is so versatile in our day-to-day lives. Perl can automate all those manual tasks and is truly platform-independent. Perl may not be in the limelight the way other languages are, but it is a remarkable language, it is still very current with ongoing development, and it has amazing online resources. Learn what makes Perl so great (including CPAN), get an introduction to Perl language syntax, find out what you can use Perl for, hear how Oracle uses Perl, discover the best way to learn Perl, and take away a small Perl project challenge. (Arjen Visser - CEO & CTO, Dbvisit Software Limited) CON10332 - Oracle RightNow CX Cloud Service’s Connect PHP API: Intro, What’s New, and Roadmap Connect PHP is a public API that enables developers to build solutions with the Oracle RightNow CX Cloud Service platform. This API is used primarily by developers working within the Oracle RightNow Customer Portal Cloud Service framework who are looking to gain access to data and services hosted by the Oracle RightNow CX Cloud Service platform through a backward-compatible API. Connect for PHP leverages the same data model and services as the Connect Web Services for SOAP API. Come to this session to get an introduction and learn what’s new and what’s coming up. (Mark Rhoads - Senior Principal Applications Engineer, Oracle. Mark Ericson - Sr. Principle Product Manager, Oracle) CON10330 - Oracle RightNow CX Cloud Service APIs and Frameworks Overview Oracle RightNow CX Cloud Service APIs are available in the following areas: desktop UI, Web services, customer portal, PHP, and knowledge. These frameworks provide access to Oracle RightNow CX Cloud Service’s Connect Common Object Model and custom objects. This session provides a broad overview of capabilities in all these areas. (Mark Ericson - Sr. Principle Product Manager, Oracle)

    Read the article

  • NUMA-aware placement of communication variables

    - by Dave
    For classic NUMA-aware programming I'm typically most concerned about simple cold, capacity and compulsory misses and whether we can satisfy the miss by locally connected memory or whether we have to pull the line from its home node over the coherent interconnect -- we'd like to minimize channel contention and conserve interconnect bandwidth. That is, for this style of programming we're quite aware of where memory is homed relative to the threads that will be accessing it. Ideally, a page is collocated on the node with the thread that's expected to most frequently access the page, as simple misses on the page can be satisfied without resorting to transferring the line over the interconnect. The default "first touch" NUMA page placement policy tends to work reasonable well in this regard. When a virtual page is first accessed, the operating system will attempt to provision and map that virtual page to a physical page allocated from the node where the accessing thread is running. It's worth noting that the node-level memory interleaving granularity is usually a multiple of the page size, so we can say that a given page P resides on some node N. That is, the memory underlying a page resides on just one node. But when thinking about accesses to heavily-written communication variables we normally consider what caches the lines underlying such variables might be resident in, and in what states. We want to minimize coherence misses and cache probe activity and interconnect traffic in general. I don't usually give much thought to the location of the home NUMA node underlying such highly shared variables. On a SPARC T5440, for instance, which consists of 4 T2+ processors connected by a central coherence hub, the home node and placement of heavily accessed communication variables has very little impact on performance. The variables are frequently accessed so likely in M-state in some cache, and the location of the home node is of little consequence because a requester can use cache-to-cache transfers to get the line. Or at least that's what I thought. Recently, though, I was exploring a simple shared memory point-to-point communication model where a client writes a request into a request mailbox and then busy-waits on a response variable. It's a simple example of delegation based on message passing. The server polls the request mailbox, and having fetched a new request value, performs some operation and then writes a reply value into the response variable. As noted above, on a T5440 performance is insensitive to the placement of the communication variables -- the request and response mailbox words. But on a Sun/Oracle X4800 I noticed that was not the case and that NUMA placement of the communication variables was actually quite important. For background an X4800 system consists of 8 Intel X7560 Xeons . Each package (socket) has 8 cores with 2 contexts per core, so the system is 8x8x2. Each package is also a NUMA node and has locally attached memory. Every package has 3 point-to-point QPI links for cache coherence, and the system is configured with a twisted ladder "mobius" topology. The cache coherence fabric is glueless -- there's not central arbiter or coherence hub. The maximum distance between any two nodes is just 2 hops over the QPI links. For any given node, 3 other nodes are 1 hop distant and the remaining 4 nodes are 2 hops distant. Using a single request (client) thread and a single response (server) thread, a benchmark harness explored all permutations of NUMA placement for the two threads and the two communication variables, measuring the average round-trip-time and throughput rate between the client and server. In this benchmark the server simply acts as a simple transponder, writing the request value plus 1 back into the reply field, so there's no particular computation phase and we're only measuring communication overheads. In addition to varying the placement of communication variables over pairs of nodes, we also explored variations where both variables were placed on one page (and thus on one node) -- either on the same cache line or different cache lines -- while varying the node where the variables reside along with the placement of the threads. The key observation was that if the client and server threads were on different nodes, then the best placement of variables was to have the request variable (written by the client and read by the server) reside on the same node as the client thread, and to place the response variable (written by the server and read by the client) on the same node as the server. That is, if you have a variable that's to be written by one thread and read by another, it should be homed with the writer thread. For our simple client-server model that means using split request and response communication variables with unidirectional message flow on a given page. This can yield up to twice the throughput of less favorable placement strategies. Our X4800 uses the QPI 1.0 protocol with source-based snooping. Briefly, when node A needs to probe a cache line it fires off snoop requests to all the nodes in the system. Those recipients then forward their response not to the original requester, but to the home node H of the cache line. H waits for and collects the responses, adjudicates and resolves conflicts and ensures memory-model ordering, and then sends a definitive reply back to the original requester A. If some node B needed to transfer the line to A, it will do so by cache-to-cache transfer and let H know about the disposition of the cache line. A needs to wait for the authoritative response from H. So if a thread on node A wants to write a value to be read by a thread on node B, the latency is dependent on the distances between A, B, and H. We observe the best performance when the written-to variable is co-homed with the writer A. That is, we want H and A to be the same node, as the writer doesn't need the home to respond over the QPI link, as the writer and the home reside on the very same node. With architecturally informed placement of communication variables we eliminate at least one QPI hop from the critical path. Newer Intel processors use the QPI 1.1 coherence protocol with home-based snooping. As noted above, under source-snooping a requester broadcasts snoop requests to all nodes. Those nodes send their response to the home node of the location, which provides memory ordering, reconciles conflicts, etc., and then posts a definitive reply to the requester. In home-based snooping the snoop probe goes directly to the home node and are not broadcast. The home node can consult snoop filters -- if present -- and send out requests to retrieve the line if necessary. The 3rd party owner of the line, if any, can respond either to the home or the original requester (or even to both) according to the protocol policies. There are myriad variations that have been implemented, and unfortunately vendor terminology doesn't always agree between vendors or with the academic taxonomy papers. The key is that home-snooping enables the use of a snoop filter to reduce interconnect traffic. And while home-snooping might have a longer critical path (latency) than source-based snooping, it also may require fewer messages and less overall bandwidth. It'll be interesting to reprise these experiments on a platform with home-based snooping. While collecting data I also noticed that there are placement concerns even in the seemingly trivial case when both threads and both variables reside on a single node. Internally, the cores on each X7560 package are connected by an internal ring. (Actually there are multiple contra-rotating rings). And the last-level on-chip cache (LLC) is partitioned in banks or slices, which with each slice being associated with a core on the ring topology. A hardware hash function associates each physical address with a specific home bank. Thus we face distance and topology concerns even for intra-package communications, although the latencies are not nearly the magnitude we see inter-package. I've not seen such communication distance artifacts on the T2+, where the cache banks are connected to the cores via a high-speed crossbar instead of a ring -- communication latencies seem more regular.

    Read the article

  • Testing Workflows &ndash; Test-First

    - by Timothy Klenke
    Originally posted on: http://geekswithblogs.net/TimothyK/archive/2014/05/30/testing-workflows-ndash-test-first.aspxThis is the second of two posts on some common strategies for approaching the job of writing tests.  The previous post covered test-after workflows where as this will focus on test-first.  Each workflow presented is a method of attack for adding tests to a project.  The more tools in your tool belt the better.  So here is a partial list of some test-first methodologies. Ping Pong Ping Pong is a methodology commonly used in pair programing.  One developer will write a new failing test.  Then they hand the keyboard to their partner.  The partner writes the production code to get the test passing.  The partner then writes the next test before passing the keyboard back to the original developer. The reasoning behind this testing methodology is to facilitate pair programming.  That is to say that this testing methodology shares all the benefits of pair programming, including ensuring multiple team members are familiar with the code base (i.e. low bus number). Test Blazer Test Blazing, in some respects, is also a pairing strategy.  The developers don’t work side by side on the same task at the same time.  Instead one developer is dedicated to writing tests at their own desk.  They write failing test after failing test, never touching the production code.  With these tests they are defining the specification for the system.  The developer most familiar with the specifications would be assigned this task. The next day or later in the same day another developer fetches the latest test suite.  Their job is to write the production code to get those tests passing.  Once all the tests pass they fetch from source control the latest version of the test project to get the newer tests. This methodology has some of the benefits of pair programming, namely lowering the bus number.  This can be good way adding an extra developer to a project without slowing it down too much.  The production coder isn’t slowed down writing tests.  The tests are in another project from the production code, so there shouldn’t be any merge conflicts despite two developers working on the same solution. This methodology is also a good test for the tests.  Can another developer figure out what system should do just by reading the tests?  This question will be answered as the production coder works there way through the test blazer’s tests. Test Driven Development (TDD) TDD is a highly disciplined practice that calls for a new test and an new production code to be written every few minutes.  There are strict rules for when you should be writing test or production code.  You start by writing a failing (red) test, then write the simplest production code possible to get the code working (green), then you clean up the code (refactor).  This is known as the red-green-refactor cycle. The goal of TDD isn’t the creation of a suite of tests, however that is an advantageous side effect.  The real goal of TDD is to follow a practice that yields a better design.  The practice is meant to push the design toward small, decoupled, modularized components.  This is generally considered a better design that large, highly coupled ball of mud. TDD accomplishes this through the refactoring cycle.  Refactoring is only possible to do safely when tests are in place.  In order to use TDD developers must be trained in how to look for and repair code smells in the system.  Through repairing these sections of smelly code (i.e. a refactoring) the design of the system emerges. For further information on TDD, I highly recommend the series “Is TDD Dead?”.  It discusses its pros and cons and when it is best used. Acceptance Test Driven Development (ATDD) Whereas TDD focuses on small unit tests that concentrate on a small piece of the system, Acceptance Tests focuses on the larger integrated environment.  Acceptance Tests usually correspond to user stories, which come directly from the customer. The unit tests focus on the inputs and outputs of smaller parts of the system, which are too low level to be of interest to the customer. ATDD generally uses the same tools as TDD.  However, ATDD uses fewer mocks and test doubles than TDD. ATDD often complements TDD; they aren’t competing methods.  A full test suite will usually consist of a large number of unit (created via TDD) tests and a smaller number of acceptance tests. Behaviour Driven Development (BDD) BDD is more about audience than workflow.  BDD pushes the testing realm out towards the client.  Developers, managers and the client all work together to define the tests. Typically different tooling is used for BDD than acceptance and unit testing.  This is done because the audience is not just developers.  Tools using the Gherkin family of languages allow for test scenarios to be described in an English format.  Other tools such as MSpec or FitNesse also strive for highly readable behaviour driven test suites. Because these tests are public facing (viewable by people outside the development team), the terminology usually changes.  You can’t get away with the same technobabble you can with unit tests written in a programming language that only developers understand.  For starters, they usually aren’t called tests.  Usually they’re called “examples”, “behaviours”, “scenarios”, or “specifications”. This may seem like a very subtle difference, but I’ve seen this small terminology change have a huge impact on the acceptance of the process.  Many people have a bias that testing is something that comes at the end of a project.  When you say we need to define the tests at the start of the project many people will immediately give that a lower priority on the project schedule.  But if you say we need to define the specification or behaviour of the system before we can start, you’ll get more cooperation.   Keep these test-first and test-after workflows in your tool belt.  With them you’ll be able to find new opportunities to apply them.

    Read the article

  • ???Flashback Log???????Redo Log?

    - by Liu Maclean(???)
    ????????????????????redo log?   RVWR( Recovery Writer)?3s??flashback generate buffer??block before image?????????? ?????block change???RVWR??block before image ?flashback log? ?????????,Oracle???????????before image????????,????????flashback database logs?????   ???????????,????? ??????????????????,???????????before image?????shared pool??flashback log buffer?,RVWR??????flashback log buffer??????????? ?DBWR???????????????,DBWR?????buffer header??FBA(Flashback Byte Address)?flashback log buffer?????????? ???? ?????? ??? ????????????? , RVWR???????????(flashback markers)?flashback database logs?? ????(flashback markers)?????????????Oracle??flashback ??????????  ??????????, Oracle ??????(flashback markers)????????????flashback database log???????????block image; ??Oracle ???????(forward recovery)?????????????????SCN?????? flashback markers for example: **** Record at fba: (lno 1 thr 1 seq 1 bno 4 bof 8184) **** RECORD HEADER: Type: 3 (Skip) Size: 8132 RECORD DATA (Skip): **** Record at fba: (lno 1 thr 1 seq 1 bno 4 bof 52) **** RECORD HEADER: Type: 7 (Begin Crash Recovery Record) Size: 36 RECORD DATA (Begin Crash Recovery Record): Previous logical record fba: (lno 1 thr 1 seq 1 bno 3 bof 316) Record scn: 0x0000.00000000 [0.0] **** Record at fba: (lno 1 thr 1 seq 1 bno 3 bof 8184) **** RECORD HEADER: Type: 3 (Skip) Size: 7868 RECORD DATA (Skip): **** Record at fba: (lno 1 thr 1 seq 1 bno 3 bof 316) **** RECORD HEADER: Type: 2 (Marker) Size: 300 RECORD DATA (Marker): Previous logical record fba: (lno 0 thr 0 seq 0 bno 0 bof 0) Record scn: 0x0000.00000000 [0.0] Marker scn: 0x0000.0060e024 [0.6348836] 06/13/2012 15:56:35 Flag 0x0 Flashback threads: 1, Enabled redo threads 1 Recovery Start Checkpoint: scn: 0x0000.0060e024 [0.6348836] 06/13/2012 15:56:12 thread:1 rba:(0x80.180.10) Flashback thread Markers: Thread:1 status:0 fba: (lno 1 thr 1 seq 1 bno 2 bof 8184) Redo Thread Checkpoint Info: Thread:1 rba:(0x80.180.10) **** Record at fba: (lno 1 thr 1 seq 1 bno 2 bof 8184) **** RECORD HEADER: Type: 3 (Skip) Size: 8168 RECORD DATA (Skip): End-Of-Thread reached ????????????????block change ????before image????????flashback log?? ?????block change???flashback log record ????????? redo log???!????flashback log ???????before image ? redo log??? change vector ?  Oracle?????????????????????????????????????,??????I/O??????????????: ??hot block??,Oracle???????????block image?????; Oracle ?????????(flashback barriers)???????????????,flashback barriers???????(???15??),??????????(flashback barriers)????(flashback markers)????????? ????, ??????change?????, ???????????????????????????, ?15????????????????????flashback log????????before image?????????????,?????????????????????,?????????????? ????????,??????????????(flashback barriers), flashback barriers???????,?????15????? ?????flashback barriers????????(flashback markers)???????????????,???????????????????(????barriers?????)??????block image ,????????????????????????????????? ??????????flashback log????redo log????! ????,????????????????, ?????????? SQL> select * from v$version; BANNER -------------------------------------------------------------------------------- Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - 64bit Production PL/SQL Release 11.2.0.3.0 - Production CORE 11.2.0.3.0 Production TNS for Linux: Version 11.2.0.3.0 - Production NLSRTL Version 11.2.0.3.0 - Production SQL> select * from global_name; GLOBAL_NAME -------------------------------------------------------------------------------- www.oracledatabase12g.com SQL> create table flash_maclean (t1 varchar2(200)) tablespace users; Table created. SQL> insert into flash_maclean values('MACLEAN LOVE HANNA'); 1 row created. SQL> commit; Commit complete. SQL> startup force; ORACLE instance started. Total System Global Area 939495424 bytes Fixed Size 2233960 bytes Variable Size 713034136 bytes Database Buffers 218103808 bytes Redo Buffers 6123520 bytes Database mounted. Database opened. SQL> update flash_maclean set t1='HANNA LOVE MACLEAN'; 1 row updated. commit; Commit complete. SQL> alter system checkpoint; System altered. SQL> select dbms_rowid.rowid_block_number(rowid),dbms_rowid.rowid_relative_fno(rowid) from flash_maclean; DBMS_ROWID.ROWID_BLOCK_NUMBER(ROWID) DBMS_ROWID.ROWID_RELATIVE_FNO(ROWID) ------------------------------------ ------------------------------------ 140431 4 datafile 4 block 140431 ??RDBA rdba: 0x0102248f (4/140431) SQL> ! ps -ef|grep rvwr|grep -v grep oracle 26695 1 0 15:56 ? 00:00:00 ora_rvwr_G11R23 SQL> oradebug setospid 26695 Oracle pid: 20, Unix process pid: 26695, image: [email protected] (RVWR) SQL> ORADEBUG DUMP FBTAIL 1; Statement processed. To dump the last 2000 flashback records , ??ORADEBUG DUMP FBTAIL 1????????2000?????? SQL> oradebug tracefile_name /s01/orabase/diag/rdbms/g11r23/G11R23/trace/G11R23_rvwr_26695.trc ? TRACE?????????block? before image **** Record at fba: (lno 1 thr 1 seq 1 bno 55 bof 2564) **** RECORD HEADER: Type: 1 (Block Image) Size: 28 RECORD DATA (Block Image): file#: 4 rdba: 0x0102248f Next scn: 0x0000.00000000 [0.0] Flag: 0x0 Block Size: 8192 BLOCK IMAGE: buffer rdba: 0x0102248f scn: 0x0000.00609044 seq: 0x01 flg: 0x06 tail: 0x90440601 frmt: 0x02 chkval: 0xc626 type: 0x06=trans data Hex dump of block: st=0, typ_found=1 Dump of memory from 0x00002B1D94183C00 to 0x00002B1D94185C00 2B1D94183C00 0000A206 0102248F 00609044 06010000 [.....$..D.`.....] 2B1D94183C10 0000C626 00000001 00014AD4 0060903A [&........J..:.`.] 2B1D94183C20 00000000 00320002 01022488 00090006 [......2..$......] 2B1D94183C30 00000CC8 00C00340 000D0542 00008000 [[email protected].......] 2B1D94183C40 006040BC 000F000A 00000920 00C002E4 [.@`..... .......] 2B1D94183C50 0017048F 00002001 00609044 00000000 [..... ..D.`.....] 2B1D94183C60 00000000 00010100 0014FFFF 1F6E1F77 [............w.n.] 2B1D94183C70 00001F6E 1F770001 00000000 00000000 [n.....w.........] 2B1D94183C80 00000000 00000000 00000000 00000000 [................] Repeat 500 times 2B1D94185BD0 00000000 00000000 2C000000 4D120102 [...........,...M] 2B1D94185BE0 454C4341 4C204E41 2045564F 4E4E4148 [ACLEAN LOVE HANN] 2B1D94185BF0 01002C41 43414D07 4E41454C 90440601 [A,...MACLEAN..D.] Block header dump: 0x0102248f Object id on Block? Y seg/obj: 0x14ad4 csc: 0x00.60903a itc: 2 flg: E typ: 1 - DATA brn: 0 bdba: 0x1022488 ver: 0x01 opc: 0 inc: 0 exflg: 0 Itl Xid Uba Flag Lck Scn/Fsc 0x01 0x0006.009.00000cc8 0x00c00340.0542.0d C--- 0 scn 0x0000.006040bc 0x02 0x000a.00f.00000920 0x00c002e4.048f.17 --U- 1 fsc 0x0000.00609044 bdba: 0x0102248f data_block_dump,data header at 0x2b1d94183c64 =============== tsiz: 0x1f98 hsiz: 0x14 pbl: 0x2b1d94183c64 76543210 flag=-------- ntab=1 nrow=1 frre=-1 fsbo=0x14 fseo=0x1f77 avsp=0x1f6e tosp=0x1f6e 0xe:pti[0] nrow=1 offs=0 0x12:pri[0] offs=0x1f77 block_row_dump: tab 0, row 0, @0x1f77 tl: 22 fb: --H-FL-- lb: 0x2 cc: 1 col 0: [18] 4d 41 43 4c 45 41 4e 20 4c 4f 56 45 20 48 41 4e 4e 41 end_of_block_dump SQL> select dump('MACLEAN LOVE HANNA',16) from dual; DUMP('MACLEANLOVEHANNA',16) -------------------------------------------------------------------- Typ=96 Len=18: 4d,41,43,4c,45,41,4e,20,4c,4f,56,45,20,48,41,4e,4e,41 ???????????????????????,??flashback log??before image????????? create table flash_maclean1 (t1 int) tablespace users; SQL> select vs.name, ms.value 2 from v$mystat ms, v$sysstat vs 3 where vs.statistic# = ms.statistic# 4 and vs.name in ('redo size','db block changes'); NAME VALUE ---------------------------------------------------------------- ---------- db block changes 0 redo size 0 SQL> select name,value from v$sysstat where name like 'flashback log%'; NAME VALUE ---------------------------------------------------------------- ---------- flashback log writes 49 flashback log write bytes 9306112 SQL> begin 2 for i in 1..5000 loop 3 update flash_maclean1 set t1=t1+1; 4 commit; 5 end loop; 6 end; 7 / PL/SQL procedure successfully completed. SQL> select vs.name, ms.value 2 from v$mystat ms, v$sysstat vs 3 where vs.statistic# = ms.statistic# 4 and vs.name in ('redo size','db block changes'); NAME VALUE ---------------------------------------------------------------- ---------- db block changes 20006 redo size 3071288 SQL> select name,value from v$sysstat where name like 'flashback log%'; NAME VALUE ---------------------------------------------------------------- ---------- flashback log writes 52 flashback log write bytes 10338304 ??????????? ??hot block,???20006 ?block changes???? ??? 3000k ?redo log ? ??1000k? flashback log ?

    Read the article

  • Cache Simulator in C

    - by DuffDuff
    Ok this is only my second question, and it's quite a doozy. It's for a school assignment, but no one (including the TAs) seems to be able to help me. It's kind of a tall order but I'm not sure where else to turn. Essentially the assignment was to make a cache simulator. This version is direct mapping and is actually only a small portion of the whole project, but if I can't even get this down I have no chance with other associativities. I'm posting my whole code because I don't want to make any assumptions about where the problem is. This is the test case: http://www.mediafire.com/?ty5dnihydnw And you run the following command: ./sims 512 direct 32 fifo wt pinatrace.out You're supposed to get: hits: 604037 misses 138349 writes: 239269 reads: 138349 But I get: Hits: 587148 Misses: 155222 Writes: 239261 Reads: 155222 If anyone could at least point me in the right direction it would be greatly appreciated. I've been stuck on this for about 12 hours. #include <stdio.h> #include <stdlib.h> #include <string.h> #include <math.h> struct myCache { int valid; char *tag; char *block; }; /* sim [-h] <cache size> <associativity> <block size> <replace alg> <write policy> <trace file> */ //God willing I come up with a better Hex to Bin convertion that maintains the beginning 0s... void hex2bin(char input[], char output[]) { int i; int a = 0; int b = 1; int c = 2; int d = 3; int x = 4; int size; size = strlen(input); for (i = 0; i < size; i++) { if (input[i] =='0') { output[i*x +a] = '0'; output[i*x +b] = '0'; output[i*x +c] = '0'; output[i*x +d] = '0'; } else if (input[i] =='1') { output[i*x +a] = '0'; output[i*x +b] = '0'; output[i*x +c] = '0'; output[i*x +d] = '1'; } else if (input[i] =='2') { output[i*x +a] = '0'; output[i*x +b] = '0'; output[i*x +c] = '1'; output[i*x +d] = '0'; } else if (input[i] =='3') { output[i*x +a] = '0'; output[i*x +b] = '0'; output[i*x +c] = '1'; output[i*x +d] = '1'; } else if (input[i] =='x') { output[i*x +a] = '0'; output[i*x +b] = '1'; output[i*x +c] = '0'; output[i*x +d] = '0'; } else if (input[i] =='5') { output[i*x +a] = '0'; output[i*x +b] = '1'; output[i*x +c] = '0'; output[i*x +d] = '1'; } else if (input[i] =='6') { output[i*x +a] = '0'; output[i*x +b] = '1'; output[i*x +c] = '1'; output[i*x +d] = '0'; } else if (input[i] =='7') { output[i*x +a] = '0'; output[i*x +b] = '1'; output[i*x +c] = '1'; output[i*x +d] = '1'; } else if (input[i] =='8') { output[i*x +a] = '1'; output[i*x +b] = '0'; output[i*x +c] = '0'; output[i*x +d] = '0'; } else if (input[i] =='9') { output[i*x +a] = '1'; output[i*x +b] = '0'; output[i*x +c] = '0'; output[i*x +d] = '1'; } else if (input[i] =='a') { output[i*x +a] = '1'; output[i*x +b] = '0'; output[i*x +c] = '1'; output[i*x +d] = '0'; } else if (input[i] =='b') { output[i*x +a] = '1'; output[i*x +b] = '0'; output[i*x +c] = '1'; output[i*x +d] = '1'; } else if (input[i] =='c') { output[i*x +a] = '1'; output[i*x +b] = '1'; output[i*x +c] = '0'; output[i*x +d] = '0'; } else if (input[i] =='d') { output[i*x +a] = '1'; output[i*x +b] = '1'; output[i*x +c] = '0'; output[i*x +d] = '1'; } else if (input[i] =='e') { output[i*x +a] = '1'; output[i*x +b] = '1'; output[i*x +c] = '1'; output[i*x +d] = '0'; } else if (input[i] =='f') { output[i*x +a] = '1'; output[i*x +b] = '1'; output[i*x +c] = '1'; output[i*x +d] = '1'; } } output[32] = '\0'; } int main(int argc, char* argv[]) { FILE *tracefile; char readwrite; int trash; int cachesize; int blocksize; int setnumber; int blockbytes; int setbits; int blockbits; int tagsize; int m; int count = 0; int count2 = 0; int count3 = 0; int i; int j; int xindex; int jindex; int kindex; int lindex; int setadd; int totalset; int writeMiss = 0; int writeHit = 0; int cacheMiss = 0; int cacheHit = 0; int read = 0; int write = 0; int size; int extra; char bbits[100]; char sbits[100]; char tbits[100]; char output[100]; char input[100]; char origtag[100]; if (argc != 7) { if (strcmp(argv[0], "-h")) { printf("./sim2 <cache size> <associativity> <block size> <replace alg> <write policy> <trace file>\n"); return 0; } else { fprintf(stderr, "Error: wrong number of parameters.\n"); return -1; } } tracefile = fopen(argv[6], "r"); if(tracefile == NULL) { fprintf(stderr, "Error: File is NULL.\n"); return -1; } //Determining size of sbits, bbits, and tag cachesize = atoi(argv[1]); blocksize = atoi(argv[3]); setnumber = (cachesize/blocksize); printf("setnumber: %d\n", setnumber); setbits = (round((log(setnumber))/(log(2)))); printf("sbits: %d\n", setbits); blockbits = log(blocksize)/log(2); printf("bbits: %d\n", blockbits); tagsize = 32 - (blockbits + setbits); printf("t: %d\n", tagsize); struct myCache newCache[setnumber]; //Allocating Space for Tag Bits, initiating tag and valid to 0s for(i=0;i<setnumber;i++) { newCache[i].tag = (char *)malloc(sizeof(char)*(tagsize+1)); for(j=0;j<tagsize;j++) { newCache[i].tag[j] = '0'; } newCache[i].valid = 0; } while(fgetc(tracefile)!='#') { setadd = 0; totalset = 0; //read in file fseek(tracefile,-1,SEEK_CUR); fscanf(tracefile, "%x: %c %s\n", &trash, &readwrite, origtag); //shift input Hex size = strlen(origtag); extra = (10 - size); for(i=0; i<extra; i++) input[i] = '0'; for(i=extra, j=0; i<(size-(2-extra)); j++, i++) input[i]=origtag[j+2]; input[8] = '\0'; // Convert Hex to Binary hex2bin(input, output); //Resolving the Address into tbits, sbits, bbits for (xindex=0, jindex=(32-blockbits); jindex<32; jindex++, xindex++) { bbits[xindex] = output[jindex]; } bbits[xindex]='\0'; for (xindex=0, kindex=(32-(blockbits+setbits)); kindex<32-(blockbits); kindex++, xindex++){ sbits[xindex] = output[kindex]; } sbits[xindex]='\0'; for (xindex=0, lindex=0; lindex<(32-(blockbits+setbits)); lindex++, xindex++){ tbits[xindex] = output[lindex]; } tbits[xindex]='\0'; //Convert set bits from char array into ints for(xindex = 0, kindex = (setbits -1); xindex < setbits; xindex ++, kindex--) { if (sbits[xindex] == '1') setadd = 1; if (sbits[xindex] == '0') setadd = 0; setadd = setadd * pow(2, kindex); totalset += setadd; } //Calculating Hits and Misses if (newCache[totalset].valid == 0) { newCache[totalset].valid = 1; strcpy(newCache[totalset].tag, tbits); } else if (newCache[totalset].valid == 1) { if(strcmp(newCache[totalset].tag, tbits) == 0) { if (readwrite == 'W') { cacheHit++; write++; } if (readwrite == 'R') cacheHit++; } else { if (readwrite == 'R') { cacheMiss++; read++; } if (readwrite == 'W') { cacheMiss++; read++; write++; } strcpy(newCache[totalset].tag, tbits); } } } printf("Hits: %d\n", cacheHit); printf("Misses: %d\n", cacheMiss); printf("Writes: %d\n", write); printf("Reads: %d\n", read); }

    Read the article

  • Enabling fastCGI for php in PLESK 9.3 or use ningx

    - by Saif Bechan
    I have a server that runs PLESK. I can set php to use 3 options: apache module, fastCGI application, cgi application And i have a different option that just says enable fastCGI support. Which option is the best to choose from? I run an php application with mysql and some ajax. Heavy reads and writes, it is a busy website. Second thing is will there be much difference if i install nginx to work with this. there is some sort of hack i can use to make ningx work on port 80 and apache on port 8080. I don't know if this is worth my while. thanks for your time folks!

    Read the article

  • Scheduled Task to show console window when logged on but still run when not logged on

    - by HeartWare
    Is it possible (and if so, how) to set up a task (console application) in Server 2008 so that it'll run both when a user is logged in and when no user is logged in, AND - if the user is logged in (either local or via RDP) - have the console appear on the screen while the program is running? Ie. the program should run under the defined user context and it writes status messages to stdout, which goes to a standard console window. This console window is either shown (if the defined user is currently logged in locally or via RDP), or not shown (but the application is still run). I have access to the source of the console application, so if it needs some additional code (like specifically opening up a new console window or what have you), then that's not a problem. At the moment, I can set up the task as "Run only when user is logged on" which will run the application when the user is logged on (local or RDP) and I can then see the status messages, or I can set it up as "Run whether user is logged or not" and no status output is visible - not even if the user is logged on.

    Read the article

  • lsyncd + csync2 : cluster of 3 or more nodes

    - by sbrattla
    I've got 3 (and potentially more) web servers hosting the same content (fronted by a load balancer). Thus, I need to make sure that files on these web servers are the same. It appears that csync2 in combination with lsyncd is able to do synchronize a cluster of nodes, but according to this article there's a problem with cyclic events in such a setup. In other words, the author writes that a file change on one machine would trigger a replication event to other machines, which again would trigger a replication event back to the original machine. It appears that this is a consequence of the setup which uses lsyncd (and inotify) to catch file modification events and from there trigger csync2 to replicate the file tree. Does anyone have experience with lsyncd in combination with csync2. Have you had trouble with cyclic events?

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 16 17 18 19 20 21 22 23 24 25 26 27  | Next Page >