Search Results

Search found 5701 results on 229 pages for 'cpu cooler'.

Page 111/229 | < Previous Page | 107 108 109 110 111 112 113 114 115 116 117 118  | Next Page >

• windows-7 x86 to x64 upgrade

  - by small_potato

  is it possible to update my system from windows 7 x86 to x64 without losing data ? cpu architecture is 32bit but I have 4g ram, so to unseal the ram, I have to go with x64.

  Read the article

• 100% utilization on amazon server

  - by user2939830

  Good day, I would just like to know if you guys have any idea what could be the possible cause for a sudden disconnection of clients and 100% cpu utilization in our amazon server. This problem just started 2 days ago and in both occasion it happened at 7 plus in the morning gmt+8. What we usually do is just reset the socket for it to normalize and then on the next day same thing happened at 7 in the morning every client is disconnected from the server.

  Read the article

• my gateway laptop will not reboot

  - by dom

  My gateway laptop model nx570xl originally rebooted normally. I don't know when it happened, but now when i try to reboot it Keeps trying but it never happens forcing me to shut down and wait a random amt of time before it will start up again. Its very annoying and wastes a lot of my time. I don't think its a cpu overheating problem because its random when it starts up after i turn it back on. Any ideas?

  Read the article

• Problem with room/screen/menu controller in python game: old rooms are not removed from memory

  - by Jordan Magnuson

  I'm literally banging my head against a wall here (as in, yes, physically, at my current location, I am damaging my cranium). Basically, I've got a Python/Pygame game with some typical game "rooms", or "screens." EG title screen, high scores screen, and the actual game room. Something bad is happening when I switch between rooms: the old room (and its various items) are not removed from memory, or from my event listener. Not only that, but every time I go back to a certain room, my number of event listeners increases, as well as the RAM being consumed! (So if I go back and forth between the title screen and the "game room", for instance, the number of event listeners and the memory usage just keep going up and up. The main issue is that all the event listeners start to add up and really drain the CPU. I'm new to Python, and don't know if I'm doing something obviously wrong here, or what. I will love you so much if you can help me with this! Below is the relevant source code. Complete source code at http://www.necessarygames.com/my_games/betraveled/betraveled_src0328.zip MAIN.PY class RoomController(object): """Controls which room is currently active (eg Title Screen)""" def __init__(self, screen, ev_manager): self.room = None self.screen = screen self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.room = self.set_room(config.room) def set_room(self, room_const): #Unregister old room from ev_manager if self.room: self.room.ev_manager.unregister_listener(self.room) self.room = None #Set new room based on const if room_const == config.TITLE_SCREEN: return rooms.TitleScreen(self.screen, self.ev_manager) elif room_const == config.GAME_MODE_ROOM: return rooms.GameModeRoom(self.screen, self.ev_manager) elif room_const == config.GAME_ROOM: return rooms.GameRoom(self.screen, self.ev_manager) elif room_const == config.HIGH_SCORES_ROOM: return rooms.HighScoresRoom(self.screen, self.ev_manager) def notify(self, event): if isinstance(event, ChangeRoomRequest): if event.game_mode: config.game_mode = event.game_mode self.room = self.set_room(event.new_room) #Run game def main(): pygame.init() screen = pygame.display.set_mode(config.screen_size) ev_manager = EventManager() spinner = CPUSpinnerController(ev_manager) room_controller = RoomController(screen, ev_manager) pygame_event_controller = PyGameEventController(ev_manager) spinner.run() EVENT_MANAGER.PY class EventManager: #This object is responsible for coordinating most communication #between the Model, View, and Controller. def __init__(self): from weakref import WeakKeyDictionary self.last_listeners = {} self.listeners = WeakKeyDictionary() self.eventQueue= [] self.gui_app = None #---------------------------------------------------------------------- def register_listener(self, listener): self.listeners[listener] = 1 #---------------------------------------------------------------------- def unregister_listener(self, listener): if listener in self.listeners: del self.listeners[listener] #---------------------------------------------------------------------- def clear(self): del self.listeners[:] #---------------------------------------------------------------------- def post(self, event): # if isinstance(event, MouseButtonLeftEvent): # debug(event.name) #NOTE: copying the list like this before iterating over it, EVERY tick, is highly inefficient, #but currently has to be done because of how new listeners are added to the queue while it is running #(eg when popping cards from a deck). Should be changed. See: http://dr0id.homepage.bluewin.ch/pygame_tutorial08.html #and search for "Watch the iteration" print 'Number of listeners: ' + str(len(self.listeners)) for listener in list(self.listeners): #NOTE: If the weakref has died, it will be #automatically removed, so we don't have #to worry about it. listener.notify(event) def notify(self, event): pass #------------------------------------------------------------------------------ class PyGameEventController: """...""" def __init__(self, ev_manager): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.input_freeze = False #---------------------------------------------------------------------- def notify(self, incoming_event): if isinstance(incoming_event, UserInputFreeze): self.input_freeze = True elif isinstance(incoming_event, UserInputUnFreeze): self.input_freeze = False elif isinstance(incoming_event, TickEvent) or isinstance(incoming_event, BoardCreationTick): #Share some time with other processes, so we don't hog the cpu pygame.time.wait(5) #Handle Pygame Events for event in pygame.event.get(): #If this event manager has an associated PGU GUI app, notify it of the event if self.ev_manager.gui_app: self.ev_manager.gui_app.event(event) #Standard event handling for everything else ev = None if event.type == QUIT: ev = QuitEvent() elif event.type == pygame.MOUSEBUTTONDOWN and not self.input_freeze: if event.button == 1: #Button 1 pos = pygame.mouse.get_pos() ev = MouseButtonLeftEvent(pos) elif event.type == pygame.MOUSEBUTTONDOWN and not self.input_freeze: if event.button == 2: #Button 2 pos = pygame.mouse.get_pos() ev = MouseButtonRightEvent(pos) elif event.type == pygame.MOUSEBUTTONUP and not self.input_freeze: if event.button == 2: #Button 2 Release pos = pygame.mouse.get_pos() ev = MouseButtonRightReleaseEvent(pos) elif event.type == pygame.MOUSEMOTION: pos = pygame.mouse.get_pos() ev = MouseMoveEvent(pos) #Post event to event manager if ev: self.ev_manager.post(ev) # elif isinstance(event, BoardCreationTick): # #Share some time with other processes, so we don't hog the cpu # pygame.time.wait(5) # # #If this event manager has an associated PGU GUI app, notify it of the event # if self.ev_manager.gui_app: # self.ev_manager.gui_app.event(event) #------------------------------------------------------------------------------ class CPUSpinnerController: def __init__(self, ev_manager): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.clock = pygame.time.Clock() self.cumu_time = 0 self.keep_going = True #---------------------------------------------------------------------- def run(self): if not self.keep_going: raise Exception('dead spinner') while self.keep_going: time_passed = self.clock.tick() fps = self.clock.get_fps() self.cumu_time += time_passed self.ev_manager.post(TickEvent(time_passed, fps)) if self.cumu_time >= 1000: self.cumu_time = 0 self.ev_manager.post(SecondEvent(fps=fps)) pygame.quit() #---------------------------------------------------------------------- def notify(self, event): if isinstance(event, QuitEvent): #this will stop the while loop from running self.keep_going = False EXAMPLE CLASS USING EVENT MANAGER class Timer(object): def __init__(self, ev_manager, time_left): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.time_left = time_left self.paused = False def __repr__(self): return str(self.time_left) def pause(self): self.paused = True def unpause(self): self.paused = False def notify(self, event): #Pause Event if isinstance(event, Pause): self.pause() #Unpause Event elif isinstance(event, Unpause): self.unpause() #Second Event elif isinstance(event, SecondEvent): if not self.paused: self.time_left -= 1

  Read the article

• Updating resources in SharpDX - why can I not map a dynamic texture?

  - by sebf

  I am trying to map a Texture2D resource in DirectX11 via SharpDX. The resource is declared as a ShaderResource, with Default usage and the 'Write' CPU flag specified. My call however fails with a generic exception from SharpDX: _Parent.Context.MapSubresource(_Resource, 0, SharpDX.Direct3D11.MapMode.Write, SharpDX.Direct3D11.MapFlags.None, out stream); I see from this question that it is supported. The MSDN docs and this other question hint that instead of using Context.MapSubresource() I should be using Texture2D.Map(), however, the DirectX11 Texture2D class does not define Map() (though it does for the DX10 equivalent). If I call the above with MapMode.WriteDiscard, the call succeeds but in this case the previous content of the texture is lost, which is no good when I only want to update a section of it. Has the Map() method been removed in DirectX11 or am I looking in the wrong place? Is the MapSubresource() method unsuitable or am I using it wrong?

  Read the article

• LWJGL - Eclipse error [on hold]

  - by Zarkopafilis

  When I try to run my lwjgl project, an error pops . Here is the log file: # A fatal error has been detected by the Java Runtime Environment: # EXCEPTION_ACCESS_VIOLATION (0xc0000005) at pc=0x6d8fcc0a, pid=5612, tid=900 # JRE version: 6.0_16-b01 Java VM: Java HotSpot(TM) Client VM (14.2-b01 mixed mode windows-x86 ) Problematic frame: V [jvm.dll+0xfcc0a] # If you would like to submit a bug report, please visit: http://java.sun.com/webapps/bugreport/crash.jsp # --------------- T H R E A D --------------- Current thread (0x016b9000): JavaThread "main" [_thread_in_vm, id=900, stack(0x00160000,0x001b0000)] siginfo: ExceptionCode=0xc0000005, reading address 0x00000000 Registers: EAX=0x00000000, EBX=0x00000000, ECX=0x00000006, EDX=0x00000000 ESP=0x001af4d4, EBP=0x001af524, ESI=0x016b9000, EDI=0x016b9110 EIP=0x6d8fcc0a, EFLAGS=0x00010246 Top of Stack: (sp=0x001af4d4) 0x001af4d4: 6da44bd8 016b9110 00000000 001af668 0x001af4e4: ffffffff 22200000 001af620 76ec39c2 0x001af4f4: 001af524 6d801086 0000000b 001afd34 0x001af504: 016b9000 016dd990 016b9000 00000000 0x001af514: 001af5f4 6d9ee000 6d9ef2f0 ffffffff 0x001af524: 001af58c 10008c85 016b9110 00000000 0x001af534: 00000000 000a0554 00000000 00000024 0x001af544: 00000000 00000000 001af6ac 00000000 Instructions: (pc=0x6d8fcc0a) 0x6d8fcbfa: e8 e8 d0 1d 08 00 8b 45 10 c7 45 d8 0b 00 00 00 0x6d8fcc0a: 8b 00 8b 48 08 0f b7 51 26 8b 40 0c 8b 4c 90 20 Stack: [0x00160000,0x001b0000], sp=0x001af4d4, free space=317k Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code) V [jvm.dll+0xfcc0a] C [lwjgl.dll+0x8c85] C [USER32.dll+0x18876] C [USER32.dll+0x170f4] C [USER32.dll+0x1119e] C [ntdll.dll+0x460ce] C [USER32.dll+0x10e29] C [USER32.dll+0x10e84] C [lwjgl.dll+0x1cf0] j org.lwjgl.opengl.WindowsDisplay.createWindow(Lorg/lwjgl/opengl/DrawableLWJGL;Lorg/lwjgl/opengl/DisplayMode;Ljava/awt/Canvas;II)V+102 j org.lwjgl.opengl.Display.createWindow()V+71 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;Lorg/lwjgl/opengl/Drawable;Lorg/lwjgl/opengl/ContextAttribs;)V+72 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;)V+12 j org.lwjgl.opengl.Display.create()V+7 j zarkopafilis.koding.io.javafx.Main.main([Ljava/lang/String;)V+16 v ~StubRoutines::call_stub V [jvm.dll+0xecf9c] V [jvm.dll+0x1741e1] V [jvm.dll+0xed01d] V [jvm.dll+0xf5be5] V [jvm.dll+0xfd83d] C [javaw.exe+0x2155] C [javaw.exe+0x833e] C [kernel32.dll+0x51154] C [ntdll.dll+0x5b2b9] C [ntdll.dll+0x5b28c] Java frames: (J=compiled Java code, j=interpreted, Vv=VM code) j org.lwjgl.opengl.WindowsDisplay.nCreateWindow(IIIIZZJ)J+0 j org.lwjgl.opengl.WindowsDisplay.createWindow(Lorg/lwjgl/opengl/DrawableLWJGL;Lorg/lwjgl/opengl/DisplayMode;Ljava/awt/Canvas;II)V+102 j org.lwjgl.opengl.Display.createWindow()V+71 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;Lorg/lwjgl/opengl/Drawable;Lorg/lwjgl/opengl/ContextAttribs;)V+72 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;)V+12 j org.lwjgl.opengl.Display.create()V+7 j zarkopafilis.koding.io.javafx.Main.main([Ljava/lang/String;)V+16 v ~StubRoutines::call_stub --------------- P R O C E S S --------------- Java Threads: ( = current thread ) 0x0179a400 JavaThread "Low Memory Detector" daemon [_thread_blocked, id=4460, stack(0x0b900000,0x0b950000)] 0x01795400 JavaThread "CompilerThread0" daemon [_thread_blocked, id=5264, stack(0x0b8b0000,0x0b900000)] 0x01790c00 JavaThread "Attach Listener" daemon [_thread_blocked, id=6080, stack(0x0b860000,0x0b8b0000)] 0x01786400 JavaThread "Signal Dispatcher" daemon [_thread_blocked, id=1204, stack(0x0b810000,0x0b860000)] 0x01759c00 JavaThread "Finalizer" daemon [_thread_blocked, id=5772, stack(0x0b7c0000,0x0b810000)] 0x01755000 JavaThread "Reference Handler" daemon [_thread_blocked, id=4696, stack(0x01640000,0x01690000)] =0x016b9000 JavaThread "main" [_thread_in_vm, id=900, stack(0x00160000,0x001b0000)] Other Threads: 0x01751c00 VMThread [stack: 0x015f0000,0x01640000] [id=4052] 0x0179c800 WatcherThread [stack: 0x0b950000,0x0b9a0000] [id=3340] VM state:not at safepoint (normal execution) VM Mutex/Monitor currently owned by a thread: None Heap def new generation total 960K, used 816K [0x037c0000, 0x038c0000, 0x03ca0000) eden space 896K, 91% used [0x037c0000, 0x0388c2c0, 0x038a0000) from space 64K, 0% used [0x038a0000, 0x038a0000, 0x038b0000) to space 64K, 0% used [0x038b0000, 0x038b0000, 0x038c0000) tenured generation total 4096K, used 0K [0x03ca0000, 0x040a0000, 0x077c0000) the space 4096K, 0% used [0x03ca0000, 0x03ca0000, 0x03ca0200, 0x040a0000) compacting perm gen total 12288K, used 2143K [0x077c0000, 0x083c0000, 0x0b7c0000) the space 12288K, 17% used [0x077c0000, 0x079d7e38, 0x079d8000, 0x083c0000) No shared spaces configured. Dynamic libraries: 0x00400000 - 0x00424000 C:\Program Files\Java\jre6\bin\javaw.exe 0x77550000 - 0x7768e000 C:\Windows\SYSTEM32\ntdll.dll 0x75a80000 - 0x75b54000 C:\Windows\system32\kernel32.dll 0x758d0000 - 0x7591b000 C:\Windows\system32\KERNELBASE.dll 0x759e0000 - 0x75a80000 C:\Windows\system32\ADVAPI32.dll 0x76070000 - 0x7611c000 C:\Windows\system32\msvcrt.dll 0x77250000 - 0x77269000 C:\Windows\SYSTEM32\sechost.dll 0x771a0000 - 0x77241000 C:\Windows\system32\RPCRT4.dll 0x76eb0000 - 0x76f79000 C:\Windows\system32\USER32.dll 0x76e60000 - 0x76eae000 C:\Windows\system32\GDI32.dll 0x77770000 - 0x7777a000 C:\Windows\system32\LPK.dll 0x75fd0000 - 0x7606e000 C:\Windows\system32\USP10.dll 0x770b0000 - 0x770cf000 C:\Windows\system32\IMM32.DLL 0x770d0000 - 0x7719c000 C:\Windows\system32\MSCTF.dll 0x7c340000 - 0x7c396000 C:\Program Files\Java\jre6\bin\msvcr71.dll 0x6d800000 - 0x6da8b000 C:\Program Files\Java\jre6\bin\client\jvm.dll 0x73a00000 - 0x73a32000 C:\Windows\system32\WINMM.dll 0x75610000 - 0x7565b000 C:\Windows\system32\apphelp.dll 0x6d7b0000 - 0x6d7bc000 C:\Program Files\Java\jre6\bin\verify.dll 0x6d330000 - 0x6d34f000 C:\Program Files\Java\jre6\bin\java.dll 0x6d290000 - 0x6d298000 C:\Program Files\Java\jre6\bin\hpi.dll 0x776e0000 - 0x776e5000 C:\Windows\system32\PSAPI.DLL 0x6d7f0000 - 0x6d7ff000 C:\Program Files\Java\jre6\bin\zip.dll 0x10000000 - 0x1004c000 C:\Users\theo\Desktop\workspace\JavaFX1\lib\natives\windows\lwjgl.dll 0x5d170000 - 0x5d238000 C:\Windows\system32\OPENGL32.dll 0x6e7b0000 - 0x6e7d2000 C:\Windows\system32\GLU32.dll 0x70620000 - 0x70707000 C:\Windows\system32\DDRAW.dll 0x70610000 - 0x70616000 C:\Windows\system32\DCIMAN32.dll 0x75b60000 - 0x75cfd000 C:\Windows\system32\SETUPAPI.dll 0x759b0000 - 0x759d7000 C:\Windows\system32\CFGMGR32.dll 0x76d70000 - 0x76dff000 C:\Windows\system32\OLEAUT32.dll 0x75db0000 - 0x75f0c000 C:\Windows\system32\ole32.dll 0x758b0000 - 0x758c2000 C:\Windows\system32\DEVOBJ.dll 0x74060000 - 0x74073000 C:\Windows\system32\dwmapi.dll 0x74b60000 - 0x74b69000 C:\Windows\system32\VERSION.dll 0x745f0000 - 0x7478e000 C:\Windows\WinSxS\x86_microsoft.windows.common-controls_6595b64144ccf1df_6.0.7600.16661_none_420fe3fa2b8113bd\COMCTL32.dll 0x75d50000 - 0x75da7000 C:\Windows\system32\SHLWAPI.dll 0x74370000 - 0x743b0000 C:\Windows\system32\uxtheme.dll 0x22200000 - 0x22206000 C:\Program Files\ESET\ESET Smart Security\eplgHooks.dll VM Arguments: jvm_args: -Djava.library.path=C:\Users\theo\Desktop\workspace\JavaFX1\lib\natives\windows -Dfile.encoding=Cp1253 java_command: zarkopafilis.koding.io.javafx.Main Launcher Type: SUN_STANDARD Environment Variables: PATH=C:/Program Files/Java/jre6/bin/client;C:/Program Files/Java/jre6/bin;C:/Program Files/Java/jre6/lib/i386;C:\Perl\site\bin;C:\Perl\bin;C:\Ruby200\bin;C:\Program Files\Common Files\Microsoft Shared\Windows Live;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Program Files\Windows Live\Shared;C:\Users\theo\Desktop\eclipse; USERNAME=theo OS=Windows_NT PROCESSOR_IDENTIFIER=x86 Family 6 Model 37 Stepping 5, GenuineIntel --------------- S Y S T E M --------------- OS: Windows 7 Build 7600 CPU:total 4 (8 cores per cpu, 2 threads per core) family 6 model 37 stepping 5, cmov, cx8, fxsr, mmx, sse, sse2, sse3, ssse3, sse4.1, sse4.2, ht Memory: 4k page, physical 2097151k(1257972k free), swap 4194303k(4194303k free) vm_info: Java HotSpot(TM) Client VM (14.2-b01) for windows-x86 JRE (1.6.0_16-b01), built on Jul 31 2009 11:26:58 by "java_re" with MS VC++ 7.1 time: Wed Oct 23 22:00:12 2013 elapsed time: 0 seconds Code: Display.setDisplayMode(new DisplayMode(800,600)); Display.create();//Error here I am using JDK 6

  Read the article

• Unity Locks Up in Live CD

  - by user212883

  I'm trying to run from the live USB to install Ubuntu 13.10 on my Windows Machine (as I've grown a touch sick of Windows). However, whenever I boot into the LiveUSB session after a few moments the Unity desktop locks up (except the mouse pointer, which I can move). Is this something to do with the fact I've got an NVidia 580 GTX? I've heard of issues with Ubuntu and this card. I've also got an SSD, but given that it's booting from USB I shouldn't think that's an issue. System Specs: Processor: Intel Core i7-2600K CPU @ 3.40 GHZ Motherboard: Asus Maximus IV Gene-Z Z68 Socket 1155 RAM: 8GB DDR3 GPU: ASUS NVidia 580 GTX

  Read the article

• Understanding and Controlling Parallel Query Processing in SQL Server

  Data warehousing and general reporting applications tend to be CPU intensive because they need to read and process a large number of rows. To facilitate quick data processing for queries that touch a large amount of data, Microsoft SQL Server exploits the power of multiple logical processors to provide parallel query processing operations such as parallel scans. Through extensive testing, we have learned that, for most large queries that are executed in a parallel fashion, SQL Server can deliver linear or nearly linear response time speedup as the number of logical processors increases. However, some queries in high parallelism scenarios perform suboptimally. There are also some parallelism issues that can occur in a multi-user parallel query workload. This white paper describes parallel performance problems you might encounter when you run such queries and workloads, and it explains why these issues occur. In addition, it presents how data warehouse developers can detect these issues, and how they can work around them or mitigate them.

  Read the article

• SQL SERVER – Database Dynamic Caching by Automatic SQL Server Performance Acceleration

  - by pinaldave

  My second look at SafePeak’s new version (2.1) revealed to me few additional interesting features. For those of you who hadn’t read my previous reviews SafePeak and not familiar with it, here is a quick brief: SafePeak is in business of accelerating performance of SQL Server applications, as well as their scalability, without making code changes to the applications or to the databases. SafePeak performs database dynamic caching, by caching in memory result sets of queries and stored procedures while keeping all those cache correct and up to date. Cached queries are retrieved from the SafePeak RAM in microsecond speed and not send to the SQL Server. The application gets much faster results (100-500 micro seconds), the load on the SQL Server is reduced (less CPU and IO) and the application or the infrastructure gets better scalability. SafePeak solution is hosted either within your cloud servers, hosted servers or your enterprise servers, as part of the application architecture. Connection of the application is done via change of connection strings or adding reroute line in the c:\windows\system32\drivers\etc\hosts file on all application servers. For those who would like to learn more on SafePeak architecture and how it works, I suggest to read this vendor’s webpage: SafePeak Architecture. More interesting new features in SafePeak 2.1 In my previous review of SafePeak new I covered the first 4 things I noticed in the new SafePeak (check out my article “SQLAuthority News – SafePeak Releases a Major Update: SafePeak version 2.1 for SQL Server Performance Acceleration”): Cache setup and fine-tuning – a critical part for getting good caching results Database templates Choosing which database to cache Monitoring and analysis options by SafePeak Since then I had a chance to play with SafePeak some more and here is what I found. 5. Analysis of SQL Performance (present and history): In SafePeak v.2.1 the tools for understanding of performance became more comprehensive. Every 15 minutes SafePeak creates and updates various performance statistics. Each query (or a procedure execute) that arrives to SafePeak gets a SQL pattern, and after it is used again there are statistics for such pattern. An important part of this product is that it understands the dependencies of every pattern (list of tables, views, user defined functions and procs). From this understanding SafePeak creates important analysis information on performance of every object: response time from the database, response time from SafePeak cache, average response time, percent of traffic and break down of behavior. One of the interesting things this behavior column shows is how often the object is actually pdated. The break down analysis allows knowing the above information for: queries and procedures, tables, views, databases and even instances level. The data is show now on all arriving queries, both read queries (that can be cached), but also any types of updates like DMLs, DDLs, DCLs, and even session settings queries. The stats are being updated every 15 minutes and SafePeak dashboard allows going back in time and investigating what happened within any time frame. 6. Logon trigger, for making sure nothing corrupts SafePeak cache data If you have an application with many parts, many servers many possible locations that can actually update the database, or the SQL Server is accessible to many DBAs or software engineers, each can access some database directly and do some changes without going thru SafePeak – this can create a potential corruption of the data stored in SafePeak cache. To make sure SafePeak cache is correct it needs to get all updates to arrive to SafePeak, and if a DBA will access the database directly and do some changes, for example, then SafePeak will simply not know about it and will not clean SafePeak cache. In the new version, SafePeak brought a new feature called “Logon Trigger” to solve the above challenge. By special click of a button SafePeak can deploy a special server logon trigger (with a CLR object) on your SQL Server that actually monitors all connections and informs SafePeak on any connection that is coming not from SafePeak. In SafePeak dashboard there is an interface that allows to control which logins can be ignored based on login names and IPs, while the rest will invoke cache cleanup of SafePeak and actually locks SafePeak cache until this connection will not be closed. Important to note, that this does not interrupt any logins, only informs SafePeak on such connection. On the Dashboard screen in SafePeak you will be able to see those connections and then decide what to do with them. Configuration of this feature in SafePeak dashboard can be done here: Settings -> SQL instances management -> click on instance -> Logon Trigger tab. Other features: 7. User management ability to grant permissions to someone without changing its configuration and only use SafePeak as performance analysis tool. 8. Better reports for analysis of performance using 15 minute resolution charts. 9. Caching of client cursors 10. Support for IPv6 Summary SafePeak is a great SQL Server performance acceleration solution for users who want immediate results for sites with performance, scalability and peak spikes challenges. Especially if your apps are packaged or 3rd party, since no code changes are done. SafePeak can significantly increase response times, by reducing network roundtrip to the database, decreasing CPU resource usage, eliminating I/O and storage access. SafePeak team provides a free fully functional trial www.safepeak.com/download and actually provides a one-on-one assistance during such trial. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

  Read the article

• What a Performance! MySQL 5.5 and InnoDB 1.1 running on Oracle Linux

  - by zeynep.koch(at)oracle.com

  The MySQL performance team in Oracle has recently completed a series of benchmarks comparing Read / Write and Read-Only performance of MySQL 5.5 with the InnoDB and MyISAM storage engines. Compared to MyISAM, InnoDB delivered 35x higher throughput on the Read / Write test and 5x higher throughput on the Read-Only test, with 90% scalability across 36 CPU cores. A full analysis of results and MySQL configuration parameters are documented in a new whitepaperIn addition to the benchmark, the new whitepaper, also includes:- A discussion of the use-cases for each storage engine- Best practices for users considering the migration of existing applications from MyISAM to InnoDB- A summary of the performance and scalability enhancements introduced with MySQL 5.5 and InnoDB 1.1.The benchmark itself was based on Sysbench, running on AMD Opteron "Magny-Cours" processors, and Oracle Linux with the Unbreakable Enterprise Kernel You can learn more about MySQL 5.5 and InnoDB 1.1 from here and download it from here to test whether you witness performance gains in your real-world applications.  By Mat Keep

  Read the article

• Improving Partitioned Table Join Performance

  - by Paul White

  The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

  Read the article

• Ubuntu 10.10 Mouse and Keyboard Freeze

  - by Kev

  I installed Ubuntu 10.10 today and have had mouse problem since. Symptoms: At some arbitrary point in time (frequency: 2-3 times per hour), the mouse and keyboard stops working for ever(may be). I start System monitor, I found out network was shutdown just before mouse freeze. Some time my keyboard keep typing one key. For example:77777777777777777777777777777777777777777777777777777.....(it keep typing for 20 sec) I found out a script just solve the freeze problem:(I hit Powerbutton) -----------------/etc/acpi/powerbtn.sh------------------------ event=button[ /]power action=/usr/sbin/fix_mouse.sh -----------------/usr/sbin/fix_mouse.sh------------------------ rmmod psmouse modprobe psmouse Yesterday I install Ubuntu 10.04 FAILED also have mouse problem. When I switch back to Windows XP. The network card is down. It kept connecting and disconnecting 1 time per sec. CPU: i5 Motherboard: ASUS P7P55D OS: Windows XP + Ubuntu 10.10 Video Card: ATI 5770 Mouse,Keyboard: PS/2

  Read the article

• Dell Latitude E6520 overheating

  - by Wu Yi Han

  I'm a newcomer to Ubuntu 11.10. My laptop is a Dell Latitude E6520, Sandy Bridge platform. The system cooling fan is crazy all the time. I don't do any intensive tasks. I really hope my laptop doesn't become a mushroom cloud. I suppose there's no perfect way to solve this... Can I lower the CPU frequency? Jupiter 0.0.51 was installed (power save mode). Cooling worked in my Windows 7 system until I deleted it. (I won't go back to Windows 7.)

  Read the article

• Ubuntu 12.04 installation aborts without giving any errors on Sony Vaio

  - by Guilherme Simoni

  I'm not able to install the release ubuntu-12.04-desktop-i386 on the laptop below: Sony Vaio VGN-FE21H CPU: Intel Core Duo T2300 1.66GHz Memory: 2GB DDR2 533MHz HDD: 100GB Graphics: NVIDIA GeForce 7400 256MB I'm using the ISO "ubuntu-12.04-desktop-i386.iso" burned into a DVD. I know the ISO is OK because I used it to successfully install on Virtualbox. Live DVD boots and runs OK, but I cannot install from it or directly from the boot menu. The installation goes through all the steps until the final part where is asked the Name, Name of PC and password. The problem is in the next step where it should start copying files and present some screens and features of Ubuntu. In this part the installation just close without any error message. If I am running the installation inside the live DVD it closes and returns to the home screen of the Live. If I am running straight from the boot it closes the graphic interface and restarts the PC. Does anybody know or faced the same problem?

  Read the article

• Looking for VPS Hosting for a LAMP Web Application

  - by Ali

  Hi guys, Trying to find a Managed VPS Hosting Solution for a LAMP Web Application. The more CPU, RAM, and Disk space the better Don't need a huge amount of bandwidth for now Would like to be able to easily grow into a stronger server Have really responsive, dedicated, smart support staff -- our current hosting is just terrible My main problem is that I can't even find a non-biased website out there that does a proper comparison of VPS Hosting providers. Can anybody either suggest a reviews/ranking site or a hosting with proven record? How would you go about finding the best hosting service? Thanks a lot! Ali

  Read the article

• Ubuntu 12.04 install DVD-RW recorder PATA as SCSI

  - by Alexandre Gatelli

  Ubuntu 12.04 64-bit installed DVD-RW recorder PATA as SCSI. My DVD-RW recorder is in /dev/sr0 as SCSI. I opened Disk Management and my IDE PATA drive is installed as SCSI. I can't use this drive because it hangs computer (I need press reset button on CPU to back to Ubuntu). What do I do for the drive back to function with the correct drive (IDE mode)? With the first kernel version of the Ubuntu 12.04 64-bit was all functioning. Help me please.

  Read the article

• Mouse and Keyboard Freeze

  - by Kev

  I installed Ubuntu 10.10 today and have had mouse problem since. Symptoms: At some arbitrary point in time (frequency: 2-3 times per hour), the mouse and keyboard stops working for ever(may be). I start System monitor, I found out network was shutdown just before mouse freeze. Some time my keyboard keep typing one key. For example:77777777777777777777777777777777777777777777777777777.....(it keep typing for 20 sec) I found out a script just solve the freeze problem:(I hit Powerbutton) -----------------/etc/acpi/powerbtn.sh------------------------ event=button[ /]power action=/usr/sbin/fix_mouse.sh -----------------/usr/sbin/fix_mouse.sh------------------------ rmmod psmouse modprobe psmouse Yesterday I install Ubuntu 10.04 FAILED also have mouse problem. When I switch back to Windows XP. The network card is down. It kept connecting and disconnecting 1 time per sec. CPU: i5 Motherboard: ASUS P7P55D OS: Windows XP + Ubuntu 10.10 Video Card: ATI 5770 Mouse,Keyboard: PS/2

  Read the article

• Tolkien’s Rivendell Rendered in LEGO

  - by Jason Fitzpatrick

  If you’re a fan of all things geeky rendered in LEGO–and we certainly are–you’ll want to take a moment to appreciate this incredible model of the mythical Rivendell from the Lord of the Rings universe. Courtesy of builders Blake Baer and Jake Bittner, the behemoth model measures nearly 4×3 ft. in size, weighs 120 pounds, and required over 50,000 LEGO bricks. Hit up the link below to check out the full set of photos. Rivendell in LEGO [via Geeks Are Sexy] How To Switch Webmail Providers Without Losing All Your Email How To Force Windows Applications to Use a Specific CPU HTG Explains: Is UPnP a Security Risk?

  Read the article

• ERRNO 5 Input/Output Error

  - by CCarey

  Going up for my first ubuntu installation and encountered a critical error. Mind that I am installing on my Macbook Pro, and have already removed all other partitions. (I'm installing with a CD) My Ubuntu version is: "ubuntu-12.10-desktop-i386" So once the installation gets to something around "Finishing copying files", a great big "[ERRNO5] Input/Output Error" pops up on the screen. Obviously this halts and crashes the whole installation. Now, I've already run a disk check, memtest, and cpu load test, and all came up green. I have also redownloaded ubuntu twice, md5 match both times, and burnt four disks. None got past this error. If anyone could help me out, that would be greatly appreciated! Cheers!

  Read the article

• World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

  - by Brian

  Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 09/30/2012.

  Read the article

• Oracle Database 12c Spatial: Vector Performance Acceleration

  - by Okcan Yasin Saygili-Oracle

  Most business information has a location component, such as customer addresses, sales territories and physical assets. Businesses can take advantage of their geographic information by incorporating location analysis and intelligence into their information systems. This allows organizations to make better decisions, respond to customers more effectively, and reduce operational costs – increasing ROI and creating competitive advantage. Oracle Database, the industry’s most advanced database,  includes native location capabilities, fully integrated in the kernel, for fast, scalable, reliable and secure spatial and massive graph applications. It is a foundation for deploying enterprise-wide spatial information systems and locationenabled business applications. Developers can extend existing Oracle-based tools and applications, since they can easily incorporate location information directly in their applications, workflows, and services. Spatial Features The geospatial data features of Oracle Spatial and Graph option support complex geographic information systems (GIS) applications, enterprise applications and location services applications. Oracle Spatial and Graph option extends the spatial query and analysis features included in every edition of Oracle Database with the Oracle Locator feature, and provides a robust foundation for applications that require advanced spatial analysis and processing in the Oracle Database. It supports all major spatial data types and models, addressing challenging business-critical requirements from various industries, including transportation, utilities, energy, public sector, defense and commercial location intelligence. Network Data Model Graph Features The Network Data Model graph explicitly stores and maintains a persistent data model withnetwork connectivity and provides network analysis capability such as shortest path, nearest neighbors, within cost and reachability. It loads partitioned networks into memory on demand, overcomingthe limitations of in-memory analysis. Partitioning massive networks into manageable sub-networkssimplifies the network analysis. RDF Semantic Graph Features RDF Semantic Graph has native support for World Wide Web Consortium standards. It has open, scalable, and secure features for storing RDF/OWL ontologies anddata; native inference with OWL 2, SKOS and user-defined rules; and querying RDF/OWL data withSPARQL 1.1, Java APIs, and SPARQLgraph patterns in SQL. Video: Oracle Spatial and Graph Overview Oracle spatial is embeded on oracle database product. So ,we can use oracle installer (OUI).The Oracle Universal Installer (OUI) is used to install Oracle Database software. OUI is a graphical user interface utility that enables you to view the Oracle software that is installed on your machine, install new Oracle Database software, and delete Oracle software that you no longer need to use. Online Help is available to guide you through the installation process. One of the installation options is to create a database. If you select database creation, OUI automatically starts Oracle Database Configuration Assistant (DBCA) to guide you through the process of creating and configuring a database. If you do not create a database during installation, you must invoke DBCA after you have installed the software to create a database. You can also use DBCA to create additional databases. For installing Oracle Database 12c you may check the Installing Oracle Database Software and Creating a Database tutorial under the Oracle Database 12c 2-Day DBA Series.You can always check if spatial is available in your database using  "select comp_id, version, status, comp_name from dba_registry where comp_id='SDO';"   One of the most notable improvements with Oracle Spatial and Graph 12c can be seen in performance increases in vector data operations. Enabling the Spatial Vector Acceleration feature (available with the Spatial option) dramatically improves the performance of commonly used vector data operations, such as sdo_distance, sdo_aggr_union, and sdo_inside. With 12c, these operations also run more efficiently in parallel than in prior versions through the use of metadata caching. For organizations that have been facing processing limitations, these enhancements enable developers to make a small set of configuration changes and quickly realize significant performance improvements. Results include improved index performance, enhanced geometry engine performance, optimized secondary filter optimizations for Spatial operators, and improved CPU and memory utilization for many advanced vector functions. Vector performance acceleration is especially beneficial when using Oracle Exadata Database Machine and other large-scale systems. Oracle Spatial and Graph vector performance acceleration builds on general improvements available to all SDO_GEOMETRY operations in these areas: Caching of index metadata, Concurrent update mechanisms, and Optimized spatial predicate selectivity and cost functions. These optimizations enable more efficient use of: CPU, Memory, and Partitioning Resulting in substantial query performance improvements.UsageTo accelerate the performance of spatial operators, it is recommended that you set the SPATIAL_VECTOR_ACCELERATION database system parameter to the value TRUE. (This parameter is authorized for use only by licensed Oracle Spatial users, and its default value is FALSE.) You can set this parameter for the whole system or for a single session. To set the value for the whole system, do either of the following:Enter the following statement from a suitably privileged account:   ALTER SYSTEM SET SPATIAL_VECTOR_ACCELERATION = TRUE;Add the following to the database initialization file (xxxinit.ora):   SPATIAL_VECTOR_ACCELERATION = TRUE;To set the value for the current session, enter the following statement from a suitably privileged account:   ALTER SESSION SET SPATIAL_VECTOR_ACCELERATION = TRUE; Checkout the complete list of new features on Oracle.com @ http://www.oracle.com/technetwork/database/options/spatialandgraph/overview/index.html Spatial and Graph Data Sheet (PDF) Spatial and Graph White Paper (PDF)

  Read the article

• Invalid Opcode 0000

  - by Mr47

  At random times (usually when watching a movie in XBMC), the computer locks up. I can still sometimes SSH in and get the 'dmesg' output before that locks up too. A hard reboot is usually required to get things going again. I have cut out the date/time/server columns for easier reading, please do ask if these seem relevant omissions... System: Ubuntu 11.04 (2.6.38-8-server) x64 X11 installed with IceWm (and XBMC) Core 2 Duo E8400 @ 3.00GHz 8 GB RAM Asus P5Q premium motherboard Primary harddrive: OCZ Vertex 2 60 GB (SSD) Other harddrives: various 750GB, 1TB, 1.5TB & 2TB (WD & samsung) Any important information I am not supplying is purely a sign of my incompetence in these matters, so please do ask and excuse me for my inabilities... invalid opcode: 0000 [#1] SMP last sysfs file: /sys/devices/system/cpu/cpu1/cache/index2/shared_cpu_map CPU 0 Modules linked in: parport_pc ppdev vesafb snd_hda_codec_analog tuner_simple tuner_types wm8775 tda9887 tda8290 tea5767 tuner cx25840 ir_lirc_codec lirc_dev snd_hda_intel snd_hda_codec snd_hwdep snd_pcm ir_sony_decoder snd_seq_midi snd_rawmidi snd_seq_midi_event snd_seq rc_rc6_mce ivtv ir_jvc_decoder cx2341x i2c_algo_bit v4l2_common mceusb videodev ir_rc6_decoder ir_rc5_decoder snd_timer ir_nec_decoder nvidia(P) btusb bluetooth rc_core v4l2_compat_ioctl32 tveeprom snd_seq_device pata_marvell psmouse shpchp serio_raw snd asus_atk0110 soundcore snd_page_alloc lp parport firewire_ohci firewire_core crc_itu_t r8169 sky2 ahci libahci Pid: 4597, comm: xbmc.bin Tainted: P 2.6.38-8-server #42-Ubuntu System manufacturer P5Q Premium/P5Q Premium RIP: 0010:[<ffffffff8119bc4a>] [<ffffffff8119bc4a>] do_mpage_readpage+0x9a/0x510 RSP: 0018:ffff88021f5a59d8 EFLAGS: 00210246 RAX: 0000000000000020 RBX: ffff88021f5a5ac8 RCX: 0000000000000000 RDX: 0000000000000001 RSI: 0000000015e36fe0 RDI: 0000000000000000 RBP: ffff88021f5a5a98 R08: ffff88021f5a5ac8 R09: ffff88021f5a5b38 R10: 0000000000000000 R11: 0000000000000000 R12: 0000000000000000 R13: 000000000000b148 R14: 0000000000000001 R15: ffff8802067034b8 FS: 00007f3f34eb1700(0000) GS:ffff8800cfc00000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007feb8d515000 CR3: 000000021d744000 CR4: 00000000000406f0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000ffff0ff0 DR7: 0000000000000400 Process xbmc.bin (pid: 4597, threadinfo ffff88021f5a4000, task ffff88021f63db80) Stack: ffff88021f5a5a28 ffffffff8116019d ffff88021f5a5b40 0000002000000003 ffff88021f5a5b38 ffff880206703370 ffffea0006d800f8 0000000000000000 ffffea0006d800f8 0000000c811270b5 ffff88021f5a5a68 ffffffff8110bdba Call Trace: [<ffffffff8116019d>] ? mem_cgroup_cache_charge+0xed/0x130 [<ffffffff8110bdba>] ? add_to_page_cache_locked+0xea/0x160 [<ffffffff8119c232>] mpage_readpages+0x102/0x150 [<ffffffff812063e0>] ? ext4_get_block+0x0/0x20 [<ffffffff812063e0>] ? ext4_get_block+0x0/0x20 [<ffffffff81149475>] ? alloc_pages_current+0xa5/0x110 [<ffffffff8120157d>] ext4_readpages+0x1d/0x20 [<ffffffff81116a9b>] __do_page_cache_readahead+0x14b/0x220 [<ffffffff81116ed1>] ra_submit+0x21/0x30 [<ffffffff81116ff5>] ondemand_readahead+0x115/0x230 [<ffffffff811171a0>] page_cache_async_readahead+0x90/0xc0 [<ffffffff8110b184>] ? file_read_actor+0xd4/0x170 [<ffffffff812de72e>] ? radix_tree_lookup_slot+0xe/0x10 [<ffffffff8110c521>] do_generic_file_read.clone.23+0x271/0x450 [<ffffffff8110d1ba>] generic_file_aio_read+0x1ca/0x240 [<ffffffff8100a82e>] ? __switch_to+0x20e/0x2f0 [<ffffffff81164c82>] do_sync_read+0xd2/0x110 [<ffffffff8108b61c>] ? hrtimer_try_to_cancel+0x4c/0xe0 [<ffffffff81279083>] ? security_file_permission+0x93/0xb0 [<ffffffff81164fa1>] ? rw_verify_area+0x61/0xf0 [<ffffffff81165463>] vfs_read+0xc3/0x180 [<ffffffff81165571>] sys_read+0x51/0x90 [<ffffffff8100bfc2>] system_call_fastpath+0x16/0x1b Code: ff ff 48 c7 85 78 ff ff ff 00 00 00 00 49 d3 ee b9 0c 00 00 00 2b 4d 8c 48 8b b2 c8 00 00 00 ba 01 00 00 00 41 0f af c6 49 d3 e5 <0f> 36 4d 8c 4c 01 e8 d3 e2 4c 8d 44 16 ff 48 8b 53 20 49 d3 f8 RIP [<ffffffff8119bc4a>] do_mpage_readpage+0x9a/0x510 RSP <ffff88021f5a59d8> ---[ end trace ac6cd2f4692205a3 ]--- Please note that the error is ALWAYS occuring at do_mpage_readpage+0x9a/0x510 with the same numbers after it. I've tried to come up with the possible meaning of these, but couldn't get any further. I've also noticed that the top block from the call trace is always the following with the exact same numbers: [<ffffffff8116019d>] ? mem_cgroup_cache_charge+0xed/0x130 [<ffffffff8110bdba>] ? add_to_page_cache_locked+0xea/0x160 [<ffffffff8119c232>] mpage_readpages+0x102/0x150 [<ffffffff812063e0>] ? ext4_get_block+0x0/0x20 [<ffffffff812063e0>] ? ext4_get_block+0x0/0x20 Could this indicate a hard drive issue, a RAM issue or something else entirely?

  Read the article

• overheating when using flash

  - by Ben

  I am having overheating issues when watching flash videos. Here is all the relevant information that I could think of. Please help. --Often when playing flash videos it operates at 95 degrees C. --Only gets that hot when on full screen (but still in the 80s otherwise) --Sometimes reaches 100 degrees and shuts down. --fan runs at around 4500 RPM when hot (not to mention I can here it speed up) --Happens under Ubuntu 12.04 and 12.10 (did not use Ubuntu before then) --Overheating happens on more than one site (for example youtube and Comedy Central) --My computer does not overheat otherwise. --Usually runs around 50-60 degrees C. --Maybe if compiling or doing updates can it get to 70 or 75 degree C. --Have a dual boot, and under Windows I do not have this issue. --I use firefox, but have the same issue if using Chrome --I have a Lenovo T410, currently running Ubuntu 12.10 --Intel® Core™ i5 CPU M 540 @ 2.53GHz × 4 --4 GB of memory, 64 bit OS --Graphics: NVS 3100M/PCIe/SSE2 Thanks and let me know if you need more info, Ben

  Read the article

• Oracle Linux and Oracle VM pricing guide

  - by wcoekaer

  A few days ago someone showed me a pricing guide from a Linux vendor and I was a bit surprised at the complexity of it. Especially when you look at larger servers (4 or 8 sockets) and when adding virtual machine use into the mix. I think we have a very compelling and simple pricing model for both Oracle Linux and Oracle VM. Let me see if I can explain it in 1 page, not 10 pages. This pricing information is publicly available on the Oracle store, I am using the current public list prices. Also keep in mind that this is for customers using non-oracle x86 servers. When a customer purchases an Oracle x86 server, the annual systems support includes full use (all you can eat) of Oracle Linux, Oracle VM and Oracle Solaris (no matter how many VMs you run on that server, in case you deploy guests on a hypervisor). This support level is the equivalent of premier support in the list below. Let's start with Oracle VM (x86) : Oracle VM support subscriptions are per physical server on which you deploy the Oracle VM Server product. (1) Oracle VM Premier Limited - 1- or 2 socket server : $599 per server per year (2) Oracle VM Premier - more than 2 socket server (4, or 8 or whatever more) : $1199 per server per year The above includes the use of Oracle VM Manager and Oracle Enterprise Manager Cloud Control's Virtualization management pack (including self service cloud portal, etc..) 24x7 support, access to bugfixes, updates and new releases. It also includes all options, live migrate, dynamic resource scheduling, high availability, dynamic power management, etc If you want to play with the product, or even use the product without access to support services, the product is freely downloadable from edelivery. Next, Oracle Linux : Oracle Linux support subscriptions are per physical server. If you plan to run Oracle Linux as a guest on Oracle VM, VMWare or Hyper-v, you only have to pay for a single subscription per system, we do not charge per guest or per number of guests. In other words, you can run any number of Oracle Linux guests per physical server and count it as just a single subscription. (1) Oracle Linux Network Support - any number of sockets per server : $119 per server per year Network support does not offer support services. It provides access to the Unbreakable Linux Network and also offers full indemnification for Oracle Linux. (2) Oracle Linux Basic Limited Support - 1- or 2 socket servers : $499 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management. It includes ocfs2 as a clustered filesystem. (3) Oracle Linux Basic Support - more than 2 socket server (4, or 8 or more) : $1199 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management. It includes ocfs2 as a clustered filesystem (4) Oracle Linux Premier Limited Support - 1- or 2 socket servers : $1399 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management, XFS filesystem support. It also offers Oracle Lifetime support, backporting of patches for critical customers in previous versions of package and ksplice zero-downtime updates. (5) Oracle Linux Premier Support - more than 2 socket servers : $2299 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management, XFS filesystem support. It also offers Oracle Lifetime support, backporting of patches for critical customers in previous versions of package and ksplice zero-downtime updates. (6) Freely available Oracle Linux - any number of sockets You can freely download Oracle Linux, install it on any number of servers and use it for any reason, without support, without right to use of these extra features like Oracle Clusterware or ksplice, without indemnification. However, you do have full access to all errata as well. Need support? then use options (1)..(5) So that's it. Count number of 2 socket boxes, more than 2 socket boxes, decide on basic or premier support level and you are done. You don't have to worry about different levels based on how many virtual instance you deploy or want to deploy. A very simple menu of choices. We offer, inclusive, Linux OS clusterware, Linux OS Management, provisioning and monitoring, cluster filesystem (ocfs), high performance filesystem (xfs), dtrace, ksplice, ofed (infiniband stack for high performance networking). No separate add-on menus. NOTE : socket/cpu can have any number of cores. So whether you have a 4,6,8,10 or 12 core CPU doesn't matter, we count the number of physical CPUs.

  Read the article

• Will Ubuntu break my RAID 0 array?

  - by Chad

  I am upgrading an older machine today with new Motherboard, RAM, and CPU. Then I am going to do a fresh install of Ubuntu 64bit. Currently the old machine has an 80gb system drive, and a 4TB RAID 0 array. The old Motherboard has no SATA ports, so I used a SATA card. Ubuntu set up the old RAID array, will it still recognize the array on a newer machine? Are there any steps I should take to ensure the array isn't damaged? It's non-crucial data, but I would rather not start over if it can be avoided. Thanks.

  Read the article

< Previous Page | 107 108 109 110 111 112 113 114 115 116 117 118  | Next Page >