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  • How to tell if you are connected to Wireless B, G or N?

    - by Raheel Khan
    I am using Windows 7 on all wired desktops and wireless laptops in my home network. I recently upgraded my Ethernet switch to Gigabit and instantly noticed an increase in throughput in wired devices. I also bought a Wireless-N WAP but with degredation in wireless file transfer speeds. I have been told that a number of reasons could affect wireless speeds including which WAP is used, how many wireless devices are connected, which security mode is used, etc. However, that remains irrelevant to my question. Each of my laptops claim to support Wireless-N but I cannot seem to figure out how to determine if the laptops are truly running Wireless-N or are connected to the WAP through some sort of mixed-mode. I do not have control of the WAP device so cannot tell what mode it is running in. Is there a way to tell which mode is being used and what the throughput is for each connected device without having access to the WAP interface?

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  • Oracle NoSQL Database: Cleaner Performance

    - by Charles Lamb
    In an earlier post I noted that Berkeley DB Java Edition cleaner performance had improved significantly in release 5.x. From an Oracle NoSQL Database point of view, this is important because Berkeley DB Java Edition is the core storage engine for Oracle NoSQL Database. Many contemporary NoSQL Databases utilize log based (i.e. append-only) storage systems and it is well-understood that these architectures also require a "cleaning" or "compaction" mechanism (effectively a garbage collector) to free up unused space. 10 years ago when we set out to write a new Berkeley DB storage architecture for the BDB Java Edition ("JE") we knew that the corresponding compaction mechanism would take years to perfect. "Cleaning", or GC, is a hard problem to solve and it has taken all of those years of experience, bug fixes, tuning exercises, user deployment, and user feedback to bring it to the mature point it is at today. Reports like Vinoth Chandar's where he observes a 20x improvement validate the maturity of JE's cleaner. Cleaner performance has a direct impact on predictability and throughput in Oracle NoSQL Database. A cleaner that is too aggressive will consume too many resources and negatively affect system throughput. A cleaner that is not aggressive enough will allow the disk storage to become inefficient over time. It has to Work well out of the box, and Needs to be configurable so that customers can tune it for their specific workloads and requirements. The JE Cleaner has been field tested in production for many years managing instances with hundreds of GBs to TBs of data. The maturity of the cleaner and the entire underlying JE storage system is one of the key advantages that Oracle NoSQL Database brings to the table -- we haven't had to reinvent the wheel.

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  • What we have to measure for measuring server performance If we can't measure the server processing time from client side?

    - by AsadYarKhan
    If we can not measure the server processing time from client side then which attributes will be good to measure in client side for measuring server side performance and What attributes are important ? I know we can get the server response time, latency and Throughput etc,but how do we understand/interpret the result of server side from these attrubutes. How can we analyse that whether my code is taking lots of time,whether Web Server, whether it is because of Server Machine(H/W).how would i know that which thing needs to be upgrade or improve.Please tell me any article or any book something that I need to study or explain here If you can so I can interpret the result of server side using these attributes response time, latency and throughput.You can tell other performance attribute if I need to understand the server result.

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  • Treeview - Hierarchical Data Template - Binding does not update on source change?

    - by ClearsTheScreen
    Greetings! I ran into this problem in my project (Silverlight 3 with C#): I have a TreeView which is data bound to, well, a tree. This TreeView has a HierarchicalDataTamplate in a resource dictionary, that defines various controls. Now I want to hide (Visibility.Collapse) some items depending on wether a node has children or not. Other items shall be visible under the same condition. It works like charm when I first bind the source tree to the TreeView, but when I change the source tree, the visibility in the treeview does not change. XAML - page: <controls:TreeView x:Name="SankeyTreeView" ItemContainerStyle="{StaticResource expandedTreeViewItemStyle}" ItemTemplate="{StaticResource SankeyTreeTemplate}"> <controls:TreeViewItem IsExpanded="True"> <controls:TreeViewItem.HeaderTemplate> <DataTemplate> <TextBlock Text="This is just for loading and will be replaced directly after the data becomes available..."/> </DataTemplate> </controls:TreeViewItem.HeaderTemplate> </controls:TreeViewItem> </controls:TreeView> XAML - ResourceDictionary <!-- Each node in the tree is structurally identical, hence only one Hierarchical Data Template that'll use itself on the children. --> <Data:HierarchicalDataTemplate x:Key="SankeyTreeTemplate" ItemsSource="{Binding Children}"> <Grid Height="24"> <TextBlock x:Name="TextBlockName" Text="{Binding Path=Value.name, Mode=TwoWay}" VerticalAlignment="Center" Foreground="Black"/> <TextBox x:Name="TextBoxFlow" Text="{Binding Path=Value.flow, Mode=TwoWay}" Grid.Column="1" Visibility="{Binding Children, Converter={StaticResource BoxConverter}, ConverterParameter=\{box\}}"/> <TextBlock x:Name="TextBlockThroughput" Text="{Binding Path=Value.throughput, Mode=TwoWay}" Grid.Column="1" Visibility="{Binding Children, Converter={StaticResource BoxConverter}, ConverterParameter=\{block\}}"/> <Button x:Name="ButtonAddNode"/> <Button x:Name="ButtonDeleteNode"/> <Button x:Name="ButtonEditNode"/> </Grid> </Data:HierarchicalDataTemplate> Now, as you can see, the TextBoxFlow and the TextBlockThroughput share the same space. What I aim at: The "Throughput" value of a node is how much of something 'flows' through this node from its children. It can't be changed directly, so I want to display a text block. Only leaf nodes have a TextBox to let someone enter the 'flow' that is generated in this leaf node. (I.E.: Node.Throughput = Node.Flow + Sum(Children.Throughput), where Node.Flow = 0 for each non-leaf.) What the BoxConverter (silly name -.-) does: public object Convert(object value, Type targetType, object parameter, System.Globalization.CultureInfo culture) { if ((value as NodeList<TreeItem>).Count > 1) // Node has Children? { if ((parameter as String) == "{box}") { return Visibility.Collapsed; } else ((parameter as String) == "{block}") { return Visibility.Visible; } } else { /* * As above, just with Collapsed and Visible switched */ } } The structure of the tree that is bound to the TreeView is essentially stolen from Dan Vanderboom (a bit too much to dump the whole code here), except that I here of course use an ObservableCollection for the children and the value items implement INotifyPropertyChanged. I would be very grateful if someone could explain to me, why inserting items into the underlying tree does not update the visibility for box and block. Thank you in advance!

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  • Why won't USB 3.0 external hard drive run at USB 3.0 speeds?

    - by jgottula
    I recently purchased a PCI Express x1 USB 3.0 controller card (containing the NEC USB 3.0 controller) with the intent of using a USB 3.0 external hard drive with my Linux box. I installed the card in an empty PCIe slot on my motherboard, connected the card to a power cable, strung a USB 3.0 cable between one of the new ports and my external HDD, and connected the HDD to a wall socket for power. Booting the system, the drive works 100% as intended, with the one exception of throughput: rather than using SuperSpeed 4.8 Gbps connectivity, it seems to be falling back to High Speed 480 Mbps USB 2.0-style throughput. Disk Utility shows it as a 480 Mbps device, and running a couple Disk Utility and dd benchmarks confirms that the drive fails to exceed ~40 MB/s (the approximate limit of USB 2.0), despite it being an SSD capable of far more than that. When I connect my USB 3.0 HDD, dmesg shows this: [ 3923.280018] usb 3-2: new high speed USB device using ehci_hcd and address 6 where I would expect to find this: [ 3923.280018] usb 3-2: new SuperSpeed USB device using xhci_hcd and address 6 My system was running on kernel 2.6.35-25-generic at the time. Then, I stumbled upon this forum thread by an individual who found that a bug, which was present in kernels prior to 2.6.37-rc5, could be the culprit for this type of problem. Consequently, I installed the 2.6.37-generic mainline Ubuntu kernel to determine if the problem would go away. It didn't, so I tried 2.6.38-rc3-generic, and even the 2.6.38 nightly from 2010.02.01, to no avail. In short, I'm trying to determine why, with USB 3.0 support in the kernel, my USB 3.0 drive fails to run at full SuperSpeed throughput. See the comments under this question for additional details. Output that might be relevant to the problem (when booting from 2.6.38-rc3): Relevant lines from dmesg: [ 19.589491] xhci_hcd 0000:03:00.0: PCI INT A -> GSI 17 (level, low) -> IRQ 17 [ 19.589512] xhci_hcd 0000:03:00.0: setting latency timer to 64 [ 19.589516] xhci_hcd 0000:03:00.0: xHCI Host Controller [ 19.589623] xhci_hcd 0000:03:00.0: new USB bus registered, assigned bus number 12 [ 19.650492] xhci_hcd 0000:03:00.0: irq 17, io mem 0xf8100000 [ 19.650556] xhci_hcd 0000:03:00.0: irq 47 for MSI/MSI-X [ 19.650560] xhci_hcd 0000:03:00.0: irq 48 for MSI/MSI-X [ 19.650563] xhci_hcd 0000:03:00.0: irq 49 for MSI/MSI-X [ 19.653946] xHCI xhci_add_endpoint called for root hub [ 19.653948] xHCI xhci_check_bandwidth called for root hub Relevant section of sudo lspci -v: 03:00.0 USB Controller: NEC Corporation uPD720200 USB 3.0 Host Controller (rev 03) (prog-if 30) Flags: bus master, fast devsel, latency 0, IRQ 17 Memory at f8100000 (64-bit, non-prefetchable) [size=8K] Capabilities: [50] Power Management version 3 Capabilities: [70] MSI: Enable- Count=1/8 Maskable- 64bit+ Capabilities: [90] MSI-X: Enable+ Count=8 Masked- Capabilities: [a0] Express Endpoint, MSI 00 Capabilities: [100] Advanced Error Reporting Capabilities: [140] Device Serial Number ff-ff-ff-ff-ff-ff-ff-ff Capabilities: [150] #18 Kernel driver in use: xhci_hcd Kernel modules: xhci-hcd Relevant section of sudo lsusb -v: Bus 012 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub Device Descriptor: bLength 18 bDescriptorType 1 bcdUSB 3.00 bDeviceClass 9 Hub bDeviceSubClass 0 Unused bDeviceProtocol 3 bMaxPacketSize0 9 idVendor 0x1d6b Linux Foundation idProduct 0x0003 3.0 root hub bcdDevice 2.06 iManufacturer 3 Linux 2.6.38-020638rc3-generic xhci_hcd iProduct 2 xHCI Host Controller iSerial 1 0000:03:00.0 bNumConfigurations 1 Configuration Descriptor: bLength 9 bDescriptorType 2 wTotalLength 25 bNumInterfaces 1 bConfigurationValue 1 iConfiguration 0 bmAttributes 0xe0 Self Powered Remote Wakeup MaxPower 0mA Interface Descriptor: bLength 9 bDescriptorType 4 bInterfaceNumber 0 bAlternateSetting 0 bNumEndpoints 1 bInterfaceClass 9 Hub bInterfaceSubClass 0 Unused bInterfaceProtocol 0 Full speed (or root) hub iInterface 0 Endpoint Descriptor: bLength 7 bDescriptorType 5 bEndpointAddress 0x81 EP 1 IN bmAttributes 3 Transfer Type Interrupt Synch Type None Usage Type Data wMaxPacketSize 0x0004 1x 4 bytes bInterval 12 Hub Descriptor: bLength 9 bDescriptorType 41 nNbrPorts 4 wHubCharacteristic 0x0009 Per-port power switching Per-port overcurrent protection TT think time 8 FS bits bPwrOn2PwrGood 10 * 2 milli seconds bHubContrCurrent 0 milli Ampere DeviceRemovable 0x00 PortPwrCtrlMask 0xff Hub Port Status: Port 1: 0000.0100 power Port 2: 0000.0100 power Port 3: 0000.0100 power Port 4: 0000.0100 power Device Status: 0x0003 Self Powered Remote Wakeup Enabled Full, non-verbose lsusb: Bus 012 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub Bus 011 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 010 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 009 Device 003: ID 04d9:0702 Holtek Semiconductor, Inc. Bus 009 Device 002: ID 046d:c068 Logitech, Inc. G500 Laser Mouse Bus 009 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 008 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 007 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 006 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 005 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 004 Device 001: ID 1d6b:0001 Linux Foundation 1.1 root hub Bus 003 Device 006: ID 174c:5106 ASMedia Technology Inc. Bus 003 Device 004: ID 0bda:0151 Realtek Semiconductor Corp. Mass Storage Device (Multicard Reader) Bus 003 Device 002: ID 058f:6366 Alcor Micro Corp. Multi Flash Reader Bus 003 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 002 Device 006: ID 1687:0163 Kingmax Digital Inc. Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 001 Device 002: ID 046d:081b Logitech, Inc. Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Full output: full dmesg full lspci full lsusb

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  • Explain difference in SQLIO numbers for RAID 0 versus RAID 5 over 6 disks

    - by markn
    When using the SQLIO benchmark tool on a 4-core Dell server with 6 15k 450GB (fast) drives, RAID 0, we found the max throughput was 2MB per second. But when configured as RAID 5, we get 30 MB per second. It seems that the RAID controller, Dell Perc 5i integrated controller, is maxing out the throughput per disk. With RAID 5, I expect to get a bump due to stripping, but not a 15x difference. Like good programmers, we suspect the hardware , but we could be missing something. This is predominately write traffic.

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  • Will an IO blocked process show 100% CPU utilization in 'top' output?

    - by Alex Stoddard
    I have an analysis that can be parallelized over a different number of processes. It is expected that things will be both IO and CPU intensive (very high throughput short-read DNA alignment if anyone is curious.) The system running this is a 48 core linux server. The question is how to determine the optimum number of processes such that total throughput is maximized. At some point the processes will presumably become IO bound such that adding more processes will be of no benefit and possibly detrimental. Can I tell from standard system monitoring tools when that point has been reached? Would the output of top (or maybe a different tool) enable me to distinguish between a IO bound and CPU bound process? I am suspicious that a process blocked on IO might still show 100% CPU utilization.

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  • java virtual machine - how does it allocate resources?

    - by Will
    I am testing the performance of a data streaming system that supports continuous queries. This is how it works: - There is a polling service which sends data to my system. - As data passes into the system, each query evaluates based on a window of the stream at the current time. - The window slides as data passes in. My problem is this, when I add more queries to the system, I should expect the throughput to decrease because it can't cope the data rate. However, I actually observe an increase in throughput. I can't understand why this is the case and I am guessing that it's something to do with the way the JVM allocates CPU, memory etc. Can anyone shed any light to my problem?

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  • How to deploy custom MBean to Tomcat?

    - by Christian
    Hi, I'm trying to deploy a custom mbean to a tomcat. This mbean is not part of a webapp. It should be instantiated when tomcat starts. My problem is, I can't find any complete documentation about how to deploy such a mbean. I'm getting different exceptions, depending on my configuration. Has anyone hints, a complete documentation or has implemented a mbean by himself and can post an example? I configured tomcat to read a configuration from his conf directory: <Engine name="Catalina" defaultHost="localhost" mbeansFile="${catalina.base}/conf/mbeans-descriptors.xml"> The content is as follows: <?xml version="1.0"?> <!-- <!DOCTYPE mbeans-descriptors PUBLIC "-//Apache Software Foundation//DTD Model MBeans Configuration File" "http://jakarta.apache.org/commons/dtds/mbeans-descriptors.dtd"> --> <!-- Descriptions of JMX MBeans --> <mbeans-descriptors> <mbean name="Performance" description="Caculate JVM throughput" type="Performance"> <attribute name="throughput" description="calculated throughput (ratio between gc times and uptime of JVM)" type="double" writeable="false"/> </mbean> </mbeans-descriptors> When name in the xml file and class name match, I get this excption: SEVERE: Error creating mbean Performance javax.management.MalformedObjectNameException: Key properties cannot be empty at javax.management.ObjectName.construct(ObjectName.java:467) at javax.management.ObjectName.<init>(ObjectName.java:1403) at org.apache.tomcat.util.modeler.modules.MbeansSource.execute(MbeansSource.java:202) at org.apache.tomcat.util.modeler.modules.MbeansSource.load(MbeansSource.java:137) at org.apache.catalina.core.StandardEngine.readEngineMbeans(StandardEngine.java:517) at org.apache.catalina.core.StandardEngine.init(StandardEngine.java:321) at org.apache.catalina.core.StandardEngine.start(StandardEngine.java:411) at org.apache.catalina.core.StandardService.start(StandardService.java:519) at org.apache.catalina.core.StandardServer.start(StandardServer.java:710) at org.apache.catalina.startup.Catalina.start(Catalina.java:581) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.catalina.startup.Bootstrap.start(Bootstrap.java:289) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.commons.daemon.support.DaemonLoader.start(DaemonLoader.java:177) When changing the name attribute in the xml file to test.example:type=Performance, I get this exception: SEVERE: Error creating mbean test.example:type=Performance javax.management.NotCompliantMBeanException: MBean class must have public constructor at com.sun.jmx.mbeanserver.Introspector.testCreation(Introspector.java:127) at com.sun.jmx.interceptor.DefaultMBeanServerInterceptor.createMBean(DefaultMBeanServerInterceptor.java:284) at com.sun.jmx.interceptor.DefaultMBeanServerInterceptor.createMBean(DefaultMBeanServerInterceptor.java:199) at com.sun.jmx.mbeanserver.JmxMBeanServer.createMBean(JmxMBeanServer.java:393) at org.apache.tomcat.util.modeler.modules.MbeansSource.execute(MbeansSource.java:207) at org.apache.tomcat.util.modeler.modules.MbeansSource.load(MbeansSource.java:137) at org.apache.catalina.core.StandardEngine.readEngineMbeans(StandardEngine.java:517) at org.apache.catalina.core.StandardEngine.init(StandardEngine.java:321) at org.apache.catalina.core.StandardEngine.start(StandardEngine.java:411) at org.apache.catalina.core.StandardService.start(StandardService.java:519) at org.apache.catalina.core.StandardServer.start(StandardServer.java:710) at org.apache.catalina.startup.Catalina.start(Catalina.java:581) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.catalina.startup.Bootstrap.start(Bootstrap.java:289) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.commons.daemon.support.DaemonLoader.start(DaemonLoader.java:177) The documentation from apache is not really helpful, as it just explains a small part. I'm aware of this question but it doesn't help me. The answer I gave worked just for a short time, after that I got some other exceptions. For additional info, the java interface public interface PerformanceMBean { public double getThroughput(); } and implementing class /* some import statements */ public class Performance implements PerformanceMBean { public double getThroughput() { ... } }

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  • Optimizing the MySQL Query Cache

    MySQL's query cache is an impressive piece of engineering if sometimes misunderstood. Keeping it optimized and used efficiently can make a big difference in the overall throughput of your application, so it's worth taking a look under the hood, understanding it, and then keeping it tuned optimally.

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Don’t Miss The Top Exastack ISV Headlines – Week Of June 5

    - by Roxana Babiciu
    Kerridge achieves Oracle Exadata Optimized status with K8, an ERP Solution for distribution, merchant and wholesale/retail sectors. The online transactional processing saw a 12x increase in the volume throughput from previous benchmarks – Watch video. Accenture achieves Oracle Exalogic Optimized status with AFPO, a unique accelerator for customer-facing solutions. Over 125 clients cut their implementation costs by up to thirty percent – Read more.

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Google I/O 2010 - Data pipelines with Google App Engine

    Google I/O 2010 - Data pipelines with Google App Engine Google I/O 2010 - Building high-throughput data pipelines with Google App Engine App Engine 301 Brett Slatkin This session will cover how to build, test, and maintain large-scale data pipelines on Google App Engine. It will cover maximizing efficiency, productionization, and how to deal with changing requirements. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 5 0 ratings Time: 01:01:52 More in Science & Technology

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  • Penalization of under performing employees, how to avoid this? [closed]

    - by Sparky
    My company's management wants to deduct from the salary of under performing employees. I'm a member of the Core Strategy committee and they want my opinion also. I believe that the throughput from an employee depends on a lot of things such as the particular work assigned to them, other members of his/her team, other reasons etc. Such penalizations will be demoralizing to the people. How can I convince my management not to do so?

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  • How to Stress Test the Hard Drives in Your PC or Server

    - by Tim Smith
    You have the latest drives for your server.  You stacked the top-of-the line RAM in the system.  You run effective code for your system.  However, what throughput is your system capable of handling, and can you really trust the capabilities listed by hardware companies? How to Stress Test the Hard Drives in Your PC or Server How To Customize Your Android Lock Screen with WidgetLocker The Best Free Portable Apps for Your Flash Drive Toolkit

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  • Deep insight into the behaviour of the SPARC T4 processor

    - by nospam(at)example.com (Joerg Moellenkamp)
    Ruud van der Pas and Jared Smolens wrote an really interesting whitepaper about the SPARC T4 and its behaviour in regard with certain code: How the SPARC T4 Processor Optimizes Throughput Capacity: A Case Study. In this article the authors compare and explain the behaviour of the the UltraSPARC T4 and T2+ processor in order to highlight some of the strengths of the SPARC T-series processors in general and the T4 in particular.

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  • CMT Blog: Virtual IO gets better for LDoms

    - by uwes
    As we all know virtual IO is of great use in the IT environments of today but when it comes to performance we often have to pay the price. In his Blog entry Improved vDisk Performance for Ldoms, Stefan Hinker explained how the new implementation of the vdisk/vds software stack in Solaris 11.1 SRU 19 (and a Solaris 10 patch sortly afterwards) significantly improves latency and throughput of the vitual disk IO.

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  • My company's management wants to deduct from the salary of under performing employees. How can I convince them not to?

    - by Sparky
    My company's management wants to deduct from the salary of under performing employees. I'm a member of the Core Strategy committee and they want my opinion also. I believe that the throughput from an employee depends on a lot of things such as the particular work assigned to them, other members of his/her team, other reasons etc. Such penalizations will be demoralizing to the people. How can I convince my management not to do so?

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  • Improving VPN performance - stronger encryption = more performance?

    - by Seth
    I have a site-to-site VPN set up with two SonicWall's (a TZ170 and a Pro1260). It was suggested to me that turning off encryption (so the VPN is tunneling only) would improve performance. (I'm not concerned with security, because the VPN is running over a trusted line.) Using FTP and HTTP transfers, I measured my baseline performance at about 130±10 kB/s. The Ipsec (Phase 2) Encryption was set to 3DES, so I set it to "none". However, the effect was opposite -- the performance dropped to 60±30 kB/s, and the transfers stall for about 25 seconds before any data comes down the line. I tried AES-128 and the throughput went UP to 160±5 kB/s. The rated speed of my line is 193 kB/s (it's a T1). Contrary to what I would think, stronger Ipsec encryption seems to improve throughput. Can anyone explain what might be going on here? Why would no encryption cause poor and highly variable performance, and cause transfers to stall? Why does AES-128 improve performance?

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  • Optimal Networking Setup for a 2-Story unit?

    - by user29336
    I am moving into a 4 bedroom two-story unit. It’s roughly 2,200 sq ft. I want absolute max throughput possible to be achieved in all focal points. We’re all in internet related industries. Between gaming and web-development latency and throughput are major factors for us. Here’s our main focal points: 1) Garage (office). downstairs 2) Each bedroom x4. upstairs 3) Living room. downstairs The fastest line we can get is Comcast 50mbdown/5up (Wideband). I am looking for the best way to achieve wireless and wired performance for our setup. Our gaming computers may be in our bedroom, and we also may bring it down to the office every now and then for “LAN” sessions. Most wireless will be happening downstairs with our laptops, but since we may do LAN sessions then hard wired latency may be important there too. My concerns: If we do only wireless there would be too much latency for gaming. I don’t know if placing one D-link DGL 4500 on the top floor would be enough; which I currently own. (http://dlink.com/us/en/home-solutions/support/product/dgl-4500-xtreme-n-gaming-router) As far as I’m aware wireless signals transfer best top down. Would this wireless router be enough on top floor and that’s it? My second strategy was a combination of wiring and wireless but I’m not sure what’s easiest way to do this? This is a place we’re renting, so I’m not sure how much leeway we have with wiring, but we’re all pretty competent... if we can’t drill through a wall we can probably “stitch” them across the edges wherever needed. Thoughts on the optimal way to do this?

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  • Best way to attach 96 tb to workstation

    - by user994179
    I'm running a workstation with dual xeon 5690's (12 physical/24 logical cores), 192 gb of ram (ie, maxed-out), Windows 7 64bit, 5 slots for adapter cards, and 1 tb of internal storage, with 5 more internal bays available. I have an app that creates data files totaling about 88 tbs. These are written once every 14 months, and the rest of the time the app only needs to read them; and 95% of the reads are sequential reads of huge chunks of data. I have some control over how big the individual files are, but ideally they would be between 5 and 8 tbs. The app will be reading from only one drive at a time, and the nature of the data is such that if (when) a drive dies I can restore the data to a new disk from tape. While it would be nice to be able to use the fastest drive/controllers available, at this point size matters more than speed. After doing lots of reading, I am leaning toward buying a bunch of cheap 2tb drives and putting them into a bunch of cheap enclosures. All this stuff is going into my home office, so I need to avoid the raised floor/refrigerated approach. My questions: Is the cheap drive/enclosure solution the best one for this situation? Given the nature of the app and the way the data is used, does RAID make sense? If so, which one? For huge sequential reads, would Usb 3.0 and eSata be a wash performance-wise? For each slot available on the workstation, can I hook up an enclosure that can hold multiple drives? Or is it one controller per drive? If I can have multiple drives on one controller, am I essentially splitting the bandwidth (throughput)? For example, if I have a 12 bay enclosure, is the throughput of the controller reduced by a factor of 12? Are there any Windows 7 volume/drive/capacity limits I should be aware of? Thanks

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  • Not getting gigbit from a gigabit link?

    - by marcusw
    I just upgraded my LAN to gigabit. This is what netperf has to say about things. Before: marcus@lt:~$ netperf -H 192.168.1.1 TCP STREAM TEST from 0.0.0.0 (0.0.0.0) port 0 AF_INET to 192.168.1.1 (192.168.1.1) port 0 AF_INET : demo Recv Send Send Socket Socket Message Elapsed Size Size Size Time Throughput bytes bytes bytes secs. 10^6bits/sec 87380 16384 16384 10.02 94.13 After: marcus@lt:~$ netperf -H 192.168.1.1 TCP STREAM TEST from 0.0.0.0 (0.0.0.0) port 0 AF_INET to 192.168.1.1 (192.168.1.1) port 0 AF_INET : demo Recv Send Send Socket Socket Message Elapsed Size Size Size Time Throughput bytes bytes bytes secs. 10^6bits/sec 87380 16384 16384 10.01 339.15 Only 340 Mbps? What's up with that? Background info: I'm connecting through a gigabit switch to a sheevaplug. I have Cat5e wiring in the walls and the run is maybe 30 feet. If you're not familiar with netperf, it has a tendency to give very stable results and never lie.

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  • How important is dual-gigabit lan for a super user's home NAS?

    - by Andrew
    Long story short: I'm building my own home server based on Ubuntu with 4 drives in RAID 10. Its primary purpose will be NAS and backup. Would I be making a terrible mistake by building a NAS Server with a single Gigabit NIC? Long story long: I know the absolute max I can get out of a single Gigabit port is 125MB/s, and I want this NAS to be able to handle up to 6 computers accessing files simultaneously, with up to two of them streaming video. With Ubuntu NIC-bonding and the performance of RAID 10, I can theoretically double my throughput and achieve 250MB/s (ok, not really, but it would be faster). The drives have an average read throughput of 83.87MB/s according to Tom's Hardware. The unit itself will be based on the Chenbro ES34069-BK-180 case. With my current hardware choices, it'll have this motherboard with a Core i3 CPU and 8GB of RAM. Overkill, I know, but this server will be doing other things as well (like transcoding video). Unfortunately, the only Mini-ITX boards I can find with dual-gigabit and 6 SATA ports are Intel Atom-based, and I need more processing power than an Atom has to offer. I would love to find a board with 6 SATA ports and two Gigabit LAN ports that supports a Core i3 CPU. So far, my search has come up empty. Thus, my dilemma. Should I hold out for such a board, go with an Atom-based solution, or stick with my current single-gigabit configuration? I know there are consumer NAS units with just one gigabit interface (probably most of them), but I think I will demand a lot more from my server than the average home user. Any advice is appreciated. Thanks.

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  • VPS goes slow at more than 20 users online at the same time

    - by hachiari
    I have 512 MB VPS (brustable to 1GB) Somehow, the site goes slow when there are about 10 users, and becomes impossible to load at 20 users online at the same time. I wonder what could be the problem for this. The bandwidth connection of the VPS is 1Gbps. Here is some settings in my VPS: KeepAlive Off <IfModule prefork.c> StartServers 7 MinSpareServers 7 MaxSpareServers 10 ServerLimit 64 MaxClients 64 MaxRequestsPerChild 0 </IfModule> my.cnf settings - calculated Max Memory 300MB Output from UNIXBENCH INDEX VALUES TEST BASELINE RESULT INDEX Dhrystone 2 using register variables 376783.7 13429727.4 356.4 Double-Precision Whetstone 83.1 1137.5 136.9 Execl Throughput 188.3 1637.4 87.0 File Copy 1024 bufsize 2000 maxblocks 2672.0 148868.0 557.1 File Copy 256 bufsize 500 maxblocks 1077.0 79430.0 737.5 File Read 4096 bufsize 8000 maxblocks 15382.0 1410009.0 916.7 Pipe Throughput 111814.6 4419722.0 395.3 Pipe-based Context Switching 15448.6 561505.1 363.5 Process Creation 569.3 10272.7 180.4 Shell Scripts (8 concurrent) 44.8 514.3 114.8 System Call Overhead 114433.5 3537373.8 309.1 ========= FINAL SCORE 295.0 I am afraid that the VPS company limit the number of connection to the VPS... is it possible? The server is in Japan, but the site has global traffic (some of the traffic are from countries with low speed connection). Could this be the problem? This is a serious problem :( my site just cant grow if this keeps on happening... please tell me if you have any idea. Thank You, Bryant

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