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  • SQL Server and Hyper-V Dynamic Memory Part 2

    - by SQLOS Team
    Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • SQL Server and Hyper-V Dynamic Memory - Part 1

    - by SQLOS Team
    SQL and Dynamic Memory Blog Post Series   Hyper-V Dynamic Memory is a new feature in Windows Server 2008 R2 SP1 that allows the memory assigned to guest virtual machines to vary according to demand. Using this feature with SQL Server is supported, but how well does it work in an environment where available memory can vary dynamically, especially since SQL Server likes memory, and is not very eager to let go of it? The next three posts will look at this question in detail. In Part 1 Serdar Sutay, a program manager in the Windows Hyper-V team, introduces Dynamic Memory with an overview of the basic architecture, configuration and monitoring concepts. In subsequent parts we will look at SQL Server memory handling, and develop some guidelines on using SQL Server with Dynamic Memory.   Part 1: Dynamic Memory Introduction   In virtualized environments memory is often the bottleneck for reaching higher VM densities. In Windows Server 2008 R2 SP1 Hyper-V introduced a new feature “Dynamic Memory” to improve VM densities on Hyper-V hosts. Dynamic Memory increases the memory utilization in virtualized environments by enabling VM memory to be changed dynamically when the VM is running.   This brings up the question of how to utilize this feature with SQL Server VMs as SQL Server performance is very sensitive to the memory being used. In the next three posts we’ll discuss the internals of Dynamic Memory, SQL Server Memory Management and how to use Dynamic Memory with SQL Server VMs.   Memory Utilization Efficiency in Virtualized Environments   The primary reason memory is usually the bottleneck for higher VM densities is that users tend to be generous when assigning memory to their VMs. Here are some memory sizing practices we’ve heard from customers:   ·         I assign 4 GB of memory to my VMs. I don’t know if all of it is being used by the applications but no one complains. ·         I take the minimum system requirements and add 50% more. ·         I go with the recommendations provided by my software vendor.   In reality correctly sizing a virtual machine requires significant effort to monitor the memory usage of the applications. Since this is not done in most environments, VMs are usually over-provisioned in terms of memory. In other words, a SQL Server VM that is assigned 4 GB of memory may not need to use 4 GB.   How does Dynamic Memory help?   Dynamic Memory improves the memory utilization by removing the requirement to determine the memory need for an application. Hyper-V determines the memory needed by applications in the VM by evaluating the memory usage information in the guest with Dynamic Memory. VMs can start with a small amount of memory and they can be assigned more memory dynamically based on the workload of applications running inside.   Overview of Dynamic Memory Concepts   ·         Startup Memory: Startup Memory is the starting amount of memory when Dynamic Memory is enabled for a VM. Dynamic Memory will make sure that this amount of memory is always assigned to the VMs by default.   ·         Maximum Memory: Maximum Memory specifies the maximum amount of memory that a VM can grow to with Dynamic Memory. ·         Memory Demand: Memory Demand is the amount determined by Dynamic Memory as the memory needed by the applications in the VM. In Windows Server 2008 R2 SP1, this is equal to the total amount of committed memory of the VM. ·         Memory Buffer: Memory Buffer is the amount of memory assigned to the VMs in addition to their memory demand to satisfy immediate memory requirements and file cache needs.   Once Dynamic Memory is enabled for a VM, it will start with the “Startup Memory”. After the boot process Dynamic Memory will determine the “Memory Demand” of the VM. Based on this memory demand it will determine the amount of “Memory Buffer” that needs to be assigned to the VM. Dynamic Memory will assign the total of “Memory Demand” and “Memory Buffer” to the VM as long as this value is less than “Maximum Memory” and as long as physical memory is available on the host.   What happens when there is not enough physical memory available on the host?   Once there is not enough physical memory on the host to satisfy VM needs, Dynamic Memory will assign less than needed amount of memory to the VMs based on their importance. A concept known as “Memory Weight” is used to determine how much VMs should be penalized based on their needed amount of memory. “Memory Weight” is a configuration setting on the VM. It can be configured to be higher for the VMs with high performance requirements. Under high memory pressure on the host, the “Memory Weight” of the VMs are evaluated in a relative manner and the VMs with lower relative “Memory Weight” will be penalized more than the ones with higher “Memory Weight”.   Dynamic Memory Configuration   Based on these concepts “Startup Memory”, “Maximum Memory”, “Memory Buffer” and “Memory Weight” can be configured as shown below in Windows Server 2008 R2 SP1 Hyper-V Manager. Memory Demand is automatically calculated by Dynamic Memory once VMs start running.     Dynamic Memory Monitoring    In Windows Server 2008 R2 SP1, Hyper-V Manager displays the memory status of VMs in the following three columns:         ·         Assigned Memory represents the current physical memory assigned to the VM. In regular conditions this will be equal to the sum of “Memory Demand” and “Memory Buffer” assigned to the VM. When there is not enough memory on the host, this value can go below the Memory Demand determined for the VM. ·         Memory Demand displays the current “Memory Demand” determined for the VM. ·         Memory Status displays the current memory status of the VM. This column can represent three values for a VM: o   OK: In this condition the VM is assigned the total of Memory Demand and Memory Buffer it needs. o   Low: In this condition the VM is assigned all the Memory Demand and a certain percentage of the Memory Buffer it needs. o   Warning: In this condition the VM is assigned a lower memory than its Memory Demand. When VMs are running in this condition, it’s likely that they will exhibit performance problems due to internal paging happening in the VM.    So far so good! But how does it work with SQL Server?   SQL Server is aggressive in terms of memory usage for good reasons. This raises the question: How do SQL Server and Dynamic Memory work together? To understand the full story, we’ll first need to understand how SQL Server Memory Management works. This will be covered in our second post in “SQL and Dynamic Memory” series. Meanwhile if you want to dive deeper into Dynamic Memory you can check the below posts from the Windows Virtualization Team Blog:   http://blogs.technet.com/virtualization/archive/2010/03/18/dynamic-memory-coming-to-hyper-v.aspx   http://blogs.technet.com/virtualization/archive/2010/03/25/dynamic-memory-coming-to-hyper-v-part-2.aspx   http://blogs.technet.com/virtualization/archive/2010/04/07/dynamic-memory-coming-to-hyper-v-part-3.aspx   http://blogs.technet.com/b/virtualization/archive/2010/04/21/dynamic-memory-coming-to-hyper-v-part-4.aspx   http://blogs.technet.com/b/virtualization/archive/2010/05/20/dynamic-memory-coming-to-hyper-v-part-5.aspx   http://blogs.technet.com/b/virtualization/archive/2010/07/12/dynamic-memory-coming-to-hyper-v-part-6.aspx   - Serdar Sutay   Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • SQL Server and Hyper-V Dynamic Memory - Part 1

    - by SQLOS Team
    SQL and Dynamic Memory Blog Post Series   Hyper-V Dynamic Memory is a new feature in Windows Server 2008 R2 SP1 that allows the memory assigned to guest virtual machines to vary according to demand. Using this feature with SQL Server is supported, but how well does it work in an environment where available memory can vary dynamically, especially since SQL Server likes memory, and is not very eager to let go of it? The next three posts will look at this question in detail. In Part 1 Serdar Sutay, a program manager in the Windows Hyper-V team, introduces Dynamic Memory with an overview of the basic architecture, configuration and monitoring concepts. In subsequent parts we will look at SQL Server memory handling, and develop some guidelines on using SQL Server with Dynamic Memory.   Part 1: Dynamic Memory Introduction   In virtualized environments memory is often the bottleneck for reaching higher VM densities. In Windows Server 2008 R2 SP1 Hyper-V introduced a new feature “Dynamic Memory” to improve VM densities on Hyper-V hosts. Dynamic Memory increases the memory utilization in virtualized environments by enabling VM memory to be changed dynamically when the VM is running.   This brings up the question of how to utilize this feature with SQL Server VMs as SQL Server performance is very sensitive to the memory being used. In the next three posts we’ll discuss the internals of Dynamic Memory, SQL Server Memory Management and how to use Dynamic Memory with SQL Server VMs.   Memory Utilization Efficiency in Virtualized Environments   The primary reason memory is usually the bottleneck for higher VM densities is that users tend to be generous when assigning memory to their VMs. Here are some memory sizing practices we’ve heard from customers:   ·         I assign 4 GB of memory to my VMs. I don’t know if all of it is being used by the applications but no one complains. ·         I take the minimum system requirements and add 50% more. ·         I go with the recommendations provided by my software vendor.   In reality correctly sizing a virtual machine requires significant effort to monitor the memory usage of the applications. Since this is not done in most environments, VMs are usually over-provisioned in terms of memory. In other words, a SQL Server VM that is assigned 4 GB of memory may not need to use 4 GB.   How does Dynamic Memory help?   Dynamic Memory improves the memory utilization by removing the requirement to determine the memory need for an application. Hyper-V determines the memory needed by applications in the VM by evaluating the memory usage information in the guest with Dynamic Memory. VMs can start with a small amount of memory and they can be assigned more memory dynamically based on the workload of applications running inside.   Overview of Dynamic Memory Concepts   ·         Startup Memory: Startup Memory is the starting amount of memory when Dynamic Memory is enabled for a VM. Dynamic Memory will make sure that this amount of memory is always assigned to the VMs by default.   ·         Maximum Memory: Maximum Memory specifies the maximum amount of memory that a VM can grow to with Dynamic Memory. ·         Memory Demand: Memory Demand is the amount determined by Dynamic Memory as the memory needed by the applications in the VM. In Windows Server 2008 R2 SP1, this is equal to the total amount of committed memory of the VM. ·         Memory Buffer: Memory Buffer is the amount of memory assigned to the VMs in addition to their memory demand to satisfy immediate memory requirements and file cache needs.   Once Dynamic Memory is enabled for a VM, it will start with the “Startup Memory”. After the boot process Dynamic Memory will determine the “Memory Demand” of the VM. Based on this memory demand it will determine the amount of “Memory Buffer” that needs to be assigned to the VM. Dynamic Memory will assign the total of “Memory Demand” and “Memory Buffer” to the VM as long as this value is less than “Maximum Memory” and as long as physical memory is available on the host.   What happens when there is not enough physical memory available on the host?   Once there is not enough physical memory on the host to satisfy VM needs, Dynamic Memory will assign less than needed amount of memory to the VMs based on their importance. A concept known as “Memory Weight” is used to determine how much VMs should be penalized based on their needed amount of memory. “Memory Weight” is a configuration setting on the VM. It can be configured to be higher for the VMs with high performance requirements. Under high memory pressure on the host, the “Memory Weight” of the VMs are evaluated in a relative manner and the VMs with lower relative “Memory Weight” will be penalized more than the ones with higher “Memory Weight”.   Dynamic Memory Configuration   Based on these concepts “Startup Memory”, “Maximum Memory”, “Memory Buffer” and “Memory Weight” can be configured as shown below in Windows Server 2008 R2 SP1 Hyper-V Manager. Memory Demand is automatically calculated by Dynamic Memory once VMs start running.     Dynamic Memory Monitoring    In Windows Server 2008 R2 SP1, Hyper-V Manager displays the memory status of VMs in the following three columns:         ·         Assigned Memory represents the current physical memory assigned to the VM. In regular conditions this will be equal to the sum of “Memory Demand” and “Memory Buffer” assigned to the VM. When there is not enough memory on the host, this value can go below the Memory Demand determined for the VM. ·         Memory Demand displays the current “Memory Demand” determined for the VM. ·         Memory Status displays the current memory status of the VM. This column can represent three values for a VM: o   OK: In this condition the VM is assigned the total of Memory Demand and Memory Buffer it needs. o   Low: In this condition the VM is assigned all the Memory Demand and a certain percentage of the Memory Buffer it needs. o   Warning: In this condition the VM is assigned a lower memory than its Memory Demand. When VMs are running in this condition, it’s likely that they will exhibit performance problems due to internal paging happening in the VM.    So far so good! But how does it work with SQL Server?   SQL Server is aggressive in terms of memory usage for good reasons. This raises the question: How do SQL Server and Dynamic Memory work together? To understand the full story, we’ll first need to understand how SQL Server Memory Management works. This will be covered in our second post in “SQL and Dynamic Memory” series. Meanwhile if you want to dive deeper into Dynamic Memory you can check the below posts from the Windows Virtualization Team Blog:   http://blogs.technet.com/virtualization/archive/2010/03/18/dynamic-memory-coming-to-hyper-v.aspx   http://blogs.technet.com/virtualization/archive/2010/03/25/dynamic-memory-coming-to-hyper-v-part-2.aspx   http://blogs.technet.com/virtualization/archive/2010/04/07/dynamic-memory-coming-to-hyper-v-part-3.aspx   http://blogs.technet.com/b/virtualization/archive/2010/04/21/dynamic-memory-coming-to-hyper-v-part-4.aspx   http://blogs.technet.com/b/virtualization/archive/2010/05/20/dynamic-memory-coming-to-hyper-v-part-5.aspx   http://blogs.technet.com/b/virtualization/archive/2010/07/12/dynamic-memory-coming-to-hyper-v-part-6.aspx   - Serdar Sutay   Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • Deploying Data Mining Models using Model Export and Import, Part 2

    - by [email protected]
    In my last post, Deploying Data Mining Models using Model Export and Import, we explored using DBMS_DATA_MINING.EXPORT_MODEL and DBMS_DATA_MINING.IMPORT_MODEL to enable moving a model from one system to another. In this post, we'll look at two distributed scenarios that make use of this capability and a tip for easily moving models from one machine to another using only Oracle Database, not an external file transport mechanism, such as FTP. The first scenario, consider a company with geographically distributed business units, each collecting and managing their data locally for the products they sell. Each business unit has in-house data analysts that build models to predict which products to recommend to customers in their space. A central telemarketing business unit also uses these models to score new customers locally using data collected over the phone. Since the models recommend different products, each customer is scored using each model. This is depicted in Figure 1.Figure 1: Target instance importing multiple remote models for local scoring In the second scenario, consider multiple hospitals that collect data on patients with certain types of cancer. The data collection is standardized, so each hospital collects the same patient demographic and other health / tumor data, along with the clinical diagnosis. Instead of each hospital building it's own models, the data is pooled at a central data analysis lab where a predictive model is built. Once completed, the model is distributed to hospitals, clinics, and doctor offices who can score patient data locally.Figure 2: Multiple target instances importing the same model from a source instance for local scoring Since this blog focuses on model export and import, we'll only discuss what is necessary to move a model from one database to another. Here, we use the package DBMS_FILE_TRANSFER, which can move files between Oracle databases. The script is fairly straightforward, but requires setting up a database link and directory objects. We saw how to create directory objects in the previous post. To create a database link to the source database from the target, we can use, for example: create database link SOURCE1_LINK connect to <schema> identified by <password> using 'SOURCE1'; Note that 'SOURCE1' refers to the service name of the remote database entry in your tnsnames.ora file. From SQL*Plus, first connect to the remote database and export the model. Note that the model_file_name does not include the .dmp extension. This is because export_model appends "01" to this name.  Next, connect to the local database and invoke DBMS_FILE_TRANSFER.GET_FILE and import the model. Note that "01" is eliminated in the target system file name.  connect <source_schema>/<password>@SOURCE1_LINK; BEGIN  DBMS_DATA_MINING.EXPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_SOURCE_DIR_OBJECT',                                 'name =''MY_MINING_MODEL'''); END; connect <target_schema>/<password>; BEGIN  DBMS_FILE_TRANSFER.GET_FILE ('MY_SOURCE_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '01.dmp',                               'SOURCE1_LINK',                               'MY_TARGET_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '.dmp' );  DBMS_DATA_MINING.IMPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_TARGET_DIR_OBJECT'); END; To clean up afterward, you may want to drop the exported .dmp file at the source and the transferred file at the target. For example, utl_file.fremove('&directory_name', '&model_file_name' || '.dmp');

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  • SQL Server and Hyper-V Dynamic Memory Part 3

    - by SQLOS Team
    In parts 1 and 2 of this series we looked at the basics of Hyper-V Dynamic Memory and SQL Server memory management. In this part Serdar looks at configuration guidelines for SQL Server memory management. Part 3: Configuration Guidelines for Hyper-V Dynamic Memory and SQL Server Now that we understand SQL Server Memory Management and Hyper-V Dynamic Memory basics, let’s take a look at general configuration guidelines in order to utilize benefits of Hyper-V Dynamic Memory in your SQL Server VMs. Requirements Host Operating System Requirements Hyper-V Dynamic Memory feature is introduced with Windows Server 2008 R2 SP1. Therefore in order to use Dynamic Memory for your virtual machines, you need to have Windows Server 2008 R2 SP1 or Microsoft Hyper-V Server 2008 R2 SP1 in your Hyper-V host. Guest Operating System Requirements In addition to this Dynamic Memory is only supported in Standard, Web, Enterprise and Datacenter editions of windows running inside VMs. Make sure that your VM is running one of these editions. For additional requirements on each operating system see “Dynamic Memory Configuration Guidelines” here. SQL Server Requirements All versions of SQL Server support Hyper-V Dynamic Memory. However, only certain editions of SQL Server are aware of dynamically changing system memory. To have a truly dynamic environment for your SQL Server VMs make sure that you are running one of the SQL Server editions listed below: ·         SQL Server 2005 Enterprise ·         SQL Server 2008 Enterprise / Datacenter Editions ·         SQL Server 2008 R2 Enterprise / Datacenter Editions Configuration guidelines for other versions of SQL Server are covered below in the FAQ section. Guidelines for configuring Dynamic Memory Parameters Here is how to configure Dynamic Memory for your SQL VMs in a nutshell: Hyper-V Dynamic Memory Parameter Recommendation Startup RAM 1 GB + SQL Min Server Memory Maximum RAM > SQL Max Server Memory Memory Buffer % 5 Memory Weight Based on performance needs   Startup RAM In order to ensure that your SQL Server VMs can start correctly, ensure that Startup RAM is higher than configured SQL Min Server Memory for your VMs. Otherwise SQL Server service will need to do paging in order to start since it will not be able to see enough memory during startup. Also note that Startup Memory will always be reserved for your VMs. This will guarantee a certain level of performance for your SQL Servers, however setting this too high will limit the consolidation benefits you’ll get out of your virtualization environment. Maximum RAM This one is obvious. If you’ve configured SQL Max Server Memory for your SQL Server, make sure that Dynamic Memory Maximum RAM configuration is higher than this value. Otherwise your SQL Server will not grow to memory values higher than the value configured for Dynamic Memory. Memory Buffer % Memory buffer configuration is used to provision file cache to virtual machines in order to improve performance. Due to the fact that SQL Server is managing its own buffer pool, Memory Buffer setting should be configured to the lowest value possible, 5%. Configuring a higher memory buffer will prevent low resource notifications from Windows Memory Manager and it will prevent reclaiming memory from SQL Server VMs. Memory Weight Memory weight configuration defines the importance of memory to a VM. Configure higher values for the VMs that have higher performance requirements. VMs with higher memory weight will have more memory under high memory pressure conditions on your host. Questions and Answers Q1 – Which SQL Server memory model is best for Dynamic Memory? The best SQL Server model for Dynamic Memory is “Locked Page Memory Model”. This memory model ensures that SQL Server memory is never paged out and it’s also adaptive to dynamically changing memory in the system. This will be extremely useful when Dynamic Memory is attempting to remove memory from SQL Server VMs ensuring no SQL Server memory is paged out. You can find instructions on configuring “Locked Page Memory Model” for your SQL Servers here. Q2 – What about other SQL Server Editions, how should I configure Dynamic Memory for them? Other editions of SQL Server do not adapt to dynamically changing environments. They will determine how much memory they should allocate during startup and don’t change this value afterwards. Therefore make sure that you configure a higher startup memory for your VM because that will be all the memory that SQL Server utilize Tune Maximum Memory and Memory Buffer based on the other workloads running on the system. If there are no other workloads consider using Static Memory for these editions. Q3 – What if I have multiple SQL Server instances in a VM? Having multiple SQL Server instances in a VM is not a general recommendation for predictable performance, manageability and isolation. In order to achieve a predictable behavior make sure that you configure SQL Min Server Memory and SQL Max Server Memory for each instance in the VM. And make sure that: ·         Dynamic Memory Startup Memory is greater than the sum of SQL Min Server Memory values for the instances in the VM ·         Dynamic Memory Maximum Memory is greater than the sum of SQL Max Server Memory values for the instances in the VM Q4 – I’m using Large Page Memory Model for my SQL Server. Can I still use Dynamic Memory? The short answer is no. SQL Server does not dynamically change its memory size when configured with Large Page Memory Model. In virtualized environments Hyper-V provides large page support by default. Most of the time, Large Page Memory Model doesn’t bring any benefits to a SQL Server if it’s running in virtualized environments. Q5 – How do I monitor SQL performance when I’m trying Dynamic Memory on my VMs? Use the performance counters below to monitor memory performance for SQL Server: Process - Working Set: This counter is available in the VM via process performance counters. It represents the actual amount of physical memory being used by SQL Server process in the VM. SQL Server – Buffer Cache Hit Ratio: This counter is available in the VM via SQL Server counters. This represents the paging being done by SQL Server. A rate of 90% or higher is desirable. Conclusion These blog posts are a quick start to a story that will be developing more in the near future. We’re still continuing our testing and investigations to provide more detailed configuration guidelines with example performance numbers with a white paper in the upcoming months. Now it’s time to give SQL Server and Hyper-V Dynamic Memory a try. Use this guidelines to kick-start your environment. See what you think about it and let us know of your experiences. - Serdar Sutay Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • Deploying Data Mining Models using Model Export and Import

    - by [email protected]
    In this post, we'll take a look at how Oracle Data Mining facilitates model deployment. After building and testing models, a next step is often putting your data mining model into a production system -- referred to as model deployment. The ability to move data mining model(s) easily into a production system can greatly speed model deployment, and reduce the overall cost. Since Oracle Data Mining provides models as first class database objects, models can be manipulated using familiar database techniques and technology. For example, one or more models can be exported to a flat file, similar to a database table dump file (.dmp). This file can be moved to a different instance of Oracle Database EE, and then imported. All methods for exporting and importing models are based on Oracle Data Pump technology and found in the DBMS_DATA_MINING package. Before performing the actual export or import, a directory object must be created. A directory object is a logical name in the database for a physical directory on the host computer. Read/write access to a directory object is necessary to access the host computer file system from within Oracle Database. For our example, we'll work in the DMUSER schema. First, DMUSER requires the privilege to create any directory. This is often granted through the sysdba account. grant create any directory to dmuser; Now, DMUSER can create the directory object specifying the path where the exported model file (.dmp) should be placed. In this case, on a linux machine, we have the directory /scratch/oracle. CREATE OR REPLACE DIRECTORY dmdir AS '/scratch/oracle'; If you aren't sure of the exact name of the model or models to export, you can find the list of models using the following query: select model_name from user_mining_models; There are several options when exporting models. We can export a single model, multiple models, or all models in a schema using the following procedure calls: BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODEL.dmp','dmdir','name =''MY_DT_MODEL'''); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODELS.dmp','dmdir',              'name IN (''MY_DT_MODEL'',''MY_KM_MODEL'')'); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('ALL_DMUSER_MODELS.dmp','dmdir'); END; A .dmp file can be imported into another schema or database using the following procedure call, for example: BEGIN   DBMS_DATA_MINING.IMPORT_MODEL('MY_MODELS.dmp', 'dmdir'); END; As with models from any data mining tool, when moving a model from one environment to another, care needs to be taken to ensure the transformations that prepare the data for model building are matched (with appropriate parameters and statistics) in the system where the model is deployed. Oracle Data Mining provides automatic data preparation (ADP) and embedded data preparation (EDP) to reduce, or possibly eliminate, the need to explicitly transport transformations with the model. In the case of ADP, ODM automatically prepares the data and includes the necessary transformations in the model itself. In the case of EDP, users can associate their own transformations with attributes of a model. These transformations are automatically applied when applying the model to data, i.e., scoring. Exporting and importing a model with ADP or EDP results in these transformations being immediately available with the model in the production system.

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  • SQL SERVER – SSMS: Memory Usage By Memory Optimized Objects Report

    - by Pinal Dave
    At conferences and at speaking engagements at the local UG, there is one question that keeps on coming which I wish were never asked. The question around, “Why is SQL Server using up all the memory and not releasing even when idle?” Well, the answer can be long and with the release of SQL Server 2014, this got even more complicated. This release of SQL Server 2014 has the option of introducing In-Memory OLTP which is completely new concept and our dependency on memory has increased multifold. In reality, nothing much changes but we have memory optimized objects (Tables and Stored Procedures) additional which are residing completely in memory and improving performance. As a DBA, it is humanly impossible to get a hang of all the innovations and the new features introduced in the next version. So today’s blog is around the report added to SSMS which gives a high level view of this new feature addition. This reports is available only from SQL Server 2014 onwards because the feature was introduced in SQL Server 2014. Earlier versions of SQL Server Management Studio would not show the report in the list. If we try to launch the report on the database which is not having In-Memory File group defined, then we would see the message in report. To demonstrate, I have created new fresh database called MemoryOptimizedDB with no special file group. Here is the query used to identify whether a database has memory-optimized file group or not. SELECT TOP(1) 1 FROM sys.filegroups FG WHERE FG.[type] = 'FX' Once we add filegroup using below command, we would see different version of report. USE [master] GO ALTER DATABASE [MemoryOptimizedDB] ADD FILEGROUP [IMO_FG] CONTAINS MEMORY_OPTIMIZED_DATA GO The report is still empty because we have not defined any Memory Optimized table in the database.  Total allocated size is shown as 0 MB. Now, let’s add the folder location into the filegroup and also created few in-memory tables. We have used the nomenclature of IMO to denote “InMemory Optimized” objects. USE [master] GO ALTER DATABASE [MemoryOptimizedDB] ADD FILE ( NAME = N'MemoryOptimizedDB_IMO', FILENAME = N'E:\Program Files\Microsoft SQL Server\MSSQL12.SQL2014\MSSQL\DATA\MemoryOptimizedDB_IMO') TO FILEGROUP [IMO_FG] GO You may have to change the path based on your SQL Server configuration. Below is the script to create the table. USE MemoryOptimizedDB GO --Drop table if it already exists. IF OBJECT_ID('dbo.SQLAuthority','U') IS NOT NULL DROP TABLE dbo.SQLAuthority GO CREATE TABLE dbo.SQLAuthority ( ID INT IDENTITY NOT NULL, Name CHAR(500)  COLLATE Latin1_General_100_BIN2 NOT NULL DEFAULT 'Pinal', CONSTRAINT PK_SQLAuthority_ID PRIMARY KEY NONCLUSTERED (ID), INDEX hash_index_sample_memoryoptimizedtable_c2 HASH (Name) WITH (BUCKET_COUNT = 131072) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO As soon as above script is executed, table and index both are created. If we run the report again, we would see something like below. Notice that table memory is zero but index is using memory. This is due to the fact that hash index needs memory to manage the buckets created. So even if table is empty, index would consume memory. More about the internals of how In-Memory indexes and tables work will be reserved for future posts. Now, use below script to populate the table with 10000 rows INSERT INTO SQLAuthority VALUES (DEFAULT) GO 10000 Here is the same report after inserting 1000 rows into our InMemory table.    There are total three sections in the whole report. Total Memory consumed by In-Memory Objects Pie chart showing memory distribution based on type of consumer – table, index and system. Details of memory usage by each table. The information about all three is taken from one single DMV, sys.dm_db_xtp_table_memory_stats This DMV contains memory usage statistics for both user and system In-Memory tables. If we query the DMV and look at data, we can easily notice that the system tables have negative object IDs.  So, to look at user table memory usage, below is the over-simplified version of query. USE MemoryOptimizedDB GO SELECT OBJECT_NAME(OBJECT_ID), * FROM sys.dm_db_xtp_table_memory_stats WHERE OBJECT_ID > 0 GO This report would help DBA to identify which in-memory object taking lot of memory which can be used as a pointer for designing solution. I am sure in future we will discuss at lengths the whole concept of In-Memory tables in detail over this blog. To read more about In-Memory OLTP, have a look at In-Memory OLTP Series at Balmukund’s Blog. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Memory, SQL Reports

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  • Python Memory leak - Solved, but still puzzled

    - by disappearedng
    Dear everyone, I have successfully debugged my own memory leak problems. However, I have noticed some very strange occurence. for fid, fv in freqDic.iteritems(): outf.write(fid+"\t") #ID for i, term in enumerate(domain): #Vector tfidf = self.tf(term, fv) * self.idf( term, docFreqDic) if i == len(domain) - 1: outf.write("%f\n" % tfidf) else: outf.write("%f\t" % tfidf) outf.flush() print "Memory increased by", int(self.memory_mon.usage()) - startMemory outf.close() def tf(self, term, freqVector): total = freqVector[TOTAL] if total == 0: return 0 if term not in freqVector: ## When you don't have these lines memory leaks occurs return 0 ## return float(freqVector[term]) / freqVector[TOTAL] def idf(self, term, docFrequencyPerTerm): if term not in docFrequencyPerTerm: return 0 return math.log( float(docFrequencyPerTerm[TOTAL])/docFrequencyPerTerm[term]) Basically let me describe my problem: 1) I am doing tfidf calculations 2) I traced that the source of memory leaks is coming from defaultdict. 3) I am using the memory_mon from http://stackoverflow.com/questions/276052/how-to-get-current-cpu-and-ram-usage-in-python 4) The reason for my memory leaks is as follows: a) in self.tf, if the lines: if term not in freqVector: return 0 are not added that will cause the memory leak. (I verified this myself using memory_mon and noticed a sharp increase in memory that kept on increasing) The solution to my problem was 1) since fv is a defaultdict, any reference to it that are not found in fv will create an entry. Over a very large domain, this will cause memory leaks. I decided to use dict instead of default dict and the memory problem did go away. My only puzzle is: since fv is created in "for fid, fv in freqDic.iteritems():" shouldn't fv be destroyed at the end of every for loop? I tried putting gc.collect() at the end of the for loop but gc was not able to collect everything (returns 0). Yes, the hypothesis is right, but the memory should stay fairly consistent with ever for loop if for loops do destroy all temp variables. This is what it looks like with that two line in self.tf: Memory increased by 12 Memory increased by 948 Memory increased by 28 Memory increased by 36 Memory increased by 36 Memory increased by 32 Memory increased by 28 Memory increased by 32 Memory increased by 32 Memory increased by 32 Memory increased by 40 Memory increased by 32 Memory increased by 32 Memory increased by 28 and without the the two line: Memory increased by 1652 Memory increased by 3576 Memory increased by 4220 Memory increased by 5760 Memory increased by 7296 Memory increased by 8840 Memory increased by 10456 Memory increased by 12824 Memory increased by 13460 Memory increased by 15000 Memory increased by 17448 Memory increased by 18084 Memory increased by 19628 Memory increased by 22080 Memory increased by 22708 Memory increased by 24248 Memory increased by 26704 Memory increased by 27332 Memory increased by 28864 Memory increased by 30404 Memory increased by 32856 Memory increased by 33552 Memory increased by 35024 Memory increased by 36564 Memory increased by 39016 Memory increased by 39924 Memory increased by 42104 Memory increased by 42724 Memory increased by 44268 Memory increased by 46720 Memory increased by 47352 Memory increased by 48952 Memory increased by 50428 Memory increased by 51964 Memory increased by 53508 Memory increased by 55960 Memory increased by 56584 Memory increased by 58404 Memory increased by 59668 Memory increased by 61208 Memory increased by 62744 Memory increased by 64400 I look forward to your answer

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  • Memory mapped files causes low physical memory

    - by harik
    I have a 2GB RAM and running a memory intensive application and going to low available physical memory state and system is not responding to user actions, like opening any application or menu invocation etc. How do I trigger or tell the system to swap the memory to pagefile and free physical memory? I'm using Windows XP. If I run the same application on 4GB RAM machine it is not the case, system response is good. After getting choked of available physical memory system automatically swaps to pagefile and free physical memory, not that bad as 2GB system. To overcome this problem (on 2GB machine) attempted to use memory mapped files for large dataset which are allocated by application. In this case virtual memory of the application(process) is fine but system cache is high and same problem as above that physical memory is less. Even though memory mapped file is not mapped to process virtual memory system cache is high. why???!!! :( Any help is appreciated. Thanks.

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  • PHP: Aggregate Model Classes or Uber Model Classes?

    - by sunwukung
    In many of the discussions regarding the M in MVC, (sidestepping ORM controversies for a moment), I commonly see Model classes described as object representations of table data (be that an Active Record, Table Gateway, Row Gateway or Domain Model/Mapper). Martin Fowler warns against the development of an anemic domain model, i.e. a class that is nothing more than a wrapper for CRUD functionality. I've been working on an MVC application for a couple of months now. The DBAL in the application I'm working on started out simple (on account of my understanding - oh the benefits of hindsight), and is organised so that Controllers invoke Business Logic classes, that in turn access the database via DAO/Transaction Scripts pertinent to the task at hand. There are a few "Entity" classes that aggregate these DAO objects to provide a convenient CRUD wrapper, but also embody some of the "behaviour" of that Domain concept (for example, a user - since it's easy to isolate). Taking a look at some of the code, and thinking along refactoring some of the code into a Rich Domain Model, it occurred to me that were I to try and wrap the CRUD routines and behaviour of say, a Company into a single "Model" class, that would be a sizeable class. So, my question is this: do Models represent domain objects, business logic, service layers, all of the above combined? How do you go about defining the responsibilities for these components?

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  • Ubuntu virtual memory caches suck up memory

    - by Tom
    Hey all, I've got an Ubuntu 9.10 64-bit server that seems to use up all available memory. According to my munin graphs, almost all of the memory used up is in the swap cache, cache, and slab cache. (I take this to mean virtual memory caches, am I right in assuming this?) Once memory usage approaches 100%, some (although not all) system services such as SSH become sluggish and unresponsive. After rebooting the system, performance and memory usage become normal for a time. Some interesting tidbits: The system runs Apache 2, MySQL, Munin, and sshd. The memory usage spikes happen at the same time every night (at 10 PM sharp.) There appears to be nothing in the crontab for any of the users, and nothing in /etc/cron.d/* out of the ordinary, let alone something that would occur at 10 PM. My question is, how do I figure out what is causing the memory suckage? I've tried the usual utilities (e.g. ps, top, etc) but I can't seem to find anything unusual. Any ideas? Thanks in advance!

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  • Oracle’s New Memory-Optimized x86 Servers: Getting the Most Out of Oracle Database In-Memory

    - by Josh Rosen, x86 Product Manager-Oracle
    With the launch of Oracle Database In-Memory, it is now possible to perform real-time analytics operations on your business data as it exists at that moment – in the DRAM of the server – and immediately return completely current and consistent data. The Oracle Database In-Memory option dramatically accelerates the performance of analytics queries by storing data in a highly optimized columnar in-memory format.  This is a truly exciting advance in database technology.As Larry Ellison mentioned in his recent webcast about Oracle Database In-Memory, queries run 100 times faster simply by throwing a switch.  But in order to get the most from the Oracle Database In-Memory option, the underlying server must also be memory-optimized. This week Oracle announced new 4-socket and 8-socket x86 servers, the Sun Server X4-4 and Sun Server X4-8, both of which have been designed specifically for Oracle Database In-Memory.  These new servers use the fastest Intel® Xeon® E7 v2 processors and each subsystem has been designed to be the best for Oracle Database, from the memory, I/O and flash technologies right down to the system firmware.Amongst these subsystems, one of the most important aspects we have optimized with the Sun Server X4-4 and Sun Server X4-8 are their memory subsystems.  The new In-Memory option makes it possible to select which parts of the database should be memory optimized.  You can choose to put a single column or table in memory or, if you can, put the whole database in memory.  The more, the better.  With 3 TB and 6 TB total memory capacity on the Sun Server X4-4 and Sun Server X4-8, respectively, you can memory-optimize more, if not your entire database.   Sun Server X4-8 CMOD with 24 DIMM slots per socket (up to 192 DIMM slots per server) But memory capacity is not the only important factor in selecting the best server platform for Oracle Database In-Memory.  As you put more of your database in memory, a critical performance metric known as memory bandwidth comes into play.  The total memory bandwidth for the server will dictate the rate in which data can be stored and retrieved from memory.  In order to achieve real-time analysis of your data using Oracle Database In-Memory, even under heavy load, the server must be able to handle extreme memory workloads.  With that in mind, the Sun Server X4-8 was designed with the maximum possible memory bandwidth, providing over a terabyte per second of total memory bandwidth.  Likewise, the Sun Server X4-4 also provides extreme memory bandwidth in an even more compact form factor with over half a terabyte per second, providing customers with scalability and choice depending on the size of the database.Beyond the memory subsystem, Oracle’s Sun Server X4-4 and Sun Server X4-8 systems provide other key technologies that enable Oracle Database to run at its best.  The Sun Server X4-4 allows for up 4.8 TB of internal, write-optimized PCIe flash while the Sun Server X4-8 allows for up to 6.4 TB of PCIe flash.  This enables dramatic acceleration of data inserts and updates to Oracle Database.  And with the new elastic computing capability of Oracle’s new x86 servers, server performance can be adapted to your specific Oracle Database workload to ensure that every last bit of processing power is utilized.Because Oracle designs and tests its x86 servers specifically for Oracle workloads, we provide the highest possible performance and reliability when running Oracle Database.  To learn more about Sun Server X4-4 and Sun Server X4-8, you can find more details including data sheets and white papers here. Josh Rosen is a Principal Product Manager for Oracle’s x86 servers, focusing on Oracle’s operating systems and software.  He previously spent more than a decade as a developer and architect of system management software. Josh has worked on system management for many of Oracle's hardware products ranging from the earliest blade systems to the latest Oracle x86 servers. 

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  • How to disable Mac OS X from using swap when there still is "Inactive" memory?

    - by Motin
    A common phenomena in my day to day usage (and several other's according to various posts throughout the internet) of OS X, the system seems to become slow whenever there is no more "Free" memory available. Supposedly, this is due to swapping, since heavy disk activity is apparent and that vm_stat reports many pageouts. (Correct me from wrong) However, the amount of "Inactive" ram is typically around 12.5%-25% of all available memory (^1.) when swapping starts/occurs/ends. According to http://support.apple.com/kb/ht1342 : Inactive memory This information in memory is not actively being used, but was recently used. For example, if you've been using Mail and then quit it, the RAM that Mail was using is marked as Inactive memory. This Inactive memory is available for use by another application, just like Free memory. However, if you open Mail before its Inactive memory is used by a different application, Mail will open quicker because its Inactive memory is converted to Active memory, instead of loading Mail from the slower hard disk. And according to http://developer.apple.com/library/mac/#documentation/Performance/Conceptual/ManagingMemory/Articles/AboutMemory.html : The inactive list contains pages that are currently resident in physical memory but have not been accessed recently. These pages contain valid data but may be released from memory at any time. So, basically: When a program has quit, it's memory becomes marked as Inactive and should be claimable at any time. Still, OS X will prefer to start swapping out memory to the Swap file instead of just claiming this memory, whenever the "Free" memory gets to low. Why? What is the advantage of this behavior over, say, instantly releasing Inactive memory and not even touch the swap file? Some sources (^2.) indicate that OS X would page out the "Inactive" memory to swap before releasing it, but that doesn't make sense now does it if the memory may be released from memory at any time? Swapping is expensive, releasing is cheap, right? Can this behavior be changed using some preference or known hack? (Preferably one that doesn't include disabling swap/dynamic_pager altogether and restarting...) I do appreciate the purge command, as well as the concept of Repairing disk permissions to force some Free memory, but those are ways to painfully force more Free memory than to actually fixing the swap/release decision logic... Btw a similar question was asked here: http://forums.macnn.com/90/mac-os-x/434650/why-does-os-x-swap-when/ and here: http://hintsforums.macworld.com/showthread.php?t=87688 but even though the OPs re-asked the core question, none of the replies addresses an answer to it... ^1. UPDATE 17-mar-2012 Since I first posted this question, I have gone from 4gb to 8gb of installed ram, and the problem remains. The amount of "Inactive" ram was 0.5gb-1.0gb before and is now typically around 1.0-2.0GB when swapping starts/occurs/ends, ie it seems that around 12.5%-25% of the ram is preserved as Inactive by osx kernel logic. ^2. For instance http://apple.stackexchange.com/questions/4288/what-does-it-mean-if-i-have-lots-of-inactive-memory-at-the-end-of-a-work-day : Once all your memory is used (free memory is 0), the OS will write out inactive memory to the swapfile to make more room in active memory. UPDATE 17-mar-2012 Here is a round-up of the methods that have been suggested to help so far: The purge command "Used to approximate initial boot conditions with a cold disk buffer cache for performance analysis. It does not affect anonymous memory that has been allocated through malloc, vm_allocate, etc". This is useful to prevent osx to swap-out the disk cache (which is ridiculous that osx actually does so in the first place), but with the downside that the disk cache is released, meaning that if the disk cache was not about to be swapped out, one would simply end up with a cold disk buffer cache, probably affecting performance negatively. The FreeMemory app and/or Repairing disk permissions to force some Free memory Doesn't help releasing any memory, only moving some gigabytes of memory contents from ram to the hd. In the end, this causes lots of swap-ins when I attempt to use the applications that were open while freeing memory, as a lot of its vm is now on swap. Speeding up swap-allocation using dynamicpagerwrapper Seems a good thing to do in order to speed up swap-usage, but does not address the problem of osx swapping in the first place while there is still inactive memory. Disabling swap by disabling dynamicpager and restarting This will force osx not to use swap to the price of the system hanging when all memory is used. Not a viable alternative... Disabling swap using a hacked dynamicpager Similar to disabling dynamicpager above, some excerpts from the comments to the blog post indicate that this is not a viable solution: "The Inactive Memory is high as usual". "when your system is running out of memory, the whole os hangs...", "if you consume the whole amount of memory of the mac, the machine will likely hang" To sum up, I am still unaware of a way of disabling Mac OS X from using swap when there still is "Inactive" memory. If it isn't possible, maybe at least there is an explanation somewhere of why osx prefers to swap out memory that may be released from memory at any time?

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  • Video memory buswidth vs video memory Bandwidth

    - by Mixxiphoid
    My current video card (9600GT) is dying and I'm searching for a new video card. Between acquiring my current one and now, I got a lot more knowledge about hardware and I want to use that to pick my new card. So I decided to not just buy some popular card blindly, but to search for a card able to handle my hardware requirements. I searched the specs at the NVidia site for the GT640 and was confused by the memory section and some questions raised. My current card's memory bus width is 256bit and has 1GB of memory. I checked Google about the importance of bus width. And all the links basically said the same 'The higher the number the more potential simultaneously traffic can be transferred'. This was already clear to me, yet there are currently a lot of new cards which are considered better than my current one with a lower bus width. To go in more detail about my question I copied the memory info from the NVidia site: GT 640 GT640 GDDR5 Memory Specs: Memory Clock 1.8 Gbps 5.0 Gbps Standard Memory Config 2048 MB 1024 MB Memory Interface DDR3 GDDR5 Memory Interface Width 128-bit 64-bit Memory Bandwidth (GB/sec) 28.5 40.0 What puzzled me is that the Memory Bandwidth seems to me the most important part, yet the lower bus width has the higher 'performance'. Is this due to the fact the memory interface is GDDR5 and is therefore able to have a higher memory clock speed (5Gbps)? If I am to buy a new video card, should I check the bus width? Memory clock? Bandwith? Amount of memory? My current card ahs 1GB memory, so I was searching for a 2GB memory card, but now I'm not so sure any more whether that is really 'better'. My main question: To me it seems that memory performance is made up by the combination of bus width and frequency. Is this true? If yes, why are there so many sites telling me I need to get a card with a high bus width? If no, then what IS important when it goes about memory performance on a video card. NOTE: The memory bandwidth is (almost) never displayed on vendor sites. How can I determine which card is better without knowing the bandwith?

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  • Dynamic Memory Allocation and Memory Management

    - by Bunkai.Satori
    In an average game, there are hundreds or maybe thousands of objects in the scene. Is it completely correct to allocate memory for all objects, including gun shots (bullets), dynamically via default new()? Should I create any memory pool for dynamic allocation, or is there no need to bother with this? What if the target platform are mobile devices? Is there a need for a memory manager in a mobile game, please? Thank you. Language Used: C++; Currently developed under Windows, but planned to be ported later.

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  • Coldfusion on VPS, how much JVM heap memory?

    - by Steven Filipowicz
    Recently I got a VPS server and I'm running Coldfusion, the website was running fine until it got more and more traffic and I started to encounter 'OutOfMemory' exceptions. I thought simply to rise the memory of the VPS server, but this didn't help. After doing some Google searches I found a setting in de CF Admin settings to set the JVM Heap memory. It was on the standard: Max Heap size 512MB and Min Heap size was empty. After playing around a bit I have now set it to Min 50MB and Max 200MB, good things is that I'm not getting the 'OutOfMemory' exceptions anymore. So far so good! But with about 50 active visitors on the website, the website starts to get slow. The CPU usage is only about 8% (Windows Taskmanager), also the taskmanager show only about 30% of the 3GB RAM in use. So I'm thinking that my values could be tweaked to use more of the RAM. Honestly I don't understand these JVM Memory heap settings, so I have no clue what is a good setting for me. I found a CF script that displays the memory usage, the details are: Heap Memory Usage - Committed 194 MB Heap Memory Usage - Initial 50.0 MB Heap Memory Usage - Max 194 MB Heap Memory Usage - Used 163 MB JVM - Free Memory 31.2 MB JVM - Max Memory 194 MB JVM - Total Memory 194 MB JVM - Used Memory 163 MB Memory Pool - Code Cache - Used 13.0 MB Memory Pool - PS Eden Space - Used 6.75 MB Memory Pool - PS Old Gen - Used 155 MB Memory Pool - PS Perm Gen - Used 64.2 MB Memory Pool - PS Survivor Space - Used 1.07 MB Non-Heap Memory Usage - Committed 77.4 MB Non-Heap Memory Usage - Initial 18.3 MB Non-Heap Memory Usage - Max 240 MB Non-Heap Memory Usage - Used 77.2 MB Free Allocated Memory: 30mb Total Memory Allocated: 194mb Max Memory Available to JVM: 194mb % of Free Allocated Memory: 16% % of Available Memory Allocated: 100% My JVM arguments are: -server -Dsun.io.useCanonCaches=false -XX:MaxPermSize=192m -XX:+UseParallelGC - Dcoldfusion.rootDir={application.home}/../ -Dcoldfusion.libPath={application.home}/../lib Can I give the JVM more memory? If so, what settings should I use? Thanks very much!!

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  • Rails model belongs to model that belongs to model but i want to use another name

    - by Micke
    Hello. This may be a stupid question but im just starting to learn Rail thats why i am asking thsi question. I have one model called "User" which handles all the users in my community. Now i want to add a guestbook to every user. So i created a model called "user_guestbook" and inserted this into the new model: belongs_to :user and this into the user model: has_one :user_guestbook, :as => :guestbook The next thing i did was to add a new model to handle the posts inside the guestbook. I named it "guestbook_posts" and added this code into the new model: belongs_to :user_guestbook And this into the user_guestbook model: has_many :guestbook_posts, :as => :posts What i wanted to achive was to be able to fetch all the posts to a certain user by: @user = User.find(1) puts @user.guestbook.posts But it doesnt work for me. I dont know what i am doing wrong and if there is any easier way to do this please tell me so. Just to note, i have created some migrations for it to as follows: create_user_guestbook: t.integer :user_id create_guestbook_posts: t.integer :guestbook_id t.integer :from_user t.string :post Thanks in advance!

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  • Java / Tomcat memory leak in RedHat Linux?

    - by black-rocky
    Hi, I've got a Red Hat box with 6G memory running Tomcat and I'm trying to figure out how much memory I have left on the box. Problem is, top & jconsole is showing one figure (around 200M), and system monitor is showing a different figure (around 2G). Does anybody know what the difference is? I'm not sure if there is a memory leak happenning here, but the highest memory consumer is a tomcat process that's taking 2.2G of memory. Screenshots below:

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  • iPad receiving memory warning with low memory use

    - by Fer
    I have an UIWebKit with a HTML, this HTML have several images and text, but just displaying it gives me the memory warning. So I did some tests: The same HTML with different images, fullsize, and after the same images but reduced 50% from it's original size, for the 50% reduced images, I went to preview and reduced all images in 50% The surprising part is the 50% test, you can see that even with 16 images, the memory peak is 4.90MB. That's really surprising. Notice that these values are not always the same, they change but there's not a huge difference between the tests. In the 50% issue, in the 8 and 16 images, although the memory is low, sometimes a memory warning appears, but the performance enhance is noticeable compared to the full size images standing still = memory after scrolling all article 1 Image = [standing still 5MB] [rotating 5.6MB] 2 Images = [standing still 6.99MB] [rotating 7.7MB] 3 Images = [standing still 9.04MB] [rotating 10.9MB] 4 Images = [standing still 10.89MB] [rotating 13.20MB] 8 Images = [standing still 23.14MB] [rotating 25.20MB] (sometimes crashes) 16 Images = [standing still 27.14MB and app crashes] 50% 1 Image = [standing still 3.2MB] [rotating 3.67MB] 2 Image = [standing still 3.2MB] [rotating 3.70MB] 3 Image = [standing still 3.3MB] [rotating 3.79MB] 4 Image = [standing still 3.3MB] [rotating 3.80MB] 8 Images = [standing still 4.29MB] [rotating 4,63MB] (sometimes crashes) 16 Images = [standing still 4.79MB] [rotating 4,90MB] (sometimes crashes) My question is: The app sometimes crashed with 16 small images. Why? The memory was much lower. What is the limit of memory use? These numbers are helpful if you also tell us the maximum. But, the maximum seemed different with the 50% size images. 13.2MB works for large images and 3.8 for small images. Anything higher sometimes crashes. That makes no sense.

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  • Oracle BI Server Modeling, Part 1- Designing a Query Factory

    - by bob.ertl(at)oracle.com
      Welcome to Oracle BI Development's BI Foundation blog, focused on helping you get the most value from your Oracle Business Intelligence Enterprise Edition (BI EE) platform deployments.  In my first series of posts, I plan to show developers the concepts and best practices for modeling in the Common Enterprise Information Model (CEIM), the semantic layer of Oracle BI EE.  In this segment, I will lay the groundwork for the modeling concepts.  First, I will cover the big picture of how the BI Server fits into the system, and how the CEIM controls the query processing. Oracle BI EE Query Cycle The purpose of the Oracle BI Server is to bridge the gap between the presentation services and the data sources.  There are typically a variety of data sources in a variety of technologies: relational, normalized transaction systems; relational star-schema data warehouses and marts; multidimensional analytic cubes and financial applications; flat files, Excel files, XML files, and so on. Business datasets can reside in a single type of source, or, most of the time, are spread across various types of sources. Presentation services users are generally business people who need to be able to query that set of sources without any knowledge of technologies, schemas, or how sources are organized in their company. They think of business analysis in terms of measures with specific calculations, hierarchical dimensions for breaking those measures down, and detailed reports of the business transactions themselves.  Most of them create queries without knowing it, by picking a dashboard page and some filters.  Others create their own analysis by selecting metrics and dimensional attributes, and possibly creating additional calculations. The BI Server bridges that gap from simple business terms to technical physical queries by exposing just the business focused measures and dimensional attributes that business people can use in their analyses and dashboards.   After they make their selections and start the analysis, the BI Server plans the best way to query the data sources, writes the optimized sequence of physical queries to those sources, post-processes the results, and presents them to the client as a single result set suitable for tables, pivots and charts. The CEIM is a model that controls the processing of the BI Server.  It provides the subject areas that presentation services exposes for business users to select simplified metrics and dimensional attributes for their analysis.  It models the mappings to the physical data access, the calculations and logical transformations, and the data access security rules.  The CEIM consists of metadata stored in the repository, authored by developers using the Administration Tool client.     Presentation services and other query clients create their queries in BI EE's SQL-92 language, called Logical SQL or LSQL.  The API simply uses ODBC or JDBC to pass the query to the BI Server.  Presentation services writes the LSQL query in terms of the simplified objects presented to the users.  The BI Server creates a query plan, and rewrites the LSQL into fully-detailed SQL or other languages suitable for querying the physical sources.  For example, the LSQL on the left below was rewritten into the physical SQL for an Oracle 11g database on the right. Logical SQL   Physical SQL SELECT "D0 Time"."T02 Per Name Month" saw_0, "D4 Product"."P01  Product" saw_1, "F2 Units"."2-01  Billed Qty  (Sum All)" saw_2 FROM "Sample Sales" ORDER BY saw_0, saw_1       WITH SAWITH0 AS ( select T986.Per_Name_Month as c1, T879.Prod_Dsc as c2,      sum(T835.Units) as c3, T879.Prod_Key as c4 from      Product T879 /* A05 Product */ ,      Time_Mth T986 /* A08 Time Mth */ ,      FactsRev T835 /* A11 Revenue (Billed Time Join) */ where ( T835.Prod_Key = T879.Prod_Key and T835.Bill_Mth = T986.Row_Wid) group by T879.Prod_Dsc, T879.Prod_Key, T986.Per_Name_Month ) select SAWITH0.c1 as c1, SAWITH0.c2 as c2, SAWITH0.c3 as c3 from SAWITH0 order by c1, c2   Probably everybody reading this blog can write SQL or MDX.  However, the trick in designing the CEIM is that you are modeling a query-generation factory.  Rather than hand-crafting individual queries, you model behavior and relationships, thus configuring the BI Server machinery to manufacture millions of different queries in response to random user requests.  This mass production requires a different mindset and approach than when you are designing individual SQL statements in tools such as Oracle SQL Developer, Oracle Hyperion Interactive Reporting (formerly Brio), or Oracle BI Publisher.   The Structure of the Common Enterprise Information Model (CEIM) The CEIM has a unique structure specifically for modeling the relationships and behaviors that fill the gap from logical user requests to physical data source queries and back to the result.  The model divides the functionality into three specialized layers, called Presentation, Business Model and Mapping, and Physical, as shown below. Presentation services clients can generally only see the presentation layer, and the objects in the presentation layer are normally the only ones used in the LSQL request.  When a request comes into the BI Server from presentation services or another client, the relationships and objects in the model allow the BI Server to select the appropriate data sources, create a query plan, and generate the physical queries.  That's the left to right flow in the diagram below.  When the results come back from the data source queries, the right to left relationships in the model show how to transform the results and perform any final calculations and functions that could not be pushed down to the databases.   Business Model Think of the business model as the heart of the CEIM you are designing.  This is where you define the analytic behavior seen by the users, and the superset library of metric and dimension objects available to the user community as a whole.  It also provides the baseline business-friendly names and user-readable dictionary.  For these reasons, it is often called the "logical" model--it is a virtual database schema that persists no data, but can be queried as if it is a database. The business model always has a dimensional shape (more on this in future posts), and its simple shape and terminology hides the complexity of the source data models. Besides hiding complexity and normalizing terminology, this layer adds most of the analytic value, as well.  This is where you define the rich, dimensional behavior of the metrics and complex business calculations, as well as the conformed dimensions and hierarchies.  It contributes to the ease of use for business users, since the dimensional metric definitions apply in any context of filters and drill-downs, and the conformed dimensions enable dashboard-wide filters and guided analysis links that bring context along from one page to the next.  The conformed dimensions also provide a key to hiding the complexity of many sources, including federation of different databases, behind the simple business model. Note that the expression language in this layer is LSQL, so that any expression can be rewritten into any data source's query language at run time.  This is important for federation, where a given logical object can map to several different physical objects in different databases.  It is also important to portability of the CEIM to different database brands, which is a key requirement for Oracle's BI Applications products. Your requirements process with your user community will mostly affect the business model.  This is where you will define most of the things they specifically ask for, such as metric definitions.  For this reason, many of the best-practice methodologies of our consulting partners start with the high-level definition of this layer. Physical Model The physical model connects the business model that meets your users' requirements to the reality of the data sources you have available. In the query factory analogy, think of the physical layer as the bill of materials for generating physical queries.  Every schema, table, column, join, cube, hierarchy, etc., that will appear in any physical query manufactured at run time must be modeled here at design time. Each physical data source will have its own physical model, or "database" object in the CEIM.  The shape of each physical model matches the shape of its physical source.  In other words, if the source is normalized relational, the physical model will mimic that normalized shape.  If it is a hypercube, the physical model will have a hypercube shape.  If it is a flat file, it will have a denormalized tabular shape. To aid in query optimization, the physical layer also tracks the specifics of the database brand and release.  This allows the BI Server to make the most of each physical source's distinct capabilities, writing queries in its syntax, and using its specific functions. This allows the BI Server to push processing work as deep as possible into the physical source, which minimizes data movement and takes full advantage of the database's own optimizer.  For most data sources, native APIs are used to further optimize performance and functionality. The value of having a distinct separation between the logical (business) and physical models is encapsulation of the physical characteristics.  This encapsulation is another enabler of packaged BI applications and federation.  It is also key to hiding the complex shapes and relationships in the physical sources from the end users.  Consider a routine drill-down in the business model: physically, it can require a drill-through where the first query is MDX to a multidimensional cube, followed by the drill-down query in SQL to a normalized relational database.  The only difference from the user's point of view is that the 2nd query added a more detailed dimension level column - everything else was the same. Mappings Within the Business Model and Mapping Layer, the mappings provide the binding from each logical column and join in the dimensional business model, to each of the objects that can provide its data in the physical layer.  When there is more than one option for a physical source, rules in the mappings are applied to the query context to determine which of the data sources should be hit, and how to combine their results if more than one is used.  These rules specify aggregate navigation, vertical partitioning (fragmentation), and horizontal partitioning, any of which can be federated across multiple, heterogeneous sources.  These mappings are usually the most sophisticated part of the CEIM. Presentation You might think of the presentation layer as a set of very simple relational-like views into the business model.  Over ODBC/JDBC, they present a relational catalog consisting of databases, tables and columns.  For business users, presentation services interprets these as subject areas, folders and columns, respectively.  (Note that in 10g, subject areas were called presentation catalogs in the CEIM.  In this blog, I will stick to 11g terminology.)  Generally speaking, presentation services and other clients can query only these objects (there are exceptions for certain clients such as BI Publisher and Essbase Studio). The purpose of the presentation layer is to specialize the business model for different categories of users.  Based on a user's role, they will be restricted to specific subject areas, tables and columns for security.  The breakdown of the model into multiple subject areas organizes the content for users, and subjects superfluous to a particular business role can be hidden from that set of users.  Customized names and descriptions can be used to override the business model names for a specific audience.  Variables in the object names can be used for localization. For these reasons, you are better off thinking of the tables in the presentation layer as folders than as strict relational tables.  The real semantics of tables and how they function is in the business model, and any grouping of columns can be included in any table in the presentation layer.  In 11g, an LSQL query can also span multiple presentation subject areas, as long as they map to the same business model. Other Model Objects There are some objects that apply to multiple layers.  These include security-related objects, such as application roles, users, data filters, and query limits (governors).  There are also variables you can use in parameters and expressions, and initialization blocks for loading their initial values on a static or user session basis.  Finally, there are Multi-User Development (MUD) projects for developers to check out units of work, and objects for the marketing feature used by our packaged customer relationship management (CRM) software.   The Query Factory At this point, you should have a grasp on the query factory concept.  When developing the CEIM model, you are configuring the BI Server to automatically manufacture millions of queries in response to random user requests. You do this by defining the analytic behavior in the business model, mapping that to the physical data sources, and exposing it through the presentation layer's role-based subject areas. While configuring mass production requires a different mindset than when you hand-craft individual SQL or MDX statements, it builds on the modeling and query concepts you already understand. The following posts in this series will walk through the CEIM modeling concepts and best practices in detail.  We will initially review dimensional concepts so you can understand the business model, and then present a pattern-based approach to learning the mappings from a variety of physical schema shapes and deployments to the dimensional model.  Along the way, we will also present the dimensional calculation template, and learn how to configure the many additivity patterns.

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  • Why model => model.Reason_ID turns to model =>Convert(model.Reason_ID)

    - by er-v
    I have my own html helper extension, wich I use this way <%=Html.LocalizableLabelFor(model => model.Reason_ID, Register.PurchaseReason) %> which declared like this. public static MvcHtmlString LocalizableLabelFor<T>(this HtmlHelper<T> helper, Expression<Func<T, object>> expr, string captionValue) where T : class { return helper.LocalizableLabelFor(ExpressionHelper.GetExpressionText(expr), captionValue); } but when I open it in debugger expr.Body.ToString() will show me Convert(model.Reason_ID). But should model.Reason_ID. That's a big problem, becouse ExpressionHelper.GetExpressionText(expr) returns empty string. What a strange magic is that? How can I get rid of it?

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  • SQL SERVER – Log File Growing for Model Database – model Database Log File Grew Too Big

    - by pinaldave
    After reading my earlier article SQL SERVER – master Database Log File Grew Too Big, I received an email recently from another reader asking why does the log file of model database grow every day when he is not carrying out any operation in the model database. As per the email, he is absolutely sure that he is doing nothing on his model database; he had used policy management to catch any T-SQL operation in the model database and there were none. This was indeed surprising to me. I sent a request to access to his server, which he happily agreed for and within a min, we figured out the issue. He was taking the backup of the model database every day taking the database backup every night. When I explained the same to him, he did not believe it; so I quickly wrote down the following script. The results before and after the usage of the script were very clear. What is a model database? The model database is used as the template for all databases created on an instance of SQL Server. Any object you create in the model database will be automatically created in subsequent user database created on the server. NOTE: Do not run this in production environment. During the demo, the model database was in full recovery mode and only full backup operation was performed (no log backup). Before Backup Script Backup Script in loop DECLARE @FLAG INT SET @FLAG = 1 WHILE(@FLAG < 1000) BEGIN BACKUP DATABASE [model] TO  DISK = N'D:\model.bak' SET @FLAG = @FLAG + 1 END GO After Backup Script Why did this happen? The model database was in full recovery mode and taking full backup is logged operation. As there was no log backup and only full backup was performed on the model database, the size of the log file kept growing. Resolution: Change the backup mode of model database from “Full Recovery” to “Simple Recovery.”. Take full backup of the model database “only” when you change something in the model database. Let me know if you have encountered a situation like this? If so, how did you resolve it? It will be interesting to know about your experience. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Data Modeling Resources

    - by Dejan Sarka
    You can find many different data modeling resources. It is impossible to list all of them. I selected only the most valuable ones for me, and, of course, the ones I contributed to. Books Chris J. Date: An Introduction to Database Systems – IMO a “must” to understand the relational model correctly. Terry Halpin, Tony Morgan: Information Modeling and Relational Databases – meet the object-role modeling leaders. Chris J. Date, Nikos Lorentzos and Hugh Darwen: Time and Relational Theory, Second Edition: Temporal Databases in the Relational Model and SQL – all theory needed to manage temporal data. Louis Davidson, Jessica M. Moss: Pro SQL Server 2012 Relational Database Design and Implementation – the best SQL Server focused data modeling book I know by two of my friends. Dejan Sarka, et al.: MCITP Self-Paced Training Kit (Exam 70-441): Designing Database Solutions by Using Microsoft® SQL Server™ 2005 – SQL Server 2005 data modeling training kit. Most of the text is still valid for SQL Server 2008, 2008 R2, 2012 and 2014. Itzik Ben-Gan, Lubor Kollar, Dejan Sarka, Steve Kass: Inside Microsoft SQL Server 2008 T-SQL Querying – Steve wrote a chapter with mathematical background, and I added a chapter with theoretical introduction to the relational model. Itzik Ben-Gan, Dejan Sarka, Roger Wolter, Greg Low, Ed Katibah, Isaac Kunen: Inside Microsoft SQL Server 2008 T-SQL Programming – I added three chapters with theoretical introduction and practical solutions for the user-defined data types, dynamic schema and temporal data. Dejan Sarka, Matija Lah, Grega Jerkic: Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft SQL Server 2012 – my first two chapters are about data warehouse design and implementation. Courses Data Modeling Essentials – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Logical and Physical Modeling for Analytical Applications – online course I wrote for Pluralsight. Working with Temporal data in SQL Server – my latest Pluralsight course, where besides theory and implementation I introduce many original ways how to optimize temporal queries. Forthcoming presentations SQL Bits 12, July 17th – 19th, Telford, UK – I have a full-day pre-conference seminar Advanced Data Modeling Topics there.

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  • release does not free up memory in low-memory condidtion

    - by user322945
    I am trying to follow the Apple's recommendation to handle low-memory warnings (found in Session 416 of WWDC 2009 videos) by freeing up resources used by freeing up my dataController object (referenced in my app delegate) that contains a large number of strings for read from a plist: - (void)applicationDidReceiveMemoryWarning:(UIApplication *)application { [_dataController release]; _dataController = nil; NSLog([NSString stringWithFormat:@"applicationDidReceiveMemoryWarning bottom... retain count:%i", [_dataController retainCount]]); } But when I run ObjectAlloc within Instruments and simulate a Low-Memory Condition, I don't see a decrease in the memory used by my app even though I see the NSLog statements written out and the retain count is zero for the object. I do pass references to the app delegate around to some of the view controllers. But the code above releases the reference to the _dataController object (containing the plist data) so I would expect the memory to be freed. Any help would be appreciated.

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  • The Ideal Platform for Oracle Database 12c In-Memory and in-memory Applications

    - by Michael Palmeter (Engineered Systems Product Management)
    Oracle SuperCluster, Oracle's SPARC M6 and T5 servers, Oracle Solaris, Oracle VM Server for SPARC, and Oracle Enterprise Manager have been co-engineered with Oracle Database and Oracle applications to provide maximum In-Memory performance, scalability, efficiency and reliability for the most critical and demanding enterprise deployments. The In-Memory option for the Oracle Database 12c, which has just been released, has been specifically optimized for SPARC servers running Oracle Solaris. The unique combination of Oracle's M6 32 Terabytes Big Memory Machine and Oracle Database 12c In-Memory demonstrates 2X increase in OLTP performance and 100X increase in analytics response times, allowing complex analysis of incredibly large data sets at the speed of thought. Numerous unique enhancements, including the large cache on the SPARC M6 processor, massive 32 TB of memory, uniform memory access architecture, Oracle Solaris high-performance kernel, and Oracle Database SGA optimization, result in orders of magnitude better transaction processing speeds across a range of in-memory workloads. Oracle Database 12c In-Memory The Power of Oracle SuperCluster and In-Memory Applications (Video, 3:13) Oracle’s In-Memory applications Oracle E-Business Suite In-Memory Cost Management on the Oracle SuperCluster M6-32 (PDF) Oracle JD Edwards Enterprise One In-Memory Applications on Oracle SuperCluster M6-32 (PDF) Oracle JD Edwards Enterprise One In-Memory Sales Advisor on the SuperCluster M6-32 (PDF) Oracle JD Edwards Enterprise One Project Portfolio Management on the SuperCluster M6-32 (PDF)

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