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  • Using Stub Objects

    - by user9154181
    Having told the long and winding tale of where stub objects came from and how we use them to build Solaris, I'd like to focus now on the the nuts and bolts of building and using them. The following new features were added to the Solaris link-editor (ld) to support the production and use of stub objects: -z stub This new command line option informs ld that it is to build a stub object rather than a normal object. In this mode, it accepts the same command line arguments as usual, but will quietly ignore any objects and sharable object dependencies. STUB_OBJECT Mapfile Directive In order to build a stub version of an object, its mapfile must specify the STUB_OBJECT directive. When producing a non-stub object, the presence of STUB_OBJECT causes the link-editor to perform extra validation to ensure that the stub and non-stub objects will be compatible. ASSERT Mapfile Directive All data symbols exported from the object must have an ASSERT symbol directive in the mapfile that declares them as data and supplies the size, binding, bss attributes, and symbol aliasing details. When building the stub objects, the information in these ASSERT directives is used to create the data symbols. When building the real object, these ASSERT directives will ensure that the real object matches the linking interface presented by the stub. Although ASSERT was added to the link-editor in order to support stub objects, they are a general purpose feature that can be used independently of stub objects. For instance you might choose to use an ASSERT directive if you have a symbol that must have a specific address in order for the object to operate properly and you want to automatically ensure that this will always be the case. The material presented here is derived from a document I originally wrote during the development effort, which had the dual goals of providing supplemental materials for the stub object PSARC case, and as a set of edits that were eventually applied to the Oracle Solaris Linker and Libraries Manual (LLM). The Solaris 11 LLM contains this information in a more polished form. Stub Objects A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be used at runtime. However, an application can be built against a stub object, where the stub object provides the real object name to be used at runtime, and then use the real object at runtime. When building a stub object, the link-editor ignores any object or library files specified on the command line, and these files need not exist in order to build a stub. Since the compilation step can be omitted, and because the link-editor has relatively little work to do, stub objects can be built very quickly. Stub objects can be used to solve a variety of build problems: Speed Modern machines, using a version of make with the ability to parallelize operations, are capable of compiling and linking many objects simultaneously, and doing so offers significant speedups. However, it is typical that a given object will depend on other objects, and that there will be a core set of objects that nearly everything else depends on. It is necessary to impose an ordering that builds each object before any other object that requires it. This ordering creates bottlenecks that reduce the amount of parallelization that is possible and limits the overall speed at which the code can be built. Complexity/Correctness In a large body of code, there can be a large number of dependencies between the various objects. The makefiles or other build descriptions for these objects can become very complex and difficult to understand or maintain. The dependencies can change as the system evolves. This can cause a given set of makefiles to become slightly incorrect over time, leading to race conditions and mysterious rare build failures. Dependency Cycles It might be desirable to organize code as cooperating shared objects, each of which draw on the resources provided by the other. Such cycles cannot be supported in an environment where objects must be built before the objects that use them, even though the runtime linker is fully capable of loading and using such objects if they could be built. Stub shared objects offer an alternative method for building code that sidesteps the above issues. Stub objects can be quickly built for all the shared objects produced by the build. Then, all the real shared objects and executables can be built in parallel, in any order, using the stub objects to stand in for the real objects at link-time. Afterwards, the executables and real shared objects are kept, and the stub shared objects are discarded. Stub objects are built from a mapfile, which must satisfy the following requirements. The mapfile must specify the STUB_OBJECT directive. This directive informs the link-editor that the object can be built as a stub object, and as such causes the link-editor to perform validation and sanity checking intended to guarantee that an object and its stub will always provide identical linking interfaces. All function and data symbols that make up the external interface to the object must be explicitly listed in the mapfile. The mapfile must use symbol scope reduction ('*'), to remove any symbols not explicitly listed from the external interface. All global data exported from the object must have an ASSERT symbol attribute in the mapfile to specify the symbol type, size, and bss attributes. In the case where there are multiple symbols that reference the same data, the ASSERT for one of these symbols must specify the TYPE and SIZE attributes, while the others must use the ALIAS attribute to reference this primary symbol. Given such a mapfile, the stub and real versions of the shared object can be built using the same command line for each, adding the '-z stub' option to the link for the stub object, and omiting the option from the link for the real object. To demonstrate these ideas, the following code implements a shared object named idx5, which exports data from a 5 element array of integers, with each element initialized to contain its zero-based array index. This data is available as a global array, via an alternative alias data symbol with weak binding, and via a functional interface. % cat idx5.c int _idx5[5] = { 0, 1, 2, 3, 4 }; #pragma weak idx5 = _idx5 int idx5_func(int index) { if ((index 4)) return (-1); return (_idx5[index]); } A mapfile is required to describe the interface provided by this shared object. % cat mapfile $mapfile_version 2 STUB_OBJECT; SYMBOL_SCOPE { _idx5 { ASSERT { TYPE=data; SIZE=4[5] }; }; idx5 { ASSERT { BINDING=weak; ALIAS=_idx5 }; }; idx5_func; local: *; }; The following main program is used to print all the index values available from the idx5 shared object. % cat main.c #include <stdio.h> extern int _idx5[5], idx5[5], idx5_func(int); int main(int argc, char **argv) { int i; for (i = 0; i The following commands create a stub version of this shared object in a subdirectory named stublib. elfdump is used to verify that the resulting object is a stub. The command used to build the stub differs from that of the real object only in the addition of the -z stub option, and the use of a different output file name. This demonstrates the ease with which stub generation can be added to an existing makefile. % cc -Kpic -G -M mapfile -h libidx5.so.1 idx5.c -o stublib/libidx5.so.1 -zstub % ln -s libidx5.so.1 stublib/libidx5.so % elfdump -d stublib/libidx5.so | grep STUB [11] FLAGS_1 0x4000000 [ STUB ] The main program can now be built, using the stub object to stand in for the real shared object, and setting a runpath that will find the real object at runtime. However, as we have not yet built the real object, this program cannot yet be run. Attempts to cause the system to load the stub object are rejected, as the runtime linker knows that stub objects lack the actual code and data found in the real object, and cannot execute. % cc main.c -L stublib -R '$ORIGIN/lib' -lidx5 -lc % ./a.out ld.so.1: a.out: fatal: libidx5.so.1: open failed: No such file or directory Killed % LD_PRELOAD=stublib/libidx5.so.1 ./a.out ld.so.1: a.out: fatal: stublib/libidx5.so.1: stub shared object cannot be used at runtime Killed We build the real object using the same command as we used to build the stub, omitting the -z stub option, and writing the results to a different file. % cc -Kpic -G -M mapfile -h libidx5.so.1 idx5.c -o lib/libidx5.so.1 Once the real object has been built in the lib subdirectory, the program can be run. % ./a.out [0] 0 0 0 [1] 1 1 1 [2] 2 2 2 [3] 3 3 3 [4] 4 4 4 Mapfile Changes The version 2 mapfile syntax was extended in a number of places to accommodate stub objects. Conditional Input The version 2 mapfile syntax has the ability conditionalize mapfile input using the $if control directive. As you might imagine, these directives are used frequently with ASSERT directives for data, because a given data symbol will frequently have a different size in 32 or 64-bit code, or on differing hardware such as x86 versus sparc. The link-editor maintains an internal table of names that can be used in the logical expressions evaluated by $if and $elif. At startup, this table is initialized with items that describe the class of object (_ELF32 or _ELF64) and the type of the target machine (_sparc or _x86). We found that there were a small number of cases in the Solaris code base in which we needed to know what kind of object we were producing, so we added the following new predefined items in order to address that need: NameMeaning ...... _ET_DYNshared object _ET_EXECexecutable object _ET_RELrelocatable object ...... STUB_OBJECT Directive The new STUB_OBJECT directive informs the link-editor that the object described by the mapfile can be built as a stub object. STUB_OBJECT; A stub shared object is built entirely from the information in the mapfiles supplied on the command line. When the -z stub option is specified to build a stub object, the presence of the STUB_OBJECT directive in a mapfile is required, and the link-editor uses the information in symbol ASSERT attributes to create global symbols that match those of the real object. When the real object is built, the presence of STUB_OBJECT causes the link-editor to verify that the mapfiles accurately describe the real object interface, and that a stub object built from them will provide the same linking interface as the real object it represents. All function and data symbols that make up the external interface to the object must be explicitly listed in the mapfile. The mapfile must use symbol scope reduction ('*'), to remove any symbols not explicitly listed from the external interface. All global data in the object is required to have an ASSERT attribute that specifies the symbol type and size. If the ASSERT BIND attribute is not present, the link-editor provides a default assertion that the symbol must be GLOBAL. If the ASSERT SH_ATTR attribute is not present, or does not specify that the section is one of BITS or NOBITS, the link-editor provides a default assertion that the associated section is BITS. All data symbols that describe the same address and size are required to have ASSERT ALIAS attributes specified in the mapfile. If aliased symbols are discovered that do not have an ASSERT ALIAS specified, the link fails and no object is produced. These rules ensure that the mapfiles contain a description of the real shared object's linking interface that is sufficient to produce a stub object with a completely compatible linking interface. SYMBOL_SCOPE/SYMBOL_VERSION ASSERT Attribute The SYMBOL_SCOPE and SYMBOL_VERSION mapfile directives were extended with a symbol attribute named ASSERT. The syntax for the ASSERT attribute is as follows: ASSERT { ALIAS = symbol_name; BINDING = symbol_binding; TYPE = symbol_type; SH_ATTR = section_attributes; SIZE = size_value; SIZE = size_value[count]; }; The ASSERT attribute is used to specify the expected characteristics of the symbol. The link-editor compares the symbol characteristics that result from the link to those given by ASSERT attributes. If the real and asserted attributes do not agree, a fatal error is issued and the output object is not created. In normal use, the link editor evaluates the ASSERT attribute when present, but does not require them, or provide default values for them. The presence of the STUB_OBJECT directive in a mapfile alters the interpretation of ASSERT to require them under some circumstances, and to supply default assertions if explicit ones are not present. See the definition of the STUB_OBJECT Directive for the details. When the -z stub command line option is specified to build a stub object, the information provided by ASSERT attributes is used to define the attributes of the global symbols provided by the object. ASSERT accepts the following: ALIAS Name of a previously defined symbol that this symbol is an alias for. An alias symbol has the same type, value, and size as the main symbol. The ALIAS attribute is mutually exclusive to the TYPE, SIZE, and SH_ATTR attributes, and cannot be used with them. When ALIAS is specified, the type, size, and section attributes are obtained from the alias symbol. BIND Specifies an ELF symbol binding, which can be any of the STB_ constants defined in <sys/elf.h>, with the STB_ prefix removed (e.g. GLOBAL, WEAK). TYPE Specifies an ELF symbol type, which can be any of the STT_ constants defined in <sys/elf.h>, with the STT_ prefix removed (e.g. OBJECT, COMMON, FUNC). In addition, for compatibility with other mapfile usage, FUNCTION and DATA can be specified, for STT_FUNC and STT_OBJECT, respectively. TYPE is mutually exclusive to ALIAS, and cannot be used in conjunction with it. SH_ATTR Specifies attributes of the section associated with the symbol. The section_attributes that can be specified are given in the following table: Section AttributeMeaning BITSSection is not of type SHT_NOBITS NOBITSSection is of type SHT_NOBITS SH_ATTR is mutually exclusive to ALIAS, and cannot be used in conjunction with it. SIZE Specifies the expected symbol size. SIZE is mutually exclusive to ALIAS, and cannot be used in conjunction with it. The syntax for the size_value argument is as described in the discussion of the SIZE attribute below. SIZE The SIZE symbol attribute existed before support for stub objects was introduced. It is used to set the size attribute of a given symbol. This attribute results in the creation of a symbol definition. Prior to the introduction of the ASSERT SIZE attribute, the value of a SIZE attribute was always numeric. While attempting to apply ASSERT SIZE to the objects in the Solaris ON consolidation, I found that many data symbols have a size based on the natural machine wordsize for the class of object being produced. Variables declared as long, or as a pointer, will be 4 bytes in size in a 32-bit object, and 8 bytes in a 64-bit object. Initially, I employed the conditional $if directive to handle these cases as follows: $if _ELF32 foo { ASSERT { TYPE=data; SIZE=4 } }; bar { ASSERT { TYPE=data; SIZE=20 } }; $elif _ELF64 foo { ASSERT { TYPE=data; SIZE=8 } }; bar { ASSERT { TYPE=data; SIZE=40 } }; $else $error UNKNOWN ELFCLASS $endif I found that the situation occurs frequently enough that this is cumbersome. To simplify this case, I introduced the idea of the addrsize symbolic name, and of a repeat count, which together make it simple to specify machine word scalar or array symbols. Both the SIZE, and ASSERT SIZE attributes support this syntax: The size_value argument can be a numeric value, or it can be the symbolic name addrsize. addrsize represents the size of a machine word capable of holding a memory address. The link-editor substitutes the value 4 for addrsize when building 32-bit objects, and the value 8 when building 64-bit objects. addrsize is useful for representing the size of pointer variables and C variables of type long, as it automatically adjusts for 32 and 64-bit objects without requiring the use of conditional input. The size_value argument can be optionally suffixed with a count value, enclosed in square brackets. If count is present, size_value and count are multiplied together to obtain the final size value. Using this feature, the example above can be written more naturally as: foo { ASSERT { TYPE=data; SIZE=addrsize } }; bar { ASSERT { TYPE=data; SIZE=addrsize[5] } }; Exported Global Data Is Still A Bad Idea As you can see, the additional plumbing added to the Solaris link-editor to support stub objects is minimal. Furthermore, about 90% of that plumbing is dedicated to handling global data. We have long advised against global data exported from shared objects. There are many ways in which global data does not fit well with dynamic linking. Stub objects simply provide one more reason to avoid this practice. It is always better to export all data via a functional interface. You should always hide your data, and make it available to your users via a function that they can call to acquire the address of the data item. However, If you do have to support global data for a stub, perhaps because you are working with an already existing object, it is still easilily done, as shown above. Oracle does not like us to discuss hypothetical new features that don't exist in shipping product, so I'll end this section with a speculation. It might be possible to do more in this area to ease the difficulty of dealing with objects that have global data that the users of the library don't need. Perhaps someday... Conclusions It is easy to create stub objects for most objects. If your library only exports function symbols, all you have to do to build a faithful stub object is to add STUB_OBJECT; and then to use the same link command you're currently using, with the addition of the -z stub option. Happy Stubbing!

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  • Oracle TimesTen In-Memory Database Performance on SPARC T4-2

    - by Brian
    The Oracle TimesTen In-Memory Database is optimized to run on Oracle's SPARC T4 processor platforms running Oracle Solaris 11 providing unsurpassed scalability, performance, upgradability, protection of investment and return on investment. The following demonstrate the value of combining Oracle TimesTen In-Memory Database with SPARC T4 servers and Oracle Solaris 11: On a Mobile Call Processing test, the 2-socket SPARC T4-2 server outperforms: Oracle's SPARC Enterprise M4000 server (4 x 2.66 GHz SPARC64 VII+) by 34%. Oracle's SPARC T3-4 (4 x 1.65 GHz SPARC T3) by 2.7x, or 5.4x per processor. Utilizing the TimesTen Performance Throughput Benchmark (TPTBM), the SPARC T4-2 server protects investments with: 2.1x the overall performance of a 4-socket SPARC Enterprise M4000 server in read-only mode and 1.5x the performance in update-only testing. This is 4.2x more performance per processor than the SPARC64 VII+ 2.66 GHz based system. 10x more performance per processor than the SPARC T2+ 1.4 GHz server. 1.6x better performance per processor than the SPARC T3 1.65 GHz based server. In replication testing, the two socket SPARC T4-2 server is over 3x faster than the performance of a four socket SPARC Enterprise T5440 server in both asynchronous replication environment and the highly available 2-Safe replication. This testing emphasizes parallel replication between systems. Performance Landscape Mobile Call Processing Test Performance System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 218,400 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 162,900 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 80,400 TimesTen Performance Throughput Benchmark (TPTBM) Read-Only System Processor Sockets/Cores/Threads Tps SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 7.9M SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 6.5M M4000 SPARC64 VII+, 2.66 GHz 4 16 32 3.1M T5440 SPARC T2+, 1.4 GHz 4 32 256 3.1M TimesTen Performance Throughput Benchmark (TPTBM) Update-Only System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 547,800 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 363,800 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 240,500 TimesTen Replication Tests System Processor Sockets/Cores/Threads Asynchronous 2-Safe SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 38,024 13,701 SPARC T5440 SPARC T2+, 1.4 GHz 4 32 256 11,621 4,615 Configuration Summary Hardware Configurations: SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 4 x 300 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head SPARC T3-4 server 4 x SPARC T3 processors, 1.6 GHz 512 GB memory 1 x 8 Gbs FC Qlogic HBA 8 x 146 GB internal disks 1 x Sun Fire X4275 server configured as COMSTAR head SPARC Enterprise M4000 server 4 x SPARC64 VII+ processors, 2.66 GHz 128 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 2 x 146 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head Software Configuration: Oracle Solaris 11 11/11 Oracle TimesTen 11.2.2.4 Benchmark Descriptions TimesTen Performance Throughput BenchMark (TPTBM) is shipped with TimesTen and measures the total throughput of the system. The workload can test read-only, update-only, delete and insert operations as required. Mobile Call Processing is a customer-based workload for processing calls made by mobile phone subscribers. The workload has a mixture of read-only, update, and insert-only transactions. The peak throughput performance is measured from multiple concurrent processes executing the transactions until a peak performance is reached via saturation of the available resources. Parallel Replication tests using both asynchronous and 2-Safe replication methods. For asynchronous replication, transactions are processed in batches to maximize the throughput capabilities of the replication server and network. In 2-Safe replication, also known as no data-loss or high availability, transactions are replicated between servers immediately emphasizing low latency. For both environments, performance is measured in the number of parallel replication servers and the maximum transactions-per-second for all concurrent processes. See Also SPARC T4-2 Server oracle.com OTN Oracle TimesTen In-Memory Database oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

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  • Using Oracle Linux iSCSI targets with Oracle VM

    - by wim.coekaerts
    A few days ago I had written a blog entry on how to use Oracle Solaris 10 (in my case), ZFS and the iSCSI target feature in Oracle Solaris to create a set of devices exported to my Oracle VM server. Oracle Linux can do this as well and I wanted to make sure I also tried out how to do this on Oracle Linux and here are the results. When you install Oracle Linux 5 update 5 (anything newer than update 3), it comes with an rpm called scsi-target-utils. To begin your quest, should you choose to accept it :) make sure this is installed. rpm -qa |grep scsi-target If it is not installed : up2date scsi-target-utils The target utils come with a tool tgtadm which is similar to iscsitadm on Oracle Solaris. There are 2 components again on the iSCSI server side. (1) create volumes - we will use lvm with lvcreate (2) expose a target using tgtadm. My server has a simple setup. All the disks are part of a single volume group called vgroot. To export a 50Gb volume I just create a new volume : lvcreate -L 50G -nmytest1 vgroot This will show up as a new volume in /dev/mapper as /dev/mapper/vgroot-mytest1. Create as many as you want for your environment. Since I already have my blog entry about the 5 volumes, I am not going to repeat the whole thing. You can just go look at the previous blog entry. Now that we have created the volume, we need to use tgtadm to set it up : make sure the service is running : /etc/init.d/tgtd start or service tgtd start (if you want to keep it running you can do chkconfig tgtd on to start it automatically at boottime) Next you need a targetname to set everything up. My recommendation would be to install iscsi-initiator-utils . This will create an iscsi id and put it in /etc/iscsi/initiatorname.iscsi. For convenience you can do : source /etc/iscsi/initiatorname.iscsi echo $InitiatorName and from here on use $InitiatorName instead of the long complex iqn. create your target : tgtadm --lld iscsi --op new --mode target --tid 1 -T $InitiatorName to show the status : tgtadm --lld iscsi --op show --mode target add the volume previously created : tgtadm --lld iscsi --op new --mode logicalunit --tid 1 --lun 1 -b /dev/mapper/vgroot-mytest1 re-run status to see it's there : tgtadm --lld iscsi --op show --mode target and just like on Oracle Solaris you now have to export (bind) it : tgtadm --lld iscsi --op bind --mode target --tid 1 -I iqn.1986-03.com.sun:01:2a7526f0ffff If you want to export the lun to every iscsi initiator then replace the iqn with ALL. Of course you have to add the iqn of each iscsi initiator or client you want to connect. In the case of my 2 node Oracle VM server setup, both Oracle VM server's initiator names would have to be added. use status again to see that it has this iqn under ACL tgtadm --lld iscsi --op show --mode target You can drop the --lld iscsi if you want, or alias it. It just makes the command line more obvious as to what you are doing. Oracle VM side : Refer back to the previous blog entry for the detailed setup of my Oracle VM server volumes but the exact same commands will be used there. discover : iscsiadm --mode discovery --type sendtargets --portal login : iscsiadm --mode node --targetname iscsi targetname --portal --login get devices : /etc/init.d/iscsi restart and voila you should be in business. have fun.

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  • Launching Ops Center 12c

    - by user12601629
    Oracle Enterprise Manager Ops Center 12c is most ambitious version of the Ops Center tooling that we've ever released. I think that make it appropriate that we launched it in grand style! When it became clear we were going to be complete with the 12c final release about this time of year, the marketing team proposed that we roll the launch of 12c into Oracle OpenWorld Tokyo.  I thought that sounded like a fine idea!  You see, I have always loved Japan.  I even studied a bit of Japanese language back in school. OpenWorld Tokyo was an outstanding even this year.  It was held in Roppongi, one of the most stylish districts in Tokyo. And, to make things even better, the Sakura (cherry blossoms) were blooming.  If you've never been in Japan for cherry blossom season, it's a must see!  Here are a couple of pics for you. Here is a picture from Roppongi, near the conference.  Here's a picture near the Imperial Palace.  A couple of friends from the local sales team took me here before my flight out. So, now back to the product launch! We choose to launch the product in John Fowler's "Engineered Systems" keynote address.  It made perfect sense because of the close ties of Ops Center to the Systems portfolio of products.  It was a packed house for the keynote.  Here's a picture I took just before we started -- there were also hundreds more people in "overflow" rooms in other parts of the venue. Here's a picture of me on stage during the launch. While there are countless new features in Ops Center 12c that customers will love, I had to limit myself to discussing just three. Mission Critical Clouds Solaris 11 Engineered Systems So, what does Mission Critical Cloud mean?  It means we've expanded EM's cloud capabilities in a couple of key areas. First, we've expanded the "self service provisioning" capabilities we have to include SPARC -- not just x86.  Now you can build clouds of Solaris Zones with ease!  Second, we've much more deeply integrated high-end storage and network management into the cloud layers.  These may our IaaS story is now much more powerful! For Solaris 11, we didn't simply port our monitoring agent to S11.  That would have been easy, but also boring! We support S11 deeply.  Full access to the power of the IPS packaging system, the new virtualized networking stack, new Zones features, the Auto Install framework.  If you're ready to try Solaris 11 then Ops Center is ready for you. Last is on the area of Engineered Systems.  These combinations of hardware and software are fast and powerful. However, we're also on a mission to make them ever easier to manage.  We've made major strides with Ops Center 12c. Manage these systems as racks, not individual components.  The new capabilities for the new engineered systems like Exalogic and SPARC SuperCluster and striking. You can read more here: Oracle Unveils Oracle Enterprise Manager Ops Center 12c So, I'll wrap this up with one final bit of fun. One of my friends from the Oracle marketing department found a super cool place to get dinner.  It's a restaurant called Gonpachi. It turns out this is the place that inspired the scene in the Quentin Taratino movie Kill Bill where Uma Thurman fights 88 Ninjas.  Here is a picture I snapped while we were there. It was surely a good time. Check it out next time you're in Tokyo.

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  • lvm disappeared after disc replacement on raid10

    - by user142295
    here my problem: I am running ubuntu 12.04 on a raid10 (4 disks), on top of which I installed an lvm with two volume groups (one for /, one for /home). The layout of the disks are as follows: Disk /dev/sda: 1500.3 GB, 1500301910016 bytes 255 heads, 63 sectors/track, 182401 cylinders, total 2930277168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x0003f3b6 Device Boot Start End Blocks Id System /dev/sda1 * 63 481949 240943+ 83 Linux /dev/sda2 481950 2910640634 1455079342+ fd Linux raid autodetect /dev/sda3 2910640635 2930272064 9815715 82 Linux swap / Solaris Disk /dev/sdb: 1500.3 GB, 1500301910016 bytes 255 heads, 63 sectors/track, 182401 cylinders, total 2930277168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x00069785 Device Boot Start End Blocks Id System /dev/sdb1 63 2910158684 1455079311 fd Linux raid autodetect /dev/sdb2 2910158685 2930272064 10056690 82 Linux swap / Solaris Disk /dev/sdc: 1500.3 GB, 1500301910016 bytes 255 heads, 63 sectors/track, 182401 cylinders, total 2930277168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x00000000 Device Boot Start End Blocks Id System /dev/sdc1 63 2910158684 1455079311 fd Linux raid autodetect /dev/sdc2 2910158685 2930272064 10056690 82 Linux swap / Solaris Disk /dev/sdd: 1500.3 GB, 1500301910016 bytes 255 heads, 63 sectors/track, 182401 cylinders, total 2930277168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x000f14de Device Boot Start End Blocks Id System /dev/sdd1 63 2910158684 1455079311 fd Linux raid autodetect /dev/sdd2 2910158685 2930272064 10056690 82 Linux swap / Solaris The first disk (/dev/sda) contains the /boot partition on /dev/sda1. I use grub2 to boot the system off this partition. On top of this raid10 I installed two volume groups, one for /, one for /home. This system worked well, I even exchanged two disks during the last two years. It always worked. But not this time. For the first time, /dev/sda broke. I do not know if this is an issue – I know I would have struggled anyways to overcome the problem with /boot installed on that disk and grub2 installed on the mbr of /dev/sda. Anyways, I did what I always did: start knoppix fire up the raid sudo mdadm --examine -scan which returns ARRAY /dev/md127 UUID=0dbf4558:1a943464:132783e8:19cdff95 start it up sudo mdadm --assemble /dev/md127 fail the failing disk (smart event) sudo mdadm /dev/md127 --fail /dev/sda2 remove the failing disk sudo mdadm /dev/md127 --remove /dev/sda2 stop the raid sudo mdadm -S /dev/md127 take out the disk replace it with a new one create the same partitions as on the failling one add it to the raid sudo mdadm --assemble /dev/md127 sudo mdadm /dev/md127 --add /dev/sda2 wait 4 hours All looks fine: cat /proc/mdstat returns: Personalities : [raid10] md127 : active raid10 sda2[0] sdd1[3] sdc1[2] sdb1[1] 2910158464 blocks 64K chunks 2 near-copies [4/4] [UUUU] unused devices: <none> and sudo mdadm --detail /dev/md127 returns /dev/md127: Version : 0.90 Creation Time : Wed Jun 10 13:08:46 2009 Raid Level : raid10 Array Size : 2910158464 (2775.34 GiB 2980.00 GB) Used Dev Size : 1455079232 (1387.67 GiB 1490.00 GB) Raid Devices : 4 Total Devices : 4 Preferred Minor : 127 Persistence : Superblock is persistent Update Time : Thu Mar 21 16:27:40 2013 State : clean Active Devices : 4 Working Devices : 4 Failed Devices : 0 Spare Devices : 0 Layout : near=2 Chunk Size : 64K UUID : 0dbf4558:1a943464:132783e8:19cdff95 (local to host Microknoppix) Events : 0.4824680 Number Major Minor RaidDevice State 0 8 2 0 active sync /dev/sda2 1 8 17 1 active sync /dev/sdb1 2 8 33 2 active sync /dev/sdc1 3 8 49 3 active sync /dev/sdd1 However, there is no trace of the volume groups. Rebooting into knoppix does not help Restarting the old system (I actually replugged and re-added the failing disk for that – the system begins to start, but then fails to see the / partition – no wonder if the volume group is gone) does not help. sudo vgscan, sudo vgdisplay, sudo lvs, sudo lvdisplay, sudo vgscan –mknodes all returned No volume groups found. I am completely at a loss. Can anyone tell me if and how I can recover my data? Thanks in advance!

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  • JBossMQ - Clustered Queues/NameNotFoundException: QueueConnectionFactory error

    - by mfarver
    I am trying to get an application working on a JBoss Cluster. It uses Queues internally, and the developer claims that it should work correctly in a clustered environment. I have jbossmq setup as a ha-singleton on the cluster. The application works correctly on whichever node currently is running the queue, but fails on the other nodes with a: "javax.naming.NameNotFoundException: QueueConnectionFactory not bound" error. I can look at JNDIview from the jmx-console and see that indeed the QueueConnectionFactory class only appears on the primary node in the Global context. Is there a way to see the Cluster's JNDI listing instead of each server? The steps I took from a default Jboss 4.2.3.GA installation were to use the "all" configuration. Then removed /server/all/deploy/hsqldb-ds.xml and /deploy-hasingleton/jms/hsqldb-jdbc2-service.xml, copying the example/jms/mysq-jdbc2-service.xml file into its place (editing that file to use DefaultDS instead of MySqlDS). Finally I created a mysql-ds.xml file in the deploy directory pointing "DefaultDS" at an empty database. I created a -services.xml file in the deploy directory with the queue definition. like the one below: <server> <mbean code="org.jboss.mq.server.jmx.Queue" name="jboss.mq.destination:service=Queue,name=myfirstqueue"> <depends optional-attribute-name="DestinationManager"> jboss.mq:service=DestinationManager </depends> </mbean> </server> All of the other cluster features of working, the servers list each other in the view, and sessions are replicating back and forth. The JBoss documentation is somewhat light in this area, is there another setting I might have missed? Or is this likely to be a code issue (is there different code to do a JNDI lookup in a clusted environment?) Thanks

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  • cisco 2900xl - SNMP - Get mac address of device connected to an interface

    - by ankit
    Hello all, Basically what i want to do is to find out what is the mac address of a device plugged in to an interface on the switch (FastEthernet0/1 for example) reading through the switch documentaion i found out that i can configure snmp trap on it to make it notify of any new mac address the switch detects by using the command snmp-server enable traps mac-notifiction but for some reason my switch does not support this feature. the only options i see are CORE_SWITCH(config)#snmp-server enable traps ? c2900 Enable SNMP c2900 traps cluster Enable Cluster traps config Enable SNMP config traps entity Enable SNMP entity traps hsrp Enable SNMP HSRP traps snmp Enable SNMP traps vlan-membership Enable VLAN Membership traps vtp Enable SNMP VTP traps <cr> so the other way would be for me to run a cronjon on my gateway to poll the switch periodically using snmp to get new mac addresses i have looked everywhere but cant seem to find the OID that would provide me this information. any help i can get would me very much appreciated ! here's the output from "show version" on my switch Cisco Internetwork Operating System Software IOS (tm) C2900XL Software (C2900XL-C3H2S-M), Version 12.0(5.4)WC(1), MAINTENANCE INTERIM SOFTWARE Copyright (c) 1986-2001 by cisco Systems, Inc. Compiled Tue 10-Jul-01 11:52 by devgoyal Image text-base: 0x00003000, data-base: 0x00333CD8 ROM: Bootstrap program is C2900XL boot loader CORE_SWITCH uptime is 1 hour, 24 minutes System returned to ROM by power-on System image file is "flash:c2900XL-c3h2s-mz.120-5.4.WC.1.bin" cisco WS-C2912-XL (PowerPC403GA) processor (revision 0x11) with 8192K/1024K bytes of memory. Processor board ID FAB0409X1WS, with hardware revision 0x01 Last reset from power-on Processor is running Enterprise Edition Software Cluster command switch capable Cluster member switch capable 12 FastEthernet/IEEE 802.3 interface(s) 32K bytes of flash-simulated non-volatile configuration memory. Base ethernet MAC Address: 00:01:42:D0:67:00 Motherboard assembly number: 73-3397-08 Power supply part number: 34-0834-01 Motherboard serial number: FAB040843G4 Power supply serial number: DAB05030HR8 Model revision number: A0 Motherboard revision number: C0 Model number: WS-C2912-XL-EN System serial number: FAB0409X1WS Configuration register is 0xF thanks, -ankit

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  • All websites migrated from server running IIS6 to IIS7

    - by Leah
    Hi, I hope someone will be able to help me with this. We have recently migrated all of our clients' sites to a new server running IIS7 - all the sites were originally running on a server running IIS6. Ever since the migration, lots of our clients are reporting error messages. There seems to be quite a number of issues related to sending emails and also, we have had the following error message reported by several different clients: Server Error in '/' Application. -------------------------------------------------------------------------------- Validation of viewstate MAC failed. If this application is hosted by a Web Farm or cluster, ensure that <machineKey> configuration specifies the same validationKey and validation algorithm. AutoGenerate cannot be used in a cluster. Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code. Exception Details: System.Web.HttpException: Validation of viewstate MAC failed. If this application is hosted by a Web Farm or cluster, ensure that <machineKey> configuration specifies the same validationKey and validation algorithm. AutoGenerate cannot be used in a cluster. I have read elsewhere that this error can appear if a button is clicked before the whole page has finished loading. But as this error has now appeared on multiple sites and only since the server migration, it seems to me that it must be something else. I was wondering if someone could tell me if there is something specific which needs to be changed for .NET sites when sites are moved from a server running IIS6 to a server running IIS7? I don't deal with the actual servers very much so I'm afraid this is very much a grey area for me. Any help would be very much appreciated.

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  • JBossMQ - Clustered Queues/NameNotFoundException: QueueConnectionFactory error

    - by mfarver
    I am trying to get an application working on a JBoss Cluster. It uses Queues internally, and the developer claims that it should work correctly in a clustered environment. I have jbossmq setup as a ha-singleton on the cluster. The application works correctly on whichever node currently is running the queue, but fails on the other nodes with a: "javax.naming.NameNotFoundException: QueueConnectionFactory not bound" error. I can look at JNDIview from the jmx-console and see that indeed the QueueConnectionFactory class only appears on the primary node in the Global context. Is there a way to see the Cluster's JNDI listing instead of each server? The steps I took from a default Jboss 4.2.3.GA installation were to use the "all" configuration. Then removed /server/all/deploy/hsqldb-ds.xml and /deploy-hasingleton/jms/hsqldb-jdbc2-service.xml, copying the example/jms/mysq-jdbc2-service.xml file into its place (editing that file to use DefaultDS instead of MySqlDS). Finally I created a mysql-ds.xml file in the deploy directory pointing "DefaultDS" at an empty database. I created a -services.xml file in the deploy directory with the queue definition. like the one below: <server> <mbean code="org.jboss.mq.server.jmx.Queue" name="jboss.mq.destination:service=Queue,name=myfirstqueue"> <depends optional-attribute-name="DestinationManager"> jboss.mq:service=DestinationManager </depends> </mbean> </server> All of the other cluster features of working, the servers list each other in the view, and sessions are replicating back and forth. The JBoss documentation is somewhat light in this area, is there another setting I might have missed? Or is this likely to be a code issue (is there different code to do a JNDI lookup in a clusted environment?) Thanks

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

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

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  • OS Analytics - Deep Dive Into Your OS

    - by Eran_Steiner
    Enterprise Manager Ops Center provides a feature called "OS Analytics". This feature allows you to get a better understanding of how the Operating System is being utilized. You can research the historical usage as well as real time data. This post will show how you can benefit from OS Analytics and how it works behind the scenes. We will have a call to discuss this blog - please join us!Date: Thursday, November 1, 2012Time: 11:00 am, Eastern Daylight Time (New York, GMT-04:00)1. Go to https://oracleconferencing.webex.com/oracleconferencing/j.php?ED=209833067&UID=1512092402&PW=NY2JhMmFjMmFh&RT=MiMxMQ%3D%3D2. If requested, enter your name and email address.3. If a password is required, enter the meeting password: oracle1234. Click "Join". To join the teleconference:Call-in toll-free number:       1-866-682-4770  (US/Canada)      Other countries:                https://oracle.intercallonline.com/portlets/scheduling/viewNumbers/viewNumber.do?ownerNumber=5931260&audioType=RP&viewGa=true&ga=ONConference Code:       7629343#Security code:            7777# Here is quick summary of what you can do with OS Analytics in Ops Center: View historical charts and real time value of CPU, memory, network and disk utilization Find the top CPU and Memory processes in real time or at a certain historical day Determine proper monitoring thresholds based on historical data View Solaris services status details Drill down into a process details View the busiest zones if applicable Where to start To start with OS Analytics, choose the OS asset in the tree and click the Analytics tab. You can see the CPU utilization, Memory utilization and Network utilization, along with the current real time top 5 processes in each category (click the image to see a larger version):  In the above screen, you can click each of the top 5 processes to see a more detailed view of that process. Here is an example of one of the processes: One of the cool things is that you can see the process tree for this process along with some port binding and open file descriptors. On Solaris machines with zones, you get an extra level of tabs, allowing you to get more information on the different zones: This is a good way to see the busiest zones. For example, one zone may not take a lot of CPU but it can consume a lot of memory, or perhaps network bandwidth. To see the detailed Analytics for each of the zones, simply click each of the zones in the tree and go to its Analytics tab. Next, click the "Processes" tab to see real time information of all the processes on the machine: An interesting column is the "Target" column. If you configured Ops Center to work with Enterprise Manager Cloud Control, then the two products will talk to each other and Ops Center will display the correlated target from Cloud Control in this table. If you are only using Ops Center - this column will remain empty. Next, if you view a Solaris machine, you will have a "Services" tab: By default, all services will be displayed, but you can choose to display only certain states, for example, those in maintenance or the degraded ones. You can highlight a service and choose to view the details, where you can see the Dependencies, Dependents and also the location of the service log file (not shown in the picture as you need to scroll down to see the log file). The "Threshold" tab is particularly helpful - you can view historical trends of different monitored values and based on the graph - determine what the monitoring values should be: You can ask Ops Center to suggest monitoring levels based on the historical values or you can set your own. The different colors in the graph represent the current set levels: Red for critical, Yellow for warning and Blue for Information, allowing you to quickly see how they're positioned against real data. It's important to note that when looking at longer periods, Ops Center smooths out the data and uses averages. So when looking at values such as CPU Usage, try shorter time frames which are more detailed, such as one hour or one day. Applying new monitoring values When first applying new values to monitored attributes - a popup will come up asking if it's OK to get you out of the current Monitoring Policy. This is OK if you want to either have custom monitoring for a specific machine, or if you want to use this current machine as a "Gold image" and extract a Monitoring Policy from it. You can later apply the new Monitoring Policy to other machines and also set it as a default Monitoring Profile. Once you're done with applying the different monitoring values, you can review and change them in the "Monitoring" tab. You can also click the "Extract a Monitoring Policy" in the actions pane on the right to save all the new values to a new Monitoring Policy, which can then be found under "Plan Management" -> "Monitoring Policies". Visiting the past Under the "History" tab you can "go back in time". This is very helpful when you know that a machine was busy a few hours ago (perhaps in the middle of the night?), but you were not around to take a look at it in real time. Here's a view into yesterday's data on one of the machines: You can see an interesting CPU spike happening at around 3:30 am along with some memory use. In the bottom table you can see the top 5 CPU and Memory consumers at the requested time. Very quickly you can see that this spike is related to the Solaris 11 IPS repository synchronization process using the "pkgrecv" command. The "time machine" doesn't stop here - you can also view historical data to determine which of the zones was the busiest at a given time: Under the hood The data collected is stored on each of the agents under /var/opt/sun/xvm/analytics/historical/ An "os.zip" file exists for the main OS. Inside you will find many small text files, named after the Epoch time stamp in which they were taken If you have any zones, there will be a file called "guests.zip" containing the same small files for all the zones, as well as a folder with the name of the zone along with "os.zip" in it If this is the Enterprise Controller or the Proxy Controller, you will have folders called "proxy" and "sat" in which you will find the "os.zip" for that controller The actual script collecting the data can be viewed for debugging purposes as well: On Linux, the location is: /opt/sun/xvmoc/private/os_analytics/collect On Solaris, the location is /opt/SUNWxvmoc/private/os_analytics/collect If you would like to redirect all the standard error into a file for debugging, touch the following file and the output will go into it: # touch /tmp/.collect.stderr   The temporary data is collected under /var/opt/sun/xvm/analytics/.collectdb until it is zipped. If you would like to review the properties for the Analytics, you can view those per each agent in /opt/sun/n1gc/lib/XVM.properties. Find the section "Analytics configurable properties for OS and VSC" to view the Analytics specific values. I hope you find this helpful! Please post questions in the comments below. Eran Steiner

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  • Big Data – Buzz Words: What is Hadoop – Day 6 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is NoSQL. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – Hadoop. What is Hadoop? Apache Hadoop is an open-source, free and Java based software framework offers a powerful distributed platform to store and manage Big Data. It is licensed under an Apache V2 license. It runs applications on large clusters of commodity hardware and it processes thousands of terabytes of data on thousands of the nodes. Hadoop is inspired from Google’s MapReduce and Google File System (GFS) papers. The major advantage of Hadoop framework is that it provides reliability and high availability. What are the core components of Hadoop? There are two major components of the Hadoop framework and both fo them does two of the important task for it. Hadoop MapReduce is the method to split a larger data problem into smaller chunk and distribute it to many different commodity servers. Each server have their own set of resources and they have processed them locally. Once the commodity server has processed the data they send it back collectively to main server. This is effectively a process where we process large data effectively and efficiently. (We will understand this in tomorrow’s blog post). Hadoop Distributed File System (HDFS) is a virtual file system. There is a big difference between any other file system and Hadoop. When we move a file on HDFS, it is automatically split into many small pieces. These small chunks of the file are replicated and stored on other servers (usually 3) for the fault tolerance or high availability. (We will understand this in the day after tomorrow’s blog post). Besides above two core components Hadoop project also contains following modules as well. Hadoop Common: Common utilities for the other Hadoop modules Hadoop Yarn: A framework for job scheduling and cluster resource management There are a few other projects (like Pig, Hive) related to above Hadoop as well which we will gradually explore in later blog posts. A Multi-node Hadoop Cluster Architecture Now let us quickly see the architecture of the a multi-node Hadoop cluster. A small Hadoop cluster includes a single master node and multiple worker or slave node. As discussed earlier, the entire cluster contains two layers. One of the layer of MapReduce Layer and another is of HDFC Layer. Each of these layer have its own relevant component. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node consists of a DataNode and TaskTracker. It is also possible that slave node or worker node is only data or compute node. The matter of the fact that is the key feature of the Hadoop. In this introductory blog post we will stop here while describing the architecture of Hadoop. In a future blog post of this 31 day series we will explore various components of Hadoop Architecture in Detail. Why Use Hadoop? There are many advantages of using Hadoop. Let me quickly list them over here: Robust and Scalable – We can add new nodes as needed as well modify them. Affordable and Cost Effective – We do not need any special hardware for running Hadoop. We can just use commodity server. Adaptive and Flexible – Hadoop is built keeping in mind that it will handle structured and unstructured data. Highly Available and Fault Tolerant – When a node fails, the Hadoop framework automatically fails over to another node. Why Hadoop is named as Hadoop? In year 2005 Hadoop was created by Doug Cutting and Mike Cafarella while working at Yahoo. Doug Cutting named Hadoop after his son’s toy elephant. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – MapReduce. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • The SPARC SuperCluster

    - by Karoly Vegh
    Oracle has been providing a lead in the Engineered Systems business for quite a while now, in accordance with the motto "Hardware and Software Engineered to Work Together." Indeed it is hard to find a better definition of these systems.  Allow me to summarize the idea. It is:  Build a compute platform optimized to run your technologies Develop application aware, intelligently caching storage components Take an impressively fast network technology interconnecting it with the compute nodes Tune the application to scale with the nodes to yet unseen performance Reduce the amount of data moving via compression Provide this all in a pre-integrated single product with a single-pane management interface All these ideas have been around in IT for quite some time now. The real Oracle advantage is adding the last one to put these all together. Oracle has built quite a portfolio of Engineered Systems, to run its technologies - and run those like they never ran before. In this post I'll focus on one of them that serves as a consolidation demigod, a multi-purpose engineered system.  As you probably have guessed, I am talking about the SPARC SuperCluster. It has many great features inherited from its predecessors, and it adds several new ones. Allow me to pick out and elaborate about some of the most interesting ones from a technological point of view.  I. It is the SPARC SuperCluster T4-4. That is, as compute nodes, it includes SPARC T4-4 servers that we learned to appreciate and respect for their features: The SPARC T4 CPUs: Each CPU has 8 cores, each core runs 8 threads. The SPARC T4-4 servers have 4 sockets. That is, a single compute node can in parallel, simultaneously  execute 256 threads. Now, a full-rack SPARC SuperCluster has 4 of these servers on board. Remember the keyword demigod.  While retaining the forerunner SPARC T3's exceptional throughput, the SPARC T4 CPUs raise the bar with single performance too - a humble 5x better one than their ancestors.  actually, the SPARC T4 CPU cores run in both single-threaded and multi-threaded mode, and switch between these two on-the-fly, fulfilling not only single-threaded OR multi-threaded applications' needs, but even mixed requirements (like in database workloads!). Data security, anyone? Every SPARC T4 CPU core has a built-in encryption engine, that is, encryption algorithms cast into silicon.  A PCI controller right on the chip for customers who need I/O performance.  Built-in, no-cost Virtualization:  Oracle VM for SPARC (the former LDoms or Logical Domains) is not a server-emulation virtualization technology but rather a serverpartitioning one, the hypervisor runs in the server firmware, and all the VMs' HW resources (I/O, CPU, memory) are accessed natively, without performance overhead.  This enables customers to run a number of Solaris 10 and Solaris 11 VMs separated, independent of each other within a physical server II. For Database performance, it includes Exadata Storage Cells - one of the main reasons why the Exadata Database Machine performs at diabolic speed. What makes them important? They provide DB backend storage for your Oracle Databases to run on the SPARC SuperCluster, that is what they are built and tuned for DB performance.  These storage cells are SQL-aware.  That is, if a SPARC T4 database compute node executes a query, it doesn't simply request tons of raw datablocks from the storage, filters the received data, and throws away most of it where the statement doesn't apply, but provides the SQL query to the storage node too. The storage cell software speaks SQL, that is, it is able to prefilter and through that transfer only the relevant data. With this, the traffic between database nodes and storage cells is reduced immensely. Less I/O is a good thing - as they say, all the CPUs of the world do one thing just as fast as any other - and that is waiting for I/O.  They don't only pre-filter, but also provide data preprocessing features - e.g. if a DB-node requests an aggregate of data, they can calculate it, and handover only the results, not the whole set. Again, less data to transfer.  They support the magical HCC, (Hybrid Columnar Compression). That is, data can be stored in a precompressed form on the storage. Less data to transfer.  Of course one can't simply rely on disks for performance, there is Flash Storage included there for caching.  III. The low latency, high-speed backbone network: InfiniBand, that interconnects all the members with: Real High Speed: 40 Gbit/s. Full Duplex, of course. Oh, and a really low latency.  RDMA. Remote Direct Memory Access. This technology allows the DB nodes to do exactly that. Remotely, directly placing SQL commands into the Memory of the storage cells. Dodging all the network-stack bottlenecks, avoiding overhead, placing requests directly into the process queue.  You can also run IP over InfiniBand if you please - that's the way the compute nodes can communicate with each other.  IV. Including a general-purpose storage too: the ZFSSA, which is a unified storage, providing NAS and SAN access too, with the following features:  NFS over RDMA over InfiniBand. Nothing is faster network-filesystem-wise.  All the ZFS features onboard, hybrid storage pools, compression, deduplication, snapshot, replication, NFS and CIFS shares Storageheads in a HA-Cluster configuration providing availability of the data  DTrace Live Analytics in a web-based Administration UI Being a general purpose application data storage for your non-database applications running on the SPARC SuperCluster over whichever protocol they prefer, easily replicating, snapshotting, cloning data for them.  There's a lot of great technology included in Oracle's SPARC SuperCluster, we have talked its interior through. As for external scalability: you can start with a half- of full- rack SPARC SuperCluster, and scale out to several racks - that is, stacking not separate full-rack SPARC SuperClusters, but extending always one large instance of the size of several full-racks. Yes, over InfiniBand network. Add racks as you grow.  What technologies shall run on it? SPARC SuperCluster is a general purpose scaleout consolidation/cloud environment. You can run Oracle Databases with RAC scaling, or Oracle Weblogic (end enjoy the SPARC T4's advantages to run Java). Remember, Oracle technologies have been integrated with the Oracle Engineered Systems - this is the Oracle on Oracle advantage. But you can run other software environments such as SAP if you please too. Run any application that runs on Oracle Solaris 10 or Solaris 11. Separate them in Virtual Machines, or even Oracle Solaris Zones, monitor and manage those from a central UI. Here the key takeaways once again: The SPARC SuperCluster: Is a pre-integrated Engineered System Contains SPARC T4-4 servers with built-in virtualization, cryptography, dynamic threading Contains the Exadata storage cells that intelligently offload the burden of the DB-nodes  Contains a highly available ZFS Storage Appliance, that provides SAN/NAS storage in a unified way Combines all these elements over a high-speed, low-latency backbone network implemented with InfiniBand Can grow from a single half-rack to several full-rack size Supports the consolidation of hundreds of applications To summarize: All these technologies are great by themselves, but the real value is like in every other Oracle Engineered System: Integration. All these technologies are tuned to perform together. Together they are way more than the sum of all - and a careful and actually very time consuming integration process is necessary to orchestrate all these for performance. The SPARC SuperCluster's goal is to enable infrastructure operations and offer a pre-integrated solution that can be architected and delivered in hours instead of months of evaluations and tests. The tedious and most importantly time and resource consuming part of the work - testing and evaluating - has been done.  Now go, provide services.   -- charlie  

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  • WebLogic job scheduling

    - by XpiritO
    Hello, overflowers :) I'm trying to implement a WebLogic job scheduling example, to test my cluster capabilities of fail-over on scheduled tasks (to ensure that these tasks are executed on fail over scenario). With this in mind, I've been following this example and trying to configure everything accordingly. Here are the steps I've done so far: Configured a cluster with 1 admin server (AdminServer) and 2 managed instances (Noddy and Snoopy); Set up database tables (using Oracle XE): ACTIVE and WEBLOGIC_TIMERS; Set up data source to access DB and associated it to the scheduling tasks under "Settings for cluster" "Scheduling"; Implemented a job (TimerListener) and a servlet to initialize the job scheduling, as follows: . package timedexecution; import java.io.IOException; import java.io.PrintWriter; import java.io.Serializable; import java.text.SimpleDateFormat; import java.util.Date; import javax.naming.InitialContext; import javax.naming.NamingException; import javax.servlet.ServletException; import javax.servlet.http.HttpServlet; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import commonj.timers.Timer; import commonj.timers.TimerListener; import commonj.timers.TimerManager; public class TimerServlet extends HttpServlet { private static final long serialVersionUID = 1L; protected static void logMessage(String message, PrintWriter out){ out.write("<p>"+ message +"</p>"); System.out.println(message); } @Override public void service(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { PrintWriter out = response.getWriter(); // out.println("<html>"); out.println("<head><title>TimerServlet</title></head>"); // try { // logMessage("service() entering try block to intialize the timer from JNDI", out); // InitialContext ic = new InitialContext(); TimerManager jobScheduler = (TimerManager)ic.lookup("weblogic.JobScheduler"); // logMessage("jobScheduler reference " + jobScheduler, out); // jobScheduler.schedule(new ExampleTimerListener(), 0, 30*1000); // logMessage("Timer scheduled!", out); // //execute this job every 30 seconds logMessage("service() started the timer", out); // logMessage("Started the timer - status:", out); // } catch (NamingException ne) { String msg = ne.getMessage(); logMessage("Timer schedule failed!", out); logMessage(msg, out); } catch (Throwable t) { logMessage("service() error initializing timer manager with JNDI name weblogic.JobScheduler " + t,out); } // out.println("</body></html>"); out.close(); } private static class ExampleTimerListener implements Serializable, TimerListener { private static final long serialVersionUID = 8313912206357147939L; public void timerExpired(Timer timer) { SimpleDateFormat sdf = new SimpleDateFormat(); System.out.println( "timerExpired() called at " + sdf.format( new Date() ) ); } } } Then I executed the servlet to start the scheduling on the first managed instance (Noddy server), which returned as expected: (Servlet execution output) service() entering try block to intialize the timer from JNDI jobScheduler reference weblogic.scheduler.TimerServiceImpl@43b4c7 Timer scheduled! service() started the timer Started the timer - status: Which resulted in the creation of 2 rows in my DB tables: WEBLOGIC_TIMERS table state after servlet execution: "EDIT"; "TIMER_ID"; "LISTENER"; "START_TIME"; "INTERVAL"; "TIMER_MANAGER_NAME"; "DOMAIN_NAME"; "CLUSTER_NAME"; ""; "Noddy_1268653040156"; "[datatype]"; "1268653040156"; "30000"; "weblogic.JobScheduler"; "myCluster"; "Cluster" ACTIVE table state after servlet execution: "EDIT"; "SERVER"; "INSTANCE"; "DOMAINNAME"; "CLUSTERNAME"; "TIMEOUT"; ""; "service.SINGLETON_MASTER"; "6382071947583985002/Noddy"; "QRENcluster"; "Cluster"; "10.03.15" Although, the job is not executed as scheduled. It should print a message on the server's log output (Noddy.out file) with a timestamp, saying that the timer had expired. It doesn't. My log files state as follows: Admin server log (myCluster.log file): ####<15/Mar/2010 10H45m GMT> <Warning> <Cluster> <test-ad> <Noddy> <[STANDBY] ExecuteThread: '1' for queue: 'weblogic.kernel.Default (self-tuning)'> <<WLS Kernel>> <> <> <1268649925727> <BEA-000192> <No currently living server was found that could host TimerMaster. The server will retry in a few seconds.> Noddy server log (Noddy.out file): service() entering try block to intialize the timer from JNDI jobScheduler reference weblogic.scheduler.TimerServiceImpl@43b4c7 Timer scheduled! service() started the timer Started the timer - status: <15/Mar/2010 10H45m GMT> <Warning> <Cluster> <BEA-000192> <No currently living server was found that could host TimerMaster. The server will retry in a few seconds.> (Noddy.log file): ####<15/Mar/2010 11H24m GMT> <Info> <Common> <test-ad> <Noddy> <[ACTIVE] ExecuteThread: '0' for queue: 'weblogic.kernel.Default (self-tuning)'> <<WLS Kernel>> <> <> <1268652270128> <BEA-000628> <Created "1" resources for pool "TxDataSourceOracle", out of which "1" are available and "0" are unavailable.> ####<15/Mar/2010 11H37m GMT> <Info> <Cluster> <test-ad> <Noddy> <[ACTIVE] ExecuteThread: '0' for queue: 'weblogic.kernel.Default (self-tuning)'> <<anonymous>> <> <> <1268653040226> <BEA-000182> <Job Scheduler created a job with ID Noddy_1268653040156 for TimerListener with description timedexecution.TimerServlet$ExampleTimerListener@2ce79a> ####<15/Mar/2010 11H39m GMT> <Info> <JDBC> <test-ad> <Noddy> <[ACTIVE] ExecuteThread: '3' for queue: 'weblogic.kernel.Default (self-tuning)'> <<WLS Kernel>> <> <> <1268653166307> <BEA-001128> <Connection for pool "TxDataSourceOracle" closed.> Can anyone help me out discovering what's wrong with my configuration? Thanks in advance for your help!

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  • Node.js vs PHP processing speed

    - by Cody Craven
    I've been looking into node.js recently and wanted to see a true comparison of processing speed for PHP vs Node.js. In most of the comparisons I had seen, Node trounced Apache/PHP set ups handily. However all of the tests were small 'hello worlds' that would not accurately reflect any webpage's markup. So I decided to create a basic HTML page with 10,000 hello world paragraph elements. In these tests Node with Cluster was beaten to a pulp by PHP on Nginx utilizing PHP-FPM. So I'm curious if I am misusing Node somehow or if Node is really just this bad at processing power. Note that my results were equivalent outputting "Hello world\n" with text/plain as the HTML, but I only included the HTML as it's closer to the use case I was investigating. My testing box: Core i7-2600 Intel CPU (has 8 threads with 4 cores) 8GB DDR3 RAM Fedora 16 64bit Node.js v0.6.13 Nginx v1.0.13 PHP v5.3.10 (with PHP-FPM) My test scripts: Node.js script var cluster = require('cluster'); var http = require('http'); var numCPUs = require('os').cpus().length; if (cluster.isMaster) { // Fork workers. for (var i = 0; i < numCPUs; i++) { cluster.fork(); } cluster.on('death', function (worker) { console.log('worker ' + worker.pid + ' died'); }); } else { // Worker processes have an HTTP server. http.Server(function (req, res) { res.writeHead(200, {'Content-Type': 'text/html'}); res.write('<html>\n<head>\n<title>Speed test</title>\n</head>\n<body>\n'); for (var i = 0; i < 10000; i++) { res.write('<p>Hello world</p>\n'); } res.end('</body>\n</html>'); }).listen(80); } This script is adapted from Node.js' documentation at http://nodejs.org/docs/latest/api/cluster.html PHP script <?php echo "<html>\n<head>\n<title>Speed test</title>\n</head>\n<body>\n"; for ($i = 0; $i < 10000; $i++) { echo "<p>Hello world</p>\n"; } echo "</body>\n</html>"; My results Node.js $ ab -n 500 -c 20 http://speedtest.dev/ This is ApacheBench, Version 2.3 <$Revision: 655654 $> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ Licensed to The Apache Software Foundation, http://www.apache.org/ Benchmarking speedtest.dev (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Finished 500 requests Server Software: Server Hostname: speedtest.dev Server Port: 80 Document Path: / Document Length: 190070 bytes Concurrency Level: 20 Time taken for tests: 14.603 seconds Complete requests: 500 Failed requests: 0 Write errors: 0 Total transferred: 95066500 bytes HTML transferred: 95035000 bytes Requests per second: 34.24 [#/sec] (mean) Time per request: 584.123 [ms] (mean) Time per request: 29.206 [ms] (mean, across all concurrent requests) Transfer rate: 6357.45 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.2 0 2 Processing: 94 547 405.4 424 2516 Waiting: 0 331 399.3 216 2284 Total: 95 547 405.4 424 2516 Percentage of the requests served within a certain time (ms) 50% 424 66% 607 75% 733 80% 813 90% 1084 95% 1325 98% 1843 99% 2062 100% 2516 (longest request) PHP/Nginx $ ab -n 500 -c 20 http://speedtest.dev/test.php This is ApacheBench, Version 2.3 <$Revision: 655654 $> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ Licensed to The Apache Software Foundation, http://www.apache.org/ Benchmarking speedtest.dev (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Finished 500 requests Server Software: nginx/1.0.13 Server Hostname: speedtest.dev Server Port: 80 Document Path: /test.php Document Length: 190070 bytes Concurrency Level: 20 Time taken for tests: 0.130 seconds Complete requests: 500 Failed requests: 0 Write errors: 0 Total transferred: 95109000 bytes HTML transferred: 95035000 bytes Requests per second: 3849.11 [#/sec] (mean) Time per request: 5.196 [ms] (mean) Time per request: 0.260 [ms] (mean, across all concurrent requests) Transfer rate: 715010.65 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.2 0 1 Processing: 3 5 0.7 5 7 Waiting: 1 4 0.7 4 7 Total: 3 5 0.7 5 7 Percentage of the requests served within a certain time (ms) 50% 5 66% 5 75% 5 80% 6 90% 6 95% 6 98% 6 99% 6 100% 7 (longest request) Additional details Again what I'm looking for is to find out if I'm doing something wrong with Node.js or if it is really just that slow compared to PHP on Nginx with FPM. I certainly think Node has a real niche that it could fit well, however with these test results (which I really hope I made a mistake with - as I like the idea of Node) lead me to believe that it is a horrible choice for even a modest processing load when compared to PHP (let alone JVM or various other fast solutions). As a final note, I also tried running an Apache Bench test against node with $ ab -n 20 -c 20 http://speedtest.dev/ and consistently received a total test time of greater than 0.900 seconds.

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  • INS-40719 error when Install Oracle RAC?

    - by Data-Base
    I'm tying to (learn how to) install Oracle RAC 11g on CentOS 6 all went OK so far but I get INS-40719 Error message regarding SCAN Name I do not have DNS server and I'm not going to try to use it on this setup I add this line to /etc/hosts 192.168.244.100 rac-cluster then used "rac-cluster" as the SCAN name and it's still not working with the same error message! any one can guide me on how to make it work? 1- do I have to add "192.168.244.100 rac-cluster" to /etc/hosts on both nodes? 2- do I need to edit/add any thing else on the nodes? cheers

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  • OpenSolaris Dead / Alive?

    - by Walter White
    Hi all, I have used Open Solaris in the past and really liked it, minus the lack of support for a few applications I use such as UFraw, Hugin, and wacom. I can compile from source, but where is the fun in that. It seems the release dates for the next Open Solaris keep getting pushed back and the release that was scheduled to happen quietly got pulled from their site. So, they're no longer saying development for 10.03 has already begun because it has come and gone and there is no release. Walter

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  • Can I have different ESX hosts accessing the same LUN over different protocols?

    - by Kevin Kuphal
    I currently have a cluster of two ESX 3.5U2 servers connected directly via FiberChannel to a NetApp 3020 cluster. These hosts mount four VMFS LUNs for virtual machine storage. Currently these LUNs are only made available via our FiberChannel initator in the Netapp configuration If I were to add an ESXi host to the cluster for internal IT use can I: Make the same VMFS LUNs available via the iSCSI initiator on the Netapp Connect this ESXi host to those LUNs via iSCSI Do all of this while the existing two ESX hosts are connected to those LUNs via FiberChannel Does anyone have experience with this type of mixed protocol environment, specifically with Netapp?

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  • Unable to access virtaul IP after configuring NLB in windows 2008

    - by krish
    I have Two webseervers(WS1, WS2). I have added NLB component on both the machines. In WS1, i have added a cluster with IP as (eg:xx.yy.zz.100) and added WS1, WS2 to the same cluster. Now i have deployed application in WS1& WS2 and tried accessing the cluster IP address from WS1 and WS2. App opened. Now i have test machine which is in the same domain of WS1 and WS2. I tried accessing the application with clustered IP in the test machine, it did not work.However it is working when accessed with the dedicated IP of WS1 and WS2. But for making NLB i have to access the app with clusted IP. Help asap would be appriciated.

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  • Doubts about Cloud Infrastructure

    - by Pravin
    Maybe a little more of the same questions that others have asked but wanted to clarify my doubt, for some years run my hosting company (reseller of esds) and I've done well so far, but I am determined to bring quality and server technology to offer another level. So far I have understood that there is a difference between cloud and cluster servers because the cluster function as load balancers that distribute in different servers roles and use the servers less overloaded in the cloud is the union of multiple servers and then the same is vitualized unlike the cluster that is allowed to use the resources of the CPU and RAM servers in the virtualized environment. My approach is to use 3 dedicated servers to create a cloud server, My doubts: Does this type of cloud servers are only reserved for big companies? (Either because the union of the servers is done by hardware or software with high price) What characteristics should these servers meet? Possibly through software which should be used? Available? Thanks for your time, Cheers!

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  • Hyper-V 2012 and P2000 SAS SAN

    - by user155950
    Hi I am having major problems setting up a Hyper-V 2012 cluster on a P2000 SAS SAN. Running System Center VMM 2012 SP1 I am unable to see any storage to create my cluster. Has anyone had experienced anything similar? Under fabric and storage I can't add the P2000, all I can do is use storage spaces in server manager to create a storage pool and virtual disk. This allows me to create a file share which I can add to VMM but I still can't see any disk to create a cluster. I am just about at the point where I want to tear my hair out wipe the servers and stick VMware on them because I know it works as I have set several systems up like this in the past. The Hyper-V servers can see the storage and in server manager on my management machine it seems to know both servers can see the same disk. VMM is running on the same machine and it can't see any disk. Help..... Thanks Mike

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  • re-point LM to a new vCenter (share same database)

    - by CapiZikus
    1) I'm planning to create a new vCenter server which database point to the same db as current vCenter (the one LM pointing to atm), Then I'm planning to repoint the LM to a new vCenter, ( the new one will see the same esx host, datastore, etc) Is LM will be okay if I do this? 2) The currect VC is a dediated server and a new vCenter will be VM, the current vCenter has database installed on local machine (inc update manager as well) I'm planning to move the local db to cluster db then point the current vCenter to this new cluster and make sure everything is working before promote a new one. Update manager will also has it own VM and point to a new db cluster. Is anythingelse I miss out or need to pay more attention on? thanks

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  • HAProxy NGInx SSL setup

    - by Niclas
    I've been looking around different setups for a server cluster supporting SSL and I would like to benchmark my idea with you. Requirements: All servers in the cluster should be under the same full domain name. (http and https) Routing to subsystems is done on URI matching in HA proxy. All URIs have support for SSL support. Wish: Centralizing routing rules ---<----http-----<-- | | Inet -->HA--+---https--->NGInx_SSL_1..N | | +---http---> Apache_1..M | +---http---> NodeJS Idea: Configure HA to route all SSL traffic (mode=tcp,algorithm=Source) to an NGInx cluster turning https traffic into http. Re-pass the http traffic from NGInx to the HA for normal load-balancing which performs load balancing based on HA config. My question is simply: Is this the best way to to configure based on requirements above?

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  • VMWARE V2V migration without shared storage

    - by TheCleaner
    I would like to do the following: Take Physical box running w2k8r2 and Sql2008R2 and do a P2V on it to a 4.1 Enterprise licensed cluster. --no worries here, I can do that part-- Take the existing physical box that is freed up and install vmware hypervisor 5.0 on it. --again I can do this part-- Do a v2v migration of the VM created in step #1 above from the Enterprise 4.1 Cluster to the standalone host. They are NOT using shared storage. Step #3 is where I'm confused as to what my best option is. I found an article online talking about using Veeam FastSCP and just shutting down the vm on the cluster, removing it from inventory, copying the files over to the new host and then adding it to inventory. Is that the best way to accomplish this?

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  • Amazon EC2 hostnames

    - by Firefly
    I'm currently trying to setup a Tigase cluster on Amazon EC2 instances in a VPC and I'm having troubles getting it to work due to the hostnames of the instances not being "full DNS names". According to the Tigase documentation: Please note the proper DNS configuration is critical for the cluster to work correctly. Make sure the 'hostname' command returns a full DNS name on each cluster node. Can anyone explain what a full DNS name is and how I can set my instances to use one? Currently my instances get a default hostname of the form "ip-10-0-0-20".

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