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Abstraction, Politics, and Software Architecture

Abstraction can be defined as a general concept and/or idea that lack any concrete details. Throughout history this type of thinking has led to an array of new ideas and innovations as well as increased confusion and conspiracy. If one was to look back at our history they will see that abstraction has been used in various forms throughout our past. When I was growing up I do not know how many times I heard politicians say “Leave no child left behind” or “No child left behind” as a major part of their campaign rhetoric in regards to a stance on education. As you can see their slogan is a perfect example of abstraction because it only offers a very general concept about improving our education system but they do not mention how they would like to do it. If they did then they would be adding concrete details to their abstraction thus turning it in to an actual working plan as to how we as a society can help children succeed in school and in life, but then they would not be using abstraction. By now I sure you are thinking what does abstraction have to do with software architecture. You are valid in thinking this way, but abstraction is a wonderful tool used in information technology especially in the world of software architecture. Abstraction is one method of extracting the concepts of an idea so that it can be understood and discussed by others of varying technical abilities and backgrounds. One ways in which I tend to extract my architectural design thoughts is through the use of basic diagrams to convey an idea for a system or a new feature for an existing application. This allows me to generically model an architectural design through the use of views and Unified Markup Language (UML). UML is a standard method for creating a 4+1 Architectural View Models. The 4+1 Architectural View Model consists of 4 views typically created with UML as well as a general description of the concept that is being expressed by a model. The 4+1 Architectural View Model: Logical View: Models a system’s end-user functionality. Development View: Models a system as a collection of components and connectors to illustrate how it is intended to be developed. Process View: Models the interaction between system components and connectors as to indicate the activities of a system. Physical View: Models the placement of the collection of components and connectors of a system within a physical environment. Recently I had to use the concept of abstraction to express an idea for implementing a new security framework on an existing website. My concept would add session based management in order to properly secure and allow page access based on valid user credentials and last user activity. I created a basic Process View by using UML diagrams to communicate the basic process flow of my changes in the application so that all of the projects stakeholders would be able to understand my idea. Additionally I created a Logical View on a whiteboard while conveying the process workflow with a few stakeholders to show how end-user will be affected by the new framework and gaining additional input about the design. After my Logical and Process Views were accepted I then started on creating a more detailed Development View in order to map how the system will be built based on the concept of components and connections based on the previously defined interactions. I really did not need to create a Physical view for this idea because we were updating an existing system that was already deployed based on an existing Physical View. What do you think about the use of abstraction in the development of software architecture? Please let me know.

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Enterprise Manager in EPM 11.1.2.x...a game of hide and seek!

- by THE

Normal 0 21 false false false DE X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} guest article: Maurice Bauhahn: Users of Oracle Hyperion Enterprise Performance Management 11.1.2.0 and 11.1.2.1 may puzzle why the URL http://<servername>:7001/em may not conjure up Enterprise Manager Fusion Middleware Control. This powerful tool has been installed by default...but WebLogic may not have been 'Extended' to allow you to call it up (we are hopeful this ‘Extend’ step will not be needed with 11.1.2.2). The explanation is on pages 425 and following of the following document: http://www.oracle.com/technetwork/middleware/bi-foundation/epm-tips-issues-1-72-427329.pdf A close look at the screen dumps in that section reveals a somewhat scary prospect, however: the non-AdminServer servlets had all failed (see the red down-arrow icons to the right of their names) after the configuration! Of course you would want to avoid that scenario! A rephrasing of the instructions might help: Ensure the WebLogic AdminServer is not running (in a default scenario that would mean port 7001 is not active). Ensure you have logged into the computer as the installing owner of EPM. Since Enterprise Manager uses a LOT of resources, be sure that there is adequate free RAM to accommodate the added load. On the machine where WebLogic AdminServer is set up (typically the Foundation Services machine), run \Oracle\Middleware\wlserver_10.3\common\bin\config (config.sh on Unix). Select the 'Extend an existing WebLogic domain' option, and click the 'Next' button. Select the domain being used by EPM System. - Typically, the default domain is created under /Oracle/Middleware/user_projects/domains and is named EPMSystem. - Click 'Next'. Under 'Extend my domain automatically to support the following added products' - place a check mark before 'Oracle Enterprise Manager - 11.1.1.0 [oracle_common]' to select it. - Continue accepting the defaults by clicking 'Next' on each page until - on the last page you click 'Extend'. - The system will grind for a few minutes while it configures (deploys?) EM. - Start the AdminServer. Sometimes there is contention in the startup order of the various servlets (resulting in some not coming up). To avoid that problem on Microsoft Windows machines you may start and stop services via the following analogous command line commands to those run on Linux/Unix (these more carefully space out timings of these events): Ensure EPM is up:\Oracle\Middleware\user_projects\epmsystem1\bin\start.bat Ensure WebLogic is up:\Oracle\Middleware\user_projects\domains\EPMSystem\bin\startWebLogic.cmd Shut down WebLogic:\Oracle\Middleware\user_projects\domains\EPMSystem\bin\stopWebLogic.cmd Shut down EPM:\Oracle\Middleware\user_projects\epmsystem1\bin\stop.bat Now you should be able to more successfully troubleshoot with the EM tool:

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DISA Cross Domain Enterprise Solutions on the NetBeans Platform

- by Geertjan

Bray 2.0 is a tool based on the NetBeans Platform that assists in creating valid Data Flow Configuration (DFC) files. The DFC Specification was developed to provide a standardized way for defining, validating, and approving data flows for use on cross-domain guarding solutions. A DFC document specifies key entities such as security domains, guards that facilitate data between security domains, data flows that describe how data travels between security domains, filters that transform and validate the data and more. Related info: http://www.disa.mil/Services/Information-Assurance/Cross-Domain-Solutions The Bray product is in development at Fulcrum IT (http://www.fulcrumco.com). The DFC Specification and Bray were developed in support of the US Department of Defense. Bray 2.0 marks the first release of Bray on the NetBeans Platform and utilizes a number of features that are core to the NetBeans Platform: Modular plugability. Bray consumers can integrate their own tools, file types, and more into the product with relative ease. Robust UI. The NetBeans Platform intuitive UI makes it easy to access and manipulate multiple aspects of a DFC. Explorer. The Explorer is a key component that makes the DFC XML easy to traverse, edit, and find errors. Context-sensitive help. JavaHelp can be readily integrated for the product as well as all the UI within. Editors. Any external file can be added to a DFC. Users can register their own editors or use the provided NetBeans editors to edit files. Printing. The NetBeans Platform Print API makes it easy to determine what should be printed and how. A screenshot: Bray 2.0 provides a lot of key features in developing valid, robust DFC files: XML validation. A DFC can be validated against the DFC schema specification. DFC Check List. An interactive, minimal guide for creating a complete DFC. Summary Window. The Summary Window functions like the Navigator in NetBeans IDE. The current "item of interest" is checked against various business rules and provides the ability to quickly find and fix errors. Change Log. Bray audits every change to a DFC and places them in a change log for users to peruse. Comments. Users can optionally add comments for other users to see. Digital signatures. DFC files can be digitally signed. A signature history and signature validation is provided in Bray. Pluggable security schemes. Bray ships with plain text and IC-ISM security schemes. If needed, users can integrate additional ones. ...and more to come! New features for Bray are constantly in development including use of the NetBeans Visual Library, language support, and more. More screenshots:

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Columnstore Case Study #1: MSIT SONAR Aggregations

- by aspiringgeek

Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014. Many of these can be found in this deck along with details such as internals, best practices, caveats, etc. The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps. In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data. The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR. By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows. New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly. Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more. Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements. Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data. "Conventional vs. Columnstore Metrics" document the raw data. Note on this linear display the magnitude of the conventional index performance vs. columnstore. The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing. I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more. Subsequent features in this series document performance enhancements that are even more significant.

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How Do You Actually Model Data?

Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols. But what is data modeling? The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago. I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects. Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines. These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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Schizophrenic Ubuntu 12.10-12.04: Atheros 922 PCI WIFI is disabled in Unity but enabled in terminal - How to getit to work?

- by zewone

I am trying to get my PCI Wireless Atheros 922 card to work. It is disabled in Unity: both the network utility and the desktop (see screenshot http://www.amisdurailhalanzy.be/Screenshot%20from%202012-10-25%2013:19:54.png) I tried many different advises on many different forums. Installed 12.10 instead of 12.04, enabled all interfaces... etc. I have read about the aht9 driver... The terminal shows no hw or sw lock for the Atheros card, nevertheless, it is still disabled. Nothing worked so far, the card is still disabled. Any help is much appreciated. Here are more tech details: myuser@adri1:~$ sudo lshw -C network *-network:0 DISABLED description: Wireless interface product: AR922X Wireless Network Adapter vendor: Atheros Communications Inc. physical id: 2 bus info: pci@0000:03:02.0 logical name: wlan1 version: 01 serial: 00:18:e7:cd:68:b1 width: 32 bits clock: 66MHz capabilities: pm bus_master cap_list ethernet physical wireless configuration: broadcast=yes driver=ath9k driverversion=3.5.0-17-generic firmware=N/A latency=168 link=no multicast=yes wireless=IEEE 802.11bgn resources: irq:18 memory:d8000000-d800ffff *-network:1 description: Ethernet interface product: VT6105/VT6106S [Rhine-III] vendor: VIA Technologies, Inc. physical id: 6 bus info: pci@0000:03:06.0 logical name: eth0 version: 8b serial: 00:11:09:a3:76:4a size: 10Mbit/s capacity: 100Mbit/s width: 32 bits clock: 33MHz capabilities: pm bus_master cap_list ethernet physical tp mii 10bt 10bt-fd 100bt 100bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=via-rhine driverversion=1.5.0 duplex=half latency=32 link=no maxlatency=8 mingnt=3 multicast=yes port=MII speed=10Mbit/s resources: irq:18 ioport:d300(size=256) memory:d8013000-d80130ff *-network DISABLED description: Wireless interface physical id: 1 bus info: usb@1:8.1 logical name: wlan0 serial: 00:11:09:51:75:36 capabilities: ethernet physical wireless configuration: broadcast=yes driver=rt2500usb driverversion=3.5.0-17-generic firmware=N/A link=no multicast=yes wireless=IEEE 802.11bg myuser@adri1:~$ sudo rfkill list all 0: hci0: Bluetooth Soft blocked: no Hard blocked: no 1: phy1: Wireless LAN Soft blocked: no Hard blocked: yes 2: phy0: Wireless LAN Soft blocked: no Hard blocked: no myuser@adri1:~$ dmesg | grep wlan0 [ 15.114235] IPv6: ADDRCONF(NETDEV_UP): wlan0: link is not ready myuser@adri1:~$ dmesg | egrep 'ath|firm' [ 14.617562] ath: EEPROM regdomain: 0x30 [ 14.617568] ath: EEPROM indicates we should expect a direct regpair map [ 14.617572] ath: Country alpha2 being used: AM [ 14.617575] ath: Regpair used: 0x30 [ 14.637778] ieee80211 phy0: >Selected rate control algorithm 'ath9k_rate_control' [ 14.639410] Registered led device: ath9k-phy0 myuser@adri1:~$ dmesg | grep wlan1 [ 15.119922] IPv6: ADDRCONF(NETDEV_UP): wlan1: link is not ready myuser@adri1:~$ lspci -nn | grep 'Atheros' 03:02.0 Network controller [0280]: Atheros Communications Inc. AR922X Wireless Network Adapter [168c:0029] (rev 01) myuser@adri1:~$ sudo ifconfig eth0 Link encap:Ethernet HWaddr 00:11:09:a3:76:4a inet addr:192.168.2.2 Bcast:192.168.2.255 Mask:255.255.255.0 inet6 addr: fe80::211:9ff:fea3:764a/64 Scope:Link UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:5457 errors:0 dropped:0 overruns:0 frame:0 TX packets:2548 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:3425684 (3.4 MB) TX bytes:282192 (282.1 KB) lo Link encap:Local Loopback inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:16436 Metric:1 RX packets:590 errors:0 dropped:0 overruns:0 frame:0 TX packets:590 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:53729 (53.7 KB) TX bytes:53729 (53.7 KB) myuser@adri1:~$ sudo iwconfig wlan0 IEEE 802.11bg ESSID:off/any Mode:Managed Access Point: Not-Associated Tx-Power=off Retry long limit:7 RTS thr:off Fragment thr:off Encryption key:off Power Management:on lo no wireless extensions. eth0 no wireless extensions. wlan1 IEEE 802.11bgn ESSID:off/any Mode:Managed Access Point: Not-Associated Tx-Power=0 dBm Retry long limit:7 RTS thr:off Fragment thr:off Encryption key:off Power Management:off myuser@adri1:~$ lsmod | grep "ath9k" ath9k 116549 0 mac80211 461161 3 rt2x00usb,rt2x00lib,ath9k ath9k_common 13783 1 ath9k ath9k_hw 376155 2 ath9k,ath9k_common ath 19187 3 ath9k,ath9k_common,ath9k_hw cfg80211 175375 4 rt2x00lib,ath9k,mac80211,ath myuser@adri1:~$ iwlist scan wlan0 Failed to read scan data : Network is down lo Interface doesn't support scanning. eth0 Interface doesn't support scanning. wlan1 Failed to read scan data : Network is down myuser@adri1:~$ lsb_release -d Description: Ubuntu 12.10 myuser@adri1:~$ uname -mr 3.5.0-17-generic i686 ![Schizophrenic Ubuntu](http://www.amisdurailhalanzy.be/Screenshot%20from%202012-10-25%2013:19:54.png) Any help much appreciated... Thanks, Philippe

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ArchBeat Link-o-Rama Top 10 for October 7-13, 2012

- by Bob Rhubart

The Top 10 items shared via the OTN ArchBeat Facebook page for the week of October 7-13, 2012. OOW12: Oracle Business Process Management/Oracle ADF Integration Best Practices | Andrejus Baranovskis The Oracle OpenWorld presentations keep coming! Oracle ACE Director Andrejus Baranovskis shares the slides from "Oracle Business Process Management/Oracle ADF Integration Best Practices," co-presented with Danilo Schmiedel from Opitz Consulting. Oracle's Analytics, Engineered Systems, and Big Data Strategy | Mark Rittman Part 1 of 3 in Oracle ACE Director Mark Rittman's series on Oracle Exalytics, Oracle R Enterprise and Endeca. Adaptive ADF/WebCenter template for the iPad | Maiko Rocha Oracle Fusion Middleware A-Team member Maiko Rocha responds to a a customer request for information about how to create an adaptive iPad template for their WebCenter Portal application, "a specific template to streamline their workflow on the iPad." Following the Thread in OSB | Antony Reynolds Antony Reynolds recently led an Oracle Service Bus POC in which his team needed to get high throughput from an OSB pipeline. "Imagine our surprise when, on stressing the system, we saw it lock up, with large numbers of blocked threads." He shares the details of the problem and the solution in this extensive technical post. WebCenter Sites Gadget Development Concepts Quickstart | John Brunswick What are Gadgets? "At their most basic level they can be thought of as lightweight portlets that run largely on the client side of an architecture," says John Brunswick. "Gadgets provide a cross-platform container to run reusable UI modules that generally expose dynamic information to an end user, allowing for some level of end user customization." Oracle Fusion Middleware Security: OAM and OIM 11g Academies Looking for technical how-to content covering Oracle Access Manager and Oracle Identity Manager? The people behind the Oracle Middleware Security blog have indexed relevant blog posts into what they call Academies. "These indexes contain the articles we've written that we believe provide long lasting guidance on OAM and OIM. Posts covered in these series include articles on key aspects of OAM and OIM 11g, best practice architectural guidance, integrations, and customizations." Fusion Applications Technical Tips | Naveen Nahata "Setting memory parameters for Admin and Managed servers of various domains in Fusion Applications can be, let us say, a little daunting," says Oracle Fusion Middleware A-Team member Naveen Nahata. "While all this may look complicated and intimidating, it is actually relatively simple once you understand how it all works." Updated Agenda for OTN Architect Day Los Angeles (Oct 25) In less than two weeks Oracle Architect Day rolls into Los Angeles, with a full slate of sessions devoted to cloud computing, engineered systems, and SOA. Follow the link for the updated event agenda. ORCLville: OOW 2012 - A Not So Brief Recap Oracle ACE Director Floyd Teter, an Applications & Apps Technology specialists, shares his personal, frank, and and extensive recap or Oracle OpenWorld 2012. SOA Suite create partition in Enterprise Manager | Peter Paul van de Beek "In Oracle SOA Suite 10g, or more specific BPEL 10g, one could group functionality in domains," says Peter Paul van de Beek. "This feature has been away in the early versions of SOA Suite 11g. They have returned in more recent version and can be used for all SCA composites (instead of BPEL only). Nowadays these 10g domains are called partitions." Thought for the Day "I strive for an architecture from which nothing can be taken away." — Helmut Jahn Source: BrainyQuote.com

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Wireless doesn't work on a Broadcom BCM4312

- by Boderick

As stated, I've just upgraded to 12.04 and my Dell Inspiron 1545 isn't recognising its wireless card and I was wondering if anybody could help? Edit: Okay, so I found the wireless card by using lspci -vvv and it returned this: 0c:00.0 Network controller: Broadcom Corporation BCM4312 802.11b/g LP-PHY (rev 01) Subsystem: Dell Wireless 1397 WLAN Mini-Card Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR+ FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast TAbort- SERR- Kernel modules: ssb lsmod Module Size Used by dm_crypt 22528 0 joydev 17393 0 dell_wmi 12601 0 sparse_keymap 13658 1 dell_wmi dell_laptop 13671 0 dcdbas 14098 1 dell_laptop psmouse 72919 0 uvcvideo 67203 0 serio_raw 13027 0 videodev 86588 1 uvcvideo snd_hda_codec_idt 60251 1 mac_hid 13077 0 snd_hda_intel 32765 5 snd_hda_codec 109562 2 snd_hda_codec_idt,snd_hda_intel snd_hwdep 13276 1 snd_hda_codec parport_pc 32114 0 rfcomm 38139 0 bnep 17830 2 ppdev 12849 0 snd_pcm 80845 3 snd_hda_intel,snd_hda_codec bluetooth 158438 10 rfcomm,bnep snd_seq_midi 13132 0 snd_rawmidi 25424 1 snd_seq_midi snd_seq_midi_event 14475 1 snd_seq_midi snd_seq 51567 2 snd_seq_midi,snd_seq_midi_event snd_timer 28931 2 snd_pcm,snd_seq snd_seq_device 14172 3 snd_seq_midi,snd_rawmidi,snd_seq binfmt_misc 17292 1 snd 62064 18 snd_hda_codec_idt,snd_hda_intel,snd_hda_codec,snd_hwdep,snd_pcm,snd_rawmidi,snd_seq,snd_timer,snd_seq_device soundcore 14635 1 snd snd_page_alloc 14108 2 snd_hda_intel,snd_pcm lp 17455 0 parport 40930 3 parport_pc,ppdev,lp sky2 53628 0 ums_realtek 17920 0 uas 17699 0 i915 414603 3 wmi 18744 1 dell_wmi drm_kms_helper 45466 1 i915 drm 197692 4 i915,drm_kms_helper i2c_algo_bit 13199 1 i915 video 19068 1 i915 usb_storage 39646 1 ums_realtek ifconfig -a eth0 Link encap:Ethernet HWaddr 00:23:ae:24:71:45 inet addr:192.168.1.158 Bcast:192.168.1.255 Mask:255.255.255.0 inet6 addr: fe80::223:aeff:fe24:7145/64 Scope:Link UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:14340 errors:0 dropped:0 overruns:0 frame:0 TX packets:10191 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:15403754 (15.4 MB) TX bytes:1262570 (1.2 MB) Interrupt:18 ham0 Link encap:Ethernet HWaddr 7a:79:05:2d:b0:f7 inet addr:5.45.176.247 Bcast:5.255.255.255 Mask:255.0.0.0 inet6 addr: fe80::7879:5ff:fe2d:b0f7/64 Scope:Link inet6 addr: 2620:9b::52d:b0f7/96 Scope:Global UP BROADCAST RUNNING MULTICAST MTU:1404 Metric:1 RX packets:0 errors:0 dropped:0 overruns:0 frame:0 TX packets:179 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:500 RX bytes:0 (0.0 B) TX bytes:27480 (27.4 KB) lo Link encap:Local Loopback inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:16436 Metric:1 RX packets:433 errors:0 dropped:0 overruns:0 frame:0 TX packets:433 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:60051 (60.0 KB) TX bytes:60051 (60.0 KB) iwconfig lo no wireless extensions. ham0 no wireless extensions. eth0 no wireless extensions. the results for sudo lshw -class network *-network description: Wireless interface product: BCM4312 802.11b/g LP-PHY vendor: Broadcom Corporation physical id: 0 bus info: pci@0000:0c:00.0 logical name: eth1 version: 01 serial: 00:22:5f:77:1f:e6 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list ethernet physical wireless configuration: broadcast=yes driver=wl0 driverversion=5.100.82.38 latency=0 multicast=yes wireless=IEEE 802.11bg resources: irq:17 memory:f69fc000-f69fffff *-network description: Ethernet interface product: 88E8040 PCI-E Fast Ethernet Controller vendor: Marvell Technology Group Ltd. physical id: 0 bus info: pci@0000:09:00.0 logical name: eth0 version: 13 serial: 00:23:ae:24:71:45 size: 100Mbit/s capacity: 100Mbit/s width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list ethernet physical tp 10bt 10bt-fd 100bt 100bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=sky2 driverversion=1.30 duplex=full firmware=N/A ip=192.168.1.158 latency=0 link=yes multicast=yes port=twisted pair speed=100Mbit/s resources: irq:45 memory:f68fc000-f68fffff ioport:de00(size=256) *-network description: Ethernet interface physical id: 2 logical name: ham0 serial: 7a:79:05:2d:b0:f7 size: 10Mbit/s capabilities: ethernet physical configuration: autonegotiation=off broadcast=yes driver=tun driverversion=1.6 duplex=full firmware=N/A ip=5.45.176.247 link=yes multicast=yes port=twisted pair speed=10Mbit/s and the results of rfkill list all 0: brcmwl-0: Wireless LAN Soft blocked: yes Hard blocked: yes 1: dell-wifi: Wireless LAN Soft blocked: yes Hard blocked: yes

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Fresh Ubuntu Install - Grub not loading

- by Ryan Sharp

System Ubuntu 12.04 64-bit Windows 7 SP1 Samsung 64GB SSD - OS' Samsung 1TB HDD - Games, /Home, Swap WD 300'ishGB HDD - Backup Okay, so I'm very frustrated, so please excuse me if I miss anything out as my head is clouded by anger and impatience, etc. I'll try me best, though. First of all, I'll explain how I got to my predicament. I finally got my new SSD. I firstly installed Windows, which completed without a hitch. Afterwards, I tried to install Ubuntu, which failed several times due to problems irrelevant to this question, but I mention this to explain my frustrations, sorry. Anyway, I finally installed Ubuntu. However, I chose the 'bootloader' to be installed on the same partition as where I was installing the Ubuntu Root partition, as that was what I believed to be the best choice. It was of my thinking that it was supposed to go on the same partition and on the SSD, which is my OS drive, though with my problem, it apparently was wrong. So I tried to fix it by checking guides and following their directions, but seemed to have messed it up even more. Here is what I receive after I use the fdisk -l command: (I also added explanations for which I used each partition for) Disk /dev/sda: 64.0 GB, 64023257088 bytes 255 heads, 63 sectors/track, 7783 cylinders, total 125045424 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: 0x324971d1 Device Boot Start End Blocks Id System /dev/sda1 * 2048 206847 102400 7 HPFS/NTFS/exFAT /dev/sda2 208896 48957439 24374272 7 HPFS/NTFS/exFAT /dev/sda3 48959486 125044735 38042625 5 Extended /dev/sda5 48959488 125044735 38042624 83 Linux sda1 --/ Windows Recovery sda2 --/ Windows 7 sda3/5 --/ Ubuntu root [ / ] Disk /dev/sdb: 1000.2 GB, 1000204886016 bytes 255 heads, 63 sectors/track, 121601 cylinders, total 1953525168 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: 0xc0ee6a69 Device Boot Start End Blocks Id System /dev/sdb1 1024208894 1953523711 464657409 5 Extended /dev/sdb3 * 2048 1024206847 512102400 7 HPFS/NTFS/exFAT /dev/sdb5 1024208896 1939851263 457821184 83 Linux /dev/sdb6 1939853312 1953523711 6835200 82 Linux swap / Solaris sdb3 --/ Partition for Steam games, etc. sdb5 --/ Ubuntu Home [ /home ] sdb6 --/ Ubuntu Swap Partition table entries are not in disk order Disk /dev/sdc: 320.1 GB, 320072933376 bytes 255 heads, 63 sectors/track, 38913 cylinders, total 625142448 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: 0x292eee23 Device Boot Start End Blocks Id System /dev/sdc1 2048 625141759 312569856 7 HPFS/NTFS/exFAT sdc1 --/ Generic backup I also used a Boot Script that other users suggested, so that I can give more details on my partitions and also where Grub is located... ============================= Boot Info Summary: =============================== => Grub2 (v1.99) is installed in the MBR of /dev/sda and looks at sector 1 of the same hard drive for core.img. core.img is at this location and looks for (,msdos5)/boot/grub on this drive. => Grub2 (v1.99) is installed in the MBR of /dev/sdb and looks at sector 1 of the same hard drive for core.img. core.img is at this location and looks for (,msdos5)/boot/grub on this drive. => Windows is installed in the MBR of /dev/sdc. Now that is weird... Why would Grub2 be installed on both my SSD and HDD? Even weirder is why is Windows on the MBR of my backup hard drive? Nothing I did should have done that... Anyway, here is the entire Output from that script... PASTEBIN So, to summarize what I need: How can I fix my setup so grub loads on startup? How can I clean my partitions to remove unnecessary grubs? What did I do wrong so that I don't do something so daft again? Thank you so much for reading, and I hope you can help me. I've been trying to have a successful setup since Friday, and I'm almost at the point that I'm really tempted to throw my computer out the window due to my frustration.

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WiFi stops working after a while in Lenovo ThinkPad W520 (Ubuntu 12.04)

- by el10780

After several minutes(I do not know how many) there is no internet connection on my laptop via Wi-Fi.Ubuntu doesn't show any kind of message that my WiFi was disconnected neither there is a signal drop,but suddenly Firefox stops connecting to web pages.I checked my modem/router and it seems that it is working fine.I tried also to reboot the WiFi device and nothing happens.The only thing that it makes it work again is a reboot of the system and if I do not want to do a reboot then I am enforced to connect to the Internet using Ethernet cable.Does anybody know what is happening? ## Some Hardware info that might be helpful ## el10780@ThinkPad-W520:~$ sudo lshw -class network *-network description: Ethernet interface product: 82579LM Gigabit Network Connection vendor: Intel Corporation physical id: 19 bus info: pci@0000:00:19.0 logical name: eth0 version: 04 serial: f0:de:f1:f1:be:10 size: 100Mbit/s capacity: 1Gbit/s width: 32 bits clock: 33MHz capabilities: pm msi bus_master cap_list ethernet physical tp 10bt 10bt-fd 100bt 100bt-fd 1000bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=e1000e driverversion=1.5.1-k duplex=full firmware=0.13-3 ip=192.168.0.10 latency=0 link=yes multicast=yes port=twisted pair speed=100Mbit/s resources: irq:50 memory:f3a00000-f3a1ffff memory:f3a2b000-f3a2bfff ioport:6080(size=32) *-network description: Wireless interface product: Centrino Advanced-N + WiMAX 6250 vendor: Intel Corporation physical id: 0 bus info: pci@0000:03:00.0 logical name: wlan0 version: 5e serial: 64:80:99:63:14:74 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list ethernet physical wireless configuration: broadcast=yes driver=iwlwifi driverversion=3.2.0-26-generic firmware=41.28.5.1 build 33926 ip=192.168.0.6 latency=0 link=yes multicast=yes wireless=IEEE 802.11abgn resources: irq:52 memory:f3900000-f3901fff *-network description: Ethernet interface physical id: 1 bus info: usb@2:1.3 logical name: wmx0 serial: 00:1d:e1:53:b2:e8 capabilities: ethernet physical configuration: driver=i2400m firmware=i6050-fw-usb-1.5.sbcf link=no el10780@ThinkPad-W520:~$ lspci 00:00.0 Host bridge: Intel Corporation 2nd Generation Core Processor Family DRAM Controller (rev 09) 00:01.0 PCI bridge: Intel Corporation Xeon E3-1200/2nd Generation Core Processor Family PCI Express Root Port (rev 09) 00:02.0 VGA compatible controller: Intel Corporation 2nd Generation Core Processor Family Integrated Graphics Controller (rev 09) 00:16.0 Communication controller: Intel Corporation 6 Series/C200 Series Chipset Family MEI Controller #1 (rev 04) 00:16.3 Serial controller: Intel Corporation 6 Series/C200 Series Chipset Family KT Controller (rev 04) 00:19.0 Ethernet controller: Intel Corporation 82579LM Gigabit Network Connection (rev 04) 00:1a.0 USB controller: Intel Corporation 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #2 (rev 04) 00:1b.0 Audio device: Intel Corporation 6 Series/C200 Series Chipset Family High Definition Audio Controller (rev 04) 00:1c.0 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 1 (rev b4) 00:1c.1 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 2 (rev b4) 00:1c.3 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 4 (rev b4) 00:1c.4 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 5 (rev b4) 00:1c.6 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 7 (rev b4) 00:1d.0 USB controller: Intel Corporation 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #1 (rev 04) 00:1f.0 ISA bridge: Intel Corporation QM67 Express Chipset Family LPC Controller (rev 04) 00:1f.2 SATA controller: Intel Corporation 6 Series/C200 Series Chipset Family 6 port SATA AHCI Controller (rev 04) 00:1f.3 SMBus: Intel Corporation 6 Series/C200 Series Chipset Family SMBus Controller (rev 04) 01:00.0 VGA compatible controller: NVIDIA Corporation GF108 [Quadro 1000M] (rev a1) 03:00.0 Network controller: Intel Corporation Centrino Advanced-N + WiMAX 6250 (rev 5e) 0d:00.0 System peripheral: Ricoh Co Ltd Device e823 (rev 08) 0d:00.3 FireWire (IEEE 1394): Ricoh Co Ltd R5C832 PCIe IEEE 1394 Controller (rev 04) 0e:00.0 USB controller: NEC Corporation uPD720200 USB 3.0 Host Controller (rev 04) el10780@ThinkPad-W520:~$ rfkill list all 0: hci0: Bluetooth Soft blocked: no Hard blocked: no 1: tpacpi_bluetooth_sw: Bluetooth Soft blocked: no Hard blocked: no 2: phy0: Wireless LAN Soft blocked: no Hard blocked: no 3: i2400m-usb:2-1.3:1.0: WiMAX Soft blocked: yes Hard blocked: no The weirdest thing is this screenshot which I took after running the **Additional Drivers** program.I mean I have a NVidia Quadro 1000M and my Intel Centrino WiFi Card and this shows that there are not proprietay drivers for my system. http://imageshack.us/photo/my-images/268/screenshotfrom201207062.png/

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Need help partitioning when reinstalling Ubuntu 14.04

- by Chris M.

I upgraded to 14.04 about a month ago on my HP Mini netbook (about 16 GB hard disk). A few days ago the system crashed (I don't know why but I was using internet at the time). When I restarted the computer, Ubuntu would not load. Instead, I got a message from the BIOS saying Reboot and Select proper Boot device or Insert Boot Media in selected Boot device and press a key I took this to mean that I needed to reinstall 14.04. When I try to reinstall Ubuntu from the USB stick, I choose "Erase disk and install Ubuntu" but then I get a message: Some of the partitions you created are too small. Please make the following partitions at least this large: / 3.3 GB If you do not go back to the partitioner and increase the size of these partitions, the installation may fail. At first I hit Continue to see if it would install anyway, and it gave the message: The attempt to mount a file system with type ext4 in SCSI1 (0,0,0), partition # 1 (sda) at / failed. You may resume partitioning from the partitioning menu. The second time I hit Go Back, and it took me to the following partitioning table: Device Type Mount Point Format Size Used System /dev/sda /dev/sda1 ext4 (checked) 3228 MB Unknown /dev/sda5 swap (not checked) 1063 MB Unknown + - Change New Partition Table... Revert Device for boot loader installation: /dev/sda ATA JM Loader 001 (4.3 GB) At this point I'm not sure what to do. I've never partitioned my hard drive before and I don't want to screw things up. (I'm not particularly tech savvy.) Can you instruct me what I should do. (P.S. I'm afraid the table might not appear as I typed it in.) Results from fdisk: ubuntu@ubuntu:~$ sudo fdisk -l Disk /dev/sda: 4294 MB, 4294967296 bytes 255 heads, 63 sectors/track, 522 cylinders, total 8388608 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 Disk /dev/sda doesn't contain a valid partition table Disk /dev/sdb: 7860 MB, 7860125696 bytes 155 heads, 31 sectors/track, 3194 cylinders, total 15351808 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: 0x0009a565 Device Boot Start End Blocks Id System /dev/sdb1 * 2768 15351807 7674520 b W95 FAT32 ubuntu@ubuntu:~$ Here is what it displays when I open the Disks utility (I tried the screenshot terminal command you suggested but it didn't seem to do anything): 4.3 GB Hard Disk /dev/sda Model: JM Loader 001 (01000001) Size: 4.3 GB (4,294,967,296 bytes) Serial Number: 01234123412341234 Assessment: SMART is not supported Volumes Size: 4.3 GB (4,294,967,296 bytes) Device: /dev/sda Contents: Unknown (There is a button in the utility that when you click it gives the following options: Format... Create Disk Image... Restore Disk Image... Benchmark but SMART Data & Self-Tests... is dimmed out) When I hit F9 Change Boot Device Order, it shows the hard drive as: SATA:PM-JM Loader 001 When I hit F10 to get me into the BIOS Setup Utility, under Diagnostic it shows: Primary Hard Disk Self Test Not Support NetworkManager Tool State: disconnected Device: eth0 Type: Wired Driver: atl1c State: unavailable Default: no HW Address: 00:26:55:B0:7F:0C Capabilities: Carrier Detect: yes Wired Properties Carrier: off When I run command lshw -C network, I get: WARNING: you should run this program as super-user. *-network description: Network controller product: BCM4312 802.11b/g LP-PHY vendor: Broadcom Corporation physical id: 0 bus info: pci@0000:01:00.0 version: 01 width: 64 bits clock: 33MHz capabilities: bus_master cap_list configuration: driver=b43-pci-bridge latency=0 resources: irq:16 memory:feafc000-feafffff *-network description: Ethernet interface product: AR8132 Fast Ethernet vendor: Qualcomm Atheros physical id: 0 bus info: pci@0000:02:00.0 logical name: eth0 version: c0 serial: 00:26:55:b0:7f:0c capacity: 100Mbit/s width: 64 bits clock: 33MHz capabilities: bus_master cap_list ethernet physical tp 10bt 10bt-fd 100bt 100bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=atl1c driverversion=1.0.1.1-NAPI latency=0 link=no multicast=yes port=twisted pair resources: irq:43 memory:febc0000-febfffff ioport:ec80(size=128) WARNING: output may be incomplete or inaccurate, you should run this program as super-user.

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Columnstore Case Study #1: MSIT SONAR Aggregations

- by aspiringgeek

Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014. Many of these can be found in this deck along with details such as internals, best practices, caveats, etc. The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps. In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data. The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR. By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows. New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly. Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more. Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements. Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data. "Conventional vs. Columnstore Metrics" document the raw data. Note on this linear display the magnitude of the conventional index performance vs. columnstore. The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing. I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more. Subsequent features in this series document performance enhancements that are even more significant.

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My LAN USB NIC is not working in ubuntu 11.10?

- by Gaurav_Java

Today i start my system its seems that my LAN port is not working . so i buy one USB to LAN adapter and i plug in ubuntu system its doen't connect automatically .when i check result lsusb its shows me that there is one DM9601 Ethernet adapter is connected when i click on network information in panel its shows me that there is something " wired NEtwork (Broadcom NetLink BCM5784M gigabit Ethernet PCIe) I think want some driver for that .i don't have any idea how it can be used ? here output of sudo lspci -nn *00:00.0 Host bridge [0600]: Intel Corporation Mobile 4 Series Chipset Memory Controller Hub [8086:2a40] (rev 07) 00:02.0 VGA compatible controller [0300]: Intel Corporation Mobile 4 Series Chipset Integrated Graphics Controller [8086:2a42] (rev 07) 00:02.1 Display controller [0380]: Intel Corporation Mobile 4 Series Chipset Integrated Graphics Controller [8086:2a43] (rev 07) 00:1a.0 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #4 [8086:2937] (rev 03) 00:1a.1 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #5 [8086:2938] (rev 03) 00:1a.7 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB2 EHCI Controller #2 [8086:293c] (rev 03) 00:1b.0 Audio device [0403]: Intel Corporation 82801I (ICH9 Family) HD Audio Controller [8086:293e] (rev 03) 00:1c.0 PCI bridge [0604]: Intel Corporation 82801I (ICH9 Family) PCI Express Port 1 [8086:2940] (rev 03) 00:1c.1 PCI bridge [0604]: Intel Corporation 82801I (ICH9 Family) PCI Express Port 2 [8086:2942] (rev 03) 00:1c.2 PCI bridge [0604]: Intel Corporation 82801I (ICH9 Family) PCI Express Port 3 [8086:2944] (rev 03) 00:1c.4 PCI bridge [0604]: Intel Corporation 82801I (ICH9 Family) PCI Express Port 5 [8086:2948] (rev 03) 00:1d.0 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #1 [8086:2934] (rev 03) 00:1d.1 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #2 [8086:2935] (rev 03) 00:1d.2 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #3 [8086:2936] (rev 03) 00:1d.3 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #6 [8086:2939] (rev 03) 00:1d.7 USB Controller [0c03]: Intel Corporation 82801I (ICH9 Family) USB2 EHCI Controller #1 [8086:293a] (rev 03) 00:1e.0 PCI bridge [0604]: Intel Corporation 82801 Mobile PCI Bridge [8086:2448] (rev 93) 00:1f.0 ISA bridge [0601]: Intel Corporation ICH9M LPC Interface Controller [8086:2919] (rev 03) 00:1f.2 SATA controller [0106]: Intel Corporation ICH9M/M-E SATA AHCI Controller [8086:2929] (rev 03) 00:1f.3 SMBus [0c05]: Intel Corporation 82801I (ICH9 Family) SMBus Controller [8086:2930] (rev 03) 02:00.0 Ethernet controller [0200]: Broadcom Corporation NetLink BCM5784M Gigabit Ethernet PCIe [14e4:1698] (rev 10) 04:00.0 Network controller [0280]: Intel Corporation WiFi Link 5100 [8086:4232]* sudo lshw -class network *-network description: Ethernet interface product: NetLink BCM5784M Gigabit Ethernet PCIe vendor: Broadcom Corporation physical id: 0 bus info: pci@0000:02:00.0 logical name: eth0 version: 10 serial: 00:1f:16:9a:56:98 capacity: 1Gbit/s width: 64 bits clock: 33MHz capabilities: pm vpd msi pciexpress bus_master cap_list ethernet physical tp 10bt 10bt-fd 100bt 100bt-fd 1000bt 1000bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=tg3 driverversion=3.119 firmware=sb v2.19 latency=0 link=no multicast=yes port=twisted pair resources: irq:47 memory:f4500000-f450ffff *-network DISABLED description: Wireless interface product: WiFi Link 5100 vendor: Intel Corporation physical id: 0 bus info: pci@0000:04:00.0 logical name: wlan0 version: 00 serial: 00:22:fa:09:02:00 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list ethernet physical wireless configuration: broadcast=yes driver=iwlagn driverversion=3.0.0-17-generic firmware=8.83.5.1 build 33692 latency=0 link=no multicast=yes wireless=IEEE 802.11abgn resources: irq:46 memory:f4600000-f4601fff *-network description: Ethernet interface physical id: 4 logical name: eth1 serial: 00:60:6e:00:f1:7d size: 100Mbit/s capacity: 100Mbit/s capabilities: ethernet physical tp mii 10bt 10bt-fd 100bt 100bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=dm9601 driverversion=22-Aug-2005 duplex=full firmware=Davicom DM9601 USB Ethernet ip=192.168.1.34 link=yes multicast=yes port=MII speed=100Mbit/s I am using Wimax internet connection which i have to connect from browser . at that time my system is not showing that i am connected to any wired connection. but when i connect internet from other system after getting conneted to internet . when i plug again my USB LAN then its shows that you are conneted to wired connetion. here is screenshot for conneting wimax from browser after connecting to internet network connection shows

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help in the Donalds B. Johnson's algorithm, i cannot understand the pseudo code

- by Pitelk

Hi , does anyone know the Donald B. Johnson's algorithm which enumarates all the elementary circuits (cycles) in a Directed graph? link text I have the paper he had published in 1975 but I cannot understand the pseudo-code. My goal is to implement this algorithm in java. Some questions i have is for example what is the matrix Ak it refers to. In the pseudo code mentions that Ak:=adjacency structure of strong component K with least vertex in subgraph of G induced by {s,s+1,....n}; Does that mean i have to implement another algorithm that finds the Ak matrix? Another question is what the following means? begin logical f; Does also the line "logical procedure CIRCUIT (integer value v);" means that the circuit procedure returns a logical variable. In the pseudo code also has the line "CIRCUIT := f;" . Does this mean? It would be great if someone could translate this 1970's pseudocode to a more modern type of pseudo code so i can understand it in case you are interested to help but you cannot find the paper please email me at [email protected] and i will send you the paper. Thanks in advance

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slicing a 2d numpy array

- by MedicalMath

The following code: import numpy as p myarr=[[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6]] copy=p.array(myarr) p.mean(copy)[:,1] Is generating the following error message: Traceback (most recent call last): File "<pyshell#3>", line 1, in <module> p.mean(copy)[:,1] IndexError: 0-d arrays can only use a single () or a list of newaxes (and a single ...) as an index I looked up the syntax at this link and I seem to be using the correct syntax to slice. However, when I type copy[:,1] into the Python shell, it gives me the following output, which is clearly wrong, and is probably what is throwing the error: array([1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6]) Can anyone show me how to fix my code so that I can extract the second column and then take the mean of the second column as intended in the original code above? EDIT: Thank you for your solutions. However, my posting was an oversimplification of my real problem. I used your solutions in my real code, and got a new error. Here is my real code with one of your solutions that I tried: filteredSignalArray=p.array(filteredSignalArray) logical=p.logical_and(EndTime-10.0<=matchingTimeArray,matchingTimeArray<=EndTime) finalStageTime=matchingTimeArray.compress(logical) finalStageFiltered=filteredSignalArray.compress(logical) for j in range(len(finalStageTime)): if j == 0: outputArray=[[finalStageTime[j],finalStageFiltered[j]]] else: outputArray+=[[finalStageTime[j],finalStageFiltered[j]]] print 'outputArray[:,1].mean() is: ',outputArray[:,1].mean() And here is the error message that is now being generated by the new code: File "mypath\myscript.py", line 1545, in WriteToOutput10SecondsBeforeTimeMarker print 'outputArray[:,1].mean() is: ',outputArray[:,1].mean() TypeError: list indices must be integers, not tuple Second EDIT: This is solved now that I added: outputArray=p.array(outputArray) above my code. I have been at this too many hours and need to take a break for a while if I am making these kinds of mistakes.

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count on LINQ union

- by brechtvhb

I'm having this link statement: List<UserGroup> domains = UserRepository.Instance.UserIsAdminOf(currentUser.User_ID); query = (from doc in _db.Repository<Document>() join uug in _db.Repository<User_UserGroup>() on doc.DocumentFrom equals uug.User_ID where domains.Contains(uug.UserGroup) select doc) .Union(from doc in _db.Repository<Document>() join uug in _db.Repository<User_UserGroup>() on doc.DocumentTo equals uug.User_ID where domains.Contains(uug.UserGroup) select doc); Running this statement doesn't cause any problems. But when I want to count the resultset the query suddenly runs quite slow. totalRecords = query.Count(); The result of this query is : SELECT COUNT([t5].[DocumentID]) FROM ( SELECT [t4].[DocumentID], [t4].[DocumentFrom], [t4].[DocumentTo] FROM ( SELECT [t0].[DocumentID], [t0].[DocumentFrom], [t0].[DocumentTo FROM [dbo].[Document] AS [t0] INNER JOIN [dbo].[User_UserGroup] AS [t1] ON [t0].[DocumentFrom] = [t1].[User_ID] WHERE ([t1].[UserGroupID] = 2) OR ([t1].[UserGroupID] = 3) OR ([t1].[UserGroupID] = 6) UNION SELECT [t2].[DocumentID], [t2].[DocumentFrom], [t2].[DocumentTo] FROM [dbo].[Document] AS [t2] INNER JOIN [dbo].[User_UserGroup] AS [t3] ON [t2].[DocumentTo] = [t3].[User_ID] WHERE ([t3].[UserGroupID] = 2) OR ([t3].[UserGroupID] = 3) OR ([t3].[UserGroupID] = 6) ) AS [t4] ) AS [t5] Can anyone help me to improve the speed of the count query? Thanks in advance!

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WHMCS - Mapping of a Manually Created Invoice with its Corresponding Domain

- by Knowledge Craving

I am using WHMCS version 4.2.1 for maintaining domain registration & website hosting services, in one of my websites. Currently, the automatic domain registration process is working great for both the existing & new clients. The main way to register domains automatically is to mark the respective invoices as paid, and automatically the domains get registered through my selected registrar. Recently, I am facing a major problem in domain renewals, where the domain has been already registered by us in the past. The problem is that some of the corresponding invoices are not already generated for the domains & so I have to manually create invoice for each of those domain renewals. However, I am unable to map that invoice with the corresponding domain. This is required because unless the domain knows that for its renewal, an invoice has been created, the marking of the invoice as paid will not instantiate the automatic renewal process through my registrar. Can anybody please tell me the probable way of mapping the invoice with its corresponding domain? I've tried to explain the problem as it is occurring in the best way possible. Still, if any more information regarding this is required, please ask. Any help is greatly appreciated.

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firefox lead dot in cookie issue

- by Jon

Hi all, We are having an annoying issue with Firefox and cookies. We have the following domains: sub1.mydomain.com sub2.mydomain.com sub3.mydomain.com otherdomain.com We have converting our framework to be multilingual and providing a drop down to change the language at any point during site. The code base is shared across all the domains above. We can not set a cookie across all "mydomain.com" sites, they have to be on each of the sub domains. To get this to work we set a JavaScript cookie when the users chooses a new language. When the page posts back to the server the code picks this up and sets the users preferences to that new language code, (this is all C# and ASP.NET). We have to set the host to be "subX.mydomain.com" and the path to be "/" in the cookie so that it is just for the subdomain and all parts of that domain. This works great on all browsers apart from FireFox. It seems that firefox will pre append a DOT to the beginning of domain so ".subX.mydomain.com". When the code posts back with FireFox the cookie is always null. Has anyone had this situation, (I imagine it is not al that uncommon). I have read a lot of people saying, remove the domain from the cookie, but that can not work for us as we have multiple subdomains that need their own cookie values. Thanks

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Java-Hibernate: How can I translate these tables to hibernate annotations?

- by penas

I need to create a simple application using these tables: http://stackoverflow.com/questions/2612848/are-these-tables-respect-the-3nf-database-normalization I have created the application using simple old JDBC, but I would like to see how the application would look like using Hibernate, but I don't know how to put the sql code in java. I have found LOTS of examples, but I'm pretty much confused about using Hibernate and I don't know If I made such a good joob. For example, for the first three tables: AUTHOR table * Author_ID, PK * First_Name * Last_Name TITLES table * TITLE_ID, PK * NAME * Author_ID, FK DOMAIN table * DOMAIN_ID, PK * NAME * TITLE_ID, FK The code in java: Table 1 @Entity @Table(name = "AUTHORS", schema = "LIBRARY") public class Author{ @Id @GeneratedValue(strategy = GenerationType.AUTO) @Column(name = "Author_ID") private int authorId; @Column(name = "First_Name", nullable = false, length = 50) private String firstName; @Column(name = "Last_Name", nullable = false, length = 40) private String lastName; @OneToMany @JoinColumn(name = "Title_ID") private List<Title> titles; Table 2 @Entity @Table(name = "TITLES") public class Title{ @Id @Column(name = "Title_ID") private int titleID; @Column(name = "Name", nullable = false, length = 50) private String name; @ManyToOne @JoinColumn(name = "Domain_ID") private Domain domains; Table 3 @Entity @Table(name = "DOMAINS") public class Domain{ @Id @GeneratedValue(strategy = GenerationType.AUTO) @Column(name = "Domain_ID") private int Domain_ID; @Column(name = "Name", nullable = false, length = 50) private String name; @OneToOne(mappedBy = "domains") private Title title; } Any good? :)

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iOS layout; I'm not getting it

- by Tbee

Well, "not getting it" is too harsh; I've got it working in for what for me is a logical setup, but it does not seem to be what iOS deems logical. So I'm not getting something. Suppose I've got an app that shows two pieces of information; a date and a table. According to the MVC approach I've got three MVC at work here, one for the date, one for the table and one that takes both these MCVs and makes it into a screen, wiring them up. The master MVC knows how/where it wants to layout the two sub MVC's. Each detail MVC only takes care of its own childeren within the bounds that were specified by the master MVC. Something like: - (void)loadView { MVC* mvc1 = [[MVC1 alloc] initwithFrame:...] [self.view addSubview:mvc1.view]; MVC* mvc2 = [[MVC2 alloc] initwithFrame:...] [self.view addSubview:mvc2.view]; } If the above is logical (which is it for me) then I would expect any MVC class to have a constructor "initWithFrame". But an MVC does not, only view have this. Why? How would one correctly layout nested MVCs? (Naturally I do not have just these two, but the detail MVCs have sub MVCs again.)

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My method is too specific. How can I make it more generic?

- by EricBoersma

I have a class, the outline of which is basically listed below. import org.apache.commons.math.stat.Frequency; public class WebUsageLog { private Collection<LogLine> logLines; private Collection<Date> dates; WebUsageLog() { this.logLines = new ArrayList<LogLine>(); this.dates = new ArrayList<Date>(); } SortedMap<Double, String> getFrequencyOfVisitedSites() { SortedMap<Double, String> frequencyMap = new TreeMap<Double, String>(Collections.reverseOrder()); //we reverse order to sort from the highest percentage to the lowest. Collection<String> domains = new HashSet<String>(); Frequency freq = new Frequency(); for (LogLine line : this.logLines) { freq.addValue(line.getVisitedDomain()); domains.add(line.getVisitedDomain()); } for (String domain : domains) { frequencyMap.put(freq.getPct(domain), domain); } return frequencyMap; } } The intention of this application is to allow our Human Resources folks to be able to view Web Usage Logs we send to them. However, I'm sure that over time, I'd like to be able to offer the option to view not only the frequency of visited sites, but also other members of LogLine (things like the frequency of assigned categories, accessed types [text/html, img/jpeg, etc...] filter verdicts, and so on). Ideally, I'd like to avoid writing individual methods for compilation of data for each of those types, and they could each end up looking nearly identical to the getFrequencyOfVisitedSites() method. So, my question is twofold: first, can you see anywhere where this method should be improved, from a mechanical standpoint? And secondly, how would you make this method more generic, so that it might be able to handle an arbitrary set of data?

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Rails nested association issue

- by Ben Langfeld

Ok, so I'm new to both Ruby and Rails and I'm trying to do what I believe is called a nested association (please correct me if this is the wrong terminology). I currently have a User model and a Domains model and I have many to many associations setup (using has_many :through) between the two, and this works fine. I now want to extend this to allow for a single role per domain per user (eg User1 is a member of Domain1 and has the role "Admin"). I have setup a Roles model with a single field (name:string) and have created three roles. I have also added a role_id column to the join table (memberships). I expected (and this is probably the issue) to be able to just use user1 = User.find(1) user1.domains.first => <some domain object> user1.domains.first.role => <some role object> but this returns a method not defined error. Can anyone tell me what I'm failing to grasp here? My model classes can be seen at http://gist.github.com/388200

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Improving Partitioned Table Join Performance

- by Paul White

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

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problem with sIFR 3 not displaying in IE just getting XXX

- by user288306

I am having a problem with sIFR 3 not displaying in IE. I get 3 larges black XXX in IE yet it displays fine in Firefox. I have checked i do have the most recent version of flash installed correctly. Here is the code on the page <div id="features"> <div id="mainmessage_advertisers"><h2>Advertisers</h2><br /><br /><h3><a href="">Reach your customers where they browse. Buy directly from top web publishers.</a></h3><br /><br /><br /><a href=""><img src="img/buyads.gif" border="0"></a></div> <div id="mainmessage_publishers"><h2>Publishers</h2><br /><br /><h3>Take control of your ad space and start generating more revenue than <u>ever before</u>.</h3><br /><br /><br /><a href=""><img src="img/sellads.gif" border="0"></a></div> </div>` Here is the code from my global.css #mainmessage_advertisers { width: 395px; height: 200px; padding: 90px 50px; border: 1px; float: left; } #mainmessage_publishers { width: 395px; height: 200px; padding: 90px 50px; float: right; } and here is what i have in my sifr.js /*********************************************************************** SIFR 3.0 (BETA 1) FUNCTIONS ************************************************************************/ var parseSelector=(function(){var _1=/\s*,\s*/;var _2=/\s*([\s>+~(),]|^|$)\s*/g;var _3=/([\s>+~,]|[^(]\+|^)([#.:@])/g;var _4=/^[^\s>+~]/;var _5=/[\s#.:>+~()@]|[^\s#.:>+~()@]+/g;function parseSelector(_6,_7){_7=_7||document.documentElement;var _8=_6.split(_1),_9=[];for(var i=0;i<_8.length;i++){var _b=[_7],_c=toStream(_8[i]);for(var j=0;j<_c.length;){var _e=_c[j++],_f=_c[j++],_10="";if(_c[j]=="("){while(_c[j++]!=")"&&j<_c.length){_10+=_c[j]}_10=_10.slice(0,-1)}_b=select(_b,_e,_f,_10)}_9=_9.concat(_b)}return _9}function toStream(_11){var _12=_11.replace(_2,"$1").replace(_3,"$1*$2");if(_4.test(_12)){_12=" "+_12}return _12.match(_5)||[]}function select(_13,_14,_15,_16){return (_17[_14])?_17[_14](_13,_15,_16):[]}var _18={toArray:function(_19){var a=[];for(var i=0;i<_19.length;i++){a.push(_19[i])}return a}};var dom={isTag:function(_1d,tag){return (tag=="*")||(tag.toLowerCase()==_1d.nodeName.toLowerCase())},previousSiblingElement:function(_1f){do{_1f=_1f.previousSibling}while(_1f&&_1f.nodeType!=1);return _1f},nextSiblingElement:function(_20){do{_20=_20.nextSibling}while(_20&&_20.nodeType!=1);return _20},hasClass:function(_21,_22){return (_22.className||"").match("(^|\\s)"+_21+"(\\s|$)")},getByTag:function(tag,_24){return _24.getElementsByTagName(tag)}};var _17={"#":function(_25,_26){for(var i=0;i<_25.length;i++){if(_25[i].getAttribute("id")==_26){return [_25[i]]}}return []}," ":function(_28,_29){var _2a=[];for(var i=0;i<_28.length;i++){_2a=_2a.concat(_18.toArray(dom.getByTag(_29,_28[i])))}return _2a},">":function(_2c,_2d){var _2e=[];for(var i=0,_30;i<_2c.length;i++){_30=_2c[i];for(var j=0,_32;j<_30.childNodes.length;j++){_32=_30.childNodes[j];if(_32.nodeType==1&&dom.isTag(_32,_2d)){_2e.push(_32)}}}return _2e},".":function(_33,_34){var _35=[];for(var i=0,_37;i<_33.length;i++){_37=_33[i];if(dom.hasClass([_34],_37)){_35.push(_37)}}return _35},":":function(_38,_39,_3a){return (pseudoClasses[_39])?pseudoClasses[_39](_38,_3a):[]}};parseSelector.selectors=_17;parseSelector.pseudoClasses={};parseSelector.util=_18;parseSelector.dom=dom;return parseSelector})(); var sIFR=new function(){var _3b=this;var _3c="sIFR-active";var _3d="sIFR-replaced";var _3e="sIFR-flash";var _3f="sIFR-ignore";var _40="sIFR-alternate";var _41="sIFR-class";var _42="sIFR-layout";var _43="http://www.w3.org/1999/xhtml";var _44=6;var _45=126;var _46=8;var _47="SIFR-PREFETCHED";var _48=" ";this.isActive=false;this.isEnabled=true;this.hideElements=true;this.replaceNonDisplayed=false;this.preserveSingleWhitespace=false;this.fixWrap=true;this.registerEvents=true;this.setPrefetchCookie=true;this.cookiePath="/";this.domains=[];this.fromLocal=true;this.forceClear=false;this.forceWidth=true;this.fitExactly=false;this.forceTextTransform=true;this.useDomContentLoaded=true;this.debugMode=false;this.hasFlashClassSet=false;var _49=0;var _4a=false,_4b=false;var dom=new function(){this.getBody=function(){var _4d=document.getElementsByTagName("body");if(_4d.length==1){return _4d[0]}return null};this.addClass=function(_4e,_4f){if(_4f){_4f.className=((_4f.className||"")==""?"":_4f.className+" ")+_4e}};this.removeClass=function(_50,_51){if(_51){_51.className=_51.className.replace(new RegExp("(^|\\s)"+_50+"(\\s|$)"),"").replace(/^\s+|(\s)\s+/g,"$1")}};this.hasClass=function(_52,_53){return new RegExp("(^|\\s)"+_52+"(\\s|$)").test(_53.className)};this.create=function(_54){if(document.createElementNS){return document.createElementNS(_43,_54)}return document.createElement(_54)};this.setInnerHtml=function(_55,_56){if(ua.innerHtmlSupport){_55.innerHTML=_56}else{if(ua.xhtmlSupport){_56=["<root xmlns=\"",_43,"\">",_56,"</root>"].join("");var xml=(new DOMParser()).parseFromString(_56,"text/xml");xml=document.importNode(xml.documentElement,true);while(_55.firstChild){_55.removeChild(_55.firstChild)}while(xml.firstChild){_55.appendChild(xml.firstChild)}}}};this.getComputedStyle=function(_58,_59){var _5a;if(document.defaultView&&document.defaultView.getComputedStyle){_5a=document.defaultView.getComputedStyle(_58,null)[_59]}else{if(_58.currentStyle){_5a=_58.currentStyle[_59]}}return _5a||""};this.getStyleAsInt=function(_5b,_5c,_5d){var _5e=this.getComputedStyle(_5b,_5c);if(_5d&&!/px$/.test(_5e)){return 0}_5e=parseInt(_5e);return isNaN(_5e)?0:_5e};this.getZoom=function(){return _5f.zoom.getLatest()}};this.dom=dom;var ua=new function(){var ua=navigator.userAgent.toLowerCase();var _62=(navigator.product||"").toLowerCase();this.macintosh=ua.indexOf("mac")>-1;this.windows=ua.indexOf("windows")>-1;this.quicktime=false;this.opera=ua.indexOf("opera")>-1;this.konqueror=_62.indexOf("konqueror")>-1;this.ie=false/*@cc_on || true @*/;this.ieSupported=this.ie&&!/ppc|smartphone|iemobile|msie\s5\.5/.test(ua)/*@cc_on && @_jscript_version >= 5.5 @*/;this.ieWin=this.ie&&this.windows/*@cc_on && @_jscript_version >= 5.1 @*/;this.windows=this.windows&&(!this.ie||this.ieWin);this.ieMac=this.ie&&this.macintosh/*@cc_on && @_jscript_version < 5.1 @*/;this.macintosh=this.macintosh&&(!this.ie||this.ieMac);this.safari=ua.indexOf("safari")>-1;this.webkit=ua.indexOf("applewebkit")>-1&&!this.konqueror;this.khtml=this.webkit||this.konqueror;this.gecko=!this.webkit&&_62=="gecko";this.operaVersion=this.opera&&/.*opera(\s|\/)(\d+\.\d+)/.exec(ua)?parseInt(RegExp.$2):0;this.webkitVersion=this.webkit&&/.*applewebkit\/(\d+).*/.exec(ua)?parseInt(RegExp.$1):0;this.geckoBuildDate=this.gecko&&/.*gecko\/(\d{8}).*/.exec(ua)?parseInt(RegExp.$1):0;this.konquerorVersion=this.konqueror&&/.*konqueror\/(\d\.\d).*/.exec(ua)?parseInt(RegExp.$1):0;this.flashVersion=0;if(this.ieWin){var axo;var _64=false;try{axo=new ActiveXObject("ShockwaveFlash.ShockwaveFlash.7")}catch(e){try{axo=new ActiveXObject("ShockwaveFlash.ShockwaveFlash.6");this.flashVersion=6;axo.AllowScriptAccess="always"}catch(e){_64=this.flashVersion==6}if(!_64){try{axo=new ActiveXObject("ShockwaveFlash.ShockwaveFlash")}catch(e){}}}if(!_64&&axo){this.flashVersion=parseFloat(/([\d,?]+)/.exec(axo.GetVariable("$version"))[1].replace(/,/g,"."))}}else{if(navigator.plugins&&navigator.plugins["Shockwave Flash"]){var _65=navigator.plugins["Shockwave Flash"];this.flashVersion=parseFloat(/(\d+\.?\d*)/.exec(_65.description)[1]);var i=0;while(this.flashVersion>=_46&&i<navigator.mimeTypes.length){var _67=navigator.mimeTypes[i];if(_67.type=="application/x-shockwave-flash"&&_67.enabledPlugin.description.toLowerCase().indexOf("quicktime")>-1){this.flashVersion=0;this.quicktime=true}i++}}}this.flash=this.flashVersion>=_46;this.transparencySupport=this.macintosh||this.windows;this.computedStyleSupport=this.ie||document.defaultView&&document.defaultView.getComputedStyle&&(!this.gecko||this.geckoBuildDate>=20030624);this.css=true;if(this.computedStyleSupport){try{var _68=document.getElementsByTagName("head")[0];_68.style.backgroundColor="#FF0000";var _69=dom.getComputedStyle(_68,"backgroundColor");this.css=!_69||/\#F{2}0{4}|rgb$255,\s?0,\s?0$/i.test(_69);_68=null}catch(e){}}this.xhtmlSupport=!!window.DOMParser&&!!document.importNode;this.innerHtmlSupport;try{var n=dom.create("span");if(!this.ieMac){n.innerHTML="x"}this.innerHtmlSupport=n.innerHTML=="x"}catch(e){this.innerHtmlSupport=false}this.zoomSupport=!!(this.opera&&document.documentElement);this.geckoXml=this.gecko&&(document.contentType||"").indexOf("xml")>-1;this.requiresPrefetch=this.ieWin||this.khtml;this.verifiedKonqueror=false;this.supported=this.flash&&this.css&&(!this.ie||this.ieSupported)&&(!this.opera||this.operaVersion>=8)&&(!this.webkit||this.webkitVersion>=412)&&(!this.konqueror||this.konquerorVersion>3.5)&&this.computedStyleSupport&&(this.innerHtmlSupport||!this.khtml&&this.xhtmlSupport)};this.ua=ua;var _6b=new function(){function capitalize($){return $.toUpperCase()}this.normalize=function(str){if(_3b.preserveSingleWhitespace){return str.replace(/\s/g,_48)}return str.replace(/(\s)\s+/g,"$1")};this.textTransform=function(_6e,str){switch(_6e){case "uppercase":str=str.toUpperCase();break;case "lowercase":str=str.toLowerCase();break;case "capitalize":var _70=str;str=str.replace(/^\w|\s\w/g,capitalize);if(str.indexOf("function capitalize")!=-1){var _71=_70.replace(/(^|\s)(\w)/g,"$1$1$2$2").split(/^\w|\s\w/g);str="";for(var i=0;i<_71.length;i++){str+=_71[i].charAt(0).toUpperCase()+_71[i].substring(1)}}break}return str};this.toHexString=function(str){if(typeof (str)!="string"||!str.charAt(0)=="#"||str.length!=4&&str.length!=7){return str}str=str.replace(/#/,"");if(str.length==3){str=str.replace(/(.)(.)(.)/,"$1$1$2$2$3$3")}return "0x"+str};this.toJson=function(obj){var _75="";switch(typeof (obj)){case "string":_75="\""+obj+"\"";break;case "number":case "boolean":_75=obj.toString();break;case "object":_75=[];for(var _76 in obj){if(obj[_76]==Object.prototype[_76]){continue}_75.push("\""+_76+"\":"+_6b.toJson(obj[_76]))}_75="{"+_75.join(",")+"}";break}return _75};this.convertCssArg=function(arg){if(!arg){return {}}if(typeof (arg)=="object"){if(arg.constructor==Array){arg=arg.join("")}else{return arg}}var obj={};var _79=arg.split("}");for(var i=0;i<_79.length;i++){var $=_79[i].match(/([^\s{]+)\s*\{(.+)\s*;?\s*/);if(!$||$.length!=3){continue}if(!obj[$[1]]){obj[$[1]]={}}var _7c=$[2].split(";");for(var j=0;j<_7c.length;j++){var $2=_7c[j].match(/\s*([^:\s]+)\s*\:\s*([^\s;]+)/);if(!$2||$2.length!=3){continue}obj[$[1]][$2[1]]=$2[2]}}return obj};this.extractFromCss=function(css,_80,_81,_82){var _83=null;if(css&&css[_80]&&css[_80][_81]){_83=css[_80][_81];if(_82){delete css[_80][_81]}}return _83};this.cssToString=function(arg){var css=[];for(var _86 in arg){var _87=arg[_86];if(_87==Object.prototype[_86]){continue}css.push(_86,"{");for(var _88 in _87){if(_87[_88]==Object.prototype[_88]){continue}css.push(_88,":",_87[_88],";")}css.push("}")}return escape(css.join(""))}};this.util=_6b;var _5f={};_5f.fragmentIdentifier=new function(){this.fix=true;var _89;this.cache=function(){_89=document.title};function doFix(){document.title=_89}this.restore=function(){if(this.fix){setTimeout(doFix,0)}}};_5f.synchronizer=new function(){this.isBlocked=false;this.block=function(){this.isBlocked=true};this.unblock=function(){this.isBlocked=false;_8a.replaceAll()}};_5f.zoom=new function(){var _8b=100;this.getLatest=function(){return _8b};if(ua.zoomSupport&&ua.opera){var _8c=document.createElement("div");_8c.style.position="fixed";_8c.style.left="-65536px";_8c.style.top="0";_8c.style.height="100%";_8c.style.width="1px";_8c.style.zIndex="-32";document.documentElement.appendChild(_8c);function updateZoom(){if(!_8c){return}var _8d=window.innerHeight/_8c.offsetHeight;var _8e=Math.round(_8d*100)%10;if(_8e>5){_8d=Math.round(_8d*100)+10-_8e}else{_8d=Math.round(_8d*100)-_8e}_8b=isNaN(_8d)?100:_8d;_5f.synchronizer.unblock();document.documentElement.removeChild(_8c);_8c=null}_5f.synchronizer.block();setTimeout(updateZoom,54)}};this.hacks=_5f;var _8f={kwargs:[],replaceAll:function(){for(var i=0;i<this.kwargs.length;i++){_3b.replace(this.kwargs[i])}this.kwargs=[]}};var _8a={kwargs:[],replaceAll:_8f.replaceAll};function isValidDomain(){if(_3b.domains.length==0){return true}var _91="";try{_91=document.domain}catch(e){}if(_3b.fromLocal&&sIFR.domains[0]!="localhost"){sIFR.domains.unshift("localhost")}for(var i=0;i<_3b.domains.length;i++){if(_3b.domains[i]=="*"||_3b.domains[i]==_91){return true}}return false}this.activate=function(){if(!ua.supported||!this.isEnabled||this.isActive||!isValidDomain()){return}this.isActive=true;if(this.hideElements){this.setFlashClass()}if(ua.ieWin&&_5f.fragmentIdentifier.fix&&window.location.hash!=""){_5f.fragmentIdentifier.cache()}else{_5f.fragmentIdentifier.fix=false}if(!this.registerEvents){return}function handler(evt){_3b.initialize();if(evt&&evt.type=="load"){if(document.removeEventListener){document.removeEventListener("DOMContentLoaded",handler,false);document.removeEventListener("load",handler,false)}if(window.removeEventListener){window.removeEventListener("load",handler,false)}}}if(window.addEventListener){if(_3b.useDomContentLoaded&&ua.gecko){document.addEventListener("DOMContentLoaded",handler,false)}window.addEventListener("load",handler,false)}else{if(ua.ieWin){if(_3b.useDomContentLoaded&&!_4a){document.write("<scr"+"ipt id=__sifr_ie_onload defer src=//:></script>");document.getElementById("__sifr_ie_onload").onreadystatechange=function(){if(this.readyState=="complete"){handler();this.removeNode()}}}window.attachEvent("onload",handler)}}};this.setFlashClass=function(){if(this.hasFlashClassSet){return}dom.addClass(_3c,dom.getBody()||document.documentElement);this.hasFlashClassSet=true};this.removeFlashClass=function(){if(!this.hasFlashClassSet){return}dom.removeClass(_3c,dom.getBody());dom.removeClass(_3c,document.documentElement);this.hasFlashClassSet=false};this.initialize=function(){if(_4b||!this.isActive||!this.isEnabled){return}_4b=true;_8f.replaceAll();clearPrefetch()};function getSource(src){if(typeof (src)!="string"){if(src.src){src=src.src}if(typeof (src)!="string"){var _95=[];for(var _96 in src){if(src[_96]!=Object.prototype[_96]){_95.push(_96)}}_95.sort().reverse();var _97="";var i=-1;while(!_97&&++i<_95.length){if(parseFloat(_95[i])<=ua.flashVersion){_97=src[_95[i]]}}src=_97}}if(!src&&_3b.debugMode){throw new Error("sIFR: Could not determine appropriate source")}if(ua.ie&&src.charAt(0)=="/"){src=window.location.toString().replace(/([^:]+)(:\/?\/?)([^\/]+).*/,"$1$2$3")+src}return src}this.prefetch=function(){if(!ua.requiresPrefetch||!ua.supported||!this.isEnabled||!isValidDomain()){return}if(this.setPrefetchCookie&&new RegExp(";?"+_47+"=true;?").test(document.cookie)){return}try{_4a=true;if(ua.ieWin){prefetchIexplore(arguments)}else{prefetchLight(arguments)}if(this.setPrefetchCookie){document.cookie=_47+"=true;path="+this.cookiePath}}catch(e){if(_3b.debugMode){throw e}}};function prefetchIexplore(_99){for(var i=0;i<_99.length;i++){document.write("<embed src=\""+getSource(_99[i])+"\" sIFR-prefetch=\"true\" style=\"display:none;\">")}}function prefetchLight(_9b){for(var i=0;i<_9b.length;i++){new Image().src=getSource(_9b[i])}}function clearPrefetch(){if(!ua.ieWin||!_4a){return}try{var _9d=document.getElementsByTagName("embed");for(var i=_9d.length-1;i>=0;i--){var _9f=_9d[i];if(_9f.getAttribute("sIFR-prefetch")=="true"){_9f.parentNode.removeChild(_9f)}}}catch(e){}}function getRatio(_a0){if(_a0<=10){return 1.55}if(_a0<=19){return 1.45}if(_a0<=32){return 1.35}if(_a0<=71){return 1.3}return 1.25}function getFilters(obj){var _a2=[];for(var _a3 in obj){if(obj[_a3]==Object.prototype[_a3]){continue}var _a4=obj[_a3];_a3=[_a3.replace(/filter/i,"")+"Filter"];for(var _a5 in _a4){if(_a4[_a5]==Object.prototype[_a5]){continue}_a3.push(_a5+":"+escape(_6b.toJson(_6b.toHexString(_a4[_a5]))))}_a2.push(_a3.join(","))}return _a2.join(";")}this.replace=function(_a6,_a7){if(!ua.supported){return}if(_a7){for(var _a8 in _a6){if(typeof (_a7[_a8])=="undefined"){_a7[_a8]=_a6[_a8]}}_a6=_a7}if(!_4b){return _8f.kwargs.push(_a6)}if(_5f.synchronizer.isBlocked){return _8a.kwargs.push(_a6)}var _a9=_a6.elements;if(!_a9&&parseSelector){_a9=parseSelector(_a6.selector)}if(_a9.length==0){return}this.setFlashClass();var src=getSource(_a6.src);var css=_6b.convertCssArg(_a6.css);var _ac=getFilters(_a6.filters);var _ad=(_a6.forceClear==null)?_3b.forceClear:_a6.forceClear;var _ae=(_a6.fitExactly==null)?_3b.fitExactly:_a6.fitExactly;var _af=_ae||(_a6.forceWidth==null?_3b.forceWidth:_a6.forceWidth);var _b0=parseInt(_6b.extractFromCss(css,".sIFR-root","leading"))||0;var _b1=_6b.extractFromCss(css,".sIFR-root","background-color",true)||"#FFFFFF";var _b2=_6b.extractFromCss(css,".sIFR-root","opacity",true)||"100";if(parseFloat(_b2)<1){_b2=100*parseFloat(_b2)}var _b3=_6b.extractFromCss(css,".sIFR-root","kerning",true)||"";var _b4=_a6.gridFitType||_6b.extractFromCss(css,".sIFR-root","text-align")=="right"?"subpixel":"pixel";var _b5=_3b.forceTextTransform?_6b.extractFromCss(css,".sIFR-root","text-transform",true)||"none":"none";var _b6="";if(_ae){_6b.extractFromCss(css,".sIFR-root","text-align",true)}if(!_a6.modifyCss){_b6=_6b.cssToString(css)}var _b7=_a6.wmode||"";if(_b7=="transparent"){if(!ua.transparencySupport){_b7="opaque"}else{_b1="transparent"}}for(var i=0;i<_a9.length;i++){var _b9=_a9[i];if(!ua.verifiedKonqueror){if(dom.getComputedStyle(_b9,"lineHeight").match(/e\+08px/)){ua.supported=_3b.isEnabled=false;this.removeFlashClass();return}ua.verifiedKonqueror=true}if(dom.hasClass(_3d,_b9)||dom.hasClass(_3f,_b9)){continue}var _ba=false;if(!_b9.offsetHeight||!_b9.offsetWidth){if(!_3b.replaceNonDisplayed){continue}_b9.style.display="block";if(!_b9.offsetHeight||!_b9.offsetWidth){_b9.style.display="";continue}_ba=true}if(_ad&&ua.gecko){_b9.style.clear="both"}var _bb=null;if(_3b.fixWrap&&ua.ie&&dom.getComputedStyle(_b9,"display")=="block"){_bb=_b9.innerHTML;dom.setInnerHtml(_b9,"X")}var _bc=dom.getStyleAsInt(_b9,"width",ua.ie);if(ua.ie&&_bc==0){var _bd=dom.getStyleAsInt(_b9,"paddingRight",true);var _be=dom.getStyleAsInt(_b9,"paddingLeft",true);var _bf=dom.getStyleAsInt(_b9,"borderRightWidth",true);var 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":"")+escape(_dd.join(" "))+">");if(_db.hasChildNodes()){_d7.push(i);i=0;_d9=_db.childNodes;continue}else{if(!/^(br|img)$/i.test(_db.nodeName)){_d8.push("</",_db.nodeName.toLowerCase(),">")}}}if(_d7.length>0&&!_db.nextSibling){do{i=_d7.pop();_d9=_db.parentNode.parentNode.childNodes;_db=_d9[i];if(_db){_d8.push("</",_db.nodeName.toLowerCase(),">")}}while(i<_d9.length&&_d7.length>0)}i++}return _d8.join("").replace(/\n|\r/g,"")}}; sIFR.prefetch({ src: 'swf/sifr/helvetica.swf' }); sIFR.activate(); sIFR.replace({ selector: 'h2, h3', src: 'swf/sifr/helvetica.swf', wmode: 'transparent', css: { '.sIFR-root' : { 'color': '#000000', 'font-weight': 'bold', 'letter-spacing': '-1' }, 'a': { 'text-decoration': 'none' }, 'a:link': { 'color': '#000000' }, 'a:hover': { 'color': '#000000' }, '.span': { 'color': '#979797' }, 'label': { 'color': '#E11818' } } }); sIFR.replace({ selector: 'h4', src: 'swf/sifr/helvetica.swf', wmode: 'transparent', css: { '.sIFR-root' : { 'color': '#7E7E7E', 'font-weight': 'bold', 'letter-spacing': '-0.8' }, 'a': { 'text-decoration': 'none' }, 'a:link': { 'color': '#7E7E7E' }, 'a:hover': { 'color': '#7E7E7E' }, 'label': { 'color': '#E11818' } } }); sIFR.replace({ selector: '#cart p', src: 'swf/sifr/helvetica-lt.swf', wmode: 'transparent', css: { '.sIFR-root' : { 'color': '#979797', 'font-weight': 'bold', 'letter-spacing': '-0.8' }, 'a': { 'text-decoration': 'none' }, 'a:link': { 'color': '#979797' }, 'a:hover': { 'color': '#000000' }, 'label': { 'color': '#979797' } } }); Thank you in advance for your help!

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PCI scan failure for SSL Certificate with Wrong Hostname?

- by Rob Mangiafico

A client had a PCI scan completed by SecurityMetrics, and it now says they failed due to the SSL certificate for the SMTP port 25 (and POP3s/IMAPS) not matching the domain scanned. Specifically: Description: SSL Certificate with Wrong Hostname Synoposis: The SSL certificate for this service is for a different host. Impact: The commonName (CN) of the SSL certificate presented on this service is for a different machine. The mail server uses sendmail (patched) and provides email service for a number of domains. The server itself has a valid SSL certificate, but it does not match each domain (as we add/remove domains all the time as clients move around). Seems SecurityMerics is the only ASV that marks this as failing PCI. Trustwave, McAfee, etc... do not see this as failing PCI. Is this issue truly a PCI failure? Or is it just SecuritMetrics being wrong?

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