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  • Fetching Partition Information

    - by Mike Femenella
    For a recent SSIS package at work I needed to determine the distinct values in a partition, the number of rows in each partition and the file group name on which each partition resided in order to come up with a grouping mechanism. Of course sys.partitions comes to mind for some of that but there are a few other tables you need to link to in order to grab the information required. The table I’m working on contains 8.8 billion rows. Finding the distinct partition keys from this table was not a fast operation. My original solution was to create  a temporary table, grab the distinct values for the partitioned column, then update via sys.partitions for the rows and the $partition function for the partitionid and finally look back to the sys.filegroups table for the filegroup names. It wasn’t pretty, it could take up to 15 minutes to return the results. The primary issue is pulling distinct values from the table. Queries for distinct against 8.8 billion rows don’t go quickly. A few beers into a conversation with a friend and we ended up talking about work which led to a conversation about the task described above. The solution was already built in SQL Server, just needed to pull it together. The first table I needed was sys.partition_range_values. This contains one row for each range boundary value for a partition function. In my case I have a partition function which uses dayid values. For example July 4th would be represented as an int, 20130704. This table lists out all of the dayid values which were defined in the function. This eliminated the need to query my source table for distinct dayid values, everything I needed was already built in here for me. The only caveat was that in my SSIS package I needed to create a bucket for any dayid values that were out of bounds for my function. For example if my function handled 20130501 through 20130704 and I had day values of 20130401 or 20130705 in my table, these would not be listed in sys.partition_range_values. I just created an “everything else” bucket in my ssis package just in case I had any dayid values unaccounted for. To get the number of rows for a partition is very easy. The sys.partitions table contains values for each partition. Easy enough to achieve by querying for the object_id and index value of 1 (the clustered index) The final piece of information was the filegroup name. There are 2 options available to get the filegroup name, sys.data_spaces or sys.filegroups. For my query I chose sys.filegroups but really it’s a matter of preference and data needs. In order to bridge between sys.partitions table and either sys.data_spaces or sys.filegroups you need to get the container_id. This can be done by joining sys.allocation_units.container_id to the sys.partitions.hobt_id. sys.allocation_units contains the field data_space_id which then lets you join in either sys.data_spaces or sys.file_groups. The end result is the query below, which typically executes for me in under 1 second. I’ve included the join to sys.filegroups and to sys.dataspaces, and I’ve  just commented out the join sys.filegroups. As I mentioned above, this shaves a good 10-15 minutes off of my original ssis package and is a really easy tweak to get a boost in my ETL time. Enjoy.

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  • The Future of Air Travel: Intelligence and Automation

    - by BobEvans
    Remember those white-knuckle flights through stormy weather where unexpected plunges in altitude result in near-permanent relocations of major internal organs? Perhaps there’s a better way, according to a recent Wall Street Journal article: “Pilots of a Honeywell International Inc. test plane stayed on their initial flight path, relying on the company's latest onboard radar technology to steer through the worst of the weather. The specially outfitted Boeing 757 barely shuddered as it gingerly skirted some of the most ferocious storm cells over Fort Walton Beach and then climbed above the rest in zero visibility.” Or how about the multifaceted check-in process, which might not wreak havoc on liver location but nevertheless makes you wonder if you’ve been trapped in some sort of covert psychological-stress test? Another WSJ article, called “The Self-Service Airport,” says there’s reason for hope there as well: “Airlines are laying the groundwork for the next big step in the airport experience: a trip from the curb to the plane without interacting with a single airline employee. At the airport of the near future, ‘your first interaction could be with a flight attendant,’ said Ben Minicucci, chief operating officer of Alaska Airlines, a unit of Alaska Air Group Inc.” And in the topsy-turvy world of air travel, it’s not just the passengers who’ve been experiencing bumpy rides: the airlines themselves are grappling with a range of challenges—some beyond their control, some not—that make profitability increasingly elusive in spite of heavy demand for their services. A recent piece in The Economist illustrates one of the mega-challenges confronting the airline industry via a striking set of contrasting and very large numbers: while the airlines pay $7 billion per year to third-party computerized reservation services, the airlines themselves earn a collective profit of only $3 billion per year. In that context, the anecdotes above point unmistakably to the future that airlines must pursue if they hope to be able to manage some of the factors outside of their control (e.g., weather) as well as all of those within their control (operating expenses, end-to-end visibility, safety, load optimization, etc.): more intelligence, more automation, more interconnectedness, and more real-time awareness of every facet of their operations. Those moves will benefit both passengers and the air carriers, says the WSJ piece on The Self-Service Airport: “Airlines say the advanced technology will quicken the airport experience for seasoned travelers—shaving a minute or two from the checked-baggage process alone—while freeing airline employees to focus on fliers with questions. ‘It's more about throughput with the resources you have than getting rid of humans,’ said Andrew O'Connor, director of airport solutions at Geneva-based airline IT provider SITA.” Oracle’s attempting to help airlines gain control over these challenges by blending together a range of its technologies into a solution called the Oracle Airline Data Model, which suggests the following steps: • To retain and grow their customer base, airlines need to focus on the customer experience. • To personalize and differentiate the customer experience, airlines need to effectively manage their passenger data. • The Oracle Airline Data Model can help airlines jump-start their customer-experience initiatives by consolidating passenger data into a customer data hub that drives realtime business intelligence and strategic customer insight. • Oracle’s Airline Data Model brings together multiple types of data that can jumpstart your data-warehousing project with rich out-of-the-box functionality. • Oracle’s Intelligent Warehouse for Airlines brings together the powerful capabilities of Oracle Exadata and the Oracle Airline Data Model to give you real-time strategic insights into passenger demand, revenues, sales channels and your flight network. The airline industry aside, the bullet points above offer a broad strategic outline for just about any industry because the customer experience is becoming pre-eminent in each and there is simply no way to deliver world-class customer experiences unless a company can capture, manage, and analyze all of the relevant data in real-time. I’ll leave you with two thoughts from the WSJ article about the new in-flight radar system from Honeywell: first, studies show that a single episode of serious turbulence can wrack up $150,000 in additional costs for an airline—so, it certainly behooves the carriers to gain the intelligence to avoid turbulence as much as possible. And second, it’s back to that top-priority customer-experience thing and the value that ever-increasing levels of intelligence can deliver. As the article says: “In the cabin, reporters watched screens showing the most intense parts of the nearly 10-mile wide storm, which churned some 7,000 feet below, in vibrant red and other colors. The screens also were filled with tiny symbols depicting likely locations of lightning and hail, which can damage planes and wreak havoc on the nerves of white-knuckle flyers.”  (Bob Evans is senior vice-president, communications, for Oracle.)  

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  • Pinterest and the Rising Power of Imagery

    - by Mike Stiles
    If images keep you glued to a screen, you’re hardly alone. Countless social users are letting their eyes do the walking, waiting for that special photo to grab their attention. And perhaps more than any other social network, Pinterest has been giving those eyes plenty of room to walk. Pinterest came along in 2010. Its play was that users could simply create topic boards and pin pictures to the appropriate boards for sharing. Yes there are some words, captions mostly, but not many. The speed of its growth raised eyebrows. Traffic quadrupled in the last quarter of 2011, with 7.51 million unique visitors in December alone. It now gets 1.9 billion monthly page views. And it was sticky. In the US, the average time a user spends strolling through boards and photos on Pinterest is 15 minutes, 50 seconds. Proving the concept of browsing a catalogue is not dead, it became a top 5 referrer for several apparel retailers like Land’s End, Nordstrom, and Bergdorfs. Now a survey of online shoppers by BizRate Insights says that Pinterest is responsible for more purchases online than Facebook. Over 70% of its users are going there specifically to keep up with trends and get shopping ideas. And when they buy, the average order value is $179. Pinterest is also scoring better in terms of user engagement. 66% of pinners regularly follow and repin retailers, whereas 17% of Facebook fans turn to that platform for purchase ideas. (Facebook still wins when it comes to reach and driving traffic to 3rd-party sites by the way). Social posting best practices have consistently shown that posts with photos are rewarded with higher engagement levels. You may be downright Shakespearean in your writing, but what makes images in the digital world so much more powerful than prose? 1. They transcend language barriers. 2. They’re fun and addictive to look at. 3. They can be consumed in fractions of a second, important considering how fast users move through their social content (admit it, you do too). 4. They’re efficient gateways. A good picture might get them to the headline. A good headline might then get them to the written content. 5. The audience for them surpasses demographic limitations. 6. They can effectively communicate and trigger an emotion. 7. With mobile use soaring, photos are created on those devices and easily consumed and shared on them. Pinterest’s iPad app hit #1 in the Apple store in 1 day. Even as far back as 2009, over 2.5 billion devices with cameras were on the streets generating in just 1 year, 10% of the number of photos taken…ever. But let’s say you’re not a retailer. What if you’re a B2B whose products or services aren’t visual? Should you worry about your presence on Pinterest? As with all things, you need a keen awareness of who your audience is, where they reside online, and what they want to do there. If it doesn’t make sense to put a tent stake in Pinterest, fine. But ignore the power of pictures at your own peril. If not visually, how are you going to attention-grab social users scrolling down their News Feeds at top speed? You’re competing with every other cool image out there from countless content sources. Bore us and we’ll fly right past you.

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  • Qt: Force QWebView to click on a web element, even one not visible on the window

    - by Pirate for Profit
    So let's say I'm trying to click a link in the QWebView, here is what I have: // extending QWebView void MyWebView::click(const QString &selectorQuery) { QWebElement el = this->page()->mainFrame()->findFirstElement(selectorQuery); if (!el) return; el.setFocus(); QMouseEvent pressEvent(QMouseEvent::MouseButtonPress, el.geometry().center(), Qt::MouseButton::LeftButton, Qt::LeftButton, Qt::NoModifier); QCoreApplication::sendEvent(this, &pressEvent); QMouseEvent releaseEvent(QMouseEvent::MouseButtonRelease, el.geometry().center(), Qt::MouseButton::LeftButton, Qt::LeftButton, Qt::NoModifier); QCoreApplication::sendEvent(this, &releaseEvent); } And you call it as so: myWebView->click("a[href]"); // will click first link on page myWebView->click("input[type=submit]"); // submits a form THE ONLY PROBLEM IS: if the element is not visible in the window, it is impossible to click. What I mean is if you have to scroll down to see it, you can't click it. I imagine this has to do with the geometry, since the element doesn't show up on the screen it can't do the math to click it right. Any ideas to get around this? Maybe some way to make the window behave like a billion x billion pixels but still look 200x200?

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  • Instructions per cycle?

    - by Matt Simmons
    I've been learning a little bit more about how processors work, but I haven't been able to find a straight answer about instructions per cycle. For instance, I was under the impression that a four core CPU could execute four instructions per cycle, so a four core CPU running at 2Ghz would execute 8 billion operations per second. Is this the case? I'm sure it's oversimplifying things, but if there's a guide or something else I can use to set myself straight, I'm definitely open to ideas.

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  • Tips for maximizing Nginx requests/sec?

    - by linkedlinked
    I'm building an analytics package, and project requirements state that I need to support 1 billion hits per day. Yep, "billion". In other words, no less than 12,000 hits per second sustained, and preferably some room to burst. I know I'll need multiple servers for this, but I'm trying to get maximum performance out of each node before "throwing more hardware at it". Right now, I have the hits-tracking portion completed, and well optimized. I pretty much just save the requests straight into Redis (for later processing with Hadoop). The application is Python/Django with a gunicorn for the gateway. My 2GB Ubuntu 10.04 Rackspace server (not a production machine) can serve about 1200 static files per second (benchmarked using Apache AB against a single static asset). To compare, if I swap out the static file link with my tracking link, I still get about 600 requests per second -- I think this means my tracker is well optimized, because it's only a factor of 2 slower than serving static assets. However, when I benchmark with millions of hits, I notice a few things -- No disk usage -- this is expected, because I've turned off all Nginx logs, and my custom code doesn't do anything but save the request details into Redis. Non-constant memory usage -- Presumably due to Redis' memory managing, my memory usage will gradually climb up and then drop back down, but it's never once been my bottleneck. System load hovers around 2-4, the system is still responsive during even my heaviest benchmarks, and I can still manually view http://mysite.com/tracking/pixel with little visible delay while my (other) server performs 600 requests per second. If I run a short test, say 50,000 hits (takes about 2m), I get a steady, reliable 600 requests per second. If I run a longer test (tried up to 3.5m so far), my r/s degrades to about 250. My questions -- a. Does it look like I'm maxing out this server yet? Is 1,200/s static files nginx performance comparable to what others have experienced? b. Are there common nginx tunings for such high-volume applications? I have worker threads set to 64, and gunicorn worker threads set to 8, but tweaking these values doesn't seem to help or harm me much. c. Are there any linux-level settings that could be limiting my incoming connections? d. What could cause my performance to degrade to 250r/s on long-running tests? Again, the memory is not maxing out during these tests, and HDD use is nil. Thanks in advance, all :)

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  • Hadron Collider – Can it unveil the hidden secrets of universe?

    - by samsudeen
    Scientist at  European Centre for Nuclear Research (CERN) today successfully simulated the Big Bang experiment finally by producing  the world’s first high-energy particle collision.This is achieved through the collision of two protons with a total energy of  around seven trillion electron volts sending sub-particles spread through in every direction.   The experiment is conducted successfully around the  European Centre for Nuclear Research (CERN) which is under 100 metres below the Franco-Swiss border. This is said to be the biggest experiment in terms on the investment (around $7 billion) and the scientific importance. This will lead to a new era of science and could change the theories about the origin of universe. You can find  more videos about the experiment at the LHC Videos Join us on Facebook to read all our stories right inside your Facebook news feed.

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  • How Many People Are In Space Right Now Tells You Just That

    - by Jason Fitzpatrick
    How Many People Are In Space Right Now is a web site with a very focused mission: to keep you abreast of just how many humans are currently exploring space. Like similar single-function sites–such as Is It Raining Now–How Many People Are In Space Right Now serves up the information with a simple interface, just the number and a link to which mission or program the space explorers are deployed under. We don’t know about you, but we’d certainly like to see the ratio of humans in space versus humans on Earth improve from the current one space explorer to several billion humans ratio. How Many People Are In Space Right Now [via Boing Boing] How to Factory Reset Your Android Phone or Tablet When It Won’t Boot Our Geek Trivia App for Windows 8 is Now Available Everywhere How To Boot Your Android Phone or Tablet Into Safe Mode

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  • Genetic algorithms with large chromosomes

    - by Howie
    I'm trying to solve the graph partitioning problem on large graphs (between a billion and trillion elements) using GA. The problem is that even one chromosome will take several gigs of memory. Are there any general compression techniques for chromosome encoding? Or should I look into distributed GA? NOTE: using some sort of evolutionary algorithm for this problem is a must! GA seems to be the best fit (although not for such large chromosomes). EDIT: I'm looking for state-of-the-art methods that other authors have used to solved the problem of large chromosomes. Note that I'm looking for either a more general solution or a solution particular to graph partitioning. Basically I'm looking for related works, as I, too, am attempting using GA for the problem of graph partitioning. So far I haven't found anyone that might have this problem of large chromosomes nor has tried to solve it.

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  • Making the Grade

    - by [email protected]
    Education Organizations Learn the Advantages of Oracle Today, K-12 school districts and state agencies nationwide have billions of reasons to come to Oracle OpenWorld 2010. Ever since the American Recovery and Reinvestment Act of 2009 set aside US$100 billion for education, schools have been eager to develop and implement statewide data systems to enhance workflow. And across the country, they've been turning to Oracle for help. According to a recent news release, Oracle already makes the grade. The Los Angeles Unified School District--the nation's second largest district--chose Oracle Business Intelligence Suite, Enterprise Edition Plus to help teachers keep track of student performance. Other educational organizations, including Fairfax County Public Schools and the North Carolina Department of Public Instruction, are also working with Oracle to improve their systemwide procedures. If you're an educator or administrator who is planning to optimize your school or agency data systems, this may be the best time to learn what Oracle can do help ensure success. Register for Oracle OpenWorld 2010 between now and July 16 and you'll save US$500 off registration.

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  • Where Facebook Stands Heading Into 2013

    - by Mike Stiles
    In our last blog, we looked at how Twitter is positioned heading into 2013. Now it’s time to take a similar look at Facebook. 2012, for a time at least, seemed to be the era of Facebook-bashing. Between a far-from-smooth IPO, subsequent stock price declines, and anxiety over privacy, the top social network became a target for comedians, politicians, business journalists, and of course those who were prone to Facebook-bash even in the best of times. But amidst the “this is the end of Facebook” headlines, the company kept experimenting, kept testing, kept innovating, and pressing forward, committed as always to the user experience, while concurrently addressing monetization with greater urgency. Facebook enters 2013 with over 1 billion users around the world. Usage grew 41% in Brazil, Russia, Japan, South Korea and India in 2012. In the Middle East and North Africa, an average 21 new signups happen per minute. Engagement and time spent on the site would impress the harshest of critics. Facebook, while not bulletproof, has become such an integrated daily force in users’ lives, it’s getting hard to imagine any future mass rejection. You want to see a company recognizing weaknesses and shoring them up. Mobile was a weakness in 2012 as Facebook was one of many caught by surprise at the speed of user migration to mobile. But new mobile interfaces, better mobile ads, speed upgrades, standalone Messenger and Pages mobile apps, and the big dollar acquisition of Instagram, were a few indicators Facebook won’t play catch-up any more than it has to. As a user, the cool thing about Facebook is, it knows you. The uncool thing about Facebook is, it knows you. The company’s walking a delicate line between the public’s competing desires for customized experiences and privacy. While the company’s working to make privacy options clearer and easier, Facebook’s Paul Adams says data aggregation can move from acting on what a user is engaging with at the moment to a more holistic view of what they’re likely to want at any given time. To help learn about you, there’s Open Graph. Embedded through diverse partnerships, the idea is to surface what you’re doing and what you care about, and help you discover things via your friends’ activities. Facebook’s Director of Engineering, Mike Vernal, says building mobile social apps connected to Facebook in such ways is the next wave of big innovation. Expect to see that fostered in 2013. The Facebook site experience is always evolving. Some users like that about Facebook, others can’t wait to complain about it…on Facebook. The Facebook focal point, the News Feed, is not sacred and is seeing plenty of experimentation with the insertion of modules. From upcoming concerts, events, suggested Pages you might like, to aggregated “most shared” content from social reader apps, plenty could start popping up between those pictures of what your friends had for lunch.  As for which friends’ lunches you see, that’s a function of the mythic EdgeRank…which is also tinkered with. When Facebook changed it in September, Page admins saw reach go down and the high anxiety set in quickly. Engagement, however, held steady. The adjustment was about relevancy over reach. (And oh yeah, reach was something that could be charged for). Facebook wants users to see what they’re most likely to like, based on past usage and interactions. Adding to the “cream must rise to the top” philosophy, they’re now even trying out ordering post comments based on the engagement the comments get. Boy, it’s getting competitive out there for a social engager. Facebook has to make $$$. To do that, they must offer attractive vehicles to marketers. There are a myriad of ad units. But a key Facebook marketing concept is the Sponsored Story. It’s key because it encourages content that’s good, relevant, and performs well organically. If it is, marketing dollars can amplify it and extend its reach. Brands can expect the rollout of a search product and an ad network. That’s a big deal. It takes, as Open Graph does, the power of Facebook’s user data and carries it beyond the Facebook environment into the digital world at large. No one could target like Facebook can, and some analysts think it could double their roughly $5 billion revenue stream. As every potential revenue nook and cranny is explored, there are the users themselves. In addition to Gifts, Facebook thinks users might pay a few bucks to promote their own posts so more of their friends will see them. There’s also word classifieds could be purchased in News Feeds, though they won’t be called classifieds. And that’s where Facebook stands; a wildly popular destination, a part of our culture, with ever increasing functionalities, the biggest of big data, revenue strategies that appeal to marketers without souring the user experience, new challenges as a now public company, ongoing privacy concerns, and innovations that carry Facebook far beyond its own borders. Anyone care to write a “this is the end of Facebook” headline? @mikestilesPhoto via stock.schng

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  • As a young student aspiring to have a career as a programmer, how should I feel about open source software?

    - by Matt
    Every once in a while on some technology websites a headline like this will pop up: http://www.osor.eu/news/nl-moving-to-open-source-would-save-government-one-to-four-billion My initial thought about government and organizations moving to open source software is that tons of programmers would lose their jobs and the industry would shrink. At the same time the proliferation and use of open source software seems to be greatly encouraged in many programming communities. Is my thinking that the full embrace of open source software everywhere will hurt the software industry a misconception? If it is not, then why do so many programmers love open source software?

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  • Podcast: Dell Perot Systems Relies on Oracle In-Memory Database Cache

    - by john.brust
    Recently we spoke with Bill Binko, Technology Consultant at Dell Perot Systems, about a high volume web-based content delivery system they implemented for a client with Oracle In-Memory Database Cache. Their client needed to respond to ~1 billion hits (web requests) per day, but hadn't been able to support this load. Oracle In-Memory Database Cache allowed for multiple & complicated queries to take place without ever hitting the disk...providing sub-millisecond response time and ability to manage much higher high volumes of data. Old System: Old SQL Server Database, over 300 servers, difficult to maintain. New System: One Oracle Database 11g instance, multiple Oracle RAC nodes, backed up by Oracle Data Guard, and Oracle In-Memory Database Cache to cut query response times by 10x. Listen to the podcast.

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  • SPARC T4-4 Delivers World Record Performance on Oracle OLAP Perf Version 2 Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered world record performance with subsecond response time on the Oracle OLAP Perf Version 2 benchmark using Oracle Database 11g Release 2 running on Oracle Solaris 11. The SPARC T4-4 server achieved throughput of 430,000 cube-queries/hour with an average response time of 0.85 seconds and the median response time of 0.43 seconds. This was achieved by using only 60% of the available CPU resources leaving plenty of headroom for future growth. The SPARC T4-4 server operated on an Oracle OLAP cube with a 4 billion row fact table of sales data containing 4 dimensions. This represents as many as 90 quintillion aggregate rows (90 followed by 18 zeros). Performance Landscape Oracle OLAP Perf Version 2 Benchmark 4 Billion Fact Table Rows System Queries/hour Users* Response Time (sec) Average Median SPARC T4-4 430,000 7,300 0.85 0.43 * Users - the supported number of users with a given think time of 60 seconds Configuration Summary and Results Hardware Configuration: SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 1 TB memory Data Storage 1 x Sun Fire X4275 (using COMSTAR) 2 x Sun Storage F5100 Flash Array (each with 80 FMODs) Redo Storage 1 x Sun Fire X4275 (using COMSTAR with 8 HDD) Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 (11.2.0.3) with Oracle OLAP option Benchmark Description The Oracle OLAP Perf Version 2 benchmark is a workload designed to demonstrate and stress the Oracle OLAP product's core features of fast query, fast update, and rich calculations on a multi-dimensional model to support enhanced Data Warehousing. The bulk of the benchmark entails running a number of concurrent users, each issuing typical multidimensional queries against an Oracle OLAP cube consisting of a number of years of sales data with fully pre-computed aggregations. The cube has four dimensions: time, product, customer, and channel. Each query user issues approximately 150 different queries. One query chain may ask for total sales in a particular region (e.g South America) for a particular time period (e.g. Q4 of 2010) followed by additional queries which drill down into sales for individual countries (e.g. Chile, Peru, etc.) with further queries drilling down into individual stores, etc. Another query chain may ask for yearly comparisons of total sales for some product category (e.g. major household appliances) and then issue further queries drilling down into particular products (e.g. refrigerators, stoves. etc.), particular regions, particular customers, etc. Results from version 2 of the benchmark are not comparable with version 1. The primary difference is the type of queries along with the query mix. Key Points and Best Practices Since typical BI users are often likely to issue similar queries, with different constants in the where clauses, setting the init.ora prameter "cursor_sharing" to "force" will provide for additional query throughput and a larger number of potential users. Except for this setting, together with making full use of available memory, out of the box performance for the OLAP Perf workload should provide results similar to what is reported here. For a given number of query users with zero think time, the main measured metrics are the average query response time, the median query response time, and the query throughput. A derived metric is the maximum number of users the system can support achieving the measured response time assuming some non-zero think time. The calculation of the maximum number of users follows from the well-known response-time law N = (rt + tt) * tp where rt is the average response time, tt is the think time and tp is the measured throughput. Setting tt to 60 seconds, rt to 0.85 seconds and tp to 119.44 queries/sec (430,000 queries/hour), the above formula shows that the T4-4 server will support 7,300 concurrent users with a think time of 60 seconds and an average response time of 0.85 seconds. For more information see chapter 3 from the book "Quantitative System Performance" cited below. -- See Also Quantitative System Performance Computer System Analysis Using Queueing Network Models Edward D. Lazowska, John Zahorjan, G. Scott Graham, Kenneth C. Sevcik external local Oracle Database 11g – Oracle OLAP oracle.com OTN SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 11/2/2012.

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  • Oracle NoSQL Database Exceeds 1 Million Mixed YCSB Ops/Sec

    - by Charles Lamb
    We ran a set of YCSB performance tests on Oracle NoSQL Database using SSD cards and Intel Xeon E5-2690 CPUs with the goal of achieving 1M mixed ops/sec on a 95% read / 5% update workload. We used the standard YCSB parameters: 13 byte keys and 1KB data size (1,102 bytes after serialization). The maximum database size was 2 billion records, or approximately 2 TB of data. We sized the shards to ensure that this was not an "in-memory" test (i.e. the data portion of the B-Trees did not fit into memory). All updates were durable and used the "simple majority" replica ack policy, effectively 'committing to the network'. All read operations used the Consistency.NONE_REQUIRED parameter allowing reads to be performed on any replica. In the past we have achieved 100K ops/sec using SSD cards on a single shard cluster (replication factor 3) so for this test we used 10 shards on 15 Storage Nodes with each SN carrying 2 Rep Nodes and each RN assigned to its own SSD card. After correcting a scaling problem in YCSB, we blew past the 1M ops/sec mark with 8 shards and proceeded to hit 1.2M ops/sec with 10 shards.  Hardware Configuration We used 15 servers, each configured with two 335 GB SSD cards. We did not have homogeneous CPUs across all 15 servers available to us so 12 of the 15 were Xeon E5-2690, 2.9 GHz, 2 sockets, 32 threads, 193 GB RAM, and the other 3 were Xeon E5-2680, 2.7 GHz, 2 sockets, 32 threads, 193 GB RAM.  There might have been some upside in having all 15 machines configured with the faster CPU, but since CPU was not the limiting factor we don't believe the improvement would be significant. The client machines were Xeon X5670, 2.93 GHz, 2 sockets, 24 threads, 96 GB RAM. Although the clients had 96 GB of RAM, neither the NoSQL Database or YCSB clients require anywhere near that amount of memory and the test could have just easily been run with much less. Networking was all 10GigE. YCSB Scaling Problem We made three modifications to the YCSB benchmark. The first was to allow the test to accommodate more than 2 billion records (effectively int's vs long's). To keep the key size constant, we changed the code to use base 32 for the user ids. The second change involved to the way we run the YCSB client in order to make the test itself horizontally scalable.The basic problem has to do with the way the YCSB test creates its Zipfian distribution of keys which is intended to model "real" loads by generating clusters of key collisions. Unfortunately, the percentage of collisions on the most contentious keys remains the same even as the number of keys in the database increases. As we scale up the load, the number of collisions on those keys increases as well, eventually exceeding the capacity of the single server used for a given key.This is not a workload that is realistic or amenable to horizontal scaling. YCSB does provide alternate key distribution algorithms so this is not a shortcoming of YCSB in general. We decided that a better model would be for the key collisions to be limited to a given YCSB client process. That way, as additional YCSB client processes (i.e. additional load) are added, they each maintain the same number of collisions they encounter themselves, but do not increase the number of collisions on a single key in the entire store. We added client processes proportionally to the number of records in the database (and therefore the number of shards). This change to the use of YCSB better models a use case where new groups of users are likely to access either just their own entries, or entries within their own subgroups, rather than all users showing the same interest in a single global collection of keys. If an application finds every user having the same likelihood of wanting to modify a single global key, that application has no real hope of getting horizontal scaling. Finally, we used read/modify/write (also known as "Compare And Set") style updates during the mixed phase. This uses versioned operations to make sure that no updates are lost. This mode of operation provides better application behavior than the way we have typically run YCSB in the past, and is only practical at scale because we eliminated the shared key collision hotspots.It is also a more realistic testing scenario. To reiterate, all updates used a simple majority replica ack policy making them durable. Scalability Results In the table below, the "KVS Size" column is the number of records with the number of shards and the replication factor. Hence, the first row indicates 400m total records in the NoSQL Database (KV Store), 2 shards, and a replication factor of 3. The "Clients" column indicates the number of YCSB client processes. "Threads" is the number of threads per process with the total number of threads. Hence, 90 threads per YCSB process for a total of 360 threads. The client processes were distributed across 10 client machines. Shards KVS Size Clients Mixed (records) Threads OverallThroughput(ops/sec) Read Latencyav/95%/99%(ms) Write Latencyav/95%/99%(ms) 2 400m(2x3) 4 90(360) 302,152 0.76/1/3 3.08/8/35 4 800m(4x3) 8 90(720) 558,569 0.79/1/4 3.82/16/45 8 1600m(8x3) 16 90(1440) 1,028,868 0.85/2/5 4.29/21/51 10 2000m(10x3) 20 90(1800) 1,244,550 0.88/2/6 4.47/23/53

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  • About floating point precision and why do we still use it

    - by system_is_b0rken
    Floating point has always been troublesome for precision on large worlds. This article explains behind-the-scenes and offers the obvious alternative - fixed point numbers. Some facts are really impressive, like: "Well 64 bits of precision gets you to the furthest distance of Pluto from the Sun (7.4 billion km) with sub-micrometer precision. " Well sub-micrometer precision is more than any fps needs (for positions and even velocities), and it would enable you to build really big worlds. My question is, why do we still use floating point if fixed point has such advantages? Most rendering APIs and physics libraries use floating point (and suffer it's disadvantages, so developers need to get around them). Are they so much slower? Additionally, how do you think scalable planetary engines like outerra or infinity handle the large scale? Do they use fixed point for positions or do they have some space dividing algorithm?

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  • What are the Crappy Code Games - What are the challenges?

    - by simonsabin
    This is part of a series on the Crappy Code Games The background Who can enter? What are the challenges? What are the prizes? Why should I attend? Tips on how to win What are the challenges? There are 4 games that you can enter. Each one is to test a different aspect of SQL Server. The High Jump: Generate the highest I/O per second The 100 m dash: Cumulative highest number of I/O’s in 60 seconds The SSIS-athon: Load one billion row fact table in the shortest time The Marathon: Generate the highest...(read more)

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  • Nokia at JavaOne

    - by Tori Wieldt
    Nokia has long been a key partner for Java Mobile, and they continue investing significantly in Java technologies. Developers can learn more about Nokia's popular Asha phone and developer platform at JavaOne. In addition to interesting technical material, all Nokia sessions will include giveaways (hint: be engaged and ask questions!). Don't miss these great sessions: CON4925 The Right Platform with the Right Technology for Huge Markets with Many Opportunities CON11253 In-App Purchasing for Java ME Apps BOF4747 Look Again: Java ME's New Horizons of User Experience, Service Model, and Internet Innovation BOF12804 Reach the Next Billion with Engaging Apps: Nokia Asha Full Touch for Java ME Developers CON6664 on Mobile Java, Asha, Full Touch, Maps APIs, LWUIT, new UI, new APIs and more CON6494 Extreme Mobile Java Performance Tuning, User Experience, and Architecture BOF6556 Mobile Java App Innovation in Nigeria

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  • Expected salary for software engineer? Am I under or over paid? [closed]

    - by Asdasd Asdasd
    I work for a reasonably large tech company in Boston, MA. My company has about 1.2 billion in revenue and around 3500 employees. I have 6 years of industry experience and my current pay package is as follows: Base salary: 97,000 bonus: 10,000/year (everyone always gets 100% of this... i don't know why they bother call it bonus) RSU stock: 8000/year at present day valuation. My vesting schedule covers me for the next 5 years. that brings my total pay to ~ 115,000/year Given that, would folks say I am under/average/over paid? I read so much about how engineers at google and facebook are making ridiculous sums of money (almost 200k with bonuses included) and it makes me question my pay package. thanks

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  • I'm the .1x programmer at my company. How can I best contribute?

    - by invaliduser
    I work at a newly-minted startup of five people. We have a Ph. D in machine learning, a former member of the RSpec core team, and the guy who compiles the Git binary for OS X. That's just the employees; the founder has a Ph. D and was CTO for a multi-billion-dollar corporation before leaving to start a (successful) startup, and has now left that to start this one. We also might get a guy with a Ph. D in math. Aaaaaaaaand then there's me, college-dropout intern. I think I'm pretty smart and I'm reading non-stop, but the delta of experience, skill, and knowledge between me and my co-workers is just breathtaking. So put yourself in their shoes: you've got a bright young intern who has a lot to learn but is at least energetic. What would be annoying? What use would you hope to get out of him in the here and now? What would be pleasantly surprising if it happened?

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  • Writing a spell checker similar to "did you mean"

    - by user888734
    I'm hoping to write a spellchecker for search queries in a web application - not unlike Google's "Did you mean?" The algorithm will be loosely based on this: http://catalog.ldc.upenn.edu/LDC2006T13 In short, it generates correction candidates and scores them on how often they appear (along with adjacent words in the search query) in an enormous dataset of known n-grams - Google Web 1T - which contains well over 1 billion 5-grams. I'm not using the Web 1T dataset, but building my n-gram sets from my own documents - about 200k docs, and I'm estimating tens or hundreds of millions of n-grams will be generated. This kind of process is pushing the limits of my understanding of basic computing performance - can I simply load my n-grams into memory in a hashtable or dictionary when the app starts? Is the only limiting factor the amount of memory on the machine? Or am I barking up the wrong tree? Perhaps putting all my n-grams in a graph database with some sort of tree query optimisation? Could that ever be fast enough?

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  • Microsoft Launches Outlook.com

    Microsoft announced the news in its Outlook blog, calling the new service modern email for the next billion mailboxes. The company is touting a fresh, clean user interface with fewer pixels in the header and no display ads; it should work well on portable devices. Another key point: it uses Exchange ActiveSync to synchronize your mail, calendar and social experience across your smartphone, tablet and desktop computer. Perhaps the biggest advance, though, is that Microsoft is connecting the email service to Facebook, Twitter, LinkedIn, Google; Skype will be part of this list, too, though i...

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  • Gone in 60 Seconds: An Insecure Database is an Easy Target

    - by Troy Kitch
    According to the recent Verizon Data Breach Investigations Report, 98% of breached data originates from database servers and nearly half are compromised in less than a minute! Almost all victims are not even aware of a breach until a third party notifies them and nearly all breaches could have been avoided through the use of basic controls. Join us for this November 28th webcast to learn more about the evolving threats to databases that have resulted in over 1 billion stolen records. Also, hear how organizations can mitigate risks by adopting a defense-in-depth strategy that focuses on basic controls to secure data at the source - the database. There's no turning back the clock on stolen data, but you can put in place controls to ensure your organization won't be the next headline. Note, this webcast will be recorded for on-demand access after November 28th. 

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  • cVidya’s MoneyMap Achieves Oracle Exadata Optimized Status

    - by Javier Puerta
    cVidya's MoneyMap running on Oracle Exadata provides extreme performance, including 4x-16x improvement in high data load rates, 4x faster data transformation and reconciliation, and query speeds - from a 2.5 billion record index –  improved from hours to few seconds! The MoneyMap solution enables operators to reconcile information from all network, operations and business support systems and through an on-going automated process, it detects problem areas which impact profitability as a result of revenue leakage, data inconsistencies or resources that are not being used efficiently. Once detected, MoneyMap provides tools to promptly correct and manage the problems to achieve profit maximization Learn more here.

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