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  • GDL Presents: Women Techmakers with Pixel Qi

    GDL Presents: Women Techmakers with Pixel Qi Jean Wang sits down with 2011 Anita Borg "Woman of Vision" Award for Innovation winner Mary Lou Jepsen of Pixel Qi to discuss overcoming technical challenges in hardware, drawing on Mary Lou's experience leading the engineering and architectural design of the $100 laptops that inspired the One Laptop Per Child (OLPC) organization. Hosts: Jean Wang - Lead Hardware Engineer for Project Glass | Vivian Cromwell - Manager, Global Chrome Developer Relations Guest: Mary Lou Jepsen - CEO and Founder, Pixel Qi From: GoogleDevelopers Views: 0 0 ratings Time: 01:00:00 More in Science & Technology

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  • SMB from fedora 12 to windows network

    - by Jean
    Hello, I got Fedora 12 samab and samba client and permitted the same via the iptables. For some reason, I cannot seem to browse my windows network. What did I do wrong, and how can I achieve this. Thanks Jean [edit] - I want to browse the windows workgroup [edit] - Set the workgroup name, restarted the smb in the services, can browse network, but only my fedora 12 system is being shown

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  • SMB from fedora 12 to windows network

    - by Jean
    Hello, I got Fedora 12 samab and samba client and permitted the same via the iptables. For some reason, I cannot seem to browse my windows network. What did I do wrong, and how can I achieve this. Thanks Jean [edit] - I want to browse the windows workgroup [edit] - Set the workgroup name, restarted the smb in the services, can browse network, but only my fedora 12 system is being shown

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  • Local Apache Web server works only when connected to the net

    - by Jean
    Hello, I installed Ubunut and got the LAMP stack installed too. Now the problem is I have to be connected to the internet for the local apache webserver to work, else it does not. I changed the IP address on the dnshost, in the apache2.conf file, got the servername in the httpd.conf, which was empty. Any ideas guys. Thanks Jean

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  • Connect Google Apps Email from Outlook via Linux ipTables

    - by Jean
    Hello, I have set up my google apps email on my outlook and I want it to connect to the google server and fetch my mails using SSL POP3 Ports 995, 465 both TCP and UDP are open. But it still not working. The server has 2 NIC, one for LAN and the other for External. Please provide assistance on solving this issue. Thanks Jean

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  • Oracle Communications Data Model

    - by jean-pierre.dijcks
    I've mentioned OCDM in previous posts but found the following (see end of the post) podcast on the topic and figured it is worthwhile to spread the news some more. ORetailDM and OCommunicationsDM are the two data models currently available from Oracle. Both are intended to capture: Business best practices and industry knowledge Pre-built advanced analytics intended to predict future events before they happen (like the Churn model shown below) Oracle technology best practices to ensure optimal performance of the model All of this typically comes with a reduced time to implementation, or as the marketing slogan goes, reduced time to value. Here are the links: Podcast on OCDM OTN pages for OCDM and ORDM

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  • Big Data&rsquo;s Killer App&hellip;

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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  • Collaborate10 &ndash; THEconference

    - by jean-pierre.dijcks
    After spending a few days in Mandalay Bay's THEHotel, I guess I now call everything THE... Seriously, they even tag their toilet paper with THEtp... I guess the brand builders in Vegas thought that once you are on to something you keep on doing it, and granted it is a nice hotel with nice rooms. THEanalytics Most of my collab10 experience was in a room called Reef C, where the BIWA bootcamp was held. Two solid days of BI, Warehousing and Analytics organized by the BIWA SIG at IOUG. Didn't get to see all sessions, but what struck me was the high interest in Analytics. Marty Gubar's OLAP session was full and he did some very nice things with the OLAP option. The cool bit was that he actually gets all the advanced calculations in OLAP to show up in OBI EE without any effort. It was nice to see that the idea from OWB where you generate an RPD is now also in AWM. I think it makes life so much simpler to generate these RPD's from your data model. Even if the end RPD needs some tweaking, it is all a lot less effort to get something going. You can see this stuff for yourself in this demo (click here). OBI EE uses just SQL to get to the calculations, and so, if you prefer APEX, you can build you application there and get the same nice calculations in an APEX application. Marty also showed the Simba MDX driver used with Excel. I guess we should call that THEcoolone... and it is very slick and wonderfully useful for all of you who actually know Excel. The nice thing is that you leverage pure Excel for all operations (no plug-ins). That means no new tools to learn, no new controls, all just pure Excel. THEdatabasemachine Got some very good questions in my "what makes Exadata fast" session and overall, the interest in Exadata is overwhelming. One of the things that I did try to do in my session is to get people to think in new patterns rather than in patterns based on Oracle 9i running on some random hardware configuration. We talked a little bit about the often over-indexing and how everyone has to unlearn all of that on Exadata. The main thing however is that everyone needs to get used to the shear size of some of the components in a Database machine V2. 5TB of flash cache is a lot of very fast data storage, half a TB of memory gets quite interesting as well. So what I did there was really focus on some of the content in these earlier posts on Upward ILM and In-Memory processing. In short, I do believe the these newer media point out a trend. In-memory and other fast media will get cheaper and will see more use. Some of that we do automatically by adding new functionality, but in some cases I think the end user of the system needs to start thinking about how to leverage all this new hardware. I think most people got very excited about these new capabilities and opportunities. THEcoolkids One of the cool things about the BIWA track was the hand-on track. Very cool to see big crowds for both OLAP and OWB hands-on. Also quite nice to see that the folks at RittmanMead spent so much time on preparing for that session. While all of them put down cool stuff, none was more cool that seeing Data Mining on an Apple iPAD... it all just looks great on an iPAD! Very disappointing to see that Mark Rittman still wasn't showing OWB on his iPAD ;-) THEend All in all this was a great set of sessions in the BIWA track. Lots of value to our guests (we hope) and we hope they all come again next year!

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  • Limiting DOPs &ndash; Who rules over whom?

    - by jean-pierre.dijcks
    I've gotten a couple of questions from Dan Morgan and figured I start to answer them in this way. While Dan is running on a big system he is running with Database Resource Manager and he is trying to make sure the system doesn't go crazy (remember end user are never, ever crazy!) on very high DOPs. Q: How do I control statements with very high DOPs driven from user hints in queries? A: The best way to do this is to work with DBRM and impose limits on consumer groups. The Max DOP setting you can set in DBRM allows you to overwrite the hint. Now let's go into some more detail here. Assume my object (and for simplicity we assume there is a single object - and do remember that we always pick the highest DOP when in doubt and when conflicting DOPs are available in a query) has PARALLEL 64 as its setting. Assume that the query that selects something cool from that table lives in a consumer group with a max DOP of 32. Assume no goofy things (like running out of parallel_max_servers) are happening. A query selecting from this table will run at DOP 32 because DBRM caps the DOP. As of 11.2.0.1 we also use the DBRM cap to create the original plan (at compile time) and not just enforce the cap at runtime. Now, my user is smart and writes a query with a parallel hint requesting DOP 128. This query is still capped by DBRM and DBRM overrules the hint in the statement. The statement, despite the hint, runs at DOP 32. Note that in the hinted scenario we do compile the statement with DOP 128 (the optimizer obeys the hint). This is another reason to use table decoration rather than hints. Q: What happens if I set parallel_max_servers higher than processes (e.g. the max number of processes allowed to run on my machine)? A: Processes rules. It is important to understand that processes are fixed at startup time. If you increase parallel_max_servers above the number of processes in the processes parameter you should get a warning in the alert log stating it can not take effect. As a follow up, a hinted query requesting more parallel processes than either parallel_max_servers or processes will not be able to acquire the requested number. Parallel_max_processes will prevent this. And since parallel_max_servers should be lower than max processes you can never go over either...

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  • Partition Wise Joins

    - by jean-pierre.dijcks
    Some say they are the holy grail of parallel computing and PWJ is the basis for a shared nothing system and the only join method that is available on a shared nothing system (yes this is oversimplified!). The magic in Oracle is of course that is one of many ways to join data. And yes, this is the old flexibility vs. simplicity discussion all over, so I won't go there... the point is that what you must do in a shared nothing system, you can do in Oracle with the same speed and methods. The Theory A partition wise join is a join between (for simplicity) two tables that are partitioned on the same column with the same partitioning scheme. In shared nothing this is effectively hard partitioning locating data on a specific node / storage combo. In Oracle is is logical partitioning. If you now join the two tables on that partitioned column you can break up the join in smaller joins exactly along the partitions in the data. Since they are partitioned (grouped) into the same buckets, all values required to do the join live in the equivalent bucket on either sides. No need to talk to anyone else, no need to redistribute data to anyone else... in short, the optimal join method for parallel processing of two large data sets. PWJ's in Oracle Since we do not hard partition the data across nodes in Oracle we use the Partitioning option to the database to create the buckets, then set the Degree of Parallelism (or run Auto DOP - see here) and get our PWJs. The main questions always asked are: How many partitions should I create? What should my DOP be? In a shared nothing system the answer is of course, as many partitions as there are nodes which will be your DOP. In Oracle we do want you to look at the workload and concurrency, and once you know that to understand the following rules of thumb. Within Oracle we have more ways of joining of data, so it is important to understand some of the PWJ ideas and what it means if you have an uneven distribution across processes. Assume we have a simple scenario where we partition the data on a hash key resulting in 4 hash partitions (H1 -H4). We have 2 parallel processes that have been tasked with reading these partitions (P1 - P2). The work is evenly divided assuming the partitions are the same size and we can scan this in time t1 as shown below. Now assume that we have changed the system and have a 5th partition but still have our 2 workers P1 and P2. The time it takes is actually 50% more assuming the 5th partition has the same size as the original H1 - H4 partitions. In other words to scan these 5 partitions, the time t2 it takes is not 1/5th more expensive, it is a lot more expensive and some other join plans may now start to look exciting to the optimizer. Just to post the disclaimer, it is not as simple as I state it here, but you get the idea on how much more expensive this plan may now look... Based on this little example there are a few rules of thumb to follow to get the partition wise joins. First, choose a DOP that is a factor of two (2). So always choose something like 2, 4, 8, 16, 32 and so on... Second, choose a number of partitions that is larger or equal to 2* DOP. Third, make sure the number of partitions is divisible through 2 without orphans. This is also known as an even number... Fourth, choose a stable partition count strategy, which is typically hash, which can be a sub partitioning strategy rather than the main strategy (range - hash is a popular one). Fifth, make sure you do this on the join key between the two large tables you want to join (and this should be the obvious one...). Translating this into an example: DOP = 8 (determined based on concurrency or by using Auto DOP with a cap due to concurrency) says that the number of partitions >= 16. Number of hash (sub) partitions = 32, which gives each process four partitions to work on. This number is somewhat arbitrary and depends on your data and system. In this case my main reasoning is that if you get more room on the box you can easily move the DOP for the query to 16 without repartitioning... and of course it makes for no leftovers on the table... And yes, we recommend up-to-date statistics. And before you start complaining, do read this post on a cool way to do stats in 11.

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  • Big Data Accelerator

    - by Jean-Pierre Dijcks
    For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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  • Partition Wise Joins II

    - by jean-pierre.dijcks
    One of the things that I did not talk about in the initial partition wise join post was the effect it has on resource allocation on the database server. When Oracle applies a different join method - e.g. not PWJ - what you will see in SQL Monitor (in Enterprise Manager) or in an Explain Plan is a set of producers and a set of consumers. The producers scan the tables in the the join. If there are two tables the producers first scan one table, then the other. The producers thus provide data to the consumers, and when the consumers have the data from both scans they do the join and give the data to the query coordinator. Now that behavior means that if you choose a degree of parallelism of 4 to run such query with, Oracle will allocate 8 parallel processes. Of these 8 processes 4 are producers and 4 are consumers. The consumers only actually do work once the producers are fully done with scanning both sides of the join. In the plan above you can see that the producers access table SALES [line 11] and then do a PX SEND [line 9]. That is the producer set of processes working. The consumers receive that data [line 8] and twiddle their thumbs while the producers go on and scan CUSTOMERS. The producers send that data to the consumer indicated by PX SEND [line 5]. After receiving that data [line 4] the consumers do the actual join [line 3] and give the data to the QC [line 2]. BTW, the myth that you see twice the number of processes due to the setting PARALLEL_THREADS_PER_CPU=2 is obviously not true. The above is why you will see 2 times the processes of the DOP. In a PWJ plan the consumers are not present. Instead of producing rows and giving those to different processes, a PWJ only uses a single set of processes. Each process reads its piece of the join across the two tables and performs the join. The plan here is notably different from the initial plan. First of all the hash join is done right on top of both table scans [line 8]. This query is a little more complex than the previous so there is a bit of noise above that bit of info, but for this post, lets ignore that (sort stuff). The important piece here is that the PWJ plan typically will be faster and from a PX process number / resources typically cheaper. You may want to look out for those plans and try to get those to appear a lot... CREDITS: credits for the plans and some of the info on the plans go to Maria, as she actually produced these plans and is the expert on plans in general... You can see her talk about explaining the explain plan and other optimizer stuff over here: ODTUG in Washington DC, June 27 - July 1 On the Optimizer blog At OpenWorld in San Francisco, September 19 - 23 Happy joining and hope to see you all at ODTUG and OOW...

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