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  • Retrieve Performance Data from SOA Infrastructure Database

    - by fip
    My earlier blog posting shows how to enable, retrieve and interpret BPEL engine performance statistics to aid performance troubleshooting. The strength of BPEL engine statistics at EM is its break down per request. But there are some limitations with the BPEL performance statistics mentioned in that blog posting: The statistics were stored in memory instead of being persisted. To avoid memory overflow, the data are stored to a buffer with limited size. When the statistic entries exceed the limitation, old data will be flushed out to give ways to new statistics. Therefore it can only keep the last X number of entries of data. The statistics 5 hour ago may not be there anymore. The BPEL engine performance statistics only includes latencies. It does not provide throughputs. Fortunately, Oracle SOA Suite runs with the SOA Infrastructure database and a lot of performance data are naturally persisted there. It is at a more coarse grain than the in-memory BPEL Statistics, but it does have its own strengths as it is persisted. Here I would like offer examples of some basic SQL queries you can run against the infrastructure database of Oracle SOA Suite 11G to acquire the performance statistics for a given period of time. You can run it immediately after you modify the date range to match your actual system. 1. Asynchronous/one-way messages incoming rates The following query will show number of messages sent to one-way/async BPEL processes during a given time period, organized by process names and states select composite_name composite, state, count(*) Count from dlv_message where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; 2. Throughput of BPEL process instances The following query shows the number of synchronous and asynchronous process instances created during a given time period. It list instances of all states, including the unfinished and faulted ones. The results will include all composites cross all SOA partitions select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype order by count(*) desc; 3. Throughput and latencies of BPEL process instances This query is augmented on the previous one, providing more comprehensive information. It gives not only throughput but also the maximum, minimum and average elapse time BPEL process instances. select composite_name Composite, component_name Process, componenttype, state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;   4. Combine all together Now let's combine all of these 3 queries together, and parameterize the start and end time stamps to make the script a bit more robust. The following script will prompt for the start and end time before querying against the database: accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)' accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)' Prompt "==== Rejected Messages ===="; REM 2012-10-24 21:00:00 REM 2012-10-24 21:59:59 select count(*), composite_dn from rejected_message where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS') and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_dn; Prompt " "; Prompt "==== Throughput of one-way/asynchronous messages ===="; select state, count(*) Count, composite_name composite from dlv_message where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; Prompt " "; Prompt "==== Throughput and latency of BPEL process instances ====" select state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime, composite_name Composite, component_name Process, componenttype from cube_instance where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;  

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  • Why lock-free data structures just aren't lock-free enough

    - by Alex.Davies
    Today's post will explore why the current ways to communicate between threads don't scale, and show you a possible way to build scalable parallel programming on top of shared memory. The problem with shared memory Soon, we will have dozens, hundreds and then millions of cores in our computers. It's inevitable, because individual cores just can't get much faster. At some point, that's going to mean that we have to rethink our architecture entirely, as millions of cores can't all access a shared memory space efficiently. But millions of cores are still a long way off, and in the meantime we'll see machines with dozens of cores, struggling with shared memory. Alex's tip: The best way for an application to make use of that increasing parallel power is to use a concurrency model like actors, that deals with synchronisation issues for you. Then, the maintainer of the actors framework can find the most efficient way to coordinate access to shared memory to allow your actors to pass messages to each other efficiently. At the moment, NAct uses the .NET thread pool and a few locks to marshal messages. It works well on dual and quad core machines, but it won't scale to more cores. Every time we use a lock, our core performs an atomic memory operation (eg. CAS) on a cell of memory representing the lock, so it's sure that no other core can possibly have that lock. This is very fast when the lock isn't contended, but we need to notify all the other cores, in case they held the cell of memory in a cache. As the number of cores increases, the total cost of a lock increases linearly. A lot of work has been done on "lock-free" data structures, which avoid locks by using atomic memory operations directly. These give fairly dramatic performance improvements, particularly on systems with a few (2 to 4) cores. The .NET 4 concurrent collections in System.Collections.Concurrent are mostly lock-free. However, lock-free data structures still don't scale indefinitely, because any use of an atomic memory operation still involves every core in the system. A sync-free data structure Some concurrent data structures are possible to write in a completely synchronization-free way, without using any atomic memory operations. One useful example is a single producer, single consumer (SPSC) queue. It's easy to write a sync-free fixed size SPSC queue using a circular buffer*. Slightly trickier is a queue that grows as needed. You can use a linked list to represent the queue, but if you leave the nodes to be garbage collected once you're done with them, the GC will need to involve all the cores in collecting the finished nodes. Instead, I've implemented a proof of concept inspired by this intel article which reuses the nodes by putting them in a second queue to send back to the producer. * In all these cases, you need to use memory barriers correctly, but these are local to a core, so don't have the same scalability problems as atomic memory operations. Performance tests I tried benchmarking my SPSC queue against the .NET ConcurrentQueue, and against a standard Queue protected by locks. In some ways, this isn't a fair comparison, because both of these support multiple producers and multiple consumers, but I'll come to that later. I started on my dual-core laptop, running a simple test that had one thread producing 64 bit integers, and another consuming them, to measure the pure overhead of the queue. So, nothing very interesting here. Both concurrent collections perform better than the lock-based one as expected, but there's not a lot to choose between the ConcurrentQueue and my SPSC queue. I was a little disappointed, but then, the .NET Framework team spent a lot longer optimising it than I did. So I dug out a more powerful machine that Red Gate's DBA tools team had been using for testing. It is a 6 core Intel i7 machine with hyperthreading, adding up to 12 logical cores. Now the results get more interesting. As I increased the number of producer-consumer pairs to 6 (to saturate all 12 logical cores), the locking approach was slow, and got even slower, as you'd expect. What I didn't expect to be so clear was the drop-off in performance of the lock-free ConcurrentQueue. I could see the machine only using about 20% of available CPU cycles when it should have been saturated. My interpretation is that as all the cores used atomic memory operations to safely access the queue, they ended up spending most of the time notifying each other about cache lines that need invalidating. The sync-free approach scaled perfectly, despite still working via shared memory, which after all, should still be a bottleneck. I can't quite believe that the results are so clear, so if you can think of any other effects that might cause them, please comment! Obviously, this benchmark isn't realistic because we're only measuring the overhead of the queue. Any real workload, even on a machine with 12 cores, would dwarf the overhead, and there'd be no point worrying about this effect. But would that be true on a machine with 100 cores? Still to be solved. The trouble is, you can't build many concurrent algorithms using only an SPSC queue to communicate. In particular, I can't see a way to build something as general purpose as actors on top of just SPSC queues. Fundamentally, an actor needs to be able to receive messages from multiple other actors, which seems to need an MPSC queue. I've been thinking about ways to build a sync-free MPSC queue out of multiple SPSC queues and some kind of sign-up mechanism. Hopefully I'll have something to tell you about soon, but leave a comment if you have any ideas.

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  • Exadata a kiskereskedelem (retail) számára

    - by Fekete Zoltán
    Egyik kedvenc blogomban a Rittman Mead honlapján hasznos eloadásra jelent meg infó és letöltési lehetoség. (Lásd a jobb oldali Top Tags dobozban a "blog" kulcsszót, és a legalsó bejegyzést.) Az eloadás címe: Exadata in the Retail Sector, azaz Exadata a felhasználása a kiskereskedelemben. Ezt az eloadást Jon Mead tartotta 2010. március 23-án Londonban az Exadata V2, Oracle Extreme Performance Data Warehousing Seminar rendezvényen. Mint láthatjuk, szinte minden gyümölcsrol beszéltek az Oracle adattárház és üzleti intelligencia virágzó gyümölcsökertjébol az Oracle BI, 11gR2 adattárház tulajdonságai és más témákban. Az eloadások a következo területekrol szóltak: - Exadata techikai ismertetés - ügyfél sztorik: LGR, Allegro, és nagy-britannia egyik legnagyobb online elektronikai kiskereskedelmi cége - Oracle BI - GoldenGate (adatreplikáció) - advanced compression (tranzakciós adatok tömörítése) - particionálás - OLAP - adatbányászat, Oracle Data Mining

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  • Can find any /.ecryptfs dir to retrieve my encrypted home dir

    - by Roberto de Armas
    At firs sorry for my english, it isn't my native languaje. I've readed some questions similar but no the exactly whith the same problem. I've moved my home directory to a separated partitión (Ubuntu 11.10) following this tutorial http://www.ubuntu-es.org/node/58233 After checking that they were all my files and folders (forgetting that one of dirs was encrypted by ecryptfs) i've installed fedora 16. Well, suprised when in my home folder was an Readme.txt advising me that my folder was unmounted for security reasons and proposing to type in comand line "ecryptfs-mount-Private" (din't work) or make click on labeled icon "acces your private data desktop" (neither din't work). After three days reading all i could find on the internet, i follow The Dustin Kirkland tutorial in http://blog.dustinkirkland.com/2011/04/introducing-ecryptfs-recover-private.html, but any /.ecryptfs was found. I'm sure that the data are somewere (the size of the moved dir is identical to the original one). Any help would by greatly appreciated. Thaks a lot.

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  • Securely sending data from shared hosted PHP script to local MSSQL

    - by user329488
    I'm trying to add data from a webhook (from a web cart) to a local Microsoft SQL Server. It seems like the best route for me is to use a PHP script to listen for new data (POST as json), parse it, then query to add to MSSQL. I'm not familiar with security concerning the connection between the PHP script (which would sit on a shared-host website) and the local MSSQL database. I would just keep the PHP script running on the same localhost (have Apache running on Windows), but the URI for the webhook needs to be publicly accessible. Alternately, I assume that I could just schedule a script from the localhost to check periodically for updates through the web carts API, though the webhooks seem to be more fool-proof for an amateur programmer like myself. What steps can I take to ensure security when using a PHP on a remote, shared-host to connect to MSSQL on my local machine?

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  • Benefit of using Data URI to embed images within HTML document and its cross-browser compatibility

    - by Manoj Agarwal
    I want to embed an image using Data URI within HTML document so that we don't need image as a separate attachment, we get just one HTML file that contains the actual image. What are its advantages and disadvantages? Does IE10 supports it? Is it useful to have such an implementation? I am working on an application, where we have html documents that link towards images stored in some location. If I use tiny online editor, as images are saved somewhere in the server, while editing the document, i can provide a link towards that image, but can't preview the final document with images from within tiny editor. If I chose to download the file locally, then i will need to download the images from server side. It looks a bit overkill, so I thought if Data URI could be used in such a situation.

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  • Sorting and Paging a Grid of Data in ASP.NET MVC

    This article is the fifth installment in an ongoing series on displaying a grid of data in an ASP.NET MVC application. Previous articles in this series examined how to sort, page, and filter a grid of data, but none have looked at combining one or more of these features in a single grid. This article and the next one show how to merge these features into a single grid. In particular, this article looks at displaying a grid that can handle both sorting and paging. The subsequent article will examine combining sorting, paging and filtering. Like with its predecessors, this article offers step-by-step instructions and includes a complete, working demo available for download at the end of the article. Read on to learn more! Read More >

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  • How do I handle 3rd party search result data (via cache)

    - by reikyoushin
    I have a search function on my site and it is taking data from 6 different 3rd party resources. The problem is, it takes too long requesting the data over and over again on the results page. I've read for questions like this on SO about session not being a good choice but for me 'memcache' is not an option, because the server doesn't have memcached installed and I have no way to install it now. Is there any other approach to do this? Storing in the database seem inappropriate because the data depends on the search terms requested. What I've been thinking is writing a file on the server that would act as a cache for this file but I don't know how I would know when to delete it after.

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  • SQL SERVER – Transaction Log Full – Transaction Log Larger than Data File – Notes from Fields #001

    - by Pinal Dave
    I am very excited to announce a new series on this blog – Notes from Fields. I have been blogging for almost 7 years on this blog and it has been a wonderful experience. Though, I have extensive experience with SQL and Databases, it is always a good idea that we consult experts for their advice and opinion. Following the same thought process, I have started this new series of Notes from Fields. In this series we will have notes from various experts in the database world. My friends at Linchpin People have graciously decided to support me in my new initiation.  Linchpin People are database coaches and wellness experts for a data driven world. In this very first episode of the Notes from Fields series database expert Tim Radney (partner at Linchpin People) explains a very common issue DBA and Developer faces in their career, when database logs fills up your hard-drive or your database log is larger than your data file. Read the experience of Tim in his own words. As a consultant, I encounter a number of common issues with clients.  One of the more common things I encounter is finding a user database in the FULL recovery model that does not make a regular transaction log backups or ever had a transaction log backup. When I find this, usually the transaction log is several times larger than the data file. Finding this issue is very significant to me in that it allows to me to discuss service level agreements with the client. I get to ask questions such as, are nightly full backups sufficient or do they need point in time recovery.  This conversation has now signed with the customer and gets them to thinking about their disaster recovery and high availability solutions. This issue is also very prominent on SQL Server forums and usually has the title of “Help, my transaction log has filled up my disk” or “Help, my transaction log is many times the size of my database”. In cases where the client only needs the previous full nights backup, I am able to change the recovery model to SIMPLE and shrink the transaction log using DBCC SHRINKFILE (2,1) or by specifying the transaction log file name by using DBCC SHRINKFILE (file_name, target_size). When the client needs point in time recovery then in most cases I will still end up switching the client to the SIMPLE recovery model to truncate the transaction log followed by a full backup. I will then schedule a SQL Agent job to make the regular transaction log backups with an interval determined by the client to meet their service level agreements. It should also be noted that typically when I find an overgrown transaction log the virtual log file count is also out of control. I clean up will always take that into account as well.  That is a subject for a future blog post. If your SQL Server is facing any issue we can Fix Your SQL Server. Additional reading: Monitoring SQL Server Database Transaction Log Space Growth – DBCC SQLPERF(logspace)  SQL SERVER – How to Stop Growing Log File Too Big Shrinking Truncate Log File – Log Full Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Rails noob - How to work on data stored in models

    - by Raghav Kanwal
    I'm a beginner to Ruby and Rails, and I have made a couple applications like a Microposts clone and a Todo-List for starters, but I'm starting work on another project. I've got 2 models - user and tracker, you log in via the username which is authenticated and you can log down data which is stored in the tracker table. The tracker has a column named "Calories" and I would like Rails to sum all of the values entered if they are on the same date, and output the result which is subtracted from, say 3000 in a new statement after the display of the model. I know what I'm talking about is just ruby code, im just not sure how to incorporate it. :( Could someone please guide me through this? And also link me to some guides/tutorials which teach working on data from models? Thank you :)

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  • SharePoint 2010 Data Retrival Techinques

    - by Jayant Sharma
    In SharePoint, we have two options to perform CRUD operation.1. using server side code2. using client side codeusing server side code, we have 1. CAML2. LINQusing client side code, we have 1. Client Object Model    1.1.      Managed Client Object Model     1.2.     Silverlight Client Object Model    1.3.     ECMA Client Object Model2. SharePoint Web Services3. ADO Data Service (based on REST Web Services)4. Using RPC Call (owssvr.dll)Which and when these options are used depend upon requirements. Every options are certain advantages and disadvantages. So, before start development of any new sharepoint project, it is important to understand the limitations of different methods.Server Object Model is used when our application is host on the same server on which sharepoint is installed. while Client Side code is used to access sharepoint from client system. In SharePoint 2010 specially Client Object Model (COM) are introduced to perform the sharepoint operations from client system. Advantage of CAML:    -  It is fast.    -  Can be use it from all kind of technology like Silverlight, or Jquery    -  You can use U2U CAML Query builder to generate CAML Query.Disadvantage Of CAML:    - Error Prone, as we can detect the error only at runtimeAdvantage of LINQ:    -  Object Oriented technique (Object Relation Model)    -  LINQ  to SharePoint provider are working with Strongly Type List Item Objects, So intellisence are present at runtime    -  No need of knowledge of CAML    -  Less Error Prone as it as it uses C# syntex.    -  You can compare two Fields of SharePoint ListDisadvantage Of LINQ:    -  List Attachment is not supported in SPMetal Tool    -  Created By, Created, Modified and Modified By Fields are not created by SPMetal Tool.    -  Custom fields are not created by SPMetal Tools    -  External Lists are not supported    -  Though at backend LINQ genenates CAML Query so it is slower than directly using CAML in Code.  Advantage of Client Object Model    -  Used to access sharepoint from client system    -  No WebServer is required at Client End    - Can use Silverlight and JavaScripts to make better and fast User experienceDisadvantage of Client Object Model    -  You cannot use RunwithEleveatedPrivilege    - Cross Site Collection query are not possible    - Lesser API's are availableADO.Net Data Services:    -  Only List based operations are possible, other type of operations are not possible.SharePoint Web Services and RPC Call:    - Previously it was used in SharePoint 2007 but after the introduction  of Client Object Model,  Microsoft recommends not to use Web Services to fetch data from SharePoint. In SharePoint 2010 it is avaliable only for backward compatibility.Ref: http://msdn.microsoft.com/en-us/library/ee539764Jayant Sharma

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  • Vertex Normals, Loading Mesh Data

    - by Ramon Johannessen
    My test FBX mesh is a cube. From what I surmise, it seems that the cube is on the extreme end of this issue, but I believe that the same issue would be able to occur in any mesh: Each vertex has 3 normals, each pointing a different direction. Of course loading in any type of mesh, potentially ones having thousands of vertices, I need to use indices and not duplicate shared verts. Currently, I'm just writing the normals to the vertex at the index that the FBX data tells me they go to, which has the effect of overwriting any previous normal data. But for lighting calculations I need more info, something that's equivalent to a normal per face, but I have no idea how this should be done. Do I average the 3 different verts' normals together or what?

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  • Is it wise to store a big lump of json on a database row

    - by Ieyasu Sawada
    I have this project which stores product details from amazon into the database. Just to give you an idea on how big it is: [{"title":"Genetic Engineering (Opposing Viewpoints)","short_title":"Genetic Engineering ...","brand":"","condition":"","sales_rank":"7171426","binding":"Book","item_detail_url":"http://localhost/wordpress/product/?asin=0737705124","node_list":"Books > Science & Math > Biological Sciences > Biotechnology","node_category":"Books","subcat":"","model_number":"","item_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=128","details_url":"http://localhost/wordpress/product/?asin=0737705124","large_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/large-notfound.png","medium_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/medium-notfound.png","small_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/small-notfound.png","thumbnail_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/thumbnail-notfound.png","tiny_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/tiny-notfound.png","swatch_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/swatch-notfound.png","total_images":"6","amount":"33.70","currency":"$","long_currency":"USD","price":"$33.70","price_type":"List Price","show_price_type":"0","stars_url":"","product_review":"","rating":"","yellow_star_class":"","white_star_class":"","rating_text":" of 5","reviews_url":"","review_label":"","reviews_label":"Read all ","review_count":"","create_review_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=132","create_review_label":"Write a review","buy_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=19186","add_to_cart_action":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/add_to_cart.php","asin":"0737705124","status":"Only 7 left in stock.","snippet_condition":"in_stock","status_class":"ninstck","customer_images":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/31FIM-YIUrL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg"],"disclaimer":"","item_attributes":[{"attr":"Author","value":"Greenhaven Press"},{"attr":"Binding","value":"Hardcover"},{"attr":"EAN","value":"9780737705126"},{"attr":"Edition","value":"1"},{"attr":"ISBN","value":"0737705124"},{"attr":"Label","value":"Greenhaven Press"},{"attr":"Manufacturer","value":"Greenhaven Press"},{"attr":"NumberOfItems","value":"1"},{"attr":"NumberOfPages","value":"224"},{"attr":"ProductGroup","value":"Book"},{"attr":"ProductTypeName","value":"ABIS_BOOK"},{"attr":"PublicationDate","value":"2000-06"},{"attr":"Publisher","value":"Greenhaven Press"},{"attr":"SKU","value":"G0737705124I2N00"},{"attr":"Studio","value":"Greenhaven Press"},{"attr":"Title","value":"Genetic Engineering (Opposing Viewpoints)"}],"customer_review_url":"http://localhost/wordpress/wp-content/ecom-customer-reviews/0737705124.html","flickr_results":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/5105560852_06c7d06f14_m.jpg"],"freebase_text":"No around the web data available yet","freebase_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/freebase-notfound.jpg","ebay_related_items":[{"title":"Genetic Engineering (Introducing Issues With Opposing Viewpoints), , Good Book","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=12165","currency_id":"$","current_price":"26.2"},{"title":"Genetic Engineering Opposing Viewpoints by DAVID BENDER - 1964 Hardcover","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=130","currency_id":"AUD","current_price":"11.99"}],"no_follow":"rel=\"nofollow\"","new_tab":"target=\"_blank\"","related_products":[],"super_saver_shipping":"","shipping_availability":"","total_offers":"7","added_to_cart":""}] So the structure for the table is: asin title details (the product details in json) Will the performance suffer if I have to store like 10,000 products? Is there any other way of doing this? I'm thinking of the following, but the current setup is really the most convenient one since I also have to use the data on the client side: store the product details in a file. So something like ASIN123.json store the product details in one big file. (I'm guessing it will be a drag to extract data from this file) store each of the fields in the details in its own table field Thanks in advance!

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  • Persisting NLP parsed data

    - by tjb1982
    I've recently started experimenting with NLP using Stanford's CoreNLP, and I'm wondering what are some of the standard ways to store NLP parsed data for something like a text mining application? One way I thought might be interesting is to store the children as an adjacency list and make good use of recursive queries (postgres supports this and I've found it works really well). Something like this: Component ( id, POS, parent_id ) Word ( id, raw, lemma, POS, NER ) CW_Map ( component_id, word_id, position int ) But I assume there are probably many standard ways to do this depending on what kind of analysis is being done that have been adopted by people working in the field over the years. So what are the standard persistence strategies for NLP parsed data and how are they used?

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