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• Etch a Circuit Board using a Simple Homemade Mixture

  - by ETC

  If you’ve been dabbling in DIY electronics projects but you’re not so excited about keeping strong acids around to etch your circuit boards, this simple DIY recipe uses common household chemicals in lieu of strong acid. Electronics hobbyist Stephen Hobley wanted to see if he could create an etching solution that wasn’t as dangerous and noxious smelling at traditional muriatic acid solutions. By combining regular white vinegar, hydrogen peroxide, and table salt, he created a homemade etching solution from ingredients safe enough to store in your pantry. The only downside to his recipe is that, compared to traditional etching solutions, the process takes a little bit longer so you’ll have to leave your board in the solution longer. Not a bad trade off for the ability to skip using any oops-I-burned-my-skin-off acids. Check out the process in the video below: Hit up the link below for more information and and interesting explanation of the chemical process (he talks about not quite understanding it in the video but two chemists write in and give him the full run down). DIY Etching Solution [Stephen Hobley via Make] Latest Features How-To Geek ETC Macs Don’t Make You Creative! So Why Do Artists Really Love Apple? MacX DVD Ripper Pro is Free for How-To Geek Readers (Time Limited!) HTG Explains: What’s a Solid State Drive and What Do I Need to Know? How to Get Amazing Color from Photos in Photoshop, GIMP, and Paint.NET Learn To Adjust Contrast Like a Pro in Photoshop, GIMP, and Paint.NET Have You Ever Wondered How Your Operating System Got Its Name? Etch a Circuit Board using a Simple Homemade Mixture Sync Blocker Stops iTunes from Automatically Syncing The Journey to the Mystical Forest [Wallpaper] Trace Your Browser’s Roots on the Browser Family Tree [Infographic] Save Files Directly from Your Browser to the Cloud in Chrome and Iron The Steve Jobs Chronicles – Charlie and the Apple Factory [Video]

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• Guidance in naming awkward objects?

  - by GlenH7

  I'm modeling a chemical system, and I'm having problems with naming my objects within an enum. I'm not sure if I should use: the atomic formula the chemical name an abbreviated chemical name. For example, sulfuric acid is H2SO4 and hydrochloric acid is HCl. With those two, I would probably just use the atomic formula as they are reasonably common. However, I have others like sodium hexafluorosilicate which is Na2SiF6. In that example, the atomic formula isn't as obvious (to me) but the chemical name is hideously long: myEnum.SodiumHexaFluoroSilicate. I'm not sure how I would be able to safely come up with an abbreviated chemical name that would have a consistent naming pattern. From a maintenance point of view, which of the options would you prefer to see and why? Audience for the code will be just programmers, not chemists. If that guides the particulars: I'm using C#; I'm starting with 10 - 20 compounds and would have at most 100 compounds. The enum is to facilitate common calculations - the equation is the same for all compounds but you insert a property of the compound to complete the equation.

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• Guidance in naming awkward domain-specific objects?

  - by GlenH7

  I'm modeling a chemical system, and I'm having problems with naming my objects within an enum. I'm not sure if I should use: the atomic formula the chemical name an abbreviated chemical name. For example, sulfuric acid is H2SO4 and hydrochloric acid is HCl. With those two, I would probably just use the atomic formula as they are reasonably common. However, I have others like sodium hexafluorosilicate which is Na2SiF6. In that example, the atomic formula isn't as obvious (to me) but the chemical name is hideously long: myEnum.SodiumHexaFluoroSilicate. I'm not sure how I would be able to safely come up with an abbreviated chemical name that would have a consistent naming pattern. From a maintenance point of view, which of the options would you prefer to see and why? Some details from comments on this question: Audience for the code will be just programmers, not chemists. I'm using C#, but I think this question is more interesting when ignoring the implementation language I'm starting with 10 - 20 compounds and would have at most 100 compounds. The enum is to facilitate common calculations - the equation is the same for all compounds but you insert a property of the compound to complete the equation. For example, Molar mass (in g/mol) is used when calculating the number of moles from a mass (in grams) of the compound. Another example of a common calculation is the Ideal Gas Law and its use of the Specific Gas Constant

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• What's wrong in this C program..? [closed]

  - by AGeek

  struct bucket { int nStrings; //No. of Strings in a Bucket. char strings[MAXSTRINGS][MAXWORDLENGTH]; // A bucket row can contain maximum 9 strings of max string length 10. };//buck[TOTBUCKETS]; void lexSorting(char array[][10], int lenArray, int symb) //symb - symbol, sorting based on character symbols. { int i, j; int bucketNo; int tBuckNStrings; bucket buck[TOTBUCKETS]; for(i=0; i<lenArray; i++) { bucketNo = array[i][symb] - 'a'; // Find Bucket No. in which the string is to be placed. tBuckNStrings = buck[bucketNo].nStrings; // temp variable for storing nStrings var in bucket structure. strcpy(buck[bucketNo].strings[tBuckNStrings],array[i]); // Store the string in its bucket. buck[bucketNo].nStrings = ++tBuckNStrings; //Increment the nStrings value of the bucket. } // lexSorting(array, lenArray, ++symb); printf("****** %d ******\n", symb); for(i=0; i<TOTBUCKETS; i++) { printf("%c = ", i+'a'); for(j=0; j<buck[i].nStrings; j++) printf("%s ",buck[i].strings[j]); printf("\n"); } } int main() { char array[][10] = {"able","aback","a","abet","acid","yawn","yard","yarn","year","yoke"}; int lenArray = 10; int i; printf("Strings: "); for(i=0; i<lenArray; i++) printf("%s ",array[i]); printf("\n"); lexSorting(array, lenArray, 0); } Well here is the complete code, that I am trying. since its been a long time since i have touched upon C programming, so somewhere i am making mistake in structure declaration. The problem goes here:- 1) I have declared a structure above and its object as array(buck[]). 2) Now when I declare this object array along with the structure, it works fine.. I have commented this thing right now. 3) But when I declare this object array inside the function.. because ultimately i have to declare inside function( as i need to build a recursive program, where objects will be created in very recursive call) then the program is throwing segmentation fault. Expected Output > [others@centos htdocs]$ ./a.out > Strings: able aback a abet acid yawn > yard yarn year yoke > ****** 0 ****** > a = able aback a abet acid > b = > c > . > . > y = yawn yard yarnyear yoke > z = Actual Output [others@centos htdocs]$ ./a.out Strings: able aback a abet acid yawn yard yarn year yoke Segmentation fault I have no idea, what difference I made in this. Kindly help. Thanks.

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• Python - calculate multinomial probability density functions on large dataset?

  - by Seafoid

  Hi, I originally intended to use MATLAB to tackle this problem but the inbuilt functions has limitations that do not suit my goal. The same limitation occurs in NumPy. I have two tab-delimited files. The first is a file showing amino acid residue, frequency and count for an in-house database of protein structures, i.e. A 0.25 1 S 0.25 1 T 0.25 1 P 0.25 1 The second file consists of quadruplets of amino acids and the number of times they occur, i.e. ASTP 1 Note, there are 8,000 such quadruplets. Based on the background frequency of occurence of each amino acid and the count of quadruplets, I aim to calculate the multinomial probability density function for each quadruplet and subsequently use it as the expected value in a maximum likelihood calculation. The multinomial distribution is as follows: f(x|n, p) = n!/(x1!*x2!*...*xk!)*((p1^x1)*(p2^x2)*...*(pk^xk)) where x is the number of each of k outcomes in n trials with fixed probabilities p. n is 4 four in all cases in my calculation. I have created three functions to calculate this distribution. # functions for multinomial distribution def expected_quadruplets(x, y): expected = x*y return expected # calculates the probabilities of occurence raised to the number of occurrences def prod_prob(p1, a, p2, b, p3, c, p4, d): prob_prod = (pow(p1, a))*(pow(p2, b))*(pow(p3, c))*(pow(p4, d)) return prob_prod # factorial() and multinomial_coefficient() work in tandem to calculate C, the multinomial coefficient def factorial(n): if n <= 1: return 1 return n*factorial(n-1) def multinomial_coefficient(a, b, c, d): n = 24.0 multi_coeff = (n/(factorial(a) * factorial(b) * factorial(c) * factorial(d))) return multi_coeff The problem is how best to structure the data in order to tackle the calculation most efficiently, in a manner that I can read (you guys write some cryptic code :-)) and that will not create an overflow or runtime error. To data my data is represented as nested lists. amino_acids = [['A', '0.25', '1'], ['S', '0.25', '1'], ['T', '0.25', '1'], ['P', '0.25', '1']] quadruplets = [['ASTP', '1']] I initially intended calling these functions within a nested for loop but this resulted in runtime errors or overfloe errors. I know that I can reset the recursion limit but I would rather do this more elegantly. I had the following: for i in quadruplets: quad = i[0].split(' ') for j in amino_acids: for k in quadruplets: for v in k: if j[0] == v: multinomial_coefficient(int(j[2]), int(j[2]), int(j[2]), int(j[2])) I haven'te really gotten to how to incorporate the other functions yet. I think that my current nested list arrangement is sub optimal. I wish to compare the each letter within the string 'ASTP' with the first component of each sub list in amino_acids. Where a match exists, I wish to pass the appropriate numeric values to the functions using indices. Is their a better way? Can I append the appropriate numbers for each amino acid and quadruplet to a temporary data structure within a loop, pass this to the functions and clear it for the next iteration? Thanks, S :-)

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• Even lighter than SQLite

  - by Richard Fabian

  I've been looking for a C++ SQL library implementation that is simple to hook in like SQLite, but faster and smaller. My projects are in games development and there's definitely a cutoff point between needing to pass the ACID test and wanting some extreme performance. I'm willing to move away from SQL string style queries, allowing it to be code driven, but I haven't found anything out there that provides SQL like flexibility while also preferring performance over the ACID test. I don't want to go reinventing the wheel, and the idea of implementing an SQL library on my own is quite daunting, even if it's only going to be simple subset of all the calls you could make. I need the basic commands (SELECT, MODIFY, DELETE, INSERT, with JOIN, and WHERE), not data operations (like sorting, min, max, count) and don't need the database to be atomic, or even enforce consistency (I can use a real SQL service while I'm testing and debugging).

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• Big Data – Buzz Words: What is NoSQL – Day 5 of 21

  - by Pinal Dave

  In yesterday’s blog post we explored the basic architecture of Big Data . In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – NoSQL. What is NoSQL? NoSQL stands for Not Relational SQL or Not Only SQL. Lots of people think that NoSQL means there is No SQL, which is not true – they both sound same but the meaning is totally different. NoSQL does use SQL but it uses more than SQL to achieve its goal. As per Wikipedia’s NoSQL Database Definition – “A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases.“ Why use NoSQL? A traditional relation database usually deals with predictable structured data. Whereas as the world has moved forward with unstructured data we often see the limitations of the traditional relational database in dealing with them. For example, nowadays we have data in format of SMS, wave files, photos and video format. It is a bit difficult to manage them by using a traditional relational database. I often see people using BLOB filed to store such a data. BLOB can store the data but when we have to retrieve them or even process them the same BLOB is extremely slow in processing the unstructured data. A NoSQL database is the type of database that can handle unstructured, unorganized and unpredictable data that our business needs it. Along with the support to unstructured data, the other advantage of NoSQL Database is high performance and high availability. Eventual Consistency Additionally to note that NoSQL Database may not provided 100% ACID (Atomicity, Consistency, Isolation, Durability) compliance.  Though, NoSQL Database does not support ACID they provide eventual consistency. That means over the long period of time all updates can be expected to propagate eventually through the system and data will be consistent. Taxonomy Taxonomy is the practice of classification of things or concepts and the principles. The NoSQL taxonomy supports column store, document store, key-value stores, and graph databases. We will discuss the taxonomy in detail in later blog posts. Here are few of the examples of the each of the No SQL Category. Column: Hbase, Cassandra, Accumulo Document: MongoDB, Couchbase, Raven Key-value : Dynamo, Riak, Azure, Redis, Cache, GT.m Graph: Neo4J, Allegro, Virtuoso, Bigdata As of now there are over 150 NoSQL Database and you can read everything about them in this single link. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Failed none and iptables

  - by Michael

  The problem is that when I ssh to my host with putty and enter user name, after that the password prompt delays. Found this is directly related to my iptables and can solve by changing default policy to ACCEPT. If default INPUT policy is ACCEPT, then password prompt is coming immediately. Mar 13 00:05:01 server-ubuntu sshd[6154]: Connection from 192.168.0.10 port 26304 Mar 13 00:05:06 server-ubuntu sshd[6154]: Failed none for acid from 192.168.0.10 port 26304 ssh2 However, if default INPUT policy is DROP, I got slight delay in getting password prompt after I enter username Mar 13 00:07:12 server-ubuntu sshd[6177]: Connection from 192.168.0.10 port 26333 Mar 13 00:07:35 server-ubuntu sshd[6177]: Failed none for acid from 192.168.0.10 port 26333 ssh2 For the second case, I tried to set default policy for FORWARD and OUTPUT chains to ACCEPT, but it didn't help. The only rule in this case is: -A INPUT -i eth1 -m mac --mac-source 00:26:XX:XX:XX:XX -j ACCEPT 00:26:XX:XX:XX:XX is the mac address from which I am trying to ssh to server's LAN(eth1). I'm sure there has to be some rule, which I can use while default INPUT chain policy is DENY in order to get password prompt immediately. I realize that the error message in the log is something normal and part of some verification procedure.

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• SQL SERVER – Concurrency Basics – Guest Post by Vinod Kumar

  - by pinaldave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions from SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. Learning is always fun when it comes to SQL Server and learning the basics again can be more fun. I did write about Transaction Logs and recovery over my blogs and the concept of simplifying the basics is a challenge. In the real world we always see checks and queues for a process – say railway reservation, banks, customer supports etc there is a process of line and queue to facilitate everyone. Shorter the queue higher is the efficiency of system (a.k.a higher is the concurrency). Every database does implement this using checks like locking, blocking mechanisms and they implement the standards in a way to facilitate higher concurrency. In this post, let us talk about the topic of Concurrency and what are the various aspects that one needs to know about concurrency inside SQL Server. Let us learn the concepts as one-liners: Concurrency can be defined as the ability of multiple processes to access or change shared data at the same time. The greater the number of concurrent user processes that can be active without interfering with each other, the greater the concurrency of the database system. Concurrency is reduced when a process that is changing data prevents other processes from reading that data or when a process that is reading data prevents other processes from changing that data. Concurrency is also affected when multiple processes are attempting to change the same data simultaneously. Two approaches to managing concurrent data access: Optimistic Concurrency Model Pessimistic Concurrency Model Concurrency Models Pessimistic Concurrency Default behavior: acquire locks to block access to data that another process is using. Assumes that enough data modification operations are in the system that any given read operation is likely affected by a data modification made by another user (assumes conflicts will occur). Avoids conflicts by acquiring a lock on data being read so no other processes can modify that data. Also acquires locks on data being modified so no other processes can access the data for either reading or modifying. Readers block writer, writers block readers and writers. Optimistic Concurrency Assumes that there are sufficiently few conflicting data modification operations in the system that any single transaction is unlikely to modify data that another transaction is modifying. Default behavior of optimistic concurrency is to use row versioning to allow data readers to see the state of the data before the modification occurs. Older versions of the data are saved so a process reading data can see the data as it was when the process started reading and not affected by any changes being made to that data. Processes modifying the data is unaffected by processes reading the data because the reader is accessing a saved version of the data rows. Readers do not block writers and writers do not block readers, but, writers can and will block writers. Transaction Processing A transaction is the basic unit of work in SQL Server. Transaction consists of SQL commands that read and update the database but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction: marked with a BEGIN TRAN and the end is marked by a COMMIT TRAN or ROLLBACK TRAN). Transactions must exhibit all the ACID properties of a transaction. ACID Properties Transaction processing must guarantee the consistency and recoverability of SQL Server databases. Ensures all transactions are performed as a single unit of work regardless of hardware or system failure. A – Atomicity C – Consistency I – Isolation D- Durability Atomicity: Each transaction is treated as all or nothing – it either commits or aborts. Consistency: ensures that a transaction won’t allow the system to arrive at an incorrect logical state – the data must always be logically correct.  Consistency is honored even in the event of a system failure. Isolation: separates concurrent transactions from the updates of other incomplete transactions. SQL Server accomplishes isolation among transactions by locking data or creating row versions. Durability: After a transaction commits, the durability property ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on data. Transaction Dependencies In addition to supporting all four ACID properties, a transaction might exhibit few other behaviors (known as dependency problems or consistency problems). Lost Updates: Occur when two processes read the same data and both manipulate the data, changing its value and then both try to update the original data to the new value. The second process might overwrite the first update completely. Dirty Reads: Occurs when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. Non-repeatable Reads: A read is non-repeatable if a process might get different values when reading the same data in two reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. Phantoms: Occurs when membership in a set changes. It occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. Isolation Levels SQL Server supports 5 isolation levels that control the behavior of read operations. Read Uncommitted All behaviors except for lost updates are possible. Implemented by allowing the read operations to not take any locks, and because of this, it won’t be blocked by conflicting locks acquired by other processes. The process can read data that another process has modified but not yet committed. When using the read uncommitted isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order) instead of a logical order scan (following page pointers). If another process doing concurrent operations changes data and move rows to a new location in the table, the allocation order scan can end up reading the same row twice. Also can happen if you have read a row before it is updated and then an update moves the row to a higher page number than your scan encounters later. Performing an allocation order scan under Read Uncommitted can cause you to miss a row completely – can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read. Read Committed Two varieties of read committed isolation: optimistic and pessimistic (default). Ensures that a read never reads data that another application hasn’t committed. If another transaction is updating data and has exclusive locks on data, your transaction will have to wait for the locks to be released. Your transaction must put share locks on data that are visited, which means that data might be unavailable for others to use. A share lock doesn’t prevent others from reading but prevents them from updating. Read committed (snapshot) ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. SQL Server generates a version of the changed row with its previous committed values. Data being changed is still locked but other processes can see the previous versions of the data as it was before the update operation began. Repeatable Read This is a Pessimistic isolation level. Ensures that if a transaction revisits data or a query is reissued the data doesn’t change. That is, issuing the same query twice within a transaction cannot pickup any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, this does allow phantom rows to appear. Preventing non-repeatable read is a desirable safeguard but cost is that all shared locks in a transaction must be held until the completion of the transaction. Snapshot Snapshot Isolation (SI) is an optimistic isolation level. Allows for processes to read older versions of committed data if the current version is locked. Difference between snapshot and read committed has to do with how old the older versions have to be. It’s possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Serializable This is the strongest of the pessimistic isolation level. Adds to repeatable read isolation level by ensuring that if a query is reissued rows were not added in the interim, i.e, phantoms do not appear. Preventing phantoms is another desirable safeguard, but cost of this extra safeguard is similar to that of repeatable read – all shared locks in a transaction must be held until the transaction completes. In addition serializable isolation level requires that you lock data that has been read but also data that doesn’t exist. Ex: if a SELECT returned no rows, you want it to return no. rows when the query is reissued. This is implemented in SQL Server by a special kind of lock called the key-range lock. Key-range locks require that there be an index on the column that defines the range of values. If there is no index on the column, serializable isolation requires a table lock. Gets its name from the fact that running multiple serializable transactions at the same time is equivalent of running them one at a time. Now that we understand the basics of what concurrency is, the subsequent blog posts will try to bring out the basics around locking, blocking, deadlocks because they are the fundamental blocks that make concurrency possible. Now if you are with me – let us continue learning for SQL Server Locking Basics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Concurrency

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• Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Relational Database and NoSQL database in the Big Data Story. In this article we will understand the role of Key-Value Pair Databases and Document Databases Supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (Yesterday’s post) NoSQL Databases (Yesterday’s post) Key-Value Pair Databases (This post) Document Databases (This post) Columnar Databases (Tomorrow’s post) Graph Databases (Tomorrow’s post) Spatial Databases (Tomorrow’s post) Key Value Pair Databases Key Value Pair Databases are also known as KVP databases. A key is a field name and attribute, an identifier. The content of that field is its value, the data that is being identified and stored. They have a very simple implementation of NoSQL database concepts. They do not have schema hence they are very flexible as well as scalable. The disadvantages of Key Value Pair (KVP) database are that they do not follow ACID (Atomicity, Consistency, Isolation, Durability) properties. Additionally, it will require data architects to plan for data placement, replication as well as high availability. In KVP databases the data is stored as strings. Here is a simple example of how Key Value Database will look like: Key Value Name Pinal Dave Color Blue Twitter @pinaldave Name Nupur Dave Movie The Hero As the number of users grow in Key Value Pair databases it starts getting difficult to manage the entire database. As there is no specific schema or rules associated with the database, there are chances that database grows exponentially as well. It is very crucial to select the right Key Value Pair Database which offers an additional set of tools to manage the data and provides finer control over various business aspects of the same. Riak Rick is one of the most popular Key Value Database. It is known for its scalability and performance in high volume and velocity database. Additionally, it implements a mechanism for collection key and values which further helps to build manageable system. We will further discuss Riak in future blog posts. Key Value Databases are a good choice for social media, communities, caching layers for connecting other databases. In simpler words, whenever we required flexibility of the data storage keeping scalability in mind – KVP databases are good options to consider. Document Database There are two different kinds of document databases. 1) Full document Content (web pages, word docs etc) and 2) Storing Document Components for storage. The second types of the document database we are talking about over here. They use Javascript Object Notation (JSON) and Binary JSON for the structure of the documents. JSON is very easy to understand language and it is very easy to write for applications. There are two major structures of JSON used for Document Database – 1) Name Value Pairs and 2) Ordered List. MongoDB and CouchDB are two of the most popular Open Source NonRelational Document Database. MongoDB MongoDB databases are called collections. Each collection is build of documents and each document is composed of fields. MongoDB collections can be indexed for optimal performance. MongoDB ecosystem is highly available, supports query services as well as MapReduce. It is often used in high volume content management system. CouchDB CouchDB databases are composed of documents which consists fields and attachments (known as description). It supports ACID properties. The main attraction points of CouchDB are that it will continue to operate even though network connectivity is sketchy. Due to this nature CouchDB prefers local data storage. Document Database is a good choice of the database when users have to generate dynamic reports from elements which are changing very frequently. A good example of document usages is in real time analytics in social networking or content management system. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• SQL – Step by Step Guide to Download and Install NuoDB – Getting Started with NuoDB

  - by Pinal Dave

  Let us take a look at the application you own at your business. If you pay attention to the underlying database for that application you will be amazed. Every successful business these days processes way more data than they used to process before. The number of transactions and the amount of data is growing at an exponential rate. Every single day there is way more data to process than before. Big data is no longer a concept; it is now turning into reality. If you look around there are so many different big data solutions and it can be a quite difficult task to figure out where to begin. Personally, I have been experimenting with a lot of different solutions which allow my database to scale immediately without much hassle while maintaining optimal database performance.  There are for sure some solutions out there, but for many I even have to learn their specific language and there is a lot of new exploration to do. Honestly, what I prefer is a product, which works with the language I know (SQL) and follows all the RDBMS concepts which I am familiar with (ACID etc.). NuoDB is one such solution.  It is an operational NewSQL database built on a patented emergent architecture with full support for SQL and ACID guarantees. In this blog post, I will explore how one can download and install NuoDB database. Step 1: Follow me and go to the NuoDB download page. Simply fill out the form, accept the online license agreement, and you will be taken directly to a page where you can select any platform you prefer to install NuoDB. In my example below, I select the Windows 64-bit platform as it is one of the most popular NuoDB platforms. (You can also run NuoDB on Amazon Web Services but I prefer to install it on my local machine for the purposes of this blog). Step 2: Once you have downloaded the NuoDB installer, double click on it to install it on the Windows platform. Here is the enlarged the icon of the installer. Step 3: Follow the wizard installation, as it is pretty straight forward and easy to do so. I have selected all the options to install as the overall installation is very simple and it does not take up much space. I have installed it on my C drive but you can select your preferred drive. It is quite possible that if you do not have 64 bit Java, it will throw following error. If you face following error, I suggest you to download 64-bit Java from here. Make sure that you download 64-bit Java from following link: http://java.com/en/download/manual.jsp If already have Java 64-bit installed, you can continue with the installation as described in following image. Otherwise, install Java and start from with Step 1. As in my case, I already have 64-bit Java installed – and you won’t believe me when I say that the entire installation of NuoDB only took me around 90 seconds. Click on Finish to end to exit the installation. Step 4: Once the installation is successful, NuoDB will automatically open the following two tabs – Console and DevCenter — in your preferred browser. On the Console tab you can explore various components of the NuoDB solution, e.g. QuickStart, Admin, Explorer, Storefront and Samples. We will see various components and their usage in future blog posts. If you follow these steps in this post, which I have followed to install NuoDB, you will agree that the installation of NuoDB is extremely smooth and it was indeed a pleasure to install a database product with such ease. If you have installed other database products in the past, you will absolutely agree with me. So download NuoDB and install it today, and in tomorrow’s blog post I will take the installation to the next level. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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• SQL Server 2005, wide indexes, computed columns, and sargable queries

  - by luksan

  In my database, assume we have a table defined as follows: CREATE TABLE [Chemical]( [ChemicalId] int NOT NULL IDENTITY(1,1) PRIMARY KEY, [Name] nvarchar(max) NOT NULL, [Description] nvarchar(max) NULL ) The value for Name can be very large, so we must use nvarchar(max). Unfortunately, we want to create an index on this column, but nvarchar(max) is not supported inside an index. So we create the following computed column and associated index based upon it: ALTER TABLE [Chemical] ADD [Name_Indexable] AS LEFT([Name], 20) CREATE INDEX [IX_Name] ON [Chemical]([Name_Indexable]) INCLUDE([Name]) The index will not be unique but we can enforce uniqueness via a trigger. If we perform the following query, the execution plan results in a index scan, which is not what we want: SELECT [ChemicalId], [Name], [Description] FROM [Chemical] WHERE [Name]='[1,1''-Bicyclohexyl]-2-carboxylic acid, 4'',5-dihydroxy-2'',3-dimethyl-5'',6-bis[(1-oxo-2-propen-1-yl)oxy]-, methyl ester' However, if we modify the query to make it "sargable," then the execution plan results in an index seek, which is what we want: SELECT [ChemicalId], [Name], [Description] FROM [Chemical] WHERE [Indexable_Name]='[1,1''-Bicyclohexyl]-' AND [Name]='[1,1''-Bicyclohexyl]-2-carboxylic acid, 4'',5-dihydroxy-2'',3-dimethyl-5'',6-bis[(1-oxo-2-propen-1-yl)oxy]-, methyl ester' Is this a good solution if we control the format of all queries executed against the database via our middle tier? Is there a better way? Is this a major kludge? Should we be using full-text indexing?

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• Postgresql has broken apt-get on Ubuntu

  - by Raphie Palefsky-Smith

  On ubuntu 12.04, whenever I try to install a package using apt-get I'm greeted by: The following packages have unmet dependencies: postgresql-9.1 : Depends: postgresql-client-9.1 but it is not going to be instal led E: Unmet dependencies. Try 'apt-get -f install' with no packages (or specify a so lution). apt-get install postgresql-client-9.1 generates: The following packages have unmet dependencies: postgresql-client-9.1 : Breaks: postgresql-9.1 (< 9.1.6-0ubuntu12.04.1) but 9.1.3-2 is to be installed apt-get -f install and apt-get remove postgresql-9.1 both give: Removing postgresql-9.1 ... * Stopping PostgreSQL 9.1 database server * Error: /var/lib/postgresql/9.1/main is not accessible or does not exist ...fail! invoke-rc.d: initscript postgresql, action "stop" failed. dpkg: error processing postgresql-9.1 (--remove): subprocess installed pre-removal script returned error exit status 1 Errors were encountered while processing: postgresql-9.1 E: Sub-process /usr/bin/dpkg returned an error code (1) So, apt-get is crippled, and I can't find a way out. Is there any way to resolve this without a re-install? EDIT: apt-cache show postgresql-9.1 returns: Package: postgresql-9.1 Priority: optional Section: database Installed-Size: 11164 Maintainer: Ubuntu Developers <[email protected]> Original-Maintainer: Martin Pitt <[email protected]> Architecture: amd64 Version: 9.1.6-0ubuntu12.04.1 Replaces: postgresql-contrib-9.1 (<< 9.1~beta1-3~), postgresql-plpython-9.1 (<< 9.1.6-0ubuntu12.04.1) Depends: libc6 (>= 2.15), libcomerr2 (>= 1.01), libgssapi-krb5-2 (>= 1.8+dfsg), libkrb5-3 (>= 1.6.dfsg.2), libldap-2.4-2 (>= 2.4.7), libpam0g (>= 0.99.7.1), libpq5 (>= 9.1~), libssl1.0.0 (>= 1.0.0), libxml2 (>= 2.7.4), postgresql-client-9.1, postgresql-common (>= 115~), tzdata, ssl-cert, locales Suggests: oidentd | ident-server, locales-all Conflicts: postgresql (<< 7.5) Breaks: postgresql-plpython-9.1 (<< 9.1.6-0ubuntu12.04.1) Filename: pool/main/p/postgresql-9.1/postgresql-9.1_9.1.6-0ubuntu12.04.1_amd64.deb Size: 4298270 MD5sum: 9ee2ab5f25f949121f736ad80d735d57 SHA1: 5eac1cca8d00c4aec4fb55c46fc2a013bc401642 SHA256: 4e6c24c251a01f1b6a340c96d24fdbb92b5e2f8a2f4a8b6b08a0df0fe4cf62ab Description-en: object-relational SQL database, version 9.1 server PostgreSQL is a fully featured object-relational database management system. It supports a large part of the SQL standard and is designed to be extensible by users in many aspects. Some of the features are: ACID transactions, foreign keys, views, sequences, subqueries, triggers, user-defined types and functions, outer joins, multiversion concurrency control. Graphical user interfaces and bindings for many programming languages are available as well. . This package provides the database server for PostgreSQL 9.1. Servers for other major release versions can be installed simultaneously and are coordinated by the postgresql-common package. A package providing ident-server is needed if you want to authenticate remote connections with identd. Homepage: http://www.postgresql.org/ Description-md5: c487fe4e86f0eac09ed9847282436059 Bugs: https://bugs.launchpad.net/ubuntu/+filebug Origin: Ubuntu Supported: 5y Task: postgresql-server Package: postgresql-9.1 Priority: optional Section: database Installed-Size: 11164 Maintainer: Ubuntu Developers <[email protected]> Original-Maintainer: Martin Pitt <[email protected]> Architecture: amd64 Version: 9.1.5-0ubuntu12.04 Replaces: postgresql-contrib-9.1 (<< 9.1~beta1-3~), postgresql-plpython-9.1 (<< 9.1.5-0ubuntu12.04) Depends: libc6 (>= 2.15), libcomerr2 (>= 1.01), libgssapi-krb5-2 (>= 1.8+dfsg), libkrb5-3 (>= 1.6.dfsg.2), libldap-2.4-2 (>= 2.4.7), libpam0g (>= 0.99.7.1), libpq5 (>= 9.1~), libssl1.0.0 (>= 1.0.0), libxml2 (>= 2.7.4), postgresql-client-9.1, postgresql-common (>= 115~), tzdata, ssl-cert, locales Suggests: oidentd | ident-server, locales-all Conflicts: postgresql (<< 7.5) Breaks: postgresql-plpython-9.1 (<< 9.1.5-0ubuntu12.04) Filename: pool/main/p/postgresql-9.1/postgresql-9.1_9.1.5-0ubuntu12.04_amd64.deb Size: 4298028 MD5sum: 3797b030ca8558a67b58e62cc0a22646 SHA1: ad340a9693341621b82b7f91725fda781781c0fb SHA256: 99aa892971976b85bcf6fb2e1bb8bf3e3fb860190679a225e7ceeb8f33f0e84b Description-en: object-relational SQL database, version 9.1 server PostgreSQL is a fully featured object-relational database management system. It supports a large part of the SQL standard and is designed to be extensible by users in many aspects. Some of the features are: ACID transactions, foreign keys, views, sequences, subqueries, triggers, user-defined types and functions, outer joins, multiversion concurrency control. Graphical user interfaces and bindings for many programming languages are available as well. . This package provides the database server for PostgreSQL 9.1. Servers for other major release versions can be installed simultaneously and are coordinated by the postgresql-common package. A package providing ident-server is needed if you want to authenticate remote connections with identd. Homepage: http://www.postgresql.org/ Description-md5: c487fe4e86f0eac09ed9847282436059 Bugs: https://bugs.launchpad.net/ubuntu/+filebug Origin: Ubuntu Supported: 5y Task: postgresql-server Package: postgresql-9.1 Priority: optional Section: database Installed-Size: 11220 Maintainer: Martin Pitt <[email protected]> Original-Maintainer: Martin Pitt <[email protected]> Architecture: amd64 Version: 9.1.3-2 Replaces: postgresql-contrib-9.1 (<< 9.1~beta1-3~), postgresql-plpython-9.1 (<< 9.1.3-2) Depends: libc6 (>= 2.15), libcomerr2 (>= 1.01), libgssapi-krb5-2 (>= 1.8+dfsg), libkrb5-3 (>= 1.6.dfsg.2), libldap-2.4-2 (>= 2.4.7), libpam0g (>= 0.99.7.1), libpq5 (>= 9.1~), libssl1.0.0 (>= 1.0.0), libxml2 (>= 2.7.4), postgresql-client-9.1, postgresql-common (>= 115~), tzdata, ssl-cert, locales Suggests: oidentd | ident-server, locales-all Conflicts: postgresql (<< 7.5) Breaks: postgresql-plpython-9.1 (<< 9.1.3-2) Filename: pool/main/p/postgresql-9.1/postgresql-9.1_9.1.3-2_amd64.deb Size: 4284744 MD5sum: bad9aac349051fe86fd1c1f628797122 SHA1: a3f5d6583cc6e2372a077d7c2fc7adfcfa0d504d SHA256: e885c32950f09db7498c90e12c4d1df0525038d6feb2f83e2e50f563fdde404a Description-en: object-relational SQL database, version 9.1 server PostgreSQL is a fully featured object-relational database management system. It supports a large part of the SQL standard and is designed to be extensible by users in many aspects. Some of the features are: ACID transactions, foreign keys, views, sequences, subqueries, triggers, user-defined types and functions, outer joins, multiversion concurrency control. Graphical user interfaces and bindings for many programming languages are available as well. . This package provides the database server for PostgreSQL 9.1. Servers for other major release versions can be installed simultaneously and are coordinated by the postgresql-common package. A package providing ident-server is needed if you want to authenticate remote connections with identd. Homepage: http://www.postgresql.org/ Description-md5: c487fe4e86f0eac09ed9847282436059 Bugs: https://bugs.launchpad.net/ubuntu/+filebug Origin: Ubuntu Supported: 5y Task: postgresql-server

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• HD failure questions ...

  - by JP

  I recently had one of my home PC hard drives fail. It was in a striped raid, so I had to rebuild the raid (no data lost, only the OS partition was there). Is there any way to diagnose exactly what went wrong with the drive? That is what caused the failure? Also, in general, what is a good way to dispose of a failed hard drive securely and realistically (I don't have thermite or muriatic acid)?

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• NoSQL Memcached API for MySQL: Latest Updates

  - by Mat Keep

  With data volumes exploding, it is vital to be able to ingest and query data at high speed. For this reason, MySQL has implemented NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB) storage engines, which bypass the SQL layer completely. Without SQL parsing and optimization, Key-Value data can be written directly to MySQL tables up to 9x faster, while maintaining ACID guarantees. In addition, users can continue to run complex queries with SQL across the same data set, providing real-time analytics to the business or anonymizing sensitive data before loading to big data platforms such as Hadoop, while still maintaining all of the advantages of their existing relational database infrastructure. This and more is discussed in the latest Guide to MySQL and NoSQL where you can learn more about using the APIs to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database The native Memcached API is part of the MySQL 5.6 Release Candidate, and is already available in the GA release of MySQL Cluster. By using the ubiquitous Memcached API for writing and reading data, developers can preserve their investments in Memcached infrastructure by re-using existing Memcached clients, while also eliminating the need for application changes. Speed, when combined with flexibility, is essential in the world of growing data volumes and variability. Complementing NoSQL access, support for on-line DDL (Data Definition Language) operations in MySQL 5.6 and MySQL Cluster enables DevOps teams to dynamically update their database schema to accommodate rapidly changing requirements, such as the need to capture additional data generated by their applications. These changes can be made without database downtime. Using the Memcached interface, developers do not need to define a schema at all when using MySQL Cluster. Lets look a little more closely at the Memcached implementations for both InnoDB and MySQL Cluster. Memcached Implementation for InnoDB The Memcached API for InnoDB is previewed as part of the MySQL 5.6 Release Candidate. As illustrated in the following figure, Memcached for InnoDB is implemented via a Memcached daemon plug-in to the mysqld process, with the Memcached protocol mapped to the native InnoDB API. Figure 1: Memcached API Implementation for InnoDB With the Memcached daemon running in the same process space, users get very low latency access to their data while also leveraging the scalability enhancements delivered with InnoDB and a simple deployment and management model. Multiple web / application servers can remotely access the Memcached / InnoDB server to get direct access to a shared data set. With simultaneous SQL access, users can maintain all the advanced functionality offered by InnoDB including support for Foreign Keys, XA transactions and complex JOIN operations. Benchmarks demonstrate that the NoSQL Memcached API for InnoDB delivers up to 9x higher performance than the SQL interface when inserting new key/value pairs, with a single low-end commodity server supporting nearly 70,000 Transactions per Second. Figure 2: Over 9x Faster INSERT Operations The delivered performance demonstrates MySQL with the native Memcached NoSQL interface is well suited for high-speed inserts with the added assurance of transactional guarantees. You can check out the latest Memcached / InnoDB developments and benchmarks here You can learn how to configure the Memcached API for InnoDB here Memcached Implementation for MySQL Cluster Memcached API support for MySQL Cluster was introduced with General Availability (GA) of the 7.2 release, and joins an extensive range of NoSQL interfaces that are already available for MySQL Cluster Like Memcached, MySQL Cluster provides a distributed hash table with in-memory performance. MySQL Cluster extends Memcached functionality by adding support for write-intensive workloads, a full relational model with ACID compliance (including persistence), rich query support, auto-sharding and 99.999% availability, with extensive management and monitoring capabilities. All writes are committed directly to MySQL Cluster, eliminating cache invalidation and the overhead of data consistency checking to ensure complete synchronization between the database and cache. Figure 3: Memcached API Implementation with MySQL Cluster Implementation is simple: 1. The application sends reads and writes to the Memcached process (using the standard Memcached API). 2. This invokes the Memcached Driver for NDB (which is part of the same process) 3. The NDB API is called, providing for very quick access to the data held in MySQL Cluster’s data nodes. The solution has been designed to be very flexible, allowing the application architect to find a configuration that best fits their needs. It is possible to co-locate the Memcached API in either the data nodes or application nodes, or alternatively within a dedicated Memcached layer. The benefit of this flexible approach to deployment is that users can configure behavior on a per-key-prefix basis (through tables in MySQL Cluster) and the application doesn’t have to care – it just uses the Memcached API and relies on the software to store data in the right place(s) and to keep everything synchronized. Using Memcached for Schema-less Data By default, every Key / Value is written to the same table with each Key / Value pair stored in a single row – thus allowing schema-less data storage. Alternatively, the developer can define a key-prefix so that each value is linked to a pre-defined column in a specific table. Of course if the application needs to access the same data through SQL then developers can map key prefixes to existing table columns, enabling Memcached access to schema-structured data already stored in MySQL Cluster. Conclusion Download the Guide to MySQL and NoSQL to learn more about NoSQL APIs and how you can use them to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database See how to build a social app with MySQL Cluster and the Memcached API from our on-demand webinar or take a look at the docs Don't hesitate to use the comments section below for any questions you may have 

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• Megjelent a MySQL 5.5

  - by Lajos Sárecz

  Rekord ido alatt készült el az új MySQL 5.5 verziót, melyet a mai nap jelentett be az Oracle. Ez újabb bizonyítéka annak, hogy az Oracle komolyan fejleszti a MySQL-t is, és igyekszik innovatív megoldásokkal megörvendeztetni a MySQL felhasználókat is. Akinek 'Déja-vu' érzése van, az nem véletlen, hiszen a szeptemberi OpenWorld konferencián került bejelentésre a MySQL 5.5 RC, azaz a Release Candidate, melyrol beszámolt például a hwsw.hu is. Az új verzióban elsosorban a teljesítményen és a skálázhatóságon fejlesztett az Oracle. Így például alapértelmezetten az InnoDB storage engine jön a MySQL-el, aminek köszönhetoen például ACID (atomicity, consistency, isolation, durability) tranzakciókat hajt végre az adatbázis-kezelo (ez mondjuk nem egy apró részlet...). Emellett újdonságot jelent még a majdnem szinkron replikáció, a fejlettebb index és tábla particionálás, valamint diagnosztika terén bevezetésre került egy új PERFORMANCE_SCHEMA, aminek köszönhetoen javult a MySQL menedzselhetosége. A RC verzióval futtatott tesztek jelentos gyorsulást mutattak a MySQL 5.1-es verziójához képest, így érdemes megfontolni a verzió frissítést.

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• Cloud Computing Architecture Patterns: Don’t Focus on the Client

  - by BuckWoody

  Normally I try to put topics in the positive in other words "Do this" not "Don't do that". Sometimes its clearer to focus on what *not* to do. Popular development processes often start with screen mockups, or user input descriptions. In a scale-out pattern like Cloud Computing on Windows Azure, that's the wrong place to start. Start with the Data    Instead, I recommend that you start with the data that a process requires. That data might be temporary or persisted, but starting with the data and its requirements helps to define not only the storage engine you need but also drives everything from security to the integrity of the application. For instance, assume the requirements show that the user must enter their phone number, and that this datum is used in a contact management system further down the application chain. For that datum, you can determine what data type you need (U.S. only or International?) the security requirements, whether it needs ACID compliance, how it will be searched, indexed and so on. From one small data point you can extrapolate out your options for storing and processing the data. Here's the interesting part, which begins to break the patterns that we've used for decades: all of the data doesn't have the same requirements. The phone number might be best suited for a list, or an element, or a string, with either BASE or ACID requirements, based on how it is used. That means we don't have to dump everything into XML, an RDBMS, a NoSQL engine, or a flat file exclusively. In fact, one record might use all of those depending on the use-case requirements. Next Is Data Management  With the data defined, we can move on to how to store the data. Again, the requirements now dictate whether we need a full relational calculus or set-based operations, or we can choose another method based on the requirements for the data. And breaking another pattern its OK to store in more than once, in more than one location. We do this all the time for reporting systems and Business Intelligence systems, so this is a pattern we need to think about even for OLTP data. Move to Data Transport How does the data get around? We can use a connection-based method, sending the data along a transport to the storage engine, but in some cases we may want to use a cache, a queue, the Service Bus, or Complex Event Processing. Finally, Data Processing Most RDBMS engines, NoSQL, and certainly Big Data engines not only store data, but can process and manipulate it as well. Its doubtful that you'll calculate that phone number right? Well, if you're the phone company, you most certainly will. And so we see that even once we've chosen the data type, storage and engine, the same element can have different computing requirements based on how it is used. Sure, We Need A Front-End At Some Point Not all data is entered by human hands in fact most data isn't. We don't really need a Graphical User Interface (GUI) we need some way for a GUI to get data into and out of the systems listed earlier.   But when we do need to allow users to enter or examine data, that should be left to the GUI that best fits the device the user has. Ever tried to use an application designed for a web browser on a phone? Or one designed for a tablet on a phone? Its usually quite painful. The siren song of "We'll just write one interface for all devices" is strong, and has beguiled many an unsuspecting architect. But they just don't work out.   Instead, focus on the data, its transport and processing. Create API calls or a message system that allows for resilient transport to the device or interface, and let it do what it does best. References Microsoft Architecture Journal:   http://msdn.microsoft.com/en-us/architecture/bb410935.aspx Patterns and Practices:   http://msdn.microsoft.com/en-us/library/ff921345.aspx Windows Azure iOS, Android, Windows 8 Mobile Devices SDK: http://www.windowsazure.com/en-us/develop/mobile/tutorials/get-started-ios/ Windows Azure Facebook SDK: http://ntotten.com/2013/03/14/using-windows-azure-mobile-services-with-the-facebook-sdk-for-windows-phone/

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• When will microsoft release IE9? [closed]

  - by Rob McKinnon

  I was one of those people early on to try their IE9 beta, and it was terribly buggy. It still does function horribly. IMO any windows release after 5(2k,nt,xp) absolutely sux the life out of my resources compared to RPM linux(opensuse), until at least service pack 2. MS is trying to push HTML5/CSS3 and they cannot pass the Acid 3 test. I am wondering when IE9 will become functional. I am a big supported of MS applications. I have a great amount of adoration for IIS7 because they have support for CGI/PHP. Is IE9 going to be released before 2012?

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• Where can I learn to write my own database?

  - by Buttons840

  I'm interested in writing my own database - a triple-store. Are there any good resources to help with the challenges of such a project? Or more generally: How can I learn to write my own database? Some specific issues I'm unsure of: How is the data actually stored on the file-system? A flat-file seems easy enough, but a database is a lot more then a flat-file. What kinds of things are typically stored (or cached) in memory? How are indexes created and stored? How is ACID compliance achieved? Etc. This is a big topic, but knowing how to store large amounts of data in a reliable way is good to know. (My investigation into existing triple-stores was summarized back in 2008; not much has changed in 4 years it seems. This is why I want write my own.)

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• Using XA Transactions in Coherence-based Applications

  - by jpurdy

  While the costs of XA transactions are well known (e.g. increased data contention, higher latency, significant disk I/O for logging, availability challenges, etc.), in many cases they are the most attractive option for coordinating logical transactions across multiple resources. There are a few common approaches when integrating Coherence into applications via the use of an application server's transaction manager: Use of Coherence as a read-only cache, applying transactions to the underlying database (or any system of record) instead of the cache. Use of TransactionMap interface via the included resource adapter. Use of the new ACID transaction framework, introduced in Coherence 3.6.   Each of these may have significant drawbacks for certain workloads. Using Coherence as a read-only cache is the simplest option. In this approach, the application is responsible for managing both the database and the cache (either within the business logic or via application server hooks). This approach also tends to provide limited benefit for many workloads, particularly those workloads that either have queries (given the complexity of maintaining a fully cached data set in Coherence) or are not read-heavy (where the cost of managing the cache may outweigh the benefits of reading from it). All updates are made synchronously to the database, leaving it as both a source of latency as well as a potential bottleneck. This approach also prevents addressing "hot data" problems (when certain objects are updated by many concurrent transactions) since most database servers offer no facilities for explicitly controlling concurrent updates. Finally, this option tends to be a better fit for key-based access (rather than filter-based access such as queries) since this makes it easier to aggressively invalidate cache entries without worrying about when they will be reloaded. The advantage of this approach is that it allows strong data consistency as long as optimistic concurrency control is used to ensure that database updates are applied correctly regardless of whether the cache contains stale (or even dirty) data. Another benefit of this approach is that it avoids the limitations of Coherence's write-through caching implementation. TransactionMap is generally used when Coherence acts as system of record. TransactionMap is not generally compatible with write-through caching, so it will usually be either used to manage a standalone cache or when the cache is backed by a database via write-behind caching. TransactionMap has some restrictions that may limit its utility, the most significant being: The lock-based concurrency model is relatively inefficient and may introduce significant latency and contention. As an example, in a typical configuration, a transaction that updates 20 cache entries will require roughly 40ms just for lock management (assuming all locks are granted immediately, and excluding validation and writing which will require a similar amount of time). This may be partially mitigated by denormalizing (e.g. combining a parent object and its set of child objects into a single cache entry), at the cost of increasing false contention (e.g. transactions will conflict even when updating different child objects). If the client (application server JVM) fails during the commit phase, locks will be released immediately, and the transaction may be partially committed. In practice, this is usually not as bad as it may sound since the commit phase is usually very short (all locks having been previously acquired). Note that this vulnerability does not exist when a single NamedCache is used and all updates are confined to a single partition (generally implying the use of partition affinity). The unconventional TransactionMap API is cumbersome but manageable. Only a few methods are transactional, primarily get(), put() and remove(). The ACID transactions framework (accessed via the Connection class) provides atomicity guarantees by implementing the NamedCache interface, maintaining its own cache data and transaction logs inside a set of private partitioned caches. This feature may be used as either a local transactional resource or as logging XA resource. However, a lack of database integration precludes the use of this functionality for most applications. A side effect of this is that this feature has not seen significant adoption, meaning that any use of this is subject to the usual headaches associated with being an early adopter (greater chance of bugs and greater risk of hitting an unoptimized code path). As a result, for the moment, we generally recommend against using this feature. In summary, it is possible to use Coherence in XA-oriented applications, and several customers are doing this successfully, but it is not a core usage model for the product, so care should be taken before committing to this path. For most applications, the most robust solution is normally to use Coherence as a read-only cache of the underlying data resources, even if this prevents taking advantage of certain product features.

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• Is redis a durable datastore?

  - by allyourcode

  By "durable" I mean, the server can crash at any time, and as long as the disk remains in tact, no data is lost (see ACID). Seems like that's what journaling mode is for, but if you enable journaling, doesn't that defeat the purpose of operating on in-memory data? Read operations might not be affected by journaling, but it seems like journaling would kill your write performance.

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• Simplest database implementation

  - by MaX

  I am looking for a really simple database implementation; basically one with no complex parsing SQL engine. What I am looking for is something demonstrating B+ trees and ACID storage (Suitable for educational purposes). What I have found up-till now form my current searches was hamster-db. I am looking for something even simpler with a smaller code-base. If there is any such opensource project in your knowledge please let me know.

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• conversion of DNA to Protein - c structure issue

  - by sam

  I am working on conversion of DNA sequence to Protein sequence. I had completed all program only one error I found there is of structure. dna_codon is a structure and I am iterating over it.In first iteration it shows proper values of structure but from next iteration, it dont show the proper value stored in structure. Its a small error so do not think that I havnt done anything and downvote. I am stucked here because I am new in c for structures. CODE : #include <stdio.h> #include<string.h> void main() { int i, len; char short_codons[20]; char short_slc[1000]; char sequence[1000]; struct codons { char amino_acid[20], slc[20], dna_codon[40]; }; struct codons c1 [20]= { {"Isoleucine", "I", "ATT, ATC, ATA"}, {"Leucine", "L", "CTT, CTC, CTA, CTG, TTA, TTG"}, {"Valine", "V", "GTT, GTC, GTA, GTG"}, {"Phenylalanine", "F", "TTT, TTC"}, {"Methionine", "M", "ATG"}, {"Cysteine", "C", "TGT, TGC"}, {"Alanine", "A", "GCT, GCC, GCA, GCG"}, {"Proline", "P", "CCT, CCC, CCA,CCG "}, {"Threonine", "T", "ACT, ACC, ACA, ACG"}, {"Serine", "S", "TCT, TCC, TCA, TCG, AGT, AGC"}, {"Tyrosine", "Y", "TAT, TAC"}, {"Tryptophan", "W", "TGG"}, {"Glutamine", "Q", "CAA, CAG"}, {"Aspargine","N" "AAT, AAC"}, {"Histidine", "H", "CAT, CAC"}, {"Glutamic acid", "E", "GAA, GAG"}, {"Aspartic acid", "D", "GAT, GAC"}, {"Lysine", "K", "AAA, AAG"}, {"Arginine", "R", "CGT, CGC, CGA, CGG, AGA, AGG"}, {"Stop codons", "Stop", "AA, TAG, TGA"} }; int count = 0; printf("Enter the sequence: "); gets(sequence); char *input_string = sequence; char *tmp_str = input_string; int k; char *pch; while (*input_string != '\0') { char string_3l[4] = {'\0'}; strncpy(string_3l, input_string, 3); printf("\n-----------%s & %s----------", string_3l, tmp_str ); for(k=0;k<20;k++) { //printf("@REAL - %s", c1[0].dna_codon); printf("@ %s", c1[k].dna_codon); int x; x = c1[k].dna_codon; pch = strtok(x, ","); while (pch != NULL) { printf("\n%d : %s with %s", k, string_3l, pch); count=strcmp(string_3l, pch); if(count==0) { strcat(short_slc, c1[k].slc); printf("\n==>%s", short_slc); } pch = strtok (NULL, " ,.-"); } } input_string = input_string+3; } printf("\nProtien sequence is : %s\n", short_slc); } INPUT : TAGTAG OUTPUT : If you see output of printf("\n-----------%s & %s----------", string_3l, tmp_str ); in both iterations, we found that values defined in structure are reduced. I want to know why structure reduces it or its my mistake? because I am stucked here

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• MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

  - by Pinal Dave

  Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• UPS and power strip interactions?

  - by chaos

  Sometimes I hear that you shouldn't plug (UPS brand X / any UPS) into (power strip brand X / any power strip) because of some interaction leading to poorly conditioned power, reduced battery life, massive explosions spattering the room with battery acid, and so on. Sometimes I hear that it's the power strip that you shouldn't plug into the UPS. What I haven't gotten is a clear idea of how reliable these recommendations are or how generally/specifically they apply. Can anyone speak precisely and non-urban-legendfully on these UPS and power strip interactions, if there are in fact ones worth thinking about?

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