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  • DocumentDB - Another Azure NoSQL Storage Service

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2014/08/25/documentdb---another-azure-nosql-storage-service.aspxMicrosoft just released a bunch of new features for Azure on 22nd and one of them I was interested in most is DocumentDB, a document NoSQL database service on the cloud.   Quick Look at DocumentDB We can try DocumentDB from the new azure preview portal. Just click the NEW button and select the item named DocumentDB to create a new account. Specify the name of the DocumentDB, which will be the endpoint we are going to use to connect later. Select the capacity unit, resource group and subscription. In resource group section we can select which region our DocumentDB will be located. Same as other azure services select the same location with your consumers of the DocumentDB, for example the website, web services, etc.. After several minutes the DocumentDB will be ready. Click the KEYS button we can find the URI and primary key, which will be used when connecting. Now let's open Visual Studio and try to use the DocumentDB we had just created. Create a new console application and install the DocumentDB .NET client library from NuGet with the keyword "DocumentDB". You need to select "Include Prerelase" in NuGet Package Manager window since this library was not yet released. Next we will create a new database and document collection under our DocumentDB account. The code below created an instance of DocumentClient with the URI and primary key we just copied from azure portal, and create a database and collection. And it also prints the document and collection link string which will be used later to insert and query documents. 1: static void Main(string[] args) 2: { 3: var endpoint = new Uri("https://shx.documents.azure.com:443/"); 4: var key = "LU2NoyS2fH0131TGxtBE4DW/CjHQBzAaUx/mbuJ1X77C4FWUG129wWk2oyS2odgkFO2Xdif9/ZddintQicF+lA=="; 5:  6: var client = new DocumentClient(endpoint, key); 7: Run(client).Wait(); 8:  9: Console.WriteLine("done"); 10: Console.ReadKey(); 11: } 12:  13: static async Task Run(DocumentClient client) 14: { 15:  16: var database = new Database() { Id = "testdb" }; 17: database = await client.CreateDatabaseAsync(database); 18: Console.WriteLine("database link = {0}", database.SelfLink); 19:  20: var collection = new DocumentCollection() { Id = "testcol" }; 21: collection = await client.CreateDocumentCollectionAsync(database.SelfLink, collection); 22: Console.WriteLine("collection link = {0}", collection.SelfLink); 23: } Below is the result from the console window. We need to copy the collection link string for future usage. Now if we back to the portal we will find a database was listed with the name we specified in the code. Next we will insert a document into the database and collection we had just created. In the code below we pasted the collection link which copied in previous step, create a dynamic object with several properties defined. As you can see we can add some normal properties contains string, integer, we can also add complex property for example an array, a dictionary and an object reference, unless they can be serialized to JSON. 1: static void Main(string[] args) 2: { 3: var endpoint = new Uri("https://shx.documents.azure.com:443/"); 4: var key = "LU2NoyS2fH0131TGxtBE4DW/CjHQBzAaUx/mbuJ1X77C4FWUG129wWk2oyS2odgkFO2Xdif9/ZddintQicF+lA=="; 5:  6: var client = new DocumentClient(endpoint, key); 7:  8: // collection link pasted from the result in previous demo 9: var collectionLink = "dbs/AAk3AA==/colls/AAk3AP6oFgA=/"; 10:  11: // document we are going to insert to database 12: dynamic doc = new ExpandoObject(); 13: doc.firstName = "Shaun"; 14: doc.lastName = "Xu"; 15: doc.roles = new string[] { "developer", "trainer", "presenter", "father" }; 16:  17: // insert the docuemnt 18: InsertADoc(client, collectionLink, doc).Wait(); 19:  20: Console.WriteLine("done"); 21: Console.ReadKey(); 22: } the insert code will be very simple as below, just provide the collection link and the object we are going to insert. 1: static async Task InsertADoc(DocumentClient client, string collectionLink, dynamic doc) 2: { 3: var document = await client.CreateDocumentAsync(collectionLink, doc); 4: Console.WriteLine(await JsonConvert.SerializeObjectAsync(document, Formatting.Indented)); 5: } Below is the result after the object had been inserted. Finally we will query the document from the database and collection. Similar to the insert code, we just need to specify the collection link so that the .NET SDK will help us to retrieve all documents in it. 1: static void Main(string[] args) 2: { 3: var endpoint = new Uri("https://shx.documents.azure.com:443/"); 4: var key = "LU2NoyS2fH0131TGxtBE4DW/CjHQBzAaUx/mbuJ1X77C4FWUG129wWk2oyS2odgkFO2Xdif9/ZddintQicF+lA=="; 5:  6: var client = new DocumentClient(endpoint, key); 7:  8: var collectionLink = "dbs/AAk3AA==/colls/AAk3AP6oFgA=/"; 9:  10: SelectDocs(client, collectionLink); 11:  12: Console.WriteLine("done"); 13: Console.ReadKey(); 14: } 15:  16: static void SelectDocs(DocumentClient client, string collectionLink) 17: { 18: var docs = client.CreateDocumentQuery(collectionLink + "docs/").ToList(); 19: foreach(var doc in docs) 20: { 21: Console.WriteLine(doc); 22: } 23: } Since there's only one document in my collection below is the result when I executed the code. As you can see all properties, includes the array was retrieve at the same time. DocumentDB also attached some properties we didn't specified such as "_rid", "_ts", "_self" etc., which is controlled by the service.   DocumentDB Benefit DocumentDB is a document NoSQL database service. Different from the traditional database, document database is truly schema-free. In a short nut, you can save anything in the same database and collection if it could be serialized to JSON. We you query the document database, all sub documents will be retrieved at the same time. This means you don't need to join other tables when using a traditional database. Document database is very useful when we build some high performance system with hierarchical data structure. For example, assuming we need to build a blog system, there will be many blog posts and each of them contains the content and comments. The comment can be commented as well. If we were using traditional database, let's say SQL Server, the database schema might be defined as below. When we need to display a post we need to load the post content from the Posts table, as well as the comments from the Comments table. We also need to build the comment tree based on the CommentID field. But if were using DocumentDB, what we need to do is to save the post as a document with a list contains all comments. Under a comment all sub comments will be a list in it. When we display this post we just need to to query the post document, the content and all comments will be loaded in proper structure. 1: { 2: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 3: "title": "xxxxx", 4: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 5: "postedOn": "08/25/2014 13:55", 6: "comments": 7: [ 8: { 9: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 10: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 11: "commentedOn": "08/25/2014 14:00", 12: "commentedBy": "xxx" 13: }, 14: { 15: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 16: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 17: "commentedOn": "08/25/2014 14:10", 18: "commentedBy": "xxx", 19: "comments": 20: [ 21: { 22: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 23: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 24: "commentedOn": "08/25/2014 14:18", 25: "commentedBy": "xxx", 26: "comments": 27: [ 28: { 29: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 30: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 31: "commentedOn": "08/25/2014 18:22", 32: "commentedBy": "xxx", 33: } 34: ] 35: }, 36: { 37: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 38: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 39: "commentedOn": "08/25/2014 15:02", 40: "commentedBy": "xxx", 41: } 42: ] 43: }, 44: { 45: "id": "xxxxx-xxxxx-xxxxx-xxxxx", 46: "content": "xxxxx, xxxxxxxxx. xxxxxx, xx, xxxx.", 47: "commentedOn": "08/25/2014 14:30", 48: "commentedBy": "xxx" 49: } 50: ] 51: }   DocumentDB vs. Table Storage DocumentDB and Table Storage are all NoSQL service in Microsoft Azure. One common question is "when we should use DocumentDB rather than Table Storage". Here are some ideas from me and some MVPs. First of all, they are different kind of NoSQL database. DocumentDB is a document database while table storage is a key-value database. Second, table storage is cheaper. DocumentDB supports scale out from one capacity unit to 5 in preview period and each capacity unit provides 10GB local SSD storage. The price is $0.73/day includes 50% discount. For storage service the highest price is $0.061/GB, which is almost 10% of DocumentDB. Third, table storage provides local-replication, geo-replication, read access geo-replication while DocumentDB doesn't support. Fourth, there is local emulator for table storage but none for DocumentDB. We have to connect to the DocumentDB on cloud when developing locally. But, DocumentDB supports some cool features that table storage doesn't have. It supports store procedure, trigger and user-defined-function. It supports rich indexing while table storage only supports indexing against partition key and row key. It supports transaction, table storage supports as well but restricted with Entity Group Transaction scope. And the last, table storage is GA but DocumentDB is still in preview.   Summary In this post I have a quick demonstration and introduction about the new DocumentDB service in Azure. It's very easy to interact through .NET and it also support REST API, Node.js SDK and Python SDK. Then I explained the concept and benefit of  using document database, then compared with table storage.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Using HBase or Cassandra for a token server

    - by crippy
    I've been trying to figure out how to use HBase/Cassandra for a token system we're re-implementing. I can probably squeeze quite a lot more from MySQL, but it just seems it has come to clinging on to the wrong tool for the task just because we know it well. Eventually will hit a wall (like happened to us in other areas). Naturally I started looking into possible NoSQL solutions. The prominent ones (at least in terms of buzz) are HBase and Cassandra. The story is more or less like this: A user can send a gift other users. Each gift has a list of recipients or is public in which case limited by number or expiration date For each gift sent we generate some token that uniquely identifies that gift. For each gift we track the list of potential recipients and their current status relating to that gift (accepted, declinded etc). A user can request to see all his currently pending gifts A can request a list of users he has sent a gift to today (used to limit number of gifts sent) Required the ability to "dump" or "ignore" expired gifts (x day old gifts are considered expired) There are some other requirements but I believe the above covers the essentials. How would I go and model that using HBase or Cassandra? Well, the wall was performance. A few 10s of millions of records per day over 2 tables kept for 2 weeks (wish I could have kept it for more but there was no way). The response times kept getting slower and slower until eventually we had to start cutting down number of days we kept data. Caching helps here but it's not an ideal solution since a big part of the ops are updates. Also, as I hinted in my original post. We use MySQL extensively. We know exactly what it can and can't do both in naive implementations followed by native partitioning and finally by horizontally sharding our dataset on the application level to reside on multiple DB nodes. It can be done, but that's not really what I'm trying to get from this. I asked a very specific question about designing a solution using a NoSQL solution since it's very hard to find examples for designs out there. Brainlag, not trying to come off as rude. I actually appreciate it a lot that you are the only one who even bothered to respond. but I see it over and over again. People ask questions and others assume they have no idea what they're talking about and give an irrelevant answer. Ignore RDBMS please. The question is about nosql.

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  • Hadoop, NOSQL, and the Relational Model

    - by Phil Factor
    (Guest Editorial for the IT Pro/SysAdmin Newsletter)Whereas Relational Databases fit the world of commerce like a glove, it is useless to pretend that they are a perfect fit for all human endeavours. Although, with SQL Server, we’ve made great strides with indexing text, in processing spatial data and processing markup, there is still a problem in dealing efficiently with large volumes of ephemeral semi-structured data. Key-value stores such as Cassandra, Project Voldemort, and Riak are of great value for ephemeral data, and seem of equal value as a data-feed that provides aggregations to an RDBMS. However, the Document databases such as MongoDB and CouchDB are ideal for semi-structured data for which no fixed schema exists; analytics and logging are obvious examples. NoSQL products, such as MongoDB, tackle the semi-structured data problem with panache. MongoDB is designed with a simple document-oriented data model that scales horizontally across multiple servers. It doesn’t impose a schema, and relies on the application to enforce the data structure. This is another take on the old ‘EAV’ problem (where you don’t know in advance all the attributes of a particular entity) It uses a clever replica set design that allows automatic failover, and uses journaling for data durability. It allows indexing and ad-hoc querying. However, for SQL Server users, the obvious choice for handling semi-structured data is Apache Hadoop. There will soon be an ODBC Driver for Apache Hive .and an Add-in for Excel. Additionally, there are now two Hadoop-based connectors for SQL Server; the Apache Hadoop connector for SQL Server 2008 R2, and the SQL Server Parallel Data Warehouse (PDW) connector. We can connect to Hadoop process the semi-structured data and then store it in SQL Server. For one steeped in the culture of Relational SQL Databases, I might be expected to throw up my hands in the air in a gesture of contempt for a technology that was, judging by the overblown journalism on the subject, about to make my own profession as archaic as the Saggar makers bottom knocker (a potter’s assistant who helped the saggar maker to make the bottom of the saggar by placing clay in a metal hoop and bashing it). However, on the contrary, I find that I'm delighted with the advances made by the NoSQL databases in the past few years. Having the flow of ideas from the NoSQL providers will knock any trace of complacency out of the providers of Relational Databases and inspire them into back-fitting some features, such as horizontal scaling, with sharding and automatic failover into SQL-based RDBMSs. It will do the breed a power of good to benefit from all this lateral thinking.

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  • How best to merge/sort/page through tons of JSON arrays?

    - by Joshiatto
    Here's the scenario: Say you have millions of JSON documents stored as text files. Each JSON document is an array of "activity" objects, each of which contain a "created_datetime" attribute. What is the best way to merge/sort/filter/page through these activities via a web UI? For example, say we want to take a few thousand of the documents, merge them into a gigantic array, sort the array by the "created_datetime" attribute descending and then page through it 10 activities at a time. Also keep in mind that roughly 25% of these JSON documents are updated every day, and updates have to make it into the view within 5 minutes. My first thought is to parse all of the documents into an RDBMS table and then it would just be a simple query such as "select top 10 name, created_datetime from Activity where user_id=12345 order by created_datetime desc". Some have suggested I use NoSQL techniques such as hadoop or map/reduce instead. How exactly would this work? For more background, see: Why is NoSQL better for this scenario?

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  • Should I try to write simple key-value storage by myself?

    - by shabunc
    I need a key-value storage in a simplest form we can think of. Keys should be some fixed-length strings, values should be some texts. This key-value storage should have an HTTP-backed API. That's basically it. As you can see, there is no big difference between such storage and some web application with some upload functionality. The thing is - it'll take few hours (including tests and coffee drinking) to write something like this. "Something like this" will be fully under my control and can be tuned on demand. Should I, in this specific case, not try to reinvent bicycles? Is it better to use some of existing NoSQL solutions. If yes, which one exactly? If, say, I'd needed something SQL-like, I won't ask and won't try to write something by myself. But with NoSQL I just don't know what is adequate and what is not.

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  • What makes Java so suitable for writing NoSQL Databases

    - by good_computer
    Looking at this page that aggregates the current NoSQL landscape, one can see that the majority of these projects are written in Java. Databases are complex systems software dealing with the file system, and so C/C++ would be a better choice than Java for this. (that's my thinking which might be flawed) Secondly, databases deal with transferring large amounts of data from disk to RAM -- which they call a working set. The JVM takes non-trivial amount of RAM for it's own purpose -- so it would be more efficient to use a platform that leaves lots of memory for data instead of hogging it for its own operations. The major relational databases are ALL written in C/C++ MySQL C, C++ Oracle Assembler, C, C++ SQL Server C++ PostgreSQL C SQLite C So what makes Java so popular in NoSQL world.

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  • TOTD #166: Using NoSQL database in your Java EE 6 Applications on GlassFish - MongoDB for now!

    - by arungupta
    The Java EE 6 platform includes Java Persistence API to work with RDBMS. The JPA specification defines a comprehensive API that includes, but not restricted to, how a database table can be mapped to a POJO and vice versa, provides mechanisms how a PersistenceContext can be injected in a @Stateless bean and then be used for performing different operations on the database table and write typesafe queries. There are several well known advantages of RDBMS but the NoSQL movement has gained traction over past couple of years. The NoSQL databases are not intended to be a replacement for the mainstream RDBMS. As Philosophy of NoSQL explains, NoSQL database was designed for casual use where all the features typically provided by an RDBMS are not required. The name "NoSQL" is more of a category of databases that is more known for what it is not rather than what it is. The basic principles of NoSQL database are: No need to have a pre-defined schema and that makes them a schema-less database. Addition of new properties to existing objects is easy and does not require ALTER TABLE. The unstructured data gives flexibility to change the format of data any time without downtime or reduced service levels. Also there are no joins happening on the server because there is no structure and thus no relation between them. Scalability and performance is more important than the entire set of functionality typically provided by an RDBMS. This set of databases provide eventual consistency and/or transactions restricted to single items but more focus on CRUD. Not be restricted to SQL to access the information stored in the backing database. Designed to scale-out (horizontal) instead of scale-up (vertical). This is important knowing that databases, and everything else as well, is moving into the cloud. RBDMS can scale-out using sharding but requires complex management and not for the faint of heart. Unlike RBDMS which require a separate caching tier, most of the NoSQL databases comes with integrated caching. Designed for less management and simpler data models lead to lower administration as well. There are primarily three types of NoSQL databases: Key-Value stores (e.g. Cassandra and Riak) Document databases (MongoDB or CouchDB) Graph databases (Neo4J) You may think NoSQL is panacea but as I mentioned above they are not meant to replace the mainstream databases and here is why: RDBMS have been around for many years, very stable, and functionally rich. This is something CIOs and CTOs can bet their money on without much worry. There is a reason 98% of Fortune 100 companies run Oracle :-) NoSQL is cutting edge, brings excitement to developers, but enterprises are cautious about them. Commercial databases like Oracle are well supported by the backing enterprises in terms of providing support resources on a global scale. There is a full ecosystem built around these commercial databases providing training, performance tuning, architecture guidance, and everything else. NoSQL is fairly new and typically backed by a single company not able to meet the scale of these big enterprises. NoSQL databases are good for CRUDing operations but business intelligence is extremely important for enterprises to stay competitive. RDBMS provide extensive tooling to generate this data but that was not the original intention of NoSQL databases and is lacking in that area. Generating any meaningful information other than CRUDing require extensive programming. Not suited for complex transactions such as banking systems or other highly transactional applications requiring 2-phase commit. SQL cannot be used with NoSQL databases and writing simple queries can be involving. Enough talking, lets take a look at some code. This blog has published multiple blogs on how to access a RDBMS using JPA in a Java EE 6 application. This Tip Of The Day (TOTD) will show you can use MongoDB (a document-oriented database) with a typical 3-tier Java EE 6 application. Lets get started! The complete source code of this project can be downloaded here. Download MongoDB for your platform from here (1.8.2 as of this writing) and start the server as: arun@ArunUbuntu:~/tools/mongodb-linux-x86_64-1.8.2/bin$./mongod./mongod --help for help and startup optionsSun Jun 26 20:41:11 [initandlisten] MongoDB starting : pid=11210port=27017 dbpath=/data/db/ 64-bit Sun Jun 26 20:41:11 [initandlisten] db version v1.8.2, pdfile version4.5Sun Jun 26 20:41:11 [initandlisten] git version:433bbaa14aaba6860da15bd4de8edf600f56501bSun Jun 26 20:41:11 [initandlisten] build sys info: Linuxbs-linux64.10gen.cc 2.6.21.7-2.ec2.v1.2.fc8xen #1 SMP Fri Nov 2017:48:28 EST 2009 x86_64 BOOST_LIB_VERSION=1_41Sun Jun 26 20:41:11 [initandlisten] waiting for connections on port 27017Sun Jun 26 20:41:11 [websvr] web admin interface listening on port 28017 The default directory for the database is /data/db and needs to be created as: sudo mkdir -p /data/db/sudo chown `id -u` /data/db You can specify a different directory using "--dbpath" option. Refer to Quickstart for your specific platform. Using NetBeans, create a Java EE 6 project and make sure to enable CDI and add JavaServer Faces framework. Download MongoDB Java Driver (2.6.3 of this writing) and add it to the project library by selecting "Properties", "LIbraries", "Add Library...", creating a new library by specifying the location of the JAR file, and adding the library to the created project. Edit the generated "index.xhtml" such that it looks like: <h1>Add a new movie</h1><h:form> Name: <h:inputText value="#{movie.name}" size="20"/><br/> Year: <h:inputText value="#{movie.year}" size="6"/><br/> Language: <h:inputText value="#{movie.language}" size="20"/><br/> <h:commandButton actionListener="#{movieSessionBean.createMovie}" action="show" title="Add" value="submit"/></h:form> This page has a simple HTML form with three text boxes and a submit button. The text boxes take name, year, and language of a movie and the submit button invokes the "createMovie" method of "movieSessionBean" and then render "show.xhtml". Create "show.xhtml" ("New" -> "Other..." -> "Other" -> "XHTML File") such that it looks like: <head> <title><h1>List of movies</h1></title> </head> <body> <h:form> <h:dataTable value="#{movieSessionBean.movies}" var="m" > <h:column><f:facet name="header">Name</f:facet>#{m.name}</h:column> <h:column><f:facet name="header">Year</f:facet>#{m.year}</h:column> <h:column><f:facet name="header">Language</f:facet>#{m.language}</h:column> </h:dataTable> </h:form> This page shows the name, year, and language of all movies stored in the database so far. The list of movies is returned by "movieSessionBean.movies" property. Now create the "Movie" class such that it looks like: import com.mongodb.BasicDBObject;import com.mongodb.BasicDBObject;import com.mongodb.DBObject;import javax.enterprise.inject.Model;import javax.validation.constraints.Size;/** * @author arun */@Modelpublic class Movie { @Size(min=1, max=20) private String name; @Size(min=1, max=20) private String language; private int year; // getters and setters for "name", "year", "language" public BasicDBObject toDBObject() { BasicDBObject doc = new BasicDBObject(); doc.put("name", name); doc.put("year", year); doc.put("language", language); return doc; } public static Movie fromDBObject(DBObject doc) { Movie m = new Movie(); m.name = (String)doc.get("name"); m.year = (int)doc.get("year"); m.language = (String)doc.get("language"); return m; } @Override public String toString() { return name + ", " + year + ", " + language; }} Other than the usual boilerplate code, the key methods here are "toDBObject" and "fromDBObject". These methods provide a conversion from "Movie" -> "DBObject" and vice versa. The "DBObject" is a MongoDB class that comes as part of the mongo-2.6.3.jar file and which we added to our project earlier.  The complete javadoc for 2.6.3 can be seen here. Notice, this class also uses Bean Validation constraints and will be honored by the JSF layer. Finally, create "MovieSessionBean" stateless EJB with all the business logic such that it looks like: package org.glassfish.samples;import com.mongodb.BasicDBObject;import com.mongodb.DB;import com.mongodb.DBCollection;import com.mongodb.DBCursor;import com.mongodb.DBObject;import com.mongodb.Mongo;import java.net.UnknownHostException;import java.util.ArrayList;import java.util.List;import javax.annotation.PostConstruct;import javax.ejb.Stateless;import javax.inject.Inject;import javax.inject.Named;/** * @author arun */@Stateless@Namedpublic class MovieSessionBean { @Inject Movie movie; DBCollection movieColl; @PostConstruct private void initDB() throws UnknownHostException { Mongo m = new Mongo(); DB db = m.getDB("movieDB"); movieColl = db.getCollection("movies"); if (movieColl == null) { movieColl = db.createCollection("movies", null); } } public void createMovie() { BasicDBObject doc = movie.toDBObject(); movieColl.insert(doc); } public List<Movie> getMovies() { List<Movie> movies = new ArrayList(); DBCursor cur = movieColl.find(); System.out.println("getMovies: Found " + cur.size() + " movie(s)"); for (DBObject dbo : cur.toArray()) { movies.add(Movie.fromDBObject(dbo)); } return movies; }} The database is initialized in @PostConstruct. Instead of a working with a database table, NoSQL databases work with a schema-less document. The "Movie" class is the document in our case and stored in the collection "movies". The collection allows us to perform query functions on all movies. The "getMovies" method invokes "find" method on the collection which is equivalent to the SQL query "select * from movies" and then returns a List<Movie>. Also notice that there is no "persistence.xml" in the project. Right-click and run the project to see the output as: Enter some values in the text box and click on enter to see the result as: If you reached here then you've successfully used MongoDB in your Java EE 6 application, congratulations! Some food for thought and further play ... SQL to MongoDB mapping shows mapping between traditional SQL -> Mongo query language. Tutorial shows fun things you can do with MongoDB. Try the interactive online shell  The cookbook provides common ways of using MongoDB In terms of this project, here are some tasks that can be tried: Encapsulate database management in a JPA persistence provider. Is it even worth it because the capabilities are going to be very different ? MongoDB uses "BSonObject" class for JSON representation, add @XmlRootElement on a POJO and how a compatible JSON representation can be generated. This will make the fromXXX and toXXX methods redundant.

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  • How do you decide what kind of database to use?

    - by Jason Baker
    I really dislike the name "NoSQL", because it isn't very descriptive. It tells me what the databases aren't where I'm more interested in what the databases are. I really think that this category really encompasses several categories of database. I'm just trying to get a general idea of what job each particular database is the best tool for. A few assumptions I'd like to make (and would ask you to make): Assume that you have the capability to hire any number of brilliant engineers who are equally experienced with every database technology that has ever existed. Assume you have the technical infrastructure to support any given database (including available servers and sysadmins who can support said database). Assume that each database has the best support possible for free. Assume you have 100% buy-in from management. Assume you have an infinite amount of money to throw at the problem. Now, I realize that the above assumptions eliminate a lot of valid considerations that are involved in choosing a database, but my focus is on figuring out what database is best for the job on a purely technical level. So, given the above assumptions, the question is: what jobs are each database (including both SQL and NoSQL) the best tool for and why?

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

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

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  • How to start with NOSQL using .net programming languages?

    - by Amr ElGarhy
    just was reading this article http://highscalability.com/blog/2010/3/23/digg-4000-performance-increase-by-sorting-in-php-rather-than.html And found this nice article http://wiki.apache.org/cassandra/DataModel I just want to know as a .net developer how to deal with NOSQL, and somethings like cassandra. I found that cassandra is apache product, is there something like this in microsoft world? or articles to know how to deal with cassandra using .net?

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  • Which third party tools/library are available for NoSQL databases?

    - by Horcrux7
    I know that NoSQL databases are very new. I am also new in this point. But exist already tools/libraries to make the life easer like for SQL databases? I think on tools for managing, maintaining, viewing or reporting of the data. There can also be libraries for easer working with the database or an abstract database layer to change the database later. I would prefer Java libraries but also other are interesting.

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

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

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  • Open Source NoSQL Databases Ramp Up

    <b>Database Journal:</b> "For most of the Web era, Relational Database Management Systems (RDBMS) based on SQL have dominated the database landscape. But over the course of the last year, a new approach has begun to take hold known as NoSQL, offering an alternative to the traditional RDBMS."

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  • NoSQL is not about object databases

    - by Bertrand Le Roy
    NoSQL as a movement is an interesting beast. I kinda like that it’s negatively defined (I happen to belong myself to at least one other such a-community). It’s not in its roots about proposing one specific new silver bullet to kill an old problem. it’s about challenging the consensus. Actually, blindly and systematically replacing relational databases with object databases would just replace one set of issues with another. No, the point is to recognize that relational databases are not a universal answer -although they have been used as one for so long- and recognize instead that there’s a whole spectrum of data storage solutions out there. Why is it so hard to recognize, by the way? You are already using some of those other data storage solutions every day. Let me cite a few: The file system Active Directory XML / JSON documents The Web e-mail Logs Excel files EXIF blobs in your photos Relational databases And yes, object databases It’s just a fact of modern life. Notice by the way that most of the data that you use every day is unstructured and thus mostly unsuitable for relational storage. It really is more a matter of recognizing it: you are already doing NoSQL. So what happens when for any reason you need to simultaneously query two or more of these heterogeneous data stores? Well, you build an index of sorts combining them, and that’s what you query instead. Of course, there’s not much distance to travel from that to realizing that querying is better done when completely separated from storage. So why am I writing about this today? Well, that’s something I’ve been giving lots of thought, on and off, over the last ten years. When I built my first CMS all that time ago, one of the main problems my customers were facing was to manage and make sense of the mountain of unstructured data that was constituting most of their business. The central entity of that system was the file system because we were dealing with lots of Word documents, PDFs, OCR’d articles, photos and static web pages. We could have stored all that in SQL Server. It would have worked. Ew. I’m so glad we didn’t. Today, I’m working on Orchard (another CMS ;). It’s a pretty young project but already one of the questions we get the most is how to integrate existing data. One of the ideas I’ll be trying hard to sell to the rest of the team in the next few months is to completely split the querying from the storage. Not only does this provide great opportunities for performance optimizations, it gives you homogeneous access to heterogeneous and existing data sources. For free.

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  • Introduction à la base de données NoSQL Cassandra, par Khanh Tuong Maudoux et François Ostyn

    La société So@t, société d'ingénierie et de conseil en informatique vous propose un article sur Cassandra.Il s'agit plus précisément d'un retour de la présentation de Nicolas Romanetti, co-fondateur de la société Jaxio qui a présenté lors de Devoxx France 2012 la base de données NoSQL Open Source Cassandra, faisant partie du projet Apache. http://soat.developpez.com/articles/cassandra/ Vous pouvez profiter de ce message pour partager vos commentaires. Mickael...

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  • NoSQL MongoDb overview

    In the current software industry that works around design patterns and OOPs there is a constant battle in converting the data from the database into the objects in the object graph and vice versa. MongoDb is a NoSQL database.  read moreBy prim sDid you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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