Search Results

Search found 874 results on 35 pages for 'scalability'.

Page 1/35 | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

  • When to start thinking about scalability?

    - by Rits
    I'm having a funny but also terrible problem. I'm about to launch a new (iPhone) app. It's a turn-based multiplayer game running on my own custom backend. But I'm afraid to launch. For some reason, I think it might become something big and that its popularity will kill my poor lonely single server + MySQL database. On one hand I'm thinking that if it's growing, I'd better be prepared and have a scalable infrastructure already in place. On the other hand I just feel like getting it out into the world and see what happens. I often read stuff like "premature optimization is the root of all evil" or people saying that you should just build your killer game now, with the tools at hand, and worry about other stuff like scalability later. I'd love to hear some opinions on this from experts or people with experience with this. Thanks!

    Read the article

  • Software development, basics of design, conventions and scalability

    - by goce ribeski
    I need to improve my programming skills in order to achieve better scalability for the software I'm working on. Purpose is to learn the rules of adding new modules and features, so when it comes to maintaining existing ones there is some concept. So, I'm looking for a good book, tutorial or websites where I can continue to read about this. Currently, what I know and what I do is: to design relational database(3NF), make separate class for each table put that in MVC implement modular programming ...write code and hope for the best... I presume that next things I need to learn more deeply are: programming codex(naming, commenting, conventions...), organize functions building interfaces organizing custom made libraries, organizing API that I'm using, documenting, team work... ... At last what my job is, it does't need to affect your answer, PHP CodeIgniter developer.

    Read the article

  • Mochiweb's Scalability Features

    - by ErJab
    From all the articles I've read so far about Mochiweb, I've heard this over and over again that Mochiweb provides very good scalability. My question is, how exactly does Mochiweb get its scalability property? Is it from Erlang's inherent scalability properties or does Mochiweb have any additional code that explicitly enables it to scale well? Put another way, if I were to write a simple HTTP server in Erlang myself, with a simple 'loop' (recursive function) to handle requests, would it have the same level scalability as a simple web server built using the Mochiweb framework?

    Read the article

  • Managing scalability and availability with two servers running Apache Httpd, Apache Mina and MySQL

    - by celalo
    Hello, I am not a developer and I don't much experience with scalable server architectures. But I am in need of a highly available and scalable system for one of my projects. There is going to be two servers I am going to use for the time being. Both with 4 core CPUs and 8 GB RAM with RAID structures running CentOS 5.4. I will also have feature called "Failover IP" which enables to direct an IP address to another server within short time. The applications which will be run on the servers: There is going to be a Java application based on Apache Mina server for handling TCP requests from some hundreds of network devices where the devices are going to send request as much as one request per minute. Handling those requests, includes parsing the requests and inserting a few rows to the Database. Parsing requests before inserting data to the DB does take neglectable time. There is going to be MySQL server, as I stated above. Also there is going to be a PHP web application running on Apache Httpd Server which uses the same DB with the Java application. What I wish to have is to make use of those two servers at the most. I was imagining to have the servers identical, sharing the work load. MySQL could be a cluster maybe? And if some application fails or the whole machine goes down, the other will continue serving the requests seamlessly. Reminding that a "Failover IP" feature will be available for me to take advantage of. Also, It should be kept in mind that number of servers could increase in time, to meet the demand. What can you suggest? Which kind of tools I can make use of? Which kind of monitoring software (paid/unpaid) I have? Thanks in advance.

    Read the article

  • scalability: when to use CDN?

    - by ajsie
    i've read about CDN but dont know exactly what it is for. lets say i've got an international social network (text and image content), and it's growing in traffic from different countries, do i have use of CDN? the picture i got from the sources i've read is that it copy your content and put it in many servers spread out over the world so that users will fetch it from the nearest point. does this mean that every server has a copy of my mysql database and the image files? is this the proper way to make your web service available for the world? cause how else could you set up a servers through out the world, contacting hosting companies for each country?

    Read the article

  • Recording Available: Oracle ETPM Performance Forum: "Scalability", Wednesday March 21st, at 1pm EST - 4:30pm EST

    - by Rick Finley
    Attached is the recording URL last months Oracle ETPM Performance forum meeting, from Wednesday, March 21st, at 1pm EST to 2:30pm EST.  The topic was “Scalability".  The topic focuses on an overview of important Scalability concetps, scalability testing and troubleshooting, and ETPM scalability characteristics we have seen in our benchmark testing.   Meeting Recording Playback URL:  https://oracletalk.webex.com/oracletalk/ldr.php?AT=pb&SP=MC&rID=67420077&rKey=73798b44e06240dd 

    Read the article

  • Poco SocketReactor Scalability

    - by Genesis
    I have written a proxy server for Linux using Poco but have since been reading up on the various approaches to achieving TCP/IP server scalability. I will need the server to handle persistent connections (not HTTP traffic) with an upper limit of about 250 simultaneous connections. Each connection typically uses about 5-10Kb/sec and the best possible latency in handling traffic is crucial. As it stands I am using the Poco SocketReactor which uses the Reactor model with a select() call at its heart however I have had a read on the C10K problem as well as few other resources and it seems that using this approach might not be the best idea. I believe there is a test implementation in the Poco libs that uses poll() so this could be an option to improve things. Does anyone have any experience using a Poco SocketReactor and do you have any idea how well it might scale for my scenario? If it will not scale well, suggestions on alternatives would be appreciated.

    Read the article

  • php vs python. scalability

    - by Quamis
    Why is PHP considered more scalable than python? I've heard may times that one of the reasons PHP is "better" than python is that PHP is more easily scalable, and that Yahoo proves that(assumig Yahoo still uses PHP). Whats the difference between PHP and Python when it comes to scalability? -- edit -- well, i have no evidence, the question arose after a discussion with a friend. -- edit2 -- here: http://www.oreillynet.com/ruby/blog/2007/09/7%5Freasons%5Fi%5Fswitched%5Fback%5Fto%5Fp%5F1.html , even if this dosent say anything about scaling..

    Read the article

  • *Client* scalability for large numbers of remote web service calls

    - by Yuriy
    Hey Guys, I was wondering if you could share best practices and common mistakes when it comes to making large numbers of time-sensitive web service calls. In my case, I have a SOAP and an XML-RPC based web service to which I'm constantly making calls. I predict that this will soon become an issue as the number of calls per second will grow. On a higher level, I was thinking of batching those calls and submitting those to the web services every 100 ms. Could you share what else works? On a lower level side of the things, I use Apache Xml-Rpc client and standard javax.xml.soap.* packages for my client implementations. Are you aware of any client scalability related tricks/tips/warnings with these packages? Thanks in advance Yuriy

    Read the article

  • Scalability of Ruby on Rails versus PHP

    - by Daniel
    Can anyone comment on which is more scalable between RoR and PHP? I have heard that RoR is less scalable than PHP since RoR has a little more overhead with its MVC framework while PHP is more low level and lighter. This is a bit vague - can anyone explain better?

    Read the article

  • Azure Service Bus Scalability

    - by phebbar
    I am trying to understand how can I make Azure Service Bus Topic to be scaleable to handle 10,000 requests/second from more than 50 different clients. I found this article at Microsoft - http://msdn.microsoft.com/en-us/library/windowsazure/hh528527.aspx. This provides lot of good input to scale azure service bus like creating multiple message factories, sending and receiving asynchronously, doing batch send/receive. But all these input are from the publisher and subscriber client perspective. What if the node running the Topic can not handle the huge number of transactions? How do I monitor that? How do I have the Topic running on multiple nodes? Any input on that would be helpful. Also wondering if any one has done any capacity testing with Topic/Queue and I am eager to see those results... Thanks, Prasanna

    Read the article

  • SQL SERVER – Core Concepts – Elasticity, Scalability and ACID Properties – Exploring NuoDB an Elastically Scalable Database System

    - by pinaldave
    I have been recently exploring Elasticity and Scalability attributes of databases. You can see that in my earlier blog posts about NuoDB where I wanted to look at Elasticity and Scalability concepts. The concepts are very interesting, and intriguing as well. I have discussed these concepts with my friend Joyti M and together we have come up with this interesting read. The goal of this article is to answer following simple questions What is Elasticity? What is Scalability? How ACID properties vary from NOSQL Concepts? What are the prevailing problems in the current database system architectures? Why is NuoDB  an innovative and welcome change in database paradigm? Elasticity This word’s original form is used in many different ways and honestly it does do a decent job in holding things together over the years as a person grows and contracts. Within the tech world, and specifically related to software systems (database, application servers), it has come to mean a few things - allow stretching of resources without reaching the breaking point (on demand). What are resources in this context? Resources are the usual suspects – RAM/CPU/IO/Bandwidth in the form of a container (a process or bunch of processes combined as modules). When it is about increasing resources the simplest idea which comes to mind is the addition of another container. Another container means adding a brand new physical node. When it is about adding a new node there are two questions which comes to mind. 1) Can we add another node to our software system? 2) If yes, does adding new node cause downtime for the system? Let us assume we have added new node, let us see what the new needs of the system are when a new node is added. Balancing incoming requests to multiple nodes Synchronization of a shared state across multiple nodes Identification of “downstate” and resolution action to bring it to “upstate” Well, adding a new node has its advantages as well. Here are few of the positive points Throughput can increase nearly horizontally across the node throughout the system Response times of application will increase as in-between layer interactions will be improved Now, Let us put the above concepts in the perspective of a Database. When we mention the term “running out of resources” or “application is bound to resources” the resources can be CPU, Memory or Bandwidth. The regular approach to “gain scalability” in the database is to look around for bottlenecks and increase the bottlenecked resource. When we have memory as a bottleneck we look at the data buffers, locks, query plans or indexes. After a point even this is not enough as there needs to be an efficient way of managing such large workload on a “single machine” across memory and CPU bound (right kind of scheduling)  workload. We next move on to either read/write separation of the workload or functionality-based sharing so that we still have control of the individual. But this requires lots of planning and change in client systems in terms of knowing where to go/update/read and for reporting applications to “aggregate the data” in an intelligent way. What we ideally need is an intelligent layer which allows us to do these things without us getting into managing, monitoring and distributing the workload. Scalability In the context of database/applications, scalability means three main things Ability to handle normal loads without pressure E.g. X users at the Y utilization of resources (CPU, Memory, Bandwidth) on the Z kind of hardware (4 processor, 32 GB machine with 15000 RPM SATA drives and 1 GHz Network switch) with T throughput Ability to scale up to expected peak load which is greater than normal load with acceptable response times Ability to provide acceptable response times across the system E.g. Response time in S milliseconds (or agreed upon unit of measure) – 90% of the time The Issue – Need of Scale In normal cases one can plan for the load testing to test out normal, peak, and stress scenarios to ensure specific hardware meets the needs. With help from Hardware and Software partners and best practices, bottlenecks can be identified and requisite resources added to the system. Unfortunately this vertical scale is expensive and difficult to achieve and most of the operational people need the ability to scale horizontally. This helps in getting better throughput as there are physical limits in terms of adding resources (Memory, CPU, Bandwidth and Storage) indefinitely. Today we have different options to achieve scalability: Read & Write Separation The idea here is to do actual writes to one store and configure slaves receiving the latest data with acceptable delays. Slaves can be used for balancing out reads. We can also explore functional separation or sharing as well. We can separate data operations by a specific identifier (e.g. region, year, month) and consolidate it for reporting purposes. For functional separation the major disadvantage is when schema changes or workload pattern changes. As the requirement grows one still needs to deal with scale need in manual ways by providing an abstraction in the middle tier code. Using NOSQL solutions The idea is to flatten out the structures in general to keep all values which are retrieved together at the same store and provide flexible schema. The issue with the stores is that they are compromising on mostly consistency (no ACID guarantees) and one has to use NON-SQL dialect to work with the store. The other major issue is about education with NOSQL solutions. Would one really want to make these compromises on the ability to connect and retrieve in simple SQL manner and learn other skill sets? Or for that matter give up on ACID guarantee and start dealing with consistency issues? Hybrid Deployment – Mac, Linux, Cloud, and Windows One of the challenges today that we see across On-premise vs Cloud infrastructure is a difference in abilities. Take for example SQL Azure – it is wonderful in its concepts of throttling (as it is shared deployment) of resources and ability to scale using federation. However, the same abilities are not available on premise. This is not a mistake, mind you – but a compromise of the sweet spot of workloads, customer requirements and operational SLAs which can be supported by the team. In today’s world it is imperative that databases are available across operating systems – which are a commodity and used by developers of all hues. An Ideal Database Ability List A system which allows a linear scale of the system (increase in throughput with reasonable response time) with the addition of resources A system which does not compromise on the ACID guarantees and require developers to learn new paradigms A system which does not force fit a new way interacting with database by learning Non-SQL dialect A system which does not force fit its mechanisms for providing availability across its various modules. Well NuoDB is the first database which has all of the above abilities and much more. In future articles I will cover my hands-on experience with it. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

    Read the article

  • Quick ways to boost performance and scalability of ASP.NET, WCF and Desktop Clients

    - by oazabir
    There are some simple configuration changes that you can make on machine.config and IIS to give your web applications significant performance boost. These are simple harmless changes but makes a lot of difference in terms of scalability. By tweaking system.net changes, you can increase the number of parallel calls that can be made from the services hosted on your servers as well as on desktop computers and thus increase scalability. By changing WCF throttling config you can increase number of simultaneous calls WCF can accept and thus make most use of your hardware power. By changing ASP.NET process model, you can increase number of concurrent requests that can be served by your website. And finally by turning on IIS caching and dynamic compression, you can dramatically increase the page download speed on browsers and and overall responsiveness of your applications. Read the CodeProject article for more details. http://www.codeproject.com/KB/webservices/quickwins.aspx Please vote for me if you find the article useful.

    Read the article

  • 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

    Read the article

  • Nike Achieves Scalability and Performance with Oracle Coherence & Exadata

    - by Michelle Kimihira
    Today, we are featuring a customer interview with with Nicole Otto, Senior Director, Consumer Digital Tech at Nike who talks about how they achieved scalability and performance with Oracle Coherence and Oracle Exadata.    Additional Information Product Information on Oracle.com: Oracle Fusion Middleware Follow us on Twitter and Facebook Subscribe to our regular Fusion Middleware Newsletter

    Read the article

  • Learn More About the Scalability, Uptime, and Agility of MySQL Cluster

    - by Antoinette O'Sullivan
    Learn more about the uncompromising scalability, uptime, and agility of MySQL Cluster by taking the authentic MySQL Cluster training course. During this three day class, you will learn how to properly configure and manage the cluster nodes to ensure high availability, how to install the different nodes as well as get a better understanding of the internals of the cluster. Events currently on the schedule for this class include:  Location  Date  Delivery Language  Wein, Austria  4 February 2013  German London, England  12 June 2013   English  Rennes, France 26 February 2013   French  Hamburg, Germany 21 January 2013   German  Munich, Germany  10 June 2013 German   Stuttgart, Germany  26 March 2013  German  Budapest, Hungary  19 June 2013  Hungarian  Milan, Italy  4 February 2013  Italy  Warsaw, Poland  18 March 2013  Polish  Barcelona, Spain  4 March 2013  Spanish  Madrid, Spain 25 February 2013   Spanish Chicago, United States  27 March 2013   English  Reston, United States  6 February 2013  English  Jakarta, Indonesia 21 January 2013  English   Singapore 18 February 2013   English To register for an event or to see further details on this or other courses in the authentic MySQL curriculum, please go to http://oracle.com/education/mysql.

    Read the article

  • scalablity of Scala over Java

    - by Marcus
    I read an article that says Scala handles concurrency better than Java. http://www.theserverside.com/feature/Solving-the-Scalability-Paradox-with-Scala-Clojure-and-Groovy ...the scalability limitation is confined specifically to the Java programming language itself, but it is not a limitation of the Java platform as a whole... The scalability issues with Java aren't a new revelation. In fact, plenty of work has been done to address these very issues, with two of the most successful projects being the programming languages named Scala and Clojure... ...Scala is finding ways around the problematic thread and locking paradigm of the Java language... How is this possible? Doesn't Scala use Java's core libraries which brings all the threading and locking issues from Java to Scala?

    Read the article

  • YouTube Scalability Lessons

    - by Bertrand Matthelié
    @font-face { font-family: "Arial"; }@font-face { font-family: "Courier New"; }@font-face { font-family: "Wingdings"; }@font-face { font-family: "Calibri"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: "Times New Roman"; }h2 { margin: 12pt 0cm 3pt; page-break-after: avoid; font-size: 14pt; font-family: "Times New Roman"; font-style: italic; }a:link, span.MsoHyperlink { color: blue; text-decoration: underline; }a:visited, span.MsoHyperlinkFollowed { color: purple; text-decoration: underline; }span.Heading2Char { font-family: Calibri; font-weight: bold; font-style: italic; }div.Section1 { page: Section1; }ol { margin-bottom: 0cm; }ul { margin-bottom: 0cm; } Very interesting blog post by Todd Hoff at highscalability.com presenting “7 Years of YouTube Scalability Lessons in 30 min” based on a presentation from Mike Solomon, one of the original engineers at YouTube: …. The key takeaway away of the talk for me was doing a lot with really simple tools. While many teams are moving on to more complex ecosystems, YouTube really does keep it simple. They program primarily in Python, use MySQL as their database, they’ve stuck with Apache, and even new features for such a massive site start as a very simple Python program. That doesn’t mean YouTube doesn’t do cool stuff, they do, but what makes everything work together is more a philosophy or a way of doing things than technological hocus pocus. What made YouTube into one of the world’s largest websites? Read on and see... Stats @font-face { font-family: "Arial"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: "Times New Roman"; }div.Section1 { page: Section1; } 4 billion Views a day 60 hours of video is uploaded every minute 350+ million devices are YouTube enabled Revenue double in 2010 The number of videos has gone up 9 orders of magnitude and the number of developers has only gone up two orders of magnitude. 1 million lines of Python code Stack @font-face { font-family: "Arial"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: "Times New Roman"; }div.Section1 { page: Section1; } Python - most of the lines of code for YouTube are still in Python. Everytime you watch a YouTube video you are executing a bunch of Python code. Apache - when you think you need to get rid of it, you don’t. Apache is a real rockstar technology at YouTube because they keep it simple. Every request goes through Apache. Linux - the benefit of Linux is there’s always a way to get in and see how your system is behaving. No matter how bad your app is behaving, you can take a look at it with Linux tools like strace and tcpdump. MySQL - is used a lot. When you watch a video you are getting data from MySQL. Sometime it’s used a relational database or a blob store. It’s about tuning and making choices about how you organize your data. Vitess- a  new project released by YouTube, written in Go, it’s a frontend to MySQL. It does a lot of optimization on the fly, it rewrites queries and acts as a proxy. Currently it serves every YouTube database request. It’s RPC based. Zookeeper - a distributed lock server. It’s used for configuration. Really interesting piece of technology. Hard to use correctly so read the manual Wiseguy - a CGI servlet container. Spitfire - a templating system. It has an abstract syntax tree that let’s them do transformations to make things go faster. Serialization formats - no matter which one you use, they are all expensive. Measure. Don’t use pickle. Not a good choice. Found protocol buffers slow. They wrote their own BSON implementation, which is 10-15 time faster than the one you can download. ...Contiues. Read the blog Watch the video

    Read the article

  • MSDN Magazine: Patterns for High Availability, Scalability, and Computing Power with Windows Azure

    In this article, Joshy Joseph, a principal architect with Microsoft Services Managed Solutions Group, examines the typical cloud platform architecture and some common architectural patterns, along with their implementation on the Windows Azure offering from Microsoft....Did 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.

    Read the article

1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >