With Tungsten Replicator Continuent is trying to deliver a better master/slave replication system. Their goal: scalability, reliability with seamless failover, no performance loss. From their website: The Tungsten Replicator implements open source database-neutral master/slave replication. Master/slave replication is a highly flexible technology that can solve a wide variety of problems including the following: * Availability - Failing over to a slave database if your master database dies * Performance Scaling - Spreading reads across many copies of data * Cross-Site Clustering - Maintaining active database replicas across WANs * Change Data Capture - Extracting changes to load data warehouses or update other systems * Zero Downtime Upgrade - Performing upgrades on a slave server which then becomes the master The Tungsten Replicator architecture is flexible and designed to support addition of new databases easily. It includes pluggable extractor and applier modules to help transfer data from master to slave. The Replicator is designed to include a number of specialized features designed to improve its usefulness for particular problems like availability. * Replicated changes have transaction IDs and are stored in a transaction history log that is identical for each server. This feature allows masters and slaves to exchange roles easily. * Smooth procedures for planned and unplanned failover. * Built-in consistency check tables and events allow users to check consistency between tables without stopping replication or applications. * Support for statement as well as row replication. * Hooks to allow data transformations when replicating between different database types. Tungsten Replicator is not a toy. It is designed to allow commercial construction of robust database cluster
We will be developing an RIA that will have a lot of database access. Think something like a QuickBooks but with about 50 transactions entered per hour per user. Users will be in the system for 7 to 9 hours a day and there will be around 20,000 users, all logged in at the same time. Reporting will be done just like a QuickBooks style app plus a lot of extra things you don't do in QuickBooks. Our operations is familiar with W2003 Server and MS SQL Server so they are recommending we stick with that. I originally requested Linux and PostgreSQL. How far can a single database server get me? If we have a 4 processor, 8 core, 128gb server, how far am I going to get before I need to shard or do something else? I know there are a lot of factors involved but in general for this size of a site, what should the strategy be? I've read almost all articles on this website but most of the applications are not RIA type of apps with this type of usage or they are architectures for sites with millions of users which we also won't have.
Disco is an open-source implementation of the MapReduce framework for distributed computing. It was started at Nokia Research Center as a lightweight framework for rapid scripting of distributed data processing tasks. The Disco core is written in Erlang. The MapReduce jobs in Disco are natively described as Python programs, which makes it possible to express complex algorithmic and data processing tasks often only in tens of lines of code.
I came across an interesting study about who are the leaders in open source content management systems market in the year of 2008. The study was just released to the public and it was conducted by Ric Sheves from Water & Stone web development company. At 50 pages, there is a significant amount of data in this study that should be of use to developers or to anyone who is looking to commit to a web publishing system (also known as a Content Management System). Read the entire article about who the open source content management systems market leader is for 2008 at MyTestBox.com - web software reviews, news, tips & tricks.
Kim Nash in an interview with Jonathan Heiliger, Facebook VP of technical operations, provides some juicy details on how Facebook handles operations. Operations is one of those departments everyone runs differently as it is usually an ontogeny recapitulates phylogeny situation. With 2,000 databases, 25 terabytes of cache, 90 million active users, and 10,000 servers you know Facebook has some serious operational issues. What are some of Facebook's secrets to better operations?
It sounds like a relatively fun environment for pushing software live. Getting software moved into production is often harder than the original coding and testing. Now I know what you are thinking. You somehow managed to procure the ssh login. So just login remotely and do the install yourself! Nobody will know. Oh so tempting. But it's not really good corporate citizenship. And you just might screw up, then there will be some esplaining to do.
Emphasing frequent releases and gutsy release policies makes it actually seem like someone is supporting developers instead of treating them like their software carries the plague. Data centers are often treated like quarantine stations and developers are treated like asymptomatic carriers of some unknown virulent disease. To be safe nothing should ever change, but that's not an attitude that makes things better. Nice to see that recognized.
To setup or not to setup a separate operations group? Facebook says "to be" and creates a seperate group. Amazon says "not to be" and has developers support their own software. Secretly I think Amazon gets better results by requiring developers to support their own software. Knowing it may be you getting the "It's Down!" call gives one proper perspective. But I like not being on call and I think most developers agree. Plus the idea "following the sun" to get 24 hour support is a smart idea.
Hi everyone, I'm researching on Scalability for a college paper, and found this site great, but it has too many tips, articles and the like, but I can't see a hierarchical organization of subjects, I would need something like a checklist of things or fields, or technologies to take into account when assesing scalability. So far I've identified these: - Hardware scalability: - scale out - scale up - Cache What types of cache are there? app-level, os-level, network-level, I/O-level? - Load Balancing - DB Clustering Am I missing something important? (I'm sure I am) I don't expect you to give a lecture here, but maybe point some things out, give me some useful links... Thanks!
I found the discussion of the available bandwidth of tree vs higher dimensional virtual networks topologies quite, to quote Spock, fascinating: A mathematical analysis by Ritter (2002) (one of the original developers of Napster) presented a detailed numerical argument demonstrating that the Gnutella network could not scale to the capacity of its competitor, the Napster network. Essentially, that model showed that the Gnutella network is severely bandwidth-limited long before the P2P population reaches a million peers. In each of these previous studies, the conclusions have overlooked the intrinsic bandwidth limits of the underlying topology in the Gnutella network: a Cayley tree (Rains and Sloane 1999) (see Sect. 9.4 for the definition). Trees are known to have lower aggregate bandwidth than higher dimensional topologies, e.g., hypercubes and hypertori. Studies of interconnection topologies in the literature have tended to focus on hardware implementations (see, e.g., Culler et al. 1996; Buyya 1999), which are generally limited by the cost of the chips and wires to a few thousand nodes. P2P networks, on the other hand, are intended to support from hundreds of thousands to millions of simultaneous peers, and since they are implemented in software, hyper-topologies are relatively unfettered by the economics of hardware. In this chapter, we analyze the scalability of several alternative topologies and compare their throughput up to 2–3 million peers. The virtual hypercube and the virtual hypertorus offer near-linear scalable bandwidth subject to the number of peer TCP/IP connections that can be simultaneously kept open.
ScaleOut StateServer is an in-memory distributed cache across a server farm or compute grid. Unlike middleware vendors, StateServer is aims at being a very good data cache, it doesn't try to handle job scheduling as well. StateServer is what you might get when you take Memcached and merge in all the value added distributed caching features you've ever dreamed of. True, Memcached is free and ScaleOut StateServer is very far from free, but for those looking a for a satisfying out-of-the-box experience, StateServer may be just the caching solution you are looking for. Yes, "solution" is one of those "oh my God I'm going to pay through the nose" indicator words, but it really applies here. Memcached is a framework whereas StateServer has already prepackaged most features you would need to add through your own programming efforts. Why use a distributed cache? Because it combines the holly quadrinity of computing: better performance, linear scalability, high availability, and fast application development. Performance is better because data is accessed from memory instead of through a database to a disk. Scalability is linear because as more servers are added data is transparently load balanced across the servers so there is an automated in-memory sharding. Availability is higher because multiple copies of data are kept in memory and the entire system reroutes on failure. Application development is faster because there's only one layer of software to deal with, the cache, and its API is simple. All the complexity is hidden from the programmer which means all a developer has to do is get and put data. StateServer follows the RAM is the new disk credo. Memory is assumed to be the system of record, not the database. If you want data to be stored in a database and have the two kept in sync, then you'll have to add that layer yourself. All the standard memcached techniques should work as well for StateServer. Consider however that a database layer may not be needed. Reliability is handled by StateServer because it keeps multiple data copies, reroutes on failure, and has an option for geographical distribution for another layer of added safety. Storing to disk wouldn't make you any safer. Via email I asked them a few questions. The key question was how they stacked up against Memcached? As that is surely one of the more popular challenges they would get in any sales cycle, I was very curious about their answer. And they did a great job differentiation themselves. What did they say? First, for an in-depth discussion of their technology take a look ScaleOut Software Technology, but here a few of the highlights:
Why use ScaleOut StateServer instead of Memcached?I've [Dan McMillan, VP Sales] included some data points below based on our current understanding of the Memcached product. We don't use and haven't tested Memcached internally, so this comparison is based in part upon our own investigations and in part what we are hearing from our own customers during their evaluation and comparisons. We are aware that Memcached is successfully being used on many large, high volume sites. We believe strong demand for ScaleOut is being driven by companies that need a ready-to-deploy solution that provides advanced features and just works. We also hear that Memcached is often seen as a low cost solution in the beginning, but development and ongoing management costs sometimes far exceed our licensing fees. What sets ScaleOut apart from Memcached (and other competing solutions) is that ScaleOut was architected from the ground up to be a fully integrated and automated caching solution. ScaleOut offers both scalability and high availability, where our competitors typically provide only one or the other. ScaleOut is considered a full-featured, plug-n-play caching solution at a very reasonable price point, whereas we view Memcached as a framework in which to build your own caching solution. Much of the cost in choosing Memcached will be in development and ongoing management. ScaleOut works right out of the box. I asked ScaleOut Software founder and chief architect, Bill Bain for his thoughts on this. He is a long-time distributed caching and parallel computing expert and is the architect of ScaleOut StateServer. He had several interesting points to share about creating a distributed cache by using an open source (i.e. build it yourself) solution versus ScaleOut StateServer. First, he estimates that it would take considerable time and effort for engineers to create a distributed cache that has ScaleOut StateServer's fundamental capabilities. The primary reason is that the open source method only gives you a starting point, but it does not include most capabilities that are needed in a distributed cache. In fact, there is no built-in scalability or availability, the two principal benefits of a distributed cache. Here is some of the functionality that you would have to build:
Do you find yourself in competition with the likes of Terracotta, GridGain, GridSpaces, and Coherence type products?Our ScaleOut technology has previously been targeted to the ASP.Net space. Now that we are entering the Java/Linux space, we will be competing with companies like the ones you mentioned above, which are mainly Java/Linux focused as well. We initially got our start with distributed caching for ecommerce applications, but grid computing seems to be a strong growth area for us as well. We are now working with some large Wall Street firms on grid computing projects that involve some (very large) grid operations. I would like to reiterate that we are very focused on data caching only. We don't try to do job scheduling or other grid computing tasks, but we do improve performance and availability for those tasks via our distributed data cache.
What architectures your customers are using with your GeoServer product?A. GeoServer is a newer, add-on product that is designed to replicate the contents of two or more geographically separated ScaleOut object stores (caches). Typically a customer might use GeoServer to replicate object data between a primary data center site and a DR site. GeoServer facilitates continuous (async.) replication between sites, so if site A goes offline, the other site B is immediately available to handle the workload. Our ScaleOut technology offers 3 primary benefits: Scalability, performance & high availability. From a single web farm perspective, ScaleOut provides high availability by making either 1 or 2 (this is configurable) replica copies of each master object and storing the replica on an alternate host server in the farm. ScaleOut provides uniform access to the object from any server, and protects the object in the case of a server failure. With GeoServer, these benefits are extended across multiple sites. It is true that distributed caches typically hold temporary, fast-changing data, but that data can still be very critical to ecommerce, or grid computing applications. Loss of this data during a server failure, worker process recycle or even a grid computation process is unacceptable. We improve performance by keeping the data in-memory, while still maintaining high availability.
Hi, we've got a web application, which runs without the common standalone application servers like tomcat or jboss, rather it runs with an embedded jetty server. Now we are planing to run instances of this application on multiple machines, with a load balancer serving the requests. The big question is: is there a common scenario on how to update these applications? Lets think of 10 instances on 10 machines (one instance per machine), where we want to update each of these applications version. The brute force approach would be, to stop all instances, update and then restart it. This is a lot of manual work ;) Another problem is down-time: so someone must only shutdown one server after another, but then there are multiple application versions around. Can someone please provide us with a hint for this problem? Perhaps papers, tools or something like that? Thanks a lot :)
Looks interesting... Abstract: Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Nonuniform bandwidth among data center nodes complicates application design and limits overall system performance. In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today’s higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.