Database index can improve query performance by a factor of thousands to millions times. Scalability Testing. This strategy is even more effective when combining with Cloud computing as adding more VM instances into the farm is just an API call. Facebook split their databases not only by tables, but also by rows. We have eliminated two single point of failure in the system. But you don't need to use the result until at a much later stage of your process. Let’s again look it up on Wikipedia: In computing, a cache /ˈkæʃ/ kash, is a hardware or software component that stores data so future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation, or the duplicate of data stored elsewhere. This usually mean the steps of execution should be relatively independent of each other. This is common for static media content. SPOFs are undesirable in any system with a goal of high availability or reliability, be it a business practice, software application, or other industrial system. I believe you’ve heard about caching and use it a lot in your projects. (e.g. Unix is designed to be extremely scalable; I've worked on systems with 12 or more processors, multiple clusters, etc. Use efficient algorithms and data structure. In fact, the query running time can get from O(n) in a full table scan, down to O(logn) in a indexed table, where n is the number of records in the table. And as long as you can scale to handle larger number of users it’s ok to have multiple single points of failures as well. There are many types of caching, all serve the same purpose: to make future requests for that data can be served faster. This part is more on keeping our system high available than enabling it to handle more requests. Improper use of caching may cause serious problems. If the master goes down, the mirror server will stand up to replace the master to make sure the web servers can still accessing the database. Back to our example, to eliminate the Single Point of Failure at the database, we can user the mirroring function of the database. This type of caching is often used in large systems that data are read a lot more often than written. Share with us in the comment! As long as we can provide a better I/O bandwidth, the whole system may benefit. If a web server breaks down, the reverse proxy will detect and route all traffic to the remaining one. This capability allows computer equipment and software programs to grow over time, rather than needing to be replaced. This article can no way cover everything in designing scalable systems. If Bob buys something on the website, the system may record that Alice buys it. Bob can now see Alice’s order history. A system (hardware + software) whose performance improves after adding more nodes, proportionally to the number of nodes added, is said to be a scalable system. It is worth noting if the underlying manycore processor architecture is scalable but the software is not, and vice versa. At first, we have our web application and our database on the same server. A wrong concurrent access model can have huge impact in your system's scalability. The plans that we prepare for our application to grow, or in other words, scale, with the growth of users or transactions, are called the scaling strategies. On the other hand, we can reduce the I/O request by optimizing database queries and indexes. Weâve learned to always design for the âmanyâ case. The idea is to create many copies of contents that are distributed geographically across servers. A system whose performance improves after adding hardware, proportionally to the capacity added, is said to be a scalable system. Lots of businesses may depend on our system. — Royans K Tharakan. What if that number of page views gets doubled by tomorrow, then ten times larger by next week, and then, a thousand times larger by the end of next month? If you can’t split it, you can’t scale it. When the traffic grows, we plans to buy a larger server. This first article is not intended to go into too much detail, but instead to give you a rough idea of what should be considered when designing a scalable web application. Nowadays, web applications are becoming more and more popular. Beside the above mentioned classification, we should also take into consideration whether our data is read-heavy or write-heavy. In polling mode, the call itself will return a "future" handle immediately. In our e-commerce system, we have 5 web servers and 1 database server, each hosted on a separate physical server instance. Let’s look at an example. Another common mistake is caching web page responses on a reverse proxy, including the response header information. The database server in this case is also a Single Point of Failure. We briefly looked at Twitterâs home timelines as an example of describing load, and response time percentiles as a way of measuring performance. Knowing the basics and the methodologies, we can do the analysis and find the most suitable solution, and prioritize what can be done first that will results in the biggest impact. Meanwhile, Bob also wants to browse product A. Dimension tables contain descriptive attributes about the business. After optimizing the database, our system can handle another million users, but looking at the resources usage, we now see that the web servers are using 99% of CPU all the time, while the database only uses less than 10% of CPU. There’s a lot more about caching, but that would be out of the scope of this blog. We setup a reverse proxy to load balance requests between the two web server. The call itself will return immediately before the actually work is done at the server side. In this design, the two major table types are dimension and fact tables. If everytime a user create a status, we have to notify all of the friends about that new activity in one database transaction, no system would be above to handle the workload, and even if it can, the user would have to wait very long before his or her status post completes, just because he or she has more than a thousand friends to update along the way. The real cause was another select query, which took too long to complete and locked the whole table during its execution, making the insert/update queue up in line. MapReduce Combiner), Increasing reading bandwidth (RAID, replication, memory caching, distributed network storage, hdfs, etc. We monitor the disk I/O, just to find out that the disk-write is always at 100 MB/s. Instead of caching the whole web page content, the system can cache objects that were read from the database into memory, so that next time, it doesn’t have to query it again from the database. The application has to be built ground up to run on multiple servers as a single application. Designing such a plan so that our application can scale, is designing for scalability. Thanks for reading. The first thing we can do is to split the system so that the web application is put on one server and the database on another. The bottleneck of the system in this case is the database server. Scalability is the property of a system to handle a growing amount of work by adding resources to the system.. As a business grows, its main objective is to continue to meet market demands. Are we going to upgrade our server to a larger one? In a scalable system, you can add processing capacity in order to remain reliable under high load. I’ve seen many times in my past projects, where the insert/update to the database took too long to complete, but the real reason was not the insert/update itself. This web server is a Single Point of Failure, which means if it fails, the whole system fails. Learn more. There were also many times in my past projects, the reason was that the database doesn’t have enough memory to cache the queries, and strange it may sound, but adding more memory could solve the disk I/O problems. Lots of large systems nowadays are split into microservices, each of which takes care of one function in the system, so that the services can be scaled separately. If your system has a lot of data that doesn’t changes for short period of time, or if the change isn’t critical and it doesn’t hurt to serve the user with an old version of the data, caching is a good candidate that can optimize your system by ten or even a hundred times. 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