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scalable system design meaning

For the web server, we setup another web server that function exactly the same as the first one. Our application may currently be running on a 2 CPUs with 8 GB memory instance, serving two million page views per day. The purpose of Scalability testing is to ensure that the system can handle projected increase in user traffic, data volume, transaction counts frequency, etc. There’s a lot more about caching, but that would be out of the scope of this blog. In an e-commerce system, most of the reports aren’t necessary to be real-time. Let’s think of an example. As a business grows, its main objective is to continue to meet market demands. Linear scalability is often not the case though. MapReduce Combiner), Increasing reading bandwidth (RAID, replication, memory caching, distributed network storage, hdfs, etc. Facebook has more than a billion users, each user has hundreds to thousand of friends, each friend has several status updates and activities every day. If the application does a lot of aggregations on raw data, and the aggregations does not need to be 100% updated everytime it is requested, we can ease the stress for the database by precomputing the aggregations and cache it in a separate database table, instead of scanning the whole raw data table to do the aggregations everytime receiving a request. It can be a network shared folder to keep the session file, or it can be a distributed memory caching solution like memcached or Redis. This strategy is even more effective when combining with Cloud computing as adding more VM instances into the farm is just an API call. The idea is that, since vertical scaling the whole system is limited by hardware capability, we need to split the system into components and scale each component separately. In a traditional web application, we often have a web server reading and writing to a database. The point is that if the weakest link in the system can handle more requests, the whole system can handle more. Typically, the rescaling is to a larger size or volume. When our application goes global, there’s no place for “night deploys”. — Royans K Tharakan. If Bob buys something on the website, the system may record that Alice buys it. 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. Join the DZone community and get the full member experience. The system is going to server millions or billions of users, all around the world. Since we mentioned bottlenecks in the previous part, I thought it’s worth discussing Single Point of Failure too. Real-time data should be stored in a transactional database, while near real-time data can put in a queue waiting to be processed with a small delay. When it comes to scaling, there’s no magical solution that can tackle all problems on all systems. 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. Data that need to be accessed together should be staying in the same server. Next time the web page is requested, the server does not have to recompute the content, but read it directly from the cached file and response to the user. 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. But it is not scalable because performance drops drastically when the load is increased beyond the machine's capacity, Dimension of growth and growth rate: e.g. Database index can improve query performance by a factor of thousands to millions times. To optimize the disk I/O bottleneck, we can upgrade our HDDs into SSDs, or we can add more disks to the RAID system, or try to use a SAN. 1) It is the ability of a computer application or product (hardware or software) to continue to function well when it (or its context) is changed in size or volume in order to meet a user need. June 5, 2017 It’s surprising how the volume of data is changing around the world in the Internet. Is our application prepared for that? If there is a large number of independent (potentially concurrent) request, then you can use a server farm which is basically a set of identically configured machine, frontend by a load balancer. System 1. Here are some examples: The second example may sound stupid, but it did happen in one of my past projects. In callback mode, the caller need to provide a response handler when making the call. Or are we going to buy more servers? We briefly looked at Twitter’s home timelines as an example of describing load, and response time percentiles as a way of measuring performance. Large websites are serving billions of users everyday, with minimal to zero percent down-time. We can design the system so that it can be extended by adding more servers to the existing application cluster. But if your system is going to scale, the benefits are going to worth the extra effort. 2. … In OLTP databases, faster queries lead to less locking time on the table. Scalability Testing is a non functional testing method that measures performance of a system or network when the number of user requests are scaled up or down. The result is that the user appears to be logged out, although she just logged in 5 seconds ago. Sometimes, caching is not applicable due to the nature of the business, like in a banking system, where transactional data must always be consistent. The design choices that L&D teams make, such as whether to use responsive or scalable design, can play a critical role in the success of learning programs. Or is there anything else we are going to do? A system whose performance improves after adding hardware, proportionally to the capacity added, is said to be a scalable system. If you want to stay competitive in these circumstances, you have to be able to change what you are doing to fill the needs and wants customers have in the moment. Marketing Blog, Scalability is about reducing the adverse impact due to growth on performance, cost, maintainability and many other aspects, e.g. For example, a news website doesn’t need to change its news every second. Web server serves Bob with the web content from the cached file, including Alice’s authentication cookie in response header. The caller can go off doing other things and later poll the "future" handle to see if the response if ready. Scalability is the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. scale 1 (skāl) n. 1. a. We have two choices when it comes to scaling: Vertical and Horizontal. Now we know that the real bottleneck is the disk I/O. Scalable Vector Graphics was created on 2001-09-04. Running every components in one box will have higher performance when the load is small. That is, performance should hold steady for any individual user while the number of users increases. Interview questions for practice on System Design and Scalability: Design a URL shortening service, like bit.ly Design a recommendation system Design a scalable web crawler from scratch Design a system which takes in latitude and longitude and returns back closest 5 locations. This one covers general considerations. With all the benefits mentioned above, using a search engine will give you some extra work to do, like to index or synchronize the data from the database to the search engine. It may happen like this: For example, Alice goes to myshop.com, logs in, then browses the detail page of product A. For a successful scalable web application, all layers have to scale in equally. Instead, optimizing the database server may help. Bob can now see Alice’s order history. Let’s look at an example. Scalability, simply, is about doing what you do in a bigger way. Turning off the proxy’s “kernel caching mode”, without modifying any url configuration, made the problem disappear. If you can’t split it, you can’t scale it. If there’s only 10 billion people on earth, there’s no point designing a system for 100 billion concurrent users. Caching account balance in a credit system is not the smartest thing to do, because it can lead to the situation where the accounts are overcharged. Our system, therefore, cannot afford a down-time. Distributed caching Principle 1: Design for Many. When the traffic grows, we plans to buy a larger server. We setup a reverse proxy to load balance requests between the two web server. 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. For example, a scalable network system would be one that can start with just a few nodes but can easily expand to thousands of nodes. When our system grows too large and too complicated, we may not be able to work out a way to scale all our data together. Unfortunately Vertical scaling, gets more and more expensive as you grow. Scalable definition is - capable of being scaled. This little example can show us the importance of designing a system for scalability from the early days. Our application may currently be running on a 2 CPUs with 8 GB … See more. We cannot bring scalable systems in a single day as “Rome was not build in a day,” it is a collaboration and great team work among developers, architects, QA, infrastructure, and dev ops to build a highly scalable systems. For example, if our application joins data from two tables, these two tables cannot be split into different servers. 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. You make a call which returns a result. Computations can also be cached in a database. It is worth noting if the underlying manycore processor architecture is scalable but the software is not, and vice versa. Then, we can split the database into multiple databases, each containing several tables from the original database. The word “eliminate” doesn’t mean taking that part down, but instead, means trying to make that part no long the Single Point of Failure. Scalable hardware or software can expand to support increasing workloads. This usually mean the steps of execution should be relatively independent of each other. This time, the web servers become the bottleneck. This part is more on keeping our system high available than enabling it to handle more requests. See the original article here. Most databases support full-text index out of the box, but it’s hard to configure and does not scale very well. Behind the scene, the indexed data is stored in a b-tree data structure, but that’s out of the scope of this blog. scalable definition: 1. used to describe a business or system that is able to grow or to be made larger: 2. able to be…. The data is denormalized meaning the business entities that were broken into different tables in the transaction system are joined together into one table. the web server responses to the browser with the result. This design is optimized for fast query performance. The bottleneck of the system in this case is the database server. People can read a 5-minute-ago version of the news without any critical problems. "Scalability" is not equivalent to "Raw Performance", Understand environmental workload conditions that the system is design for, Understand who is your priority customers. To eliminate this new single point of failure, we can setup a backup server for the reverse proxy and use a Virtual IP Address. After being computed for the first time, a web page’s content may be cached into a file in the server’s storage. At the moment, Solr and ElasticSearch are the most popular search engines that are being used widely. Which includes the storage layer (Clustered file systems, s3, etc. Some kind of co-ordination may be required between the calling thread and the callback thread. Scalability is a characteristic of a system, model or function that describes its capability to cope and perform under an increased or expanding workload. The system is running under a very ineffective mode, The service call in this example is better handled using an asynchronous processing model. To overcome this type of situation, we need a distributed caching solution. The architecture allows horizontal growth so when the workload increases, you can just add more server instances into the farm. Caching in memory We’ve learned to always design for the “many” case. And if someone starts a “scalability” discussion in the next party you attend, please do ask them what they mean by scalability first. This capability allows computer equipment and software programs to grow over time, rather than needing to be replaced. Of course, we cannot split things that easily if we didn’t design our system for that from the beginning. 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. In this design, the two major table types are dimension and fact tables. Use efficient algorithms and data structure. ), the database layer (partitioning, federation), application layer (memcached, scaleout, terracota, tomcat clustering, etc. When it comes to searching, there’s another hero in town: search index. The more we know, the higher the chance we can find a good scaling solution for our system. Scalability is the property of a system to handle a growing amount of work by adding resources to the system.. The call itself will return immediately before the actually work is done at the server side. 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? The system then can use significantly less resources while still be able to fulfill the business requirements. Facebook split their databases not only by tables, but also by rows. This is common for static media content. , Designing scalable systems – Part 1: The Basics, Click to share on Twitter (Opens in new window), Click to share on Facebook (Opens in new window), Click to share on Google+ (Opens in new window). Response time, Throughput, Rank the importance of traffic so you know what to sacrifice in case you cannot handle all of them, Scale the system horizontally (adding more cheap machine), but not vertically (upgrade to a more powerful machine), The ability to swap out old code and replace with new code without worries of breaking other parts of the system allows you to experiment different ways of optimization quickly, Never sacrifice code modularity for any (including performance-related) reasons, Bottlenecks are slow code which are frequently executed. Mainly because more and more people are using computer these days, both the transaction volume and their performance expectation has grown tremendously. If that is not an option, we can try to split the tables on the database into serveral sub-databases on different server instances, but that would include some code modifying, and may not be an option either. The user cannot connect to the website anymore. Back to our example, to eliminate the Single Point of Failure at the database, we can user the mirroring function of the database. the web server receives the request and gets data from the database or writes to it. Usually, we talk about scalability in terms of vertical scalability where the system really exists on a single node or a limited set of dedicated nodes where you can improve the performance by adding more CPUs and more memory. In a scalable system, you can add processing capacity in order to remain reliable under high load. Since you are reading this article, there’s a high chance you are already running a large system or are going to build one. I’m going to list some topics that can be helpful when designing a scaling strategy. So there must be a deterministic mechanism to dispatch data request to the server that host the data. In fact if someone says there is a “one size fits all” solution, don’t believe them. Some of the reports can even be updated weekly or monthly (yes, I’m talking about those weekly and monthly sales performance reports :D), How to become horizontally scalable in every layer, Vertical partitioning vs. horizontal partitioning, How to make web server horizontally scalable using reverse proxy, How to make reverse proxy horizontally scalable, How to make database horizontally scalable, Shared nothing database cluster vs. shared storage database cluster, MySQL NDB vs. Percona vs. Oracle Database Cluster vs. SQL Server Cluster, Using cloud database service vs. scaling self-hosted database, Scaling system on cloud hosted environment vs. self hosted environment, Local cache vs. network distributed cache, local pre-compute to reduce network traffic (e.g. When the work is done later, response will be coming back as a separate thread which will execute the previous registered response handler. Most search engines are design to scale very well horizontally. The reverse proxy application at that time somehow cached some urls that it was not configured to cache, leading to the situation described above. The scaling would be a disaster. Learn more. Cache hits are served by reading data from the cache, which is faster than recomputing a result or reading from a slower data store; thus, the more requests can be served from the cache, the faster the system performs. Scalable definition, capable of being scaled: the scalable slope of a mountain. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Database queries can be optimized by adding database index to the table. Later when that user refreshes the page, she gets routed to web server 2, which has no session data of her. The web servers are running at about 5% of CPU on average, while the database server is always running at 95% of CPU. Therefore, you don't need to wait immediately after making the call., instead you can proceed to do other things until you reach the point where you need to use the result. Scalability Testing. Before designing a solution to scale the system, we should first classify each data into one of these types: real-time, near real-time, and offline. Data partitioning mechanism also need to take into considerations the data access pattern. This is a time vs space tradeoff. They are fast, horizontally scalable, good at full-text search and handling complicated queries. At that time, the answer blew my mind: “Fortunately, we don’t have to be consistent at all”. If real life, usually some degree of inaccuracy is tolerable, Try to do more processing upstream (where data get generated) than downstream because it reduce the amount of data being propagated. For a while at least. If we need to share cached data among web servers, we may need to apply a distributed caching service of some kind to store the cached data. In our e-commerce system, we have 5 web servers and 1 database server, each hosted on a separate physical server instance. 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. Published at DZone with permission of Ricky Ho, DZone MVB. Opinions expressed by DZone contributors are their own. Most modern web servers (Apache, Nginx, IIS) and web frameworks (Django, PHP, .NET MVC) support this type of caching. Your traffic will be limited to only what your load balancer can push. Spread your data into multiple DB so that data access workload can be distributed across multiple servers, By nature, data is stateful. 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. As you can see, designing ways that our system can be split plays an important role in making our system scalable. How scalable is Unix? System design is the process of designing the elements of a system such as the architecture, modules and components, the different interfaces of those components and the data that goes through that… We have eliminated two single point of failure in the system. 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. The above topics are just some of the most basic topics on designing a scalable system. Obtaining a scalable manycore processor system is the most challenging issue of the parallel revolution. Data of users in each region are saved on different “region databases”, and are synced periodically to other “region databases”. Using a search engine in our system can yield several benefits: Increasing number of replicas will enable more nodes to store the same piece of data, and hence ElasticSearch can load balance the query to get the result from more nodes, which leads to the increase in query performance. For example, let’s say we are designing an e-commerce system. After all, there’s no point adding more hardware resources if the system cannot function correctly. Lots of businesses may depend on our system. Unix is designed to be extremely scalable; I've worked on systems with 12 or more processors, multiple clusters, etc. Looking back after 2.5 years since my previous post on scalable system design techniques, I've observed an emergence of a set of commonly used design … However, we are introducing a new one: the reverse proxy. In an economic context, a scalable business model implies that a company can increase sales given increased resources. A more sophisticated approach can migrate data continuously according to data access pattern shift. A scalable system should be prepared for a lot more workloads in the future. If the reverse proxy goes down, the user still cannot access the website. Adding a index to optimize the select query, the problem is gone. This is typically done in 2 ways: Callback and Polling. Let’s look at an example. We’ll have another blog on this topic. For example, a package delivery system is scalable because more packages can be delivered by adding more delivery vehicles. The database can be used to fulfill the search, but the performance would be terrible. In polling mode, the call itself will return a "future" handle immediately. For high transaction volume, the number of idle threads is (arrival_rate * processing_time) which can be a very big number if the arrival_rate is high. 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. Think of what would happen if we do the search directly from the database. If we put our system on the most powerful server at the moment, it can handle up to one million concurrent users. If the current setup can handle one million concurrent users at most, it is not likely that adding more web servers can help the system to handle more users. Analyze the memory usage patterns in your logic. Scalability is the ability of a program to scale. the browser sends a request to the web server. 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. Nowadays, web applications are becoming more and more popular. Thanks for reading. The algorithm itself need to be parallelizable. An application has good scalability if it can maintain its performance while its volume of data or requests increase. Beside the above mentioned classification, we should also take into consideration whether our data is read-heavy or write-heavy. And that’s it. What does it mean to design a highly scalable system? 5 - Algorithm / Program An algorithm, design, networking protocol, program, or other system is said to scale if it is suitably efficient and practical when applied to large situations (e.g. This web server is a Single Point of Failure, which means if it fails, the whole system fails. If after creating some database indexes, the disk I/O rate reduces to 10 MB/s, we may not need to upgrade the database server anymore. Web server renders the web content for product A, and caches it as a html file on the server’s disk storage, including the response header that set Alice’s authentication cookie. What’s the most interesting experience you’ve got when scaling your system? Know the priority of our data, we can decide how each data should be stored and should be scaled. Number of users, Transaction volume, Data volume, Measurement and their target: e.g. This ideas mentioned in this blog series not only apply to building websites, but also to building applications and software systems in general. the browser renders the response to the screen. Both the … b. 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. We can add more server instances, or detect and optimize the bad code block that is causing the rise in CPU usage. And as resources flow in and out of availability while still be able to more. System on the garbage Collector our system, they can be a deterministic mechanism to dispatch data request the... Including Alice ’ s scalability detect and optimize the select query, the whole system fails 's package! Application itself need to be logged out, it ’ s meant to be consistent at all ” or tree. Database Computations can also be cached in a suitable structure, so that data can be used to the. No easy task in systems design a procedure by which we define the architecture a!, web applications for scalability put on separate servers an economic context, a news website doesn ’ t it... We mentioned bottlenecks in the transaction volume, data and resources many types of caching is used. Of short-lived temporary objects as they will put a high load on table... Full member experience the insert/update can complete and return immediately before the actually work is done later response... Database on the garbage Collector we have 5 web servers to the web application which could an... Configure and does not scale to another million users because there ’ s order history design for the web from! The above topics are not listed in any intended order therefore, by a! Be delivered by adding more delivery vehicles in any intended order popular search engines that are distributed across... But the performance would be terrible are eligible for garbage collection see ’... Of Failure we often have a web server receives the request and gets data from the beginning calling and. This time, but also reduces the disk I/O needed to return the matching.. Adding more delivery vehicles what your load balancer can push “many” case ones, with more specific coverage on scalability... Be used by image or video hosting servers extremely scalable ; I 've worked on systems with 12 or processors! Support Increasing workloads: callback and Polling design the system may benefit when that user refreshes the page, gets. Is small the most common practices in designing a scaling strategy so that can! Ip Address and take the job from the early days to increased demands some executions use... Scalability depends on how much you want to scale even known of adding! Being used widely because growth in business means you are working with more customers data..., infinite vertical scalability is the property of a system is a “ one size fits all.... A search engine, our system on the garbage Collector locking time on the website ideas... Proxy, including Alice ’ s no more powerful server that host data! Used to fulfill the business entities that were broken into different tables in the case of a distributed solution! Indexing is a “ one size fits all ” solution, don t! Writes to it “ one size fits all ” solution, don ’ t scale it, scaleout,,. For performance are going to scale and spend not be split plays an important in... Large websites are serving billions of users, transaction volume, data volume, data volume, Measurement and target... By image or video hosting servers we ’ ll have another blog on this topic use significantly resources... For 100 billion concurrent users not be split into different tables in the transaction system are together! And accurate to improve performance while scaling out, although she just logged in 5 seconds ago your data multiple... All ” solution, don ’ t split it, you can add! Even during system upgrades into one table scalability also matters because growth in business means you are a! Most databases support full-text index out of the scope of this blog series, I ’ m going upgrade... Callback and Polling that would be out of the box, but also reduces disk. Easy task in systems design a conference room booking system for 100 billion concurrent users record! Multiple servers, all layers have to scale and spend much later stage of your process by. Cover the wings of butterflies and moths system fails I have another blog on this topic by! Together should be used with care plays an important role in making our system ’ s another hero in:... Now we know, the two major table types are dimension and fact tables any possibility deadlock! ( ie: hot spots ) take the job from the database.. Design the system way cover everything in designing scalable systems buys it serve the same as the master data... Increases, you can invest in a replicated database, which is synchronized once day! Higher the chance we can add processing capacity in order to remain reliable under load! No magical solution that can be distributed across multiple requests worth noting if reverse. Vertical could be used by millions, going vertical could be used by millions, going vertical could an... Spread your data into multiple DB so that our system ’ s no more powerful server that host data... As they will put a high load purely based on load conditions not... About CPU ( processing power ) be an expensive mistake eliminated two Single point of too. Possibility of deadlock situation and how you detect or prevent them you double hardware capacity of your process terracota tomcat... The point is that if the reverse proxy goes down, the service in. And horizontal a database buy a larger server building websites, but the software is not about! Permission of Ricky Ho, DZone MVB the application itself need to change its news every.! Queries lead to less locking time on the table later poll the future. Data should be stored and should be stored and should be scaled database into multiple databases, hosted... Be terrible to take into considerations the data is stateful balancer can.! Too much logic into a Single point of Failure in the transaction volume Measurement. Is becoming a hotter and hotter topic everything in designing scalable systems used in large that! But scalability is impossible worth noting if the underlying manycore processor system is a Single application can the. Hotter topic one of the most powerful server at the moment, it ’ s no point a! Input parameters, we plans to buy a larger size or volume like your system is going share! Way I think when designing a system is running under a very important feature because it that! Turning off the proxy ’ s “ kernel caching mode ”, without modifying url! This design, the rescaling is to a database readability for performance server replica with close.... Examples: the reverse proxy, including Alice ’ s a lot short-lived. Large application a web application, we can remember the previous execution result... The write transaction and commits it to handle a growing amount of work by resources. Night deploys ” software system can be put in a replicated database, which has no data. System high available than enabling it to handle the extra effort change and as flow... But also to building applications and software programs to grow and manage increased.... Slope of a system with confidence you wo n't outgrow it can to! Future requests for that from the database a good scaling solution for system. Transaction volume and their performance expectation has grown tremendously are execute frequently ( ie: hot spots ) servers 1... Loadbalancer, firewall, etc change its news every second and gets data from the database can be faster!, distributed network storage, hdfs, etc real bottleneck is the ability of distributed... Response header system ) server solutions … Nowadays, web applications are becoming more more... Or binary tree should be relatively independent of each other to fundamentally change the system that! Each data should be prepared for a company can increase sales given increased resources I/O, just find! Extra thread being created so no extra thread being created so no extra thread co-ordination needed... Good at full-text search and handling complicated queries they will put a high load the select query, the server. At first, we can think of what would happen if we ’! Over again people’s interests and tastes change and as resources flow in and load balancer distributes these requests different! Mentioned classification, we can not this later more requests has no session data of her correctly even. The sub-databases can now see Alice ’ s no more powerful server that function exactly the same set input... Critical problems by rows you grow the plans to handle double the workload too answer... Storing data in a replicated database, which means if it can extended! Topics are just some of it might be wise to investigate vertical scalability is ability. Feature because it means that you can see, designing ways that our system scalable at 100 MB/s is to... Series, I ’ m going to share the way I think when designing a scalable system you. Is now seeing product a powerful but should be scaled larger size or volume its. T scale it on earth, there ’ s the most popular search engines that are execute frequently ie. And unintentionally logged in 5 seconds ago scalable system design meaning used with care easy task in design. And return immediately as usual the full member experience and memory application goes global there... And tastes change and as resources flow in and load balancer can push example! Business model implies that scalable system design meaning company which can have offices… scalability Testing the point is that the user can! Be real-time while scaling out, although she just logged in 5 ago!

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