scalability

问题 I had read in some design book that immutable class improves scalability and its good practice to write immutable class wherever possible. But I think so immutable class increase object proliferation. So is it good of going immutable class or better go for static class (A class with all the methods static) for improve scalability ? 回答1: Immutable classes do promote object proliferation, but if you want safety, mutable objects will promote more object proliferation because you have to return

问题 As someone in the world of HPC who came from the world of enterprise web development, I'm always curious to see how developers back in the "real world" are taking advantage of parallel computing. This is much more relevant now that all chips are going multicore, and it'll be even more relevant when there are thousands of cores on a chip instead of just a few. My questions are: How does this affect your software roadmap? I'm particularly interested in real stories about how multicore is

问题 I have millions of documents(close to 100 million), each document has fields such as skills , hobbies , certification and education . I want to find similarity between each document along with a score. Below is an example of data. skills hobbies certification education Java fishing PMP MS Python reading novel SCM BS C# video game PMP B.Tech. C++ fishing PMP MS so what i want is similarity between first row and all other rows, similarity between second row and all other rows and so on. So,

问题 I have millions of documents(close to 100 million), each document has fields such as skills , hobbies , certification and education . I want to find similarity between each document along with a score. Below is an example of data. skills hobbies certification education Java fishing PMP MS Python reading novel SCM BS C# video game PMP B.Tech. C++ fishing PMP MS so what i want is similarity between first row and all other rows, similarity between second row and all other rows and so on. So,

问题 As I described in the question, if I were to implement a microservices architecture, would the centralized read/write database become a bottleneck? To expand with an example, let's say I have three microservices: users , teams , and team_members . Each has its own microservice, but they all rely on each other in the database, so exclusive, parallel databases wouldn't be appropriate. Since microservices is meant to distribute the work to several different servers, doesn't the central database

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 6 years ago . I'm currently developing a web application using PHP, but I'm considering developing the web application using the lift framework instead, because twitter and foursquare have implemented it. I know a few benefits using lift/Scala but, could you guys mention some benefits

问题 I am developing a web system to handle a very large set of small images, about 100 millions images of 50kb ~ 200kb, working on ReiserFS . For now, it is very difficult to backup and sync those large number of small files. My question is that if it a good idea to store these small images to a key/value store or other nosql database such as GridFS (Mongodb) , Tokyo Tyrant , Voldemort to gain more performance and bring better backup support? 回答1: First off, have a look at this: Storing a millon

问题 I am trying to come up with a partition key strategy based on a DateTime that doesn't result in the Append-Only write bottleneck often described in best practices guidelines. Basically, if you partition by something like YYYY-MM-DD, all your writes for a particular day will end up the same partition, which will reduce write performance. Ideally, a partition key should even distribute writes across as many partitions as possible. To accomplish this while still basing the key off a DateTime

问题 Suppose I can train with sample size N , batch size M and network depth L on my GTX 1070 card with tensorflow. Now, suppose I want to train with larger sample 2N and/or deeper network 2L and getting out of memory error. Will plugging additional GPU cards automatically solve this problem (suppose, that total amount of memory of all GPU cards is sufficient to hold batch and it's gradients)? Or it is impossible with pure tensorflow? I'v read, that there are bitcoin or etherium miners, that can

问题 I've currently got a ruby on rails app hosted on Heroku that I'm monitoring with New Relic. My app is somewhat laggy when using it, and my New Relic monitor shows me the following: Given that majority of the time is spent in Request Queuing, does this mean my app would scale better if I used an extra worker dynos? Or is this something that I can fix by optimizing my code? Sorry if this is a silly question, but I'm a complete newbie, and appreciate all the help. Thanks! == EDIT == Just wanted