scalability

问题 We have simple batch processes which are working fine. Recently we have new reqmnt to implement new batch process to generate reports. We have diff source of data to read to prepare this reports. Specifically we might have one view for each report. Now we want to scale this process in such a way that, it can be scaled and be completed as early as possible. I am familiar with multithread step but not sure about other strategy(Remote chunking and partition step) and which one to use when. In

问题 I am going to use memcache(not memcacheD) for my PHP application . I have few confusions . I found this wrapper class for memcache. 1)As on connect method it adds all servers to pull. If this is done on every single request wont it slow down the permanence cause of network latency ? 2)if there are 2 servers in a pool and one goes offline requests will still be made to offline server ? There is no other way to automatically remove offline server from memcache ? If not what does memcache.allow

问题 Social networking website probably maintain tables for users, friends and events... How do they use these tables to compute friends events in an efficient and scalable manner? 回答1: Many of the social networking sites like Twitter don't use an RDBMS at all but a Message Queue application. A lot of them start out with a already present application like RabbitMQ. Some of them get big enough they have to heavily customize or build their own. Twitter is in the process of doing this for the second

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . Are there any open source alternatives to oracle coherence? (btw, how much does coherence cost anyways?) 回答1: EhCache provides a decent replicated cache but has nowhere near the feature set Coherence provides. 回答2: Hazelcast is an open source, transactional, distributed caching solution for Java. It is released

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 4 years ago . I've been considering using Parse.com's service for my backend, but I'm skeptical about its scalability. Can it really handle several thousand simultaneous users? If not, is their any good way transitioning away from it? 回答1: I know the question may be old, but wanted to

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . How do you design/architect a scalable application? Any suggestion of books or websites that could help to understand how to scale out applications? Thanks 回答1: Over the past year I've had to come up to speed on this question for a project my company's working on, and I've found these resources extremely helpful

问题 This question have been asked many times in SO but none of the answers is satisfying to my situation. Question 1 Question 2 Question 3 Question 4 I am dealing with a DataObjectVersions table that contains multiple versions for around 1.2 million unique objects (and increasing). I need to concatenate changes from a specific field for each unique object. Right now I am using the solution with the XML Path presented in Q3 but running such a query on this table is a total performance disaster.

问题 I have a math problem that I solve by trial and error (I think this is called brute force), and the program works fine when there are a few options, but as I add more variables/data it takes longer and longer to run. My problem is although, the prototype works, it is useful with thousands of variables and large data sets; so, I'm wondering if it is possible to scale brute force algorithms. How can I approach scaling it? I was starting to learn and play around with Hadoop (and HBase); although

问题 I have a math problem that I solve by trial and error (I think this is called brute force), and the program works fine when there are a few options, but as I add more variables/data it takes longer and longer to run. My problem is although, the prototype works, it is useful with thousands of variables and large data sets; so, I'm wondering if it is possible to scale brute force algorithms. How can I approach scaling it? I was starting to learn and play around with Hadoop (and HBase); although

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . I'm building a web application with Django. The reasons I chose Django were: I wanted to work with free/open-source tools. I like Python and feel it's a long-term language, whereas regarding Ruby I wasn't sure, and PHP seemed like a huge hassle to learn. I'm building a prototype