massive games

high concurrency, the best solution is to use specific methods for specific requirements, including locking, queuing, and so on. Another key is to simplify transactions and reduce transactions as much as possible.There is this awareness, as long as to think, always solve, there is no need to make these technologies very God, technically speaking, the massive data processing of the ideas and algorithms are not difficult.PS: These days many people desp

can we see the true meaning of the Microsoft AI product "Cortana" and "Xiaoice". Microsoft's cloud computing platform has tremendous computational power to ensure that "Cortana" and "Xiaoice" can easily answer thousands of of people within a second.The key to artificial intelligence based on massive data is: Semantic search. What is semantic search? Microsoft's network robot must have the ability to communicate with the user semantics, that is, in th

) {if ($i >=20000 $i Description: 1, the massive data import, you should pay attention to some of the limitations of PHP, you can temporarily adjust, or will error. Allowed memory size of 33554432 bytes exhausted (tried to allocate bytes)2,php Operation TXT file file_get_contents () file_put_contents ()3, Mass import, the best batch import, the chance of failure a little bit 4, before the massive import, th

ThinkPHP processing massive data table sharding mechanism detailed Code application ThinkPHP built-in table sharding algorithm to process millions of user data. Data Table: house_member_0house_member_1house_member_2house_m ThinkPHP processing massive data table sharding mechanism detailed code Use the built-in table sharding algorithm of ThinkPHP to process millions of user data. data table: house_member_

Massive log data, extract a day to visit Baidu the most times the IP.The first is this day, and is to visit Baidu's log in the IP out to write to a large file. Note that the IP is 32-bit and has a maximum of 2^32 IP. The same can be used to map the method, such as module 1000, the entire large file mapping to 1000 small files, and then find out the frequency of each of the most frequent IP (can be used hash_map frequency statistics, and then find the

How to Save massive online shopping order data? How can we store large B2C orders with tens of thousands of orders per day? It is impossible to save a table, right? What should I do if the sub-tables are saved? How can we combine queries ?, How many times a day would be less than yuan a year? if one table is too old to use the master/slave database server for read/write splitting, how can I store massive on

guardian to each aggregate object. All operations on this resource are handed over to this guardian, and a lock-free queue is used to quickly queue up and hand over to the guardian. Through reactive programming, we can also avoid the occupation of CPU threads for a resource operation for too long. As long as we finish the competition for resources, we can immediately dispatch another thread to do the rest. In this way, serial sequential operations and parallel operations can be perfectly combi

($ senti_value! = 0){If ($ I >= 20000 $ I {$ Mm = explode ("", $ arr [4]);Foreach ($ mm as $ m) // [adductive #1 adducting #1 adducent #1] This TXT record must be converted to 3 SQL records {$ Nn = explode ("#", $ m );$ Word = $ nn [0];$ SQL. = "(\" $ word \ ", 1, $ senti_value, 2),"; // note that word may contain single quotes (such as jack's ), therefore, we must use double quotation marks to include word (escape)}}$ I ++;}}// Echo $ I;$ SQL = substr ($ SQL, 0,-1); // remove the last comma//

If you want to transfer massive Oracle data (more than 80 Mb) to another user or related tablespace in actual operations. We recommend that you use the following methods to quickly transfer data. That is, the method for creating a new table and the method for directly inserting it. I. How to create a new table Create table target_tablename tablespace Target_tablespace_name nologging Pctfree 10 pctused 60 Storage (initial 5 M next 5 M mine

($ senti_value! = 0){If ($ I >= 20000 $ I {$ Mm = explode ("", $ arr [4]);Foreach ($ mm as $ m) // [adductive #1 adducting #1 adducent #1] This TXT record must be converted to 3 SQL records {$ Nn = explode ("#", $ m );$ Word = $ nn [0];$ SQL. = "(\" $ word \ ", 1, $ senti_value, 2),"; // note that word may contain single quotes (such as jack's ), therefore, we must use double quotation marks to include word (escape)}}$ I ++;}}// Echo $ I;$ SQL = substr ($ SQL, 0,-1); // remove the last comma//

Tags: determine allow not to process massive amounts of data need CLU set effect percent　 Because of the actual project involved, it is found that when the data volume of MySQL table reaches millions, the efficiency of normal SQL query decreases linearly, and the query speed is simply intolerable if the query condition in where is more. Once tested on a table containing 400多万条 records (indexed) to perform a conditional query, its query time unexpected

is 10 million, but if you remove the duplicates, no more than 3 million. The higher the repetition of a query string, the more users are queried for it, the more popular it is, please count the hottest 10 query strings, which requires no more than 1G of memory.Solution: Although there are 10 million query, but because of the high repetition, so in fact only 3 million of the query, each query255byte, (300w*255b Hash statistics: This batch of massive