Mysql Composite Key

MySQL optimization is very important. The most common and most needed optimization is limit. 　　The limit of MySQL brings great convenience to paging, but when the amount of data is large, the performance of limit is reduced dramatically. The same is 10 data select * FROM Yanxue8_visit limit 10000,10 and select * from Yanxue8_visit limit 0,10 is not a quantitative level ...

1, use the index to traverse the table faster. The index created by default is a non-clustered index, but sometimes it is not optimal. Under non-clustered indexes, the data is physically stored on the data page. Reasonable index design should be based on the analysis and prediction of various inquiries. In general: a. There are a large number of duplicate values, and often range query (>, <,> =, <=) and order by, group by occurred columns, consider the establishment of cluster index; Column, and each column contains duplicate values ​​can be ...

1, use the index to traverse the table faster. The index created by default is a non-clustered index, but sometimes it is not optimal. Under non-clustered indexes, the data is physically stored on the data page. Reasonable index design should be based on the analysis and prediction of various inquiries. In general: a. A large number of duplicate values, and often range query (>, <,> =, <=) and order by, group by occurred column, consider the establishment of cluster index; b. Column, ...

MySQL database sql statement commonly used optimization methods 1. Query optimization, should try to avoid full table scan, should first consider where and order by the columns involved in the establishment of the index. 2. Should be avoided in the where clause on the field null value judgment, otherwise it will cause the engine to abandon the use of indexes and full table scan, such as: select id from t where num is null You can set the default value of num 0, to ensure that Num column table does not null value ...

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"Guide" the author (Xu Peng) to see Spark source of time is not long, note the original intention is just to not forget later. In the process of reading the source code is a very simple mode of thinking, is to strive to find a major thread through the overall situation. In my opinion, the clue in Spark is that if the data is processed in a distributed computing environment, it is efficient and reliable. After a certain understanding of the internal implementation of spark, of course, I hope to apply it to practical engineering practice, this time will face many new challenges, such as the selection of which as a data warehouse, HB ...

The hardware environment usually uses a blade server based on Intel or AMD CPUs to build a cluster system. To reduce costs, outdated hardware that has been discontinued is used. Node has local memory and hard disk, connected through high-speed switches (usually Gigabit switches), if the cluster nodes are many, you can also use the hierarchical exchange. The nodes in the cluster are peer-to-peer (all resources can be reduced to the same configuration), but this is not necessary. Operating system Linux or windows system configuration HPCC cluster with two configurations: ...

In 2017, the double eleven refreshed the record again. The transaction created a peak of 325,000 pens/second and a peak payment of 256,000 pens/second. Such transactions and payment records will form a real-time order feed data stream, which will be imported into the active service system of the data operation platform.

2010 should be remembered because SQL will die this year. This year, the relational database is on the verge of falling, and this year developers found they no longer needed long, laborious columns or tables to store data. 2010 will be the starting year for document databases. Although this momentum has lasted for many years, it is now the era of more and broader document-based databases. From cloud-based Amazon to Google, a large number of open source tools, and the ensuing CouchDB and MongoDB. So what is MongoD ...

So far, there's not a universally accepted definition of big data, which means that the big data you're talking about may not be the same thing as the big data that I'm talking about, but that doesn't prevent big data applications from becoming a recognized trend. According to the IDC report, in the next four years big data market growth, in the meantime the online data information will explode growth. According to IDC forecast, 2017 Big Data technology and service market compound average annual growth rate (CAGR) is estimated to be 27%, the market scale reaches 324 US dollars (see: 2017 Big Data market value of 32.4 billion US dollars), its increase is ICT ...