Sql Select Distinct Only One Column

SQL statement for querying and deleting duplicate records (i) For example, there is a field "name" in Table A, and the "name" value may be the same between different records, and now you need to query between the records in the table, the "name" value has duplicates, and select name, Count (*) from-a group by name has count (*) > 1 as ...

Method 1 delete yourtable where [id] not in (select max ([id]) from yourtable group by (name + value) b on a.id = b.id where b.id ...

This article describes the SQL name for filtering duplicate records using a having group by and various select in federated queries to implement a variety of different methods. -1, find redundant records in the table, duplicate records are based on a single field (Peopleid) to determine the code as follows select * from arranges where Peopleid in (select Peopleid from&n ...

Intermediary transaction http://www.aliyun.com/zixun/aggregation/6858.html ">seo diagnose Taobao guest cloud host technology Hall in the consideration of a website if the host million or tens of millions of PV, We tend to think in two directions instinctively: static and distributed. Of course, for Sina and other portal types of Web sites, static + read and write separation and distribution, can solve most of the problems. But we are facing more, may be large data + high and ...

Hive in the official document of the query language has a very detailed description, please refer to: http://wiki.apache.org/hadoop/Hive/LanguageManual, most of the content of this article is translated from this page, Some of the things that need to be noted during the use process are added. Create tablecreate [EXTERNAL] TABLE [IF not EXISTS] table_name [col_name data_t ...

Using hive, you can write complex MapReduce query logic efficiently and quickly. In some cases, however, the Hive Computing task can become very inefficient or even impossible to get results, because it is unfamiliar with data attributes or if the Hive optimization convention is not followed. A "good" hive program still needs to have a deep understanding of the hive operating mechanism. Some of the most familiar optimization conventions include the need to write large tables on the right side of the join, and try to use UDF instead of transfrom ... Like。 Here are 5 performance and logic ...

First, the importance of the index The index is used to quickly find a column in a particular value of the line. Instead of using an index, MySQL must start with the first record and then read the entire table until it finds the relevant row. The larger the table, the more time it takes. If the table in the query column index, MySQL can quickly reach a location to search the middle of the data file, there is no need to see all the data. Note that if you need to access most of the rows, sequential reads are much faster since we avoid disk searches. If you use Xinhua Dictionary to find "Zhang" the Chinese characters, do not use the directory, then ...

Hive is the most widely used SQL on Hadoop tool, and recently many major data companies have introduced new SQL tools such as Impala,tez,spark, based on column or memory hot data, although many people have a view of hive, inefficient, slow query, and many bugs. But Hive is still the most widely used and ubiquitous SQL on Hadoop tool. Taobao survey analysis of the previous report, Taobao 90% of the business run in hive above. The ratio of the storm audio and video ...

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Doug cutting is based on Google's published academic paper. Users can easily develop and run applications that process massive amounts of data in Hadoop without knowing the underlying details of the distribution. The features of low cost, high reliability, high scalability, high efficiency and high fault tolerance make Hadoop the most popular large data analysis system, yet its HDFs and mapred ...

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Dougcutting based on Google's published academic paper. Users can easily develop and run applications that process massive amounts of data in Hadoop without knowing the underlying details of the distribution. The features of low cost, high reliability, high scalability, high efficiency and high fault tolerance make Hadoop the most popular large data analysis system, yet its HDFs and mapreduc ...