Where Subquery

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 ...

We want to do not only write SQL, but also to do a good performance of the SQL, the following for the author to learn, extract, and summarized part of the information to share with you! (1) Select the most efficient table name order (valid only in the Rule-based optimizer): The ORACLE parser processes the table names in the FROM clause in Right-to-left order, and the last table in the FROM clause (the underlying table driving tables) is processed first, In the case where multiple tables are included in the FROM clause, you must select the table with the least number of records as the underlying table. If...

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 optimized for different queries, and optimization can be controlled by configuration, this article will introduce some of the optimization strategies and optimization control options. Column cropping (columns pruning) When reading data, read only the columns that are needed in the query, ignoring the other columns. For example, for queries: SELECT a,b from T WHERE e < 10; Where T contains 5 columns (a,b,c,d,e), the column c,d will be ignored and only read A, B, e column ...

The greatest fascination with large data is the new business value that comes from technical analysis and excavation. SQL on Hadoop is a critical direction. CSDN Cloud specifically invited Liang to write this article, to the 7 of the latest technology to do in-depth elaboration. The article is longer, but I believe there must be a harvest. December 5, 2013-6th, "application-driven architecture and technology" as the theme of the seventh session of China Large Data technology conference (DA data Marvell Conference 2013,BDTC 2013) before the meeting, ...

"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 ...

MySQL large table repeated fields should be how to find it? This is a lot of people have encountered the problem, here is to teach you a MySQL table repeated fields of inquiry, for your reference. The database has a large table, you need to find the name of the duplicate record id, in order to compare. If only to find the name of the database does not repeat the field, it is easy SELECT min (`id`),` name` FROM `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 ...

The complete collection of SQL statement operations deserves to be permanently stored the following statements are part of the MSSQL statement and are not available in Access. SQL classification: ddl-data Definition language (create,alter,drop,declare) dml-Data Manipulation Language (Select,delete,update,insert) dcl-Data Control Language (Grant,revoke, Commit,rollback first, briefly introduce the basic statement: 1, Description: Create number ...

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 ...