inplace sort

MySQL versions, for the Add index processing is different, there are three main types: (1) Copy table methodThis is the earliest supported way to create an index INNODB. As the name suggests, creating indexes is done through temporary table copies.

MySQL Online DDL Improvement and Application, mysqlddlThis article analyzes the implementation principle and precautions of Online DDL. For any DDL operation, the performer must perform a pre-test or clearly understand whether the impact of this

1. Online DDLIn MySQL 5.1 (with InnoDB Plugin) and 5.5, a new feature called Fast Index Creation (FIC), is to add or remove the two-level index , you can not copy the original table. For the previous version of the addition of the index to remove

Lintcode: Sort Colors II, lintcodecolors Given an array of n objects with k different colors (numbered from 1 to k), sort them so that objects of the same color are adjacent, with the colors in the order 1, 2, ... k.NoteYou are not suppose to use

1 Import pandas as PD, NumPy as NP 2 dates = Pd.date_range ('20130101', periods=6)3 df = PD. DataFrame (Np.random.randn (6,4), Index=dates, Columns=list ('ABCD'))1 mutate + IfElse1df['E'] = Np.where (df['D'] >= 0,'>=0','')2df['F'] =

BackgroundOne of the DBA's daily work must be a DDL change, a DDL change that locks the table, which can be said to be a perpetual pain in the DBA's mind, especially when a DDL change is performed, causing a large number of threads on the library to

Objective:5.1 and 5.5 InnoDB Plugin support for fast index create:How is Fast index create implemented? Only for secondary index, no copy table data is required. Execution process:1. Determine if the current table has an transaction (commit or

How to adjust the DB2 databaseSharing performance and practical skills. If you have read the relevant materials, it may be helpful for you to learn more. 1. SQL Cost AnalysisIn many cases, a simple SQL statement may make DB2 in an embarrassing state.

1, Pandas IntroductionThe Python data analysis Library or pandas is a numpy-based tool that was created to solve the data analytics task. Pandas incorporates a number of libraries and a number of standard data models, providing the tools needed to

The main tasks of data preprocessing are: First, data preprocessing 1. Data cleaning 2. Data integration 3. Data Conversion 4. Data reduction 1. Data cleaningReal-world data is generally incomplete, noisy, and inconsistent. The data cleanup