巧用xmltype解析clob数据
对于clob的数据,很多场合中都使用xml的格式,但是对于数据的查取和处理总是感觉力不从心。在条件允许的情况下,如果能够巧妙的使
对于clob的数据,很多场合中都使用xml的格式,但是对于数据的查取和处理总是感觉力不从心。在条件允许的情况下,如果能够巧妙的使用xmltype来做数据处理,无意中是对于clob的一个处理利器。
简单说下需求。
数据库里存放的clob类似下面的格式
现在有一个需求是能够把RelatedObjectInfo 中的objID查取,整理后得到一个以逗号分隔的串。
比如上面的clob数据,需要输出成为下面的形式:
##PC4.0##118146,##PC4.0##30369,##PC4.0##118145,##PC4.0##118211,##PC4.0##117696,##PC4.0##119094,##PC45.0##118203,
如果直接通过sql语句来写,确实很难实现,,如果通过Pl/sql也需要做不少的工作。
下面尝试使用xmltype来直接读取clob数据。
简单创建一个测试表,插入数据。
create table AA(id number,c_cml clob);
insert into aa values(5,to_clob('
'));
来看看xmltype的效果,根据根节点,找到最终的叶子节点。
select extract(xmltype(c_cml),'/ObjectInfo/Relations/RelationInfo/RelatedObjects/RelatedObjectInfo') a,
id
from aa where id=5;
A ID
---------------------------------------------------------------------------------------------------- --------
可以看到已经查到了
更进一步,把xml标记进行清除。可以直接使用replace
SQL> select replace(extract(xmltype(c_cml),'/ObjectInfo/Relations/RelationInfo/RelatedObjects/RelatedObjectInfo'),'
9094"/>##PC4.0##118203"/>##PC4.0##118133"/>##PC4.0##118135"/>##PC4.0##118583"/>##PC4.0##30313"/>##PC
4.0##30310"/>##PC4.0##110154"/>##PC4.0##30317"/>##PC4.0##30314"/>##PC4.0##30315"/>##PC4.0##30318"/>#
#PC4.0##118131"/>##PC4.0##30309"/>##PC4.0##118160"/>##PC4.0##119101"/>
然后直接清除尾部标记。
SQL> select replace(replace(extract(xmltype(c_cml),'/ObjectInfo/Relations/RelationInfo/RelatedObjects/RelatedObjectInfo'),'
2 id
3 from aa where id=5;
A ID
---------------------------------------------------------------------------------------------------- --------
##PC4.0##118146,##PC4.0##30369,##PC4.0##118145,##PC4.0##118211,##PC4.0##117696,##PC4.0##119094,##PC4 5
.0##118203,##PC4.0##118133,##PC4.0##118135,##PC4.0##118583,##PC4.0##30313,##PC4.0##30310,##PC4.0##11
0154,##PC4.0##30317,##PC4.0##30314,##PC4.0##30315,##PC4.0##30318,##PC4.0##118131,##PC4.0##30309,##PC
4.0##118160,##PC4.0##119101,
这样就能很快实现需求,把clob的数据当做xml来做处理,当然了对于clob的数据格式也是有一些限定的。
本文永久更新链接地址:

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

Full table scanning may be faster in MySQL than using indexes. Specific cases include: 1) the data volume is small; 2) when the query returns a large amount of data; 3) when the index column is not highly selective; 4) when the complex query. By analyzing query plans, optimizing indexes, avoiding over-index and regularly maintaining tables, you can make the best choices in practical applications.

Yes, MySQL can be installed on Windows 7, and although Microsoft has stopped supporting Windows 7, MySQL is still compatible with it. However, the following points should be noted during the installation process: Download the MySQL installer for Windows. Select the appropriate version of MySQL (community or enterprise). Select the appropriate installation directory and character set during the installation process. Set the root user password and keep it properly. Connect to the database for testing. Note the compatibility and security issues on Windows 7, and it is recommended to upgrade to a supported operating system.

InnoDB's full-text search capabilities are very powerful, which can significantly improve database query efficiency and ability to process large amounts of text data. 1) InnoDB implements full-text search through inverted indexing, supporting basic and advanced search queries. 2) Use MATCH and AGAINST keywords to search, support Boolean mode and phrase search. 3) Optimization methods include using word segmentation technology, periodic rebuilding of indexes and adjusting cache size to improve performance and accuracy.

MySQL is an open source relational database management system. 1) Create database and tables: Use the CREATEDATABASE and CREATETABLE commands. 2) Basic operations: INSERT, UPDATE, DELETE and SELECT. 3) Advanced operations: JOIN, subquery and transaction processing. 4) Debugging skills: Check syntax, data type and permissions. 5) Optimization suggestions: Use indexes, avoid SELECT* and use transactions.

The difference between clustered index and non-clustered index is: 1. Clustered index stores data rows in the index structure, which is suitable for querying by primary key and range. 2. The non-clustered index stores index key values and pointers to data rows, and is suitable for non-primary key column queries.

MySQL and MariaDB can coexist, but need to be configured with caution. The key is to allocate different port numbers and data directories to each database, and adjust parameters such as memory allocation and cache size. Connection pooling, application configuration, and version differences also need to be considered and need to be carefully tested and planned to avoid pitfalls. Running two databases simultaneously can cause performance problems in situations where resources are limited.

In MySQL database, the relationship between the user and the database is defined by permissions and tables. The user has a username and password to access the database. Permissions are granted through the GRANT command, while the table is created by the CREATE TABLE command. To establish a relationship between a user and a database, you need to create a database, create a user, and then grant permissions.

Data Integration Simplification: AmazonRDSMySQL and Redshift's zero ETL integration Efficient data integration is at the heart of a data-driven organization. Traditional ETL (extract, convert, load) processes are complex and time-consuming, especially when integrating databases (such as AmazonRDSMySQL) with data warehouses (such as Redshift). However, AWS provides zero ETL integration solutions that have completely changed this situation, providing a simplified, near-real-time solution for data migration from RDSMySQL to Redshift. This article will dive into RDSMySQL zero ETL integration with Redshift, explaining how it works and the advantages it brings to data engineers and developers.
