最近在研究Hue,遇到一个问题,在HiveEditor写一个HQL,提交后会报权限错误,类似这样的Authorizationfailed:NoprivilegeSelectfoundforinputs&n..
最近在研究Hue,遇到一个问题,在Hive Editor写一个HQL,提交后会报权限错误,类似这样的
Authorization failed:No privilege 'Select' found for inputs {database:xxx, table:xxx, columnName:xxx}. Use show grant to get more details. Hue的登录用户是hadoop,使用cli方式查询的时候,是没问题的,但是使用Hue连接HiveServer2的方式,就查询不了对应的表了,排除Hue的干扰,使用Beeline来连接HiveServer2,同样报权限的错误,堆栈信息如下图
根据堆栈信息大概梳理了下源码(只列出比较重要的代码),Hive提交SQL的权限验证流程如下
Hive的权限验证首先会调用authorizeUserDBAndTable验证用户是否对访问的DB和Table有访问权限,对应到MetaStore的DB_PRIVS和TBL_PRIVS表,在进行验证的时候,会通过thrift与HiveMetaStore进程进行交互来获取MetaStore库中对应表的相关信息。如果用户对更大粒度的资源有访问权限,则会直接返回,不会再继续进行更细粒度的验证,也就是说如果用户对DB有相关的权限,则不会继续验证对Table和Column的访问权限。
查看了下DB_PRIVS表,hadoop用户对访问的数据库是有Select权限的,所以再传统CLI模式下访问是没有问题的。看上述代码也都是在预料之中,因为实际上CLI模式和HiveServer模式的权限验证是一套代码。决定remote debug下,进而发现this.getAuthenticator().getUserName()的值为hive,也即是启动HiveServer2的用户,而不是提交SQL的用户hadoop,顺藤摸瓜,找到了设置authenticator相关属性的代码

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.


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