解剖 SQLSERVER 第六篇 对OrcaMDF的 系统 测试 里 避免 regressions(译) http://improve.dk/avoiding-regressions-in-orcamdf-by-system-testing/ 当我继续添加新功能和新的数据结构支持进去OrcaMDF软件的时候,bug的风险不断增加 特别是当我开发一个很大
解剖SQLSERVER 第六篇 对OrcaMDF的系统测试里避免regressions (译)
http://improve.dk/avoiding-regressions-in-orcamdf-by-system-testing/
当我继续添加新功能和新的数据结构支持进去OrcaMDF软件的时候,bug的风险不断增加
特别是当我开发一个很大的未知功能时,我不能预估结构和该结构的关联,为了降低风险,测试是很有必要的
单元测试
单元测试是在面向对象编程里测试源代码某一个功能的最小一部分的测试。一个测试的例子是SqlBigInt数据类型解析类,
他应该长这个样子
<span>using</span><span> System; </span><span>using</span><span> NUnit.Framework; </span><span>using</span><span> OrcaMDF.Core.Engine.SqlTypes; </span><span>namespace</span><span> OrcaMDF.Core.Tests.Engine.SqlTypes { [TestFixture] </span><span>public</span> <span>class</span><span> SqlBigIntTests { [Test] </span><span>public</span> <span>void</span><span> GetValue() { </span><span>var</span> type = <span>new</span><span> SqlBigInt(); </span><span>byte</span><span>[] input; input </span>= <span>new</span> <span>byte</span>[] { <span>0xFF</span>, <span>0xFF</span>, <span>0xFF</span>, <span>0xFF</span>, <span>0xFF</span>, <span>0xFF</span>, <span>0xFF</span>, <span>0x7F</span><span> }; Assert.AreEqual(</span><span>9223372036854775807</span><span>, Convert.ToInt64(type.GetValue(input))); input </span>= <span>new</span> <span>byte</span>[] { <span>0x82</span>, <span>0x5A</span>, <span>0x03</span>, <span>0x1B</span>, <span>0xD5</span>, <span>0x3E</span>, <span>0xCD</span>, <span>0x71</span><span> }; Assert.AreEqual(</span><span>8200279581513702018</span><span>, Convert.ToInt64(type.GetValue(input))); input </span>= <span>new</span> <span>byte</span>[] { <span>0x7F</span>, <span>0xA5</span>, <span>0xFC</span>, <span>0xE4</span>, <span>0x2A</span>, <span>0xC1</span>, <span>0x32</span>, <span>0x8E</span><span> }; Assert.AreEqual(</span>-<span>8200279581513702017</span><span>, Convert.ToInt64(type.GetValue(input))); } [Test] </span><span>public</span> <span>void</span><span> Length() { </span><span>var</span> type = <span>new</span><span> SqlBigInt(); Assert.Throws</span><argumentexception>(() => type.GetValue(<span>new</span> <span>byte</span>[<span>9</span><span>])); Assert.Throws</span><argumentexception>(() => type.GetValue(<span>new</span> <span>byte</span>[<span>7</span><span>])); } } }</span></argumentexception></argumentexception>
这个测试包含了SqlBigInt 类的主入口点,测试long bigint 数据类型是否会造成上溢或下溢的情况,也包含长度检查。
对于像SqlBigInt这样简单的类型单元测试会工作得很好。有时候单元测试会很复杂当相关联的类需要调用相应方法,类等支持他运行的底层结构的时候(mock测试)
虽然这是一个工作策略,测试需要不断进行,特别在项目早期阶段,整个架构都是动态的
系统测试
在测试范围上,我们需要更大的范围测试 -系统测试。系统测试旨在测试系统作为一个整体,基本上忽略系统内部工作原理
如果要分类的话可以被分为 黑盒测试。对于OrcaMDF,我估计可以捕获90%的所有的regressions 只使用10%的时间,
相比起单元测试使用更多时间只捕获少量的regressions 。
因此,这是一个很好的方法在开发期间的测试,同时可以引入关键的单元测试和集成测试。
例如我想测试DatabaseMetaData 类里面的用户表名字的解析,我可以模拟SysObjects的值列表,同时对于DatabaseMetaData 类
的构造函数也能模拟MdfFile 所必须的参数,为了做到这一点,我必须从MdfFile 提取出一个接口并且在上面使用mocking framework
系统测试的方法执行以下流程:
1、连接到SQLSERVER实例
2、在测试固件(Test fixture)里创建测试架构
3、分离数据库
4、运行OrcaMDF 并加载分离的数据库验证结果
一个测试样例,创建两个用户表并且验证DatabaseMetaData类的输出
<span>using</span><span> System.Data.SqlClient; </span><span>using</span><span> NUnit.Framework; </span><span>using</span><span> OrcaMDF.Core.Engine; </span><span>namespace</span><span> OrcaMDF.Core.Tests.Integration { </span><span>public</span> <span>class</span><span> ParseUserTableNames : SqlServerSystemTest { [Test] </span><span>public</span> <span>void</span><span> ParseTableNames() { </span><span>using</span>(<span>var</span> mdf = <span>new</span><span> MdfFile(MdfPath)) { </span><span>var</span> metaData =<span> mdf.GetMetaData(); Assert.AreEqual(</span><span>2</span><span>, metaData.UserTableNames.Length); Assert.AreEqual(</span><span>"</span><span>MyTable</span><span>"</span>, metaData.UserTableNames[<span>0</span><span>]); Assert.AreEqual(</span><span>"</span><span>XYZ</span><span>"</span>, metaData.UserTableNames[<span>1</span><span>]); } } </span><span>protected</span> <span>override</span> <span>void</span><span> RunSetupQueries(SqlConnection conn) { </span><span>var</span> cmd = <span>new</span> SqlCommand(<span>@"</span><span> CREATE TABLE MyTable (ID int); CREATE TABLE XYZ (ID int);</span><span>"</span><span>, conn); cmd.ExecuteNonQuery(); } } }</span>
在实际的真实生活场景里这样可以非常快速的进行测试。想测试转发记录的解析?只需要简单地创建一个新的测试
编写TSQL代码来生成目标数据库状态然后验证扫描到的表数据
系统测试的缺点
不幸的是系统测试不是万能药,它也有它的缺点。最明显的一个缺点是性能。
单元测试通常需要运行非常快,基本上允许您在每个文件保存后在后台运行它们。从绑定CPU开始到运行 ,每一个这样的系统测试都需要半秒
幸运的是,它们可以并行运行没有问题。在一台四核的机器能让我每分钟运行480个测试。这能够让一个完整的测试集合控制在合理的时间,
同时依然保持测试子集能够很快运行。通常代码的更改不会对测试造成太多的影响
第六篇完

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.

SQL commands in MySQL can be divided into categories such as DDL, DML, DQL, DCL, etc., and are used to create, modify, delete databases and tables, insert, update, delete data, and perform complex query operations. 1. Basic usage includes CREATETABLE creation table, INSERTINTO insert data, and SELECT query data. 2. Advanced usage involves JOIN for table joins, subqueries and GROUPBY for data aggregation. 3. Common errors such as syntax errors, data type mismatch and permission problems can be debugged through syntax checking, data type conversion and permission management. 4. Performance optimization suggestions include using indexes, avoiding full table scanning, optimizing JOIN operations and using transactions to ensure data consistency.

InnoDB achieves atomicity through undolog, consistency and isolation through locking mechanism and MVCC, and persistence through redolog. 1) Atomicity: Use undolog to record the original data to ensure that the transaction can be rolled back. 2) Consistency: Ensure the data consistency through row-level locking and MVCC. 3) Isolation: Supports multiple isolation levels, and REPEATABLEREAD is used by default. 4) Persistence: Use redolog to record modifications to ensure that data is saved for a long time.

MySQL's position in databases and programming is very important. It is an open source relational database management system that is widely used in various application scenarios. 1) MySQL provides efficient data storage, organization and retrieval functions, supporting Web, mobile and enterprise-level systems. 2) It uses a client-server architecture, supports multiple storage engines and index optimization. 3) Basic usages include creating tables and inserting data, and advanced usages involve multi-table JOINs and complex queries. 4) Frequently asked questions such as SQL syntax errors and performance issues can be debugged through the EXPLAIN command and slow query log. 5) Performance optimization methods include rational use of indexes, optimized query and use of caches. Best practices include using transactions and PreparedStatemen

MySQL is suitable for small and large enterprises. 1) Small businesses can use MySQL for basic data management, such as storing customer information. 2) Large enterprises can use MySQL to process massive data and complex business logic to optimize query performance and transaction processing.

InnoDB effectively prevents phantom reading through Next-KeyLocking mechanism. 1) Next-KeyLocking combines row lock and gap lock to lock records and their gaps to prevent new records from being inserted. 2) In practical applications, by optimizing query and adjusting isolation levels, lock competition can be reduced and concurrency performance can be improved.


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

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

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

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SecLists
SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment