This document discusses high availability solutions for MySQL databases. It covers MySQL replication, MySQL Cluster, and DRBD. MySQL replication allows for one master and multiple slaves but has some limitations. MySQL Cluster provides automatic partitioning and failover between data nodes. DRBD can synchronize block-level replication between nodes for increased availability. The document provides an overview of each technology and their strengths for high availability use cases.
This document discusses high availability solutions for MySQL databases. It covers MySQL replication, MySQL Cluster, and DRBD. MySQL replication allows for one master and multiple slaves but has some limitations. MySQL Cluster provides automatic partitioning and failover between data nodes. DRBD can synchronize block-level replication between nodes for increased availability. The document provides an overview of each technology and their strengths for high availability use cases.
This document provides an overview of setting up MySQL replication between a master database server and one or more slave servers. It discusses creating a user for replication, configuring the master with binary logging and a server ID, configuring slaves with unique server IDs, obtaining the replication information from the master, and using mysqldump or raw data files to initialize slaves with data from the master. It also covers starting new replication environments and adding additional slaves.
Vertical application partitioning allows a database system to scale to nearly unlimited levels by distributing application processing across all system resources. It involves splitting the database backend, application server, web server, and client display into separate tiers. Further, it involves partitioning entities like users across multiple database clusters based on attributes like user IDs, to distribute the load. This approach avoids bottlenecks and allows scaling simply by adding more database clusters as user numbers increase.
This document summarizes new replication features in MySQL versions 5.1, 6.0, and 5.4. Key features include row-based replication in 5.1, ignoring servers in circular replication in 6.0/5.4, replication heartbeat and slave position synchronization in 6.0/5.4, automatic relay log recovery in 6.0/5.4, and semi-synchronous replication as a pluggable architecture in 6.0/5.4. Ongoing projects previewed include multi-threaded replication slaves and time-delayed replication.
The document discusses various topics related to optimizing performance in MySQL databases, including:
1. Choosing optimal data types can improve performance by reducing storage size and simplifying queries. Smaller and simpler types are generally better.
2. Indexing is important for performance. The document discusses different index types like B-Tree indexes and their uses. Indexes should be chosen based on the types of queries.
3. Benchmarking and profiling are important to identify bottlenecks. Benchmarking tests overall performance while profiling identifies specific costly operations. The results can help optimize the database, queries, or hardware configuration.
This document discusses software refactoring and provides examples. It defines refactoring as improving the design of existing code without changing its observable behavior. Refactoring is important for software evolution as designs inevitably become outdated. Examples of refactoring techniques include extracting methods, using null objects, replacing switch statements with polymorphism, and inheritance. The document also discusses bad code smells and anti-patterns that refactoring can help address, such as long methods, large classes, duplicate code, and high coupling between classes.
The document discusses refactoring, which is improving the internal structure of code without changing its external behavior or functionality. It covers the goals of refactoring, provides examples of refactoring code, and discusses principles and techniques for refactoring safely and incrementally through testing. Reasons to refactor include improving software design over time, reducing bugs, and enabling faster development by keeping code easy to understand and modify.
The document provides examples of refactoring code and writing unit tests. In the refactoring example, a team reviews and refactors Java code that calculates fees for investment accounts using techniques like Extract Method, Replace Magic Number with Symbolic Constant, and Introduce Explaining Variable. In the unit testing example, JUnit tests are constructed to verify the refactored code handles typical, boundary, and error cases. The tests validate expected behavior and failures.
The document describes refactoring an existing video rental program to make it easier to add a new requirement of generating HTML statements. The program's structure is not well-suited for adding new features. To address this, the author extracts a method from the long statement() method to calculate rental amounts, improving the design and making it easier to add the new HTML feature. This is an example of how refactoring can restructure code to enable adding new capabilities without rewriting existing code.
This document summarizes steps taken to refactor code for calculating and printing a customer's video rental charges. The initial code was not well structured, with the statement() method doing too much. Through a series of small refactoring steps like extracting methods and moving methods to appropriate classes, the code was improved. Key steps included decomposing statement() into logical chunks, moving the amount calculation to the Rental class, and eliminating unnecessary variables. The end goal was to make the code easier to understand and modify.
This document provides an overview of setting up MySQL replication between a master database server and one or more slave servers. It discusses creating a user for replication, configuring the master with binary logging and a server ID, configuring slaves with unique server IDs, obtaining the replication information from the master, and using mysqldump or raw data files to initialize slaves with data from the master. It also covers starting new replication environments and adding additional slaves.
Vertical application partitioning allows a database system to scale to nearly unlimited levels by distributing application processing across all system resources. It involves splitting the database backend, application server, web server, and client display into separate tiers. Further, it involves partitioning entities like users across multiple database clusters based on attributes like user IDs, to distribute the load. This approach avoids bottlenecks and allows scaling simply by adding more database clusters as user numbers increase.
This document summarizes new replication features in MySQL versions 5.1, 6.0, and 5.4. Key features include row-based replication in 5.1, ignoring servers in circular replication in 6.0/5.4, replication heartbeat and slave position synchronization in 6.0/5.4, automatic relay log recovery in 6.0/5.4, and semi-synchronous replication as a pluggable architecture in 6.0/5.4. Ongoing projects previewed include multi-threaded replication slaves and time-delayed replication.
The document discusses various topics related to optimizing performance in MySQL databases, including:
1. Choosing optimal data types can improve performance by reducing storage size and simplifying queries. Smaller and simpler types are generally better.
2. Indexing is important for performance. The document discusses different index types like B-Tree indexes and their uses. Indexes should be chosen based on the types of queries.
3. Benchmarking and profiling are important to identify bottlenecks. Benchmarking tests overall performance while profiling identifies specific costly operations. The results can help optimize the database, queries, or hardware configuration.
This document discusses software refactoring and provides examples. It defines refactoring as improving the design of existing code without changing its observable behavior. Refactoring is important for software evolution as designs inevitably become outdated. Examples of refactoring techniques include extracting methods, using null objects, replacing switch statements with polymorphism, and inheritance. The document also discusses bad code smells and anti-patterns that refactoring can help address, such as long methods, large classes, duplicate code, and high coupling between classes.
The document discusses refactoring, which is improving the internal structure of code without changing its external behavior or functionality. It covers the goals of refactoring, provides examples of refactoring code, and discusses principles and techniques for refactoring safely and incrementally through testing. Reasons to refactor include improving software design over time, reducing bugs, and enabling faster development by keeping code easy to understand and modify.
The document provides examples of refactoring code and writing unit tests. In the refactoring example, a team reviews and refactors Java code that calculates fees for investment accounts using techniques like Extract Method, Replace Magic Number with Symbolic Constant, and Introduce Explaining Variable. In the unit testing example, JUnit tests are constructed to verify the refactored code handles typical, boundary, and error cases. The tests validate expected behavior and failures.
The document describes refactoring an existing video rental program to make it easier to add a new requirement of generating HTML statements. The program's structure is not well-suited for adding new features. To address this, the author extracts a method from the long statement() method to calculate rental amounts, improving the design and making it easier to add the new HTML feature. This is an example of how refactoring can restructure code to enable adding new capabilities without rewriting existing code.
This document summarizes steps taken to refactor code for calculating and printing a customer's video rental charges. The initial code was not well structured, with the statement() method doing too much. Through a series of small refactoring steps like extracting methods and moving methods to appropriate classes, the code was improved. Key steps included decomposing statement() into logical chunks, moving the amount calculation to the Rental class, and eliminating unnecessary variables. The end goal was to make the code easier to understand and modify.