Working with Amazon Aurora MySQL Simplified 101

Databases are required to drive the applications that contribute to the company’s success. Databases should develop in size, speed, and reliability as the business grows, and should be robust, quicker, and more dependable to give better service.

Amazon Aurora is a Relational Database, designed to be compatible with MySQL so that current applications can continue to run without interruption. On the same hardware, it gives 5 times the performance of MySQL. Amazon RDS manages AWS Aurora, which handles complex database functions including deployment, monitoring, and patching.

In this article, you will gain a basic understanding of Amazon Aurora MySQL DB Clusters and various methods to migrate data into them. You will also discover some of the best practices for Aurora MySQL working. So, before moving forward, let’s get introduced to some of the key features of Amazon Aurora and MySQL.

Table of Contents

• What is Amazon Aurora?
  • Key Features of Amazon Aurora
• What is MySQL?
  • Key Features of MySQL
• What is Amazon Aurora MySQL?
• What are the Methods for Migrating Data to an Amazon Aurora MySQL DB Cluster?
  • Physical Migration
  • Logical Migration
  • Load Data To Amazon Aurora MySQL using Hevo’s No-Code Data Pipeline
• Best Practices for Working with Amazon Aurora MySQL
  • Optimize Large Aurora MySQL Join Queries with Hash Joins
  • Use Amazon Aurora to Scale Reads for MySQL Database
  • Use Amazon Aurora for Disaster Recovery with MySQL Databases
  • Use Multithreaded Replication in Aurora MySQL
  • Invoke AWS Lambda Functions using Native MySQL Functions
• Conclusion

What is Amazon Aurora?

Amazon Aurora is a prominent database engine with a robust feature set that provides full MySQL and PostgreSQL compatibility. It provides enterprise-level performance and automates all Database processes. As a result, you won’t have to worry about handling operations like data backups, hardware provisioning, software upgrades, and so on manually.

Aurora’s multiAZ deployment tool enables tremendous scalability and can replicate data across many zones. This means that consumers can choose from a variety of hardware configurations to meet their own needs. Furthermore, Aurora’s server-less functionality manages the scalability of your Databases and helps scale up or down your storage as needed. This method only costs you for the period that the Database is active.

On the same hardware, Amazon Aurora has 5X the throughput of standard MySQL and 3X the throughput of standard PostgreSQL, making it a great choice for many enterprise applications, SaaS applications, Web and Mobile Games, and other applications that require a Database with high throughput, massive storage scalability, and high availability. Among the company’s noteworthy clients are Autodesk, Dow Jones, Netflix, Pokemon, Samsung, and others.

Key Features of Amazon Aurora

Amazon Aurora’s popularity is aided by the following features:

• Scalability: Based on your Database usage, Amazon Aurora will automatically scale from a minimum of 10 GB storage to 64 TB storage in increments of 10 GB. This will have no effect on the Database’s performance, and you won’t have to worry about allocating storage space as your business expands.
• Data Backups: Automatic, incremental, and continuous backups are supported. These data backups don’t slow down Database performance. This eliminates the need to take data snapshots regularly to keep your data safe.
• High Availability and Durability: Amazon RDS keeps track of the health of your Amazon Aurora database and the underlying Amazon Elastic Compute Cloud (Amazon EC2) instance. In the case of a database failure, Amazon RDS will immediately resume the database and associated operations. With Amazon Aurora, you don’t need to replay database redo logs for crash recovery, which cuts restart times in half. 
• Fully Managed: Thanks to Amazon Aurora, your Database will be maintained up to date with the latest fixes. You can select if and when your instance is patched with DB Engine Version Management. You can manually stop and start an Amazon Aurora database with a few clicks. This makes it simple and cost-effective to use Aurora for development and testing where the Database does not need to be up all of the time.
• Developer ML-Toolkit: Aurora delivers Machine Learning capabilities straight from the Database, allowing you to add ML-based predictions to your applications using the regular SQL programming language. You can use a wide range of Machine Learning algorithms without having to build new integrations or move data around.

What is MySQL?

MySQL is a Relational Database Management System (RDBMS) based on SQL. In RDBMS, tables are made up of rows and columns, and all data relationships follow a rigid logical structure. MySQL is an important part of most popular software stacks for creating and maintaining everything from customer-facing web applications to data-driven B2B services. It works with a variety of operating systems and languages, including PHP, PERL, JAVA, C++, and C.

When compared to Microsoft SQL Server and Oracle Database, MySQL is a faster, more scalable, and easier-to-use Database Management System. It uses a basic Client-Server Model to assist users in managing Relational Databases, or data stored in rows and columns across tables. It makes use of the well-known query language Structured Query Language (SQL), which enables users to conduct all CRUD (Create, Read, Update, and Delete) operations.

Key Features of MySQL

Despite fierce competition in the Database industry today, MySQL remains the preferred Database for over 5000 businesses, including Uber, Netflix, Pinterest, Amazon, Airbnb, Twitter, and others. Some of the remarkable features that contribute to the success of MySQL are:

• High performance: MySQL enables quick data processing and offers a user-friendly interface. It can also handle a large number of users at the same time, and you may access it from anywhere. It supports several performance-enhancing features such as Table Index Partitioning and Distinct Memory Caches.
• Compatibility: You can take advantage of MySQL’s high-speed and low-latency statistics transactions.
• Scalability: The MySQL platform enables you to smoothly scale up or down your business at any moment. It also works with the most popular operating systems, such as Linux, OS X, and Windows.
• Robust Transactional Support: ACID (Atomicity, Consistency, Isolation, and Durability) properties are used to ensure that no data is lost or inconsistent.
• Ease of Use: As it uses SQL to query data, anyone with a basic understanding of SQL can quickly do the essential tasks.

What is Amazon Aurora MySQL?

Amazon Aurora MySQL is a MySQL-compatible, fully managed relational Database engine that combines the performance and reliability of high-end commercial Databases with the simplicity and low cost of open-source Databases. Aurora MySQL is a drop-in alternative for MySQL that makes setting up, operating, and scaling new and current MySQL deployments straightforward and cost-effective, allowing you to focus on your company and applications.

Amazon RDS (Relational Database Service) is the main backbone that handles the Aurora tasks such as provisioning, patching, backup, recovery, failure detection, and repair. Amazon RDS also offers one-click migration tools for converting your Amazon RDS for MySQL applications to Aurora MySQL.

Some of the advanced features of Aurora MySQL Database (DB) Clusters include:

• One primary instance that allows read and write operations, as well as up to 15 Aurora Replicas that enable read-only operations. These can be promoted to the primary role automatically if the current primary instance fails.
• In the event of a failover, a cluster endpoint automatically follows the primary instance.
• A reader endpoint for all Aurora Replicas is automatically updated as Aurora Replicas are added or removed.
• For enhanced scalability, internal server connection pooling and thread multiplexing are used.
• Database restarts and recovery in a fraction of a second.
• Application Developers can design “smart drivers” using near-real-time cluster information, connecting directly to specific instances depending on their read-write or read-only roles.

What are the Methods for Migrating Data to an Amazon Aurora MySQL DB Cluster?

When moving data from an existing Database to an Amazon Aurora MySQL DB cluster, you have various alternatives. Your migration options are also influenced by the Database from which you’re migrating and the volume of data you’re moving. 

Mainly, there are 2 different types of migration: 

• Physical Migration
• Logical Migration

Physical Migration

The term “Physical Migration” refers to the usage of physical copies of Database files to move the Database.

Advantages of Physical Migration:

• Physical Migration, especially for large Databases, is faster than Logical Migration.
• When a backup is taken for physical migration, Database performance is unaffected.
• Everything in the source Database, including complex Database components, can be migrated via Physical Migration.

Limitations of Physical Migration:

• The innodb_page_size parameter must be set to its default value (16KB).
• This approach cannot be used to migrate Databases that have 2 data files or a data file with a different name.
• The innodb_log_files_in_group parameter must be set to its default value (2).

Logical Migration

The term “Logical Migration” refers to a migration that is carried out using logical Database modifications such as inserts, updates, and deletes.

Advantages of Logical Migration:

• Subsets of the Database, such as single tables or sections of a table, can be migrated.
• Regardless of the physical storage arrangement, data can be moved.

Limitations of Logical Migration:

• Compared to Physical Migration, Logical Migration is typically slower.
• The Logical Migration operation might be slowed down by complex Database components. They can even block Logical Migration in some circumstances.

Refer to Migrating Data to an Amazon Aurora MySQL DB cluster page to understand the methods and the type of migration to an Amazon Aurora MySQL DB Cluster for each option.

Load Data To Amazon Aurora MySQL using Hevo’s No-Code Data Pipeline

Hevo provides an Automated No Code Data Pipeline that helps you move data from or to Amazon Aurora MySQL. You can set up a Data Warehouse managed by Hevo on the fly, as part of the Integration process. The ingested data from the source is first stored in a Data Warehouse managed by Hevo and then loaded to the destination such as Databases, BI tools, or Business Applications.

Hevo’s fault-tolerant architecture will enrich and transform your data securely and consistently and load it to your destination without any assistance from your side. You can entrust us with your data transfer process by both ETL and ELT processes to a data warehouse, Reverse ETL processes to CRMS, etc and enjoy a hassle-free experience.

Here are more reasons to try Hevo:

• Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
• Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility, designed for everyone.
• Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
• Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
• Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

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Best Practices for Working with Amazon Aurora MySQL

This section explains how to use or migrate data to an Amazon Aurora MySQL DB cluster by leveraging the recommended practices. Some of the best practices of working with Amazon Aurora MySQL are:

• Optimize Large Aurora MySQL Join Queries with Hash Joins
• Use Amazon Aurora to Scale Reads for MySQL Database
• Use Amazon Aurora for Disaster Recovery with MySQL Databases
• Use Multithreaded Replication in Aurora MySQL
• Invoke AWS Lambda Functions using Native MySQL Functions

1) Optimize Large Aurora MySQL Join Queries with Hash Joins

When applying an equijoin to join a huge quantity of data, instead use a hash join that can increase query speed. Hash joins can be enabled in Aurora MySQL easily. Any complex expression can be a hash join column.

To see if a query can benefit from a hash join, first profile the query with the EXPLAIN command. The EXPLAIN statement informs you about the query execution strategy that will be used.

2) Use Amazon Aurora to Scale Reads for MySQL Database

You can integrate Amazon Aurora with your MySQL DB instance to take advantage of Amazon Aurora’s read scaling features and expand your MySQL DB instance’s read workload. To enable Aurora to read scale MySQL, create an Aurora MySQL DB cluster and set it up as a read replica of your MySQL DB instance. Then, to perform the read queries, connect to the Aurora MySQL cluster. In addition, RDS for MySQL DB instance or a MySQL database operating external to Amazon RDS can be used as the source database.

3) Use Amazon Aurora for Disaster Recovery with MySQL Databases

Using Amazon Aurora and your MySQL DB instance, you can make an offsite backup for disaster recovery. To do this, create an Amazon Aurora DB cluster and make it a read replica of your MySQL DB instance. This applies to a MySQL database running outside of Amazon RDS or an RDS for MySQL DB instance.

When you set up replication between a MySQL DB instance and an Amazon Aurora MySQL DB cluster, you need to keep an eye on it to make sure it stays healthy and, if required, repair it.

4) Use Multithreaded Replication in Aurora MySQL

When an Aurora MySQL DB cluster is used as a read replica for binary log replication, Aurora defaults to single-threaded replication. Multithreaded replication is not prohibited in Aurora MySQL, however, it is only supported in Aurora MySQL version 3 and higher.

Aurora MySQL versions 1 and 2 inherited from MySQL many difficulties with multithreaded replication. Hence, AWS doesn’t advocate using multithreaded replication in production for those versions.

5) Invoke AWS Lambda Functions using Native MySQL Functions

If you’re using Amazon Aurora version 1.16 or later, AWS recommends calling Lambda functions with the native MySQL functions lambda_sync and lambda_async.

• Use the synchronous lambda_sync function when you need to know the outcome of a Lambda function before going on to another action. 
• Use the asynchronous lambda_async function when you don’t need to know the result of the Lambda function before proceeding to the next step.

AWS suggests wrapping calls to the mysql.lambda_async procedure in a stored procedure if you’re using the deprecated mysql.lambda_async procedure. This stored procedure can be called from a variety of places, including triggers and client code. This strategy can assist your Database Programmers to avoid impedance mismatch issues and make calling Lambda functions easier.

Conclusion

In a nutshell, you gained a basic understanding of Amazon Aurora, and MySQL and why they are so popular in the industry. You also learned about Amazon Aurora MySQL DB Clusters and their advanced features. In addition, you explored the various methods to migrate your data to Aurora MySQL clusters. At the end of this article, you discovered the noteworthy and recommended practices you should follow while working with Amazon Aurora MySQL DB Clusters.

However, knowing where to start and how to combine data from various applications to Amazon Aurora MySQL can be a challenge for many companies. This is where Hevo can help save your day!

Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 100+ Data Sources including 40+ Free Sources, into your Data Warehouse such as Amazon Aurora MySQL to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.

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