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Do You Have A Good Architectural Sense?
AWS Well-Architected Framework can assist in understanding both advantages and disadvantages when building systems on the cloud.
By understanding its six pillars, you'll discover ways of designing systems that are reliable, secure and efficient; furthermore, the AWS Management Console offers a free tool called AWS Well-architected, which helps users compare workloads against these best practices by answering questions about each of its six pillars; further guidance regarding cloud architecture deployments diagrams whitepapers can also be found within AWS Architecture Center.
The Following Is A Brief Introduction To The Topic:
Relational databases have been the go-to choice for data storage and persistence for decades. Unfortunately, many of them still feature monolithic architecture that doesn't take advantage of cloud infrastructure; such architecture creates numerous costs, availability, and flexibility issues that Amazon Aurora seeks to address.
Amazon Aurora is an open-source MySQL and PostgreSQL-compatible relational database engine designed to combine high-end commercial motors' speed, availability and security with the simplicity and cost-effectiveness of open-source engines.
Aurora can deliver up to five times better performance than MySQL and three times better than PostgreSQL for comparable high-end performance - costing 1/10th of commercial machines!
Amazon Aurora can be accessed via Amazon RDS database services, providing access in an automated fashion. Amazon RDS handles most database management tasks for Aurora, such as hardware provisioning and software patches, as well as automatically setup, configuration, monitoring and backup duties.
Amazon Aurora was designed for high-availability workloads. Aurora clusters span multiple availability zones within each Region for fault tolerance and data durability across data centers.
A region may include one or multiple highly available data centers operated by Amazon; each Availability Zone features low latency connections separating itself from each other for fault-tolerance; six copies of every segment exist across Availability Zones.
Create up to 15 Aurora Replicas easily for applications requiring read-only copies with minimal replica lag using the same storage as their parent instance, thus lowering costs and eliminating the need for replica nodes to write data directly back to storage.
Aurora Global Database can also support high throughput reads across six regions for up to 90 replicas - perfect for high throughput applications!
Amazon Aurora provides an extremely secure way to encrypt databases. Key management service allows for creating keys and managing them efficiently; Amazon Aurora encryption encrypts data at rest stored in an underlying storage volume - such as automated backups and snapshots - encrypts replicas within clusters as well as providing transit security via SSL (AES-256).
Amazon Aurora's product page lists its many features. Amazon Aurora is rapidly becoming one of the go-to databases for mission-critical applications due to its rich feature set and affordable costs.
Amazon Aurora Serverless is an automatic configuration that adjusts capacity according to application needs, instantly scaling from hundreds of thousands of transactions up to billions within milliseconds, with database capacity tailored exactly as necessary by an application's needs - saving time by not worrying about managing database capacity while paying only for what your application utilizes.
Aurora Serverless V2 offers an economical and straightforward solution to customers with irregular workloads, multiple databases, or who prefer cost certainty in their decision-making processes.
Fixed-size instances could also be beneficial in meeting such needs more precisely.
Amazon Aurora features discussed here are intended to apply equally to both MySQL and PostgreSQL database engines unless specifically mentioned otherwise; migration practices outlined are relevant only for Aurora MySQL engines; please consult the Amazon Aurora User Guide for details regarding best practices specific to PostgreSQL engines.
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Considerations For Database Migration
Data storage and management is at the core of most applications, making database migration an integral element in improving their functionality, performance and reliability.
When beginning Amazon Aurora migration projects, there are various factors you must take into consideration before starting this endeavor.
Considerations For Application
Evaluate Aurora features:
Amazon Aurora was designed to be fully wire compatible with MySQL 5.6 & 5.7 databases; therefore, most applications, drivers and code used today for creating MySQL databases can run smoothly in Aurora without needing modifications.
Amazon Aurora does not support certain MySQL features like MyISAM due to being managed. At the same time, SSH cannot access database hosts, potentially restricting you from accessing third-party tools and plugins.
Performance Considerations:
Migration should include considering database performance. Most successful database migrations start by evaluating their new platform's performance.
Typically, the Amazon Aurora Performance Assessment gives an idea about overall database performance but doesn't mirror the data access patterns of your application. Therefore, directly running queries (or a subset thereof) will provide an accurate representation.
Take A Look At These Ideas:
- Migration of your MySQL database to Amazon Aurora should take no downtime; you can perform tests of its performance using either staging/test versions of your app or by recreating the production workload.
- It would be best to use non-MySQL engines to test busy table queries as this is an initial starting point; testing after data migration is the only real way of knowing exactly how your application will operate.
- Amazon Aurora delivers performance comparable to commercial engines and significantly better than MySQL by tightly integrating a virtualized SSD storage layer designed specifically for database workloads with its database engine, thus decreasing writes to storage, eliminating database thread delays and decreasing lock contention.
- SysBench tests with Amazon Aurora at its core version r5.16xlarge delivered approximately 800,000.000 reads per second with nearly as many writes (200,000 per second ) being output, providing five times faster performance when running this benchmark on identical hardware than when tested against MySQL.
- Amazon Aurora stands out as an impressive alternative to MySQL when handling highly concurrent workloads, offering greater performance at running multiple concurrent queries simultaneously. To optimize throughput for Amazon Aurora workloads, we advise creating applications designed specifically to utilize this technology and run more queries concurrently than before.
Considerations For Sharding And Reading Replica
As part of your migration, the Aurora database may enable you to combine database shards that span different nodes into a single Aurora instance - offering up to 128TB of storage with AWS data engineer thousands of tables at much faster read/write rates than traditional MySQL databases.
Consider Aurora replicas to offload read-only workloads from your primary database, increasing concurrency while improving read/write and multi-AZ configuration performance.
Aurora read images have near-zero replication delays, allowing up to 15 representations per cluster configuration - perfect for multi-AZ arrangements!
Considerations For Reliability
High availability and disaster relief should both be top priorities when considering databases. Calculate your Recovery Time Objective (RTO) and Recovery Point Objective (RPO), using Amazon Aurora as your resource to help strengthen these two factors.
Amazon Aurora makes restarting databases faster in many situations by moving buffer caches out of database processes and making them available instantly at restart time.
Furthermore, this database platform automatically recovers in cases related to hardware or Availability Zone failure.
Aurora provides zero Recovery Point Objective within AWS regions, an impressive feat compared to traditional on-premise databases.
Aurora maintains six copies of your database across three Availability Zones for automatic restore with minimal data loss; should data become inaccessible at Amazon Aurora, you may restore from DB Snapshots or perform restore point-in-time on another instance if necessary.
Amazon Aurora provides cross-region Disaster Recovery (DR) through a feature called global databases, designed for applications supporting globally distributed transaction environments and across multiple AWS regions.
Aurora replicates your data across areas using storage-based copying, with typical latency time being less than one second; this does not impact database performance but allows fast reads in each Region while disaster recovery when one Region goes offline - quickly moving read/write workloads over to an alternative AWS region within minutes in case there's an outage, or regional outage occurs! Binlog allows you to set up Aurora Read Replicas across different AWS Regions by creating up to five Aurora Read Replicas per cluster across other AWS Regions.
Considerations On Licensing And Cost
Costs associated with owning and operating databases vary significantly, making TCO analysis essential before planning any database migration.
Migrating to another platform aims to lower total ownership while still offering similar or enhanced features for applications; costs associated with an open-source engine such as MySQL or Postgres typically consist of hardware costs, server administration expenses and database maintenance activities, while for commercial engines like Oracle SQL Server DB2, the majority of prices is likely related to licensing arrangements.
Amazon Aurora costs are one-tenth of other commercial engines, so many applications can reduce total cost of ownership (TCO) by switching.
You could save money switching over even if your application uses open-source engines such as MySQL or Postgres; Aurora provides high-performance dual-purpose read replicas at low costs - for more details, visit the Amazon Aurora Pricing Page.
Read More: Aws Cloud Application Development Is The Top Choice For Businesses Why?
Considerations For Other Migrations
Once you've examined various criteria such as application compatibility, performance, total cost of ownership (TCO), reliability and infrastructure cost effectiveness, it may be time to migrate your current platform onto another one.
Estimating code change effort: When migrating MySQL-compatible databases to Amazon Aurora, you must count how many schema and code changes must be made during migration.
While minimal code modifications should be necessary when migrating databases that are MySQL compatible directly from other engines such as Azure SQL Database or MongoDB, when migrating now from these engines, it may require changes that must be estimated with AWS Schema Conversion Tool (for more details, see: "Schema Migration with AWS Schema Conversion Tool in this document). This tool provides estimates using the AWS Schema Conversion Tool; further information in this document).
Applications Available during Migration: When migrating applications, consider either a low-downtime or predictable approach depending on their availability needs and database size.
It is also wise to evaluate how the migration impacts business operations and applications before beginning its execution; both methods will be explored further below.
As part of the migration, it may be necessary to modify connection strings for all applications so they use the new database.
One possible approach may include changing all application connection strings. At the same time, DNS could provide another means without using host names in this instance. Consider creating a CNAME record pointing directly at your database hostname instead.
This way, you can modify the connection string for your application in just one location rather than manually managing multiple settings. Monitoring how long its TTL value remains valid is also important before adding or changing its settings. You could set this value too high, which causes the hostname pointed at by this CNAME to stay cached longer or put it too low, causing additional processing overhead as this CNAME needs to be resolved frequently by applications, although each use case varies.
An initial TTL value of 5 seconds might provide sufficient starting points.
Planning Your Database Migration Process
As previously outlined in this section, we outlined key considerations when migrating your databases to Amazon Aurora.
Once it is determined that Aurora meets your application needs, an action plan and decision strategy need to be developed as to how best to proceed with migration.
Homogeneous Migration:
Your source database must be compatible with MySQL-5.6 or MySQL-5.7 databases like MariaDB or Percona for a seamless transition to Aurora.
Migration between MySQL 5.6/5.7 and Aurora should be easy.
Migration Homogeneous With No Downtime:
Migration with downtime, when applicable and feasible for your application, is typically the quickest and least disruptive solution to database migrations; it is highly recommended as most apps have set maintenance periods to facilitate such activities.
There are other available methods of migrating databases without downtime, however.
- RDS Snapshot Migration -- You can migrate your database from Amazon RDS MySQL 5.5.6 or 5.5.7 to Amazon Aurora by migrating a snapshot. When performing snapshot migrations and test migrations, applications or users must stop updating databases during snapshot migrations; test migrations will help determine how long migration will take, depending on its size. To learn more, refer to this document's RDS Snapshot Migration section.
- Migrating to Aurora using native MySQL Tools -- If you want more control of the migration process and prefer native MySQL tools over alternative migration techniques, using native MySQL tools for migration could be ideal. Other migration techniques might not work as effectively: Create mysqldump dump and import into an Amazon Aurora MySQL DB Cluster using mysqldump; copy incremental/full backup files from MySQL into Amazon S3 buckets, then restore these into an Aurora MySQL cluster; this method may prove faster for migrating data than mysqldump for migrating data- migrate MySQL data using S3 buckets as described here.
- Migration with AWS Database Migration Service - AWS DMS offers one-time migration if your Amazon Aurora database requires migration. First, copy the schema between databases using native MySQL tools before migrating data via AWS DMS; please see this document's Migrating Data section for instructions and step-by-step guidelines on migrating data with AWS DMS. It may be more suitable if you don't possess experience working with native MySQL tools directly.
Migration Homogeneous With Almost Zero Downtime:
Your database could easily move between SQL and Aurora quickly - here are two scenarios to get you going:
- If the database size is large and your maintenance window does not permit enough downtime options, consider hiring outside help to handle it for an alternative maintenance approach.
- Test both databases simultaneously by running them simultaneously in parallel mode.
Replication is an ideal way of replicating any changes made in MySQL onto Aurora databases, with two available solutions.
These options may include:
- Amazon Aurora supports traditional MySQL binlog replication for near-zero downtime migration using MySQL binlog replication. If you manage a MySQL database, you may already be familiar with it; using one-time loads with native tools gives more control.
- Migration with AWS Database Migration Service -- AWS DMS offers past and real-time replication using Change Data Capture (CDC), taking care of initial copying, setting up replica instances and monitoring replication synchronization after migration. AWS DMS offers another great alternative if you don't already use binlog replica; its implementation ensures homogenous migration with nearly zero downtime lag times. This document includes a section about it for reference.
- Migrate with minimal downtime using Aurora Read Replica: If you run Amazon RDS MySQL 5.6.x or 5.7.x, create an Aurora Read Replica of its source instance; when its replica lag reaches zero between it and MySQL DB source instance, point client applications at it instead and migrate. Please refer to this document's Migration with Aurora Read Replica part for guidance in using this migration option.
Migration Heterogeneous
Migration options exist for moving non-MySQL databases over to Amazon Aurora.
Schema Migration:
AWS Schema Conversion Tool can assist with non-MySQL database migration to Amazon AWS cloud migration Aurora.
As a desktop tool for Oracle, Microsoft SQL Server or PostgreSQL database users who cannot convert their schema automatically using another method or when necessary, when they cannot automatically convert, create an equivalent schema when possible (please refer to Migrating database schema section of this document for details).
Data Migration:
AWS Database Migration Service offers consistent and near-zero-downtime database migrations and continuous replication for heterogeneous database data.
AWS DMS manages all aspects of migration, including compression and parallel transfers, for faster transfer speeds.
Third-party tools such as Qlik Replicate (formerly Qlik Replicate), Tungsten Replicator or Oracle Golden Gate can also help facilitate Amazon Aurora migrations; before selecting one for use during your transition process, take into consideration performance costs and licensing fees when choosing.
Migration Of Large Databases To Amazon Aurora
Every database migration project faces unique obstacles when migrating large datasets, and many successful large database migrations apply strategies such as those listed below:
- Continuous Replication: Moving large databases requires longer downtime; to reduce it further, load baseline data onto your target database first and enable replication afterward. This may help cut your rest in half!
- First copy static tables: When moving databases that rely heavily on stationary data tables, it is recommended to move these first. AWS DMS allows for export and import functionality and makes this step simple.
- Multiphase Migration: Migration should occur over multiple phases when migrating large databases containing thousands of tables. You could, for instance, move tables without cross-join queries each weekend until your source database has been fully migrated. To accomplish this goal, your application would need to connect simultaneously to both databases while your data resides on separate nodes; although less common, this form of migration still offers options.
- Database Clean-up: Spillover from Database Migrations: Large databases often hold unneeded tables and data unused by DBAs and developers alike, making copies within their same database or forgetting to delete unused ones. Migration provides the perfect opportunity for database cleaners to tidy up before their migration, archive or drop tables no longer needed or archive them into flat files to free up space in large tables.
Amazon Aurora: Partitioning and Consolidation of Shards
You can combine all your functional partitions into a single Aurora Database if you run multiple divisions to achieve high performance.
Amazon Aurora instances support up to 128TB of data and have much higher read and write rates than MySQL databases; consolidating sections onto one Aurora database simplifies database administration, reduces TCO, and enhances query performance across divisions.
- Functional Partitioning: Functional partitioning refers to assigning different functions or nodes within an ecommerce app to different databases; one database could be dedicated to catalogs while the other processes orders; these partitions typically exist separately and non-overlapping.
- Strategy for consolidation: Use AWS DMS or native MySQL tools for schema migration to Aurora instances on AWS, while AWS Schema Conversion Tool may also help convert non-MySQL compliant database sources to Aurora tables; then AWS DMS may be used either once or continuously when loading data through DMS.
- Data Sharding: When multiple nodes use the same database schema with distinct data sets stored on them simultaneously, database sharding occurs. A high-traffic blog service might utilize database shards with identical table structures to spread users' activity data over numerous nodes of its database sharding system.
- Strategy for Consolidation: All shards will use a common schema database, so only create it once. Use native tools or AWS Schema Conversion Tool if using MySQL databases; for other databases, use native tools instead or AWS Schema Conversion Tool as applicable for migration purposes. After completing your database schema migration, it is best to stop writing to database shards until data loading can take place using native tools or AWS DMS replication if your writing will continue over an extended period.
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Conclusion
Amazon Aurora makes dynamic resizing of database storage space simple. Aurora clusters expand as your data grows without negatively affecting performance or availability, eliminating the need to estimate and provision large amounts of database storage in advance.
Your aws data analytics Amazon Aurora database cluster's capacity may expand to 128 terabytes and decrease automatically with deleted records.
