Monitoring and alerting tools and best practices for Amazon RDS for MySQL

and MariaDB

AWS Prescriptive Guidance

Copyright © 2024 Amazon Web Services, Inc. and/or its affiliates. All rights reserved.

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

MySQL and MariaDB

AWS Prescriptive Guidance: Monitoring and alerting tools and best

practices for Amazon RDS for MySQL and MariaDB

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

MySQL and MariaDB

Table of Contents

Targeted business outcomes ...................................................................................................................... 3

Tools included in Amazon RDS .................................................................................................................. 8

CloudWatch namespaces ............................................................................................................................ 8

CloudWatch alarms and dashboards ........................................................................................................ 9

Amazon RDS Performance Insights ........................................................................................................ 11

Performance Insights metrics for DB instances ................................................................................... 18

Database load ........................................................................................................................................ 18

Dimensions ............................................................................................................................................. 19

Counter metrics ..................................................................................................................................... 20

SQL statistics ......................................................................................................................................... 23

CloudWatch metrics for DB instances .................................................................................................... 24

Publishing Performance Insights metrics to CloudWatch .................................................................. 24

Amazon RDS events .................................................................................................................................. 33

EventBridge rules ....................................................................................................................................... 47

Specifying actions, enabling, and disabling alarms ............................................................................ 49

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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# ..................................................................................................................................................................... 52

A ..................................................................................................................................................................... 53

B ..................................................................................................................................................................... 56

C ..................................................................................................................................................................... 58

D ..................................................................................................................................................................... 61

E ..................................................................................................................................................................... 65

F ..................................................................................................................................................................... 67

G ..................................................................................................................................................................... 68

H ..................................................................................................................................................................... 69

R ..................................................................................................................................................................... 83

S ..................................................................................................................................................................... 86

T ..................................................................................................................................................................... 90

U ..................................................................................................................................................................... 91

V ..................................................................................................................................................................... 92

W .................................................................................................................................................................... 92

Z ..................................................................................................................................................................... 93

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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June 2024 (document history)

You can monitor your resources to ensure that they are performing as expected, and to detect and

remediate any issues before they impact your customers. You should use the metrics, logs, events,

and traces that you monitor to raise alarms when thresholds are breached.

This guide describes database observability and monitoring tools and best practices for Amazon

Relational Database Service (Amazon RDS) databases. The guide focuses on MySQL and MariaDB

databases, although most of the information also applies to other Amazon RDS database engines.

This guide is for solutions architects, database architects, DBAs, senior DevOps engineers, and

other team members who engage in designing, implementing, and managing monitoring and

observability solutions for their database workloads running in the AWS Cloud.

• Alerting

• Next steps and resources

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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Monitoring and alerting are included in four pillars of the AWS Well-Architected Framework.

• The operational excellence pillar prescribes that your workload should be designed to include

telemetry and monitoring. AWS services such as Amazon Relational Database Service (Amazon

RDS) provide the information necessary for you to understand your workload's internal state (for

example, metrics, logs, events, and traces). When you operate your Amazon RDS databases, you

will want to understand the health of your databases instances, detect operational events, and

be able to respond to both planned and unplanned events. AWS provides monitoring tools that

parameters―that caused it. You can raise alarms automatically when measurements are outside

expected boundaries. We recommend that you use alarms not only for notifications, but also to

initiate automated actions in response to the detected events. You can evaluate the metrics you

collect against predefined thresholds or use machine learning algorithms to identify anomalous

behavior. For example, to detect a trend of increased CPU utilization, you can gather and

analyze the cpuUtilization.total metric over a period of time. Alerting on that anomaly

proactively, before CPU utilization hits the hard limit, can help you remediate the issue before it

impacts your customers.

• The reliability pillar defines monitoring and alerting as critical to ensure that you are meeting

your availability requirements. Your monitoring solution must be able to detect failures

• The security pillar discusses detection and prevention of unexpected or unwanted configuration

changes, and unexpected behavior. You can configure your Amazon RDS for MySQL and MariaDB

DB instances with the MariaDB Audit Plugin to record database activity such as user logins and

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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specific operations run against the database. The plugin stores the record of database activity in

a log file, which can be integrated and imported into monitoring and alerting tools. The log file

is analyzed in real time for unexpected or suspicious behavior in your database. Such unexpected

or suspicious behavior can indicate that your Amazon RDS DB instance has been compromised,

which signals potential risks to your business. If the monitoring tool detects such an event, it

activates an alarm to initiate a response to the security incident, which helps address suspicious

and malicious activities.

Targeted business outcomes

Implementing best practices in monitoring and alerting mechanisms helps you ensure a high-

performing, resilient, efficient, secure, and cost-optimized infrastructure for your applications and

workloads. You can use observability tools that gather, store, and visualize metrics, events, traces,

and logs in real time to observe and analyze the bigger picture of the health and performance of

your databases, and thus prevent the degradation or disruption of your associated IT services. If

unplanned degradation or service disruption still occurs, monitoring and alerting tools help you

monitor and set up alerts on your databases to detect problems quickly, investigate them

efficiently, and remediate them as soon as possible to minimize downtime and other disruptions,

you can enhance the availability, security, and performance of the digital service for your

customers.

• Build customer trust. Better performance and a smoother user experience help you win your

customers' trust, which can result in more business on your platform. For example, a payment

processing service provider that offers a reliable online service can expect high customer

trust and loyalty, which results in more customers and better retention, an increase in billable

transactions, and new, innovative services that generate more revenue.

• Avoid financial losses. Any unexpected downtime in your database infrastructure can impact

Targeted business outcomes 3

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basis for expensive trials, where customers might demand compensation based on your liability

and warranty contracts. According to a study by Atlassian Corporation, a software company, the

average costs of service outage are in the range of $140K‒$540K per hour, depending on the

type and size of the business. A stable database environment is key to preventing long outages

and loss of business.

existing cloud workloads, and introduce new services. New services provide additional value to

your customers and more revenue to your business, creating a flywheel effect on the growth of

your business.

• Improve developer productivity. Developers who are productive and efficient, and who don't

encounter issues and bottlenecks in their development tasks, can deliver high-quality products in

a shorter time. However, software engineering and IT operations often have complex challenges,

and this complexity increases with the scale of workloads and their architectures. To analyze

performance and consistency across distributed applications, developers need tools that

can provide correlated metrics and traces. These help identify defective code artifacts and

infrastructure components as quickly as possible, and help determine impacts on end users. The

Targeted business outcomes 4

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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You have to enable Performance Insights during the DB instance creation process to collect metrics

for the Amazon RDS DB instances in your account. The free tier includes seven days of performance

data history and one million API requests per month. Optionally, you can purchase longer retention

periods. For complete pricing information, see Performance Insights Pricing.

Enhanced Monitoring

Enhanced Monitoring is a tool that captures metrics in real time for the operating system (OS)

that your Amazon RDS DB instance runs on. These metrics provide up to one second granularity

for CPU, memory, Amazon RDS and OS processes, file system, and disk I/O data, among others.

You can access and analyze these metrics in the Amazon RDS console. As with Performance

Insights, Enhanced Monitoring metrics are delivered from Amazon RDS to CloudWatch, where you

can benefit from additional features such as the long-term preservation of metrics for analysis,

creating metrics filters, displaying graphs on the CloudWatch dashboard, and setting up alarms.

By default, Enhanced Monitoring is disabled when you create a new Amazon RDS DB instance. You

can enable the feature when you create or modify a DB instance. Pricing is based on the amount

Enhanced Monitoring 12

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Additional AWS services

deliver events from your applications and AWS resources, including your Amazon RDS DB

instances. An Amazon RDS event indicates a change in the Amazon RDS environment. For

example, when a DB instance changes its status from Available to Stopped, Amazon RDS

generates the event RDS-EVENT-0087 / The DB instance has been stopped. Amazon

RDS delivers events to CloudWatch Events and EventBridge in near real time. Using EventBridge

and CloudWatch Events, you can define rules to send alerts on specific Amazon RDS events of

interest and automate actions to be taken when an event matches the rule. A variety of targets

are available in response to an event, such as an AWS Lambda function that can perform a

corrective action, or an Amazon SNS topic that can send an email or SMS to notify DBAs or

DevOps engineers about the event.

• General log

• Audit log

You can use CloudWatch Logs Insights to query and analyze the log data. The feature

includes a purpose-built query language that helps you search for log events that match

patterns, which you define. For example, you can track table corruption in your MySQL DB

instance by monitoring the error log file for the following pattern: "ERROR 1034 (HY000):

Incorrect key file for table '*'; try to repair it OR Table * is marked

as crashed". Filtered log data can be converted into CloudWatch metrics. You can then use

the metrics to create dashboards with graphs or tabular data, or set an alarm if the defined

threshold value is breached. This is particularly useful when using the audit log, because you can

Additional AWS services 13

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behavior is detected. You can access and manage database logs by using the AWS Management

Console, the AWS CLI, the Amazon RDS API, or the AWS SDK for CloudWatch Logs.

a new Amazon RDS DB instance, an event is detected, and the log includes information

about the requested action ("eventName": "CreateDBInstance"), the date and

time of the action ("eventTime": "2022-07-30T22:14:06Z"), request parameters

("requestParameters": {"dBInstanceIdentifier": "test-instance", "engine":

"mysql", "dBInstanceClass": "db.m6g.large"}), and so on. Events that are logged by

CloudTrail include both calls from the Amazon RDS console and calls from code that uses the

Amazon RDS API.

Third-party monitoring tools

In some scenarios, in addition to the full suite of cloud-native observability and monitoring tools

that AWS provides for Amazon RDS, you might want to use monitoring tools from other software

system that runs the DB instance, because access to the database host machine is denied. However,

you can integrate many third-party monitoring solutions with Amazon RDS by building on top of

CloudWatch and other AWS Cloud services. For example, Amazon RDS metrics, logs, events, and

traces can be exported and then imported into the third-party monitoring tool for further analysis,

visualization, and alerting. Some of these third-party solutions include Prometheus, Grafana, and

Percona.

Prometheus is an open-source monitoring solution that collects metrics from configured targets

at given intervals. It is a general-purpose monitoring solution that can monitor any application

Third-party monitoring tools 14

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or service. When you monitor Amazon RDS DB instances, CloudWatch collects the metrics from

Amazon RDS. The metrics are then exported to the Prometheus server by using an open-source

exporter such as YACE exporter or CloudWatch Exporter.

• YACE exporter optimizes data export tasks by retrieving several metrics in a single request to the

• CloudWatch Exporter is officially maintained by Prometheus. It retrieves CloudWatch metrics

through the CloudWatch API and stores them on the Prometheus server in a format that's

compatible with Prometheus, by using REST API requests to the HTTP endpoint.

and Region to monitor hundreds of Amazon RDS DB instances could result in a throttling error

(ThrottlingException) and code 400 errors. To overcome such limitations, consider using YACE

exporter, which is optimized to collect up to 500 different metrics in a single request. Additionally,

to deploy a large number of Amazon RDS DB instances, you should consider using multiple AWS

accounts, instead of centralizing the workload into a single AWS account, and limiting the number

of exporter instances in each AWS account.

Alerts are generated by the Prometheus server and handled by Alertmanager. This tool takes care

of deduplicating, grouping, and routing alerts to the correct receiver such as email, SMS, or Slack,

or initiating an automated response action. Another open-source tool called Grafana displays

visualizations for these metrics. Grafana provides rich visualization widgets, such as advanced

Prometheus and Grafana 15

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Percona Monitoring and Management (PMM) is a free, open-source database monitoring,

management, and observability solution for MySQL and MariaDB. PMM collects thousands of

performance metrics from DB instances and their hosts. It provides a web UI to visualize data in

dashboards and additional features such as automatic advisors for database health assessments.

You can use PMM to monitor Amazon RDS. However, the PMM client (agent) isn't installed on

the underlying hosts of the Amazon RDS DB instances, because it doesn't have access to the

hosts. Instead, the tool connects to the Amazon RDS DB instances, queries server statistics,

(IAM) user access key (IAM role) and automatically discovers the Amazon RDS DB instances that

are available for monitoring. The PMM tool is profiled for database monitoring and collects

more database-specific metrics than Prometheus. To use the PMM Query Analytics dashboard,

you must configure the Performance Schema as the query source, because the Query Analytics

agent isn't installed for Amazon RDS and can't read the slow query log. Instead, it queries the

performance_schema from the MySQL and MariaDB DB instances directly to obtain metrics. One

of the prominent features of PMM is its ability to alert and advise DBAs on issues that the tool

identifies in their databases. PMM offers sets of checks that can detect common security threats,

performance degradation, data loss, and data corruption.

In addition to these tools, there are several commercial observability and monitoring solutions

Percona 16

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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other detailed, low-level monitoring information, which is collected by Amazon RDS Performance

Insights.

Performance Insights metrics for DB instances 18

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Dimensions 19

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• SQL shows which SQL statements are contributing the most to the total database load. The Top

dimensions table, which is located under the Database load chart in Amazon RDS Performance

Insights, is interactive. You can obtain a detailed list of wait events associated with the SQL

statement by clicking the bar in the Load by waits (AAS) column. When you select an SQL

statement in the list, Performance Insights displays the associated wait events in the Database

load chart and the SQL statement text in the SQL text section. SQL statistics are displayed on

the right side of the Top dimensions table.

Counter metrics 20

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Manage Metrics, choose the Database metrics tab, and then select the metrics of interest, as

shown in the following illustration.

Counter metrics 21

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The following chart shows similar metrics displayed in the CloudWatch dashboard.

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

MySQL and MariaDB

The other set of OS metrics is collected by Enhanced Monitoring for Amazon RDS. This tool gives

you deeper visibility into the health of your Amazon RDS for MariaDB and Amazon RDS for MySQL

DB instances, by providing real-time system metrics and OS process information. When you enable

Enhanced Monitoring on your DB instance and set the desired granularity, the tool collects the

operating system metrics and process information, which you can display and analyze on the

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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•

memory.active – The amount of assigned memory, in kilobytes.

•

memory.cached – The amount of memory used for caching file system-based I/O.

•

loadAverageMinute.one – The number of processes that requested CPU time during the last

minute.

For a complete list of metrics, see OS metrics in Enhanced Monitoring in the Amazon RDS

documentation.

On the Amazon RDS console, the OS process list provides details for each process that's running in

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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• OS processes ‒ This section represents an aggregated summary of all kernel and system

processes. These processes generally have minimal impact on database performance.

The following screen illustration shows the OS process list in the Amazon RDS console.

Amazon RDS delivers the metrics from Enhanced Monitoring into your CloudWatch Logs account.

The monitoring data that is shown on the Amazon RDS console is retrieved from CloudWatch Logs.

You can also retrieve the metrics for a DB instance as a log stream from CloudWatch Logs. These

metrics are stored in JSON format. You can consume the Enhanced Monitoring JSON output from

CloudWatch Logs in a monitoring system of your choice.

In order to display graphs on the CloudWatch dashboard and create alarms that would initiate an

action if a metric breaches the defined threshold, you must create metric filters in CloudWatch

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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from CloudWatch Logs. For detailed instructions, see the AWS re:Post article on how to filter

Enhanced Monitoring CloudWatch Logs to generate automated custom metrics for Amazon RDS.

The following example illustrates the custom metric CPU.User in the Custom/RDS namespace.

This custom metric is created by filtering the cpuUtilization.user Enhanced Monitoring metric

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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and best practices that help you achieve and maintain the reliability, availability, performance,

• Database logs

• Audit trails

Amazon RDS events

An Amazon RDS event indicates a change in the Amazon RDS environment. For example, when

the DB instance status changes from Starting to Available, Amazon RDS generates the event RDS-

EVENT-0088 The DB instance has been started. Amazon RDS delivers events to Amazon

EventBridge in near real time. You can access events though the Amazon RDS console, the AWS CLI

command describe-events, or the Amazon RDS API operation DescribeEvents. The following screen

Amazon RDS events 33

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Amazon RDS events 34

AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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Amazon RDS emits different types of events, including DB instance events, DB parameter

group events, DB security group events, DB snapshot events, RDS Proxy events, and blue/green

deployment events. The information includes:

•

Amazon RDS events 35

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Amazon RDS events 36

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Database logs

were accessed, and which server variables were changed.

Database logs 37

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Alerts are one of the most important information sources when it comes to the security,

availability, performance, and reliability of your IT infrastructure and IT services. They notify and

(for items that are automatically actionable) about changes, high-risk actions, or failures in the IT

environment. IT alerts are the first line of defense against system outages or changes that can turn

into major incidents. By automatically monitoring systems and generating alerts for outages and

risky changes, IT teams can minimize downtime and reduce the high cost that comes with it.

As best practices, the AWS Well-Architected Framework prescribes that you use monitoring to

generate alarm-based notifications, and monitor and alarm proactively. Use CloudWatch or a

third-party monitoring service to set alarms that indicate when metrics are outside of expected

boundaries.

The purpose of alert management is to establish efficient, standardized procedures for handling IT-

related events and incidents through logging, classification, action definition and implementation,

When you operate your Amazon RDS DB instances, you want to monitor and generate alerts

on different kinds of metrics, events, and traces. For MySQL and MariaDB databases, the critical

sources of information are DB instance metrics, OS metrics, events, logs, and audit trails. We

CloudWatch alarms 44

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recommend that you use CloudWatch alarms to watch a single metric over a time period that you

specify.

The following example illustrates how you can set an alarm that watches the CPUUtilization

metric (percentage of CPU utilization) on all your Amazon RDS DB instances. You configure the

alarm to be triggered if the CPU utilization on any DB instance is greater than 80 percent for the

evaluation period of 5 minutes.

CloudWatch alarms 45

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This means that the alarm goes into the ALARM state if any of your databases experiences a high

CPU utilization (over 80 percent) for 5 minutes or more. The alarm remains in the OK state if the

CloudWatch alarms support metric and composite alarms.

• A metric alarm watches a single CloudWatch metric and can perform mathematical expressions

CloudWatch alarms 46

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CloudWatch alarms can watch only CloudWatch metrics. If you want to create an alarm based

on the error, slow query, or general logs, you must create CloudWatch metrics from the logs.

You can accomplish that as discussed earlier in the OS monitoring and Events, logs, and audit

trails sections, by using filters to create metrics from log events. Similarly, to alert on Enhanced

Monitoring metrics, you must create metric filters in CloudWatch from CloudWatch Logs.

EventBridge rules 47

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EventBridge rules 48

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Specifying actions, enabling, and disabling alarms

Specifying actions, enabling, and disabling alarms 49

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AWS Prescriptive Guidance Monitoring and alerting tools and best practices for Amazon RDS for

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Example:Migrate a Microsoft Hyper-V application to AWS.

• Retain (revisit) – Keep applications in your source environment. These might include

applications that require major refactoring, and you want to postpone that work until a later

time, and legacy applications that you want to retain, because there’s no business justification

for migrating them.

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• Retire – Decommission or remove applications that are no longer needed in your source

environment.

A

ABAC

See attribute-based access control.

abstracted services

See managed services.

small, controlled batches instead of requiring a one-time cutover. It’s more flexible but requires

more work than active-passive migration.

active-passive migration

A database migration method in which in which the source and target databases are kept in

sync, but only the source database handles transactions from connecting applications while

data is replicated to the target database. The target database doesn’t accept any transactions

during migration.

aggregate function

A SQL function that operates on a group of rows and calculates a single return value for the

group. Examples of aggregate functions include SUM and MAX.

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cold cache

A buffer cache that is empty, not well populated, or contains stale or irrelevant data. This

affects performance because the database instance must read from the main memory or disk,

which is slower than reading from the buffer cache.

for CV.

C 60

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CV

A meaningful variation between the production data and the data that was used to train an ML

model, or a meaningful change in the input data over time. Data drift can reduce the overall

quality, accuracy, and fairness in ML model predictions.

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data preprocessing

An ML subfield that uses multiple layers of artificial neural networks to identify mapping

between input data and target variables of interest.

D 62

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defense-in-depth

An information security approach in which a series of security mechanisms and controls are

delegated administrator for that service. For more information and a list of compatible services,

See environment.

detective control

A security control that is designed to detect, log, and alert after an event has occurred.

These controls are a second line of defense, alerting you to security events that bypassed the

preventative controls in place. For more information, see Detective controls in Implementing

security controls on AWS.

development value stream mapping (DVSM)

A process used to identify and prioritize constraints that adversely affect speed and quality in

a software development lifecycle. DVSM extends the value stream mapping process originally

designed for lean manufacturing practices. It focuses on the steps and teams required to create

and move value through the software development process.

D 63

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digital twin

A virtual representation of a real-world system, such as a building, factory, industrial

dimension table

like text. These attributes are commonly used for query constraining, filtering, and result set

deployed location. These events can be natural disasters, technical failures, or the result of

human actions, such as unintentional misconfiguration or a malware attack.

disaster recovery (DR)

The strategy and process you use to minimize downtime and data loss caused by a disaster. For

more information, see Disaster Recovery of Workloads on AWS: Recovery in the Cloud in the

AWS Well-Architected Framework.

Software (Boston: Addison-Wesley Professional,2003). For information about how you can use

domain-driven design with the strangler fig pattern, see Modernizing legacy Microsoft ASP.NET

(ASMX) web services incrementally by using containers and Amazon API Gateway.

DR

See disaster recovery.

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drift detection

Tracking deviations from a baselined configuration. For example, you can use AWS

CloudFormation to detect drift in system resources, or you can use AWS Control Tower to detect

changes in your landing zone that might affect compliance with governance requirements.

DVSM

See development value stream mapping.

E

EDA

A computing process that transforms plaintext data, which is human-readable, into ciphertext.

encryption key

A cryptographic string of randomized bits that is generated by an encryption algorithm. Keys

can vary in length, and each key is designed to be unpredictable and unique.

endianness

The order in which bytes are stored in computer memory. Big-endian systems store the most

significant byte first. Little-endian systems store the least significant byte first.

endpoint

See service endpoint.

endpoint service

E 65

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or to AWS Identity and Access Management (IAM) principals. These accounts or principals

can connect to your endpoint service privately by creating interface VPC endpoints. For more

information, see Create an endpoint service in the Amazon Virtual Private Cloud (Amazon VPC)

documentation.

enterprise resource planning (ERP)

computing:

• development environment – An instance of a running application that is available only to the

core team responsible for maintaining the application. Development environments are used

to test changes before promoting them to upper environments. This type of environment is

• lower environments – All development environments for an application, such as those used

for initial builds and tests.

• production environment – An instance of a running application that end users can access. In a

CI/CD pipeline, the production environment is the last deployment environment.

• upper environments – All environments that can be accessed by users other than the core

development team. This can include a production environment, preproduction environments,

provide a high-level description of requirements and implementation tasks. For example, AWS

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ERP

aggregate data and then perform initial investigations to find patterns, detect anomalies,

and check assumptions. EDA is performed by calculating summary statistics and creating data

visualizations.

F

fact table

The central table in a star schema. It stores quantitative data about business operations.

Typically, a fact table contains two types of columns: those that contain measures and those

that contain a foreign key to a dimension table.

fail fast

A philosophy that uses frequent and incremental testing to reduce the development lifecycle. It

See branch.

features

The input data that you use to make a prediction. For example, in a manufacturing context,

features could be images that are periodically captured from the manufacturing line.

feature importance

How significant a feature is for a model’s predictions. This is usually expressed as a numerical

score that can be calculated through various techniques, such as Shapley Additive Explanations

F 67

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(SHAP) and integrated gradients. For more information, see Machine learning model

interpretability with :AWS.

feature transformation

To optimize data for the ML process, including enriching data with additional sources, scaling

values, or extracting multiple sets of information from a single data field. This enables the ML

The use of multiple conditions to allow or deny an access request.

flash-cut migration

A database migration method that uses continuous data replication through change data

capture to migrate data in the shortest time possible, instead of using a phased approach. The

objective is to keep downtime to a minimum.

G

geo blocking

See geographic restrictions.

geographic restrictions (geo blocking)

In Amazon CloudFront, an option to prevent users in specific countries from accessing content

modern, preferred approach.

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greenfield strategy

The absence of existing infrastructure in a new environment. When adopting a greenfield

strategy for a system architecture, you can select all new technologies without the restriction

of compatibility with existing infrastructure, also known as brownfield. If you are expanding the

existing infrastructure, you might blend brownfield and greenfield strategies.

guardrail

A high-level rule that helps govern resources, policies, and compliance across organizational

units (OUs). Preventive guardrails enforce policies to ensure alignment to compliance standards.

They are implemented by using service control policies and IAM permissions boundaries.

Detective guardrails detect policy violations and compliance issues, and generate alerts

Migrating your source database to a target database that uses a different database engine

(for example, Oracle to Amazon Aurora). Heterogeneous migration is typically part of a re-

that helps with schema conversions.

high availability (HA)

The ability of a workload to operate continuously, without intervention, in the event of

challenges or disasters. HA systems are designed to automatically fail over, consistently deliver

high-quality performance, and handle different loads and failures with minimal performance

impact.

historian modernization

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migration factory

Cross-functional teams that streamline the migration of workloads through automated, agile

metadata include the target subnet, security group, and AWS account.

migration pattern

A repeatable migration task that details the migration strategy, the migration destination, and

An online tool that provides information for validating the business case for migrating to

the AWS Cloud. MPA provides detailed portfolio assessment (server right-sizing, pricing, TCO

comparisons, migration cost analysis) as well as migration planning (application data analysis

and data collection, application grouping, migration prioritization, and wave planning). The

MPA tool (requires login) is available free of charge to all AWS consultants and APN Partner

consultants.

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ML

milestones for the modernization process, and an action plan for addressing identified gaps. For

more information, see Evaluating modernization readiness for applications in the AWS Cloud.

monolithic applications (monoliths)

Applications that run as a single service with tightly coupled processes. Monolithic applications

have several drawbacks. If one application feature experiences a spike in demand, the

entire architecture must be scaled. Adding or improving a monolithic application’s features

also becomes more complex when the code base grows. To address these issues, you can

use a microservices architecture. For more information, see Decomposing monoliths into

microservices.

MPA

"Which product category is most interesting to this customer?"

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mutable infrastructure

A model that updates and modifies the existing infrastructure for production workloads. For

improved consistency, reliability, and predictability, the AWS Well-Architected Framework

recommends the use of immutable infrastructure as a best practice.

O

OAC

See origin access control.

OAI

See origin access identity.

OCM