“Local,” “fog,” and “cloud” resources refer to different levels of computing infrastructure and data storage, each with its own characteristics and applications. Here’s a breakdown of each:
Local Resources:
Local resources refer to computing resources (such as servers, storage devices, and networking equipment) that are located on-premises, within an organization’s physical facilities.
These resources are typically owned, operated, and maintained by the organization itself.
Local resources offer direct control and physical access, which can be advantageous for certain applications that require high performance, low latency, or strict security measures.
However, managing local resources requires significant upfront investment in hardware, software, and IT personnel, and scalability may be limited by physical constraints.
Fog Resources:
Fog computing extends the concept of cloud computing to the edge of the network, closer to where data is generated and consumed.
Fog resources typically consist of computing devices (such as edge servers, routers, and gateways) deployed at the network edge, such as in factories, retail stores, or IoT (Internet of Things) devices.
The term “fog” emphasizes the idea of bringing the cloud closer to the ground, enabling real-time data processing, low-latency communication, and bandwidth optimization.
Fog computing is well-suited for applications that require rapid decision-making, real-time analytics, or offline operation in environments with intermittent connectivity.
By distributing computing tasks across fog nodes, organizations can reduce the reliance on centralized cloud data centers and improve overall system performance and reliability.
Cloud Resources:
Cloud resources refer to computing services (such as virtual machines, storage, databases, and applications) that are delivered over the internet by third-party providers.
These resources are hosted in remote data centers operated by cloud service providers (e.g., Amazon Web Services, Microsoft Azure, Google Cloud Platform).
Cloud computing offers scalability, flexibility, and cost-effectiveness, as organizations can provision resources on-demand and pay only for what they use.
Cloud services are accessed over the internet from anywhere with an internet connection, enabling remote access, collaboration, and mobility.
Cloud computing is ideal for a wide range of use cases, including web hosting, data storage and backup, software development and testing, big data analytics, machine learning, and more.
In summary, while local resources provide direct control and physical proximity, fog resources enable edge computing capabilities for real-time processing and low-latency communication, and cloud resources offer scalability, flexibility, and accessibility over the internet. Organizations may choose to leverage a combination of these resource types to meet their specific requirements for performance, reliability, security, and cost-effectiveness.
Availability and reachability is improved by adding one more server. However, the entire system can again become unavailable if there is a capacity issue. Let’s look at that load issue with both types of systems we discussed, active-passive and active-active.
Vertical Scaling
If there are too many requests sent to a single active-passive system, the active server will become unavailable and hopefully failover to the passive server. But this doesn’t solve anything. With active-passive, you need vertical scaling. This means increasing the size of the server. With EC2 instances, you select either a larger type or a different instance type. This can only be done while the instance is in a stopped state. In this scenario, the following steps occur:
Stop the passive instance. This doesn’t impact the application since it’s not taking any traffic.
Change the instance size or type, then start the instance again.
Shift the traffic to the passive instance, turning it active.
The last step is to stop, change the size, and start the previous active instance as both instances should match.
When the amount of requests reduces, the same operation needs to be done. Even though there aren’t that many steps involved, it’s actually a lot of manual work to do. Another disadvantage is that a server can only scale vertically up to a certain limit.
Once that limit is reached, the only option is to create another active-passive system and split the requests and functionalities across them. This could require massive application rewriting.This is where the active-active system can help. When there are too many requests, this system can be scaled horizontally by adding more servers.
Horizontal Scaling
As mentioned above, for the application to work in an active-active system, it’s already created as stateless, not storing any client session on the server. This means that having two servers or having four wouldn’t require any application changes. It would only be a matter of creating more instances when required and shutting them down when the traffic decreases.
The Amazon EC2 Auto Scaling service can take care of that task by automatically creating and removing EC2 instances based on metrics from Amazon CloudWatch.
You can see that there are many more advantages to using an active-active system in comparison with an active-passive. Modifying your application to become stateless enables scalability.
Integrate ELB with EC2 Auto Scaling
The ELB service integrates seamlessly with EC2 Auto Scaling. As soon as a new EC2 instance is added to or removed from the EC2 Auto Scaling group, ELB is notified. However, before it can send traffic to a new EC2 instance, it needs to validate that the application running on that EC2 instance is available.
This validation is done via the health checks feature of ELB. Monitoring is an important part of load balancers, as it should route traffic to only healthy EC2 instances. That’s why ELB supports two types of health checks.
Establishing a connection to a backend EC2 instance using TCP, and marking the instance as available if that connection is successful.
Making an HTTP or HTTPS request to a webpage that you specify, and validating that an HTTP response code is returned.
Differentiate Between Traditional Scaling and Auto Scaling
With a traditional approach to scaling, you buy and provision enough servers to handle traffic at its peak. However, this means that at night time, there is more capacity than traffic. This also means you’re wasting money. Turning off those servers at night or at times where the traffic is lower only saves on electricity.
The cloud works differently, with a pay-as-you-go model. It’s important to turn off the unused services, especially EC2 instances that you pay for On-Demand. One could manually add and remove servers at a predicted time. But with unusual spikes in traffic, this solution leads to a waste of resources with over-provisioning or with a loss of customers due to under-provisioning.
The need here is for a tool that automatically adds and removes EC2 instances according to conditions you define—that’s exactly what the EC2 Auto Scaling service does.
Use Amazon EC2 Auto Scaling
The EC2 Auto Scaling service works to add or remove capacity to keep a steady and predictable performance at the lowest possible cost. By adjusting the capacity to exactly what your application uses, you only pay for what your application needs. And even with applications that have steady usage, EC2 Auto Scaling can help with fleet management. If there is an issue with an EC2 instance, EC2 Auto Scaling can automatically replace that instance. This means that EC2 Auto Scaling helps both to scale your infrastructure and ensure high availability.
Configure EC2 Auto Scaling Components
There are three main components to EC2 Auto Scaling.
Launch template or configuration: What resource should be automatically scaled?
EC2 Auto Scaling Group: Where should the resources be deployed?
Scaling policies: When should the resources be added or removed?
Learn About Launch Templates
There are multiple parameters required to create EC2 instances: Amazon Machine Image (AMI) ID, instance type, security group, additional Amazon Elastic Block Store (EBS) volumes, and more. All this information is also required by EC2 Auto Scaling to create the EC2 instance on your behalf when there is a need to scale. This information is stored in a launch template.
You can use a launch template to manually launch an EC2 instance. You can also use it with EC2 Auto Scaling. It also supports versioning, which allows for quickly rolling back if there was an issue or to specify a default version of your launch template. This way, while iterating on a new version, other users can continue launching EC2 instances using the default version until you make the necessary changes.
You can create a launch template one of three ways.
The fastest way to create a template is to use an existing EC2 instance. All the settings are already defined.
Another option is to create one from an already existing template or a previous version of a launch template.
The last option is to create a template from scratch. The following options will need to be defined: AMI ID, instance type, key pair, security group, storage, and resource tags.
Note: Another way to define what Amazon EC2 Auto Scaling needs to scale is by using a launch configuration. It’s similar to the launch template, but it doesn’t allow for versioning using a previously created launch configuration as a template. Nor does it allow for creating one from an already existing EC2 instance. For these reasons and to ensure that you’re getting the latest features from Amazon EC2, use a launch template instead of launch configuration.
Get to Know EC2 Auto Scaling Groups
The next component that EC2 Auto Scaling needs is an EC2 Auto Scaling Group (ASG). An ASG enables you to define where EC2 Auto Scaling deploys your resources. This is where you specify the Amazon Virtual Private Cloud (VPC) and subnets the EC2 instance should be launched in.
EC2 Auto Scaling takes care of creating the EC2 instances across the subnets, so it’s important to select at least two subnets that are across different Availability Zones.
ASGs also allow you to specify the type of purchase for the EC2 instances. You can use On-Demand only, Spot only, or a combination of the two, which allows you to take advantage of Spot instances with minimal administrative overhead.To specify how many instances EC2 Auto Scaling should launch, there are three capacity settings to configure for the group size.
Minimum: The minimum number of instances running in your ASG even if the threshold for lowering the amount of instances is reached.
Maximum: The maximum number of instances running in your ASG even if the threshold for adding new instances is reached.
Desired capacity: The amount of instances that should be in your ASG. This number can only be within or equal to the minimum or maximum. EC2 Auto Scaling automatically adds or removes instances to match the desired capacity number.
When EC2 Auto Scaling removes EC2 instances because the traffic is minimal, it keeps removing EC2 instances until it reaches a minimum capacity. Depending on your application, using a minimum of two is a good idea to ensure high availability, but you know how many EC2 instances at a bare minimum your application requires at all times. When reaching that limit, even if EC2 Auto Scaling is instructed to remove an instance, it does not, to ensure the minimum is kept.
On the other hand, when the traffic keeps growing, EC2 Auto Scaling keeps adding EC2 instances. This means the cost for your application will also keep growing. That’s why it’s important to set a maximum amount to make sure it doesn’t go above your budget.
The desired capacity is the amount of EC2 instances that EC2 Auto Scaling creates at the time the group is created. If that number decreases, then EC2 Auto Scaling removes the oldest instance by default. If that number increases, then EC2 Auto Scaling creates new instances using the launch template.
Ensure Availability with EC2 Auto Scaling
Using different numbers for minimum, maximum, and desired capacity is used for dynamically adjusting the capacity. However, if you prefer to use EC2 Auto Scaling for fleet management, you can configure the three settings to the same number, for example four. EC2 Auto Scaling will ensure that if an EC2 instance becomes unhealthy, it replaces it to always ensure that four EC2 instances are available. This ensures high availability for your applications.
Enable Automation with Scaling Policies
By default, an ASG will be kept to its initial desired capacity. Although it’s possible to manually change the desired capacity, you can also use scaling policies.
In the AWS Monitoring module, you learned about Amazon CloudWatch metrics and alarms. You use metrics to keep information about different attributes of your EC2 instance like the CPU percentage. You use alarms to specify an action when a threshold is reached. Metrics and alarms are what scaling policies use to know when to act. For example, you set up an alarm that says when the CPU utilization is above 70% across the entire fleet of EC2 instances, trigger a scaling policy to add an EC2 instance.
There are three types of scaling policies: simple, step, and target tracking scaling.
Simple Scaling Policy
A simple scaling policy allows you to do exactly what’s described above. You use a CloudWatch alarm and specify what to do when it is triggered. This can be a number of EC2 instances to add or remove, or a specific number to set the desired capacity to. You can specify a percentage of the group instead of using an amount of EC2 instances, which makes the group grow or shrink more quickly.
Once this scaling policy is triggered, it waits a cooldown period before taking any other action. This is important as it takes time for the EC2 instances to start and the CloudWatch alarm may still be triggered while the EC2 instance is booting. For example, you could decide to add an EC2 instance if the CPU utilization across all instances is above 65%. You don’t want to add more instances until that new EC2 instance is accepting traffic.
However, what if the CPU utilization was now above 85% across the ASG? Only adding one instance may not be the right move here. Instead, you may want to add another step in your scaling policy. Unfortunately, a simple scaling policy can’t help with that.
Step Scaling Policy
This is where a step scaling policy helps. Step scaling policies respond to additional alarms even while a scaling activity or health check replacement is in progress. Similar to the example above, you decide to add two more instances in case the CPU utilization is at 85%, and four more instances when it’s at 95%.
Deciding when to add and remove instances based on CloudWatch alarms may seem like a difficult task. This is why the third type of scaling policy exists: target tracking.
Target Tracking Scaling Policy
If your application scales based on average CPU utilization, average network utilization (in or out), or based on request count, then this scaling policy type is the one to use. All you need to provide is the target value to track and it automatically creates the required CloudWatch alarms.
Load balancing refers to the process of distributing tasks across a set of resources. In the case of the corporate directory application, the resources are EC2 instances that host the application, and the tasks are the different requests being sent. It’s time to distribute the requests across all the servers hosting the application using a load balancer.
To do this, you first need to enable the load balancer to take all of the traffic and redirect it to the backend servers based on an algorithm. The most popular algorithm is round-robin, which sends the traffic to each server one after the other.
A typical request for the application would start from the browser of the client. It’s sent to a load balancer. Then, it’s sent to one of the EC2 instances that hosts the application. The return traffic would go back through the load balancer and back to the client browser. Thus, the load balancer is directly in the path of the traffic.
Although it is possible to install your own software load balancing solution on EC2 instances, AWS provides a service for that called Elastic Load Balancing (ELB).
FEATURES OF ELB
The ELB service provides a major advantage over using your own solution to do load balancing, in that you don’t need to manage or operate it. It can distribute incoming application traffic across EC2 instances as well as containers, IP addresses, and AWS Lambda functions.
The fact that ELB can load balance to IP addresses means that it can work in a hybrid mode as well, where it also load balances to on-premises servers.
ELB is highly available. The only option you have to ensure is that the load balancer is deployed across multiple Availability Zones.
In terms of scalability, ELB automatically scales to meet the demand of the incoming traffic. It handles the incoming traffic and sends it to your backend application.
HEALTH CHECKS
Taking the time to define an appropriate health check is critical. Only verifying that the port of an application is open doesn’t mean that the application is working. It also doesn’t mean that simply making a call to the home page of an application is the right way either.
For example, the employee directory application depends on a database, and S3. The health check should validate all of those elements. One way to do that would be to create a monitoring webpage like “/monitor” that will make a call to the database to ensure it can connect and get data, and make a call to S3. Then, you point the health check on the load balancer to the “/monitor” page.
After determining the availability of a new EC2 instance, the load balancer starts sending traffic to it. If ELB determines that an EC2 instance is no longer working, it stops sending traffic to it and lets EC2 Auto Scaling know. EC2 Auto Scaling’s responsibility is to remove it from the group and replace it with a new EC2 instance. Traffic only sends to the new instance if it passes the health check.
In the case of a scale down action that EC2 Auto Scaling needs to take due to a scaling policy, it lets ELB know that EC2 instances will be terminated. ELB can prevent EC2 Auto Scaling from terminating the EC2 instance until all connections to that instance end, while preventing any new connections. That feature is called connection draining.
ELB COMPONENTS
The ELB service is made up of three main components.
Listeners: The client connects to the listener. This is often referred to as client-side. To define a listener, a port must be provided as well as the protocol, depending on the load balancer type. There can be many listeners for a single load balancer.
Target groups: The backend servers, or server-side, is defined in one or more target groups. This is where you define the type of backend you want to direct traffic to, such as EC2 Instances, AWS Lambda functions, or IP addresses. Also, a health check needs to be defined for each target group.
Rules: To associate a target group to a listener, a rule must be used. Rules are made up of a condition that can be the source IP address of the client and a condition to decide which target group to send the traffic to.
APPLICATION LOAD BALANCER
Here are some primary features of Application Load Balancer (ALB).
ALB routes traffic based on request data. It makes routing decisions based on the HTTP protocol like the URL path (/upload) and host, HTTP headers and method, as well as the source IP address of the client. This enables granular routing to the target groups.
Send responses directly to the client. ALB has the ability to reply directly to the client with a fixed response like a custom HTML page. It also has the ability to send a redirect to the client which is useful when you need to redirect to a specific website or to redirect the request from HTTP to HTTPS, removing that work from your backend servers.
ALB supports TLS offloading. Speaking of HTTPS and saving work from backend servers, ALB understands HTTPS traffic. To be able to pass HTTPS traffic through ALB, an SSL certificate is provided by either importing a certificate via Identity and Access Management (IAM) or AWS Certificate Manager (ACM) services, or by creating one for free using ACM. This ensures the traffic between the client and ALB is encrypted.
Authenticate users. On the topic of security, ALB has the ability to authenticate the users before they are allowed to pass through the load balancer. ALB uses the OpenID Connect protocol and integrates with other AWS services to support more protocols like SAML, LDAP, Microsoft AD, and more.
Secure traffic. To prevent traffic from reaching the load balancer, you configure a security group to specify the supported IP address ranges.
ALB uses the round-robin routing algorithm. ALB ensures each server receives the same number of requests in general. This type of routing works for most applications.
ALB uses the least outstanding request routing algorithm. If the requests to the backend vary in complexity where one request may need a lot more CPU time than another, then the least outstanding request algorithm is more appropriate. It’s also the right routing algorithm to use if the targets vary in processing capabilities. An outstanding request is when a request is sent to the backend server and a response hasn’t been received yet.
For example, if the EC2 instances in a target group aren’t the same size, one server’s CPU utilization will be higher than the other if the same number of requests are sent to each server using the round-robin routing algorithm. That same server will have more outstanding requests as well. Using the least outstanding request routing algorithm would ensure an equal usage across targets.
ALB has sticky sessions. In the case where requests need to be sent to the same backend server because the application is stateful, then use the sticky session feature. This feature uses an HTTP cookie to remember across connections which server to send the traffic to.Finally, ALB is specifically for HTTP and HTTPS traffic. If your application uses a different protocol, then consider the Network Load Balancer (NLB).
NETWORK LOAD BALANCER
Here are some primary features of Network Load Balancer (NLB).Network Load Balancer supports TCP, UDP, and TLS protocols. HTTPS uses TCP and TLS as protocol. However, NLB operates at the connection layer, so it doesn’t understand what a HTTPS request is. That means all features discussed above that are required to understand the HTTP and HTTPS protocol, like routing rules based on that protocol, authentication, and least outstanding request routing algorithm, are not available with NLB.
NLB uses a flow hash routing algorithm. The algorithm is based on:
The protocol
The source IP address and source port
The destination IP address and destination port
The TCP sequence number
If all of these parameters are the same, then the packets are sent to the exact same target. If any of them are different in the next packets, then the request may be sent to a different target.
NLB has sticky sessions. Different from ALB, these sessions are based on the source IP address of the client instead of a cookie.
NLB supports TLS offloading. NLB understands the TLS protocol. It can also offload TLS from the backend servers similar to how ALB works.
NLB handles millions of requests per second. While ALB can also support this number of requests, it needs to scale to reach that number. This takes time. NLB can instantly handle this amount of requests.
NLB supports static and elastic IP addresses. There are some situations where the application client needs to send requests directly to the load balancer IP address instead of using DNS. For example, this is useful if your application can’t use DNS or if the connecting clients require firewall rules based on IP addresses. In this case, NLB is the right type of load balancer to use.
NLP preserves source IP address. NLB preserves the source IP address of the client when sending the traffic to the backend. With ALB, if you look at the source IP address of the requests, you will find the IP address of the load balancer. While with NLB, you would see the real IP address of the client, which is required by the backend application in some cases.
SELECT BETWEEN ELB TYPES
Selecting between the ELB service types is done by determining which feature is required for your application. Below you can find a list of the major features that you learned in this unit and the previous.
Feature
Application Load Balancer
Network Load Balancer
Protocols
HTTP, HTTPS
TCP, UDP, TLS
Connection draining (deregistration delay)
✔
IP addresses as targets
✔
✔
Static IP and Elastic IP address
✔
Preserve Source IP address
✔
Routing based on Source IP address, path, host, HTTP headers, HTTP method, and query string
The great thing about CloudWatch is that all you need to get started is an AWS account. It is a managed service, which enables you to focus on monitoring, without managing any underlying infrastructure.
The employee directory app is built with various AWS services working together as building blocks. It would be difficult to monitor all of these different services independently, so CloudWatch acts as one centralized place where metrics are gathered and analyzed. You already learned how EC2 instances post CPU utilization as a metric to CloudWatch. Different AWS resources post different metrics that you can monitor. You can view a list of services that send metrics to CloudWatch in the resources section of this unit.
Many AWS services send metrics automatically for free to CloudWatch at a rate of one data point per metric per 5-minute interval, without you needing to do anything to turn on that data collection. This by itself gives you visibility into your systems without you needing to spend any extra money to do so. This is known as basic monitoring. For many applications, basic monitoring does the job.
For applications running on EC2 instances, you can get more granularity by posting metrics every minute instead of every 5 minutes using a feature like detailed monitoring. Detailed monitoring has an extra fee associated. You can read about pricing on the CloudWatch Pricing Page linked in the resources section of this unit.
Break Down Metrics
Each metric in CloudWatch has a timestamp and is organized into containers called namespaces. Metrics in different namespaces are isolated from each other—you can think of them as belonging to different categories.
AWS services that send data to CloudWatch attach dimensions to each metric. A dimension is a name/value pair that is part of the metric’s identity. You can use dimensions to filter the results that CloudWatch returns. For example, you can get statistics for a specific EC2 instance by specifying the InstanceId dimension when you search.
Set Up Custom Metrics
Let’s say for your application you wanted to record the number of page views your website gets. How would you record this metric to CloudWatch? It’s an application-level metric, meaning that it’s not something the EC2 instance would post to CloudWatch by default. This is where custom metrics come in. Custom metrics allows you to publish your own metrics to CloudWatch.
If you want to gain more granular visibility, you can use high-resolution custom metrics, which enable you to collect custom metrics down to a 1-second resolution. This means you can send one data point per second per custom metric. Other examples of custom metrics are:
Web page load times
Request error rates
Number of processes or threads on your instance
Amount of work performed by your application
Note: You can get started with custom metrics by programmatically sending the metric to CloudWatch using the PutMetricData API.
Understand the CloudWatch Dashboards
Once you’ve provisioned your AWS resources and they are sending metrics to CloudWatch, you can then visualize and review that data using the CloudWatch console with dashboards. Dashboards are customizable home pages that you use for data visualization for one or more metrics through the use of widgets, such as a graph or text.
You can build many custom dashboards, each one focusing on a distinct view of your environment. You can even pull data from different Regions into a single dashboard in order to create a global view of your architecture.
CloudWatch aggregates statistics according to the period of time that you specify when creating your graph or requesting your metrics. You can also choose whether your metric widgets display live data. Live data is data published within the last minute that has not been fully aggregated.
You are not bound to using CloudWatch exclusively for all your visualization needs. You can use external or custom tools to ingest and analyze CloudWatch metrics using the GetMetricData API.
As far as security goes, you can control who has access to view or manage your CloudWatch dashboards through AWS Identity and Access Management (IAM) policies that get associated with IAM users, IAM groups, or IAM roles.
Get to Know CloudWatch Logs
CloudWatch can also be the centralized place for logs to be stored and analyzed, using CloudWatch Logs. CloudWatch Logs can monitor, store, and access your log files from applications running on Amazon EC2 instances, AWS Lambda functions, and other sources.
CloudWatch Logs allows you to query and filter your log data. For example, let’s say you’re looking into an application logic error for your application, and you know that when this error occurs it will log the stack trace. Since you know it logs the error, you query your logs in CloudWatch Logs to find the stack trace. You also set up metric filters on logs, which turn log data into numerical CloudWatch metrics that you graph and use on your dashboards.
Some services are set up to send log data to CloudWatch Logs with minimal effort, like AWS Lambda. With AWS Lambda, all you need to do is give the Lambda function the correct IAM permissions to post logs to CloudWatch Logs. Other services require more configuration. For example, if you want to send your application logs from an EC2 instance into CloudWatch Logs, you need to first install and configure the CloudWatch Logs agent on the EC2 instance.
The CloudWatch Logs agent enables Amazon EC2 instances to automatically send log data to CloudWatch Logs. The agent includes the following components.
A plug-in to the AWS Command Line Interface (CLI) that pushes log data to CloudWatch Logs.
A script that initiates the process to push data to CloudWatch Logs.
A cron job that ensures the daemon is always running.
After the agent is installed and configured, you can then view your application logs in CloudWatch Logs.
Learn the CloudWatch Logs Terminology
Log data sent to CloudWatch Logs can come from different sources, so it’s important you understand how they’re organized and the terminology used to describe your logs.
Log event: A log event is a record of activity recorded by the application or resource being monitored, and it has a timestamp and an event message.
Log stream: Log events are then grouped into log streams, which are sequences of log events that all belong to the same resource being monitored. For example, logs for an EC2 instance are grouped together into a log stream that you can then filter or query for insights.
Log groups: Log streams are then organized into log groups. A log group is composed of log streams that all share the same retention and permissions settings. For example, if you have multiple EC2 instances hosting your application and you are sending application log data to CloudWatch Logs, you can group the log streams from each instance into one log group. This helps keep your logs organized.
Configure a CloudWatch Alarm
You can create CloudWatch alarms to automatically initiate actions based on sustained state changes of your metrics. You configure when alarms are triggered and the action that is performed.
You first need to decide what metric you want to set up an alarm for, then you define the threshold at which you want the alarm to trigger. Next, you define the specified time period of which the metric should cross the threshold for the alarm to be triggered.
For example, if you wanted to set up an alarm for an EC2 instance to trigger when the CPU utilization goes over a threshold of 80%, you also need to specify the time period the CPU utilization is over the threshold. You don’t want to trigger an alarm based on short temporary spikes in the CPU. You only want to trigger an alarm if the CPU is elevated for a sustained amount of time, for example if it is over 80% for 5 minutes or longer, when there is a potential resource issue.
Keeping all that in mind, to set up an alarm you need to choose the metric, the threshold, and the time period. An alarm has three possible states.
OK: The metric is within the defined threshold. Everything appears to be operating like normal.
ALARM: The metric is outside of the defined threshold. This could be an operational issue.
INSUFFICIENT_DATA: The alarm has just started, the metric is not available, or not enough data is available for the metric to determine the alarm state.
An alarm can be triggered when it transitions from one state to another. Once an alarm is triggered, it can initiate an action. Actions can be an Amazon EC2 action, an Auto Scaling action, or a notification sent to Amazon Simple Notification Service (SNS).
Use CloudWatch Alarms to Prevent and Troubleshoot Issues
CloudWatch Logs uses metric filters to turn the log data into metrics that you can graph or set an alarm on. For the employee directory application, let’s say you set up a metric filter for 500-error response codes.
Then, you define an alarm for that metric that will go into the ALARM state if 500-error responses go over a certain amount for a sustained time period. Let’s say if it’s more than five 500-error responses per hour, the alarm should enter the ALARM state. Next, you define an action that you want to take place when the alarm is triggered.
In this case, it makes sense to send an email or text alert to you so you can start troubleshooting the website, hopefully fixing it before it becomes a bigger issue. Once the alarm is set up, you feel comfortable knowing that if the error happens again, you’ll be notified promptly.
You can set up different alarms for different reasons to help you prevent or troubleshoot operational issues. In the scenario just described, the alarm triggered an SNS notification that went to a person who looked into the issue manually. Another option is to have alarms trigger actions that automatically remediate technical issues.
For example, you can set up an alarm to trigger an EC2 instance to reboot, or scale services up or down. You can even set up an alarm to trigger an SNS notification, which then triggers an AWS Lambda function. The Lambda function then calls any AWS API to manage your resources, and troubleshoot operational issues. By using AWS services together like this, you respond to events more quickly.
Google Cloud Platform (GCP) provides a range of data management tools and services to help organizations store, process, analyze, and visualize their data. Here are some key Google Cloud data management tools and services:
Google Cloud Storage: Google Cloud Storage is a scalable object storage service that allows organizations to store and retrieve data in the cloud. It offers multiple storage classes for different use cases, including Standard, Nearline, Coldline, and Archive, with varying performance and cost characteristics.
Google BigQuery: Google BigQuery is a fully managed, serverless data warehouse service that enables organizations to analyze large datasets using SQL queries. It offers high performance, scalability, and built-in machine learning capabilities for advanced analytics and data exploration.
Google Cloud Firestore and Cloud Bigtable: Google Cloud Firestore is a scalable, fully managed NoSQL document database service for building serverless applications, while Cloud Bigtable is a highly scalable NoSQL database service for real-time analytics and IoT applications. Both services offer low-latency data access and automatic scaling.
Google Cloud SQL: Google Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server. It automates backups, replication, patch management, and scaling, allowing organizations to focus on their applications instead of database administration.
Google Cloud Spanner: Google Cloud Spanner is a globally distributed, horizontally scalable relational database service that offers strong consistency and high availability. It is suitable for mission-critical applications that require ACID transactions and global scale.
Google Cloud Dataflow: Google Cloud Dataflow is a fully managed stream and batch processing service that allows organizations to process and analyze data in real-time. It offers a unified programming model based on Apache Beam for building data pipelines that can scale dynamically with demand.
Google Cloud Dataproc: Google Cloud Dataproc is a fully managed Apache Hadoop and Apache Spark service that enables organizations to run big data processing and analytics workloads in the cloud. It offers automatic cluster provisioning, scaling, and management, along with integration with other GCP services.
Google Cloud Pub/Sub: Google Cloud Pub/Sub is a fully managed messaging service that allows organizations to ingest and process event streams at scale. It offers reliable message delivery, low-latency message ingestion, and seamless integration with other GCP services.
Google Data Studio: Google Data Studio is a free, fully customizable data visualization and reporting tool that allows organizations to create interactive dashboards and reports from various data sources. It offers drag-and-drop functionality and real-time collaboration features.
These are just a few examples of the data management tools and services available on Google Cloud Platform. Depending on specific requirements and use cases, organizations can leverage GCP’s comprehensive portfolio of data services to meet their data management needs.
Arturo Gutiérrez Loza Blog 