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Review of the Important Qualities in a Monitoring and Alerting System
At the conclusion of our guide to metrics, alerting, and monitoring systems, we explored what qualities make up an effective monitoring system.
Because soon, we'll be exploring all their main components. It was beneficial for us to review what qualities make an ideal monitoring solution.
Monitoring Components Should be Separate: For accurate data collection and to prevent negative impacts on performance, most monitoring components should be kept apart.
This allows accurate collection and helps avoid potential negative repercussions of monitoring components with other infrastructure.
Trustworthy and Reliable: Monitoring is used to assess the health of systems; thus, any monitored system must be reliable and accessible for effective functioning.
Summary and detail views are simple for users: Information that cannot be easily comprehended can quickly become useless, so providing operators with summary views that enable them to see critical areas and obtain further details when needed can be enormously helpful in increasing productivity and making information readily available for analysis.
An Effective Strategy to Preserve Historical Data: To recognize anomalous behavior quickly and identify patterns more easily, knowing typical behavior requires having access to older data over a longer period.
This may necessitate keeping older records accessible over time.
Ability to Correlate Different Factors: Being able to display disjointed information in an organized fashion is vital in order to detect patterns.
Tracking new metrics or infrastructure is easy: your monitoring system needs to evolve with your evolving applications and infrastructure.
Otherwise, trust in them could diminish quickly. Insufficient coverage diminishes this trust as tools or data become outdated or less trustworthy.
Powerful and Flexible Alerting: For effective alerting to take place, multiple notification channels with different priorities based on your conditions should be made available to you.
Let's consider all the components involved with a monitoring system.
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The Monitoring System Is Made Up Of Several Parts
Monitoring systems consist of various components and interfaces working in concert to monitor, collect, visualize and report on deployment statuses.
Below, we discuss each element.
Distributed Monitoring Agents & Data Exporters
Monitoring systems may be located on servers, but data must also be gathered from various locations within your infrastructure.
A monitoring agent - an application that collects and sends collected information directly to an endpoint - must be installed on every machine in your network for efficient data collection and collection by central monitoring software. They collect statistics on hosts where agents have been installed before sending this data over for analysis by their central monitoring software.
Agents run as daemons on every host in a system. They should always remain running, serving to connect securely to remote data, set sampling or frequency policies and create unique identifiers.
Their basic configuration may involve creating secure connections, setting sampling/frequency policies and creating unique identifiers; using as few resources as possible and running without much oversight so as to reduce impact on other services while making setup and sending metrics from nodes easy and effective monitoring tools for managers.
Agents for monitoring web servers or database servers can be easily located. Monitoring agents typically collect metrics at a host level and are generic.
Most types of specialized software will need their data exported either by building your agent from scratch or editing its program code directly. However, there are libraries for many popular monitoring tools that help add custom instrumentation if that becomes necessary.
In order to avoid impacting the performance or health of an application negatively, custom solutions must minimize their footprint as much as possible.
Push-based monitoring models assume agents send data directly to a central repository; pull-based architectures also exist and involve individual hosts gathering metrics, aggregating them and serving it at specific endpoints - similar to an agent, but easier and requiring less configuration due to not needing access to other computers.
Metrics ingress
Metrics components are an integral component of any monitoring system. Since data generation occurs continuously, its collection system must be built for speedy collection while working effectively in tandem with storage layers to manage this volume of information.
Metrics ingress points provide a central location where all monitoring agents or stats aggregators send collected data for processing.
Their endpoints must have the capacity to receive and authenticate multiple hosts simultaneously while being load-balanced or distributed so as to be reliable under high traffic volumes. For metrics systems, this data ingress could also include ingress endpoints distributed throughout their network for scaling with increasing volumes.
Polling components for pull-based systems connect directly with hosts and collect metrics, though many requirements and responsibilities overlap.
Suppose individual hosts rely on authentication credentials for access and login purposes. In that case, polling must provide credentials that enable users to gain entry and log onto secure endpoints.
Data Management Layer
Data management involves organizing, recording and responding to administrative layers' requests and queries for data.
Records often reflect changes over time using time series databases specialized for this function, allowing users to query it directly.
Data management's primary role is collecting and storing information from hosts. At a minimum, storage should record metrics being reported along with observed value, time of generation and host.
Storage layers must provide export options when data collection becomes too large to process locally, store locally, or access locally. Furthermore, import capabilities should also allow re-ingestion of past data when necessary.
This layer should also provide organized access; systems using time series databases typically employ APIs or built-in querying languages to facilitate this functionality, which enables data exploration and interactive querying; their primary users will likely be dashboards presenting data or alert systems.
Dashboard and Visualization Layer
Interfaces built atop this layer enable users to interact with collected data. Best represented as graphs with a time axis plotted on it, you can easily see how values have fluctuated with time as well as track metrics across different timescales to detect trends or recent changes in behavior.
Visualization and data layers enable data from multiple hosts or parts of an application stack to be visualized simultaneously and overlaid, as well as to easily spot changes or events across infrastructure layers simultaneously.
Furthermore, their interactive nature enables users to select which data needs overlaying for specific tasks at hand.
Dashboards are created by saving graphs and data into graph files, then using these saved graphs and data sets for display on an always-on display or troubleshooting specific parts of a system.
They may serve multiple roles, such as being constantly present as health metrics display on an always-on screen; portals for troubleshooting specific parts; capacity planning dashboards are helpful but may not always be needed day to day; it is, therefore, crucial that creating both focused as well as general dashboards is easy - this way data becomes actionable and accessible!
The Threshold And Alerting Functionality
Dashboards and graphs may provide the ideal way to visualize data within your system. Yet, their usefulness is restricted by human interpretation alone.
Monitoring systems play an essential role in relieving team members of active monitoring of your system so they can focus on more productive activities; to accomplish this task, monitoring systems must alert when something needs to be addressed using alerts or thresholds that users define themselves.
An alert system's mission is to reliably notify users when there are significant changes in data while leaving them alone otherwise.
You must first define alerting criteria so the system understands your definition of significant change and continuously evaluates metric threshold and notification method based on new information received, where the threshold measures maximum or minimum averages over given periods while the notification method defines how alerts will be delivered.
Finding an appropriate balance between alerting and responding to problems is challenging. You need to identify which metrics represent real issues that require immediate attention, as well as which notification methods work best in different circumstances.
Your threshold definition must clearly define criteria, whereas notification needs should provide communication methods tailored toward various levels of severity.
Monitors for Black Boxes and White Boxes
Now, we will discuss how you can set thresholds and alarms that suit the best your team's needs. First, let's define white-box and black-box monitoring: two monitoring models described as black-box or white-box; these types don't contradict each other but often coexist within systems in order to maximize strengths and minimize weaknesses.
Monitoring in the black box: this is an approach that monitors only externally visible elements and takes an external viewpoint in order to maintain focus on public behavior.
Black-box monitoring does not require special knowledge about any component being monitored. Rather, it provides data from users about the functionality of systems under scrutiny from a perspective they would be viewing it from.
Though limited, such views provide excellent indicators of issues affecting customers.
White-Box Monitoring: White-box monitoring can also prove immensely valuable.
"White Box" refers to any method that utilizes insider information for evaluation; such an approach would likely become more prevalent if your processes are internal and not visible externally, providing more complete insights than any external monitoring method. Additionally, it allows more predictive decisions by providing comprehensive sets of data that alert when resource consumption changes and when services need to increase accordingly.
Black-box and white-box categories serve to organize different perspectives within your system. White-box data is useful for investigating known problems, evaluating root causes and discovering correlations.
At the same time, black-box monitoring helps detect serious issues by quickly showing users their impact.
Read More: Implementing Network Security Monitoring Solutions
The Severity Of An Alert Is Matched With The Type
An effective monitoring system is of crucial importance. Without notifications sent out regularly and monitoring dashboards regularly to remain informed, your team could miss events that affect their system affecting it negatively and will have to stay abreast of everything happening with their systems at all times - while aggressive messages with high false-positive rates, irrelevant events or ambiguous language could potentially prove hazardous for their performance.
This section will address different levels of notification and how best to apply them in order to maximize their efficiency.
In particular, we'll look at selecting alerts you'd like and their intended goals.
Page
Pages are the cornerstone alert, serving to draw attention to an issue identified with the system quickly. Use of Pages should only occur in situations that necessitate swift resolution due to severity.
An aggressive but reliable paging system must reach out quickly and effectively in order to address them successfully.
Only use pages when it is truly urgent; their importance lies in what they indicate. An ideal paging system must be reliable, persistent and aggressive enough not to be disregarded by its recipient(s).
Many systems provide options to notify another person or group in case no response comes within an allotted period if you fail to respond within the allotted time frame.
Pages, by their very nature, are disruptive and should only be implemented when there is an obvious operational problem in your system.
Pages should only be utilized when they can directly correspond with symptoms observed through black-box methods - for instance, it can be hard to tell the impact of an overloaded web server, but unreachability of the domain could require such action.
Second Notifications
Notifications such as emails or tickets should only be issued if an immediate response can wait until tomorrow, not when immediate attention must be provided immediately.
Pages provide more immediate alerts that should be managed immediately by staff on call than notifications do; therefore, notifications should only be distributed when responding immediately is impossible or immediate action cannot be taken by emergency staff members.
Monitoring generates emails and tickets that give team members a clear sense of what tasks need to be completed when they next become available.
Notifications should only be sent for issues that directly impact production; often, these notifications are based on indicators or white boxes that predict or identify emerging problems quickly and predictively.
Notification alarms monitor similar behaviors as paging alarms but are set at lower thresholds. You might, for instance, create a notification when there is even a slight increase in application latency; when this rise reaches an unacceptable threshold level, a page would be sent as well.
Notifications should only be utilized in situations that demand fast action without endangering system stability, like alerting team members of an issue and providing enough time for investigation and resolution before it impacts users or becomes worse.
Logging Information
Although not an alert, you might wish to record specific behaviors you observe without immediately alerting others.
Setting thresholds that only record data under specific conditions could help. You could write these to a database or use them to increase counters within your monitoring system - the important goal here is reducing how many questions operators need to construct in order to collect this information and providing easily compiled data sets instead.
Strategy should only be employed in situations that do not demand immediate responses or require correlating factors and summarizing data at specific intervals for later reference.
These triggers might not come in handy every day but may prove invaluable if an issue recurs; saved queries and customized investigative dashboards also offer great advantages when used effectively.
What to Avoid When Alerting?
Once again, be absolutely clear about what alerts mean to the team. Every alert should signal monitoring system company an event requiring human action or decision.
As you explore metrics related to alerts, keep your eye out for opportunities to automate reactions.
Automated remediation may be suitable in several instances:
- Signing the issue with an easily recognizable signature is the surest way of identifying it, as it will never change over time.
- Automation eliminates human decision-making or involvement, providing more autonomy over any situation that meets these criteria. While some scenarios are easier than others to script out, you should have no problem automating any situation that meets these parameters.
- Threshold alerts could still trigger response plans by opening scripted solutions, providing further useful insight into system health as well as monitoring metrics thresholds more closely.
Bear in mind that even automated processes may encounter challenges, so to maximize success with them, add alerts into the scripted response so an operator will be alerted should automation fail - this way, you can handle most cases with little intervention while alerting your team of an incident that requires intervention.
Designing Effective Thresholds And Alerts
We'll now discuss the qualities of good alerts.
Triggered By Events With Real User Impact
As previously discussed, the best alerts are those based on real-world scenarios that directly impact users. Therefore, it is imperative to analyze various failure and performance degradation scenarios to see where these could manifest into layers where users interact with one another.
Understanding your infrastructure redundancy, its component relationships and goals for performance and availability are vitally important in order to identify metrics that accurately represent user-impacting issues that might surface in the future.
Thresholds of Gradually Increasing Severity
Next, identify threshold values appropriate to each metric. It may require trial and error before you find an ideal set.
Review historical values to identify which situations required remediation; setting an "emergency threshold" that triggers messages as soon as the threshold falls under some threshold, along with one or more "canary thresholds," may help with this step. Request feedback after creating new alerts so you can fine-tune them so they meet team expectations more effectively.
Include Appropriate Content
Recover faster from an incident by decreasing investigation time. Alert messages should provide enough context so operators are able to grasp their situation and begin planning the next steps immediately.
An alert should contain details regarding affected components or systems as well as the trigger metric that was reached, the date, time and cause of an incident.
Furthermore, links could include accessing related dashboards or ticketing systems (if tickets have been automatically generated).
Assuring operators have enough information so they can concentrate their attention on an incident is of utmost importance.
While it is not necessary or recommended to share every bit of detail about events occurring around you, providing basic details with suggestions as to what you could do next will greatly aid their initial responses.
Send It To The Right People
Alerts will become ineffective unless they can be responded to effectively, with action taken depending on each respondent's knowledge, expertise and consent.
In organizations of various sizes, it may be challenging to determine who or which groups will receive alert messages; to reduce uncertainty, you could create rotating on-call teams for each group and an effective escalation strategy as a solution for making these decisions more quickly and confidently.
On-call rotations must include enough experienced individuals in order to avoid burnout or alert fatigue; if your alerting system doesn't have its system for scheduling on-call hours, then manually rotating alert contacts might need to happen according to schedule.
You could include separate lists for different systems with rotating on-call personnel whose names can be seen here as well.
Escalation plans provide an effective means of making sure incidents reach the right people quickly and reliably.
To maximize efficiency, having staff continuously monitor systems would be preferable over rotating on-call operators as this allows for immediate mitigation or any required assistance or expertise from operators on-call being made available when required. Establishing when and how an issue should escalate will decrease unnecessary alerts while upholding urgency pages effectively.
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The Conclusion Of The Article Is:
This explores how alerting and monitoring work within actual systems.
In it, we explored various components of monitoring systems as they work to meet organizational awareness requirements, black-and-white box monitoring as an approach for alerting, alert types, how best to match alert media with incident severity levels, as well as features of efficient alert systems designed to increase team responsiveness.
