Full Stack Observability

Log Analytics

What is log analytics? Log analytics entails searching, investigating, and visualizing time-sequenced data from IT system logs. It extends log monitoring by enabling teams to detect patterns and anomalies, aiding in issue resolution and providing operational insights. Historical data from archived logs can also be analyzed for

Why is log analytics important? 

Log data is growing exponentially. Logging tools must scale to manage the influx of data from human and machine sources. Traditional analytics struggle with the volume and diversity of today's logging data in complex systems. Without a centralized logging platform, challenges and costs can increase. Data is crucial for understanding current business processes and planning for the future. 

What are the benefits of log analytics?

• Enhance customer experiences: Analyze user engagement to optimize application usability and retention.
• Improve resource efficiency: Identify and resolve performance bottlenecks within the organization.
• Understand customer behavior: Utilize log data to personalize sales and marketing strategies based on customer interests and activity.
• Detect suspicious activity: Monitor logs for signs of malicious behavior and prevent security breaches.
• Ensure audit compliance: Use log analytics to meet regulatory requirements and minimize audit risks.

What are the challenges of log analytics?

• Scalability: Growing log volumes pose challenges for teams. Many tools struggle with enterprise-scale logs, prompting exploration of AI for IT Operations (AIOps) solutions.
• Centralization: Achieving a unified view of organizational activity is vital but hindered by diverse and siloed log data. Outdated architectures may not integrate well with modern tools, requiring log standardization for streamlined analysis.
• Cost Efficiency: Not all log data needs immediate access, but rapid retrieval is crucial. Cost-effective storage with data tiering minimizes overhead
• Data Diversity: Distributed applications generate diverse log data across services and systems, from structured to unstructured formats. Normalizing and understanding log data is essential for efficient querying amid this complexity.

What are the use cases for log analytics?

Log analytics can transform your business, offering real-time application monitoring, performance insights, root cause analysis, and SIEM capabilities. Beyond these, organizations can use log analysis to enhance security policy compliance, study online user behavior, and drive better business decisions overall.

Synthetic Monitoring

What is synthetic monitoring? 

Synthetic monitoring emulates user interactions with applications using scripts, testing various scenarios, locations, and device types. This practice provides insights into application performance, monitors uptime automatically, and identifies issues in critical business transactions, like completing  

Why use synthetic monitoring? 

Poor application performance can drive customers away, resulting in high bounce rates and lost market share. Troubleshooting such issues can be challenging and time-consuming for IT teams. Emulating user behavior paths in a test environment helps prevent these problems by monitoring system health, improving performance, and increasing resiliency. Synthetic monitoring also aids in meeting service level agreements (SLAs) and holding 
third-party providers accountable for issues. 

Challenges of synthetic monitoring 

Modern applications are complex and accessed from diverse locations, making synthetic monitoring inadequate for capturing all potential errors. DevOps teams prioritize early application testing. However, setting up synthetic monitoring requires specialized technical knowledge and is time-consuming. Synthetic tests lack resilience and can fail with minor UI changes, leading to unnecessary alerts. Many tools lack context to explain failures or their business impact, delaying resolution and complicating issue prioritization. 

Synthetic monitoring use cases 

Synthetic monitoring typically involves three types: availability monitoring, web performance monitoring, and transaction monitoring.

• Availability monitoring confirms site or application availability and specific content or API call success.
• Web performance monitoring assesses page load speed, element performance, errors, and response times.
• Transaction monitoring completes tasks like logging in, form completion, and checkout.

Synthetic tests fall into two categories:

• Browser tests simulate user transactions  (e.g., making a purchase).
• API tests monitor endpoints like HTTP, SSL, and DNS for uptime, security, and performance. For instance, HTTP tests ensure application responsiveness, SSL tests validate secure transactions, and DNS tests verify resolution times. Multistep API tests monitor end-to-end workflows effectively.

REAL USER MONITORING 

What is real user monitoring? 

Real user monitoring (RUM) captures detailed data on user interactions with an application, including metrics like navigation start and speed index. User sessions, or click paths, vary widely within applications, from filling forms to uploading files. RUM tracks each action's completion time to identify patterns for  

How real user monitoring works 

Real user monitoring injects code into applications to capture metrics during use. For browser-based apps, JavaScript detects and tracks page loads and XHR requests. Native mobile apps integrate monitoring libraries into their packages. Data is streamed to a data store for querying and visualization. Some tools offer automatic setup, while others need manual configuration. 

Benefits and limitations of RUM

Real user monitoring (RUM) offers valuable insights into user experiences and helps identify performance issues early. It aids in verifying SLA compliance  and guides UX improvements based on user behavior.

However, RUM has limitations. It struggles to establish performance baselines due to diverse user actions and generates large data  on new service versions volumes requiring efficient query tools. RUM effectiveness depends on user activity; low usage times yield limited data. Additionally, RUM lacks data until users engage with them, undermining proactive issue detection—a gap synthetic monitoring can fill.

Service Level Indicator

What is Service Level Indicator? 

An SLI (service level indicator) is a defined quantitative measure of service performance. Common SLIs include request latency, error rate, and system throughput. Measurements are often aggregated over a window and converted into rates, averages, or percentiles. SLIs ideally directly measure the desired service level, but proxies are used when direct measurement is challenging. Availability, the fraction of time a service is usable, is crucial. It's often expressed in "nines" (e.g., %99 is "2 nines," %99.999 is "5 nines"). Google Compute Engine aims for "three and a half nines" availability (%99.95). 

What is Service Level Objectives? 

An SLO (service level objective) sets a target or range for a service level measured by an SLI. Typically structured as SLI ≤ target or lower bound ≤ SLI ≤ upper bound. Choosing an SLO can be complex; for instance, external HTTP request rates are dictated by user demand, making it challenging to set an SLO for this metric. 

Benefits of SLOs

• Enhanced software quality: SLOs balance innovation with delivery by setting acceptable downtime levels.
• Proactive business continuity: Prevent disruptions by monitoring critical applications, infrastructure, and services.
• Informed decision-making: DevOps and SRE teams use SLO data for release decisions and focus areas.
• Reduced alert fatigue: Contextual alerts prevent storming and unnecessary notifications.

Use Cases:

• User-facing systems prioritize availability, latency, and throughput. Storage systems emphasize latency, availability, and durability. Big data systems focus on throughput and end-to-end latency.
• All systems should  prioritize correctness as an indicator of health, although it's often data-related and not solely an SRE responsibility.