DevOps Monitoring And Logging Tools

What Are Logs?

All software, networking hardware, workstations, and servers generate logs or records of events, which are, by default, written to files on local disks. Critical data can be found in these logs. For instance, a web server's event log may include data such as a user's IP address, date, time, request type, and more. Logs give admins an audit trail to follow as they fix issues and identify problems' underlying causes. As a result of the variety of logs, their formats frequently change. Consider utilizing JavaScript Object Notation (JSON) as a common structured format throughout your IT ecosystem to reduce complexity.

What is DevOps monitoring?

Monitoring covers various processes, including planning, development, testing, deployment, and operations. It offers a comprehensive and real-time view of the status of the production environment with insights into applications, Infrastructure, and services. Collecting data from logs and metrics allows you to observe performance and compliance at each stage of the SDLC pipeline.

What is log monitoring?

Log monitoring is the process of regularly checking logs for particular occurrences or patterns to spot potential faults or difficulties. DevOps and developers use log monitoring technologies to gather, analyze, and understand network performance information while continuously monitoring logs as they are being created.

Log monitoring is frequently used to maintain system stability, spot security gaps, and monitor system modifications or updates. It can be utilized in various contexts, including IT departments, web servers, and cloud-based applications.

Log monitoring is the collective term for collecting procedures involved in log management and analysis to assist IT teams in managing their Infrastructure and operations. Monitoring can be divided into various categories depending on the scope and methods employed. Distributed tracing may be necessary for contemporary cloud-native systems built using microservices to follow a request's progress.

Log monitoring use cases

Various monitoring methods can be established depending on the scope and methodologies used. To track a request's progress in contemporary cloud-native systems constructed using microservices, distributed tracing may be required. Log, event, and metric monitoring are still necessary for other causes. Here are some actual usage cases.

  • Cloud infrastructure surveillance
    The operational processes of an IT infrastructure are examined, monitored, and managed through cloud monitoring. Manual or automated management processes confirm website, server, app, and other cloud infrastructure performance and availability. This continuous evaluation of resource utilization, server response times, and speed spots potential security holes before they arise.
  • Monitoring application performance
    Many enterprises use distributed tracing and monitoring (metrics) to monitor applications. Log data is the best source for connecting the dots across an application stack when troubleshooting or debugging programs.
    1. Finding problems early—before they impact actual customers—is beneficial.
    2. Monitoring many program elements, including servers, databases, and much more.
    3. To get rid of application bottlenecks, it delivers alerts and a dashboard.
  • Network monitoring: identifying system issues
    Hardware such as load balancers, routers, and firewalls form the foundation of enterprise networks. Check that these devices are functioning correctly by monitoring and tracking network problems. Additionally, it aids in the development of secure communications, auditing, and resolving network problems.
  • Web server monitoring by debugging
    Whether the website or application for your business is hosted by Apache, IIS, or another web server, log monitoring is crucial to providing a better user experience. Server logs may keep track of errors, failed services, and more, including traffic flow. Looking at web server logs will help you improve your online applications, identify traffic peaks, and handle issues more rapidly.

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Why do we need log monitoring?

Although they are similar concepts, log monitoring and log analytics are separate from one another. They work as a team to maintain the health and efficiency of apps and essential services.

  • Improved compliance
    According to numerous governmental and regulatory authorities, businesses must abide by the law and present proof of conformity. The GDPR, HIPAA, PCI, and many more laws and regulations can all be complied with by businesses with the help of log monitoring.
  • Increased security
    Several cyberattacks target specific programs and applications. Monitoring logs enables companies to spot code flaws and take preventative action to improve program performance.
    1. Finding areas of poor performance.
    2. Debugging and assessing an application's condition.
    3. Finding the root of installation and run-time problems in apps.
  • Improve team performance
    Teams may discover potential bottlenecks and inefficient configurations through log monitoring, which optimizes system performance.
    Real-time monitoring of the system's health is done to look for anomalies or inactivity.
    1. Finding issues with settings or performance.
    2. Comply with SLAs and operational objectives.
  • Create a better team collaboration
    To create more trustworthy multi-cloud configurations, cloud operators and architects benefit from a single-log monitoring system.
    1. Teams can respond to situations more quickly and spot problems before they impact end customers.
    2. Encourages stakeholders to work together effectively to solve problems.

7 Best Practices for Log Monitoring and Analysis

  • Utilize Logging Levels
  • Implement structured log formats
  • Standardize Your Logs
  • Include tags or distinctive identifiers
  • Switch on real-time monitoring
  • Configure alerts
  • Logging CI/CD Pipeline Integration

Leverage Logging Levels

While some systems produce data continuously, others only do so when an exceptional occurrence occurs. Teams must constantly improve their systems to ensure that only valuable data is gathered from logs.

You may filter and retrieve helpful information and prevent information overload by logging levels (warn, fatal, error, etc.). You can monitor some important events and disregard others using logging levels.

Implement structured log formats

Analyzing log files can be difficult because unstructured text data is typically contained in them. While modern tools can assist you in analyzing various varieties of structured and unstructured logs, doing so can be time-consuming and frequently error-prone.

When logs are presented in a standardized and recognizable format, log analyzers can process or parse them more easily. In light of this, you should transform your unstructured data into a structured log format, such as JSON. Logs can speed up search queries during troubleshooting if they are written in a standard format.

Normalize Your Logs

A log parser can organize and make this information more legible because every log contains numerous bits of information, allowing you to utilize search queries to extract valuable insights. You may now monitor particular event log fields thanks to this. You can find out who is accessing a server by keeping an eye on the "user" and "source IP" sections. Most log analyzers now support automated parsing for popular log formats.

Include tags or distinctive identifiers

As it makes it simple to segment and filter the logs, tagging logs is quite helpful when troubleshooting or debugging programs. The alphanumeric strings serve as distinctive identifiers and can limit search results, monitor specific user sessions, and more.

When studying logs in container systems, tags take on even greater significance. Tracking all the logs becomes more difficult because applications in Docker Swarm can have numerous containers. In these circumstances, you can alter your tags and give other container properties to make them more meaningful.

Enable Real-Time Monitoring

Any performance stumbling blocks or lingering problems in your live environment can impact application performance, user experience, compliance slip-ups, and even monetary and reputational damages.

Because of this, it's essential to monitor production environments in real-time. Teams frequently rely on real-time log viewers, which offer live tail functions like the Linux tail -f command. You may find problems as they arise and fix them before they become significant problems with live monitoring.

Set up Alerts

It's only sometimes possible to monitor everything continuously because IT teams sometimes share many duties. You should establish baselines for all your monitoring metrics and set up alerts for changes from these baselines to remain on top of your surroundings.

With notification services like Slack, Hip Chat, and Pager Duty, most contemporary logging technologies offer simple connections. Remember that threshold-based warnings may require regular reviews to maintain appropriate signal-to-noise ratios.

Integrate Logging with Your CI/CD Pipeline

The teams in charge of constantly enhancing their automation pipelines should strive to make the most of logs as DevOps becomes increasingly popular. They can integrate logging with their source code management systems to maintain an audit trail of application performance and availability across many settings. They can monitor the success rates of their code integrations using logging integration, which also makes mistake detection and debugging easier.

Log monitoring tools

  1. Prometheus
  2. Grafana
  3. Elasticsearch


Every DevOps specialist should be acquainted with the tool Prometheus. It is a time series data model-based open-source monitoring solution for services and warnings. A unique identifier— the metric name—and a time stamp are used by Prometheus to store the data and metrics that it gathers from various services. Prometheus can instantly query metrics from this storage system, making it possible to edit data sets for visualization easily. The dimensional data model of Prometheus is made possible through labels, another feature. Metrics can be used with labels to extract a particular dimension from a given measure. So, queries are more accurate and effective.

Prometheus employs exporters instead of other monitoring tools, which communicate with an agent installed on the host of the service being tracked and assessed. To utilize Prometheus, customers must either instrument their code to implement the metric types specified by Prometheus or, if this is not possible, have the monitored service push the metrics to the appropriate exporter. To create a Prometheus metric, the exporter aggregates the log entries and sends them to the Prometheus server.

Characteristics of Prometheus

The key traits of Prometheus are.

A multi-dimensional data paradigm where time series data are identifiable by metric names and key/value pairs

  • To use this dimensionality, use PromQL, a flexible query language.
  • Independent server nodes without relying on distributed storage
  • An HTTP pull mechanism is used for time series gathering.
  • An intermediary gateway is available to push time series.
  • Targets are found using service discovery or static configuration.
  • Dashboarding and many graphing modes are supported.


Grafana is an open-source observability platform for displaying metrics, logs, and traces gathered from your applications. It's a cloud-native tool for fast putting together data dashboards that help you look at and evaluate your stack. Grafana connects to various data sources such as Prometheus, Influx DB, Elastic Search, and traditional relational database engines. Complex dashboards are created using these sources to select relevant fields from your data. Dashboards can incorporate various visualization components such as graphs, heat maps, and histograms.

What is Grafana?

Grafana is an open-source tool for performing data analytics, retrieving metrics that assist in making sense of the enormous amount of data, and monitoring our apps with stylish, customized dashboards. To alert you to issues as they arise, Grafana has an integrated alerting solution. Several endpoints can receive notifications, including email, Slack, and webhooks. Grafana provides a centralized monitoring view by consuming the Prometheus, Loki, and Alert manager set Alert rules.

What is Grafana Cloud?

An open SaaS Software as a Service metrics platform that is cloud-native, highly accessible, quick, and ultimately managed is called Grafana Cloud. Quite useful for people who don't want to bother about managing the full deployment infrastructure and want to avoid shouldering the burden of hosting the solution on-prem.

What Is a Grafana Dashboard?

Here is an image of a Grafana dashboard that is being used to monitor things.

The dashboards pull data from plugged-in data sources such as Graphite, Prometheus, Influx DB, Elastic Search, MySQL, PostgreSQL, etc. These are a few of the many data sources that Grafana supports by default.

The dashboards contain a gamut of visualization options such as geo maps, heat maps, and histograms, all the variety of charts & graphs which a business typically requires to study data. A dashboard contains several different individual panels on the grid. Each panel has different functionalities.

Other Considerations

Engineering and operational processes that prioritize data can be facilitated using Grafana. You can still use it for straightforward dashboards and monitoring solutions, but displaying large amounts of data from several sources will be advantageous.

Your organization's goals and the viewpoints you employ should be particular. Before putting together a dashboard, it is wise to list the information you want to track and how it should be displayed. The opposite of helpful is true when false info is presented.

While developing your dashboards, you could encounter data "dark patches." These appear when a component of your stack isn't supplying metrics or when a Grafana data source cannot receive measurements. Grafana monitoring is worthwhile if the component is essential to your application. This can be achieved by adequately instrumenting the component. Consumers can feel unjustly secure if your dashboards only provide a partial picture.



A grouping of documents with a typical relationship is called an elasticsearch. Elasticsearch uses JSON documents to store data. Every document associates a set of keys (field or property names) with the appropriate values (strings, numbers, Booleans, dates, arrays of values, geolocations, or other data types).

To enable extremely quick full-text searches, Elasticsearch uses a data structure called an inverted index. A word's unique occurrences in each document are listed in an inverted index, along with the documents in which they are found.

During the indexing process, ElasticSearch stores documents and builds an inverted index to make the document data searchable in near real-time. Indexing is initiated with the index API, through which you can add or update a JSON document in a specific index.

How does ElasticSearch work?

To better understand how ElasticSearch works, let's cover some basic concepts of how it organizes data and its backend components.

Logical Concepts

  1. Documents
    Documents are the basic unit of information that can be indexed in Elasticsearch expressed in JSON, which is the global internet data interchange format. You can think of a document like a row in a relational database, representing a given entity — the thing you're searching for. In Elasticsearch, a document can be more than just text; it can be any structured data encoded in JSON.
  2. Indices
    An index is a collection of documents that have similar characteristics. An index is the highest level entity that you can query against in Elasticsearch. The index is similar to a database in a relational database schema. Any documents in an index are typically logically related.
  3. Inverted Index
    An index in Elasticsearch is called an inverted index, the mechanism by which all search engines work. It is a data structure that stores a mapping from content, such as words or numbers, to its locations in a document or a set of documents.

Top Benefits of Using Elastic Search

  • High Performance
    Using distributed inverted indices, ElasticSearch can conduct rapid searches. For every search query that includes a cached filter, it searches the cached results since it caches all of the queries for the result set. ElasticSearch can obtain the searched data faster than a normal SQL database, which often takes more than 10 seconds.
  • Easily Scalable
    ElasticSearch can scale to thousands of machines and thousands of gigabytes of storage thanks to its distributed architecture. It is designed to operate without issue on any system or cluster with multiple nodes. It is almost entirely automatic and only slightly painful to expand from a small cluster to a huge one, then a very massive one.
  • Distributed Architecture
    In addition to handling search queries flawlessly, ElasticSearch's distributed architecture makes it strong enough to manage massive amounts of data. In the distributed approach, the indices are split into shards, which can produce countless replicas. Routing and rebalancing actions are carried out automatically as and when new documents are introduced.
  • Document-oriented Database
    For storing complicated things in documents, ElasticSearch uses JSON (JavaScript Object Notation), which it then indexes on its own. Due to its compatibility with a number of computer languages, JSON serialization has developed into a standard format for NoSQL databases. Higher performance is the consequence since it is clear, concise, and easy to read.
  • Schema Free
    ElasticSearch is schema-free, which means it doesn't require any data definition and uses some defaults to index the date unless you specify the data type. It accepts JSON documents, detects the data type, indexes the records, and makes them searchable. When the object with a new property is indexed later, it is automatically added to the mapping definitions.

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