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A Machine Learning Approach to Log Analytics – How to Analyze Logs?

Logs play a big role in the development and maintenance of software systems. Using logs, developers and engineers analyze what’s happening at every layer of a system and track down problems. Despite a large amount of distributed log data, analyzing it all adequately is still a huge challenge.

In this article, we’ll go through the main problems of manual log analysis, and see why Machine Learning is the solution for this challenge.

What is a log analysis tool?

After collecting and parsing logs from different sources, log analysis tools analyze large amounts of data to find the main cause of an issue concerning any application or system error. 

These tools are essential for monitoring, collecting, and evaluating logs in a centralized location. This way, users get system-level insights from collected log data. You can rapidly troubleshoot, fix issues, and find meaningful behavioral patterns to guide business decisions, investigations, and security.

Modern software systems generate a huge volume of logs, making it impractical to inspect logs with traditional log analysis tools, based on manual query-level matching or rule-based policies.

Traditional log analysis problem

Before traditional log analysis, first we need to define log analysis itself, and see why it’s crucial for companies.

In fact, log analysis is reviewing and making sense of computer-generated log messages, such as log events or audit trail records (generated from computers, networks, firewalls, applications servers, and other IT systems). 

It’s used by organizations to improve performance and solve issues. It also mitigates a variety of risks, responds with security policies, comprehends online user behavior, and conducts forensics during an investigation.

Steps of Log Analysis
Steps of log analysis (source)

Increasing scale and complexity of modern software systems expanded the volume of logs, making the traditional, manual log inspections unreasonable. In fact, modern software systems generate tons of logs. For example, gigabytes of data per hour are generated for any commercial cloud application. It’s impossible to make the distinction between data from everyday business activities and from malicious activities using the traditional way.

Manual log analysis depends on the proficiency of the person running the analysis. If they have a deep understanding of the system, they may gain some momentum reviewing logs manually. However, this has serious limitations. It puts the team at the mercy of one person. As long as that person is unreachable, or unable to resolve the issue, the entire operation is put at risk.

The answer is Machine Learning log analysis

Machine learning could be part of the solution if not the solution to the challenges of traditional log analysis.

Computers have proven that they can beat humans. In tasks where there’s a huge volume of data, this ability makes machines capable of driving cars, recognizing images, and detecting cyber threats. 

With machine learning-powered log analysis, tech teams get rid of routine and repeatable tasks, and engineers can focus on other tasks that can’t be accomplished by machines. Like problem-solving tasks, and thinking of innovative new products.

Benefits of Machine Learning for log analysis 

Using machine learning with log analysis tools lets us:

  • Categorize data rapidly: Logs can be seen as textual data, which means that NLP techniques can be applied to gather the same logs in an organized manner, making it possible to search for specific types of logs.
  • Automatically identify issues: one of the benefits of ML is that it detects issues and problems automatically, even if there’s a huge number of logs.
  • Alert critical information: many log analysis tools create excessive alerts where they’re, in most cases, not the cause of real issues. With ML, it’s possible to be alerted when there’s something that deserves attention. This way, we overcome the issue of false positive alerts.
  • Early anomaly detection: in most disastrous events, there’s always an initial anomaly that wasn’t detected. Machine learning can detect this anomaly before it creates a major problem.

Best ML-powered log analysis tools  

In this section, we’re going to list the best log analysis tools that use machine learning for monitoring, and define how to choose between them. We’ll do that by reviewing the top 10 log analysis tools.

1.Coralogix

Log analysis tools - Coralogix

Coralogix is a startup that wants to bring automation and intelligence to logging. They’re building a remote monitoring and management tool powered by machine learning, which offers an analytics platform to improve the delivery and maintenance process of the network. Users have an ideal platform to view all live log streams, define dashboard widgets for maximum control over the data, and cluster log data back into original patterns.

2. Datadog

Log analysis tools - Datadog

Datadog is a log analysis tool, providing monitoring of servers, databases, tools, and services through a SaaS-based data analytics platform. Datadog’s visualization displays log data in the form of graphs, which let you visualize network performance over time. Datadog uses centralized data storage to protect log data from being compromised, along with machine learning to detect anomalous log patterns and issues.

3. SolarWinds / Loggly

Log analysis tools - Solarwinds

Loggly is a SaaS solution for log data management. Users can simply aggregate logs from the entire infrastructure, and bring them together in one place to track activity and analyze trends. Loggly serves multiple purposes, such as monitoring application analytics, troubleshooting server and application issues, transaction correlation, and alerting. It offers different advanced features like dynamic field explorer, automatic alerts, default or custom dashboards, and derived log fields.

4. Logic Monitor

Log analysis tools - LogicMonitor

Logic Monitor is a SaaS-based performance monitoring platform with the ability to monitor the data that matters to the business, so that you can react quickly to problems and be proactive with solutions. It provides full-stack visibility for networks, cloud, servers, and more, all in a combined view.

5. Logz.io

Log analysis tools - Logz

Logz.io provides a scalable and intelligent machine data analytics platform, built on ELK and Grafana, for monitoring modern applications. It combines cloud-native simplicity and scalability with crowdsourced machine learning to identify big issues before they happen. Users can monitor, troubleshoot, and secure mission-critical applications using one unified platform.

6. Sematext

Log analysis tools - Sematex

Sematext is a log management and analysis tool on the cloud. It’s an online implementation of the ELK. It’s also available for a self-hosted solution via Sematext Enterprise. Sematext is a unified platform with all-in-one solutions for infrastructure monitoring, application performance monitoring, log management, real user monitoring, and synthetic monitoring to provide unified, real-time observability of your entire technology stack.

7. Splunk

Log analysis tools - Splunk

Splunk is one of the popular commercial log centralizing tools. The typical deployment is on-premises (Splunk Enterprise), although it’s also offered as a service (Splunk Cloud). It has real-time alerts. They can be sent by email or RSS. Alerts have configurable thresholds and trigger conditions to determine what activity will generate a notification. The supporting information included with alerts helps reduce event resolution time.

8. SumoLogic

Log analysis tools - Sumologic

Sumo Logic is a log management tool for collaborating, operating, developing, and securing applications. It has a powerful search syntax, where it helps define operations in a similar way to UNIX pipes. It’s also a cloud-based machine data analytics platform, designed to proactively identify performance issues, ensure seamless device availability, and enhance application rollouts. In addition, Sumo Logic includes built-in pattern detection, predictive analytics, and anomaly detection.

9. Xpolog

Log analysis tools - Xpolog

XpoLog is an end-to-end solution for fully automated log management. It’s designed to collect and parse log data from IT infrastructures, cloud applications, and servers. Moreover, it provides analysis tools, report engines, monitoring engines, correlation capabilities, transaction tracking, and monitoring search engines for logs. Xpolog supports both agentless and agent-based architectures, which means that it can access logs via standard protocols like SSH.

10. Zebrium

Log analysis tools - Zebrium

Zebrium is a software used to monitor log structure using unsupervised machine learning to automatically catch software incidents and show the root cause of it. The tool works by finding hotspots of correlated anomalous patterns across logs and metrics. The software offers AES-256 encryption and receives alerts via Slack.

ML log analysis tools – comparison table 

We have constituted a 10 log analysis tools comparison table for easy and better reviewing.

Tool Pros Cons Best fit for
Coralogix Faster search experience, with great log aggregation and alerting capabilities. Awesome customer support. Automatic data clustering, alerts to Slack and email. Log amount limited per day, not per month. Small enterprises and startups.
Datadog Powerful alert and warning configuration to drastically reduce false positives. Good API documentation, very responsive customer service. Some users complain about cost getting out of control (due to flexible pricing possibility). Small and medium-sized companies.
SolarWinds/ loggly Good search capabilities, option to collect and analyze logs from many different sources in a centralized place. Users can also distribute alerts and create tickets on different platforms like Slack, HipChat, or Jira. The UI isn’t very pretty. Basic features, like API access or more than a few users, are only available in higher pricing plans. Organizations that deploy to cloud environments rather than on-prem.
Logic Monitor Monitors a broad range of devices and environments with great detail and precision, both on cloud and on-prem environments. Custom visual dashboards and many pre-configured rich dashboards. Web UI can sometimes require refreshing for changes to display, which is annoying. Reconsidering pricing on the entry-level side so that smaller organizations can get into this tool. Medium-sized and large companies.
Logz.io Good search, easy to use, including filtering and formatting capabilities. Great alerting mechanism, especially for monitoring applications. Limited to create sub accounts, which is a major problem for big companies. Data retention isn’t great. Sometimes we could lose the events if we reach maximum event ingestion. Cloud-based applications.
Sematext Easy navigation, nice interface environment without complicated clutter. Good documentation and excellent customer support. Only parses Syslog and JSON on the server-side. Custom parsing has to be done in the log shipper. Can’t mix Kibana and native UI widgets in the same dashboard. Enterprise and consumer-oriented companies.
Splunk Extensive list of features includes machine data indexing, real-time and historical searching, with advanced reporting functionalities. Very expensive. Steep learning curve, with expensive deployments and high maintenance. Organizations looking for solid technology and confidence in company and brand.
SumoLogic Informative insights into different aspects of modern apps, dashboards for monitoring and visualizations, machine learning functionalities, predictive analysis functions. Pricing is per ingested byte, which means that data retention is expensive (high price to keep a long history). Small organizations having a few logs.
Xpolog Very easy to maintain and deploy with algorithms that automate analysis, including a huge variety of log analysis and management features. Smaller community than other tools, the product focuses more on IT and security than the developer’s community. Enterprises and SMEs looking for quick deployment with an affordable solution.
Zebrium Easy to use with automatic detection of problems and root causes, without needing manual rules. Can be used as a standalone log management tool, or an ML add-on to an existing log management tool such as the ELK Stack. Free plan is limited to 500 MB a day, with 3-day retention. Also, it’s not well-known as its competitors. Large Enterprises, Medium and Small Business.

Conclusion

Several log analysis platforms use machine learning that help in the automatic detection of root causes and issues, without needing much or any manual analysis. 

When you’re choosing a log analysis tool, look beyond functionalities and budget, and consider the amount of time you can gain. Do you want to spend time developing your own log analysis tool, or prefer a solution that does everything out-of-the-box so you can focus on your business?

The final decision is yours. I hope this article helps you choose the right tool!

References


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15 Best Tools for Tracking Machine Learning Experiments

Pawel Kijko | Posted February 17, 2020

While working on a machine learning project, getting good results from a single model-training run is one thing, but keeping all of your machine learning experiments organized and having a process that lets you draw valid conclusions from them is quite another. That’s what machine learning experiment management helps with. 

In this article, I will explain why you, as data scientists and machine learning engineers, need a tool for tracking machine learning experiments and what is the best software you can use for that.

Tools for tracking machine learning experiments – who needs them and why?

  • Data Scientists: In many organizations, machine learning engineers and data scientists tend to work alone. That makes some people think that keeping track of their experimentation process is not that important as long as they can deliver that one last model. This is true to an extent, but when you want to come back to an idea, re-run a model from a couple of months ago or simply compare and visualize the differences between runs, the need for a system or tool for tracking ML experiments becomes (painfully) apparent. 
  • Teams of Data Scientists: A specialized tool for tracking ML experiments is even more useful for the whole team of data scientists. It allows them to see what others are doing, share the ideas and insights, store experiment metadata, retrieve it at any time and analyze it whenever they need to. It makes the teamwork much more efficient, prevents situations where several people work on the same task, and makes onboarding of new members way easier.
  • Managers/Business people: tracking software creates an opportunity to involve other team members like managers or business stakeholder in your machine learning projects. Thanks to the possibility to prepare visualizations, add comments and share the work, managers and co-workers can easily track the progress and cooperate with the machine learning team.

Here is an in-depth article about experiment management for those of you who want to learn more.

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