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.
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.
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.
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
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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!
The Best MLOps Tools and How to Evaluate Them
12 mins read | Jakub Czakon | Updated August 25th, 2021
In one of our articles—The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups—Jean-Christophe Petkovich, CTO at Acerta, explained how their ML team approaches MLOps.
According to him, there are several ingredients for a complete MLOps system:
- You need to be able to build model artifacts that contain all the information needed to preprocess your data and generate a result.
- Once you can build model artifacts, you have to be able to track the code that builds them, and the data they were trained and tested on.
- You need to keep track of how all three of these things, the models, their code, and their data, are related.
- Once you can track all these things, you can also mark them ready for staging, and production, and run them through a CI/CD process.
- Finally, to actually deploy them at the end of that process, you need some way to spin up a service based on that model artifact.
It’s a great high-level summary of how to successfully implement MLOps in a company. But understanding what is needed in high-level is just a part of the puzzle. The other one is adopting or creating proper tooling that gets things done.
That’s why we’ve compiled a list of the best MLOps tools. We’ve divided them into six categories so you can choose the right tools for your team and for your business. Let’s dig in!Continue reading ->