What is Log Management? The Complete Logging Guide
If you’re struggling with syslog, juggling multiple log management tools to collect, parse, store, archive, and analyze log data, or drowning in the sheer volume of logs generated by your applications, you’re not alone. Wondering exactly why the ELK Stack is so widespread, or why businesses shell out big dollars for log management services like Splunk?
Here’s a complete guide to what modern log management is and what your open source, SaaS, DIY options are as you develop your logging strategy.
Firstly, what are logs?
A log file is a continuous and time stamped record of events and messages automatically generated by your IT systems and software applications. They record what happened, when, and by whom.
These events and messages are either written into a single log file or reside in many files locally or in remote locations. Logging is the act of collecting unstructured data as an audit trail for root cause analysis as well as live stream of activity.
Logs are ubiquitous – almost all computing systems and software produce logs. They can be found on servers, computers, operating systems, networks, load balancers, applications, threads, and services in applications, application frameworks, and containers (to name a few!). Logs can record faults, exceptions, help debug what went wrong, identify security breaches, and provide useful insight for developers, DevOps, and SecOps to analyze.
A log can be useful for a lot of different purposes. From developers who write code, to DevOps who are searching and fixing production issues, to system administrators who need to ensure everything’s running smoothly.
What does a log look like?
The data found in logs can be unstructured and structured in many ways. At its most basic, there is a timestamp, level and message. Other details can include the hostname, log type, application, tags, IP address, MAC address, and TCP socket status. There are also many formats of log types, like the common log format, Windows events, syslog, JSON, Cron, and many more. Check here for all the formats that LogDNA automatically parses.
Logs are often created by developers for debugging the operation of an application or to understand user behavior. That means the data within the log message can vary between developers, applications, vendors and systems.
What is log management?
Log management is the approach taken to deal with large volumes of log data continuously generated by nearly every computing device and software application and covers the end to end process of data collection, ingestion, log aggregation, analysis, search, reporting, long-term storage and retention.
With modern cloud applications and their infrastructure logs growing every minute in production, it’s crucial to get an end to end view of what’s going on inside the application, framework, container, servers and network devices. With a proper logging solution, you can search massive amounts of data quickly, pinpoint issues in real-time with custom views, create rules and alerts, and support the analysis and reporting you need.
A scalable and centralized log management solution should automate this process and enables the necessary monitoring, alerts, reporting, and insights will allow your business operations to thrive with minimal server and production issues.
Importance of logging
Logs provide an inside view of the health of applications and network infrastructure. Without logs, developers, sysops, DevOps, SecOps would not have any visibility into how the applications are operating, whether the infrastructure is working normally or if there have been any breaches, security attacks, or anomalies in activity. Getting insight through logs is especially important with modern cloud applications because they use infrastructure they don’t own, services from multiple sources, and are prone to many points of network communication failure.
IT professionals, security teams and developers rely on logs to do their job. They analyze performance, troubleshoot issues, and find root causes of unexpected outages and security breaches.
Logs are collected, accessed and analyzed in real-time to help teams understand what transpired in the network and application. Each log file contains nuggets of important information, context and actionable data that help with identifying and resolving issues that impact the IT and the business.
This help businesses be more proactive in detecting and resolving issues, resulting in better availability, fewer system outages, and better customer experiences. Increasingly they are being used to as a live stream of activity across a company’s infrastructure.
Logs are also necessary for compliance with security policies, audit, regulation, and forensics. Developers can also use logs to improve their code quality. In addition to listening to user feedback and opinions, they also help companies understand customer behavior and improve their product.
Massive amounts of data from many sources are meaningless unless it enables you to do your job or gain actionable insights. Log management is more than just looking at events in a file and doing grep searches on them. With logs spread across hundreds of systems, stacks, and devices, it’s impossible to manage all of them wherever they sit locally.
The adoption of microservices, containers, and serverless computing, in particular, is transforming the way software organizations generate and analyze log data. Effective log management solutions must be powerful enough to ingest terabytes of unstructured log data from many sources, flexible enough to parse dozens of different log formats, and scalable enough to tolerate unexpected spikes in log volume.
A log management solution that works well ingests and indexes every new line of logs no matter the source and provides a simple interface for users to quickly search and analyze an incident of interest, seeing all logs in chronological order across multiple systems.
Log management allows users to create their own rules, views, and alerts so that patterns can be easily recognized and the data is contextual and actionable. For network operations and security operations, log management helps detect issues and patterns proactively so that the team can intervene and resolve before issues escalate. Teams increase their operating efficiency by using intelligent log management tools.
Increasing compliance regulations and requirements also require that you log the data, but store it securely and respond to certain events. Log management helps alert the right teams to meet compliance requirements.
The importance of log management is obvious. It is a necessary part of every production software environment, especially to quickly find that needle in the haystack when you need it most.
Log Monitoring – A Crucial Part of Log Management
Log monitoring oversees important activity within your infrastructure, inspects events, detects errors, logs user actions, and provides you with alerts in the event of a data breach or abnormal behavior.
Effective log monitoring systems rely on centralized log data that are well indexed to be, quickly searchable and provide real-time alerts about important events. These events don’t just have to be for root cause analysis, but can also be used to gain insight into critical events such as loss in sales, server warnings, HTTP errors, performance, shopping cart abandonment, and others.
Log monitoring can also be set up for security, suspicious behavior, repeated failed admin login attempts, ACL changes or new accounts created.
How log management works:
- Log collection
- Log data ingestion
- Search & analysis
- Log monitoring & alerts
- Visualization and reporting
Log analysis – best practices
We analyze logs to make sense of events, properly detect patterns, anomalies, and help users make data-driven decisions. Logs can be filtered based on text patterns, tagged and categorized, and correlated from different sources to see all pertinent information to a certain event.
Log analysis helps connect the dots between the network, infrastructure, server, application framework, and user behavior to better understand what is broken and give a comprehensive view across all activity, across all sources, servers, and locations. Log analysis helps identify potential issues and threats, do root cause analysis and mitigate risks. It also feeds into log monitoring, alerting, security, audits, and regulatory compliance.
Teams that need to search through vast volumes of logs will require a fast and simplified process to find all the information they need to debug an incident, jump to the right timeline, and solve issues.
With modern cloud applications producing gigabytes or terabytes a day, the log management tool must be able to centralize all logs and retrieve data within seconds.
It’s also important for your tools to have a user-intuitive dashboard and support natural search. For instance, LogDNA’s logging tools do not require special language to perform search functions, so teams can onboard almost instantly, compared to months for most other logging tools.
Features to look for in a logging platform:
1. Log Collection
Various strategies can be implemented to collect logs, since they can be found anywhere on your software stack, operating system, containers, cloud infrastructure and network. The applications can add to syslog or directly write to an ingestion service on the cloud via REST API or code libraries . Collector agents can be installed on the OS level, platform level, and application level to facilitate the ingestion. The level of log granularity is also a consideration factor when choosing the right collector strategy and the control over how and when the logs are collected.
Syslog is a logging system that allows logging on a server locally and to remote syslog servers. This has been available since the 1980s on all Linux/UNIX installations and supports logging across all types of system devices like printers and routers and system level events, kernel level events and authentication services like Apache and NGINX. The main problem with syslog (and rsyslog, syslog-ng) is the lack of structure. Journald (joutnalctl) attempts to improve syslog by replacing the plain text files with more structure which has also become very popular.
Platforms like Heroku, Kubernetes, Docker, Fluentd, Flynn, Elastic Beanstalk, and Cloud Foundry can also be configured to collect logs but aren’t as straightforward to do from scratch. LogDNA supports all these integrations and has documentation here.
Always collect, and centralize logs outside of the live application servers because if they were collected and stored locally there is no visibility into what happened when the servers fails. The role of the log collection agent is to send these logs over to centralized infrastructure in real-time.
2. Log Ingestion
Log ingestion, or data ingestion, is the process of collecting, formatting, and importing data from external sources like applications, servers, and platforms. To utilize this data in an insightful way, logs should be formatted properly to include timestamps, input type classification, files and directories, network events, sources, and any other information your organization needs to easily find and understand this data in the future.
The ingestion service needs to be able to handle:
- Ingesting data from many sources and formats
- Efficiently storing and indexing the data to enable blazing fast searches
- Making this data available for teams to perform analysis and monitoring
For instance, LogDNA does not just ingest raw data. We recognize most popular log line types and automatically parse it before storing, making us exponentially faster than all logging tools in the industry.
Here are some of the log line types that LogDNA will recognize and parse. For an updated list, check out our comprehensive log ingestion docs.
- AWS ELB
- AWS S3
- Windows Events
3. Search and Log Analysis
There are various ways that a log management solution is supports searching. UNIX based developers who are used to debugging locally are used to the file system function, grep and formulating complicated regex text/pattern matching to find lines that they might be looking for. Both from a usability and search performance perspective, grep has its limitations.
Modern log management solutions enable users to use natural language, similar to Google search to find what users are looking for. The most important benchmark of a good log management solution is how fast is it able to return all the lines you’re looking for versus the volume of data that it’s searching through. If you have to wait minutes or hours for search results, this will be a big barrier to fixing production issues.
It is also important to notice how near-real time it is to see the live stream of activity across the infrastructure and the ability to jump quickly to any moment in time to begin root cause analysis.
4. Log Monitoring and Alerts
A key component of log management is providing the ability to set up rules and alerts, no matter the use case. For example, in order to monitor server performance, rules need to be created to monitor certain events like CPU usage spikes. Sales staff might want to set up rules to be alerted when events like abandoned shopping carts happen. Suspicious activities should also be set up to notice failed login attempts for the security team to investigate. Log management should also be able to integrate with Slack, Pagerduty, email, and more to properly alert the right people when something happens.
Based on specific patterns in the logs like 100 errors in 10 seconds or 1000 errors in 10 minutes, alerting should be able to be set up to get notified before users are impacted.
Logging applications should have the ability to set up these rules and filters to monitor and alert you in real-time. LogDNA makes log monitoring and smart alerting simple, so regardless how massive your amounts of data are, you won’t require extra staff or a dedicated team to do this manually.
5. Visualization and Reporting
Doing root cause analysis usually means involving cross-functional team members. It’s so important for everyone to be on the same page, have access to the same data sources, and share views and reports with each other. Live tail is useful to see the current state of the system and in addition to that, being able to visualize log volume over time also allows the company to detect anomalies in behavior and drill down to the log lines.
It is useful to quickly spot problematic trends by graphing log data and visually identifying spikes, for example in 404 errors. Before determining the level of visualization and reporting necessary, be sure to identify who the customer will be. Developers and operations will most likely have different requirements for reporting than marketing, sales, privacy, security and compliance teams and likely require different solutions.
Types of Log Management - How to Choose the Best Tools
It’s important to get a log management solution that works well with your company’s unique needs and helps empower your business to work more efficiently.
There are a few different types of centralized log management platforms. The ELK stack has been downloaded millions of times and is the most popular log management platform if your organization is willing to deploy and manage these open source projects on your own.
There are also SaaS cloud logging providers like LogDNA, Sumologic, and Logz.io that let you quickly send, live-tail and analyze your logs in a centralized easily accessible place on the cloud within minutes.
If you are an enterprise with strict requirements on keeping your logs on-premise or self-hosted in your own servers, you can consider the historical enterprise logging provider in the space, Splunk if you have an astronomical budget and the man power to learn their special search queries and functions.
But if your modern deployment environment includes Kubernetes or Docker, very few multi-cloud logging providers exist that was built to seamlessly streamline your logging as you scale other than LogDNA’s On-prem, private cloud or multi-cloud solution
ELK – Self-Managed & Open Source Logging
Many companies opt to self manage through the Elastic Stack (Elasticsearch, Logstash, and Kibana), basically building your own log management service in the process. Before implementing a custom solution like this, it’s important to consider the costs that come with maintaining and managing your own system. There will be a lot of positive things to note as there will be greater design flexibility, but at the cost of much higher operational complexity. Download our Total Cost of Ownership white paper to learn more. ELK is a collection of open source projects and has the ability to run both on-premise and in the cloud. This has made it a popular choice for companies seeking a log management solution at a low cost.
However, there are many long term and hidden costs as the log volume increases, especially in FTE headcount and building in-house expertise to maintain this custom log management stack.
Open source components of the ELK Stack
- Elasticsearch: a search and analytics engine
- Logstash: a log ingestor and processing pipeline
- Kibana: a data visualization tool for Elasticsearch
- Beats: a set of agents that collect and send log data to Logstash
Cloud-based logs are not always the easiest to collect, ingest, or parse, yet with modern software, servers, and multi-cloud applications, it’s become increasingly crucial. When you’re faced with a development issue, one of the easiest things you can use is a centralized cloud log management solution. Rather than circle through endless text-files in a scattered and chaotic manner, or dedicating more resources and manpower to create your own centralized tools, create ingestors and parsers before you debug.
One of the best advantages of cloud management tools is that they can be used to easily pinpoint the main cause of any software or application error, within one simple search.
Most cloud logging providers have a collection of agents and ingestors that work with popular stacks, frameworks, and log types. They abstract common issues like volume spikes in log data, dropped log lines, and can easily handle real-time searching and filtering. What differentiates them is speed, and pinpointing the exact events and issues you’re looking for with real-time accuracy of the live tail, especially as your log volume spikes and accumulates.
Another great factor is that you’ll be equipped with a visual overview of how your customers are using your software. All of this information in one packaged and single dashboard. Look for a cloud logging provider that can keep up with your volume and grow as you grow and consider how much data retention your company needs. If you are generating terabytes of log volume daily, you might move from cloud solutions to on prem solutions.
Not all logging solutions are created equal. The danger of a shopping list approach to finding a logging provider is the nuance of the ease of use for your team members that in the time and pressure sensitive scenarios they need to use logging tools to get to the root issues, that they can jump right in, get the search results they need right away and step through exactly what is happening in real-time. If you notice the times where your engineering and DevOps teams are figuring out how to resolve a hard issue, these are the conditions under which your log management strategy needs to support. Cloud logging solutions help you focus on building great products instead of having to worry about designing, creating, maintaining and scaling a log management platform. An intuitive user experience that requires minimal or no on-boarding training, fast search results, real-time and accurate live tail views of your systems are areas you’ll want to consider prioritizing in your evaluation.
Deploying On Your Own Infrastructure & Hybridization
Often, there are clear requirements in a business to have full control and ownership of all that is happening in your infrastructure whether it’s for compliance, security, privacy. Other managing your own ELK stack, there is a lack of options when it comes to on-premise. Legacy players like Splunk have astronomical costs and FTE costs.
LogDNA is one of the few multi-cloud logging providers that draws our expertise of scaling cloud logging for thousands of companies to providing on-premise solutions that work with your infrastructure. We’re the only solution that is tightly integrated with Kubernetes on all cloud infrastructure (GKE, EKS, IKS) as well as Packet and bare metal deployments. This way, you can use your FTE headcount to build great products and know that there are logging experts that will help with your logging infrastructure, hardware, software upgrades, scaling issues.
Legacy solutions include expensive hardware requirements and the cost of scaling are important factors to consider. The landscape of enterprise infrastructure also include both on-premise hardware and cloud infrastructure. Amazon S3, Microsoft Azure, Google Cloud Platform all can be used to centralize your log management solution. It will require more setup and focus on the operations and scaling issues than to use a cloud log management solution.
The challenges with deploying on your own infrastructure will boil down to the TCO and costs associated to the hardware requirements and strategies in customizing the solution to the requirements of your business, updating the software and scaling to handle spikes, unexpected behavior, and growing pains. Open source projects may not come up with bug fixes and features quickly enough that will support your needs.
How to Choose the Best Log Management System for Your Organization
Every business may have different logging requirements based on log volume, scalability, compliance, or log retention. Here are the main factors to consider.
Healthcare data is incredibly sensitive and important to keep track of and protected. Before the cloud existed the Health Insurance Portability and Accountability Act of 1996 Title II (HIPAA) was the first important law that addressed these concerns.
Regulations through the Hitech Act amendment have been created to protect electronic health information and patient information. Log management and auditing requirements are covered extensively by HIPAA as well.
- Protected information being changed/exchanged
- Who accessed what information when
- Employee logins
- Software and security updates
- User and system activity
- Irregular Usage patterns
It’s grown increasingly more important for healthcare professionals and business partners alike to maintain HIPAA compliance indefinitely. Log files (where healthcare data may exist) must be collected, protected, stored and ready to be audited at all times. A data breach can end up costing a company millions of dollars.
GDPR (General Data Protection Regulation) is to help companies strengthen and standardize user data privacy all organizations that handle EU citizen’s personal data, regardless of where the organizations themselves are located. PCI compliance is necessary for anyone involved in the processing, transmission or storage of payment card data.
Check out more details on LogDNA Compliance.
Log Volume and Retention
You will need to figure out what your daily volume will be, account for data spikes and abnormal behaviors. You’re also going to need to figure out how long to store that data, whether your use cases are for real-time debugging and live tail, or if you have to keep logs for compliance.
Scalability and Flexibility
The cost is going to be an important deciding factor. Pay per gig is one of the most flexible and smartest ways of using a logging platform. Depending on your product, you could go from processing a few thousand logs a day to a few million overnight. Your log management platform needs to grow as you grow.
Here is a good checklist to think about when determining your total cost to operate, and what you’ll need out of a log management platform.
- Free trial & easy installation
- Free plan with live tail available
- Ability to track log volume
- Storage retention costs
- User limits & plan of action if they’re exceeded
- Features offered per each plan
- Granular billing rate per GB
- Compliance and security
Comparing Self-Managed vs Hosted Log Management Solutions
When comparing between self-managed and hosted logging systems, be sure to analyze the total cost of ownership. Even though deploying the ELK stack is free to start, it quickly becomes a core part of your infrastructure and will require extra resources, training, and personnel to customize and manage the system indefinitely. As applications grow and succeed, the corresponding log volume and storage needs will change.
10 Components to Look For In Your Ideal Logging Solution
- Use a framework with flexible output options
- Utilize standard format like JSON
- Visualization of console logs without direct server access
- Custom format for storage outside your data center
- User experience intuitive for all users
- Low latency for live monitoring
- Test search performance at full query capacity
- Ingestion time less than a few seconds
- Automatically parsed logs at ingestion
- Easy onboarding and integration for pre-existing systems
Why LogDNA’s Log Management System?
Created by engineers for engineers, LogDNA is the easiest, most intuitive, and affordable log management system for both cloud and hybrid cloud logging. With the fastest search and simplest interface for log analysis, you can get started within minutes and quickly aggregate system and application logs into one efficient platform. As the leading provider of multi-cloud log management, no matter your budget or infrastructure needs, we’re one of the few providers who can provide both cloud based and on-prem solutions.
Our customers love the ease and real-time nature of our live tail and ease of using search, filtering and graphing. Your team can hit the ground running whether it’s root cause analysis, performance monitoring, application security analysis, forensics, compliance and/or understanding user behavior. We do the heavy legwork when it comes to speed, scale and management.
Your data is also safe with LogDNA, as we were built from the ground up for HIPAA compliance, SOC 2 Compliance, and GDPR. See more information on our compliance and security.
Log From Anywhere
From Kubernetes, python to REST APIs we support over 30+ integrations to ingest data. The beauty of our cloud-based logging platform is you can start logging in under 2 minutes with almost no onboarding required.
Here is a list of popular operating systems, platforms and code libraries that LogDNA supports. Check our docs for a complete list of integrations.
- Operating Systems:
- Linux Debian based: Ubuntu, Debian, LinuxMint
- Linux RPM based: Redhat, centOS, Amazon Linux, Gentoo, Windows, Mac OS X Server
- Platforms: Kubernetes, Heroku, Docker, Fluentd, Flynn, CloudWatch, Elastic, Beanstalk, Cloud Foundry
- Code Libraries: REST, Node.js, Ruby/Rails, Python, Go Lang, Java, PHP, iOS
Affordable Pay-Per-Gig Pricing
LogDNA makes it easy to get started for companies of all sizes with our “pay as you grow” pricing structure. You also don’t have to worry about fixed storage buckets. Check with us about pricing for the self hosted solution.
Constrained usage buckets and complicated licensing agreements are wrought with inefficiencies. Nobody has an accurate view of their log volume and as dynamic as your company grows, this volume will fluctuate and grow. Our pricing system is innovative and has set a precedent in the industry that cannot be beat.
Creating and maintaining your own log management system might be tempting when you are just launching your product. Then you will run into scaling issues and see the true costs of log management. LogDNA is capable of handling hundreds of thousands of log events per second and dozens of terabytes per customer per day. Whether you go from 1GB to 10PB, we’ve got you covered.
The log management strategy you choose will quickly become essential to the IT and operations and health of your business. Logs are generated by every part of your IT infrastructure and the right strategy will enable your organization to monitor important activities, anomalies, user actions and handle data breaches and failures accordingly.
When formulating your log management strategy, take into consideration your business’ unique needs, systems, log formats. Get an idea of the log volumes, retention needs, and what management solution best aligns with your overall business strategy. Get your engineering team involved in this process and have a trial period where the engineers can use the system with real use cases. Pay attention to the infrastructure requirements and hidden costs of “free” self-managed solutions as your log volume spikes and your organization scales. LogDNA takes care of log management so you can focus on building great products.
We hope this complete guide to log management provides you with a good starting point as your formulate your log management strategy.