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Log Collection and Analysis Best Practices

  • By Gcore
  • November 2, 2023
  • 9 min read
Log Collection and Analysis Best Practices

In the IT industry, logs provide invaluable insights into system behavior, performance, and security, enabling timely troubleshooting and data-driven decision making. As a result, generating a vast quantity of logs is often considered a valuable goal in itself. However, the indiscriminate logging of every single step of your code can lead to chaos in log storage, failing to deliver the expected benefits of log collection. In this article, we’ll look at best practices for log generation, collection, and analysis to help you get the most from your logs.

Understanding Logs and Log Collection

Logs are short messages that capture significant events within a software system, along with associated metadata. Log collection refers to the generation, aggregation, and storage of the historical data represented by the logs.

Typically, log messages are generated within a software’s source code or by infrastructure components. These messages are either stored locally on disk or sent to a dedicated server; in both locations, the log entries are processed, stored, and analyzed.

The main use cases for log collection include:

  • Troubleshooting bugs: Log messages can help to reconstruct the sequence of events leading to a bug and provide useful data that gives context.
  • Detecting errors: You may be unaware of a specific bug or failure until an anomaly appears in your log files. Monitoring logs helps detect errors and system malfunctions.
  • Investigating security incidents: Unauthorized access attempts, cyberattacks, and other suspicious activity can be revealed by logs, which track unusual events happening in the system.
  • Usage analytics: Logs can mark various milestones or steps in users’ interaction with your services and applications, enhancing your understanding of how they use your software.

Server applications often use logs to analyze how their API is used, monitor outages, and measure latency when exchanging data between subsystems, or the system and the user, in order to recognize performance bottlenecks. In mobile apps, it’s common to use logs while investigating crash reports and analyzing A/B testing results.

The entire process of logging can be divided into two categories of activities: log collection and log analysis. The first group encompasses everything that produces log messages, including logs generation and saving them to a file or sending them to a remote storage. The second group relates to activities on the logs consumer side—logs storage, processing, combining, filtering, and, ultimately, their analysis. In some cases, the edge between two groups is blurred, therefore some practices recommended in this article affect the entire process, not just the group they logically relate to.

Log Collection Best Practices

In order to get the most from your logs, it’s important to follow certain best practices. Here are some simple practices to ensure that your logs are useful tools for the efficient maintenance of your app, instead of a pile of unsorted data with no practical value.

#1 Planning

Good results start with a good plan, and logs are no exception. Although logs can be introduced at any stage of the application lifecycle, planning what you want to log before you start writing code optimizes the process. This allows you to integrate logging seamlessly where it’s useful, efficient, and maintainable.

#2 Including All Layers and Subsystems

It’s important to include all system components and code modules in your logging. Otherwise, you may find a bug located in an area that’s only partially logged or not logged at all, and then you’ll either need another way to tackle the issue or you’ll have to add logging retroactively and wait until the problem occurs again. Adding new logs on the go, redeploying the system, and waiting until the elusive bug reoccurs—while your users are expecting a fix—does not inspire trust in your company because it’ll result in subpar user experience.

#3 Structure

Good logs have good structure. Here are four elements that lend structure to logs, and the reasons it’s important to keep these four things in mind while generating log messages:

Categorization

To navigate easily through large amounts of data, log data should be organized systematically. Using different categories for different subsystems enables you to filter logs for the specific part of the application that is relevant to your current analysis.

Log Levels

Virtually all log systems support different groups of messages, which are commonly referred to as log levels. While different log systems may use different names for levels or offer a slightly different number, a number of levels are common across systems.

  • Debug logs help software developers with the problem at hand by providing technical and specific information, such as the context necessary to reproduce a bug.
  • Info logs are usually bound to certain events related to the software’s business value, such as starting and stopping a service or creating and removing a file. Such logs are useful when gathering statistics and analyzing various usage scenarios.
  • Warning logs warn about potentially dangerous events in the system or circumstances that might lead to such events. For instance, they will notify you if a disc has almost run out of space.
  • Error logs are exactly what their name suggests: the description and meta-data of the errors that happen in your system. For example, a mobile application can generate an error log when its backend server is not available.
  • Fault or fatal-level logs show critical failures of the software that prevent its proper functioning. They usually mean that an intervention from an engineer is required. For instance, a microservice can log a fault when its connected database is down.

Using log levels consistently allows entries to be filtered, limiting the output to the necessary minimum. Together with categorizing, this would make it possible, for example, to display warnings related solely to the database layer.

Tagging

Some log systems allow you to add custom tags to log messages. Similarly to how categories help distinguish log messages produced by different subsystems, tags allow you to group your log messages by custom criteria, for example, “A/B Testing” or “Performance.”

Format

Adhering to a well-known formatting structure, such as JSON or XML, makes processing and storing of the log data more efficient. However, there’s a catch: A fixed format imposes rigid constraints on the log message. Thus, applying a strict schema to each log message might result in partial loss of context, which is less likely with a free-form message.

#4 Consistent Text Formatting

Software developers are sometimes reluctant to write any kind of documentation, including log messages, and generating consistent log messages requires discipline and long-term commitment. However, using established terminology and unified formatting always pays off, because it helps you skim through vast amounts of log messages more easily and reduces the possibility of human error.

Reading logs can be a challenge even when they are properly organized. At the very minimum, using units and date formats consistently is necessary if you don’t want to spend hours reading scattered entries, and editing messages that use different terms for the same concept.

#5 Including Relevant Data and Context

Before adding logging, it’s worth deciding which data will be useful for your use case. Relevant data provides your log messages with context. For example, attaching unique identifiers to user API requests makes it easier to find information about a specific request. Even if a log message is in plain text, consistently including a timestamp will eventually give the message context during log filtering and analysis.

On the other hand, it’s equally beneficial to avoid generating unnecessary log messages. Logging irrelevant data introduces noise, slows down the search for relevant information, and wastes both your and your users’ disk space.

The key to remember when it comes to relevance is that detailed and meaningful messages are the core of your log entries. Even if they are collected, stored, and processed by sophisticated automated systems, ultimately logs are read and interpreted by humans, so they need to convey relevant meaning.

#6 Security and Privacy

When planning which data to incorporate into logs, users’ private information should be excluded or at least encrypted. Some log systems can redact personal details, such as names or credit card numbers. This obfuscates sensitive data but still allows log entries containing a specific encrypted identifier to be collated.

If sending sensitive data to your log server is unavoidable, precautions should be taken. The internet connection must be secure, data should be encrypted, and access to logs should be restricted to a select few individuals whose roles require it.

It’s also advisable to keep your software updated, because new patches often contain fixes to known security breaches. However, this comes with its own pitfalls: New updates sometimes include known issues, so always read release notes and bulletins.

Finally, if your company must comply with certain regulations, such as GDPR for companies operating within the EU, logs require particular attention. Regulations may require that certain data types, including logs, have a finite retention period.

What Is Log Analysis?

Log analysis involves the set of activities related to reading, searching, and interpreting collected logs. While effective log generation and collection are integral parts of the efficient log system, they make up just half of its success. When it comes to actually using the resulting logs, analysis comes to the fore.

IT professionals generally use log analysis in a precisely targeted way, focusing on specific sections of the entire log to answer questions, analyze aspects of performance, or investigate incidents. For instance, the focus might be on logs related to a user session during the time when a particular bug occurred.

Log Analysis Best Practices

Given the challenges of processing large volumes of stored data and network delays in the case of remote log servers, log analysis has its own challenges. In this section, we’ll take a look at best practices that make the process easier.

After logs are generated and collected, they are stored in or sent to a logs storage. This is where IT professionals access generated logs to analyze them.

#1 Accessible Storage

To facilitate efficient analysis, a good storage system for your logs should possess the following qualities:

  • Friendly interface: Browsing log messages starts with accessibility; a cryptic API can easily make reading logs torturous. Choose a log system or service that makes your logs easily accessible, preferably one that has an intuitive user interface.
  • Security: As log entries can contain sensitive data or business-critical information, access should be restricted. Implement a storage system that allows individual or role-based access control.
  • Easy browsing: The storage should allow easy browsing, sorting, and filtering. Centralized storage allows logs to be easily and quickly collated from different subsystems, giving you a holistic view of your logs—crucial, given the growth of distributed systems and cloud-based services.
  • Rotation: To comply with regulations and reduce costs, the storage system should support log rotation. This means that when storage limits are exceeded or retention periods expire, old or irrelevant data is automatically deleted.
  • Scalability: Scalability will ensure that your logs will not be lost if your service usage grows rapidly. To limit the cost of storing vast amounts of data, a storage service might compress files containing log messages, especially if the data is old or is being stored only because of a retention policy.
  • Indexing: Like other kinds of databases, logs benefit from indexing for faster access and more efficient filtering and sorting.

#2 Normalization

Since log messages may come from disparate sources, such as different modules of an application or different microservices of the server, data received by the log system may be in diverse formats. Many software systems use the legacy syslog format, while others have their own, e.g., Apple’s unified logging system, which is used in macOS and iOS applications. Data should be automatically converted to a consistent format when it is received and stored.

Modern log services support various filters and parsers that will format logs to one standard. However, data should follow the standards which are recognized by your log analysis tools.

#3 Correlating

An effective log analysis tool should allow the collation of log messages from different sources. This is crucial for investigation of incidents that occur and are logged in one subsystem, but are actually caused by a failure in another subsystem. Such scenarios require a deep understanding of the context and juxtaposition of all relevant events from all involved software components.

#4 Monitoring

Regular monitoring of incoming log messages allows for quicker identification of unusual activity, prompt reactions to security incidents, and earlier handling of performance drops. As a result, cyberattacks can be averted and the service operates without interruption. This is especially important for services where even a brief downtime can result in significant loss of revenue, like the financial services industry or enterprises.

Nowadays, monitoring often includes machine learning, which adapts to the specifics of your use case and constantly learns to predict events by analyzing more and more data. With the help of machine learning, logging systems can detect patterns in log messages that could, for instance, be a sign of a cyberattack, but aren’t obvious to human interpreters.

#5 Real-Time Updates

In order to receive timely alerts from a managed logging system, log messages should be written into the system as close to real time as possible. While this may seem obvious, it’s not that simple to implement.

Log collection demands computational powers, and sending log messages to a server consumes network bandwidth. However, as log collection is not critical to the immediate user experience, it’s not always a priority. As a result, many systems send log messages using buffers and background queuing.

As often happens with IT systems, actual “real-timeness” is also a matter of a tradeoff: If logs are processed in the background with low priority, they may be analyzed and interpreted later than desired. On the other hand, if logging is given a high priority, the software’s responsiveness may suffer.

Managed Logging

Managed logging systems, also known as logging as a service (LaaS,) are centralized storage systems for log collection and analysis. These services save companies the investment of building and maintaining their own solutions.

Managed logging systems typically offer flexible options for log storage and rotation, and a user-friendly interface for displaying, sorting, and filtering historical data. However, different systems offer different feature sets, so it’s advisable to familiarize yourself with what a service provides before making a final decision; switching between managed logging systems can be both costly and cumbersome.

Gcore Managed Logging

Gcore Managed Logging stores logs collected from different sources and compiles them into a single, intuitive system that can be browsed using OpenSearch Dashboards. For better reliability, we use Kafka servers as an intermediary buffer and retain received log messages, ensuring they remain available even if the log source is currently down.

Conclusion

Log collection and analysis ultimately lead to a better user experience delivered by your services and applications. The best practices discussed in this article can help make working with the collected logs easier and more efficient.

Managed logging services, such as Gcore Managed Logging, help you to get the most from your logs. You get a centralized persistent storage that accumulates logs from all your services and displays them on a dashboard, where you can easily collate, filter and monitor your log messages.

Get LaaS now

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Workloads that need high processing power, such as video encoding, machine learning, or simulations, will benefit from VMs with multiple CPU cores. However, be mindful of CPU architecture—look for VMs that offer the latest processors (e.g., Intel Xeon, AMD EPYC) for better performance per core.It’s also important that the VM has enough memory to avoid paging, which occurs when the system uses disk space as virtual memory, significantly slowing down performance. Consider a configuration with more RAM and support for faster memory types like DDR4 for memory-heavy applications.﹟4 Assess storage performance and capacityStorage performance and capacity can significantly impact the performance of your virtual machine, especially for applications requiring large data volumes. Key considerations include:Disk type: For faster read/write operations, opt for solid-state drives (SSDs) over traditional hard disk drives (HDDs). Some cloud providers also offer NVMe storage, which can provide even greater speed for highly demanding workloads.Disk size: Choose the right size based on the amount of data you need to store and process. Over-allocating storage space might seem like a safe bet, but it can also increase costs unnecessarily. You can always resize disks later, so avoid over-allocating them upfront.IOPS and throughput: Some workloads require high input/output operations per second (IOPS). If this is a priority for your workload (e.g., databases), make sure that your VM configuration includes high IOPS storage options.﹟5 Weigh up your network requirementsWhen working with cloud-based VMs, network performance is a critical consideration. High-speed and low-latency networking can make a difference for applications such as online gaming, video conferencing, and real-time analytics.Bandwidth: Check whether the VM configuration offers the necessary bandwidth for your workload. For applications that handle large data transfers, such as cloud backup or file servers, make sure that the network interface provides high throughput.Network latency: Low latency is crucial for applications where real-time performance is key (e.g., trading systems, gaming). Choose VMs with low-latency networking options to minimize delays and improve the user experience.Network isolation and security: Check if your VM configuration provides the necessary network isolation and security features, especially when handling sensitive data or operating in multi-tenant environments.﹟6 Factor in cost considerationsWhile it’s essential that your VM has the right configuration, cost is always an important factor to consider. Cloud providers typically charge based on the resources allocated, so optimizing for cost efficiency can significantly impact your budget.Consider whether a pay-as-you-go or reserved model (which offers discounted rates in exchange for a long-term commitment) fits your usage pattern. The reserved option can provide significant savings if your workload runs continuously. You can also use monitoring tools to track your VM’s performance and resource usage over time. This data will help you make informed decisions about scaling up or down so you’re not paying for unused resources.﹟7 Evaluate security featuresSecurity is a primary concern when selecting a VM configuration, especially for workloads handling sensitive data. Consider the following:Built-in security: Look for VMs that offer integrated security features such as DDoS protection, web application firewall (WAF), and encryption.Compliance: Check that the VM configuration meets industry standards and regulations, such as GDPR, ISO 27001, and PCI DSS.Network security: Evaluate the VM's network isolation capabilities and the availability of cloud firewalls to manage incoming and outgoing traffic.﹟8 Consider geographic locationThe geographic location of your VM can impact latency and compliance. Therefore, it’s a good idea to choose VM locations that are geographically close to your end users to minimize latency and improve performance. In addition, it’s essential to select VM locations that comply with local data sovereignty laws and regulations.﹟9 Assess backup and recovery optionsBackup and recovery are critical for maintaining data integrity and availability. Look for VMs that offer automated backup solutions so that data is regularly saved. You should also evaluate disaster recovery capabilities, including the ability to quickly restore data and applications in case of failure.﹟10 Test and iterateFinally, once you've chosen a VM configuration, testing its performance under real-world conditions is essential. Most cloud providers offer performance monitoring tools that allow you to assess how well your VM is meeting your workload requirements.If you notice any performance bottlenecks, be prepared to adjust the configuration. This could involve increasing CPU cores, adding more memory, or upgrading storage. Regular testing and fine-tuning means that your VM is always optimized.Choosing a virtual machine that suits your requirementsSelecting the best virtual machine configuration is a key step toward optimizing your workloads efficiently, cost-effectively, and without unnecessary performance bottlenecks. By understanding your workload’s needs, considering factors like CPU, memory, storage, and network performance, and continuously monitoring resource usage, you can make informed decisions that lead to better outcomes and savings.Whether you're running a small application or large-scale enterprise software, the right VM configuration can significantly improve performance and cost. Gcore offers a wide range of virtual machine options that can meet your unique requirements. Our virtual machines are designed to meet diverse workload requirements, providing dedicated vCPUs, high-speed storage, and low-latency networking across 30+ global regions. You can scale compute resources on demand, benefit from free egress traffic, and enjoy flexible pricing models by paying only for the resources in use, maximizing the value of your cloud investments.Contact us to discuss your VM needs

How to get the size of a directory in Linux

Understanding how to check directory size in Linux is critical for managing storage space efficiently. Understanding this process is essential whether you’re assessing specific folder space or preventing storage issues.This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.What is a Linux directory?A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.#1 Get Linux directory size using the du commandUsing the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).Check the size of a specific directory in LinuxTo get the size of a specific directory, open your terminal and type the following command:du -sh /path/to/directoryIn this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.Check the size of all directories in LinuxTo get the size of all files and directories within the current directory, use the following command:sudo du -h /path/to/directoryExample: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.#2 Get Linux directory size using ncduIf you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.For Debian or Ubuntu, use this command:sudo apt-get install ncduOnce installed, run ncdu followed by the path to the directory you want to analyze:ncdu /path/to/directoryThis will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:#3 Get Linux directory size using 1s -1aYou can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:-l (long listing format)Displays the detailed information for each file and directoryShows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name-a (all files)Instructs ls to include all files, including hidden files and directoriesIncludes hidden files on Linux that typically have names beginning with a . (dot)ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.Example: When you enter ls -la command and press Enter, you will see an output similar to this:Each line includes:File type and permissions (e.g., drwxr-xr-x):The first character indicates the file type- for a regular filed for a directoryl for a symbolic linkThe next nine characters are permissions in groups of three (rwx):r = readw = writex = executePermissions are shown for three classes of users: owner, group, and others.Number of links (e.g., 2):For regular files, this usually indicates the number of hard linksFor directories, it often reflects subdirectory links (e.g., the . and .. entries)Owner and group (e.g., user group)File size (e.g., 4096 or 1045 bytes)Modification date and time (e.g., Jan 7 09:34)File name (e.g., .bashrc, notes.txt, Documents):Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)ConclusionThat’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:Affordable shared compute resources starting from €3.2 per monthDeploy across 50+ cloud regions with dedicated servers for low-latency applicationsSecure apps and data with DDoS protection, WAF, and encryption at no additional costGet started today

How to Run Hugging Face Spaces on Gcore Inference at the Edge

Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization.  Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. Step 1: Log In to the Gcore Customer PortalGo to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. Step 2: Go to Inference at the EdgeIn the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. Step 3: Choose a Hugging Face ModelOpen huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. Click on Files in the Space and locate the Docker option. Copy the Docker image link and startup command from Hugging Face Space. Step 4: Deploy the Model on GcoreReturn to the Gcore Customer Portal deployment page and enter the following details: Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest Startup command: python app.py Container port: 7860 Configure the pod as follows: GPU-optimized: 1x L40S vCPUs: 16 RAM: 232GiB For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.Step 5: Interact with Your ModelOnce the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.Powerful, Simple AI Deployment with GcoreGcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. Explore Gcore Inference at the Edge

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