An In-Depth Exploration of eBPF Technology

Think of eBPF as a well-kept secret that has recently emerged into the limelight. For years, its core concepts existed, but only now has it gained significant recognition in the IT realm. The increasing awareness of eBPF is due to its transformative impact on Kubernetes observability and other crucial tasks. This article delves into the history, functionality, and benefits of eBPF, and why it’s a technology worth exploring.

Understanding eBPF

eBPF, or extended Berkeley Packet Filter, is a feature of the Linux kernel that allows sandboxed programs to run within kernel space. This capability enhances the operating system’s functionality securely and efficiently by leveraging the kernel’s access to system resources and data without compromising security or efficiency.

As indicated by its name, eBPF is an extension of an earlier technology called the Berkeley Packet Filter (BPF), introduced in 1993. BPF provided the Linux kernel with tools to view, control, and filter network traffic through the system call interface. This innovation allowed developers to dynamically modify network policies, introducing a novel approach to kernel programming.

eBPF advances BPF in several ways:

• Unlike BPF, eBPF is not confined to the networking subsystem of the Linux kernel. It can access nearly any resource that the Linux kernel can “attach” to.
• BPF supported only basic, network-oriented operations, while eBPF is equipped with a broad array of tools to enhance the capabilities of a program.
• A large community has formed around eBPF, creating numerous Software Development Kits (SDKs) and tools that simplify the development of eBPF programs.

How eBPF Functions

eBPF operates by enabling developers to execute custom code in the kernel through a specific process:

• Writing an eBPF Program: Developers write programs in “Restrictive C,” a subset of the C programming language tailored for eBPF.
• Loading the Program: The program is loaded into the kernel using user space tools like bpftool (a utility for inspecting and interacting with eBPF programs).
• Verification Process: The eBPF verifier checks the program for issues, such as unauthorized memory access, to ensure it won’t destabilize the system.
• Execution: Upon passing verification, the kernel runs the program as specified, and developers can view the output wherever the program is designed to expose it.

eBPF System Structure: Kernel and User Space

The architecture of eBPF involves the concepts of user space and kernel space. User space, also known as user land, is where standard applications run, controlled by non-privileged users. Kernel space is reserved for processes and programs associated with the kernel. This separation ensures that ordinary programs are isolated from the kernel, preventing a buggy or malicious user land program from crashing the entire system. If user space programs need low-level system data, they must request it from the kernel, which only the kernel can access directly. This traditional approach is slow and consumes significant memory and CPU resources. However, eBPF programs run directly in the kernel, allowing direct access to kernel-level resources. For instance, if there is a need to inspect every call to the exec() system call (responsible for creating new processes), eBPF can be used. This approach provides several advantages over traditional applications for deep system visibility.

Flexibility and Customizability of eBPF

eBPF’s ability to inspect system calls with custom code offers tremendous flexibility for addressing various use cases. It can access all user land and kernel space memory and resources. This makes eBPF different from other tools that provide system data access in predefined ways. For example, dmesg prints the contents of the kernel buffer, offering some kernel event visibility. But with dmesg, you’re limited to the kernel buffer’s information. eBPF, however, allows customization of the data types you can view and how you view it. eBPF’s programmability also enables control over how output is exposed and structured, enhancing its customizability.

Efficiency and Performance Monitoring with eBPF

Running programs in kernel space results in significant efficiency gains. Since eBPF programs can access data directly, they don’t require the overhead associated with user space applications requesting data from the kernel.

Enhanced Security and System Insight

eBPF programs run in sandboxed environments and must pass a verification process before execution, reducing the risk of security or stability issues. This approach is more secure compared to Linux kernel modules, another method of inserting custom code into the kernel. Buggy kernel modules can crash the system, and insecure modules could lead to security breaches affecting other system parts.

Dynamic Tracing and Observability

The ability to dynamically insert custom code into the Linux kernel without modifying the kernel source code makes eBPF highly dynamic for tracing and observability. Programs can be loaded and executed on demand within a running kernel, allowing for dynamic tracking of a “living and breathing” system.

Implementing an eBPF Program

Deploying an eBPF program involves writing and compiling the code, verifying and loading it into the kernel, and then running it to monitor the desired code flow. eBPF code is typically written in “Restrictive C,” compiled into eBPF bytecode using Clang (a compiler for the C language family), and then loaded using the bpf() system call (a syscall for loading eBPF programs into the kernel). Once verified and executed, program input or output can be accessed using eBPF maps or predefined file descriptors.

Key Features and Benefits of eBPF

eBPF offers several benefits, especially for monitoring and observability:

• Custom programs can be run on demand without modifying the kernel source code or deploying special user space applications (beyond eBPF tools).
• Minimal CPU and memory consumption of eBPF programs leaves more resources for workloads.
• eBPF standardizes the monitoring and observability process across modern Linux releases, being built into the kernel source code.

eBPF Use Cases

• Network Observability: Inspecting all network events and traffic flows helps troubleshoot networking issues and provides context for performance problems.
• Performance Monitoring: eBPF enables granular performance metrics calculation for applications and processes, aiding in anomaly detection.
• Tracing: Collecting traces and contexts from running applications helps identify bottlenecks.
• Profiling: eBPF offers deep insights into resource utilization by applications, facilitating optimization.
• Security Auditing: Monitoring privileged operations and unusual process behavior helps detect and investigate security threats.

Getting Started with eBPF in Kubernetes

To use eBPF in Kubernetes, ensure the kernel supports eBPF (Linux 4.16 or later). User space utilities like bpftrace (a high-level tracing language for Linux) can be used to interact with eBPF. Deployment across nodes can be managed using methods like DaemonSets, which ensure efficient and scalable implementation.

Best Practices

• Keeping up with eBPF developments and community efforts to utilize the latest features and tools.
• Maintaining a positive attitude and learning from errors to overcome the steep learning curve of eBPF.
• Testing eBPF programs across different Linux distributions to ensure consistent performance and avoid compatibility issues.

Leveraging eBPF SDKs

Recent developments have simplified eBPF programming through various SDKs and toolchains, such as BCC (BPF Compiler Collection) and libpf (a library for working with eBPF programs). These tools, along with eBPF-friendly compilers like Clang, have made eBPF more accessible to developers.

Challenges and Downsides

• Complex Verification: Writing eBPF code that passes the verifier can be challenging, especially for newcomers.
• Stack Space Limitations: eBPF programs have limitations on stack space, requiring efficient coding practices.
• Portability Issues: Differences between kernel versions can affect eBPF program portability across different Linux distributions.

Ensuring eBPF Security

While eBPF is designed to be safe, additional measures like using CAP_BPF for granular privilege control and keeping systems updated are essential for maintaining security. CAP_BPF is a Linux capability that provides fine-grained control over what privileges a process has for interacting with eBPF.

Simplifying Kubernetes Management with eBPF

Managing Kubernetes can often feel like solving a complex puzzle. The complexity arises because Kubernetes is an intricate system with numerous components, each exposing logs and metrics in different ways. Traditionally, this meant there was no straightforward way to pull and analyze all the data necessary to understand what’s happening inside a Kubernetes cluster. However, eBPF introduces a new approach to managing Kubernetes that offers significant efficiencies. By leveraging eBPF, Kubernetes observability, security, and other challenges become more manageable, transforming what could be a headache-inducing task into a much more pleasant experience.

The Role of eBPF in Kubernetes

Given that Kubernetes nodes typically run on a Linux-based operating system, eBPF utilizes these kernels to collect the necessary data for informed decision-making. This approach provides consistency and efficiency in gathering observability data, unlike the traditional method that relied on the sidecar pattern and scattered log files.

How eBPF Works in Kubernetes

Using eBPF in Kubernetes involves a straightforward process:

• Identify the Information Needed: Determine the data you need to collect, such as networking, CPU, memory usage, and storage operations.
• Write an eBPF Program: Create a program that instructs the kernel on which data to expose.
• Deploy the eBPF Program: Implement the program on the relevant nodes in your cluster, either manually or using methods like DaemonSets for automation.
• Data Collection and Analysis: Gather and analyze the data produced by the eBPF program using your preferred analytics tools.

Practical Applications of eBPF in Kubernetes

eBPF’s ability to collect detailed and varied data from the Linux kernel allows it to enhance Kubernetes in numerous ways:

• Performance Monitoring: eBPF can gather granular performance data for individual containers and microservices, aiding in identifying bottlenecks and optimizing resource usage.
• Cluster Observability: By collecting data from control plane components like Etcd and the API server, eBPF provides a comprehensive view of cluster health and performance. This detailed observability helps correlate control plane metrics with worker node data for deeper insights.
• Security Monitoring: eBPF’s ability to monitor both process behavior and network traffic makes it a powerful tool for detecting security anomalies and investigating suspicious activities.
• Network Traffic Filtering: eBPF can map network traffic to specific processes, offering granular visibility into network usage by containers, pods, and applications.
• Tracing and Profiling: eBPF traces how requests flow through the stack, helping pinpoint performance issues and providing deep insights into resource utilization by applications.

The Power of eBPF for Modern Observability

Imagine discovering a tool so effective that once you start using it, you can’t imagine how you ever managed without it. This is what eBPF (extended Berkeley Packet Filter) brings to the world of observability. While teams managed observability long before eBPF gained traction, this technology enables a much more efficient and secure approach to gathering critical data through the Linux kernel. Although eBPF might not be the perfect solution for every observability need, its advantages make it an essential tool for modern, complex applications.

Understanding eBPF Observability

eBPF observability uses eBPF to collect necessary data from applications directly from kernel space. As part of the Linux kernel, eBPF collects information about network usage, application processes, and system resources. This means you don’t need to deploy user-space agents to gather deep insights into your applications and servers. Instead, you can use eBPF for tracing this data.

The ability to collect observability data from the Linux kernel rather than from agents running in user space offers several critical benefits:

• Efficiency: eBPF programs consume minimal resources
• Security: eBPF programs run in sandboxed environments, minimizing the risk of security breaches.
• Reliability: The Linux kernel verifies eBPF programs for safety before execution, reducing the risk of buggy code causing system crashes.
• Simplicity: eBPF is built into modern Linux kernel versions, so no special frameworks or kernel modules are required. You only need tools to interact with the eBPF framework.

How eBPF Observability Agents Improve Application Performance

Traditional observability software requires deploying a monitoring agent in user space. These agents, whether installed on each node or deployed inside a pod using the sidecar pattern, often consume significant resources. Because these agents act as intermediaries between applications and the kernel, they request data from the kernel, leading to higher resource consumption.

In contrast, eBPF observability agents run in kernel space, accessing necessary data directly without needing intermediaries. This results in lower resource usage, with eBPF programs typically consuming a fraction of the resources required by traditional agents. The performance overhead is minimal, freeing up more CPU and memory for the actual workloads.

How to Use eBPF to Collect Observability Data

There are two main approaches to using eBPF for observability:

• Standalone eBPF Tools: Various command-line tools like BCC (BPF Compiler Collection) allow you to deploy and run eBPF programs to collect data. These tools are useful for quick experiments or one-off data collection.
• eBPF-Based Observability Platforms: Some observability platforms, such as groundcover, leverage eBPF under the hood. These platforms simplify the deployment and management of eBPF programs, making it easier to collect and analyze observability data.

Popular eBPF Tools and Frameworks

Several open-source eBPF tools and frameworks are available:

• BCC: A general-purpose toolkit for running eBPF programs.
• Bpftrace: A high-level tracing language for eBPF.
• eCapture: A tool for capturing SSL data.
• Tracee: An eBPF-based security monitoring and incident investigation toolkit.
• Kubectl Trace: A tool for scheduling and managing eBPF programs on Kubernetes.

Best Practices for eBPF Observability

To get the most out of eBPF, consider these best practices:

• Avoid Unprivileged Mode: Running eBPF in unprivileged mode is insecure. Ensure that eBPF programs are executed with proper permissions.
• Keep the Kernel Up-to-Date: Regularly update your Linux kernel to access the latest eBPF features and improvements.
• Ensure Kernel Consistency: Use the same kernel version across servers to maintain consistent eBPF output.
• Write Specialized Programs: Create separate eBPF programs for different tasks to maximize visibility and efficiency.

OpenTelemetry and eBPF: A Powerful Combination for Observability

For those deeply involved in observability, efficiency, and consistency, OpenTelemetry and eBPF are two innovative technologies that have revolutionized the field. These solutions offer unique advantages for achieving faster, more scalable, and effective visibility into complex, cloud-native environments. This article delves into how these technologies work together to enhance observability and how Groundcover leverages them.

What is OpenTelemetry?

OpenTelemetry, often abbreviated as OTel, is an open-source observability framework that includes a collection of APIs, Software Development Kits (SDKs), and other tools designed to collect observability data from applications. It provides a standardized approach to telemetry, enabling the collection of metrics, traces, and logs from remote systems.

Benefits of OpenTelemetry

OpenTelemetry offers several advantages:

• Standardization: Provides a consistent way to collect observability data from different applications.
• Flexibility: Compatible with any OpenTelemetry-compliant tool, making it easier to switch observability platforms.
• Ease of Implementation: Developers can use prebuilt libraries to integrate observability into their applications quickly.

Combining OpenTelemetry and eBPF

By combining OpenTelemetry and eBPF, you can leverage the strengths of both technologies for a comprehensive observability solution. In your current deployment, application instrumentation is not used, but all logs, traces, and metrics are still captured due to the synergy between OpenTelemetry and eBPF.

How It Works Without Application Instrumentation

1. System-Level Data Collection: eBPF gathers data directly from the Linux kernel, capturing detailed metrics, traces, and logs about system performance and behavior.
2. Data Transformation: The collected data using eBPF is transformed to adhere to OpenTelemetry standards. This transformation ensures that the data is formatted and standardized correctly, making it compatible with OpenTelemetry tools.
3. OpenTelemetry Integration: OpenTelemetry uses the eBPF-collected and transformed data to provide comprehensive observability information without needing application-level instrumentation. This integration allows OpenTelemetry to collect detailed observability data from the system level.
4. Data Processing and Analysis: The transformed and collected data is processed by OpenTelemetry-compliant tools, providing insights into both application and system performance.

eBPF-Based OpenTelemetry Collectors

The OpenTelemetry eBPF project has developed collectors that use eBPF to gather data directly from the kernel and format it for OpenTelemetry tools. These collectors provide the efficiency of eBPF with the standardized data export capabilities of OpenTelemetry.

OpenTelemetry-eBPF Project Components

The OpenTelemetry-eBPF project includes several tools designed to collect and translate eBPF data:

• Kernel-Collector: Monitors kernel events and sends data to a specified host.
• K8s-Collector: Observes Kubernetes-specific events, such as pod creation, and sends data to a remote collector.
• Cloud-Collector: Gathers observability data from supported cloud platforms (currently AWS and GCP).
• Reducer: Translates raw eBPF data into OpenTelemetry-compatible metrics.

Example Integration of OpenTelemetry and eBPF

To illustrate how these tools work together, let’s consider an example of using the kernel-collector to gather network observability data:

1. Compile the Kernel-Collector: Build the kernel-collector for your system.
2. Deploy the Reducer: Run the reducer to format eBPF data into an OpenTelemetry-compatible format.
3. Configure Environment Variables: Set the necessary environment variables, such as EBPF_NET_INTAKE_HOST, to direct data to the reducer.
4. Start the Kernel-Collector: Launch the kernel-collector to begin collecting and sending data to the reducer.
5. Analyze the Data: Use an OpenTelemetry-compliant tool to scrape and analyze the formatted data.

Using eBPF and OpenTelemetry with Groundcover

Groundcover is an observability platform that seamlessly integrates both OpenTelemetry and eBPF, offering a powerful solution for monitoring modern, cloud-native environments.

• Integrated Observability: Groundcover combines the strengths of OpenTelemetry and eBPF, allowing you to collect data from both application and kernel levels without extensive manual setup.
• Ease of Use: With built-in support for OpenTelemetry and eBPF, Groundcover simplifies the deployment and management of observability tools, making it easier to gather and analyze data.
• Efficiency: By utilizing eBPF for data collection, Groundcover minimizes resource consumption, leaving more CPU and memory available for your actual workloads.
• Scalability: Groundcover is designed to scale with your infrastructure, providing consistent observability across large, dynamic environments.

How Groundcover Integrates OpenTelemetry and eBPF

• OpenTelemetry Integration: Groundcover fully supports OpenTelemetry, allowing it to ingest data formatted using the OpenTelemetry standard. This means you can collect observability data without application instrumentation.
• Data Collection: Groundcover collects this data, ensuring that it adheres to the OpenTelemetry standard.
• Analysis and Visualization: Groundcover processes and visualizes the data, providing insights into application performance, health, and behavior.
• eBPF Integration: Groundcover leverages eBPF under the hood to collect observability data from the operating system level. This approach enhances efficiency and depth of visibility:
• Kernel-Level Data Collection: eBPF programs run in the kernel space, gathering detailed data about system performance and behavior with minimal overhead.
• Security and Stability: The sandboxed nature of eBPF programs ensures that data collection is secure and does not destabilize the system.
• Seamless Integration: Groundcover manages the deployment and execution of eBPF programs, simplifying the process for users.

Benefits of Combining OpenTelemetry and eBPF with Groundcover

By integrating OpenTelemetry and eBPF, Groundcover offers a unique set of benefits:

• Holistic Observability: Collect and correlate data from both the application and operating system levels, providing a comprehensive view of your environment.
• Reduced Overhead: eBPF’s efficient data collection minimizes the performance impact on your systems.
• Enhanced Insights: The combination of OpenTelemetry’s standardized data and eBPF’s detailed kernel-level data provides deep insights into both application and system performance.

Leveraging eBPF for Advanced Profiling and Tracing

Traditional monitoring and observability tools often feel like using blunt instruments when you need precision tools. They track total resource usage and provide some basic projections, but they fall short in delivering detailed, specific, and granular data. This is where eBPF (extended Berkeley Packet Filter) comes into play, offering continuous profiling and tracing with minimal overhead on your applications. Let’s explore how eBPF transforms profiling and tracing, making it possible to optimize the performance of Linux-based workloads without the drawbacks of conventional methods.

Advanced Profiling with eBPF

Profiling involves determining which resources are being consumed by individual applications or processes. Unlike traditional tools that monitor total resource consumption, eBPF allows for continuous profiling, offering detailed insights into CPU, memory, network data, and other resource usage.

• CPU Profiling: eBPF monitors stack traces to track CPU utilization by individual processes. This helps in identifying processes that consume excessive CPU resources, potentially affecting other workloads.
• Memory Profiling: eBPF traces memory events and allocation requests, providing insights into memory allocation and usage by individual processes. This helps in detecting memory leaks and optimizing memory distribution.
• Network Profiling: eBPF maps network traffic to processes, offering granular visibility into network usage by individual applications. This helps in troubleshooting network performance issues and detecting malicious network activity.

How eBPF Profiling Works

eBPF profiling leverages its ability to run custom code within the Linux kernel, allowing it to monitor stack traces and resource utilization directly. This method is highly efficient compared to traditional tools that rely on user space data and can provide more detailed insights.

Implementing eBPF for Continuous Profiling in Kubernetes

To leverage eBPF for profiling in Kubernetes, follow these steps:

1. Ensure that every node in your cluster runs a Linux kernel that supports eBPF.
2. Deploy eBPF agents on each node to collect profiling data continuously.
3. Push the collected data to an analytics tool for detailed analysis.

For instance, to profile CPU usage on Ubuntu, you can install bpfcc-tools and use the profile tool to gather CPU utilization data. Redirect the output to a file or data stream for further analysis in your preferred analytics tool.

eBPF for System Tracing

System tracing involves monitoring the execution of processes and the flow of data within the system. Traditional tracing methods often involve resource-intensive software that can degrade system performance. eBPF provides a more efficient alternative.

• Enhanced System Troubleshooting and Debugging: eBPF can trace function calls and kernel routines, providing granular visibility into system operations. This aids in debugging and performance optimization.
• Increased Granularity and Accuracy: eBPF traces performance and debugging data on a process-by-process basis, offering detailed insights that traditional tools might miss.
• Avoiding System Disruption: eBPF programs undergo a verification process to ensure they don’t disrupt system operations. This makes eBPF safer than traditional kernel modules, which can cause system crashes if buggy.

Implementing eBPF Tracing

To get started with eBPF tracing:

• Write eBPF code to specify the data you want to collect.
• Deploy the eBPF program using tools like bpftrace or bcc.
• Run the program in kernel space and collect the traced data.

For example, using bpfcc-tools, you can run tools like killsnoop-bpfcc to monitor system calls. The collected data can help identify the cause of random application crashes or other issues.

Combining eBPF Profiling and Tracing for Maximum Optimization

Using eBPF for both profiling and tracing offers a comprehensive approach to performance optimization:

• Profiling: Helps identify resource-intensive processes and optimize resource allocation.
• Tracing: Provides detailed insights into the execution of processes, aiding in debugging and performance tuning.

By combining these capabilities, you can achieve a highly efficient and granular view of your system’s performance and behavior.

Best Practices for eBPF Profiling and Tracing

• Choose the Right Tools: Select eBPF tools that match your needs. For experimentation, use open-source CLI tools like bpftrace. For production, consider observability platforms with built-in eBPF support.
• Optimize eBPF Code: Efficiently written eBPF programs provide better performance and insights.
• Stay Updated: Keep up with the latest developments in eBPF to leverage new features and improvements.
• Secure eBPF Data: Ensure that the granular data collected by eBPF is protected and accessible only to authorized personnel.