But there is more to ensuring a bug-free product than a strong team. DevOps need to have the right methods and tools in place to better manage bugs in the system. 

Two of the most effective methods are monitoring and observability. Although they may seem like the same process at a glance, they have some apparent differences beneath the surface. In this article, we look at the meaning of monitoring and observability, explore their differences and examine how they complement each other.

What is monitoring in DevOps?

In DevOps, monitoring refers to the supervision of specific metrics throughout the whole development process, from planning all the way to deployment and quality assurance. By being able to detect problems in the process, DevOps personnel can mitigate potential issues and avoid disrupting the software’s functionality.   

DevOps monitoring aims to give teams the information to respond to bugs or vulnerabilities as quickly as possible. 

DevOps Monitoring Metrics

To correctly implement the monitoring method, developers need to supervise a variety of metrics, including:

• Lead time or change lead time
• Mean time to detection
• Change failure rate
• Mean time to recovery
• Deployment frequency
  

  What is Observability in DevOps?

  Observability is a system where developers receive enough information from external outputs to determine its current internal state. It allows teams to understand the system’s problems by revealing where, how, and why the application is not functioning as it should, so they can address the issues at their source rather than relying on band-aid solutions. Moreover, developers can assess the condition of a system without interacting with its complex inner workings and affecting the user experience. There are a number of observability tools available to assist you with the software development lifecycle.

  The Three Pillars of Observability

  Observability requires the gathering and analysis of data released by the application’s output. While this flood of data can become overwhelming, it can be broken down into three fundamental data pillars developers need to focus on:

  1. Logs

  Logs refer to the structured and unstructured lines of text an application produces when running certain lines of code. The log records events within the application and can be used to uncover bugs or system anomalies. They provide a wide variety of details from almost every system component. Logs make the observability process possible by creating the output that allows developers to troubleshoot code by simply analyzing the logs and identifying the source of an error or security alert.

  2. Metrics

  Metrics numerically represent data that illustrates the application’s functioning over time. They consist of a series of attributes, such as name, label, value, and a timestamp that reveals information on the system’s overall performance and any incidents that may have occurred. Unlike logs, metrics don’t record specific incidents but return values representing the application’s overall performance. In DevOps, metrics can be used to assess the performance of a product throughout the development process and identify any potential problems. In addition, metrics are ideal for observability as it’s easy to identify patterns gathered from various data points to create a complete picture of the application’s performance.

  

  3. Trace

  While logs and metrics provide enough information to understand a single system’s behavior, they rarely provide enough information to clarify the lifetime of a request located in a distributed system. That’s where tracing comes in. Traces represent the passage of the request as it travels through all of the distributed system’s nodes. 

  Implementing traces makes it easier to profile and observe systems. By analyzing the data the trace provides, your team can assess the general health of the entire system, locate and resolve issues, discover bottlenecks, and select which areas are high-value and their priority for optimization.

   

  Monitoring vs. Observability: What’s the Difference?

  We’ve compiled the below table to better distinguish between these two essential DevOps methods:

  Monitoring	Observability
  Practically any system can be monitored	The system has to be designed for observation
  Asks if your system is working	Asks what your system is doing
  Includes metrics, events, and logs	Includes traces
  Active (pulls and collects data)	Passive (pushes and publishes data)
  Capable of providing raw data	Heavily relies on sampling
  Enables rapid response to outages	Reduces outage duration
  Collects metrics	Generates metrics
  Monitors predefined data	Observes general metrics and performance
  Provides system information	Provides actionable insights
  Identifies the state of the system	Identifies why the system failed

  Observability vs. Monitoring: What do they have in common?

  While we’ve established that observability and monitoring are entirely different methods, this doesn’t make them incomparable. On the contrary, monitoring and observability are generally used together, as both are essential for DevOps. Despite their differences, their commonalities allow the two methods to co-exist and even complement each other. 

  Monitoring allows developers to identify when there is an anomaly, while observability gives insights into the source of the issue. Monitoring is almost a subset of, and therefore key to, observability. Developers can only monitor systems that are already observable. Although monitoring only provides solutions for previously identified problems, observability simplifies the DevOps process by allowing developers to submit new queries that can be used to solve an already identified issue or gain insight into the system as it is being developed.

  Why both are essential?

  Monitoring and observability are both critical to identifying and mitigating bugs or discrepancies within a system. But to fully utilize the advantages of each approach, developers must do both thoroughly. Manually implementing and maintaining these approaches is an enormous task. Luckily, automated tools like Lightrun allow developers to focus their valuable time and skills on coding. The tool enables developers to add logs, metrics, and traces to their code without restarting or redeploying software in real-time, preventing delays and guaranteeing fast deployment.

The post Observability vs. Monitoring appeared first on Lightrun.

With over 74% of CEOs expressing concerns that increased complexity will continue to lead to performance management difficulties, it’s clear that investing in observability tools is a must. But with the wide range of tools available on the market, how do you choose the tool that’s right for you? And what features are more suitable for your organization’s needs? This article lists the top 9 observability tools of 2022 to help you make the best choice for your business. 

What is an Observability Tool?

As infrastructure becomes increasingly complex, observability grows more challenging. Observability tools perform the tasks required for observability, including monitoring systems and applications through monitors and logs. In contrast to individual monitoring tools, observability tools allow organizations to receive constant insights and feedback from their systems. Organizations receive actionable insights into their business faster than they would from tools focusing solely on monitoring or logging. Observability tools allow organizations to understand system behavior, giving them the information they need to prevent system problems by predicting them before they occur.

Features to look for in Observability Tools

Before we look at specific tools, let’s examine some of the features you should look for when choosing the right observability tool for your organization. Some features to have in mind include:

• A dashboard that provides monitoring services, such as a clear view of your system
• Alerts in case of events or anomalies
• Tracking abilities to track significant events
• Long term monitoring with comparisons, allowing the system to detect anomalies
• Automated issue detection and analysis
• Event logging for speedy resolution
• The ability to use SLA tracking to measure meta-data and data quality compared to pre-set standards

Top 9 Observability Tools for 2022

The market for observability tools continues to grow, and the variety of choices can become overwhelming. We’ve collected the top nine tools, divided into four categories: shift-left observability, serverless monitoring, incident response, and application performance monitoring.

Shift-Left Observability

The shift-left concept refers to taking processes traditionally performed during later stages of the product lifecycle and implementing them earlier on. Shift-left observability simply means implementing observability practices earlier into the product’s lifecycle.

1. Dynatrace

Dynatrace is a more comprehensive SaaS tool that addresses a wide range of monitoring services, particularly for large-scale organizations. The system uses an AI engine called Davis to automate anomaly detection and root cause analysis services. While pricing may vary depending on the package, it starts at $69 per month for 8 GB per host if billed annually.

Dynatrace’s AI and advanced anomaly detection tools have made it a popular option for large organizations looking to monitor complex infrastructure while quickly detecting vulnerabilities. Unfortunately, the solution does have its downsides, such as being on the more expensive end of observability solutions and lacking updated technical documentation.

2. Lightrun

Lightrun offers a developer-native observability platform that allows users to add logs, metrics, and traces to production and staging. It gives you full observability of your infrastructure, enabling you to quickly detect and mitigate any potential issues without adding extra code. The solution’s logs and metrics can be added in real-time and even while the product is running.

The solution offers a free 14-day trial and affordable pricing. It is already used by companies like Nokia, Taboola, DealHub, and WhiteSource. Lightrun facilitates early debugging in real-time for various systems, from monolith applications to microservices.

Serverless Monitoring

Serverless monitoring allows users to only access infrastructure and resources as they need them instead of pre-purchasing unnecessary server capacity from the get-go. By using serverless monitoring, organizations save money as they only pay for the resources they use.

3. Lumigo

Lumigo is a solution that builds a virtual stack trace of all the services that participate in a process. The tool presents all the data it gathers in a clear visual map with search and filter capabilities, allowing organizations to identify and mitigate issues quickly. Its features include creating data visibility across infrastructure and giving organizations the data they need to remove bottlenecks. A free version is available, and paid versions begin at $99 per month.

Although the system offers many unique capabilities and visual tools such as graphs and timelines, some reports are oversaturated, and it is difficult to sort through for relevant information. The solution is still in its early stages and therefore missing some crucial capabilities.

Incident Response

Incident response is the process used by DevOps, IT, and dev teams to manage any issues or incidents, including damage control and prevention. It generally includes a guideline that delineates the response to follow in the event of an incident.

4. Lightstep

Lightstep is a solution that collects data and presents it clearly and concisely, allowing users to monitor their applications and respond to any unusual changes or anomalies. Lightstep enables users to minimize the effects of outages and other crises on operations. The company offers a free option and group prices starting at $100 per active service per month.

Lightstep provides clear visibility into the required tasks and gives teams insight into what they need to prioritize. Some users report that the solution can perform somewhat slowly at times and that the mobile application doesn’t perform as well as the desktop app.

Application Performance Monitoring

Applications performance monitoring allows organizations to monitor their IT environment,  assess whether it meets performance standards, and identify bugs and other potential problems. This allows organizations to upgrade their performance and offer a stellar user experience.  

5. Anodot

Anodot’s solution uses machine learning software to constantly assess and compare performance, allowing it to provide real-time anomaly alerts and even predictions of anomaly sources. The solution enables businesses to cut their detection and manage issues faster. Anodot states that most users miss out on 85% of their usable data and claim to minimize detection and resolution time by 80%.

Anodot services are used by major tech companies such as Payoneer and TripAdvisor. The system offers a variety of payment plans and a free demo. Although it provides many valuable features, the system’s UI has room for improvement, and its algorithm is not always accurate.

6. Datadog

Datadog is a monitoring, security, and analytics platform designed for developers, security engineers, IT operations teams, and business users who interact with the cloud. The SaaS platform automates performance monitoring, infrastructure monitoring, and log management. The platform provides users with real-time observability across their entire architecture.

The solution offers a free version, a free trial, and two pricing options that allow users to pay per host starting at $15 or per log, starting at $1.27 per million log events. Datadog is trusted by Shell, Samsung, 21st Century Fox, and many well-known corporations. Despite its many benefits, the system can be challenging to navigate, and the documentation is not always up to par.

7. Grafana

Grafana is an observability tool that creates reports and usage insights for developers and builds dashboards to make data easily viewable and readable.  Grafana is trusted by several large corporations, including Siemens, eBay, and PayPal. The platform can be used in conjunction with other similar platforms, including Datadog and Dynatrace, and can report on these platforms’ performance.

A free version of the platform is available, and paid versions start at $8 for a single user. Although the tool is free and includes features such as an alert and notification system, the platform has limited dashboard designs and organization.

8. Honeycomb

Honeycomb is an analysis tool that allows developers to identify application issues quickly. The platform also gives developers the ability to resolve problems using the same interface.  The solution enables teams to understand their software better, simplifying the debugging and upgrading process and allowing the team to resolve issues more quickly.

The platform offers a free plan for individuals and a 14-day free trial for enterprises. It is excellent for analyzing systems and identifying the source of incidents, but it is less effective for traditional monitoring purposes.

9. New Relic

New Relic’s platform is designed to speed up the repair process and reduce downtime, increasing productivity and allowing engineers to focus on enhancing application performance. The system is easy to set up and offers real-time analytics to help developers troubleshoot their applications. The platform is flexible and can even provide teams with guidelines offering response suggestions.

The company offers a variety of pricing plans, including a free program and several plans that require contact with the company for pricing details. The system’s application monitoring and infrastructure monitoring stand out for their effectiveness. Still, the system is less effective as a proactive monitoring system and tends to send false alarms.

Observability Is Essential

Observability tools are critical to monitoring growing and increasingly more complex infrastructure. While choosing the correct monitoring tool can be complicated, finding the most suitable one to meet your organization’s needs can streamline your monitoring and maintenance processes. Lightrun offers a solution that provides both monitoring and observability services. To see for yourself, request a Lightrun demo today. 

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Application Programming Interfaces (APIs) are a crucial building block in modern software development, allowing applications to communicate with each other and share data consistently. APIs are used to exchange data inside and between organizations, and the widespread adoption of microservices and asynchronous patterns boosted API adoption inside the application itself.

The central role of APIs is also evident with the emergence of the API-first approach, where the application’s design and implementation start with the API, thus treating APIs as first-class citizens and developing reusable and consistent APIs.

In the last decade, Representational state transfer (REST) APIs have come to dominate the scene, becoming the predominant API technology on the web. REST is more of an architectural approach than a strict specification: This free-formedness is probably the key to REST success as it has been essential in making REST popular and one of the critical enablers of a loose coupling between API providers and consumers. However, sometimes this bites back as a lack of consistency in the API behavior and interface. This is sometimes alleviated using specification frameworks like OpenAPI or JSON Schema.

Also, it’s worth pointing out the role of developers in designing and consuming APIs, as frequently, the development of an API requires strict collaboration between backend developers, frontend developers, and mobile developers since the role of API is the integration of different applications and systems.

Challenges in API integration

Despite being central to modern application development, API integration remains challenging. Those challenges mainly originate from the fact that the systems connected by APIs form a distributed system, with the usual complexities involved in distributed computing. Also, the connected systems are mostly heterogeneous (different tech stacks, data models, ownership, hosting, etc.), leading to integration challenges. Here are the most common ones:

• Incorrect data. Improper data formatting or conversion errors (due to inaccurate data type or incompatible data structures) can cause issues with the exchanged data. This often results in malformed JSON, errors in deserialization, and type casting errors.
• Lack of proper documentation. Poorly documented endpoints may require extensive debugging to infer data format or API behavior. This is particularly problematic when dealing with third-party services without access to the source code or the architecture.
• Incorrect or unexpected logic or behavior. The loosely defined REST model does not allow for specifying the callee behavior formally, or such behavior can be undocumented or implemented wrong for some edge cases.
• Poor query parameter handling. Query parameters are the way for the callee to modify the provided results. Often, edge cases arise where parameters are not handled correctly, requiring a trial-and-error debugging process.
• Error handling. Even if HTTP provides the basic mechanism of response codes for error handling, each API implementation tends to customize it, either using custom codes or adding JSON error messages. Error handling is not always coherent, even between different endpoints on the same system, and it may be undocumented.
• Authentication and authorization errors. The way in which authorization is handled on the API producer can generate errors and unexpected behavior, sometimes manifesting incoherence between different endpoints on the same system.

Errors can be present on the provider side or the consumer side. On the provider side, we often cannot intervene in the implementation, which necessitates implementing workarounds on the consumer side.

For errors on the consumer (wrong deserialization, incorrect handling of pagination, or states, etc.), troubleshooting usually involves examining logs for request/response patterns and adding logs to examine parameters and payloads.

Lightrun Dynamic Observability for API debugging

Ligthrun‘s Developer Observability Platform implements a new approach to observability by overcoming the difficulties of troubleshooting applications in a live setting. It enables developers to dynamically instrument logs for applications that run remotely on a production server by adding logs, metrics, and virtual breakpoints, without the need to code changes, redeployment, or application restarts.

In the context of API debugging, the possibility of debugging on the production environment provides significant advantages, as developers do not need to reproduce locally the entire API ecosystem surrounding the application, which can result difficult: think, for example, to the need to authenticate to third-parties API, or to provide a realistic database to operate the application locally. Also, it is only sometimes possible to reproduce realistic API calls locally, as the local development environment tends to be simplified with respect to the production one.

Lightrun allows debugging API-providing and consuming applications directly on the live environment, in real-time and on-demand, regardless of the application execution environment. In particular, Lightrun makes it possible to:

• Add dynamic logs. Adding new logs without stopping the application allows obtaining the relevant information for the API exchange (request/response/state) without leaving the IDE and without losing the state (for example, authentication tokens, complex API interactions, pagination, and real query parameters). It’s also possible to log conditionally only when a specific code-level condition is true, for example, to debug a particular API edge case taken out from a high number of API requests.
• Take snapshots. Adding virtual breakpoints that can be triggered on a specific code condition to show the change in time of request parameters and response payloads.
• Add Lightrun metrics for method duration and other insights. It makes it possible to measure the execution times of APIs and count the time a specific endpoint is being called.

Lightrun is integrated with developer IDEs, making it ideal for developers, as it allows them to stay focused on their local environment. Doing so, Lightrun works as a debugger that works everywhere the application is deployed, allowing for a faster feedback loop during the API development and debugging phases.

Bottom Line

Troubleshooting APIs returning incorrect data or behaving erratically is essential to ensure reliable communication between systems and applications. By understanding the common causes of this issue and using the right tools and techniques, developers can quickly identify and fix API problems, delivering a better user experience and ensuring smooth software operations. Lightrun is a developer observability platform giving backend and frontend developers the ability to add telemetry to live API applications, thus representing an excellent resolution to API integration challenges. Try it now on the playground, or book a demo!

The post Dynamic Observability Tools for API Live Debugging appeared first on Lightrun.

Platform engineering has been one of the hottest keywords in the software community in recent years. As a natural extension of DevOps and the shift-left mentality it fosters, platform engineering is a subfield within software engineering that focuses on building and maintaining tools, workflows, and frameworks that allow developers to build and test their applications efficiently. While platform engineering can take many forms, most commonly, the byproduct of platform engineering is an Internal Developer Platform (IDP) that enables self-services capabilities for developers.

One of the notable challenges with building a successful platform engineering organization is that there still exists a big gap between dev and ops teams in terms of the tools and the domains they operate in. While the promise of DevOps is to bridge that gap, oftentimes traditional tools designed by and for operations teams are blindly applied to internal developer platforms, drastically reducing their effectiveness. In order for IDPs to be truly self-service and beneficial for all parties involved, observability must play a key role. Without observability, developers will not be able to gather insights into their applications and debug as true owners of their code. 

Important to note that platform engineering comes to serve the wider organization at maximum scale across multi-cloud providers (AWS, GCP, Azure), multi-environments (QA, CI, Pre-Production, Production), and multi-runtime languages (Java, C#, .Net, Python, etc.). Being able to debug and troubleshoot all of the above mentioned configurations and code bases in a standardized way is a huge challenge as well as a critical pillar for success. 

As a matter of fact, in a recent article that covers the core skills that are required from a platform engineer, 2 out of the top 8 skills were around developer observability and debugging.

Core Skills Required from a Platform Engineer (Source: SpiceWorks)

In this article, we will explore some of the key components of platform engineering and how they manifest in internal developer platforms. We will then shift our focus to the growing importance and adoption of developer focused real-time observability in IDPs and how traditional observability tooling often falls short. Finally, we’ll look at how Lightrun’s dynamic observability tooling can unlock the true value of IDPs. 

Key Components of Platform Engineering

Platform engineering came largely as a response to the difference in the idealistic promises and the stark realities of DevOps in practice. While the “you write it, then you run it” ethos of DevOps sounds good, the reality is not so simple. With the rise of cloud native architectures and microservices, we now have more complex moving components to run an application. It is unrealistic to ask developers to not only write their code but also be well-versed in what traditionally falls under the Ops bucket (e.g., IaC, CI/CD, etc). 

So platform engineering is a more practical response to carry on the spirit of DevOps while acknowledging the real-world constraints. Some of the key components of platform engineering includes:

• Promoting DevOps Practices: This includes IaC, CI/CD, fast iterations, modular deployments, etc. 
• Enabling Self-Service: Platform engineering teams should enable developers to build and test their applications easily. This touches not only on the build pipeline, but also the infrastructure and other related third-party APIs and services that developers can spin up and connect to on demand. 
• Providing Tools and Automation: As a follow up to the first two points, platform engineering teams should provide a collection of tools, scripts, and frameworks to automate various tasks to speed up developer lifecycles and reduce human error. 
• Balancing Abstraction and Flexibility: There should be a good balance between abstracting away the underlying infrastructure to support a scalable and performant platform with exposing important metrics, logs, and other observability data points for engineers to troubleshoot issues. In addition, this allows ownership of services by developers (DevOps practice) without the overhead of understanding all infrastructure parts. Basically shifting left to the developers without the cost of infrastructure complexity.

In short, the platform engineering team acts as a liaison between developers and other infrastructure-related teams to provide tools and platforms for developers to write, build, and deploy code without diving too deep into the complexities of modern infrastructure stacks. 

Internal Developer Platforms

These principles are best seen in internal developer platforms. IDPs cover the entire application lifecycle beyond the traditional CI/CD pipeline responsibilities. IDPs provide developers with a flexible platform in which they can quickly iterate on testing their applications as if it is done locally. More specifically, this includes:

• Provisioning a new and isolated environment to deploy and test their applications.
• Ability to add, modify, and remove configuration, secrets, services, and dependencies on demand.
• Fast iteration between building and deploying new versions as well as the ability to rollback.
• Scaling up or down based on load.
• Production-like environment with guardrails built in to not accidentally cause outages or degradation in service for other teams.
• Enablement for developers to understand at all times their application costs and allow them to participate and own the overall cost optimization efforts.

In other words, IDPs provide developers a self-service platform that glues together all the tools behind the scenes in a cohesive manner. 

Importance of Real-Time Debugging within an IDP

One of the critical components of a self-service platform is observability through real-time debugging. Without exposing adequate levels of observability to the developers, IDPs will remain a black box that will trigger more support tasks once things go wrong, which defeats the purpose of setting up a self-service platform in the first place. Ideally, developers have access to logs, metrics, traces, and other important pieces of information to troubleshoot the issue and iterate based on the feedback. 

As such, real-time observability plays a critical role in creating a successful platform engineering organization and a robust IDP. Platform engineers and VP’s of platform engineering that are building IDPs today are investing and prioritizing the need to efficiently collect logs, metrics, and traces and surface the most relevant signals for developers to detect, troubleshoot, and respond to those issues. 

Real-Time Debugging within IDP using Lightrun 

Lightrun offers a unique solution that aligns with the principles of platform engineering and adds observability in a way that fits with existing developer workflows. Lightrun provides a standard developer observability platform for real-time debugging that allows developers across multiple clouds, environments, runtime languages and IDEs the ability to debug complex issues fast without a need for iterative SDLC cycle and redeployments. 

Specifically, provide developers in real time with:

• Dynamic logging: developers can add new logs without stopping or restarting their applications to simply add a new log. This can be added conditionally to only show up in certain scenarios to reduce the noise. 
• Snapshots: snapshots emulate what breakpoints would give in a local context. It takes a snapshot of the current execution including environment variables, configuration, and other stack traces at run time. 
• Metrics: developers often don’t think about preemptively adding metrics. Now with Lightrun, they can be collected on demand. 

These dynamic observability tools are as mentioned integrated into IDEs that developers already use to write their code. Compared to traditional observability tools like APMs or logging aggregators, Lightrun allows developers to add or remove various logs, snapshots, or metrics on demand without having to go through the expensive iteration cycle or adding logs, raising a PR for review, and waiting for changes to take effect. Especially in the context of IDPs, this dynamic approach enables developers a truly self-service method to troubleshoot and debug their applications. 

Summary

The rise of platform engineering in recent years has significantly improved developer productivity and experience. Internal developer platforms address a growing problem of increased complexities in developing and deploying modern applications. As more organizations embrace platform engineering and build out internal developer platforms, observability is becoming an imperative tool in standardizing real-time debugging within the IDP tool stack for a truly self-service platform. With Lightrun’s suite of dynamic observability tooling, platform engineering teams can unlock the true potential of IDPs for increased developer productivity. 

The post Why Real-Time Debugging Becomes Essential in Platform Engineering appeared first on Lightrun.

When it comes to debugging performance related issues, the range of these issues together with their root cause can be overwhelming to developers. 

Resolving performance issues is a challenging task due to the multitude of potential factors that can contribute to their occurrence. These factors range from inefficient code or architecture that lacks scalability, to specific infrastructure problems related to hardware and storage. Additionally, reproducing performance issues in a local environment that mimics the production setup can be difficult, as well as identifying and addressing these issues for specific sets of users. Developers often encounter these common challenges when attempting to resolve performance problems. Furthermore, developers may also face a lack of expertise in utilizing Application Performance Monitoring (APM) tools, which, in any case, may not offer code-level insights and actionable information.

For developers who want to address code-specific inquiries when troubleshooting performance issues, such as: 

• Determining how many times a particular line of code is executed 

• Whether a specific line of code is reached

• The execution time of a method

• The execution time of a code block

Pinpointing the exact lines of code responsible for downtime or performance problems can be a challenging and intimidating task. Moreover, identifying performance anomalies within a specific area of the product becomes exceedingly difficult without access to comprehensive insights and meaningful correlations between metrics obtained from various sources, such as the database, CPU usage, network latency, and so on.

Marketplace Gap

While the APM and profiling marketplace today is extremely advanced and offers a range of tools and solutions for monitoring and alerting the developers and Ops when a degraded service occurs, these tools are not operating within the source code or the IDE itself, and requires the developers to be very well versed in these tools and domain instead of him focusing on the code areas that are causing these issues. Having the ability to combine APM tools and dashboards with a developer native observability solution is a perfect bridge to the existing gap and the above-mentioned challenges. That was the rationale behind launching the advanced Lightrun Metrics for performance observability and debugging.

Solution Overview

When trying to address the above mentioned gaps as well as challenges, Lightrun Metrics comes to focus on the following 4 use cases and shift left performance observability:

It does so by providing developers with 4 key metrics that are being collected and consumed within the Java IntelliJ IDE as well as piped to leading APM tools dashboards.

4-type Metrics Collection

The Lightrun Metrics dedicated tab within the IDE plugin consists of the following:

Counters* (coming Soon), TicToc, Method Duration, and Custom Metrics. Below are some details on what each of these provide.

• Counters

A Lightrun Counter is added to a single line of code. It counts the number of times the code line is reached. You can add a counter to any and as many lines of code you need. From the Lightrun IDE plugin, you can specify the conditions (as Boolean expressions) when to record the line execution

• TicToc (Block Duration)

The Lightrun Tic & Toc metric measures the elapsed time for executing a specified block of code, within the same function

•  Method Duration

The Method Duration metric measures the elapsed time for executing a given method

•   Custom Metric

Lightrun metrics enables developers to design their own custom metric, using conditional expressions that evaluate to a numeric result of type long. Custom metrics are all about value distribution and correctness within a given Java application, and it allows developers to transform a specific variable value into statistics and detect anomalies or other distribution trends. Custom metrics can be created using the configuration form in the Lightrun IDE plugin. 

Example/How To Use

To get started with Lightrun Metrics, developers would need to create a new Lightrun account and install the IntelliJ dedicated plugin. Once the setup is complete developers can start creating metrics across the 4 types highlighted above. Here are examples of the creation and understanding of them.

Note that you can use the Lightrun actions against a single application instance or a few based on the amount of agents attached to these instances and collect metrics and averages.

TicToc

To add a TicToc and measure a block of code execution duration, go to the Lightrun plugin within the IntelliJ IDE and right click the line of code to add the specific action.

As you can see in the above screenshot, developers can gain runtime code-level execution performance within the TicToc graph and examine averages across deployments and over time.

Custom Metric

To build a custom expression around the code under investigation, go to the Lightrun plugin within the IntelliJ IDE and right click the line of code to add this specific action.

Visualizing the action output within 3rd party tools can be done through our various integrations and by setting the target output within the IDE plugin user interface.

Below is a complete demo video that shows the entire solution and the different metric options used in a Java application that’s running with 20 agents attached.

Bottom Line

Shifting left developer observability and employing the observability driven development workflow can be much easier through such tools and capabilities. When developers are better equipped with tools that fit their skillset and that operate from within their native environments, they are much more productive, hence, can resolve the issues at hand much faster. They can specifically analyze performance issues from the code level and reduce the overall MTTR for such issues, and they can do so by not having to change the application state since these actions are all added and consumed in runtime. 

Get Started with Lightrun Metrics!

The post Lightrun Empowers Developers with Next Generation Metric Tools for Java Performance Troubleshooting appeared first on Lightrun.

In celebration of that I thought I’d write about some of the tricks we discussed amongst ourselves in the past to give you a sense of what to expect when joining us for the workshop but also a useful tool in its own right.

The Problem

Before we begin I’d like to take a moment to talk about production and the role of developers within a production environment. As a hacker I often do everything. That’s OK for a small company but as companies grow we add processes.

Production doesn’t go down in flames as much. Thanks to staging, QA, CI/CD and DevOps who rein in people like me…

So we have all of these things in place. We passed QA, staging and everything’s perfect. Right?

Well… Not exactly.

Sure. Modern DevOps made a huge difference to production quality, monitoring and performance. No doubt. But bugs are inevitable. The ones that slither through are the worst types of vermin. They’re hard to detect and often only happen on scale.

Some problems, like performance issues. Are only noticeable in production against a production database. Staging or dev environments can’t completely replicate modern complex deployments. Infrastructure as Code (IaC) helps a lot with that but even with such solutions, production is at a different scale.

It’s the One Place that REALLY Matters

Everything that isn’t production is in place to facilitate production. That’s it. We can have the best and most extensive tests. With 100% coverage for our local environments. But when our system is running in production behavior is different. We can’t control it completely.

A knee jerk reaction is “more testing”. I see that a lot. If only we had a test for that… The solution is to somehow think of every possible mistake we can make and build a test for that. That’s insane. If we know the mistake, we can just avoid it. The idea that a different team member will have that insight is again wrong. People make similar mistakes and while we can eliminate some bugs in this way. More tests create more problems… CI/CD becomes MUCH slower and results in longer deploy times to production.

That means that when we do have a production bug. It will take much longer to fix because of redundant tests. It means that the whole CI quality process which we need to go through, will take longer. It also means we’ll need to spend more on CI resources…

Logging

Logging solves some of the problems. It’s an important part of any server infrastructure. But the problems are similar to the ones we run into with testing.

We don’t know what will be important when we write a log. Then in production we might find it’s missing. Overlogging is a huge problem in the opposite direction. It can:

• Demolish performance & caching
• Incur huge costs due to log retention
• Make debugging harder due to hard to wade through verbosity

It might still be missing the information we need…

I recently posted to a reddit thread where this comment was also present:

“A team at my company accidentally blew ~100k on Azure Log Analytics during the span of a few days. They set the logging verbosity to a hitherto untested level and threw in some extra replicas as well. When they announced their mistake on Slack, I learned that yes, there is such a thing as too much logging.”  – full thread here.

Again, logging is great. But it doesn’t solve the core problem.

Agility

Our development team needs to be fast and responsive. We need to respond quickly to issues. Sure, we need to try and prevent them in the first place… But like most things in life the law of diminishing returns is in effect here too. There are limits to tests, logs, etc.

For that we need to fully understand the bug fast. Going through the process of reproducing something locally based on hunches is problematic at best. We need a way to observe the problem.

This isn’t new. There are plenty of solutions to look at issues in production e.g. APM tools provide us invaluable insight into our performance in production. They don’t replace profilers. They provide the one data point that matters: how fast is the application that our customers are using!

But most of these tools are geared towards DevOps. It makes sense. DevOps are the people responsible for production, so naturally the monitoring tools were built for them. But DevOps shouldn’t be responsible for fixing R&D bugs or even understanding them… There’s a disconnect here.

Enter Developer Observability

Developers observability is a pillar of observability targeted at developers instead of DevOps. With tools in this field we can instantly get feedback that’s tailored for our needs and reduce the churn of discovering the problem.  Before these tools if a log didn’t exist in the production and we didn’t understand the problem… We had to redeploy our product with “more logs” and cross our fingers…

In Practice and The Workshop…

I got a bit ahead of myself explaining the problem longer than I will explain the solution. I tend to think that’s because the solution is so darn obvious once we “get it”. It’s mostly a matter of details.

Like we all know: the devil is in the details…

Developer observability tools can be very familiar to developers who are used to working with debuggers and IDEs. But they are still pretty different. One example is breakpoints.

It’s Snapshots Now

We all know this drill. Set a breakpoint in the code that doesn’t work and step over until you find the problem. This is so ingrained into our process that we rarely stop to think about this at all.

But if we do this in a production environment the server will be stuck while waiting for us to step over. This might impact all users in the server and I won’t even discuss the security/stability implications (you might as well take a hammer and demolish the server. It’s that bad).

Snapshots do everything a breakpoint does. They can be conditional, like a conditional breakpoint. They contain the stack trace and you can click on elements in the stack. Each frame includes the value of the variables in this specific frame. But here’s the thing: they don’t stop.

So you don’t have “step over” as an option. That part is unavoidable since we don’t stop. You need to rethink the process of debugging errors.

currentTimeMillis()

I love profilers. But when I need to really understand the cost of a method I go to my trusted old currentTimeMillis() call. There’s just no other way to get accurate/consistent performance metrics on small blocks of code.

But as I said before. Production is where it’s at. I can’t just stick micro measurements all over the code and review later.

So developer observability tools added the ability to measure things. Count the number of times a line of code was reached. Or literally perform a tictoc measurement which is equivalent to that currentTimeMillis approach.

See You There

“Only when the tide goes out do you discover who’s been swimming naked.” –   Warren Buffett

I love that quote. We need to be prepared at all times. We need to move fast and be ready for the worst. But we also need practicality. We aren’t original, there are common bugs that we run into left and right. We might notice them faster but mistakes aren’t original.

In the workshop we’ll focus on some of the most common mistakes and demonstrate how we can track them using developer observability. We’ll give real world examples of failures and problems we ran into in the past and as part of our work. I’m very excited about this and hope to see you all there!

The post When Disaster Strikes: Production Troubleshooting appeared first on Lightrun.

The purpose of both monitoring and observability is to find issues in an application. However, monitoring aims to capture already known issues and display them on a dashboard to understand their root cause and the time they occurred. 

On the other hand, observability takes a much low-level approach where developers debug the code to understand the internal state of an application. Thus, observability is the latest evolution of application monitoring that helps detect unknown issues.

Three pillars facilitate observability. They are logs, metrics, and traces. 

• Metrics indicate that there is an issue.
• Traces tell you where the issue is. 
• Logs help you to find the root cause. 

Observability offers several benefits, such as the following:

• Find issues that would otherwise be difficult to detect. Usually, troubleshooting production issues is a disaster. 
• Reduce alert fatigue.
• Faster release of products.
• Increased automation.
• Increased productivity of developers. 

According to Gartner, by 2024, 30% of enterprises will use observability to improve the performance of their digital businesses. It’s a rise from what was less than 10% in 2020.

What is OpenTracing?

Logs help understand what is happening in an application. Most applications create logs on the server on which they’re running. However, logs won’t be sufficient for distributed systems as it is challenging to find the location of an issue with logs. Distributed tracing comes in handy here, as it tracks a request from its inception to the end. 

Although tracing provides visibility into distributed applications, instrumenting traces is a very tedious task. Each tracing tool available works in its way, and they are constantly evolving. Besides, different tools may be required for different situations, so developers shouldn’t have to be stuck with one tool throughout the whole software development process. This is where OpenTracing comes into play. 

OpenTracing is an open-source vendor-agnostic API that allows developers to add tracing into their code base. It’s a standard framework for instrumentation and not a specific, installable program. By providing standard specifications to all tracing tools available, developers can choose the tools that suit their needs at different stages of development. The API works in nine languages, including Java, JavaScript, and Python. 

OpenTracing Features 

OpenTracing consists of four main components that are easy to understand. These are:

Tracer 

A Tracer is the entry point of the tracing API. Tracers are used to create spans. They also let us extract and inject trace information from and to external sources. 

Span

Spans are the primary building block or a unit of work in a trace. Once you make a web request that creates a new trace, it’s called a “root span.” If that request initiates another request in its workflow, the second request will be a child span. Span can support more complex workflows, even involving asynchronous messaging.

SpanContext 

SpanContext is a serializable form of a Span that transfers Span information across process boundaries. It contains trace id, span id, and baggage items.

References 

References build connections between spans. There are two types of references called ChildOf and FollowsFrom. 

What is OpenTelemetry?

Telemetry data is a common term across different scientific fields. It is a collection of datasets gathered from a remote location to measure a system’s health. In DevOps, the system is the software application, while the data we collect are logs, traces, and metrics.  

OpenTelemetry is an open-source framework with tools, APIs, and SDKs for collecting telemetry data. This data is then sent to the backend platform for analysis to understand the status of an application. OpenTelemetry is a Cloud Native Computing Foundation (CNCF) incubating project created by merging OpenTracing and OpenCensus in May 2019. 

OpenTelemetry aims to create a standard format for collecting observability data. Before the invention of solutions like OpenTelemetry, collecting telemetry data across different applications was inconsistent. It was a considerable burden for developers. OpenTelemetry provides a standard for observable instrumentation with its vendor-agnostic APIs and libraries. It saves companies a lot of valuable time spent on creating mechanisms to collect telemetry data. 

You can install and use OpenTelemetry for free. This guide will tell you more about this framework. 

OpenTelemetry features

You have to know the critical components of OpenTelemetry to understand how it works. They are as follows: 

API 

APIs help to instrument your application to generate traces, metrics, and logs. These APIs are language-specific and written in various languages such as Java, .Net, and Python. 

SDK

SDK is another language-specific component that works as a mediator between the API and the Exporter. It defines concepts like configuration, data processing, and exporting. The SDK also handles transaction sampling and request filtering well.

Collector 

The collector gathers, processes, and exports telemetry data. It acts as a vendor-agnostic proxy. Though it isn’t an essential component, it is helpful because it can receive and send application telemetry data to the backend with great flexibility. For example, if necessary, you can handle multiple data formats from OTLP, Jaeger, and Prometheus and send that data to various backends. 

In-process exporter 

You can use the Exporter to configure the backend to which you want to send telemetry data. The Exporter separates the backend configuration from the instrumentation. Therefore, you can easily switch the backend without changing the instrumentation. 

Differences between OpenTracing and OpenTelemetry

OpenTracing and OpenTelemetry are both open-source projects aimed at providing vendor-agnostic solutions. However, OpenTelemetry is the latest solution created by merging OpenTracing and OpenCensus. Thus, it is more robust than OpenTracing.

While OpenTracing collects only traces in distributed applications, OpenTelemetry gathers all types of telemetry data such as logs, metrics, and traces. Moreover, OpenTelemetry is a collection of APIs, SDK, and libraries that you can directly use. One of the critical advantages of OpenTelemetry is its ability to quickly change the backend used to process telemetry data. 

Overall, there are many benefits of using OpenTelemetry over OpenTracing, so developers are migrating from one to the other.

Summary

Logs, traces, and metrics are essential to detect anomalies in your application. They help to avoid any adverse effects on the user experience. While logs can be less effective in distributed systems, traces can indicate the location of an issue. Solutions like OpenTracing and OpenTelemetry provide standards for collecting this telemetry data. 

You can simplify the observability by using Lightrun. This tool allows you to insert logs and metrics in real-time even while the server is running. You can debug all types of applications, including monolithic applications, microservices, Kubernetes clusters, and Docker Swarm. Amongst many other benefits, Lightrun enables you to quickly resolve bugs, increase productivity, and enhance site reliability. Get started with Lightrun today!

The post OpenTracing vs. OpenTelemetry appeared first on Lightrun.

To summarize the core problem: the hashcode method is central to the java collection API. Specifically, with the performance of hash tables (specifically the Map interface hash table). The same is true with the equals method. If we have anything more complex than a string object or a primitive, the overhead can quickly grow.

But the main problem is nuanced behavior. The example given in the article is that of the Java SE URL class. The API for that class specifies that the following comparison of these distinct objects would evaluate to true:

new URL("http://127.0.0.1/").equals(new URL("http://localhost/")); 

new URL("http://127.0.0.1/").hashcode() == new URL("http://localhost/").hashcode(); 

This is a bug in the specification. Notice this applies to all domains, so lookup is necessary to perform hashing or equals. That can be very expensive.

TIP: performance of equals/hashcode must be very efficient for usage in key values containers such as maps and other hash-based collections

There are so many pitfalls with these methods. Some of them would only be obvious at scale. E.g. a friend showed me a method that compared objects and had an external dependency on a list of potential values. This performed great locally but slowly in production where the list had more elements.

How can you tell if a hash function is slow in production?

How do you even find out that it’s the fault of the hash function?

Measuring Performance

For most intents and purposes, we wouldn’t know the problem is in the equals method or hash code. We would need to narrow the problem down. It’s likely that a server process would take longer than we would expect and possibly show up on the APM.

What we would see on the APM is the slow performance of a web service. We can narrow that down by using the metrics tools provided by Lightrun.

Note: Before we proceed, I assume you’re familiar with the basics of Lightrun and have it installed. If not, please check out this introduction.

Lightrun includes the ability to set several metric types:

• A counter which counts the amount of times a specific line of code is reached
• Time measure (tictoc) which measures the performance of a specific code block
• Method duration – same as tictoc for a full method
• Custom metric – measure based on a custom expression

Notice that you can use conditions on all metrics. If performance overhead impacts a specific user, you can limit the measurement only to that specific user.

We can now use these tools to narrow down performance problems and find the root cause, e.g. here I can check if these two lines in the method are at fault:

Adding this tictoc provides us with periodical printouts like this:

INFO: 13 Feb 2022, 14:50:06 TicToc Stats::
{
  "VetListBlock" : {"breakpointId" : "fc27d745-b394-400e-83ee-70d7644272f3","count" : 33,"max" : 32,"mean" : 4.971277332041485,"min" : 1,"name" : "VetListBlock","stddev" : 5.908043099655046,"timestamp" : 1644756606939  }
}

You can review these printouts to get a sense of the overhead incurred by these lines. You can also use the counter to see the frequency at which we invoke a method.

NOTE: You can pipe these results to Prometheus/Grafana for better visualization, but that requires some configuration that’s outside of the scope of this tutorial.

If you see a collection or map as the main performance penalty in the application, it’s very possible that a wayward hash code or equals method are at fault. At this point, you can use metrics in the method itself to gauge its overhead.

This is very similar to the way in which we would often debug these things locally. Surround a suspect area by measurements and rerun the test. Unfortunately, that approach is slow as it requires recompiling/deploying the app. It’s also impractical in production. With this approach we can quickly review all the “suspicious” areas and narrow them all down quickly.

Furthermore, we can do that on a set of servers using the tag feature. In this way we can scale our measurements as we scale our servers.

Checking Thread Safety

Mutable objects can be changed from multiple threads while we try to debug them. This might trigger problems that appear to be performance issues. By verifying that we have single thread access, we can also reduce synchronization in critical sections.

E.g. in a key values store, if a separate thread mutates the key, the store might get corrupted.

The simplest way to do this is log the current thread using the condition Current thread is: {Thread.currentThread().getName()}:

The problem is that a condition like this can trigger output that’s hard to follow, you might see hundreds of printouts. So once we find out the name of the thread, we can add a condition:

!Thread.currentThread().getName().equals("threadName")

This will only log access from different threads. This is something I discussed in my previous post here.

TL;DR

The performance metrics of the equals and hashcode methods in Java SE are crucial. They have a wide reaching impact on the Java collection API, especially in the key values related calls. Objects must implement this efficiently, but it’s often hard to determine the Java class that’s at fault.

We can use Lightrun metrics to time arbitrary methods in production, sign up for Lightrun if you want to try doing it. It’s important to measure class performance in the “real world” environment, which might differ from our local test cases. Objects can behave radically differently with production scale data, and minor changes to a class can make a big difference..

We can narrow down the overhead of hashes, use logs to determine threading issues, and use counters to determine usage of an API.

The post Debugging Java Equals and Hashcode Performance in Production appeared first on Lightrun.

Version control streamlines database management, giving your entire team a common place to manage the codebase, communicate effectively, and collaborate easily, even from remote locations.

Managing databases using version control has been an oversight for most DevOps teams. Some of the challenges that database version control solves are:

• Low productivity due to delays in code reviews
• Distributed workforce leading to slow commits & merges
• Complicated asset management as the software complexity grows
• Databases scalability issues due to the tedious process 

In this article, we will learn what database version control is, its benefits, and the top tools to version control your database.

What is Database Version Control?

Database version control is the practice of tracking every change made to the database by every team member. Like application version control, database version control acts as a single source of truth. It empowers you with complete visibility, traceability, and continuous monitoring of the changes in your database.

Database versioning constitutes information such as database schema, indexes, views, stored procedures, functions, and database configurations. With different teams like developers and system admins working on the same database, it becomes crucial to version control your databases.

The existing market scenario demands faster application releases, made possible by simplifying application and database changes regardless of complexity. Unfortunately, the significance of database changes is often overlooked. According to the State of Database Deployments in Application Delivery, more than 57% of application alterations require corresponding database changes.

Accelerating database changes through version control has a slew of benefits. Some of them are listed below:

• Greater visibility – You will get improved observability into your database. Tracking changes made, team members who made the changes, historical changes, and everyone’s fingerprint. Database version control also helps you locate a bug’s source and resolve it rapidly.
• Better collaboration – Irrespective of where your team works, they’ll always be on the same page concerning database changes. As version control forms the single source of truth, all the changes ever made are pushed to the source repository. The changes can be approved and merged in real-time after the verification by other team members.
• Database rollbacks – Version control is an excellent backup strategy. If anything fails or doesn’t go as desired, you can quickly revert to the earlier version. These rollbacks can be utilized for root cause analysis of the issue, saving you significant time.
• Compliance management – You can easily implement compliance and governance guidelines with a repository acting as a single source. Also, every change is tracked and logged, which makes auditing simpler.

Although version control has been a powerful concept to keep up with the software development complexity, teams often skip putting databases in version control because of challenges such as using multiple databases for development, demand for niche skills, and lack of tools for integration. With the number of tools available in the market, the task is to find the right database version control tool that fits your needs.

What to look for in Database Version Control tools?

The core idea behind version control is to ensure seamless collaboration between teams to accelerate software development. CI/CD (Continuous Integration and Continuous Delivery) is a DevOps practice integrating different code versions across stages and application deployment. When picking a database version control solution, you need to focus on the below aspects:

• Communication capabilities – A communication channel for teams to come together to discuss or update each other is crucial in avoiding confusion and mistakes.
• Security – As your team will be connecting to the tool from possible unsecured locations and networks, the solution should have robust security features.
• Real-time editing – DevOps hinges on a continuous improvement concept, which means the team should be able to make the changes in real-time.
• Traceability – Every code change should be reviewed and accounted for to avoid unwanted issues.
• Integrations – Today’s software development scenario features a variety of development tools, and your database versioning tool must allow easy integration with different environments like microservices and Kubernetes.

Top 8 Database Version Control tools

1. Git

Git is a free, open-source, widely used version control system. This distributed source control system allows you to host your data on locally saved folders called repositories. 

Your team can access all files from the local repository, which can also be stored online. With Git, a copy of a particular functionality is called a branch, and each branch comes with its history. It becomes a part of the main project only when you merge them through pull requests.

Pros:

• Allows experimentation as you can keep your work private
• Enables flexible workflow or process that fits you best
• Safeguard data by effectively detecting data corruption

Cons:

• The learning curve is pretty steep and can be overwhelming
• Requires you to make a lot of decisions to implement changes

What are users saying?

“I like the options it provides developers to maintain repositories and help them collaborate in the best possible way.”

2. Mercurial

Mercurial (Hg) is a free, open-source, distributed source control management tool with an intuitive interface. Built with Python, Hg is a platform-independent tool. However, it lacks change control as you can’t edit earlier commits.

Pros

• An easy-to-use tool that is fast and requires no maintenance. 
• Good documentation makes it easy for non-technical contributors
• It has better security features

Cons

• Not as flexible as other database version control tools
• It allows only two parent profiles

What are users saying?

“Ease-of-use when performing operations like branching, merging, rebasing, and reverting file changes.”

3. CVS

CVS (Concurrent Version System) is a solution that allows you to manage various versions of your source code. Your team can easily collaborate on the platform by sharing version files through a common repository. Unlike other tools, CVS doesn’t create multiple copies of your source code files. Instead, it keeps a single code copy but records all the changes made.

Pros:

• High reliability since it doesn’t allow commits with errors
• It only saves the revisions made to the code, making code reviews easy

Cons:

• Working on CVS is a time-consuming affair
• You can only store files in repositories

What are users saying?

“It’s simpler and less complex and has a good UI to make it easier.”

4. Lightrun

Lightrun is an open-source web interface and observability platform that follows Git-like methodology. Every action and change your team makes is logged and can be audited readily. You can also add logs, metrics, and traces to your app in real-time and on-demand to resolve bugs faster in any environment. It offers significant security features like an encrypted communication channel, blocklisting, and a hardened authentication process.

Pros:

• It comes with solid observability capabilities
• Works transparently along with applications enabling zero downtime
• You can significantly reduce time spent on debugging
• Easy, command-based workflows

What are users saying?

“Great tool for faster incident resolution and real-time debugging without needing to add new code.”

5. Dolt

Dolt is a SQL database that follows the Git-like versioning paradigm, unlike other version control tools. However, Dolt versions tables instead of files, ensuring your updates and changes are never lost.

Pros

• Partially open-source, lightweight, and easy to use
• Convenient to analyze data because of the SQL interface

Cons

• You will be bound to Dolt to realize its benefits
• It is yet to be adopted widely
• Dolt only versions tables, not any other data format

What are users saying?

“Easy to use and integrate with reports and dashboards.”

6. HelixCore

HelixCore is the version control solution from Perforce. It simplifies complex product development by tracking and managing changes to source code and other files. It uses the Streams feature to branch and merge your configuration changes. HelixCore makes it easy to investigate change history and is highly scalable.

Pros:

• It comes with a native command-line tool
• Capability to integrate with third-party tools
• Better security with multiple authentications & access features

Cons:

• It involves a complex workflow and user management
• Higher resource provisions are needed, so it can get expensive

What are users saying?

“It’s extremely simple to find what you are looking for and use it to complete tasks and the ability to track assets easily.”

7. LakeFS

LakeFS is an open-source data versioning tool that enables you to scale your data to Petabytes using S3 or GCS for storage. It follows a Git-like branching and committing practice in line with ACID (Atomicity, Consistency, Isolation, and Durability) compliance. This way, you can make changes in private and isolation that can be created, merged, and rolled back immediately.

Pros:

• Seamless scalability enabling large data lakes
• Allows version control for both development & production stages
• Offers advanced features like ACID transactions with cloud storage

Cons:

• Being a new product, it will have frequent feature changes
• You will need to integrate it with other tools

What are users saying?

“It is possible to develop schema changes in YAML and JSON, which is the order of the game nowadays.”

8. Liquibase

Liquibase is a migration-based version control database tool that uses changelog functionality to track the changes you make to your database. It defines its changesets in XML format that allows you to utilize the database schema on other database platforms. It comes in two variants – open-source and premium.

Pros:

• Allows targeted rollbacks to undo changes
• Supports a variety of database types
• Enables you to specify changes in multiple formats, including SQL, XML, and YAML

Cons:

• Advanced features are only available in the paid version
• It needs significant time and effort to use the tool better

What are users saying?

“Easy to integrate, and we can version control the changes by maintaining all the changeset.”

Summary

Database version control is a powerful concept that can give your application development methodology an extra edge. There are multiple tools available today – both free and paid. We have listed the top 8 database versioning tools used widely today. However, you must thoroughly understand your requirements and development pipeline before choosing the tool.

Lightrun can be an ideal pick to complement your development landscape as it has strong security and observability features. Start using Lightrun today, or request a demo to learn more.

The post Top 8 Database Version Control Tools appeared first on Lightrun.

SRE is a new field that combines aspects of software engineering and operations. Job openings for Site Reliability Engineers surged by more than 72% in the US in 2019, making it one of the most sought-after roles. SREs provide critical value for an organization’s cyber security policy implementation and upgrades. 

What is Site Reliability Engineering?

Site reliability engineering (SRE) is an area that combines aspects of software engineering and operations. The average cost of a system’s downtime comes to around $5,600 per minute, equivalent to more than $300,000 per hour.

The main goal of SRE is to ensure that a site or service is available and performing well. SREs do so by designing and building systems that are resilient to failure and by monitoring and responding to incidents when they occur.

While this sounds a lot like DevOps – it’s not. The main difference between SRE and DevOps is that SRE places a greater emphasis on reliability and availability, while DevOps focuses on speed and agility. A Site Reliability Engineer’s role is to ensure that systems are reliable and available while providing DevOps-style automation and efficiency.

Some of the specific benefits of SRE include:

1. Reduced downtime: By designing systems to be resilient to failure and monitoring and responding to incidents quickly, SRE can help reduce the time a site or service is unavailable.
2. Improved quality: SRE can help improve the overall quality of service by making it more reliable and easier to operate.
3. Reduced costs: SRE can prevent outages and disruptions and ensure that systems can recover quickly when problems occur.

Top 12 Site Reliability Engineer (SRE) Tools

SRE tools can be divided into the following categories: 

• APM (Application Performance Management) and Monitoring Tools
• Automated Incident Response System
• Real-Time Communication tools
• Configuration Management tools

APM (Application Performance Management) and Monitoring Tools

APM tools help businesses identify and diagnose issues with their applications. Monitoring tools enable companies to identify and diagnose problems with their infrastructure. Both tools are essential for businesses to ensure that their applications and infrastructure run smoothly.

1. Datadog

Rated 4.3 out of 5 by over 300 reviews on G2, Datadog is a monitoring service for cloud-scale applications, providing end-to-end visibility across the application stack. Organizations of all sizes use it to troubleshoot issues, gain insight into their applications, and ensure business continuity.

Datadog has many advantages, including scalability, integrations with over 350 technologies, and monitoring infrastructure and applications in a single platform. Datadog provides features specifically designed for large organizations, such as role-based access control and auditing.

However, Datadog can be expensive for large organizations. It can also lack some of the features of more specialized monitoring tools, such as application performance management (APM).

Pros: 

• Allows for monitoring of multiple servers at once
• Flexible and easily customizable
• Detailed information and graphs are available
• It can set up alerts to notify you of any issues

Cons: 

• Can be expensive
• It may be overwhelming if you are monitoring a lot of servers
• Not as widely known/used as some other monitoring tools

2. Lightrun

Lightrun is the perfect tool for developers who want to test and debug their code in real-time. It is a cloud-based application that enables developers to identify and fix errors in their code faster and more efficiently.

Lightrun tools help developers and ops teams to work together more efficiently and to improve the quality of their services. It’s also an excellent way to test code changes in a live environment without affecting all users.

Overall, LightRun is a helpful tool for developers who want to test their code in a production environment, especially when things go wrong and an outage is impossible. It’s quick and easy to use and can save time and headaches in the long run.

Pros: 

• Easy to use
• It can be used to test various aspects of applications while in production 
• It can be used to track bugs on-prem and in real-time
• Good for keeping on top of security compliance through active monitoring
• A free trial is available

3. New Relic 

New Relic’s software provides real-time data about web application performance. Developers use this data to identify and diagnose issues. The software also provides insights into the performance of mobile applications. 

New Relic has a free and paid subscription. The free subscription provides data on up to 100 applications, while the paid subscription provides data on an unlimited number of applications.

Pros: 

• It offers a wide range of features 
• It has a strong community support 
• It can be easily integrated with other tools 
• A free trial is available

Cons: 

• Relatively expensive 
• It slows down some servers 
• Some features can be confusing to set up

Automated Incident Response System

An automated incident response system is a system that automates incident response tasks, such as identifying, containing, and eradicating incidents. This can be done by integrating multiple security tools and technologies to streamline the incident response process. Automated incident response systems can help businesses by reducing the time and resources needed to respond to incidents and improving the effectiveness of the incident response.

4. Grafana 

Grafana is a data visualization tool that allows you to see and analyze data in real-time. Developers and data scientists use it to debug applications and understand data flows. Grafana has various uses, including monitoring server performance, visualizing database queries, and monitoring application performance.

Grafana is open source and free to use. It is available for Windows, Mac, and Linux. Grafana is easy to use and has a wide variety of plugins. Grafana also provides built-in data sources and has alerting capabilities.

Pros: 

• Allows for easy creation and visualization of complex data queries
• It can be used to monitor multiple data sources easily
• It is highly customizable and allows for the creation of custom dashboards

Cons: 

• It may be overwhelming for users who are not familiar with data visualization
• It can be challenging to set up and configure
• Limited documentation and support

5. PagerDuty

PagerDuty is an automated incident response system that organizations use to help manage and respond to incidents. It is a cloud-based platform that provides users with the ability to create and manage incidents, as well as to track and monitor response times and incident resolution.

PagerDuty has some features that make it a valuable tool for managing critical incidents. It allows organizations to create and manage incident response plans, track and manage incidents, and communicate with incident response teams. It also provides a variety of reports and tools for analyzing and responding to incidents.

PagerDuty also has some drawbacks: it can be challenging to set up and use, and it can be expensive. It also lacks some features that would be useful for managing critical incidents, such as the ability to integrate with other incident response systems.

Pros:

• Easily integrate with other tools and systems
• Flexible and customizable
• It can be used for on-call scheduling
• Real-time visibility into incidents

Cons:

• Can be expensive
• Complex to set up
• Not all features are available in all plans
• It can be challenging to use for some users

6. HoneyComb

One of the key benefits of using Honeycomb is that it can help organizations save time and resources when responding to security incidents. The system’s automated incident response capabilities can help organizations quickly identify and investigate the root cause of an incident. Additionally, the integration with SIEM systems can help organizations automate many tasks associated with incident response, such as threat analysis and classification.

While Honeycomb can be a valuable tool for incident response, the system can be expensive to purchase and implement. Additionally, Honeycomb requires a high degree of technical expertise to configure and use effectively. The system’s reliance on data from multiple sources can make it challenging to use in environments where data is siloed.

Pros: 

• Can help identify slow or inefficient queries
• Can track database activity over time
• Can help optimize database performance
• Provides a web-based interface for easy access

Cons:

• Requires a paid subscription
• It may be challenging to set up and configure
• It may not be compatible with all database systems
• Limited customer support

Real-Time Communication tools

Real-Time Communication (RTC) tools are software applications that allow users to communicate with each other in real-time. RTC tools are typically used for voice and video communication but can also be used for text-based communication, file sharing, and collaboration.

RTC tools are suitable for businesses and their teams because they allow for quick and efficient communication between team members. Teams can use RTC tools for various purposes, such as team meetings, training sessions, and customer support. RTC tools also help improve communication between remote team members.

7. Microsoft Teams

Microsoft Teams is a real-time communication tool part of the Microsoft Office 365 suite of productivity tools. It is designed for businesses of all sizes and offers a variety of features, including file sharing, chat, video conferencing, and more. However, it requires a subscription to Office 365. 

Pros: 

• Allows for accessible communication and collaboration between team members
• It can be accessed from anywhere with an internet connection
• Integrates with other Microsoft products
• It has a variety of features and tools to improve productivity

Cons: 

• It may be challenging to learn how to use all the features
• It can be glitchy or slow at times
• Some features may not be available in all countries

8. Slack

Slack is a real-time communication tool that allows users to communicate with each other via messaging. It is similar to other messaging tools such as WhatsApp and Facebook Messenger but has some unique features that make it stand out.

The pros of Slack include user-friendliness and integrating well with a wide variety of tools and services. However, keeping up with all the messages can be overwhelming if team members are part of too many channels.

Pros: 

• Allows for clear and concise communication within a team
• It helps to keep everyone organized and on the same page
• It can be accessed from anywhere
• It makes it easy to find old conversations

Cons: 

• It can be a distraction if not used properly
• It can be overwhelming if there are too many channels
• People can easily get lost in conversation threads

9. Telegram

Telegram is a messaging app focused on speed and security. It’s super-fast, simple, and accessible. You can use Telegram on all your devices — your messages sync seamlessly across any number of your phones, tablets, or computers.

With Telegram, you can send messages, photos, videos, and files of any type (doc, zip, mp3, etc.), as well as create groups for up to 200,000 people or channels for broadcasting to unlimited audiences. 

You can write to your phone contacts and find people by their usernames, like SMS and email combined. The main drawback of Telegram is that it is banned in some countries, which may be a significant pain if your team members are spread across the globe.

Pros: 

• It can be used on multiple devices 
• It has a self-destruct feature 
• It can be used without a phone number 

Cons: 

• Security concerns 
• It may be blocked in some countries 
• It is less popular than other messaging apps

Configuration Management tools

Configuration management tools help businesses and their teams manage configurations, or settings, across their environment. Configuration management tools automate and simplify setting and maintaining consistent configurations across multiple servers and devices. This can help businesses avoid configuration drift, leading to inconsistency and errors. Configuration management tools can also help companies to recover from configuration changes that cause problems.

10. Ansible 

Ansible is a configuration management tool that automates tasks, such as software deployments, provisioning, and configuration. It is often used for managing server deployments and managing both small and large-scale infrastructure. It is also open source and is available for free.

The tool is simple and easy to use. It is agentless, meaning it does not require any software installed on the target machines. Ansible is also idempotent, so running a task multiple times will have the same effect as running it once.

It is a popular configuration management tool because it is easy to use and doesn’t require any special software installed on the target machines. However, because Ansible is agentless, it can be difficult to troubleshoot when things go wrong.

Pros: 

• It is straightforward to use and doesn’t require any unique setup or configuration
• Ansible playbooks are easy to read and understand
• It can be used to manage a large number of servers from a central location
• It can be used to automate many system administration tasks

Cons: 

• Ansible playbooks can become very complex and challenging to maintain
• It can be slow to run, especially on large systems
• Ansible can be tricky to debug
• It is not a good choice for real-time management of servers

11. SaltStack 

SaltStack is a Python-based configuration management tool to manage server configurations, deployments, and orchestration.

However, it is not as widely used as some other configuration management tools, so there is less community support and fewer resources available. Additionally, SaltStack can only work on Linux servers.

Pros: 

• Saltstack can manage large numbers of servers very efficiently
• Saltstack’s declarative approach to configuration management means that configurations are easy to understand and maintain
• It is very scalable and can be used to manage thousands of servers
• It is fast and can apply changes to a large number of servers very quickly

Cons: 

• Saltstack can be complex to learn and use
• Requires a good understanding of system administration to be used effectively
• Saltstack can be difficult to debug when things go wrong
• It can be resource-intensive and may not be suitable for minimal deployments

12. Terraform

Terraform is a configuration management tool used to manage infrastructure as code. It is popular among DevOps professionals because it is declarative, meaning that it describes the desired state of the infrastructure. It is also idempotent, so running the same Terraform configuration multiple times will result in the same final form.

Advantages of Terraform include infrastructure as code and execution plans. However, a significant drawback for teams is its learning curve and potential vendor lock-in for complex designs.

Pros: 

• It can manage large-scale deployments
• It can easily provision resources
• It can an manage dependencies between resources
• It can automate deployment processes

Cons: 

• Difficult to learn
• Difficult to manage complex configurations and to debug
• It can be slow

Next Steps

DevOps teams can’t overstate the importance of having an SRE tool, as having the right tool can make all the difference in keeping your business up and running.

Lightrun provides all of the features you need to manage your applications effectively. It offers application performance monitoring, application management, and even application security features. If you’re looking for a way to automate the implementation and maintenance of your logging, metrics, and tracing, then Lightrun is the tool for you. Start using Lightrun today.

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