Options in Observability

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There are different options in observability. I&O leaders will need to sieve through the possibilities in observability to pick the right choice for their enterprise. Deciding on what observability pipeline to settle for entails a lot of decisive steps and considerations. Many people root for open-source solutions, while a couple of others depend on solutions provided by their existing vendors. They all have areas of advantage and disadvantage. Some could be long-term, while others may be short-term.

In this post, we’ll discuss some primary considerations to guide you towards adopting suitable options in observability. 

If you’re looking at an observability option, you should consider these;

  • Protocol support
  • Management system
  • Supports both cloud and local instances
  • Supports many destinations while having the flexibility to add new ones
  • Designed to reduce data costs

There’s an ever-increasing rate of log data a company needs to analyze, store, and easily access or retrieve. This has resulted in a race by many organizations towards adopting new technologies towards becoming more software-centric. This is to keep up with business demand. Some have rapidly embraced microservices architectures. A good number have adopted Kubernetes, while a significant number of others are going serverless. Other companies are still managing traditional systems.

Many organizations incorporate a hybrid IT environment. This leverages both on-premises systems and off-premises cloud/hosted resources in an integrated fashion. Following the rapidly increasing data volumes, combining an observability solution can increase an organization’s data visibility.

What is Observability?

Observability is the ability to answer any question about your business or application at any given time. Regardless of the complexity of one’s infrastructure. Operations engineers have become widely interested in observability tools to enhance uptime and service performance. Also, modern observability platforms can sieve data most relevant to business users.

For instance, while being able to collect process IDs, they can also collect transaction IDs. That way, business organizations can quickly identify the business impact of problems in their applications. This is done by instrumenting systems and applications to collect metrics, traces, and logs. You then send all of this data to a system that can store and analyze it while helping you gain insights.

Furthermore, observability entails assembling log fragments and monitoring tools and organizing them to derive actionable knowledge of the whole environment. That way, you can gain valuable insight. Observability looks at getting data out of your infrastructure and applications to observe, monitor properly, and secure their running state. While minimizing overlap, wasted resources, and cost.

Options in Observability

With the help of observability options, you can quickly receive data from any source. This includes object storage, transforming it into any shape, and routing it to any destination. It means that you can channel data from any source to any chosen destination with a choice of altering what you’re sending. It is vital that your data is always made available for the entire enterprise.

However, having a large number of tools and products can become a drawback. Also, teams need to vary from one another. For this reason, a decoupled approach is encouraged. This is what an observability pipeline stands for. By building an observability pipeline, you break down data and channel such to various systems.

Building a customized observability pipeline requires several properties, including protocol support for existing agents, an easily manageable system, performance, etc.

Protocol Support

The primary challenge while attempting to implement an observability pipeline on your own is protocol support. Most organizations have a wide range of agents. From tens to hundreds of thousands, in situations where your agents are proprietary, for instance, the Splunk universal forwarder. For such cases, you should look at replacing all your existing agents. On the other end, you should look at installing a second agent collecting the same data.

In conditions where your agents are already open source agents like Elastic search’s Beats or Fluentd, you could implement Apache Kafka to serve as a temporary receiver to pre-process before sending it onto Elastic.

Worthy of note is that you’d be adding a new distributed system to your pipeline. This, however, does not solve the end-to-end use case.

Solving the real problem, including transforming and processing the data, your pipeline would become complex. It will include multiple processing steps, with additional processing software like Fluentd pulling data off of topics and a second copy on a different matter.

Programming Your Pipeline

Another significant challenge often observed with building an observability pipeline on top of a generic stream processing engine is the administrator’s workload.

Leveraging systems like Apache NiFi, you can rely on to transport even binary data from the edge. You can also depend on it as a passthrough, routing arbitrary bytes from one Kafka topic to another.

Apache NiFi is a graphical environment, but most generic stream processing engines provide a programming environment to their end-users. Programming a stream processing engine requires end-users to work at a much lower level. Extreme flexibility comes at the cost of requiring the system’s user to reimplement many things done out of the box in logging systems.

Building Your Management

There is always a great focus on the day one problem while implementing a system. Over time, new issues arise and are left to the implementer of the system. Questions such as;

  • What are the simple steps users can take to make changes safely and reliably?
  • What are the indicators to show the user if the system is performing optimally?
  • Which ways can the user troubleshoot this system?

Fluentd and Logstash

Fluentd and Logstash are log processing tools most appropriate for Day 1. Working through it, building an initial configuration could be seamless. But the big question lies in its scalability and how long it lasts.

For Fluentd or logstash, managing configurations with the corresponding configuration changes requires a significant system investment.

One success trait with organizations building observability pipelines is that they’ve created unique personal unit testing frameworks for configurations and intense code and configuration review processes.

These processes weren’t invented for no reason. Before implementing these processes, minor configuration errors would regularly break production. This process also involves implementing your continuous deployment pipeline to roll changes safely to production. On top of CI/CD, which needs to be built, monitoring comes next. Suppose data is coming in delayed or the system slows down for any reason. In that case, metrics need to be available to determine which system is causing back pressure or which particular set of configurations might be the culprit.

One wrong regular expression can destroy the performance of a data processing system. Building up rich monitoring dashboards is a considerable part of the Day 2 cost of building your system. Lastly, when data is not being processed in how the end-users are expecting, the operators of the system need to reproduce data processing to validate why a given configuration produces a given output. There is no environment like production. Attempting to take a configuration and an environment where the given conditions reproduce the problem can be hugely time-consuming. Tools like Fluentd and Logstash provide little introspection, data capture, or troubleshooting capabilities.

Performance

Moving large data volumes, a little above 20%, can largely improve processing speed. Likewise, impact tens to hundreds of thousands of dollars a year in infrastructure costs. Companies such as Fluentd and Logstash have had long known performance challenges. Apache NiFi and other generic systems are not designed for a petabyte/day scale. Another challenge is that building your system can be capital intensive from an infrastructure perspective at scale.

An Alternative: The Out-of-the-Box Solution

As seen before now, building a personal observability pipeline could be quite a challenge. This is because it has to deal with issues of insufficient protocol support, overburdening workload for the administrator, troubleshooting challenges, and generally increasing infrastructure costs. This is where an out-of-the-box solution comes in. Depending on an enterprise-ready solution with a turn-key deployment and easy manageability is key.

Role-Based Access Control (RBAC)

Another substantive alternative is role-based access control (RBAC). This allows you to manage teams’ access to data, so they can only receive what’s necessary for their job implementation. One advantage of it is data safety. It schedules how each data is allocated.

Adopting a no-code configurable solution that works natively on events is recommended. This is simply because most sources and destinations also deal with events. One great advantage of an observability pipeline is that it enables organizations to handle event breaking natively in their observability tool when sent a raw byte stream.

Options in Observability – Putting It All Together

In conclusion, an observability platform solves a couple of problems. Organizations can solve problems without running helter-skelter as results are always readily available. There are a couple of unique custom solutions. However, solving business problems requires a complete out-of-box solution. Several custom solutions require a lot of integration and building effort on users to address all of their challenges.

Not sure what the difference is between Observability and Monitoring? Read our blog to find out more!

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