Full-stack observability is a term that you may have heard being tossed around in many conversations on the topic of observability. What does it mean? Full-stack observability constitutes having visibility into all layers of your technology stack. Collecting, correlating, and aggregating all telemetry in the components provides insight into the behavior, performance, and health of the system. A well-designed full-stack observability strategy with the right platform can not only provide forensics but also help avoid potential problems by forecasting them ahead of time.
In order to achieve this, you need data from all layers of the technology stack in order to get a complete view of the health and performance of your system. This is where MELT comes in. MELT is the set of components that give you data from all layers of the technology stack, allowing you to have full-stack observability.
What is MELT?
MELT is an acronym for:
- (M)etrics: Data that represents the performance of your system. This can be things like response times, CPU utilization, or memory usage.
- (E)vents: Data that represent significant changes in state in your system. This can be things like application restarts, deployments, or errors.
- (L)ogs: Data that represents the actions taken by your system. This can be things like access logs, application logs, or database queries.
- (T)races: Data that represents the flow of a request as it goes through your system. This can be used to diagnose performance issues or identify errors.
MELT and Root cause analysis
Now that we understand MELT, let us see how root cause analysis needs MELT. Root cause analysis is the process of identifying the underlying cause of a problem. For e.g. lets us say you have an application showing a performance degradation say and API showing slower response time. In order to root cause, you first need a way to see that the API is running slow. This is where metrics play a role. By looking at the metrics of the API, you can see that the response time has increased.
But this is not enough to get to the root cause, you need to understand why the API is running slow. This is where traces come in. Traces show you the flow of a request as it goes through your system. By looking at the trace data, you can see that the slow down is happening at the database query.
Now that you know where the problem is, you need to understand why the database query is slow. This is where logs come in. By looking at the logs of the database query, you can see that there was a recent change to the schema that is causing the slowdown.
Events also help in this process. By raising an event when the database connectivity fails, you can create an alert to notify you when this happens.
So as you can see, MELT is essential for root cause analysis. But is full-stack observability enough with MELT? Does MELT address all of your needs? What if you need the data available for other consumers? How do you know the data you are collecting is relevant and optimal. How do you handle compliance? There are many other things to consider as well. This is where a Full-stack Observability Data Fabric comes in.
Beyond MELT to an observability data fabric
A Full-stack Observability Data Fabric is a platform that allows you to not only collect, store, and query data from all layers of your technology stack but also provides you with controls to transform and connect your data to consumers on-demand. Data sources could be any component that generates data like cloud services, virtual machines, Kafka streams, networked devices, etc.
The key to a full-stack observability data fabric is to create an observability data lake first which is the master repository of all your data. This data lake should be able to ingest data from all sources with minimal transformation and provide you with the ability to query this data in real-time.
From this observability data lake, you can then connect your data to consumers on-demand using a variety of methods such as streaming, batch jobs, or API calls. This allows you to not only provide observability data to the consumers that need it but also gives you the ability to control how this data is used.
Data control is an important part of any observability strategy. Data control includes things such as data filtering, data augmentation, volume reduction, control license spending, and having rapid control for flexible data retention on demand. e.g. some parts of your observability data may need to be retained for 30 days while others for 1 year.
Understanding data control
Let us look at a few other examples of data control. Data filtering is the process of removing sensitive or unwanted data from your observability data before it is made available to consumers. This can be done using a variety of methods such as whitelisting, blacklisting, or user-defined filters. Data augmentation or enrichment is the process of adding additional context to your observability. There are many more components to it. Here’s an infographic that can help you visualize the various aspects and benefits of implementing an observability data fabric for your organization.
Let us see how this can help with your observability implementations. Let us take an example of a Datadog observability implementation. How can an observability data fabric help with Datadog?
The same gains can be realized by using the LOGIQ.AI observability data fabric with many vendor solutions such as Datadog, Splunk, NewRelic, Elastic to name a few.
A Full-stack Observability Data Fabric provides you with a complete view of your system by collecting data from all layers of your technology stack and making this data available to consumers on-demand. This allows you not only to root cause problems quickly but also to prevent problems from happening in the first place.
If you are not using a Full-stack Observability Data Fabric, you are missing out on the benefits of Full-stack Observability.
What are you waiting for? Start using a Full-stack Observability Data Fabric today!