Blog RootPerformance Performance Engineering & Monitoring Blog

What is Observability and Why You Need It

Note: This post was originally written for the Cprime blog. You can check out the original here.

As more organizations move from on-prem to cloud infrastructure, IT teams are finding that traditional monitoring solutions just aren’t getting the job done. Many monitoring vendors have moved beyond monitoring to observability. Sadly, some are doing nothing more than putting a good spin on the same old monitoring solution.

The bottom line is that traditional monitoring isn’t enough for today’s cloud applications and infrastructure. You need real observability, which can tell you not only when there’s a problem but also what its underlying cause is. In this post, let’s discuss what observability is and how it can help.

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Elasticsearch Alternatives for Event Data: 5 Options

Note: This post was originally written for the Scalyr blog. You can check out the original here.

The amount of event data to collect has seen a dramatic increase in the last few years. It continues to grow as more companies move to microservices, containers, and the modern infrastructure stack. For many, Elasticsearch has been the solution to help.

With more data comes some common scaling problems, so you may consider solutions that are Elasticsearch alternatives.

Choosing the wrong alternative can be risky. So in this post, you’re going to learn about five Elasticsearch alternatives you should consider. You’ll learn about some of their benefits and drawbacks, and also how they’re priced.

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How to Prevent Capturing Data for Security Reasons

Security should be top of mind these days. I wrote an article years ago about three lessons you can learn from doing a network security analysis. Taking the appropriate steps is even more crucial now than then.

But how do you protect your organization when you need to help troubleshoot an application problem and need to capture packets? In this post, let’s talk about how you can go about doing that.

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The Essential Guide to Scaling Elasticsearch

Note: This post was originally written for the Scalyr blog. You can check out the original here.

Some things aren’t always what they seem.

You’re tasked with engineering a solution that your organization needs. You implement it with a tool that seems relatively easy to set up. But over time, you realize that there’s no Easy button.

Elasticsearch is an example of one of those things. It’s a great product for collecting event data fairly quickly and easily. You start with one data node in one cluster and go from there. And because it’s free and open-source (for now), it’s even better. But as your Elasticsearch cluster grows and collects more data, you start to have some scaling issues. In this post, I’m going to provide some information on scaling an Elasticsearch implementation, as well as some general recommendations for proactive ways to scale Elasticsearch.

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Where is the Best Location for a Packet Capture?

You just got a call about an application slowness issue. You’ve been told that it’s not an application issue and that it must be somewhere on the network.

When you need to capture some data, for whatever reason, one question that inevitably comes up is where you should capture. What’s the best location for a packet capture? That’s a good question to ask yourself.

If you don’t, you should be. So let’s talk about that now.

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StatsD: How to Measure Anything in Your System

Note: This post was originally written for the Scalyr blog. You can check out the original here.

In his book “How to Measure Anything,” management consultant and author Douglas Hubbard states that “anything can be measured.” Hubbard argues that something that can be observed lends itself to being measured.

How can this apply to software development and operations? Well, in today’s world of increasingly complex IT systems, you can’t afford not to measure anything and everything. But in order to observe and then measure something, it needs to meet the literal definition of observability, meaning that a system’s internal state must be exposed externally. This allows you to measure it.

With observability, you find out not only that your system malfunctioned, but also why. This is done with data from logs, metrics, and traces.

In 2011, the Etsy Engineering team made things a little bit easier to measure and observe metrics in your IT system with the introduction of StatsD. Historically, collecting data about networks and servers has always been easier to do than gaining the same information about applications.

StatsD made collecting application metrics simpler for developers by instrumenting your code with specific metrics you want to observe. As a result, StatsD has become one of the most popular tools for gathering metrics data.

In this post, I’m going to give you a brief tutorial of StatsD and how you can use it to measure anything in your application.

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How to Simplify SNMP Implementation

Note: This post was originally written for the Sensu blog. You can check out the original here.

You want implementations to be simple. Who doesn’t?

But often, that’s not the case.

SNMP is one of the oldest used protocols to manage a network. SNMP stands for simple network management protocol, but it might not have been all that simple for you.

Maybe it was simple back in the late 1980s, when SNMP became a standard protocol for network monitoring. But that was over 30 years ago, and while SNMP is ubiquitous, a lot has changed.

In this post, you’ll learn what SNMP is and some challenges you may have when using it to monitor your infrastructure. You’ll also learn some best practices you should consider when implementing SNMP monitoring.

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Performance Monitoring in the Cloud: Does it Still Matter?

Editor’s note: This post was originally written for the eG Innovations blog. You can check out the original here.

So your organization has decided that they’re moving to the cloud.

Smart move! Many companies today are either making the move to the cloud or have already made it.

According to RightScale’s 2019 State of the Cloud Survey, 94% of IT professionals say their companies use the cloud. Over 90% is public cloud.

Gartner predicts that public cloud revenue will increase by 17% in 2020. The biggest percentage increase is expected with IaaS.

So you’re clearly not alone.

But what’s the plan after the move for making sure your applications and infrastructure perform at expected levels in the cloud?

When you no longer have full control of your infrastructure, monitoring becomes even more important than it did on-premise. Unfortunately, it seems some companies get the impression that moving to the cloud will solve all their issues.

The cloud has many benefits, but not having to monitor your workloads isn’t one of them. I want to show you why, and provide some tips for cloud performance monitoring.

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Review: How New Relic APM Alerts Performs

New Relic APM, 3/2020

New Relic APM helps you to understand the performance of your applications. I wanted to run some performance tests with it for my review. In this video, I walk through the Alerts feature. Here it is….

How New Relic APM Monitors – The Alerts

Transcription

Coming soon!

What Is Cloud Data Migration? A Detailed Guide

Editor’s note: This post was originally written for DataOpsZone You can check out the original here.

Do believe the hype!

Many companies are migrating their data to the cloud. Cisco forecasts that by 2021, 94% of all workloads and compute instances will be processed by cloud data centers.

But Gartner’s 2018 hype cycle did have cloud migration falling into the “trough of disillusionment.” This could be because migrating data to the cloud isn’t easy. Organizations encounter numerous issues when trying to take advantage of the cloud’s benefits.

Organizations need to plan properly before undertaking such an endeavor. Let’s look at what you can do to minimize the issues that can plague a cloud data migration project.

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