Revolutionizing Perfomance Management

Dan Kuebrich

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Top Stories by Dan Kuebrich

We’re excited to announce three important changes to our TraceView pricing and product. First, and most importantly, we are launching a free tier! Project includes all the data we collect on requests, hosts, service calls, JVMs and everything else, and we’re giving it away, free forever. We want you to have this, so we’ve packed everything you need into this version to help you get as much visibility into production as possible. Second, we’re changing the name of Tracelytics to TraceView. Since Tracelytics joined AppNeta in June of last year, there has been a lot of work under the hood on both products. Stay tuned for more cross-talk between these products — we think there’s a lot unexplored territory here, and this name change is just the first step down that path. Third, we’re also excited to change our pricing structure to a per-host model. While the first itera... (more)

Introducing a New Look for Traces

Our fundamental unit of performance data is the trace, an incredibly rich view into the performance of an individual request moving through your web application. Given all this data and the diversity of the contents of any individual trace, it’s important to have an interface for understanding what exactly was going on when a request was served. How did it get handled? What parts were slow, and what parts were anomalous? Over the past year, the TraceView team has been listening to your thoughts on this topic as well as hatching some of our own. Today we get to share the fruit of... (more)

Tracing Celery Performance for Web Applications

Are you using Celery to process Python back-end tasks asynchronously?  Have you wanted to get insight into their resource consumption and efficiency?  Here’s a few useful ways to get insight into Celery performance when running tasks. A simple celery task For a quick review, Celery lets you turn any python method into an asynchronous task.  Here’s a simple one: 1 2 3 4 from celery.task import task @task def add(x, y): return x + y Let’s trace Celery We’ll start with the good stuff. In the latest release of our Python instrumentation, oboeware-1.0, we have an updated API that makes ... (more)

The Taming of the Queue

A few weeks back webserver request queueing came under heightened scrutiny as rapgenius blasted Heroku for not using as much autotune as promised in their “intelligent load balancing”. If you somehow missed the write-up (or response), check it out for its great simulations of load balancing strategies on Heroku. What if you’re not running on Heroku? Well, the same wisdom still applies – know your application’s load balancing and concurrency and measure its performance. Let’s explore how request queueing affects applications in the non-PaaS world and what you can do about it. Fu... (more)

Performing Under Pressure | Part 1

Many types of performance problems can result from the load created by concurrent users of web applications, and all too often these scalability bottlenecks go undetected until the application has been deployed in production.  Load-testing, the generation of simulated user requests, is a great way to catch these types of issues before they get out of hand.  Last month I presented about load testing with Canonical's Corey Goldberg at the Boston Python Meetup last week and thought the topic deserved blog discussion as well. In this two-part series, I'll walk through generating lo... (more)