InfluxData prides itself on prioritizing developer happiness. A large part of maintaining developer happiness is providing client libraries that allow users to interact with the database through the language and library of their choosing. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.
On the sleepy island of Gozo, security isn’t a concern. Tourists can leave their bags on the beach and go off on an adventure without worrying that their belongings will be stolen. In my home city, however, we say that “if you don’t tie it down, it’s not yours.” Everything can be stolen. Similarly, the internet is the biggest and busiest city in the world! If it can be read, copied, written, or injected with SQL, it’s not yours.
We’re big fans of AWS Lambda at Honeycomb. As you may have read, we recently made some major improvements to our storage engine by leveraging Lambda to process more data in less time. Making a change to a complex system like our storage engine is daunting, but can be made less so with good instrumentation and tracing. For this project, that meant getting instrumentation out of Lambda and into Honeycomb.
Previously we showed you how to Write Points from CSV to InfluxDB with Telegraf. Today we will learn how you can write millions of points to InfluxDB 2.0 with the InfluxDB Python Client on your local machine in a matter of seconds. The inspiration for this blog and this exercise comes from Mark Litwintschik’s Benchmark. In the benchmark, Mark compares the query times for data from the Billion Taxi Rides Dataset against several databases.
We’re excited to release version 7 of the Datadog Agent. It has all of the same functionality as Agent 6, but it is the first version to ship with only the Python 3 runtime. With Python 2 reaching its end of life on January 1, 2020, migrating your services to Python 3 will ensure that they continue working as expected. We’ve tested all of our more than 350 integrations to ensure they work with Python 3.
With 200+ plugins, Telegraf has a wide variety of data collection applications. However, sometimes you need to collect custom data or maybe you want to integrate external tools into your time data analysis. In that case, it makes sense to take advantage of InfluxDB’s Client Libraries. Today we will focus on how to use the latest InfluxDB Python Client Library with InfluxDB v2.0. If you are running InfluxDB v1.x, please take a look at this tutorial instead.
In “Getting Started with OpenTelemetry Alphas: Python”, we scratched the surface of distributed tracing with OpenTelemetry in Python. In this article, we’ll dive deeper into span exporters and learn how to get traces into LightStep from your Python application. You can create a free-forever LightStep account here to follow along.
New Alpha versions of the Python OpenTelemetry API and SDK packages have just been released, and it’s time to take them for a test drive! This guide walks through installing the required packages, generating tracing information and propagating context across processes via HTTP.
