Written by Evan Mullins and Gang Tao
Originally posted at https://docs.livepeer.org/tutorials/developing/analyze-engagement-timeplus
Evan Mullins is a data scientist and analytics expert who is responsible for Livepeer's data engineering team.
Video engagement metrics are important to video creators, which serve as valuable indicators of content quality, help users manage their time effectively, facilitate interaction with content creators and other viewers, and contribute to the overall user experience on video-sharing platforms.
In May 2023, Livepeer released their version of these engagement metrics offering detailed information on viewer behavior and playback quality on your platform. The API includes engagement metrics such as view counts and watch time, as well as performance metrics such as error rate, time to first frame, rebuffer ratio, and exit-before-starts across a variety of dimensions.
There are many different data analysis tools available today which can be used to help analyze these engagement metrics. Typically, users need to import the data into a data platform, and then build queries (SQL or non-SQL based) and dashboards on that platform to support interactive data exploration or monitoring what’s happening with those metrics data using visualizations.
Timeplus is a real-time streaming data analytics platform, it provides analytics functionality combined with both real-time streaming data and historical batching data. You can take it as a combination of streaming processing (such as Apache Flink) + real-time OLAP (such as Apache Druid).
Timeplus is a great tool that can be used for engagement metrics analysis due to:
Timeplus offers query analytics capabilities based on SQL, making it user-friendly for those already proficient in SQL.
Timeplus delivers ultra-low latency real-time queries, instantly delivering analytical results to users as events occur.
Timeplus facilitates the extraction of query time information, eliminating the need for traditional ETL processes. Users can swiftly create analytics by combining diverse data sources.
How to get started using Timeplus with Livepeer's Engagement Data
Analyzing Livepeer engagement metrics on Timeplus is super easy. Based on the Timeplus terraform provider, users can create the whole analytic solution with a few simple commands.
Here is the process assuming you have already had both a Livepeer studio account and Timeplus workspace created.
Create your Livepeer API Key https://docs.livepeer.org/guides/developing/quickstart
Create your Timeplus API Key https://docs.timeplus.com/apikey
Download the Livepeer terraform resource definition from https://github.com/timeplus-io/livepeer-source/blob/main/stacks/main.tf to your local directory
Open a terminal from the directory and add following environments
export TF_VAR_timeplus_apikey=your_timeplus_api_key export TF_VAR_timeplus_workspace=your_timeplus_workspace_id export TF_VAR_timeplus_endpoint=timeplus_cloud_endpoint export TF_VAR_livepeer_apikey=your_livepeer_apikey
6. Deploy the resources to Timeplus Cloud by run the following commands
terraform init terraform apply
This resource template will help users to create following resources in Timeplus:
A stream of engagement metrics with name livepeer_viewership_metrics_kv
A Livepeer source which will periodically pull data from Livepeer API and store the metrics data into the defined stream
A user defined function (UDF) that turns the geohash into geo locations with longitude and latitude.
A dashboard that contains the following:
Hourly Views and Watch Time
Engagement by OS
View count by Video (Top 5)
View count by Device Type (Top 5)
Time to First Frame
View By Geo Locations
Here is the dashboard that you will instantly get after the resources being deployed to Timeplus:
In today’s blog, we show how Timeplus can be used to create analytic solutions for Livepeer’s engagement metrics with a few commands. We will show you how to build more advanced video stream data analytics functions based on Timeplus in follow up blogs. Stay tuned!