In Part I in our series outlining best practices for scaling observability, we reviewed the data analysis capabilities that can help engineers troubleshoot faster during high pressure situations during a game launch.
Nobody wants lag time or crashes in their game launch. Similarly, no one wants terminated sessions or for your gamer customers to log off and play a competitor’s game.
Collecting and analyzing telemetry data – including logs, metrics, and traces – is critical to gain visibility into your system’s health and performance, and to quickly troubleshoot issues impacting user experience.
In this post, we’ll examine telemetry data management best practices to ensure your team can extract meaningful insights from large volumes of telemetry data while keeping observability costs under control.
When many players access your game at once – increasing the load on your infrastructure – your cloud workloads will generate increasingly large telemetry data volumes that are needed for visibility into production.
Observability is a big data analytics problem. So let’s review some considerations for preparing and storing the data that enables enhanced visibility for your team – all while reducing observability costs that can quickly skyrocket without measures in place.
Data Enrichment and Optimization at Scale
Observability data can be noisy, overwhelming, and confusing. As cloud workloads grow, they generate huge volumes of data – some provide helpful and actionable insights, while other data is useless.
This is especially true of unstructured log data. All too often, engineering teams struggle to search through mountains of log data that don’t contain the insights they need to effectively troubleshoot their environment.
With log parsing, you can structure logs into easy-to-search fields. Encouraging good logging hygiene is an easy thing to suggest. But frankly, if it was easy, everyone would do it. There is a reason why it’s a pervasive problem in the monitoring and observability world: it’s unintuitive! For those less experienced with parsing and filtering data, there are technologies and expertise available to simplify things.
This article can provide some helpful examples to parse your logs with Grok – the most popular log parsing language. Logz.io’s custom parser can also simplify this process. Those who want to outsource log parsing entirely can also use Logz.io’s parsing-as-a-service, where our Customer Support Engineers will parse all your logs for you.
De-noise your data and keep costs under control on game day
We mentioned before that large data volumes mean larger costs. The volume of your data could be highest on the day of your launch – due to high numbers of user sessions – which can increase the amount of telemetry data and associated costs.
But most collected observability data is never actually used. Large volumes of useless data can needlessly drive up costs. Plus, noisy data can obscure the critical insights needed to quickly identify and troubleshoot production issues impacting user sessions.
By filtering out useless information and enriching the data you need, the overall quality of your observability data will improve. Plus, you won’t be paying as much for data you won’t use.
Similarly to enriching your log data, if it was easy, everyone would do it.
Unfortunately, it can be difficult to identify and filter out the data you don’t need. Most observability solutions require you to reconfigure your data collection components – which could be hundreds of separate agents and other technologies. This can be difficult and time-consuming.
Solutions like Logz.io provide a central place to inventory all of your data and filter out everything you don’t need. You can utilize data optimization and filtering tools to reduce noise from your observability environment – reducing costs and ensuring you’re only focusing on the high value data.
These are the kinds of challenges Mediatonic had to overcome as they launched Fall Guys – see this post to learn about their observability journey during this massive event.
If you’re interested in learning more about using Logz.io’s data optimization capabilities to remove noisy data, learn more about our data optimization capabilities.
Or, sign up for a free trial of Logz.io to see how our observability platform can scale to meet your game launch needs.
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