InfluxDB Downsampling to Avoid Burning the Midnight Oil

InfluxDB Downsampling to Avoid Burning the Midnight Oil

 PDT
Online

Go-Jek is a startup that specializes in ride-hailing, logistics and digital payments in Indonesia with recent expansion into Vietnam, Singapore, and Thailand. They use InfluxDB for storing and collecting metrics from systems and applications. They use these infrastructure and business metrics for monitoring and alerting, gathering 55,153 points per second during peak times, all written into an InfluxDB instance. With such a heavy load, they faced the issue of high memory and disk space utilization and instead of scaling the InfluxDB cluster horizontally, they solved the disk space problem by downsampling their metrics data.

In this webinar, Aishwarya and Anugrah from Go-JEK will share how they used downsampling in InfluxDB to solve their disk space problem. They will also share how they used InfluxDB and Grafana to build their monitoring solution—a solution that saved them from downtimes, rising machine costs and countless pages buzzing in the night forcing them to burn the midnight oil to address performance issues. This will also talk about how they automated this solution using Chef and Terraform for all the InfluxDB and Grafana instances.

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