Customer Success Story: PayPal
PayPal needed to find a scalable end-to-end host monitoring solution to replace its old one. As the company was modernizing its infrastructure and transitioning many of its applications to a container-based architecture, the new monitoring solution needed to be designed to work with containers and to provide metrics collection, storage, alerting and visualization all at once since the team’s preference was to select a single-vendor platform.
PayPal chose InfluxData’s InfluxDB Enterprise and leveraged all components of InfluxData’s open source core platform to build a solution using Telegraf aggregators, message queues, and publishers in order to control data payload size, manage message flow, and avoid single point of failure (SPOF).
PayPal was looking for a “Host Monitoring” solution to replace antiquated monitoring systems. The monitoring solution had to be scalable to keep pace with the company’s infrastructure. PayPal has nine data centers, with 30,000 instances each, and they all have their own clusters. The company was migrating all their old applications — some 20 years old and compiled in C++ — into containers and more modern operating systems, with many of their Docker hypervisors hosting 50 to 100 containers at once. PayPal also wanted to meet their technical requirements through one vendor to solve all of their host monitoring problems.
InfluxData’s InfluxDB Enterprise (the Enterprise edition of InfluxDB) provided an end-to-end solution from one vendor as PayPal was seeking. Using the InfluxData platform, PayPal built a resilient monitoring solution that works at scale, and in the process, the Monitoring Ingest/Collectors & Alerting Platforms team at PayPal has developed best practices formulated for scaling InfluxDB Enterprise in their environment.
Dennis Brazil, Sr. Manager, SRE Monitoring Ingest/Collectors & Alerting Platforms at PayPal, presented a talk at InfluxDays San Francisco 2018 titled “Best Practices for Scaling an InfluxDB Enterprise Cluster”. Click below to watch the video or view the presentation.
Ingesting Data Used in Machine Learning
The development team at PayPal uses InfluxDB — InfluxData’s purpose-built time series database — to extract real-time, run-time data from jobs and scheduler objects which are fed through InfluxDB and sent to their Machine Learning (ML) Model for training and to improve run-time predictions.
Senior Software Engineer, Sagar Jain, found that InfluxDB had a number of compelling features including high performance queries written in InfluxQL, a SQL-like query language for interacting with InfluxDB, as well as InfluxDB’s APIs. Sagar found InfluxQL easy to learn and said it took minimal effort to write queries in InfluxDB. The InfluxDB API provides a simple way to interact with the database, which Sagar also found simple and easy to use.