# Glassdoor Decreases Latency Overhead and Improves Data Monitoring with WhyLabs

DevFeed: [Glassdoor Decreases Latency Overhead and Improves Data Monitoring with WhyLabs](<https://devfeed.tech/articles/glassdoor-decreases-latency-overhead-and-improves-data-monitoring-with-whylabs-22609.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/glassdoor-decreases-latency-overhead-and-improves-data-monitoring-with-whylabs-ad399576624d?source=rss----288d984af747---4>)

Author: Lanqi Fei

Published: 2023-09-06T21:57:52Z

Content type: article

Language: en

Sources: [Glassdoor Engineering](<https://devfeed.tech/sources/glassdoor-engineering.md>)

Topics: [Latency](<https://devfeed.tech/topics/latency.md>), [Logging](<https://devfeed.tech/topics/logging.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [Library](<https://devfeed.tech/topics/library.md>), [async](<https://devfeed.tech/topics/async.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [async](<https://devfeed.tech/tags/async.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [graph](<https://devfeed.tech/tags/graph.md>), [latency](<https://devfeed.tech/tags/latency.md>), [latency-optimization](<https://devfeed.tech/tags/latency-optimization.md>), [library](<https://devfeed.tech/tags/library.md>), [logging](<https://devfeed.tech/tags/logging.md>), [model-monitoring](<https://devfeed.tech/tags/model-monitoring.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [open-source](<https://devfeed.tech/tags/open-source.md>)

## AI overview

This article examines how Glassdoor and WhyLabs addressed latency when integrating data monitoring into a real-time service. It describes changes to whylogs, an open-source data logging library, and compares architectural options including asynchronous calls, DAG-based restructuring, and keeping work in a linear execution path.

## Source excerpt

Authors: Lanqi Fei, Jamie, Natalia This blog was written by Lanqi Fei, Senior ML Scientist at Glassdoor, Jamie Broomall, Senior Software Engineer at WhyLabs, and Natalia Skaczkowska-Drabczyk, Customer Success Data Scientist at WhyLabs. The challenge of integration latency Consider the scenario where we want to integrate a new tool into an existing service that potentially operates in real-time and involves some user interface. We need to make sure that the latency of the service in production is acceptable after the integration, while still keeping the overall maintenance costs low. In this scenario, there are trade-offs to be made and the right choice will depend on the individual characteristics of the service and the newly integrated function. Simplifying this function is a common path to gaining a significant advantage in this optimization game. This blog, written in collaboration between Glassdoor and WhyLabs, describes a real-world instance of an integration latency challenge and gives a detailed walk-through of the changes applied within whylogs (an open-source data logging library maintained by WhyLabs) to mitigate it. What are the best options for reducing latency? There are a couple of options for reducing latency when integrating a new function into an existing service. Restructuring your service or architecture to allow an early response to the caller before doing the additional work (this may be as simple as using an async call pattern with a log statement or as complex as a DAG framework). You can think of your service as a graph -- its nodes should be the latency-critical tasks and ideally those should be executed, instrumented and tested independently. Using a DAG can be a good way of scaling out a service to a large number of new features and integrations while maintaining latency requirements and managing the complexity of the critical path to generating a high quality user response. The downside of this approach is the additional complexity as well