What Is Continuous Verification (CV)?

Updated 10 months ago by Michael Katz

This topic introduces you to Harness' Continuous Verification features, in the following sections:

Visual Overview

In a hurry? Here's a one-minute video summary of how Harness helps you monitor the health of your deployments through a streamlined, comprehensive interface:

Verifying Services

The more often you deploy software, the more you need to validate the health of newly deployed service instances. You need the ability to rapidly detect regressions or anomalies, and to rapidly roll back failed deployments.

You have your choice of state-of-the-art APM (application performance monitoring) and logging software to continually measure your deployment data. But before Harness, you needed to connect your data to these multiple systems, and manually monitor each provider for unusual, post-deployment activity.

Harness' Continuous Verification (CV) approach simplifies verification. First, Harness aggregates monitoring from multiple providers into one dashboard. Second, Harness uses machine learning to identify normal behavior for your applications. This allows Harness to identify and flag anomalies in future deployments, and to perform automatic rollbacks.

APM/Time-Series Data

Application performance monitoring (APM) platforms like AppDynamics continuously measure and aggregate performance metrics across your service's transactions, database calls, third-party API calls, etc. We can mine these metrics to provide an excellent snapshot of the service's current state, and to predict its near-future behavior.

Harness Continuous Verification uses real-time, semi-supervised machine learning to model and predict your service's behavior. We then apply anomaly-detection techniques to the modeled representation, to predict regressions in behavior or performance.

Log Data

Harness Continuous Verification can also consume data from log providers like Sumo Logic and Elastic/ELK. Using semi-supervised machine learning, Harness analyzes and extracts clusters of log messages, based on textual and contextual similarity. This builds a further signature (model) of your service's current state and future behavior.

Using this learned signature—and using real-time comparisons of the current signature to past versions—Harness then predict service anomalies and regressions, starting at deployment time and extending beyond.

Getting Alerts

Harness Continuous Verification enables you to flexibly configure alerts, and alert thresholds, based on Harness' dynamic analysis of both time-series and log data.

Next Up

Next, take a look at:

How did we do?