Metrics Overview

Essential knowledge

Author:

Kirill Gaiduk

Changed on:

19 June 2024

Overview

This document is intended to introduce the Metrics and describe their place within the Fluent Big Inventory product.

Pre-requisites:

You should have knowledge of the Inventory.
You should have knowledge of OMX Workflow Framework.
You should have knowledge of Events.
You should have knowledge of GraphQL.

Key points

Metrics are available for all Fluent Order Management and Fluent Big Inventory customers.
Metrics data is captured, uploaded, and stored in a monitoring system.
Metrics data is retrieved via the Metrics API.
Metrics data can be visualized with the OMX UX Framework, Business intelligence tools, and third-party Metrics analytics platforms.

What are Metrics for?

Metrics data visualization provides visibility of Fluent platform operational data and allows to monitor and observe platform/inventory availability and orchestration insights.

Value Proposition

Metrics enablement brings the necessary tools to:

Improve the visibility and insights of the Fluent platform.
Identify potential problems or anomalies in the Fluent platform before an escalation into critical issues.
Provides an aggregate view of the internal state of the platform, allows the detection of problems quickly, reduces the time spent diagnosing issues, and speeds up the resolution process.

Functional Model

There are two main stages to achieve the objective of improving the visibility/insights of the Fluent Platform:

Metrics capturing
Metrics querying

Note

For more details, check the How Metrics works article.

Features

Metrics enable the following functionality:

Capturing, uploading, and storing Metrics Data

While the Fluent platform and applications process customers' requests, the instrumentation is integral to the Fluent application source code to enhance processing visibility and capture the Metrics. The Fluent Platform source code is instrumented to collect Metrics and upload those Metrics data into the Metrics workspace. The Metrics workspace is then queried to retrieve the Metrics data.

Note

For more details, check the Metrics Managing Flow section described in the How Metrics works article.

Available Metrics Data

The following Metrics data is currently available:

General:
- The number of events received by the Fluent platform.
- The last timestamp of events received by the Fluent platform.
- The number of events received by the orchestration engine (Rubix).
- The amount of time an event stays in the internal queue.
  i.e., the time after Fluent API sends the event and before Rubix receives the event.
- The amount of time the orchestration engine (Rubix) takes to execute an event.
Inventory specific:
- The amounts of all batch items processed, filtered out and considered “changed” by the Batch Pre-Processing job.
- The timestamp for when the given Batch Pre-Processing operation has completed.
- The total number of records exported by the Inventory Feeds job, split by data type.
- The timestamp for when the Inventory Feeds job is completed.

For more details, check the Available Metrics section described in the Metrics usage for Platform Observability article.

Querying Metrics Data

The Metrics monitoring system provides a dimensional data model and flexible query language to select the Metrics data and aggregate the time series data in real-time.

See the Query Syntax section described in the Getting Started with Metrics API article for query details.

Note

For more details, check the Metrics API Querying Flow section described in the How Metrics works article.

Available Metrics Queries

The following Metrics GraphQL queries are created in terms of a Reference solution to retrieve the Metrics data:

```
`metricInstant`
```
GraphQL query is used to query one instant vector, for example:
- The total number of events received by Fluent APIs.
- The total number of events received by orchestration engine (Rubix).
- The total number of failed/successful events processed by orchestration engine (Rubix).
```
`metricRange`
```
GraphQL query is used to query range vector, for example:
- The average time of event execution in orchestration engine (Rubix).
- The average time of event waiting in the queue.

For more details, check the Queries section described in the Getting Started with Metrics API article.

Visualizing Metrics Data

Metrics API query operations provide the necessary Metrics data to display the charts, diagrams, and dashboards.

Customer Metrics data could be visualized by hooking up the Metrics query data with:

1. OMX UX Framework, for example:

Sources Dashboard

Feeds Dashboard

2. Metrics analytics and visualisation platforms. For example:

Grafana

Related content

How Metrics works

Edited 14 days ago

This document is intended to describe to implementers Metrics technical details within the Fluent Big Inventory product.

Pre-requisites:

You should have knowledge of Metrics
You should have knowledge of OMX Workflow Framework.
You should have knowledge of Events.
You should have knowledge of GraphQL.

Metrics usage for Platform Observability

Edited 14 days ago

This document is intended to describe to implementers which Metrics are available within the Fluent Big Inventory product and what kind of data they provide to achieve Platform Observability.

Pre-requisites:

You should have knowledge of How Metrics works.
You should have knowledge of Events.
You should have knowledge of Fluent APIs.

Getting Started with Metrics API

Edited 14 days ago

This guide is intended to provide implementers with information about the Metrics API within the Fluent Big Inventory product, describing its functionalities and utilization principles within the Fluent Platform.

Pre-requisites:

You should have knowledge of How Metrics works.
You should have knowledge of Metrics usage for Platform Observability.
You should have knowledge of GraphQL.
You should configure your User role with
```
`METRICS_VIEW`
```
permission for Metrics data access.

Metrics Overview

Overview

Key points

What are Metrics for?

Value Proposition

Functional Model

Features

Capturing, uploading, and storing Metrics Data

Querying Metrics Data

Visualizing Metrics Data

Related content

How Metrics works

Metrics usage for Platform Observability

Getting Started with Metrics API

Kirill Gaiduk

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