Confluent cloud integration

New Relic offers an integration for collecting your Confluent Cloud managed streaming for Apache Kafka data. This document explains how to activate this integration and describes the data that can be reported.

Prerequisites

A New Relic account
An active Confluent Cloud account
A Confluent Cloud API key and secret
MetricsViewer access on the Confluent Cloud account

Activate integration

To enable this integration, go to Integrations & Agents, select Confluent Cloud -> API Polling and follow the instructions.

Important

If you have IP Filtering set up, add the following IP addresses to your filter.

162.247.240.0/22
152.38.128.0/19

For more information about New Relic IP ranges for cloud integration, refer this document. For instructions to perform this task, refer this document.

Configuration and polling

Default polling information for the Confluent Cloud Kafka integration:

New Relic polling interval: 5 minutes
Confluent Cloud data interval: 1 minute

You can change the polling frequency only during the initial configuration.

View and use data

You can query and explore your data using the following event type:

Entity	Data type	Provider
Cluster	`Metric`	`Confluent`

For more on how to use your data, see Understand and use integration data.

Metric data

This integration records Amazon Managed Kafka data for cluster, partition, and topic entities.

Metric	Unit	Description
`cluster_load_percent`	Percent	A measure of the utilization of the cluster. The value is between 0.0 and 1.0. Only dedicated tier clusters has this metric data.
`hot_partition_ingress`	Percent	An indicator of the presence of a hot partition caused by ingress throughput. The value is 1.0 when a hot partition is detected, and empty when there is no hot partition detected.
`hot_partition_egress`	Percent	An indicator of the presence of a hot partition caused by egress throughput. The value is 1.0 when a hot partition is detected, and empty when there is no hot partition detected.
`request_bytes`	Bytes	The delta count of total request bytes from the specified request types sent over the network. Each sample is the number of bytes sent since the previous data point. The count is sampled every 60 seconds.
`response_bytes`	Bytes	The delta count of total response bytes from the specified response types sent over the network. Each sample is the number of bytes sent since the previous data point. The count is sampled every 60 seconds.
`received_bytes`	Bytes	The delta count of bytes of the customer's data received from the network. Each sample is the number of bytes received since the previous data sample. The count is sampled every 60 seconds.
`sent_bytes`	Bytes	The delta count of bytes of the customer's data sent over the network. Each sample is the number of bytes sent since the previous data point. The count is sampled every 60 seconds.
`received_records`	Count	The delta count of records received. Each sample is the number of records received since the previous data sample. The count is sampled every 60 seconds.
`sent_records`	Count	The delta count of records sent. Each sample is the number of records sent since the previous data point. The count is sampled every 60 seconds.
`partition_count`	Count	The number of partitions.
`consumer_lag_offsets`	Milliseconds	The lag between a group member's committed offset and the partition's high watermark.
`successful_authentication_count`	Count	The delta count of successful authentications. Each sample is the number of successful authentications since the previous data point. The count sampled every 60 seconds.
`active_connection_count`	Count	The count of active authenticated connections.

What's next

Data and UI

Learn how to use New Relic to monitor your Kafka clusters

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