Sumo Logic OpenTelemetry Collector

OpenTelemetry Collector is our next-generation collector, built on OpenTelemetry, that provides a single unified agent to send Logs, Metrics, Traces, and Metadata for Observability to Sumo Logic. This can help simplify and streamline the process of monitoring and understanding the performance and behavior of complex distributed systems, making it easier to identify and diagnose issues and improve overall system reliability and efficiency.

What makes the Sumo Logic OTel Collector unique is its flexibility and scalability. It can be easily deployed as a containerized application on any cloud platform, and it supports a wide range of data sources, including AWS CloudWatch, Prometheus, and Jaeger. This means that organizations can use the collector to gain deeper visibility into their distributed systems, no matter where they are hosted.

Once the data is collected, the Sumo Logic platform provides powerful analytics capabilities, enabling users to gain insights into their applications and systems, troubleshoot issues, and optimize their operations. With its user-friendly interface and powerful features, the Sumo Logic OTel Collector is an ideal choice for organizations looking to gain a deeper understanding of their distributed systems and improve their overall performance and reliability.

Guides

In this section, we'll introduce the following concepts:

Install Collector on Linux

Step-by-step instructions for installing the OpenTelemetry collector on Linux.

Install Collector on macOS

Step-by-step instructions for installing the OpenTelemetry collector on macOS.

Install Collector on Windows

Step-by-step instructions for installing the OpenTelemetry collector on Windows.

Data Source and Configurations

Learn how to collect logs, metrics, and tracing data, as well as how to add configurations for the OpenTelemetry collector.

Sumo Logic OTel vs OTel

Understand the relationship between the Sumo Logic OpenTelemetry Collector and the OpenTelemetry upstream project.

Troubleshooting and FAQ

Find solutions to common issues and answers to frequently asked questions about the OpenTelemetry collector.

Performance Benchmarks

Logs collection

The following benchmark has been compiled on an Amazon m4.large instance, which has 2 CPU cores and 8 GB of memory available.

It can be used when estimating the required CPU resources for logs collection using filelogreceiver.

CPU usage guidelines

Benchmark - CPU usage for particular average message size and EPS

Measured CPU usage for particular Events Per Second (EPS) average message size.

	100B	512B	1KB	5KB	10KB
EPS	-	-	-	-	-
100	1.14%	1%	1.01%	1.4%	3.78%
200	1.29%	1.4%	1.41%	2.57%	5.36%
500	2.75%	2.71%	2.95%	5.7%	10.68%
1000	4.74%	5.07%	5.32%	11.3%	20.12%
1500	7.08%	7.29%	7.99%	16.93%	27.96%
2000	9.64%	9.56%	10.39%	22.51%	36.59%

Benchmark - EPS for average message size and CPU usage

Events Per Second (EPS) achieved for a particular average message size and CPU usage.

	100B	512B	1KB	5KB	10KB
Average CPU usage	-	-	-	-	-
5%	2000	1100	1000	150	200
10%	3500	2100	1500	450	300
20%	6500	4100	3000	1200	700
50%	14000	10100	8500	-*	-*
90%	-*	19100	-*	-*	-*

^* Cells without a resulting EPS come from the fact that the CPU utilization didn't reach the designated CPU utilization during the benchmark run.

Using the information from the above table, if you had an average CPU usage of 5%:

• 10 KB logs can be ingested at 200 logs/sec (2000 KB/sec).
• 1 KB logs can be ingested at 1000 logs/sec (1000 KB/sec).

This shows that the collector performs better when it is made to ingest bigger log entries (which is expected due to less overhead coming from timestamp parsing, etc.).

Memory usage guidelines

Benchmark - memory usage for particular average message size and EPS

Measured memory usage (in MB) for particular Events Per Second (EPS) average message size.

	100B	512B	1KB	5KB	10KB
EPS	-	-	-	-	-
100	113.14	116.16	117.1	116.99	112.59
200	115.16	118.55	116.8	119.67	127.02
500	118.24	121.79	122.78	127.87	142.73
1000	121.6	126.75	127.94	140.11	106.82
1500	128.54	131.9	137.69	95.21	113.89
2000	130.62	125.27	144.59	98.62	134.61

Fine Tuning Performance

There are a couple configuration options that can help with performance in specific scenarios.

Sumo Logic Exporter

The Sumo Logic Exporter sends data to Sumo Logic.

It has the following features that can help with performance:

• retry_on_failure with its initial_interval, max_interval and max_elapsed_time settings,
• sending_queue with its num_consumers, queue_size settings,
• timeout.

Read more about these features in the Sumo Logic Exporter docs.

Batch Processor

The Batch Processor joins records of each type in batches.

It has the following features that can help with performance:

• send_batch_size,
• send_batch_max_size,
• timeout.

Read more about these features in the Batch Processor docs.

Memory Limiter Processor

The Memory Limiter Processor prevents out-of-memory crashes for the collector process by monitoring the amount of memory used by the collector and forcing it to lower its memory consumption.

Read more about its features in the Memory Limiter Processor docs.