What Drives iLogtail Adoption? Insights from a Two‑Year Community Survey

Survey Overview

The two‑year anniversary of iLogtail, Alibaba Cloud’s open‑source observability data collector, was marked by a community usage survey. The survey collected quantitative data on deployment scale, environment, plugin usage, configuration management, and community participation.

Key Adoption Metrics

51.85% of respondents run the open‑source iLogtail in gray‑scale or large‑scale production; 22.22% operate it on more than 1,000 machines.

85.19% deploy iLogtail in containerized environments; of these, 81.48% use Kubernetes and 18.52% use Docker.

66.67% express willingness to contribute to the project; among them, 40% have already contributed. 85% say better documentation would help, and 55% request timely updates on development needs.

40% provided contact information and are open to sharing use‑case articles or presenting at developer meetings.

Deployment Characteristics

All respondents run iLogtail on Linux; a minority also deploy on Windows.

In Kubernetes clusters, the dominant deployment pattern is a DaemonSet, ensuring one iLogtail instance per node.

Production usage is prevalent, with 22.22% of deployments exceeding 1,000 hosts.

Plugin Usage Distribution

Respondents reported the most frequently used plugins in three categories:

Input plugins

File log collection ( file_log/input_file) – 70.37%

Container stdout ( service_docker_stdout/input_container_stdio) – 48.15%

Kafka consumer ( service_kafka) – 48.15%

Processor plugins

JSON parsing ( processor_json) – 59.26%

Regex parsing ( processor_regex) – 59.26%

Add fields ( processor_add_fields) – 44.44%

Flusher plugins

Kafka output ( flusher_kafka/flusher_kafka_v2) – 74.07%

Stdout ( flusher_stdout) – 25.93%

Elasticsearch output ( flusher_elasticsearch) – 25.93%

The data indicate that file and container‑stdout inputs remain foundational, while Kafka has become the primary data‑streaming backbone. JSON and regex processors are the most common parsing mechanisms, and Kafka is the dominant destination for processed logs.

ConfigServer Adoption

76.67% of respondents have never used ConfigServer, the community‑provided configuration management tool.

Among non‑users, 65.21% were unaware of ConfigServer, 21.74% found its features mismatched to their needs, and 13.04% had no configuration‑management requirement.

Among users, 57.14% performed custom modifications. The most requested improvements are richer functionality, an improved UI, and stronger configuration control. An upgraded version is planned for the summer.

Community Participation

Two‑thirds of respondents are willing to contribute to iLogtail.

Primary barriers for non‑contributors are lack of knowledge on how to develop (75%) and unclear development direction (33.33%).

Planned actions include expanding documentation, providing clearer development guides, and adding practical code examples to lower the entry threshold.

Roadmap Highlights

External communication

Launch a new website for community updates and requirements.

Continuously improve documentation and add development samples.

Plugin ecosystem

Focus on high‑interest features and release C++ implementations for high‑frequency plugins.

Configuration management

Provide out‑of‑the‑box releases, enhance the front‑end UI, and add monitoring and alerting capabilities.

The project will be rebranded as LoongCollector, aiming to strengthen its role in the observability space through technical innovation and deeper community co‑creation.