rtcollector is an open-source observability agent designed specifically for RedisTimeSeries. Its modular architecture allows users to collect metrics from various sources using plugins, and directly ingest them into RedisTimeSeries. It aims to be a lightweight and efficient solution, leveraging the speed and capabilities of RedisTimeSeries for metric storage and analysis. The project supports collecting metrics from system resources, Prometheus exporters, and custom applications, offering a flexible way to consolidate and monitor time series data.
The project rtcollector, hosted on GitHub, introduces a novel approach to observability by leveraging RedisTimeSeries as its primary backend. It's designed as a modular agent, offering flexibility and extensibility for collecting, processing, and storing various metrics. The core functionality revolves around ingesting metrics from diverse sources, transforming them as needed, and persisting them within RedisTimeSeries for efficient querying and analysis.
This agent distinguishes itself by being specifically tailored for RedisTimeSeries, allowing it to capitalize on the database's inherent capabilities for handling time-series data. Instead of relying on traditional monitoring systems or complex agents, rtcollector streamlines the process by directly integrating with RedisTimeSeries. This potentially simplifies deployment and reduces the overhead associated with managing multiple components.
The modular architecture of rtcollector facilitates customization and adaptation to different monitoring needs. Users can select and configure specific modules, referred to as "collectors," to gather metrics from various sources, such as system performance counters, application logs, or external APIs. This modularity promotes a focused approach, where only the necessary components are activated, thereby minimizing resource consumption and improving overall efficiency.
Furthermore, rtcollector incorporates processing capabilities, enabling users to transform and enrich collected metrics before storage. This might include aggregations, calculations, or applying custom logic to derive more meaningful insights from raw data. By performing these transformations within the agent, rtcollector optimizes the data flow and reduces the burden on downstream analysis tools.
The project emphasizes simplicity and ease of use, aiming to provide a straightforward method for collecting and storing time-series data within RedisTimeSeries. Its native integration with the database, combined with the modular design and processing features, makes it a potentially valuable tool for building observability solutions. The project leverages the Go programming language, contributing to its performance and portability.
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https://news.ycombinator.com/item?id=44066120
Hacker News users discussed rtcollector's niche appeal, questioning its advantages over existing solutions like Prometheus. Some commenters appreciated its simplicity and ease of use, especially for smaller projects or those already invested in RedisTimeSeries. Concerns were raised about the potential performance implications of using Lua scripting within Redis, and the lack of features like service discovery. The project's modularity and potential for customization were seen as positives, though some doubted the necessity of a dedicated agent for this purpose. Overall, the reaction was mixed, with some interest but also skepticism about its broader applicability and long-term viability.
The Hacker News post titled "Show HN: rtcollector - A modular, RedisTimeSeries-native observability agent" generated several comments discussing various aspects of the project.
One commenter questioned the necessity of another agent, expressing skepticism about the value proposition over established solutions like Prometheus. They specifically mentioned Prometheus' service discovery, alerting, and visualization capabilities, wondering how rtcollector compares in these areas.
Another commenter pointed out the potential benefit of using RedisTimeSeries for reduced cardinality issues compared to other time-series databases like Prometheus. They highlighted how high cardinality can lead to performance problems and increased storage costs, suggesting that rtcollector, by leveraging RedisTimeSeries, might offer a solution to these challenges.
A subsequent comment built upon this by noting that the project's modular design could be appealing. The ability to collect metrics from various sources and consolidate them within RedisTimeSeries was seen as a potential strength. However, the same commenter also echoed the earlier sentiment about the need for a clear comparison with existing solutions to better understand rtcollector's niche.
Another user expressed interest in the project specifically for its RedisTimeSeries integration, mentioning their existing use of Redis and the desire to avoid adding another dependency like Prometheus. They saw rtcollector as a potentially convenient way to leverage their current infrastructure for metrics collection.
One comment touched upon the potential advantages of a push-based system like rtcollector compared to Prometheus' pull-based approach. They suggested that push-based systems can be more efficient in certain scenarios, although they didn't elaborate further on the specific use cases where this advantage would be most pronounced.
Finally, a commenter raised the point that many existing Redis monitoring tools offer similar functionality. They questioned the uniqueness of rtcollector and suggested that the project author should clarify what distinguishes it from these existing tools. This reinforced the recurring theme of needing a clearer differentiation from the established landscape of monitoring solutions.