Pi Cluster Documentation
Pi Cluster Documentation

Logging Architecture

EFK vs PLG Stacks

Two different stacks can be deployed as centralized logging solution for the kubernetes cluster:

  • EFK stack (Elasticsearch-Fluentd/Fluentbit-Kibana), where:
    • Elasticsearch is used as logs storage and search engine
    • Fluentd/Fluentbit used for collecting, aggregate and distribute logs
    • Kibana is used as visualization layer.

    This is a mature open-source stack for implementing centralized log management and log analytics capabilities. Since Elasticsearch indexes the whole content of the logs the resources required by the solution in terms of storage and memory are high.

  • PLG stack (Promtail - Loki - Grafana), where:
    • Promtail is used as log collector
    • Loki as log storage/aggregator
    • Grafana as visualization layer.

    Loki is a lightweigh alternative to Elasticsearch providing a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus for Kubernetes environments.

    Loki consumption of resources is lower than ES because it does not index the contents of the logs, it rather indexes a set of labels for each log stream.

In the cluster both stacks can be deployed to deliver complimentary logs-based monitoring (observability) and advance log analytics capabilities.

The logging architecture will have the following components:

  1. Loki as key component of the Observability platform. Loki, managing logs like prometheus metrics, with the same labels, makes possible to join in the same Grafana dashboards metrics (prometheus), logs (Loki) and traces (Tempo) belonging to the same context (pod, application, container). This way Grafana can be used as single plane of glass for monitoring cluster services.

  2. ElasticSearh/Kibana providing advance log analytics capabilities. Loki indexing capabilities are limited to logs labels while ES indexes whole content of the logs. Kibana provides many visualization tools to do analysis on ES indexed data, such as location maps, machine learning for anomaly detection, and graphs to discover relationships in data.

  3. Common architecture for log collection, aggregation and distribution based on Fluentbit/Fluentd. Fluentbit/Fluentd can be used to distribute logs to both logs storage platform (ES and Loki) instead of deploying two separate log collectors (Fluentbit and Promtail).

    Fluentbit/Fluentd selected over Promtail, because it is a general purpose log colletor/distributor, that can be used to ingest logs from different sources (not only kubernetes), parsing and filtering them, and route them to different destinations. Promtail is tailored only to work only with Loki.

The architecture is shown in the following picture

pi-cluster-logging-architecture

Log collection, aggregation and distribution architectures

Two different architectures can be implemented with Fluentbit and Fluentd

Forwarder-only architecture

forwarder-only

This pattern includes having a logging agent, based on fluentbit or fluentd, deployed on edge (forwarder), generally where data is created, such as Kubernetes nodes, virtual machines or baremetal servers. These forwarder agents collect, parse and filter logs from the edge nodes and send data direclty to a backend service.

Advantages

Disadvantages

  • Hard to change configuration across a fleet of agents (E.g., adding another backend or processing)
  • Hard to add more end destinations if needed

Forwarder/Aggregator Architecture

forwarder-aggregator

Similar to the forwarder-only deployment, lightweight logging agent instance is deployed on edge (forwarder) close to data sources (kubernetes nodes, virtual machines or baremetal servers). In this case, these forwarders do minimal processing and then use the forward protocol to send data to a much heavier instance of Fluentd or Fluent Bit (aggregator). This heavier instance may perform more filtering and processing before routing to the appropriate backend(s).

Advantages

  • Less resource utilization on the edge devices (maximize throughput)

  • Allow processing to scale independently on the aggregator tier.

  • Easy to add more backends (configuration change in aggregator vs. all forwarders).

Disadvantages

  • Dedicated resources required for an aggregation instance.

With this architecture, in the aggregation layer, logs can be filtered and routed to different logs backends: Elastisearch and Loki. In the future different backend can be added to do further online processing. For example Kafka can be deployed as backend to build a Data Streaming Analytics architecture (Kafka, Apache Spark, Flink, etc) and route only the logs from a specfic application.

Log Management solution for the Pi Cluster

Forwarder/Aggregator architecture will be deployed in the cluster.

Both fluentbit and fluentd can be deployed as forwarder and/or aggregator.

The differences between fluentbit and fluentd can be found in Fluentbit documentation: “Fluentd & Fluent Bit”.

Main differences are:

  • Memory footprint: Fluentbit is a lightweight version of fluentd (just 640 KB memory)

  • Number of plugins (input, output, filters connectors): Fluentd has more plugins available, but those plugins need to be installed as gem libraries. Fluentbit’s plugins do not need to be installed.

In this deployment fluentbit is installed as forwarder (plugins available are enough for collecting and parsing kubernetes logs and host logs) and fluentd as aggregator to leverage the bigger number of plugins available.

For additional details about all common architecture patterns that can be implemented with Fluentbit and Fluentd see “Common Architecture Patterns with Fluentd and Fluent Bit”.

Logging solution installation procedure

The procedure for deploying logging solution stack is described in the following pages:

  1. Elasticsearch and Kibana installation

  2. Loki installation

  3. Fluentd installation.

  4. Fluent-bit installation .

References

Last Update: Jun 18, 2025

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