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Log aggregation

As explained in ScyllaDB in Action

Log aggregation is the centralized collection and organization of log entries from multiple systems or nodes.

Log aggregation collects logs from multiple systems or nodes in a centralized store, making them searchable and easier to correlate. With a small ScyllaDB cluster and one application instance, operators can inspect logs manually; as systems grow, centralized aggregation becomes critical. section 11.1.5

A practical monitoring setup can use log aggregation to:

  • Query and correlate log entries from multiple ScyllaDB nodes and application instances. section 11.4
  • Pair logs with application traces to follow a request across services and identify where it spends time. section 11.1.5
  • Use Grafana Loki when adopting the Prometheus and Grafana ecosystem, or select an existing organizational tool. section 11.1.5

Tracing a growing cluster

When an application begins reporting errors, centralized logs let an operator search application and ScyllaDB entries together. Traces can then connect increased latency to a particular endpoint or database partition. section 11.1.5

How ScyllaDB in Action covers it

How deep it goes
Defines the practice and names tool choices, but does not detail collection pipelines, schemas, or configuration.
Examples
Uses Grafana Loki and correlates logs from ScyllaDB nodes with application errors and traces.
What it assumes
Assumes familiarity with application and ScyllaDB logs, while introducing centralized searching and correlation as systems scale.
What you can do after
Diagnose distributed-system incidents by searching and correlating logs across nodes and applications.
How it connects
Connects log aggregation with ScyllaDB, application traces, Prometheus, Grafana, and Alertmanager.

Also explained in

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of ScyllaDB in Action. The section links point to the parts of the book it draws on.