Log Processor Overview

This document provides an overview of the Log Processor, a Go-based service that continuously consumes, processes, and structures logs from microservices. It is designed to work closely with Kafka (as a log stream), TimeSeries/relational databases (for structured data storage), and optionally Neo4j (for visualizing microservice dependencies).

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Table of Contents

1. Purpose
2. High-Level Architecture
3. Key Components
4. Data Flow
5. Typical Use Cases
6. Pitfalls and Recommendations
7. Extensibility
8. Contributing

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Purpose

The Log Processor’s primary goals:

• Ingest and store raw logs from Kafka topics (published by microservices).
• Reconstruct complete request flows (traces) by correlating data with trace_id and span_id.
• Convert raw data into a structured format, enabling easy querying, analytics, and continuous updates to runtime models.
• Periodically update a service dependency graph (if desired, in a graph database like Neo4j).

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High-Level Architecture

1. Kafka Consumer

   • Reads log messages published by microservices to Kafka.
   • Each message typically includes:
     • trace_id / span_id
     • Source service, destination service
     • Request/response details
     • Timestamps
     • Possible parent–child relationships for hierarchical call chains

2. Database Storage

   • A time-series or relational database is used for:
     • Raw Logs: storing individual log records as they arrive.
     • Structured Logs: storing aggregated records (grouped by trace_id to rebuild full request flows).

3. Dependency Graph (Optional)

   • A scheduler periodically pulls processed data from the database and updates a dependency graph, capturing:
     • Service relationships (which service calls which)
     • Call metrics (call frequency, average latency, error counts)

4. Dashboard / Analytics

   • Analytical dashboards (e.g., Grafana) or custom UIs can query the Log Processor’s outputs for real-time observability.

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Key Components

1. Kafka Consumer

• Language: Go
• Responsibility:
  • Subscribes to one or more Kafka topics (e.g. microservice-logs-topic).
  • Batches or streams log entries to the Log Processor’s core logic.

2. Processing / Enrichment

• Description:
  • Groups log entries by trace_id.
  • Reconstructs call hierarchies using parent–child span_id.
  • Performs transformations or adds metadata (e.g. user sessions, environment markers).

3. Time-Series / Relational Database

• Purpose:
  • Holds both raw and enriched log data for querying.
  • Typically includes tables for raw_logs and structured_logs, keyed by trace_id.

4. Dependency Graph Updater

• Purpose:
  • Reads from structured_logs in small batches (e.g., every few seconds/minutes).
  • Inserts or updates edges in a graph database to reflect new inter-service calls or changes in latencies.

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Data Flow

1. Microservice -> Kafka
   Each microservice logs events to Kafka, tagging each entry with a trace_id and span_id for correlation.

2. Kafka -> Raw Logs Table
   The Log Processor consumes messages from Kafka and writes them to a raw_logs table. This ensures minimal overhead during ingestion.

3. Log Processor -> Structured Logs
   After correlation and trace reconstruction, the processor writes aggregated data (per trace) to structured_logs. This can include total duration, involved services, and error info.

4. Structured Logs -> Dependency Graph
   The processor (or a background job) uses the aggregated data to update a dependency graph, capturing service-to-service calls and associated metrics.

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Typical Use Cases

• Real-Time Monitoring
  Continuously track call frequency, average/95th percentile latency, error rates, and feed data into dashboards or alerts.

• Distributed Tracing
  Reconstruct full call flows from trace_id and span_id, enabling quick identification of bottlenecks or anomalies.

• Architecture Insights
  Maintain an always-fresh overview of how services interact at runtime. Detect cyclical dependencies, newly introduced services, or changes in network topologies.

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Pitfalls and Recommendations

1. High-Volume Logging

   • With many services, logs can be massive. Consider partitioning Kafka topics and scaling the Log Processor horizontally.

2. Indexing

   • Ensure critical columns (e.g. trace_id, span_id) are properly indexed in your database to prevent query bottlenecks.

3. Partial Traces

   • Logs may arrive out-of-order or may be missing. Handle incomplete data gracefully (e.g., partial correlation, skipping incomplete spans).

4. Fault Tolerance

   • Use replication for Kafka or database.
   • Log Processor itself should be stateless, allowing easy re-deployment or autoscaling.

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Extensibility

• Custom Enrichment
  Add domain-specific metadata (e.g., attach a user token, geo-location) to each structured log entry.

• Alternative Storage
  Replace or add other data stores (e.g. Elasticsearch, InfluxDB) for specialized indexing or analytics demands.

• Integration with Analytics
  Pipe your structured logs into ML pipelines or anomaly detection services for advanced performance insights.

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Contributing

1. Fork the repository and create a feature branch.
2. Implement and test changes or bug fixes.
3. Open a Pull Request describing your changes.

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