Logstash vs Apache Spark: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare Logstash and Apache Spark across features, pricing, integrations, and community metrics. Logstash / Apache Spark.

Feature

Logstash

Logging

Apache Spark

Data Processing

Side-by-side comparison of developer tools
Server-side data processing pipeline
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 14,928
⭐ 43,909
Contributors
👥 620
👥 3,575
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Java
Scala
Features
  • • Etl Framework
  • • Java
  • • Jruby
  • • Logging
  • • Real Time Processing
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
No integrations listed
No integrations listed
Momentum Score
61/100 (stable)
81/100 (stable)
Community Health
34/100 (needs-attention)
91/100 (excellent)
Maturity Index
49/100 (emerging)
89/100 (mature)
Innovation Score
51/100 (evolving)
91/100 (pioneering)
Risk Score (higher is safer)
53/100 (low)
94/100 (minimal)
Developer Experience
53/100 (needs-improvement)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Logstash Strengths

Apache Spark Strengths

  • ✓ More popular (43,909 stars)
  • ✓ Larger community (3,575 contributors)

When to Use Logstash vs Apache Spark

Use Logstash when its strengths align better with your stack and team needs, and choose Apache Spark when its ecosystem, integrations, or cost profile is a better fit.

Related comparisons

Logstash vs Vector → Apache Spark vs Apache Flink → Logstash vs Fluentd → Apache Spark vs Apache Beam → Logstash vs Fluent Bit → Apache Spark vs Elasticsearch → Logstash vs Elasticsearch → Apache Spark vs Grafana → Logstash vs Grafana → Apache Spark vs Redis → Logstash vs Redis → Apache Spark vs Prometheus →

More Comparisons

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Data source: GitHub API

Last updated: 8/30/2026