# databricks/learning-spark

Example code from Learning Spark book

Repository: https://github.com/databricks/learning-spark
Canonical: https://ross.abutalabs.com/products/learning-spark
Language: Java
License: MIT
License Family: permissive
Last push: 2026-06-30T07:11:37+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 100
- inputs: {"age_days": 4461, "days_push": 64, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3893, forks 2391 (observed 2026-08-28T04:08:28.421400+00:00)

## What it is
Example code accompanying the O'Reilly 'Learning Spark' book, with implementations in Java, Scala, and Python. The examples target Spark 1.3 and demonstrate core Spark APIs and usage patterns.

## Use cases
- learn Apache Spark from book examples
- run sample Spark jobs in Java Scala and Python
- see how to build and submit Spark assemblies with sbt or maven
- follow along with the Learning Spark book exercises
- reference pyspark example scripts

## When to choose
- you are reading the Learning Spark book and want runnable code
- you want simple, small examples for learning Spark fundamentals

## When to avoid
- you need examples for modern Spark versions or Spark SQL/DataFrame APIs
- you want production-ready Spark application templates

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools, data-science, etl
- domain: big-data, tutorials
- platform: jvm, python, cross-platform
- tags: apache-spark, book-examples, scala, java, pyspark, sample-code, data-engineering

## Member repositories
- databricks/learning-spark (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.421400+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:25:36.060345+00:00, confidence not recorded.
  - readme: https://github.com/databricks/learning-spark (fetched 2026-08-28T04:08:28.421400+00:00, sha a433e2e43e99)
- Data as of 2026-08-30T08:39:29.467469+00:00.