# endymecy/spark-ml-source-analysis

spark ml 算法原理剖析以及具体的源码实现分析

Repository: https://github.com/endymecy/spark-ml-source-analysis
Canonical: https://ross.abutalabs.com/products/spark-ml-source-analysis
Homepage: https://github.com/endymecy/spark-ml-source-analysis
License: Apache-2.0
License Family: permissive
Topics: spark, machine-learning, source-analysis
Last push: 2019-03-25T13:24:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3890, "days_push": 2718, "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 1957, forks 815 (observed 2026-08-28T04:05:59.140356+00:00)

## What it is
A Chinese-language tutorial collection analyzing the principles and source code of Spark MLlib's machine learning algorithms. It covers classification, regression, clustering, dimensionality reduction, feature engineering, and optimization with their distributed implementations.

## Use cases
- understand how spark mllib algorithms are implemented internally
- learn distributed implementations of machine learning algorithms
- study spark ml source code for classification and clustering algorithms
- learn how k-means or ALS work in spark
- prepare for spark machine learning interviews
- understand gradient descent and L-BFGS implementations in spark

## When to choose
- you work with Spark 1.6/2.x MLlib and want deep algorithm and source-level understanding
- you prefer Chinese-language explanations of ML theory plus code walkthroughs
- you want to learn how ML algorithms are distributed across a cluster

## When to avoid
- you need up-to-date coverage of Spark 3.x or the newer spark.ml DataFrame API
- you want runnable production code rather than analysis articles
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, documentation
- domain: machine-learning, big-data, tutorials
- platform: jvm, python
- tags: spark, spark-ml, source-code-analysis, distributed-machine-learning, chinese-language

## Member repositories
- endymecy/spark-ml-source-analysis (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.140356+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 2026-08-30T03:06:01.201976+00:00, confidence not recorded.
  - readme: https://github.com/endymecy/spark-ml-source-analysis (fetched 2026-08-28T04:05:59.140356+00:00, sha 6a1acf2d8098)
  - homepage: https://github.com/endymecy/spark-ml-source-analysis (fetched 2026-08-29T10:46:19.149624+00:00, sha 1ef4adeb7d3e)
- Data as of 2026-08-30T08:39:29.467469+00:00.