Distributed Machine Learning and Graph Processing

Once data no longer fits on one machine, training models and traversing relationships must be distributed too. Big data analytics frameworks provide scalable versions of familiar algorithms (classification, clustering, recommendation) and of graph processing, the analysis of entities (nodes) and their relationships (edges). For the engines underneath, see Apache Spark and Hadoop and MapReduce.

Machine Learning at Scale

Distributed ML spreads data across a cluster so each node computes partial results (for example gradients or cluster sums) that are combined. It powers predictive analytics, recommendation, fraud and anomaly detection, and customer segmentation.

Library	Built on	Highlights
Apache Mahout	Hadoop MapReduce, later Spark	Clustering (k-means), classification (Naive Bayes), collaborative filtering; largely overtaken by MLlib in practice
Spark MLlib	Apache Spark	Classification, regression, clustering, recommendation (ALS), dimensionality reduction, pipelines; in-memory iteration makes training much faster than disk-based MapReduce

Source: Spark MLlib guide.

MLlib example: logistic regression

from pyspark.sql import SparkSession
from pyspark.ml.classification import LogisticRegression
 
spark = SparkSession.builder.appName("ML Example").getOrCreate()
data = spark.read.format("libsvm").load("sample_libsvm_data.txt")
 
lr = LogisticRegression(maxIter=10)
model = lr.fit(data)
model.summary.predictions.show()

For deep learning, hyperparameter search, and RL on a cluster, see Ray (Ray Train, Tune, RLlib). MLlib’s Pipeline chains feature transformers and an estimator so the same steps run identically in training and inference. For model background see Machine Learning MOC.

Common ML use cases

Fraud detection: flag anomalous transactions.
Healthcare analytics: find disease patterns and predict outcomes.
Customer segmentation: cluster buyers by behavior.
Search relevance: learn ranking signals.

Graph Processing

Graph data models highly connected domains: social networks, financial transactions, supply chains, protein interactions. Two different tool families exist.

Kind	Tools	Role
Graph databases (store and query)	Neo4j, Amazon Neptune, ArangoDB	Native graph storage with fast relationship traversal (Database Types)
Graph computation frameworks (analyze at scale)	Apache Giraph, Spark GraphX	Iterative algorithms over graphs too large for one machine

Neo4j

A native graph database queried with Cypher, a declarative pattern-matching language.

// Accounts that share a phone number with a flagged account
MATCH (flagged:Account {status: "flagged"})-[:HAS_PHONE]->(p:Phone)<-[:HAS_PHONE]-(other:Account)
RETURN other.id, p.number

Typical uses: fraud rings, recommendation engines, knowledge graphs. Source: Neo4j Cypher manual.

Apache Giraph

Distributed graph processing on Hadoop using the Pregel model: computation is vertex-centric and proceeds in supersteps. In each superstep every vertex reads messages, updates its value, and sends messages to neighbors, until all vertices vote to halt. PageRank is the canonical example.

flowchart LR
    S1["Superstep N<br/>vertices receive messages"] --> S2["Compute<br/>update vertex value"]
    S2 --> S3["Send messages<br/>to neighbors"]
    S3 --> S4{"All vertices<br/>halted?"}
    S4 -- "no" --> S1
    S4 -- "yes" --> S5["Done"]

Spark GraphX

A graph library inside Spark that supports graph-parallel computation (PageRank, connected components, triangle counting) alongside DataFrames, SQL, and MLlib in one program. Source: GraphX programming guide.

Graph use cases

Social network analysis: influencers and communities.
Recommendation: products or friends from connection patterns.
Fraud detection: hidden relationships between accounts.
Biological networks: protein-protein interactions and disease links.

Choosing

Use a graph database when you need low-latency queries over live relationship data. Use a graph framework (GraphX, Giraph) for offline, whole-graph analytics. Pair either with the pipelines in Big Data Integration and Orchestration to keep the graph fed.

Explorer