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Apache Spark
Fast, unified engine for large-scale data analytics
Apache Spark delivers a fast, unified analytics engine supporting Scala, Java, Python, and R, with built-in libraries for SQL, machine learning, graph processing, and streaming at scale.
Overview
Overview
Apache Spark is a unified analytics engine designed for large‑scale data processing. It offers high‑level APIs in Scala, Java, Python, and a deprecated R interface, enabling developers and data scientists to write applications in their language of choice. Built‑in libraries such as Spark SQL, MLlib, GraphX, and Structured Streaming extend the core engine to cover batch queries, machine‑learning pipelines, graph analytics, and real‑time stream processing.
Deployment
Spark can run locally for development, on standalone clusters, or be managed by resource managers like YARN, Mesos, and Kubernetes. Integration with Hadoop storage systems allows seamless access to HDFS, Hive, and other compatible data sources. Users start interactive sessions via spark-shell (Scala) or pyspark (Python) and submit jobs with spark-submit or the example runner.
Ecosystem
The project provides extensive documentation, a vibrant community, and a flexible build system based on Apache Maven, making it suitable for a wide range of big‑data workloads.
Highlights
Unified engine with batch and streaming support
APIs for Scala, Java, Python, and (deprecated) R
Built-in libraries: Spark SQL, MLlib, GraphX, Structured Streaming
Runs on local, YARN, Mesos, Kubernetes, and standalone clusters
Pros
- High performance in-memory computation
- Extensive language and library ecosystem
- Seamless integration with Hadoop storage
- Active open-source community and documentation
Considerations
- Heavy JVM memory footprint
- Steep learning curve for cluster concepts
- Requires careful tuning for optimal resource use
- Limited support for sub-millisecond latency workloads
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Fit guide
Great for
- Large-scale batch ETL pipelines
- Interactive data science notebooks
- Real-time stream processing applications
- Machine-learning model training on big data
Not ideal when
- Tiny datasets that fit in a single machine
- Ultra-low latency transaction processing
- Environments without Java runtime
- Simple SQL reporting where a lightweight engine suffices
How teams use it
Nightly data warehouse ETL
Processes terabytes of raw logs into curated tables within minutes.
Ad-hoc analytics with PySpark
Data scientists explore large datasets in Jupyter notebooks using familiar pandas syntax.
Fraud detection streaming pipeline
Ingests transaction streams, applies ML models, and alerts in near-real time.
Social network graph analysis
Computes PageRank and community detection on billions of edges using GraphX.
Tech snapshot
Scala67%
Python17%
Java7%
Jupyter Notebook5%
HiveQL2%
R1%
Frequently asked questions
How do I launch an interactive Spark session?
Use ./bin/spark-shell for Scala or ./bin/pyspark for Python; both connect to a local or configured cluster.
Which programming languages are officially supported?
Scala, Java, Python, and (deprecated) R APIs are provided out of the box.
Can Spark run on existing Hadoop clusters?
Yes, Spark integrates with Hadoop storage and can be launched on YARN, Mesos, or Kubernetes alongside Hadoop services.
How do I submit a job to a cluster?
Use ./bin/run-example or spark-submit with the MASTER environment variable set to spark://host:port, yarn, or local[N].
Project at a glance
Active- Stars
- 42,673
- Watchers
- 42,673
- Forks
- 29,014
LicenseApache-2.0
Repo age11 years old
Last commit7 hours ago
Primary languageScala
Last synced 2 hours ago