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Apache Spark is a distributed computing framework for processing large datasets across clusters. It provides APIs in Scala, Java, Python, and R, with specialized tools for SQL queries, machine learning, graph processing, and real-time streaming.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | apache/spark |
| Owner | apache |
| Primary language | Scala |
| License | Apache-2.0 — OSI-approved |
| Stars | 43.6k |
| Forks | 29.3k |
| Open issues | 444 |
| Latest release | Unknown |
| Last updated | 2026-07-08 |
| Source | https://github.com/apache/spark |
What spark is
Spark offers a unified engine for batch and streaming workloads with RDD, DataFrame, and SQL abstractions. It supports multiple language bindings, integrates with Hadoop ecosystems, and includes MLlib, GraphX, and Structured Streaming for domain-specific computation graphs.
Get the spark source
Clone the repository and explore it locally.
git clone https://github.com/apache/spark.gitcd spark# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Java 17+ required; ensure JVM resource allocation and tuning (memory, GC, executor parallelism) is planned from the start.
- PySpark adds serialization overhead; CPU-bound Python logic may require Scala/Java implementation for acceptable performance.
- Cluster size and storage backend (HDFS, S3, GCS) must be pre-selected; Spark does not abstract these architectural decisions.
- R support is marked deprecated in README; prioritize Scala, Java, or Python for new projects.
- Structured Streaming API differs semantically from batch DataFrames; stateful operations and exactly-once guarantees require careful design.
When to avoid it — and what to weigh
- Sub-Millisecond Latency Requirements — Spark has task scheduling and serialization overhead; not suitable for applications requiring single-digit millisecond response times.
- Simple Single-Machine Workloads — Spark's cluster overhead and distributed coordination make it inefficient for datasets and queries that fit comfortably in a single server.
- Highly Specialized Graph or ML Algorithms — Domain-specific tools (GraphQL engines, TensorFlow, PyTorch) often outperform GraphX and MLlib for advanced use cases.
- Limited Operational Infrastructure — Requires mature DevOps, cluster management (Kubernetes, YARN, Mesos), and monitoring expertise; not plug-and-play for small teams.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license.
Apache 2.0 permits commercial use, modification, and distribution with proper license attribution and liability disclaimers. No explicit patent grant or protection clause beyond standard Apache 2.0 terms; review Apache Software Foundation legal documentation for comprehensive clarity on your use case.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
Spark does not encrypt data in motion or at rest by default; configure TLS for RPC, storage encryption at the backend, and Kerberos/LDAP for cluster access control. Review cluster network isolation, executor sandbox limitations, and dependency vulnerability scanning (Maven Central). Serialized Python pickles from untrusted sources pose code injection risk.
Alternatives to consider
Presto / Trino
Fast distributed SQL query engine for multi-source analytics; lower latency and simpler operational model than Spark for SQL-only workloads.
Flink
Unified batch and stream processing with lower latency, native state management, and stronger exactly-once guarantees; steeper learning curve.
DuckDB
In-process OLAP engine for analytical queries on local or remote data; eliminates cluster complexity for sub-terabyte datasets and development workflows.
Build on spark with DEV.co software developers
Spark demands infrastructure expertise and cluster design. Devco's DevOps and Cloud Deployment teams can architect your cluster, configure production operations, and optimize performance for your workload.
Talk to DEV.coRelated on DEV.co
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spark FAQ
Is R support still maintained?
What is the minimum cluster size for Spark?
Does Spark support exactly-once semantics in streaming?
How do I choose between RDD, DataFrame, and SQL APIs?
Software developers & web developers for hire
spark rarely ships on its own. As a software development company, DEV.co handles the surrounding work — data modeling, service integration, deployment, and the open-source databases glue code that production actually requires.
Ready to Deploy Spark at Scale?
Spark demands infrastructure expertise and cluster design. Devco's DevOps and Cloud Deployment teams can architect your cluster, configure production operations, and optimize performance for your workload.