Open-Source Databases · apache

spark

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.

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Source: GitHub — github.com/apache/spark

43.6k

GitHub stars

29.3k

Forks

Scala

Primary language

Apache-2.0

License (OSI-approved)

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.

Quickstart

Get the spark source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/apache/spark.git2cd spark3# follow the project's README for install & configuration

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Best use cases

Large-Scale Batch Data Processing

Process multi-terabyte datasets across commodity clusters with fault tolerance and optimized execution plans via Catalyst optimizer.

SQL Analytics and Data Warehousing

Run ANSI SQL queries on structured data at scale; Spark SQL with DataFrames provides performance comparable to specialized data warehouses.

Real-Time Streaming and ML Pipelines

Combine structured streaming with MLlib for continuous feature engineering and model scoring on streaming data at production scale.

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

Security considerations

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.

Software development agency

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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.

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spark FAQ

Is R support still maintained?

R is marked deprecated in the README. New projects should prioritize Scala, Java, or Python; existing SparkR code will continue to function but receive reduced investment.

What is the minimum cluster size for Spark?

Spark can run on a single node (local[*] mode) for development and small workloads, but cluster deployment requires multiple machines. Overhead typically favors clusters with 4+ nodes for production analytics.

Does Spark support exactly-once semantics in streaming?

Structured Streaming offers exactly-once guarantee for stateless operations and idempotent sinks. Stateful operations (aggregations, joins) and transactional sinks require careful design and are documented in the official guide.

How do I choose between RDD, DataFrame, and SQL APIs?

DataFrames and SQL are preferred for structured data and enable Catalyst optimization. Use RDDs only for unstructured data or low-level control; they lack optimizer support and are slower.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If spark is part of your open-source databases roadmap, our team can implement, customize, migrate, and maintain it.

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