Spark (software)
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| Spark (software) | |
|---|---|
| Name | Spark |
| Developer | Apache Software Foundation |
| Released | 2014 (incubation 2013) |
| Programming language | Scala, Java, Python |
| Operating system | Cross-platform |
| Genre | Cluster computing |
| License | Apache License 2.0 |
Spark (software) is an open-source cluster-computing framework designed for distributed data processing and analytics, developed under the auspices of the Apache Software Foundation, with core contributions from institutions such as UC Berkeley's AMPLab, corporate contributors like Databricks, and organizations including Intel and Yahoo!. The project emphasizes in-memory computation, fault tolerance, and extensible libraries for processing workloads common to Hadoop, Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Spark integrates with ecosystem projects including HDFS, YARN, Mesos, and Kubernetes while supporting languages associated with Scala (programming language), Java (programming language), Python (programming language), and R (programming language).
Overview
Spark provides a unified analytics engine that supports batch processing, stream processing, machine learning, and graph computation; its core abstractions and libraries are designed to interoperate with systems such as HDFS, Apache Hive, Apache HBase, Cassandra (database), and Amazon S3. The architecture centers on resilient distributed datasets and directed acyclic graph scheduling concepts influenced by research from UC Berkeley AMPLab, with runtime characteristics suitable for workloads found in Facebook, Netflix, Airbnb, and Uber. Spark's libraries—Spark SQL, MLlib, GraphX, and Spark Streaming—align with analytical needs across enterprises, research labs like Lawrence Berkeley National Laboratory, and cloud providers such as Google Cloud Platform and Microsoft Azure.
History and Development
Spark originated from research at UC Berkeley AMPLab led by contributors affiliated with institutions including MIT, Stanford University, and companies like Cloudera and Databricks; it entered the Apache Software Foundation incubator before becoming a top-level project alongside other projects such as Hadoop, Hive, and Mahout. Milestones include academic publications presented at venues like NSDI and SIGMOD, major releases influenced by contributors from Intel and Yahoo!, and commercial adoption accelerated by entities such as Twitter, eBay, and Spotify. Governance evolved under the Apache Software Foundation's meritocratic model, with release engineering, community conferences such as Strata Data Conference, and ecosystem initiatives coordinated by companies including Databricks and foundations like Linux Foundation.
Architecture and Components
The core runtime implements a driver and executor model with a scheduler that builds directed acyclic graphs (DAGs), a lineage-based fault tolerance mechanism inspired by RDDs, and a memory manager influenced by work from UC Berkeley AMPLab and contributors from Intel. Storage and data source connectors integrate with HDFS, Amazon S3, Apache Parquet, Apache ORC, Apache Kafka, and databases such as Cassandra (database) and HBase. Libraries include Spark SQL for structured queries interoperable with Apache Hive metastore and ANSI SQL, MLlib for algorithms used by teams at Netflix and Uber, GraphX for graph-parallel computation used by research groups at Stanford University and MIT, and Spark Streaming/Structured Streaming for integration with Apache Kafka and Apache Flume.
Programming Interfaces and APIs
APIs are provided for languages associated with projects like Scala (programming language), Java (programming language), Python (programming language), and R (programming language), and include high-level abstractions such as DataFrame and Dataset influenced by Pandas and R DataFrame semantics. SQL interfaces interoperate with tools such as Apache Hive, Presto, and Impala, while machine learning pipelines align with standards used by libraries like scikit-learn and TensorFlow. Connectors and I/O APIs support formats and systems including Apache Avro, Apache Parquet, ORC, JDBC, and cloud storage from Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Deployment and Ecosystem Integrations
Spark can be deployed on resource managers and platforms including Hadoop YARN, Apache Mesos, Kubernetes, and standalone cluster modes used by organizations such as Databricks and Cloudera. Integrations include notebook interfaces like Jupyter Notebook and Zeppelin, workflow schedulers such as Apache Airflow and Oozie, and monitoring/observability stacks using Prometheus, Grafana, and Elasticsearch. Commercial distributions and managed services are offered by vendors including Databricks, Cloudera, Hortonworks (merged into Cloudera), and cloud providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
Performance and Scalability
Performance optimizations include in-memory caching, whole-stage code generation, Tungsten project improvements, and query planning via cost-based optimizers influenced by Catalyst and research from UC Berkeley. Scalability targets deployment at petabyte scale with production use cases at companies like Facebook, Alibaba Group, Twitter, and Netflix; benchmark studies often compare Spark to Hadoop MapReduce, Presto, and Flink (software). Fault tolerance relies on lineage and checkpointing strategies adopted by teams at Uber and Airbnb, while resource isolation and elasticity are managed through integrations with Kubernetes, YARN, and Mesos.
Use Cases and Adoption
Common use cases include ETL pipelines for enterprises such as Walmart, interactive analytics for platforms like Facebook and LinkedIn, recommendation systems deployed by Netflix and Spotify, real-time event processing for Uber and Lyft, and scientific computing in labs like Lawrence Berkeley National Laboratory and CERN. Adoption spans sectors including finance firms such as Goldman Sachs, healthcare organizations like Johns Hopkins University, and retailers including Amazon (company) and eBay, with training and certification programs provided by companies such as Databricks and community initiatives associated with Apache Software Foundation.