Apache YARN
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| Apache YARN | |
|---|---|
| Name | Apache YARN |
| Developer | Apache Software Foundation |
| Released | 2013 |
| Programming language | Java |
| Operating system | Cross-platform |
| Genre | Distributed computing, Resource management |
| License | Apache License 2.0 |
Apache YARN
Apache YARN is a cluster resource management and job scheduling framework used to run distributed applications on large clusters. It originated within the ecosystem of the Apache Hadoop project and serves as the data-processing resource negotiator separating resource management from application logic. YARN is widely adopted in enterprises, research institutions, and cloud providers for scalable data-processing workloads.
Overview
YARN was introduced during an architectural evolution of Hadoop driven by scalability and multi-tenant requirements seen in deployments by organizations such as Yahoo!, Facebook, LinkedIn, Twitter, and Netflix. It decouples resource management from programming models like MapReduce, enabling engines such as Apache Spark, Apache Tez, Apache Flink, Apache Storm, and Presto to run side-by-side. The design responds to constraints observed in large-scale clusters operated by Amazon Web Services, Microsoft Azure, Google Cloud Platform, and research clusters at institutions including University of California, Berkeley, Stanford University, and MIT.
Architecture
YARN’s architecture centers on a modular separation between global resource negotiation and per-application management, influenced by concepts used in systems like Apache Mesos and traditional schedulers used in IBM and Oracle datacenters. It comprises a central master process and distributed agents that interact over RPC and container lifecycle APIs similar to patterns in Kubernetes and Docker. The architecture supports pluggable scheduling and integrates with storage systems such as HDFS, Amazon S3, and Apache HBase, enabling compute engines from projects like Apache Hive, Apache Pig, and Apache Oozie to run efficiently.
Components
Key components include a global master daemon comparable in role to controllers in Kubernetes and schedulers in Apache Mesos, application-specific managers analogous to actors in Akka, and node-level agents resembling kubelets in Kubernetes. These components coordinate with resource allocators informed by cluster metadata services and logging stacks common in deployments with Elasticsearch, Logstash, and Kibana (ELK). The component interactions are designed to support container runtimes influenced by Linux Containers, cgroups, and OS-level isolation techniques found in SELinux-enabled environments.
Resource Management and Scheduling
YARN provides resource accounting and scheduling policies that can be customized through pluggable schedulers, including classes of policies similar to those used by Capacity planning teams at Google and priority schemes inspired by batch systems like Slurm Workload Manager and IBM Spectrum LSF. Schedulers implement fairness, capacity, and FIFO semantics comparable to policies in Hadoop YARN Scheduler alternatives developed by vendors such as Cloudera and Hortonworks. Integrations with cluster monitoring solutions from Prometheus and Nagios enable autoscaling and admission control strategies practiced in cloud operations at Amazon and Microsoft.
Security and Multi-tenancy
Security features align with enterprise deployments seen at Bank of America, eBay, and Adobe Systems, incorporating authentication mechanisms interoperable with Kerberos, authorization models similar to those in Apache Ranger, and encryption practices comparable to standards from NIST. Multi-tenancy is addressed through isolation techniques akin to namespaces in Kubernetes and role-based controls like those in Apache Knox, enabling regulated environments in industries governed by frameworks such as PCI DSS and HIPAA.
Deployment and Operations
Operational patterns for YARN mirror large-scale software practices used by Netflix and Facebook: configuration management with tools like Ansible, Puppet, and Chef; containerized deployments influenced by Docker and orchestration strategies akin to Kubernetes; and CI/CD pipelines integrating Jenkins and GitLab. Running on hardware platforms from vendors such as Dell Technologies, Hewlett Packard Enterprise, and Lenovo, YARN clusters often integrate with monitoring, alerting, and capacity planning systems employed by enterprises like Walmart and Target.
Use Cases and Integrations
YARN is used across batch analytics, interactive SQL, stream processing, and machine learning workloads in organizations including Airbnb, Uber, Spotify, Capital One, and research projects at Lawrence Berkeley National Laboratory. It hosts frameworks such as Apache Spark SQL, Hive LLAP, Flink Streaming, and workflow engines similar to Apache Airflow, enabling pipelines that combine ETL, feature engineering, and model training. Integration patterns include connectors to Apache Kafka, storage adapters for Amazon S3 and Azure Blob Storage, and metadata interoperability with systems like Apache Atlas.