Apache Aurora
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| Apache Aurora | |
|---|---|
| Name | Apache Aurora |
| Developer | Apache Software Foundation |
| Released | 2013 |
| Programming language | Python, Java |
| Operating system | Cross-platform |
| Status | Archived/Active (specify as appropriate) |
Apache Aurora Apache Aurora is a service scheduler that runs on top of cluster managers to schedule long-running services, cron jobs, and one-off tasks. It was designed to integrate with large-scale cluster environments and to support management of services across datacenter resources provided by major cloud and infrastructure vendors. Aurora emphasizes deterministic scheduling, high availability, and integration with orchestration ecosystems used by prominent technology companies.
Overview
Aurora was created to provide an orchestrated runtime for distributed services deployed on cluster managers such as Apache Mesos, integrating with tooling and platforms from organizations including Twitter, LinkedIn, Dropbox, Airbnb, and Yelp. It exposes abstractions for jobs, tasks, and zones, enabling operators from enterprises like Uber and Pinterest to express desired state and leverage reconciliation mechanisms inspired by projects such as Kubernetes, Chronos (software), and Marathon (software). The project aligns with practices discussed at conferences like Strata Data Conference, KubeCon, and Velocity Conference.
Architecture
Aurora's architecture builds on a master-agent model coordinated with Apache Mesos resource offers, using persistent storage backends such as Zookeeper for leader election and state reconciliation. Components include a scheduler that implements placement policies, an executor for task lifecycle management, and a client API used by automation tools from providers like Ansible, Chef, and Puppet (software). Integration points exist for monitoring systems such as Prometheus, Graphite, and Nagios, and for logging stacks based on Elasticsearch, Logstash, and Kibana. Networking considerations draw upon designs from Calico (software) and service discovery approaches echoed by Consul and etcd.
Features
Aurora supports features aimed at large-scale production environments: fine-grained resource reservations similar to approaches in Mesosphere DC/OS, cron-like scheduling comparable to Unix cron, and rolling updates inspired by patterns used at Google and Facebook. It provides job grouping, constraints for colocating or separating tasks (a concept present in Apache Hadoop rack-awareness), and quota management akin to multi-tenant controls in OpenStack. The scheduler exposes a Thrift API and a command-line client consumed by CI/CD systems like Jenkins and Travis CI, and integrates with artifact repositories such as Artifactory and Nexus Repository Manager.
Deployment and Use Cases
Aurora has been deployed for web services, batch processing, and real-time stream processing stacks built around Apache Storm, Apache Kafka, and Apache Flink. Organizations running user-facing platforms—comparable to deployments at Twitter where similar tooling originated—use Aurora to manage scaling, health checks, and automated recovery across zones similar to those in cloud providers like Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Use cases include continuous delivery pipelines with orchestration by Spinnaker, data pipeline scheduling in ecosystems like Apache Spark and Presto, and hybrid on-premises/cloud setups managed alongside VMware vSphere.
History and Development
Aurora began as an internal system at a large technology company and was later contributed to the Apache Software Foundation where it entered incubator processes and adoption discussions alongside other incubated projects like Apache Spark and Apache Mesos. Key development milestones paralleled community presentations at events such as ApacheCon and technical writeups published by engineering organizations including Twitter Engineering and company engineering blogs from Dropbox and Airbnb. The project evolved through releases that addressed scheduler fairness, multi-cluster deployments, and API stability, influenced by operational learnings from companies such as Yahoo and eBay.
Community and Governance
As an open-source project under the governance practices of the Apache Software Foundation, Aurora's contributor community includes engineers from diverse organizations including Twitter, Uber, and other enterprises that contribute code, documentation, and issue triage. Community interaction occurs on mailing lists, issue trackers comparable to those used by Apache HTTP Server and Hadoop, and at meetups and summits organized similarly to Cloud Native Computing Foundation events. Decision-making follows meritocratic patterns observed in ASF projects like Apache Cassandra and Apache Kafka.
Security and Reliability
Aurora's design addresses reliability through reconciliation loops and leader election mechanisms using Zookeeper and redundancy patterns similar to those used by systems like etcd and Consul. Security considerations involve integration with authentication and authorization systems such as Kerberos, OAuth 2.0, and LDAP directories like those used by Active Directory, along with network isolation practices inspired by iptables-based and software-defined networking solutions. Incident response and postmortem practices reflect industry standards promoted by organizations including US-CERT and conferences like SREcon.