Apache Myriad
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| Apache Myriad | |
|---|---|
| Name | Apache Myriad |
| Developer | Apache Software Foundation |
| Released | 2014 |
| Programming language | Java |
| Operating system | Cross-platform |
| License | Apache License 2.0 |
Apache Myriad Apache Myriad is an open-source project that integrates Apache Hadoop and Apache Mesos to enable co-scheduling of Apache Spark, Apache YARN, and other distributed applications on shared clusters. It acts as a bridge between YARN ResourceManager concepts and Mesos master resource offers, allowing elastic scaling of YARN containers on Mesos-managed nodes for workloads such as Hadoop MapReduce, Spark Streaming, and HBase region servers. The project originated within the Apache Software Foundation ecosystem and was designed to improve resource utilization and operational flexibility in data-center environments managed with Mesos frameworks.
Overview
Myriad provides a framework that maps YARN NodeManager instances to Mesos tasks, enabling cluster operators to run HDFS client workloads alongside native Mesos frameworks such as Chronos (software), Aurora (service scheduler), and Kubernetes. By leveraging Mesos’ two-level scheduling model, Myriad aims to reconcile YARN’s container lifecycle with Mesos resource allocation, supporting heterogeneous workloads including Tez, Hive (data warehouse), and Presto (SQL query engine). The design targets environments operated by organizations similar to Cloudera, Hortonworks, and MapR Technologies that require elastic scaling across multi-tenant clusters.
Architecture
The architecture couples YARN components with Mesos primitives: a Mesos framework scheduler negotiates resource offers from the Mesos slave fleet and launches tasks that host YARN NodeManagers, while a YARN ResourceManager remains the logical scheduler for container placement within the NodeManagers. Core modules interact with ZooKeeper for leader election and coordination, use Apache Thrift or Protocol Buffers for RPC in some integrations, and employ Docker (software) containerization for isolating NodeManager processes on Mesos agents. The system also integrates with monitoring stacks like Prometheus, Grafana, and Nagios and logging tools such as Fluentd and ELK Stack.
Deployment and Configuration
Deployments typically involve installing Mesos components, a YARN ResourceManager and HDFS NameNode, and configuring Myriad as a Mesos framework scheduler with an assigned principal registered in Apache ZooKeeper. Operators tune settings such as task launch constraints, CPU and memory profiles, and containerizer choices (Mesos containerizer versus Docker) through configuration files and environment variables. For production, integration with Systemd, Apache Ambari, or Cloudera Manager can manage lifecycle and observability; security setups often involve Kerberos authentication and TLS certificates issued by internal Certificate Authoritys.
Resource Management and Scheduling
Myriad implements elasticity by dynamically adjusting the number of YARN NodeManagers via Mesos task lifecycle events, enabling bursty workloads to grow and shrink without manual provisioning. It cooperates with YARN schedulers like the Capacity Scheduler and Fair Scheduler while relying on Mesos’ offer-driven allocation model; metrics and policies can be informed by telemetry from Ganglia or Datadog. The framework supports fine-grained CPU shares and memory reservations using Mesos resource primitives, and can integrate with resource-aware placement systems such as Apache Slider or cluster autoscaling solutions used by Amazon EC2 and Google Cloud Platform deployments.
Use Cases and Integrations
Common use cases include running mixed workloads—batch processing with Apache Spark, interactive SQL with Presto (SQL query engine), and long-running services like HBase—on a unified Mesos cluster to maximize utilization. Myriad has been used in hybrid on-premises and cloud scenarios with OpenStack and Apache CloudStack, and in continuous data pipelines combining Apache Kafka with Apache Storm or Flink. Integrations with orchestration and CI/CD tools such as Jenkins, Ansible, and Terraform enable automated testing and scaling workflows in enterprise environments managed by teams similar to those at Netflix, Twitter, and LinkedIn.
History and Development
The project emerged around 2014 from contributors interested in unifying YARN and Mesos scheduling models; initial development involved engineers from organizations active in the Big data space. It was incubated and supported within the Apache Software Foundation community, receiving commits and discussions across repositories and mailing lists. Over time, shifts in industry adoption—such as the rise of Kubernetes and changing vendor strategies at Cloudera and Hortonworks—affected contributor activity and deployment patterns.
Community and Support
Community interaction occurred via Apache Software Foundation mailing lists, issue trackers hosted on project repositories, and discussion at conferences like Strata Data Conference, MesosCon, and ApacheCon. Documentation and user guides were produced by contributors and third-party blogs from companies including Mesosphere and various consultancies. For enterprise support, organizations often relied on commercial vendors or internal SRE teams familiar with Hadoop and Mesos ecosystems.