Understanding Apache Mesos - A Comprehensive Guide for Data Engineers
If you are a data engineer, you might have come across the term "Apache Mesos". Apache Mesos is a distributed systems kernel that abstracts computing resources away from machines, enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.
In this comprehensive guide, we will cover everything you need to know about Apache Mesos, including its fundamental concepts, architecture, and usage.
Table of Contents
- Introduction to Apache Mesos
- Apache Mesos Fundamental Concepts
- Resource Allocation
- Failover and Failback
- Fine-grained Sharing
- Multi-Framework Support
- Apache Mesos Architecture
- Using Apache Mesos in Data Engineering
- Running Apache Spark on Apache Mesos
- Running Apache Hadoop on Apache Mesos
- Conclusion
- Category: Distributed System
Introduction to Apache Mesos
Apache Mesos is a distributed systems kernel designed for the efficient execution of distributed applications. It provides efficient resource isolation, sharing, and abstraction across cluster applications or frameworks. It allows the system to optimize the usage of physical systems' resources, including CPU, memory, and storage, by running multiple workloads in the same cluster.
Apache Mesos Fundamental Concepts
Resource Allocation
Apache Mesos enables various distributed applications to share resources seamlessly. Apache Mesos provides the resources to various applications using fine-grained controls that enable the application to request and receive only the necessary resources. A resource may involve CPUs, memory, disks, etc. Apache Mesos leverages the cgroups Linux kernel feature to limit resources.
Failover and Failback
Failover refers to the ability to transfer tasks, jobs, or requests from one resource to another without a noticeable interruption to service. Apache Mesos enables this by monitoring resources for failures and automatic recovery or retrieval of tasks. In addition, Apache Mesos provides a fail-safe mechanism that ensures that scheduling rules are not lost and that minimal services are affected.
Fine-grained Sharing
Apache Mesos offers fine-grained sharing of resources across different distributed applications or workloads to help reduce cluster costs. Consolidating several workloads or applications into a single cluster ensures that the underlying physical resources are underutilized. Apache Mesos provides a way to allocate and control the transportation of the physical resources to save costs without compromising quality.
Multi-Framework Support
Apache Mesos supports several frameworks to enable efficient provisioning of resources. These frameworks include Apache Spark, Apache Hadoop, Docker, Kubernetes, Elasticsearch, and many more.
Apache Mesos Architecture
Apache Mesos is designed to be distributed, fault-tolerant, and scalable. Apache Mesos relies on the following major components:
- Master
- Slave
- Framework
Master
The Master is the primary coordination point in the Apache Mesos architecture. It is responsible for making scheduling decisions and resource allocation. The Master runs the resource allocation algorithm, and the Slave manages the execution of tasks on the host. The Master receives resource offers from Slaves in the form of Offer Status Updates.
Slave
The Slaves are the workhorses of the Apache Mesos architecture that enable the execution of tasks in a cluster. The Slave daemon running on the host manages the execution of tasks or workloads. The Slave daemon receives offers from the Master about the resources that the host has to offer.
Framework
The framework defines the workload, scheduling mechanism, and execution policy for tasks. The framework communicates with the Master to acquire resources and schedule tasks.
Using Apache Mesos in Data Engineering
Data engineers can use Apache Mesos to deploy data processing clusters and run popular data processing frameworks like Apache Spark, Apache Hadoop, and more.
Running Apache Spark on Apache Mesos
Apache Spark is widely used for data processing and can be run on Apache Mesos. Running Apache Spark on Apache Mesos offers a few benefits, including sharing resources between Spark and other applications that use the Mesos cluster. Also, developers can specify the resources allocated to a specific Spark application, and it can scale elastically with little or no intervention.
Running Apache Hadoop on Apache Mesos
Apache Hadoop is a popular data processing framework that can be run on Apache Mesos to enable efficient resource allocation and sharing. Running Hadoop on Mesos offers improved resource utilization, simplified resource management, and a simplified dependency on a single resource manager.
Conclusion
In conclusion, Apache Mesos is an efficient distributed systems kernel that abstracts computing resources away from machines, enabling fault-tolerant and elastic distributed systems to easily be built and run effectively. It provides an ideal solution for running data processing frameworks like Apache Spark, Apache Hadoop, and more.