Yeedu Features
The following sections detail the range of unique features provided by Yeedu to help developers manage their Spark jobs efficiently, as well as platform features that enable organizations to optimize their Spark infrastructure and reduce costs.
Job Management Features
Universal CLI
Yeedu offers a customized Command Line Interface (CLI) that allows developers to interact with the platform via the command line. The CLI is available on various operating systems, including Windows, Linux, Mac OS, and others. This makes it easy for developers to create, manage, and monitor jobs on the Yeedu platform using simple and intuitive commands.
Logging
Yeedu provides comprehensive logging functionality that enables developers to monitor and debug their Spark jobs in real-time. The platform offers both standard and custom logging options, giving developers the flexibility to choose the level of detail they need. This helps developers to quickly identify and troubleshoot issues, saving time and reducing downtime.
Dependency Management
Yeedu simplifies dependency management by automatically detecting and installing dependencies for Spark jobs. The platform also provides pre-built dependencies for commonly used libraries, making it easy for developers to get started quickly. This reduces the time and effort required for developers to manage dependencies, allowing them to focus on their core tasks.
Apache History Server
Yeedu provides an Apache History Server that enables developers to view and analyze the performance of their Spark jobs. The history server allows developers to track the progress of their jobs, view detailed job metrics, and identify performance bottlenecks. This helps developers optimize their jobs and improve overall performance.
Cluster Monitoring
Yeedu offers comprehensive cluster monitoring functionality that enables developers to monitor the performance of their Spark clusters in real-time. The platform provides detailed metrics and alerts for CPU usage, memory usage, network traffic, and other key performance indicators. This helps developers optimize their clusters for performance and cost-efficiency.
Overall, these unique features make Yeedu a powerful and user-friendly platform for managing Spark jobs. With its customized CLI, comprehensive logging, automatic dependency management, Apache History Server, and cluster monitoring, Yeedu simplifies job management and optimization, enabling developers to focus on delivering high-quality applications and services.
Platform Features
Autoscaling
Yeedu provides autoscaling functionality that allows organizations to automatically scale their Spark clusters based on demand. This helps organizations optimize their cluster usage and reduce costs by only paying for the resources they need.
Downscaling
Yeedu also offers downscaling functionality that allows organizations to automatically reduce the size of their Spark clusters during periods of low demand. This helps organizations reduce their infrastructure costs by not paying for unused resources.
Auto-Stop / Auto-Start
Yeedu enables organizations to automatically stop and start their Spark clusters based on a schedule or specific conditions. This helps organizations further reduce their infrastructure costs by only paying for the resources they need when they need them.
Multi-Spark Version
Yeedu supports multiple versions of Apache Spark, allowing organizations to run their Spark workloads on the version that best meets their needs. This provides organizations with greater flexibility and control over their Spark infrastructure.
Billing Breakdown
Yeedu provides detailed billing breakdowns that allow organizations to track their infrastructure costs and identify areas where they can optimize their usage. This helps organizations reduce their infrastructure costs and achieve greater cost-efficiency.
CUDA Support
Yeedu supports CUDA, enabling organizations to accelerate their Spark workloads using NVIDIA GPUs. This provides organizations with a powerful and cost-efficient way to process large-scale data and accelerate machine learning workloads.