Welcome to our AWS Certified Solutions Architect Professional tutorial series. This tutorial will cover two main areas. The first is Amazon EMR design patterns and architectural and operational best practices. The goal here is to demonstrate how to best use EMR and what we’re seeing with our largest and most successful customers. The second is a deep dive on cost optimization and how you can ensure you’re running Amazon EMR in the most cost-efficient way.
Before we get started, let’s provide a quick overview of Amazon EMR. It’s a core component of any data lake that forms a mature foundation for organizations to become data-driven and leverage machine learning to innovate faster. EMR allows you to launch compute clusters and select from 21 different big data frameworks such as Apache Spark, HBase, Hoodie, and more. What sets EMR apart is its decoupling of compute and storage. Unlike on-premises solutions, you can scale each independently and pay for only what you use. This flexibility enables your jobs to operate independently of each other. EMR can also integrate with EC2 Spot Instances, Reserved Instances, and Savings Plans, providing per-second billing, so you only pay for what you use at the lowest cost possible. Lastly, EMR is fully managed, launching clusters in minutes, relieving you of tasks like provisioning infrastructure setup and Hadoop configuration. EMR takes care of these tasks, allowing you to focus on the analysis.
Now, let’s dive into some architectural and operational best practices.
Leverage Amazon S3 as Your Persistent Data Store with EMR File System (EMRFS)
One of the core components that differentiate EMR from on-premises systems is EMRFS, the EMR File System. EMRFS is an implementation of HDFS that all Amazon EMR clusters use for reading and writing regular files directly to Amazon S3. EMRFS provides the convenience of storing persistent data in Amazon S3 for use with Hadoop while offering features like consistent view and data encryption.
The biggest benefit of using S3 is decoupling storage and compute. Unlike traditional Hadoop data lakes, where data storage and compute resources are tightly coupled, S3 allows your storage to grow infinitely independently of your compute resources, and at S3’s low storage rates. It costs significantly less than maintaining replicated data in HDFS. Additionally, S3 is designed for eleven nines of durability, eliminating the need for re-replication once your data is there. EMR can read directly from S3, eliminating the need to ingest data into HDFS before using it. Applications like Spark and Hive can read directly from S3, providing flexibility in cluster resizing and isolation.
Another significant advantage is the ability to point multiple clusters at the same source of truth. Different departments within your organization can operate different jobs in isolation, with clusters billed to their respective business units. This approach allows you to split interactive query workloads from ETL type workloads, offering more operational flexibility.
Furthermore, S3 allows you to evolve your analytical infrastructure seamlessly. The Hadoop ecosystem continually evolves with new technologies emerging frequently. With EMR and S3, you can provision a new cluster with the latest technology and operate it in parallel with your core production environment. Once you decide which technology to adopt, transitioning from one to another is straightforward, providing future-proofing and flexibility without costly re-platforming or data transformation.
Amazon S3 Tips: Partitions, Compression, and File Formats
When using S3 as your persistent data store, it’s crucial to optimize it for your ETL and analytical workloads. Some key considerations include partitioning and organizing your data to reduce the amount of data scanned, optimizing file sizes, and employing compression to reduce storage costs and improve performance. Additionally, choosing the right file format for your data can significantly impact query performance.
Optimized File Format
For analytical workloads, column-oriented formats like Parquet can be highly efficient. Parquet’s design optimizes analytics by grouping data by column, enabling better performance for queries that involve only a subset of columns. Comparing file formats, we often see substantial performance improvements when using Parquet, especially for tasks like counting records. Parquet’s metadata also aids in data processing by providing statistics on columns, which can be used to skip unnecessary file scans or perform pre-aggregation efficiently.
HDFS Is Still There If You Need It
Although S3 is the recommended persistent data store, HDFS is still available on Amazon EMR clusters. It can be useful for workloads with iterative reads on the same data set or disk I/O-intensive tasks. However, HDFS data is ephemeral and gets lost when EMR clusters are terminated. Thus, it’s suitable primarily for intermediate or staging data.
Choose the Right Hardware for Your Job
When provisioning EMR clusters, it’s essential to select the right hardware for your specific workload. You can choose from a variety of EC2 instance types, each optimized for different types of tasks. Properly sizing containers and executors to match the instance’s core-to-memory ratio is crucial for maximizing resource utilization. For memory-intensive jobs, instance types from the R series may be more suitable, while IO-intensive workloads may benefit from instance types like the I3 series with SSD-based instance stores.
Cloud-native Architectural Patterns
When running Amazon EMR, two common architectural patterns emerge: long-running clusters that auto-scale based on demand and transient clusters designed for specific, short-lived jobs. Choosing between these patterns depends on the nature of your workloads and can significantly impact resource utilization.
Long-running clusters are ideal for scenarios where you have continuous workloads, such as real-time use cases or ad-hoc query servers. In contrast, transient clusters work well for job-specific pipelines, isolating jobs, and reducing the blast radius in case of cluster failures. They are also easier to upgrade and restart, making them suitable for jobs that run intermittently.
Reliability Considerations
Ensuring the reliability of your EMR clusters in production is crucial. Disaster recovery is essential, and you should ensure that your metadata is stored outside of your cluster. Amazon Glue Catalog or running your Hive Metastore on a multi-AZ RDS cluster are reliable options. Additionally, spreading EMR clusters across Availability Zones (AZs) enhances fault tolerance.
Amazon EMR provides multi-master support, allowing automatic failover of applications in case of hardware failure. Even for long-running clusters, it’s best practice to design them to be transient, making resource provisioning, upgrades, and job restarts more manageable.
Automate Resource Provisioning and Job Submission
To streamline resource provisioning and job submission, automation is essential. AWS offers several tools, such as the EMR Job Flow and Steps API, Lambda, and third-party options like Oozie and Airflow. Automating these processes ensures quick and reliable resource provisioning and simplifies tasks like upgrades and job restarts.
Managing Amazon EMR Clusters and Cost Optimization
Now, let’s delve deeper into managing EMR clusters efficiently and optimizing costs.
Managing EMR Clusters
1. Workflow Automation with Step Functions
One of the key features that Amazon EMR offers is the ability to automate your workflows using AWS Step Functions. Step Functions provide a way to orchestrate complex ETL (Extract, Transform, Load) workflows with multiple steps, making it easier to manage and monitor your data processing tasks.
In the example provided, we saw how you can use Step Functions to create, manage, and terminate EMR clusters based on your specific needs. The visual workflow representation and event history allow you to track each step of the process, from cluster creation to job submission and termination.
2. Stay Up to Date with Amazon EMR Upgrades
Staying up to date with Amazon EMR upgrades is essential to leverage the latest features and improvements in the platform. By keeping your EMR clusters up-to-date, you ensure that your organization benefits from bug fixes, new features, and performance enhancements.
One significant improvement to highlight is the performance-optimized runtime environment for Apache Spark, known as EMR Runtime for Apache Spark. This runtime environment is fine-tuned for Spark workloads and can significantly improve query performance, as demonstrated with the TPC-DS benchmark.
Cost Optimization Strategies
1. Amazon EMR Nodes
Understanding the different types of nodes in an Amazon EMR cluster is crucial for cost optimization. In an EMR cluster, you have master nodes, core nodes, and task nodes. Each type serves a specific purpose, and choosing the right purchasing option for each can impact your costs.
- Master Node: Typically, the master node runs core services like the resource manager, name node, and job tracker. For long-running and critical workloads, it’s recommended to use on-demand instances for the master node to ensure stability.
- Core Nodes: Core nodes handle HDFS (Hadoop Distributed File System) data. It’s essential to use on-demand instances for core nodes to prevent instability if spot instances are reclaimed.
- Task Nodes: Task nodes are used for computation and don’t handle HDFS data. They are suitable for running on spot instances, making them cost-effective for transient workloads.
2. Turning on Spot Instances
Spot instances offer substantial cost savings, but their availability can be less predictable than on-demand instances. However, for EMR clusters, spot instances are a great fit, especially for transient workloads. Spot instances can be used for master nodes, core nodes, and task nodes.
3. Scale Up with Spot Instances
Combining on-demand and spot instances in your EMR cluster can help optimize both cost and performance. By provisioning enough on-demand capacity to meet your Service Level Agreements (SLAs) and using spot instances to bring down the average costs, you can strike a balance between cost-efficiency and reliability.
In the example provided, adding spot instances to the cluster not only reduced costs but also improved job completion times by leveraging more resources.
4. Leverage Auto Scaling to Reduce Costs
Auto scaling is a powerful tool for adjusting the number of Amazon EC2 instances in an EMR cluster automatically. It helps match cluster resources to the actual workload demands, reducing costs during idle periods.
With the introduction of EMR Managed Scaling, the process becomes more straightforward. You can define minimum and maximum constraints for your cluster, and EMR will handle the scaling based on actual resource requirements, improving both performance and cost efficiency.
Conclusion
Managing Amazon EMR clusters effectively and optimizing costs is crucial for organizations working with big data analytics. By decoupling storage and compute, staying up to date with EMR upgrades, and making informed decisions about instance types and pricing options, you can ensure that your EMR workflows are both efficient and cost-effective.
Furthermore, by taking advantage of automation with Step Functions, leveraging spot instances, and implementing auto scaling, you can create a flexible and responsive environment that adapts to workload changes while minimizing unnecessary expenses.
In the ever-evolving landscape of big data analytics, mastering these strategies will empower your organization to harness the full potential of Amazon EMR while keeping costs under control.
Stay tuned for more updates and best practices in the world of Amazon EMR, and remember that optimizing your EMR clusters is an ongoing process that can lead to significant benefits for your organization.