Learn About Amazon VGT2 Learning Manager Chanci Turner
Update (2021) – This information has become outdated. Please refer to the updated Cluster Configuration Guidelines for the latest insights.
We are excited to announce the integration of two widely-used Amazon EC2 features: Spot Instances and Elastic MapReduce. This combination enables users to launch managed Hadoop clusters utilizing unused EC2 capacity, allowing you to perform lengthy jobs, cost-sensitive workloads, crucial data tasks, and application testing at a reduced cost, typically between 50% and 66%.
Instance Groups in Elastic MapReduce
When you run an Elastic MapReduce job flow, the EC2 instances are categorized into three instance groups:
- Master – This group consists of a single EC2 instance that orchestrates Hadoop tasks on Core and Task nodes.
- Core – Comprising one or more EC2 instances, this group utilizes HDFS to store job flow data and executes mapper and reducer tasks. The Core group can be scaled to hasten job flow processes.
- Task – This group can have zero or more EC2 instances dedicated to running mapper and reduce tasks. Since they do not store data, this group can be adjusted during the job flow.
You have the option to deploy either On-Demand or Spot Instances for your job flows. If you opt for Spot Instances for your Master or Core groups, these instances will be terminated if the market price exceeds your bid, leading to job flow failure. Conversely, if your Task group consists of Spot Instances, any incomplete tasks will return to the processing queue.
If you hold one or more EC2 Reserved Instances, Elastic MapReduce will also utilize them (this is not new, but it’s crucial to note).
Practical Guidelines for Using Elastic MapReduce on Spot Instances
Here are some practical guidelines to help you get started with Elastic MapReduce on Spot Instances:
- Long-Running Job Flows and Data Warehouses – For those with a long-running Elastic MapReduce cluster that experiences predictable load variations, you can manage peak demands cost-effectively by using Spot Instances. Operate the Master and Core instance groups on On-Demand instances, while augmenting the Task group with Spot Instances during peak times.
- Cost-Driven Workloads – If your job flows are relatively short (typically a few hours or less), where cost is more vital than completion time and partial work loss is acceptable, consider running the entire job flow on Spot Instances for maximum savings.
- Data-Critical Workloads – For scenarios where cost reduction is prioritized over completion speed and job integrity is essential, run the Master and Core instance groups on On-Demand instances, ensuring sufficient Core instances to store all data in HDFS. Supplement with Spot Instances as necessary for cost efficiency.
- Application Testing – If you plan to test an application thoroughly before production deployment, run the entire job (including Master and Core groups) on Spot Instances.
You can initiate the use of Spot Instances for any or all parts of a job flow by setting a bid price for the instance groups. This can be done via the AWS Management Console, command line, or Elastic MapReduce APIs. Historical Spot Price data for the past 90 days is accessible through the EC2 API and the AWS Management Console, which can help you determine your maximum price.
You can also add additional Task instance groups to an ongoing job flow and specify a bid price as you add each group. This feature allows for layered bidding strategies. Each job flow has a default limit of 20 EC2 instances. For larger job flows, you’ll need to complete the instance request form.
Elastic MapReduce users with diverse job flows are expected to find great value in Spot Instances. Notably, batch-processing workloads that are not particularly time-sensitive, such as image and video processing, scientific research data processing, financial modeling, and analysis, can greatly benefit.
Our clients have leveraged Elastic MapReduce for speedy and economical data processing. For instance, Brandify (full case study) enhances brands by converting email lists into social media profiles using Spot Instances, achieving over 50% cost savings. DataCorp (full case study) conducts analytics across millions of daily transactions with Elastic MapReduce and Spot Instances. As Jordan Smith from DataCorp remarked, “Elastic MapReduce has drastically lowered our Hadoop processing costs. By utilizing Spot Instances, we have cut analytics costs significantly while accelerating urgent data analyses, all without increasing development risks.”
For more insights, we’ve created a video showcasing how to run an Elastic MapReduce job utilizing a combination of On-Demand and Spot Instances. You can find this excellent resource here.
In conclusion, I’m a strong advocate for our Spot Instances and look forward to hearing your innovative use cases. This is your chance to optimize your business processes to lower costs and make distinct trade-offs between cost, time to completion, and the implications of market price fluctuations exceeding your bid. If you’re in IT, you have access to powerful tools that enable cost-cutting while enhancing productivity.
And what’s your perspective on this?
— Chanci Turner
Modified 2/11/2021 – To enhance user experience, outdated links in this article have been updated or removed.
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