Asserting Amazon EMR Serverless (Preview): Run massive information functions with out managing servers
16 mins read

Asserting Amazon EMR Serverless (Preview): Run massive information functions with out managing servers

Right this moment we’re completely satisfied to announce Amazon EMR Serverless, a brand new choice in Amazon EMR that makes it straightforward and cost-effective for information engineers and analysts to run petabyte-scale information analytics within the cloud. With EMR Serverless, you’ll be able to run functions constructed utilizing open-source frameworks reminiscent of Apache Spark, Hive, and Presto with out having to configure, handle, optimize, or safe clusters. EMR Serverless routinely provisions and scales the compute and reminiscence assets required by your functions, and also you solely pay for the assets that the functions use.

On this submit, we talk about the advantages of EMR Serverless, stroll you thru the core ideas of EMR Serverless and the way you should utilize it, and present you a fast demo.

Overview of EMR Serverless

Tens of 1000’s of consumers use Amazon EMR, a managed service for operating open-source analytics frameworks reminiscent of Apache Spark, Hive, and Presto for large-scale information analytics functions. With Amazon EMR, you’ll be able to provision clusters of any measurement in minutes. Amazon EMR routinely installs and configures the frameworks you select, and offers a performance-optimized runtime that’s suitable with and over twice as quick as customary open-source.

Amazon EMR prospects have full management over cluster configuration. The power to customise clusters lets you optimize for price and efficiency primarily based on workload necessities. For instance, you should utilize Amazon Elastic Compute Cloud (Amazon EC2) reminiscence optimized situations to run SQL workloads with low latency, or use the EC2 Graviton2-based situations to enhance efficiency. You may as well use EC2 Spot Situations, that are built-in in Amazon EMR so to reap the benefits of unused EC2 capability within the AWS Cloud to acquire situations at as much as a 90% low cost in comparison with On-Demand costs. In case you run your functions on Kubernetes, you should utilize Amazon EMR on Amazon EKS to run your Amazon EMR analytics functions on Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

Nevertheless, tuning clusters for optimum price and efficiency requires engineers to have deep data of the underlying analytics frameworks. Moreover, the precise compute and reminiscence assets wanted to optimally run functions depend upon varied components, such because the schedule and complexity of information processing jobs and the quantity of information being processed. When these traits change over time, it is advisable reevaluate and reconfigure clusters. As well as, directors need to safe and monitor clusters to make sure that they’re compliant with company safety insurance policies. Many shoppers don’t want this degree of customization and management, and desire a easier option to course of information utilizing open-source frameworks and Amazon EMR’s performance-optimized runtime.

With this in thoughts, we constructed EMR Serverless. With EMR Serverless, you will get all the advantages of operating Amazon EMR, however with a serverless atmosphere. We had the next objectives in thoughts once we constructed EMR Serverless:

  • Present a less complicated expertise – EMR Serverless is straightforward to make use of since you don’t need to configure, optimize, function, or safe clusters. You don’t have to fret about occasion sorts or cluster sizes, or about making use of OS updates. You merely specify the framework and model that you just wish to use to your utility, and submit your information processing jobs. You continue to get all the advantages that you just count on out of Amazon EMR—open-source compatibility, open-source model forex, and performance-optimized runtime—however with out the necessity to handle clusters.
  • No have to guess cluster sizes – EMR Serverless eliminates the necessity to right-size clusters for various jobs and information sizes. With EMR Serverless, you create an utility utilizing an open-source framework model, and submit jobs to the appliance. EMR Serverless routinely provides and removes employees at completely different phases of processing your job. In consequence, you don’t need to reconfigure when information volumes change, and also you solely pay for what your jobs require. You’ll be able to management prices by specifying the minimal and most variety of concurrent employees, and the VCPU and reminiscence per employee.
  • Retain Amazon EMR’s performance-optimized runtime and open-source forex – EMR Serverless consists of the Amazon EMR performance-optimized runtime for Apache Spark, Hive, and Presto. The Amazon EMR runtime is API-compatible and over twice as quick as customary open-source, so your jobs run quicker and incur much less compute prices.
  • Seamless integration with EMR Studio – EMR Serverless consists of EMR Studio, which offers absolutely managed serverless Jupyter Notebooks and acquainted open-source instruments reminiscent of Spark UI and Tez UI that will help you develop, visualize, and debug your functions.
  • Computerized and fine-grained scaling – EMR Serverless routinely scales up employees at every stage of processing your job and scales them down once they’re not required. You’re charged for mixture vCPU, reminiscence, and storage assets used from the time a employee begins operating till it stops, rounded as much as the closest second with a 1-minute minimal. For instance, your job might require 10 employees for the primary 10 minutes of processing the job, and 50 employees for the following 5 minutes. With fine-grained automated scaling, you solely incur price for 10 employees for 10 minutes and 50 employees for five minutes. In consequence, you don’t need to pay for underutilized assets.
  • Resilience to Availability Zone failures – EMR Serverless is a Regional service. Whenever you submit jobs to an EMR Serverless utility, it could possibly run in any Availability Zone within the Area. A job is run in a single Availability Zone to keep away from efficiency implications of community visitors throughout Availability Zones. In case an Availability Zone is impaired, a job submitted to your EMR Serverless utility is routinely run in a special (wholesome) Availability Zone. When utilizing assets in a personal VPC, EMR Serverless recommends that you just specify the non-public VPC configuration for a number of Availability Zones in order that EMR Serverless can routinely choose a wholesome Availability Zone.
  • Allow shared functions – Whenever you submit jobs to an EMR Serverless utility, you’ll be able to specify the AWS Identification and Entry Administration (IAM) function that should be utilized by the job to entry AWS assets reminiscent of Amazon Easy Storage Service (Amazon S3) objects. In consequence, completely different IAM principals can run jobs on a single EMR Serverless utility, and every job can solely entry the AWS assets that the IAM principal is allowed to entry. This lets you arrange eventualities the place a single utility with a pre-initialized pool of employees is made accessible to a number of tenants whereby every tenant can submit jobs utilizing a special IAM function, however use the widespread pool of pre-initialized employees to instantly course of requests.
  • Allow interactive functions – Interactive functions that enable information scientists and analysts to run SQL queries and scripts for information exploration require a quick response time to person requests. For such functions, EMR Serverless lets you pre-initialize a pool of employees. You can begin your EMR Serverless utility and pre-initialize the pool of employees as quickly as a person begins the appliance, and cease the appliance to cease employees when no interactive customers are lively. If processing person requests requires extra employees than what have been pre-initialized, EMR Serverless routinely provides extra employees as much as the utmost concurrent limits that you just specify. Due to this fact, by controlling the variety of employees to pre-initialize and the utmost concurrent employees, you’ll be able to optimize person expertise and price to your interactive functions.
  • Make it straightforward to modify from one deployment mannequin to a different – The identical Amazon EMR releases are offered for functions utilizing EMR clusters, Amazon EMR on EKS, and EMR Serverless. Whenever you construct an utility utilizing an Amazon EMR launch (for instance a Spark job utilizing Amazon EMR launch 6.4), you’ll be able to select to run it on an EMR cluster, Amazon EMR on EKS, or EMR Serverless with out having to rewrite the appliance. This lets you construct functions for a given framework model, and retain the pliability to alter the deployment mannequin primarily based on future operational wants.

Core ideas

On this part, we talk about the core ideas in EMR Serverless: functions, jobs, employees, and pre-initialized employees.


With EMR Serverless, you’ll be able to create a number of functions that use open-source analytics frameworks. To create an utility, you specify the open-source framework that you just wish to use (for instance, Apache Spark or Apache Hive), the Amazon EMR launch for the open-source framework model (for instance, Amazon EMR launch 6.4, which corresponds to Apache Spark 3.1.2), and a reputation to your utility. After you create an utility, you’ll be able to submit information processing jobs or interactive requests to your utility.

The next are a couple of examples the place it’s possible you’ll wish to create a number of functions:

  • To make use of completely different open-source frameworks (for instance, Hive or Spark)
  • To make use of completely different variations of open-source frameworks for various use circumstances (for instance, use a more moderen model of Spark for a brand new utility with out having to improve older functions)
  • To carry out A/B testing when upgrading from one model to a different (for instance, migrating from Spark 2.4 to Spark 3.1)
  • To take care of separate logical environments for check and manufacturing eventualities
  • To offer separate logical environments for various groups with unbiased price controls and utilization monitoring
  • To logically separate completely different line-of-business functions (for instance, finance vs. advertising)


A job is a request submitted to an EMR Serverless utility that’s asynchronously run and tracked via completion. You’ll be able to run a number of jobs concurrently in an utility.


An EMR Serverless utility internally makes use of employees to run your jobs. Relying on the open-source framework, EMR Serverless makes use of a default variety of VCPU, reminiscence, and native storage per employee. You’ll be able to override these defaults to your utility.

Pre-initialized employees

EMR Serverless offers an optionally available characteristic to pre-initialize employees when your utility begins up, in order that the employees are able to course of requests instantly when a job is submitted to the appliance. Pre-initialized employees help you keep a heat pool of employees for the appliance in order that it could possibly present a sub-second response to begin processing requests.

Widespread utilization patterns utilized to EMR Serverless

Now let’s look at some widespread utilization eventualities and the way EMR Serverless offers you a easy resolution.

Sample #1: Information pipelines

Information pipelines are the spine of your analytics workloads. A typical sample with information pipelines is to begin a cluster, run a job, and cease the cluster when the job is full. As a result of information is separated from compute, the inputs and outputs for every job are persevered individually from the cluster (for instance, in Amazon S3). These steps are often automated utilizing workflow orchestration functions reminiscent of Apache Airflow. You may as well use AWS companies reminiscent of AWS Step Features and AWS Managed Workflows for Apache Airflow (Amazon MWAA) to create such workflows.

Though automating these steps isn’t advanced, information engineers need to spend time figuring out the suitable EC2 occasion and cluster measurement. They’ve to find out the Availability Zone the place the cluster is run, and deal with failover. They’ve to check their functions when adopting OS updates. When information sizes change over time, they need to resize clusters, or use options like Amazon EMR managed scaling that routinely resize clusters. EMR Serverless offers a less complicated resolution by eliminating the necessity so that you can deal with these eventualities. You merely select the open-source framework and model to your utility, and submit jobs. You don’t have to fret about occasion choice, cluster sizes, cluster startup, cluster resize, stopping nodes, Availability Zone failover, or OS updates.

Sample #2: Shared clusters

One other widespread sample is for groups to make use of a shared long-running cluster to run a number of jobs. On this case, engineers implement queues in Apache YARN for various workloads on a standard cluster, and arrange guidelines to routinely scale the cluster up or down primarily based on general workload. With Amazon EMR on EC2 clusters, you should utilize Amazon EMR managed scaling, a characteristic that routinely scales clusters up or down relying on the workload. With EMR Serverless, employees are assigned to every job when required, so your jobs get the assets they want. Furthermore, since you solely pay for the employees that your jobs require, you don’t incur price for over-provisioned assets. Lastly, as a result of every job can specify the IAM function that needs to be used to entry AWS assets when operating the job, you don’t need to arrange advanced configurations to handle queues and permissions.

Sample #3: Interactive workloads

A 3rd sample of use is when groups maintain a cluster of situations accessible to help interactive evaluation. On this case, the cluster is ready up and initialized with functions that anticipate interactive person requests. Functions are pre-initialized in order that they will instantly begin processing person requests and supply an interactive person expertise. EMR Serverless allows this state of affairs with out requiring you to handle clusters. You’ll be able to specify the variety of employees that you just wish to pre-initialize once you begin an EMR Serverless utility. Subsequently, when customers submit requests, the pre-initialized employees can be utilized to instantly course of person requests. If processing the person requests requires extra employees than what you will have chosen to pre-initialize, EMR Serverless routinely provides extra employees (as much as the utmost concurrent restrict that you just specify). After the requests are processed, EMR Serverless routinely reverts again to sustaining the pre-initialized employees that you just specified. You’ll be able to management when the pre-initialized employees are lively by controlling when to begin and cease your EMR Serverless utility. For instance, you can begin your utility when customers start interactive evaluation and switch it off when there aren’t any person requests and the appliance is idle.



On this submit, we mentioned the core ideas and customary utilization patterns of EMR Serverless, and confirmed you a fast demonstration video. EMR Serverless is in Preview, and you may enroll for the preview to run workloads utilizing Spark 3.1.2 and Hive 2.0 utilizing the API, AWS Command Line Interface (AWS CLI), and SDK. For extra info, see EMR Serverless documentation.

Concerning the Authors

Damon Cortesi is a Principal Developer Advocate with Amazon Net Providers.

Mehul Y. Shah is the GM for Amazon EMR.

Abhishek Sinha is a Principal Product Supervisor at Amazon Net Providers.

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