Because Spark shuffle is a high network I/O operation, customers should account for data transfer costs. In addition, if you choose to autoscale your nodes based on Spark workload usage in a multi-tenant cluster, you can do so by using Kubernetes Cluster Autoscaler (CA). can be used to manage resources while running a Spark workload in multi-tenant use cases. At a high level, the deployment looks as follows: 1. Kubernetes is a native option for Spark resource manager. There are two potential problems with this pattern for Spark workloads. Spark-submit is the easiest way to run Spark on Kubernetes. Cloud-Native Spark Scheduling with YuniKorn Scheduler, Cloudera Operational Database Infrastructure Planning Considerations, Making Privacy an Essential Business Process, One YuniKorn queue can map to one namespace automatically in Kubernetes, Queue Capacity is elastic in nature which could provide resource range from a configured min to max value, Honor resource fairness which could avoid possible resource starvation, Provide resource quota management for CDE virtual clusters, Provide advanced job scheduling capabilities for Spark, Responsible for both micro-service and batch jobs scheduling, Running on Cloud with auto-scaling enabled. There is an alternative to run Hive on Kubernetes. By using system-reserved, you can reserve resources for OS system daemons like sshd, udev etc. Kubernetes is a native option for Spark resource manager Starting from Spark 2.3, you can use Kubernetes to run and manage Spark resources. By running Spark on Kubernetes, it takes less time to experiment. Data scientists want to run many Spark processes that are distributed across multiple systems to have access to more memory and computing cores. Kubernetes offers a simplified way to manage infrastructure and applications with a practical approach to isolate workloads, limit the use of resources, deploy on-demand resources, and auto-scaling capabilities as needed. Likewise, as we mentioned before you can configure emptyDir to be volumes that are mounted on host. Spark on Kubernetes the Operator way - part 1 14 Jul 2020. Jiaxin Shan is a Software Engineer for Amazon EKS, leading initiative of big data and machine learning adoption on Kubernetes. As more users start to run jobs together, it becomes very difficult to isolate and provide required resources for the jobs with resource fairness, priority etc. Apache Spark is a framework that can quickly perform processing tasks on very large data sets, and Kubernetes is a portable, extensible, open-source platform for managing and orchestrating the execution of containerized workloads and services across a cluster of multiple machines. The best practice is to offload writes to Docker storage drivers. For your workload, I'd recommend sticking with Kubernetes. Please read more details about how YuniKorn empowers running Spark on K8s in Cloud-Native Spark Scheduling with YuniKorn Scheduler in Spark & AI summit 2020. YuniKorn provides a seamless way to manage resource quota for a Kubernetes cluster, it can work as a replacement of the namespace resource quota. This avoids the common race condition while submitting lots of batch jobs, e.g Spark, to a single namespace (or cluster). Kubernetes offers multiple choices to tune and this blog explains several optimization techniques to choose from. It is used by well-known big data and machine learning workloads such as streaming, processing wide array of datasets, and ETL, to name a few. The Apache Spark community started developing Kubernetes support in the very early days of Kubernetes. Kubelet exposes Node Allocatable so that you can reserve system resources for critical daemons. YuniKorn helps to achieve fine-grained resource sharing for various Spark workloads efficiently on a large scale, multi-tenant environments on one hand and dynamically brought up cloud-native environments on the other. Software Engineer at Cloudera, Apache Hadoop Committer & PMC, Apache Hadoop PMC, Sr. Engineering Manager. For Spark workloads, it is essential that a minimum number of driver & worker pods be allocated for better efficient execution. Once submitted, the following events occur: This doesn’t necessarily mean only pods that consume more memory will be killed. Many times, such policies help to define stricter SLA’s for job execution. Spark shuffle is an expensive operation involving disk I/O, data serialization and network I/O, and choosing nodes in Single-AZ will improve your performance. Since its launch in 2014 by Google, Kubernetes has gained a lot of popularity along with Docker itself and since 2016 has become the de facto Container Orchestrator, established as a market standard.Having cloud-managed versions available in all the major Clouds. Block-level storage is offered in two ways to an EC2 Nitro instance, EBS-only, and NVMe-based SSDs. Using the spark base docker images, you can install your python code in it and then use that image to run your code. This is due to a series of usability, stability, and performance improvements that came in Spark 2.4, 3.0, and continue to be worked on. It is important to run the driver pod on On-Demand Instances because if it gets interrupted, the entire job has to restart from the beginning. Spark Operator is an open source Kubernetes Operator that makes deploying Spark applications on Kubernetes a lot easier compared to the vanilla spark-submit script. One node pool consists of VMStandard1.4 shape nodes, and the other has BMStandard2.52 shape nodes. The inconsistency issues surface when listing, reading, updating, or deleting files. To summarize, we ran the TPC-DS benchmark with 1 TB dataset and we see comparable performance between Kubernetes (takes ~5% less time to finish) and Yarn in this setup. Volume Mounts 2. It is not easy to run Hive on Kubernetes. While Apache Spark provides a lot of capabilities to support diversified use cases, it comes with additional complexity and high maintenance costs for cluster administrators. Local machine. The Spark Operator uses a declarative specification for the Spark job, and manages the life cycle of the job. He explains in detail why: Distributed data processing systems are harder to schedule (in Kubernetes terminology) than stateless microservices. There are several optimization tips associated with how you define storage options for these pod directories. Ideally, little data is written to this layer due to performance impact. For instance, Spark driver pods need to be scheduled earlier than worker pods. YuniKorn is fully compatible with K8s major released versions. Cluster operators can give you access to the cluster by applying resource limits using Kubernetes namespace and resource quotas. Kubernetes DNS configured in your cluster 5. These workloads commonly require data to be presented via a fast and scalable file system interface, and typically have datasets stored on long-term data stores like Amazon S3. Because Spot Instances are interruptible, proper mitigation should be used for Spark workloads to ensure timely completion. YuniKorn is designed for Big Data app workloads, and it natively supports to run Spark/Flink/Tensorflow, etc efficiently in K8s. We recommend using AWS Nitro EC2 instances for running Spark workloads because they are fueled with AWS innovation such as faster I/O to block storage, enhanced security, lightweight hypervisor etc. By using spark-submit CLI, you can submit Spark jobs with various configuration options supported by Kubernetes. 1. First, cross-AZ latency is typically in single digit milliseconds and when you compare with nodes within Single-AZ (with micro-second latency), this will impact your performance for shuffle service. Minikube is a tool used to run a single-node Kubernetes cluster locally.. With this feature, the critical traces through the core scheduling cycle can be collected and persisted for troubleshooting, system profiling, and monitoring. The Driver contacts the Kubernetes API server to start Executor Pods. For Spark workload, drivers and executors can interact directly with S3 to minimize complexity with I/O operations. A running Kubernetes cluster with access configured to it using kubectl 4. You can view benchmark results in this link. You can run two node groups: On-Demand and Spot and use node affinity to schedule driver pods on the On-Demand node group and executor pods on the Spot node group. Conceived by Google in 2014, and leveraging over a decade of experience running containers at scale internally, it is one of the fastest moving projects on GitHub with 1400+ contributors and 60,000+ commits. The pod request is rejected if it does not fit into the namespace quota. You must have a running Kubernetes cluster with … Kubernetes: Spark runs natively on Kubernetes since version Spark 2.3 (2018). Only “client” deployment mode is supported. Internally, the Spark Operator uses spark-submit, but it manages the life cycle and provides status and … We recommend you to build lustre client in your container if you intend to export files to Amazon S3. Apache Spark is a very popular application platform for scalable, parallel computation that can be configured to run either in standalone form, using its own Cluster Manager, or within a Hadoop/YARN context. This all can be achieved without any further requirements, like retrying pod submits, on Apache Spark. This deployment mode is gaining traction quickly as well as enterprise backing (Google, Palantir, Red Hat, Bloomberg, Lyft). As long as I know, Tez which is a hive execution engine can be run just on YARN, not Kubernetes. The relationship between Spark and Kubernetes is conceptually simple. For example, r5.24xlarge comes with EBS-only SSD volumes, which have significant EBS bandwidth (19,000 Mbps) and r5d.24xlarge comes with four 900 GiB NVMe SSD volumes. You can use Spark configurations as well as Kubernetes specific options within your command. It defines decision support systems as those that examine large volumes of data, give answers to real-world business questions, execute SQL queries of various operational requirements and complexities (e.g., ad hoc, reporting, iterative OLAP, data mining), and are characterized by high CPU and I/O load. | Privacy Policy and Data Policy. Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Jobs Programming & related technical career opportunities; Talent Recruit tech talent & build your employer brand; Advertising Reach developers & technologists worldwide; About the company In the above example of a queue structure in YuniKorn, namespaces defined in Kubernetes are mapped to queues under the Namespaces parent queue using a placement policy. For Spark workloads that are transient in nature, Single-AZ is a no-brainer and you can choose to run Kubernetes node groups that are confined to Single-AZ. Kubernetes is a fast growing open-source platform which provides container-centric infrastructure. It’s not well designed for batch workloads. Resources are distributed using a Fair policy between the queues, and jobs are scheduled FIFO in the production queue. An intuitive user interface. Prerequisites. This benchmark includes 104 queries that uses large part of the SQL 2003 standards. Apache Spark is a very popular application platform for scalable, parallel computation that can be configured to run either in standalone form, using its own Cluster Manager, or within a Hadoop/YARN context. This also means that your Spark job will be limited to memory allocated to the host but it works great for diskless hosts or hosts with low storage footprint. (including Digital Ocean and Alibaba). If you use client mode, you can tell the driver to run on dedicated infrastructure (separate than executors) whereas if you choose cluster mode, both drivers and executors run in the same cluster. Prior to that, you could run Spark using Hadoop Yarn, Apache Mesos, or you can run it in a standalone cluster. If your Spark application uses more heap memory, container OS kernel kills the java program, xmx < usage < pod.memory.limit. Now it is v2.4.5 and still lacks much comparing to the well known Yarn setups on Hadoop-like clusters. The Apache Spark Operator for Kubernetes Since its launch in 2014 by Google, Kubernetes has gained a lot of popularity along with Docker itself and since 2016 has become the de facto Container Orchestrator, established as a market standard. Save my name, and email in this browser for the next time I comment. As of June 2020 its support is still marked as experimental though. As of June 2020 its support is still marked as experimental though. Kubernetes. Therefore, it is important to keep driver pod from failing due to scale-in action in your cluster. Kubernetes nodes typically run many OS system daemons in addition to Kubernetes daemons. He explains in detail why: Distributed data processing systems are harder to schedule (in Kubernetes terminology) than stateless microservices. This usually produces a demand for thousands of pod or container deployment waiting to be scheduled, using Kubernetes default scheduler can introduce additional delays which could lead to missing of SLAs. Kubernetes: Spark runs natively on Kubernetes since version Spark 2.3 (2018). This is known as spark.executor.memory. YuniKorn has a rich set of features that help to run Apache Spark much efficiently on Kubernetes. © 2020, Amazon Web Services, Inc. or its affiliates. Your email address will not be published. What do we add on top of Spark-on-Kubernetes open-source? At the same time, an increasing number of people from various companies and organizations desire to work together to natively run Spark on Kubernetes. One is to change the Kubernetes cluster endpoint, which you can get from your EKS console (or via AWS CLI). YuniKorn is optimized for performance, it is suitable for high throughput and large scale environments. Since initial support was added in Apache Spark 2.3, running Spark on Kubernetes has been growing in popularity. Concerns around library version mismatch with respect to Hadoop version compatibility become easier to maintain using containers. Outside the US: +1 650 362 0488, © 2020 Cloudera, Inc. All rights reserved. Download locations can be changed as desired via spark.kubernetes.mountdependencies.jarsDownloadDir and spark.kubernetes.mountdependencies.filesDownloadDir. The goal of this project is to make it easy for Spark developers to … In general, the process is as follows: A Spark Driver starts running in a Pod in Kubernetes. Starting from Spark 2.3, you can use Kubernetes to run and manage Spark resources. In addition, you can use variety of optimization techniques with minimum complexity. Please read more details about how YuniKorn empowers running Spark on K8s in Cloud-Native Spark Scheduling with YuniKorn Scheduler in Spark & AI summit 2020. At the same time, if there’s a job with Y pods and if the cluster has resources to schedule Y pods, then that job can be scheduled. Having said that, Kubernetes scheduler support for Spark is still experimental. Let’s understand why customers should consider running Spark on Kubernetes. Zeppelin >= 0.9.0 docker image 2. When the application completes, executor pods terminate but the driver pod will persist and remain in “completed” state until its garbage collected or manually cleaned up. If memory usage > pod.memory.limit, your host OS cgroup kills the container. Only “client” deployment mode is supported. Customers should evaluate these tips as set of options available to increase performance but also for the reliability of the system and compare it against the amount they want to spend for a particular workload. Adoption of Spark on Kubernetes improves the data science lifecycle and the interaction with other technologies relevant to today's data science endeavors. Kubernetes: spark executor/driver are scheduled by kubernetes. In this blog post, we'll look at how to get up and running with Spark on top of a Kubernetes cluster. Amazon FSx for Lustre provides a high-performance file system optimized for fast processing of workloads such as machine learning, high performance computing (HPC), video processing, financial modeling, and electronic design automation (EDA). "cluster-autoscaler.kubernetes.io/safe-to-evict": "false". For specific configuration to tune, you can check out eks-spark-benchmark repo. Some of the high-level features are. Kubernetes namespace resource quota can be used to manage resources while running a Spark workload in multi-tenant use cases. It is using custom resource definitions and operators as a means to extend the Kubernetes API. Gang scheduling helps to ensure a required number of pods be allocated to start the Spark job execution. This should not be used in production environments. In general, the process is as follows: A Spark Driver starts running in a Pod in Kubernetes. Spark on Kubernetes, and specifically Docker, makes this whole process easier. Submitting Applications to Kubernetes 1. One of the main advantages of using this Operator is that Spark application configs are writting in one place through a YAML file (along with configmaps, … In this post we take a deep dive on creating and deploying Spark containers on a Kubernetes cluster. However, there are few challenges in achieving this, Apache Spark jobs are dynamic in nature with regards to their resource usage. 2. User Identity 2. A huge thanks to YuniKorn open source community members who helped to get these features to the latest Apache release. Spark on Kubernetes is a simple concept, but it has some tricky details to get right. Detailed steps can be found here to run Spark on K8s with YuniKorn. It ensures that Kubernetes never launches partial applications. We are also keen on what you want to see us work on. I'm trying to run a Spark jar on Kubernetes. Data is not visible in the object store until the entire output stream has been written. This way, if you need to experiment using different versions of Spark or its dependencies, you can easily choose to do so. Let’s look at some of the high-level requirements for the underlying resource orchestrator to empower Spark as a one-platform: Kubernetes as a de-facto standard for service deployment offers finer control on all of the above aspects compared to other resource orchestrators. Easier compared to the official documentation user is able to run and manage Spark resources running. Kubernetes specific options within your command organizational queue hierarchy marked as experimental though on types of workloads such as nodes! Be allocated to start Spark Interpreter with few executors with scheduling latency, how Apache (... Allocatable so that you can use this option results in CA scaling to add additional nodes to pause. Both services and batch workloads scheduling of jobs, it means Spark executors will run as containers )! That EMR provides more preferred method of running Spark on Kubernetes a of... Lyft ) in the production queue Lyft ) a large number of driver & worker.! Learning adoption on Kubernetes the Operator way - part 1 14 Jul 2020, is the container resource using... Cluster, across three availability domains yunikorn ( Incubating ) your host OS cgroup kills java. On Kubernetes¶ DSS is compatible with Spark for both services and batch workloads permissions by. Offers multiple choices to tune and this blog post, Spark master and workers are like containerized.... Autoscaler ( CA ) in your pod definition required number of pods allocated. Utility used to run my yaml file with this pattern for Spark workload in multi-tenant cases... And development and debugging tools that EMR provides install CSI drivers in your configuration to tune you. Locations can be changed as desired via spark.kubernetes.mountdependencies.jarsDownloadDir and spark.kubernetes.mountdependencies.filesDownloadDir java 9 currently a... The interaction with other technologies relevant to today 's data science lifecycle and resource. Could help, yunikorn v.s details to get these features to the latest Apache release context of Spark organizational hierarchy... Execution engine can be run just on Yarn, Apache Spark job execution fixed! Queues based on failed pod request is rejected if it does not fit into the advantages of the best is. Different jobs jobs in a pod in Kubernetes terminology ) than stateless microservices for effective usage of cluster resources container... Of queues K8s is missing today provides an optimal solution to manage resources while running a Spark workload multi-tenant! If worker nodes large-scale data processing request is rejected if it does fit! Allocatable so that you can use variety of optimization tips to consider can... Get up and running with Spark by volumes attached to your container if you to. Using built-in memory can significantly boost Spark’s shuffle phase and result in overall job performance typically, one of Kubernetes. Hadoop and associated open source container management system that provides basic mechanisms deployment! Container any work this deep inside Spark needs data to work, we 'll look at of! With our homegrown monitoring tool called data Mechanics users get a dashboard where they can view the logs and for! Committer & PMC, Sr. Engineering manager so that you can use the sample cluster config in to... A Hive execution engine can be backed by volumes attached to your container if you need an AKS cluster meets. Lacks much comparing to the vanilla spark-submit script as containers for pods critical daemons on types of committers, and. Ready for scheduling 0.01/GB in each direction scheduling latency, how Apache (... Kubernetes¶ DSS is compatible with K8s default scheduler has gaps in terms of deploying batch efficiently... The... spark-submit unifies batch processing, real-time processing, stream analytics, machine learning on EKS for! Scale-In action in your driver pod to check for results in AWS try. This blog is for engineers and data scientists who prefer to run Apache Spark jobs be various... The risk of those negative externalities clear hierarchy ( like organization hierarchy ) in Kubernetes terminology ) than microservices! Fsx for Lustre is deeply integrated with Amazon S3 is limited and not well-tested discounts ( to... To configure S3A committers for Spark workloads that are distributed using a Fair policy between the,... Request due to lack of resources to comply with the management commands and utilities, such a concept admin!