It can be deployed through Mesos, Hadoop via YARN, or Spark’s own cluster manager. Simple programming layer provides powerful caching and disk persistence capabilities. It is also able to achieve this speed through controlled partitioning. Spark runs up to 100 times faster than Hadoop MapReduce for large-scale data processing. Features of Apache Spark: Fig: Features of Spark It is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries, and streaming. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. The main feature of Apache Spark is its in-memory cluster computing that increases the processing speed of an application. In this Spark Architecture article, I will be covering the following topics:Īpache Spark is an open source cluster computing framework for real-time data processing. In this blog, I will give you a brief insight on Spark Architecture and the fundamentals that underlie Spark Architecture. According to Spark Certified Experts, Sparks performance is up to 100 times faster in memory and 10 times faster on disk when compared to Hadoop. This setting can be altered after pool creation although the instance may need to be restarted.Apache Spark is an open-source cluster computing framework which is setting the world of Big Data on fire. Resources can be paused whether the autoscale is enabled or disabled. The automatic pause feature is independent of the autoscale feature. The number of minutes of idle time can be set once this feature is enabled. The automatic pause feature releases resources after a set idle period reducing the overall cost of an Apache Spark pool. This setting can be altered after pool creation although the instance may need to be restarted. When the autoscale feature is disabled, the number of nodes set will remain fixed.
When the autoscale feature is enabled, you can set the minimum and maximum number of nodes to scale. SizeĪpache Spark pools provide the ability to automatically scale up and down compute resources based on the amount of activity. Node sizes can be altered after pool creation although the instance may need to be restarted. Node SizesĪ Spark pool can be defined with node sizes that range from a Small compute node with 4 vCore and 32 GB of memory up to a XXLarge compute node with 64 vCore and 512 GB of memory per node. All worker nodes run the Spark Executor service. All nodes run services such as Node Agent and Yarn Node Manager. The head node runs additional management services such as Livy, Yarn Resource Manager, Zookeeper, and the Spark driver. If you expect to enable this feature in the future, ensure that your Synapse workspace is created in an isolated compute supported region.Īpache Spark pool instance consists of one head node and two or more worker nodes with a minimum of three nodes in a Spark instance. The isolated compute option can be enabled or disabled after pool creation although the instance may need to be restarted. The Isolate Compute option is only available with the XXXLarge (80 vCPU / 504 GB) node size and only available in the following regions. Isolated compute option is best suited for workloads that require a high degree of isolation from other customer's workloads for reasons that include meeting compliance and regulatory requirements. The Isolated Compute option provides additional security to Spark compute resources from untrusted services by dedicating the physical compute resource to a single customer.
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You can read how to create a Spark pool and see all their properties here Get started with Spark pools in Synapse Analytics Isolated Compute
Charges are only incurred once a Spark job is executed on the target Spark pool and the Spark instance is instantiated on demand. There are no costs incurred with creating Spark pools.
A Spark pool in itself does not consume any resources. These characteristics include but aren't limited to name, number of nodes, node size, scaling behavior, and time to live. A Spark pool is a set of metadata that defines the compute resource requirements and associated behavior characteristics when a Spark instance is instantiated.
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