Dive into Databricks Clusters: The Engine for Data Revolution

?A cluster is a collection of virtual machines that helps to achieve distributed data processing.??

??? Clusters can be classified into two types:??

1. Job Cluster (used for automated workloads): These types of clusters are used for running fast and robust automated tasks. They are created when you run a job on your new Job Cluster and terminate the Cluster once the job ends. A Job Cluster cannot be restarted.??
2. Interactive/All Purpose Cluster (used for Interactive and ad hoc analysis): These types of Clusters are used to analyze data collaboratively via interactive notebooks. An All-purpose Cluster can be terminated and restarted manually. They can also be shared by multiple users to do collaborative tasks interactively.?

Based on the cluster usage, there are three modes of clusters that Databricks supports:?

1. Standard Clusters:?Standard cluster mode is also called a No Isolation shared cluster, which means these clusters can be shared by multiple users with no isolation between the users. In the case of single users, the standard mode is suggested. Workload supports in these modes of clusters are in Python, SQL, R, and Scala can all be run on standard clusters.?
2. High Concurrency Clusters:?A managed cloud resource is a high-concurrency cluster. High-concurrency clusters have the advantage of fine-grained resource sharing for maximum resource utilization and low query latencies.?Workloads written in SQL, Python, and R can be run on high-concurrency clusters. Running user code in separate processes, which is not possible in Scala, improves the performance and security of High Concurrency clusters.??Table access control is also only available on High Concurrency clusters.?
3. Single Node Clusters:?Single node clusters as the name suggests will only have one node i.e. for the driver. There would be no worker node available in this mode. In this mode, the spark job runs on the driver note itself.?This mode is more helpful in the case of small data analysis and Single-node machine learning workloads that use Spark to load and save data.?

? [Note: To execute Spark jobs in a Standard cluster, at least one Spark worker node is required in addition to the driver node.]??

Points to Remember:?

1. We cannot change the cluster mode once a cluster is created. If we want a different cluster mode, we must create a new one.?
2. Standard and Single Node clusters terminate automatically after 120 minutes by default.?
3. High Concurrency clusters do not terminate automatically by default.??