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Apache Spark, being a powerful distributed computing engine, doesn't have a traditional user interface (UI) like many other software applications. However, it offers several interfaces and monitoring tools for interacting with and managing Spark applications: - Web UI: Spark provides a web-based user interface that gives insights into the status and performance of Spark applications. You can access this UI through a web browser by default on port 4040 of the Spark driver node. It provides information about running Spark jobs, stages, tasks, RDD (Resilient Distributed Dataset) storage, and more. You can monitor job progress, resource usage, and bottlenecks through this interface.
- Spark History Server: This component allows you to view information about completed Spark applications. It's particularly useful for analyzing past jobs, troubleshooting, and performance tuning. The history server typically runs as a separate service and can be configured to retain logs and event data for a certain duration.
- Third-party Monitoring Tools: Spark integrates with various third-party monitoring tools like Ganglia, Graphite, and Prometheus. These tools provide additional insights into cluster performance, resource utilization, and application behavior.
- Spark Shell: Spark provides interactive shells for Scala (spark-shell), Python (pyspark), and R (sparkR), which offer REPL (Read-Eval-Print Loop) environments for running Spark code interactively. While not a traditional UI, these shells provide an interactive interface for testing Spark code snippets, running queries, and exploring data.
- Notebook Interfaces: Many users prefer to interact with Spark through notebook interfaces like Jupyter, Zeppelin, or Databricks notebooks. These notebooks provide an interactive environment where users can write and execute code, visualize data, and annotate their analyses in a single document. Databricks notebooks, in particular, offer tight integration with Spark and additional collaborative features.
- Spark UI in Spark Streaming and Structured Streaming: When working with Spark Streaming or Structured Streaming applications, you can monitor the progress of streaming jobs through a specialized UI. It provides insights into batch durations, processing times, and input/output rates for streaming sources and sinks.
- Spark unified interface. It is spark-submit.
Jobs, stages, storage, environment, executor, and SQL are included within the Apache Spark User Interface.
You can monitor jobs in progress running as tasks in the executors by using the Apache Spark application UI.
We can use Spark Application UI to monitor the application progress because it helps we identify failed jobs and tasks and optimizes the application workflow:
- The application creates a job. Spark divides the job into one or more stages. The first stage starts with the tasks. The tasks run, and as one stage completes, the next stage starts. When tasks and stages are complete, the next job can begin. Tasks within a stage are processed in parallel, and stages are processed sequentially as dependencies are resolved. The tasks here are referring to the work items that Spark workers are responsible for executing within a Spark job. In the Spark ecosystem, a "task" is the smallest unit of work that is sent to the executor, which is a process running on a worker node. Each worker (see page3309) has a set number of executor processes, and these executors perform the tasks.
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