Apache Spark is a powerful open-source distributed computing system that provides an easy-to-use and performant platform for big data processing. One of the key aspects of working with any big data system is the ability to monitor and diagnose applications effectively. The Spark History Server is a tool that aids in inspecting Spark application executions after they have completed. It gives insights into various aspects of the application run which can be used for debugging, performance tuning, and resource optimization. In this comprehensive guide, we will explore how to use the Spark History Server to monitor applications, understanding its functionality, setting it up, and interpreting the information it provides.
What is the Spark History Server?
The Spark History Server is a web-based UI for monitoring and troubleshooting Spark applications after they have finished. It allows you to review historical application data that the Spark event log mechanism records during execution. This data includes stages, tasks, job execution time, resource utilization, and more. By studying this retrospective data, developers and administrators can analyze the performance and resource consumption of their Spark jobs and take necessary action to optimize them.
Setting Up the Spark History Server
Enabling Event Logging in Spark
Before we dive into using the Spark History Server, it’s important to make sure that event logging is enabled in your Spark applications. This can be done by setting the following configuration properties in your `spark-defaults.conf` file or passing them directly to Spark when submitting a job:
spark.eventLog.enabled true
spark.eventLog.dir hdfs:///path/to/eventlog/directoryReplace `hdfs:///path/to/eventlog/directory` with the actual HDFS path where you want to store your event logs. These logs will be used by the History Server to reconstruct the application’s UI.
Starting the Spark History Server
Once event logging is set up, you can start the Spark History Server by running the `start-history-server.sh` script that comes bundled with Spark’s binary distribution. It’s usually located in the `sbin` directory of your Spark installation.
./sbin/start-history-server.shAfter starting the server, you can access the History Server UI by navigating to `http://:18080` in your web browser, where “ is the machine where the server is running.
Monitoring Spark Applications with the History Server
Understanding the Spark History Server UI
The History Server UI is the gateway to your application’s monitoring data and is divided into several sections, each giving details about different aspects of your Spark jobs.
Application List
The main landing page of the History Server shows a list of applications that have finished. Here you’ll see information such as the application ID, the user who submitted the job, its name, when it started and ended, and its overall duration.
Jobs and Stages
When you click on a specific application, the Server shows detailed information about the jobs that were part of that application. This includes the sequence of jobs, their stages, and tasks. It also presents a DAG visualisation of job stages that help in understanding the job flow.
Task Summary
This section gives an overview of the tasks within a stage, including their status, duration, GC time, input and output sizes, and shuffle read and write metrics. It helps in identifying which tasks took the longest and might be candidates for optimization.
Environment
The Environment tab provides valuable information regarding the configuration parameters with which the Spark application was launched. This is instrumental in debugging configuration-related issues.
Executors
The Executors page shows detailed information about each of the executors that were involved in the application. This includes memory and disk usage, active and completed tasks, and total shuffle read and write metrics.
Interpreting Data from the Spark History Server
Now that we understand what information is available to us, let’s talk about how to interpret this data effectively to monitor and improve the performance and efficiency of Spark applications.
Identifying Bottlenecks
Spark applications might run slower than expected for a variety of reasons, such as skewed data, resource constraints, or inefficient transformations. Using the Stages tab, you can identify stages with a high task failure rate or a significant duration difference between tasks which indicates potential bottlenecks.
Analyzing Task Metrics
When you select a task, you can analyze its summary, including input and output metrics, shuffle read and shuffle write details, and task duration.
For example, a task with prolonged shuffle read times could indicate a need for repartitioning the data or adjusting the number of partitions. Similarly, high shuffle write times may necessitate a review of the amount of data being shuffled and the available network resources.
Auditing Executor Performance
Executors are the workhorses of a Spark application, and their performance can directly affect the overall application’s performance. By looking at the Executors tab, you can determine whether resources like memory and CPU are being used optimally or if there’s room for adjustment.
Tuning Garbage Collection
Long garbage collection (GC) times can significantly affect task performance. The Task Summary can provide insights into how much time is spent in GC. If this number is high, you might consider tuning the JVM’s garbage collection settings or the application’s memory usage.
Making Use of Advanced Features
Searching and Filtering
The Spark History Server UI comes with search and filtering capabilities that allow users to pinpoint specific jobs, stages, or tasks based on different criteria such as status, duration, or submission time.
REST API for Programmatic Access
The History Server also exposes a REST API that provides programmatic access to application metrics. This can be used to build custom monitoring solutions or to integrate with other monitoring systems.
import scalaj.http.Http
val applicationId = "app-20170101010101-0001"
val url = s"http://:18080/api/v1/applications/$applicationId"
val response = Http(url).asString
println(response.body)Example REST API Usage
The above Scala code snippet demonstrates a simple usage of the Spark History Server’s REST API. It fetches details for a given application ID and prints out the response.
Best Practices for Monitoring
Regularly Check the Spark History Server
Regularly inspect your Spark History Server UI to stay on top of any issues that may crop up and to maintain a historical record of how your applications are performing over time.
Analyze Failed Applications
Always analyze failed applications using the History Server to understand the root cause of the failure. It can reveal if the failure was due to a resource issue, a data related issue, or something else.
Optimize Resource Usage
Use the information provided by the Spark History Server to optimize resource allocation. For instance, you might discover that your application is not using the full potential of the allocated memory, or it’s spilling data to disk frequently, indicating that you need to tweak memory settings.
Integrate with Cluster Manager Metrics
To get a holistic view of your application’s health and performance, integrate Spark History Server metrics with those from the cluster manager (like YARN, Mesos, or Kubernetes). This will help you to understand how your Spark jobs are affecting other jobs running on the same cluster.
Monitoring applications is an integral part of Spark application development and maintenance. With the help of the Spark History Server and proper interpretation of its rich set of data, it becomes much easier to gain valuable insights into your applications’ performance and troubleshoot effectively.