Experiencing a Spark OutOfMemory Error? Here’s the best solution
Today, Apache Spark engineers are in high demand since the technology is being utilised widely across a wide range of sectors, resulting in an increase in need for them over time. These individuals are responsible for collecting and analysing unstructured data in order to uncover insights that will aid in strategic decision-making.
Every year, the Apache Spark analytics business grows in income, not just locally but also internationally, as the company becomes more engaged in analytics export to nations all over the world. And it’s always been observed that as an industry grows at an exponential rate, the need for human resources, in this instance Apache Spark developers, grows at an exponential rate as well.
Your Apache Spark application failed due to an OutOfMemoryError issue that was not handled. It is possible that you may get an error. As a result of reading this blog article, you will get a better understanding of the most frequent OutOfMemoryException that occurs in Apache Spark applications.
The purpose of this blog is to record the learning and familiarity with Apache Spark, as well as to use that knowledge to improve the performance of Apache Spark in general. You will be guided through the specifics of what would have occurred in the background and resulted in this exception being raised. In addition, you will learn how to deal with such exceptions in real-world situations throughout this course.
What is the primary source of the problem?
The most probable reason of this error is because the Java virtual machines are not given enough heap memory to work with, as described above (JVMs). As part of the Apache Spark application, these JVMs are started as executors or drivers, depending on their role.
Exceptions due to a lack of available memory
Spark tasks may fail as a result of out of memory errors occurring at the driver or executor end of the process. Since part of the debugging process for out of memory errors, you should determine how much memory and cores the application needs, as these are the two most important factors to consider while improving the Spark program’s performance. You may change the Spark application settings to resolve the out-of-memory errors based on the resources that are available in your environment.
Out of memory issues and how to analyse them in Spark
- The first thing that comes to mind is that you may want to raise the heap size till it works. It may be sufficient, but there are occasions when it is preferable to grasp what is actually going on. Follow these procedures, just as you would for any other bug:
- Make the system as repeatable as possible. Because Spark tasks may take a long time to complete, attempt to recreate the issue on a smaller dataset to reduce the length of the debugging loop.
- Make the system visible so that it may be studied. Spark logging and all metrics should be enabled, and the JVM verbose Garbage Collector (GC) logging should be configured.
- Make use of the scientific procedure. Understand the system, form hypotheses, test them, and maintain a record of the observations you make as you learn more.
What are some of the most common fundamental troubleshooting techniques?
- An OutOfMemory issue may occur here if Spark is used incorrectly, like in the example above. We have two options for dealing with this issue: either utilise spark.driver.maxResultSize or repartition.
- The table is initially realised at the driver side before being broadcasted to the executors when executing a BroadcastJoin operation. To fix this problem, there are two options: either increase the driver memory or decrease the value for spark.sql.autoBroadcastJoinThreshold. In this instance, increasing the driver memory is the preferred option.
- If the number of spark cores available to our executors is insufficient, we will be forced to process an excessive number of partitions. Spark.default.parallelism and spark.executor.cores may be configured to get around this problem.
- It is possible that the application will fail as a result of YARN memory constraints. In this case, the setup must be done correctly in order to prevent output from spilling over the disc.
There are many divisions within Apache Spark that may be pursued as a profession. It has a large number of activities in its data cycle, and it typically has a variety of specialists working on each of them.
By thoroughly comprehending the issue, spark programmers may get a knowledge of how to set up the settings that are needed for their use situation and app in the correct manner. In order to optimise the operational efficiency or questions to be performed inside the software application, it will be necessary to analyse the error and its potential causes.