One of the primary operations in memory programming in Apache Spark is shuffling. The process is responsible for managing data […]
Apache Spark is a well-known word in the big data world. A powerful software that is 100 times faster than any other platform. Apache Spark might be fantastic but has its share of challenges. As an Apache Spark service provider, Ksolves’ has thought deeply about the challenges faced by Apache Spark developers.
Best solutions to overcome the five most common challenges of Apache Spark.
Serialization is key
Serialization is very important during the performance of distributed applications. Those formats which are slow to serialize objects, and also those that consume large bytes will automatically slow down the computation.
The issue here is that you’ll have to distribute codes for running and the data needs to be executed. Thus you need to make sure that your programs can serialize, deserialize, and send objects across the wire. This is probably the first thing that you need to tune to optimize a Spark application. We also recommend you to use the Kryo serializer, as the java serializer gives a mediocre performance.
Getting partition recommendations and sizing
Any performance management software that sees data skew will refer to more partitions. Let that fact sink in that the more partitions you have, the better your serialization will be.
The perfect way to decide the number of partitions in an RDD is to equate the number of partitions to the number of crores in the cluster. We do this so that the partitions will process in parallel and the resources receive optimal utilization. We recommend you avoid any situation where you have four executors and five partitions.
Monitoring executor size and yarn memory overhead
Apache Spark developers often try to subdivide data sets into the smallest pieces that can be easily consumed by spark executors, but they don’t want them to be extremely small. Well, there are some ways through which you can find a middle ground, and one needs to find a solution for data skew by ensuring a well-distributed keyspace.
You should make a rough guess at the size of the executor based on the data amount you want to be processed at a single time. There are two values in spark on YARN to track the size of the executor, and the YARN memory overhead. Well, this function is to prevent the YARN scheduler from killing any application that uses huge amounts of data from NIO memory.
Utilizing the full potential of DAG management
It is always better to track the complexity of the executive plan. DAG is the directed acyclic graph visualization tool and comes with a SparkUI for one visual map. If Apache Spark developers think that something that should be straightforward is taking 10 long stages, they can look at their code and reduce it to two to three stages.
We suggest you look at all the stages in parallelization. Track the DAG and don’t focus just on the overall complexity. You need to make sure that the code you have is running in parallel. If you find that you have a non-parallel stage that uses less than 60% of the unavailable executors, you should keep in mind some questions like-
-Should that computer be rolled into other stages?
-Is there any issue with separate partitioning?
Managing library conflicts
In terms of shading, we will recommend you to ensure that any external dependencies classes are available in the environment that you are using, and also they do not conflict with the internal libraries used by Spark. One such example is Google Protobuf, which is a popular binary format for storing data that is more compact than JSON.
Ksolves’ Apache Spark Services
We have discussed the most common challenges and their solutions. But the work doesn’t stop here. There are a lot more things which you need to know. So, which is the best place? Well, there is no better place than Ksolves for Apache Kafka consulting. Our customized Apache Spark services offer the best solution with the most budget-friendly plans. We have a great and experienced team of Apache Spark developers who are qualified to handle all the complexity and provide you with a fault-free service. To know more about Apache Spark services, give us a call or write in the comments section.
Email : email@example.com
Call : +91 8130704295
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