Apache Spark has begun revolutionizing the way Big Data projects are built and the way modern data warehousing solutions are thought of. While organizations are striving to gain the ground on Data Management, Data Handling, Analytics, and Data operation, it is not easy for a developer to shift focus from the traditional SQL way of managing data to a programming framework like Apache Spark.
Learners and enthusiasts are trying to get their hands on Scala, the de facto language of Apache Spark. If you know Python, you can hang in there with it for a while, but sooner or later, Scala is what you need. There are some best practices and conventions with every tool and programming language framework you use. Here are some of the tips for Apache Spark to ensure you follow the recommended practices suggested by the industry experts.
Use Spark DataFrames API
With a recent release of updated Apache Spark, developers have been able to get their hands on the new and updated Dataframes API. Apache Spark has matured enough to come with a built-in optimizer so that a sloppy code makes little difference. Also, using the DataFrames API ensures that the code you write is readable to a TSQL expert, too. As DataFrames don’t use lambda functions, the code becomes easily readable for someone who has worked with SQL code.
Minimize code shuffling
When Apache Spark transfers data over a network, it serializes data into the binary form which has an effect on the performance of the program when shuffling takes place or operations that require large amounts of data transfer are carried out. For an example, use group ByKey only as a last resort and prefer reduce ByKey. It is better to use connection pools rather than creating dedicated connections for connecting to external data sources.
Don’t install the IDE, not just yet
If you don’t have experience with Java or Scala, you are better off without the IDE for the time being. Focus on your data and ignore the IDE and its hassles for a while. You have a great option that comes with Spark installation- the spark-shell. Use the Spark-shell to drop in individual commands for the engine to process. This is a great way of learning the syntax when you’ve just started off.
With large datasets of more than 200 Gigabytes, garbage collection process on the JVM Spark runs can probably lead to a performance issue. It is advisable to switch to G1 GC from the default ParallelGC for an enhanced performance. If you use Spark Streaming for a pure streaming architecture, it may not be that efficient. If your micro batches do not give you a low enough latency for your processing needs, you should consider a different framework for the same, like- Storm, Flink, or Samza.
Don’t worry about RDDs at all
It is recommended to spend 95 percent of your time on DataFrames. For their lack of support for lambda functions, you should opt for Datasets and not resort back to RDDs. It is being considered a waste of time to learn RDDs at this point of maturity in Apache Spark.
Use Hive SQL Parser instead of Spark’s
Using org.apache.spark.sql.hive.HiveContext instead of the Spark SQL context is better, and you don’t even need to run Hive for using it. Rumors have it that the Spark SQL context as we know it today will go away and be replaced by Hive Context in the future.
Don’t lose sight of the good old software development best practices. Carry out comprehensive unit tests and integration tests on the Spark framework and conform to reuse of code between streaming and batch jobs whenever possible.
Follow the recommended practices and gear yourself up for the day when Big Data with a push from Apache Spark will change the shape of all existing industries.