#Compare java to r full
#Compare java to r pro
#Compare java to r code

Scala/Java: Spark is written in Scala and runs on the JVM however DataFrames are a custom columnar abstraction so performance is not necesarilly guranteed.For these experiments I purposefully chose UDFs that can be represented in native Spark SQL, to provider a benchmark, however in real life that may not always be the case. Native/SQL: There are many built in UDFs for DataFrames and presumably they are optimized for performance.

#Compare java to r full

In each case a where clause and a count are used to bypass any optimizations which might result in the full table not being processed. NoOp: Takes an int and returns it unmodified.Numeric: Takes an int, caps it at 10 and takes the log.String: Takes a string column and selects the first 5 characters.Two relatively simple custom UDFs were compared:

#Compare java to r code

Wikipedia ClickStream data from April 2018 (available here: )ДЄll the code is available on Github here.

#Compare java to r pro

2016 15' Macbook Pro 2.6ghz 16gb ram (4 cores, 8 with hyperthreading).

UDFs which take in all the rows for a group and return back a subset of those rows.

UDFs that take in a single value and return a single value.

To remove the impact of disk reads all input DataFrames were cached. The DataFrame interface abstracts away most performance differences so in comparing performance we'll be focusing on custom UDFs. select uppercase(name) as name, mean(legs) as legs Given how closely the DataFrame API matches up with SQL it's easy to switch between SQL and non-SQL APIs. One nice feature is that you can write custom SQL UDFs in Scala, Java, Python or R. Spark is capable of running SQL commands and is generally compatible with the Hive SQL syntax (including UDFs). Since Spark 2.3 the new Structured Streaming API is available in R which finally allows for stream processing support. The R API is also idiomatic R rather than a clone of the Scala API as in Python which makes it a lower barrier to entry for existing R users. Support for R is relatively new and in the past support for various APIs has lagged behind Scala/Python however there is now relatively parity. As a note, Vectorized UDFs have many limitations including what types can be returned and the potential for out of memory errors. Since Spark 2.3 there is experimental support for Vectorized UDFs which leverage Apache Arrow to increase the performance of UDFs written in Python. The Python API, however, is not very pythonic and instead is a very close clone of the Scala API. Python is one of the de-facto languages of Data Science and as a result a lot of effort has gone into making Spark work seamlessly with Python despite being on the JVM. spark.udf().register('uppercase', (String string) -> string.toUpperCase(), DataTypes.StringType) One definite upside of Java support is that other JVM languages such as Kotlin can use it to run Spark seamlessly. Furthermore, the Dataset API is not available and interactive notebook environments do not support Java. Scala is somewhat interoperable with Java and the Spark team has made sure to bridge the remaining gaps.The limitations of Java mean that the APIs aren't always as concise as in Scala however that has improved since Java 8's lambda support. def uppercase = udf((string: String) => string.toUpperCase()) Scala is the only language that supports the typed Dataset functionality and, along with Java, allows one to write proper UDAFs (User Defined Aggregation Functions). Spark is written in Scala and as a result Scala is the de-facto API interface for Spark. As a note, this post focused on the DataFrame/DataSet APIs rather than the now deprecated RDD APIs. However not all language APIs are created equal and in this post we'll look at the differences from both a syntax and performance point of view. One of its selling point is the cross-language API that allows you to write Spark code in Scala, Java, Python, R or SQL (with others supported unofficially). It has since become one of the core technologies used for large scale data processing. Apache Spark is an open source distributed computing platform released in 2010 by Berkeley's AMPLab.