maxilooki.blogg.se - Java reflection performance

JAVA REFLECTION PERFORMANCE HOW TO
JAVA REFLECTION PERFORMANCE FULL
JAVA REFLECTION PERFORMANCE CODE

This makes sense since the dplyr verb approach actually works by translating the commands into Spark SQL via dbplyr and then sends those translated commands to Spark via that interface. We see multiple invocations do the sql method and also the columns method. First, we will close the previous connection and create a new one with the configuration containing the set to TRUE, and copy in the flights dataset: sparklyr::spark_disconnect(sc) We will use TRUE in our article to keep the output short and easily manageable.

"callstack" will use message() to communicate short info on what is being invoked and the callstack.

"cat" will use cat() to communicate short info on what is being invoked.

TRUE will use message() to communicate short info on what is being invoked.

We can choose one of the following 3 values based on our preferences: To obtain the information, we use the property. In fact, the communication is a set of invocations that can be very different depending on which of the approches we choose for our purposes. Now that we have and overview of the invoke() interface, we can take a look under the hood of sparklyr and see how it actually communicates with the Spark instance. How sparklyr communicates with Spark, invoke logging # " private transient .config.ConfigReader .org$apache$spark$SparkConf$$reader" # " private final .org$apache$spark$SparkConf$$settings" The sparklyr package itself provides facilities of nature similar to those above, looking at some of them, even though they are not exported: sparklyr:::jobj_class(spark_conf)Ĭapture.output(sparklyr:::jobj_inspect(spark_conf)) %>% head(10) We could therefore improve our helper to be more detailed in the return value information.

# Invoke the `getAll` method and look at part of the result We see that there is a getAll method that could prove useful, returning a list of tuples and taking no arguments as input: # Returns a list of tuples, takes no arguments: # "getTimeAsSeconds" "getTimeAsSeconds" "getWithSubstitution" # "getSizeAsMb" "getTimeAsMs" "getTimeAsMs" # "getSizeAsKb" "getSizeAsKb" "getSizeAsMb" # "getSizeAsBytes" "getSizeAsGb" "getSizeAsGb"

# "getOption" "getSizeAsBytes" "getSizeAsBytes" # "getDeprecatedConfig" "getDouble" "getenv" # "getAvroSchema" "getBoolean" "getClass" Spark_conf_methods % getAvailableMethods() ScMethodNames % spark_context() %>% invoke("conf") Similarly, we can look at a SparkContext class and show some available methods that can be invoked: scMethods % spark_context() %>% Params % invoke("getClass") %>% invoke("getMethods") Let us write a small wrapper that will return a some of the method’s details in a more readable fashion, for instance the return type and an overview of parameters: getMethodDetails % invoke("getReturnType") %>% invoke("toString") This is a good result, but the output is not very user-friendly from the R user perspective. We can see that the invoke() chain has returned a list of Java object references, each of them of class . # public .RpcEndpointRef .org$apache$spark$SparkContext$$_heartbeatReceiver() # public scala.Option .org$apache$spark$SparkContext$$_ui() # public scala.Option .org$apache$spark$SparkContext$$_progressBar() # public .org$apache$spark$SparkContext$$_env() # public .org$apache$spark$SparkContext$$_conf() # public .CallSite .org$apache$spark$SparkContext$$creationSite() # Connect and copy the flights dataset to the instance

JAVA REFLECTION PERFORMANCE CODE

You can then use the user name rstudio and password pass to login and continue experimenting with the code in this post.

Should make RStudio available by navigating to your browser. If you have docker available, running docker run -d -p 8787:8787 -e PASSWORD=pass -name rstudio jozefhajnala/sparkly:add-rstudio

JAVA REFLECTION PERFORMANCE FULL

The full setup of Spark and sparklyr is not in the scope of this post, please check the first one for some setup instructions and a ready-made Docker image. How sparklyr communicates with Spark, invoke logging.Investigating DataSet and SparkContext class methods.Using the Java reflection API to list the available methods.In this fifth part, we will look into more details around sparklyr’s invoke() API, investigate available methods for different classes of objects using the Java reflection API and look under the hood of the sparklyr interface mechanism with invoke logging.

JAVA REFLECTION PERFORMANCE HOW TO

In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs, SQL query generators that can be executed by Spark and how to use the lower-level API to invoke methods on Java object references from R. For the best experience, charts and code formatting, read the article on its original home.