# "getSizeAsKb" "getSizeAsKb" "getSizeAsMb" # "getOption" "getSizeAsBytes" "getSizeAsBytes" # "getDeprecatedConfig" "getDouble" "getenv" # "getAvroSchema" "getBoolean" "getClass" Spark_conf_methods % getAvailableMethods() Looking at the object reference however does not provide useful information, so we can list the methods for that class and look for "get" methods that would show us the configuration: spark_conf % spark_context() %>% invoke("conf") For instance, we see that there is a getConf method avaiable to us. We can also use the helper functions to investigate more. # "getExecutorThreadDump" "getLocalProperties" # "getExecutorIds" "getExecutorMemoryStatus" # "eventLogger" "executorAllocationManager"
# "dagScheduler_$eq" "defaultMinPartitions" # "binaryFiles$default$2" "binaryRecords" # "addSparkListener" "applicationAttemptId" # "$lessinit$greater$default$5" "accumulable" # "$lessinit$greater$default$3" "$lessinit$greater$default$4" ScMethodNames <- sort(unique(names(scMethods))) Similarly, we can look at a SparkContext class and show some available methods that can be invoked: scMethods % spark_context() %>% Using the above defined function we can explore the methods available to a DataFrame reference, showing a few of the names first: dfMethods % spark_dataframe() %>% Investigating DataSet and SparkContext class methods Nms <- vapply(mthds, invoke, "getName", FUN.VALUE = character(1)) Params % invoke("getClass") %>% invoke("getMethods") Let us write a small wrapper that will return a some of the method’s details in a more readable fashion, for instance the return type and an overview of parameters: getMethodDetails % invoke("getReturnType") %>% invoke("toString") This is a good result, but the output is not very user-friendly from the R user perspective. We can see that the invoke() chain has returned a list of Java object references, each of them of class. # public .RpcEndpointRef .org$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() We can circumvent that using the getMethods method which (in short) returns an array of Method objects reflecting public member methods of the class.įor instance, retrieving a list of methods for the class: mthds % spark_context() %>% The invoke() interface is powerful, but also a bit hidden from the eyes as we do not immediately know what methods are available for which object classes. Superclass of crunchifyDouble: class the Java reflection API to list the available methods Let's find out if class object represents an Array class = Example 2
#Invoke method in java reflection example how to
How to get the Class's Name, CanonicalName and SimpleName? = Example 1ġ.
Protected void setWebAddress(String webAddress) Result: Crunchify Object: = Create a Setters and Getters for all variables Let’s create POJO class called CrunchifyPOJO. No app server, no base classes, no interfaces required to use. It’s just what the name says: an object that compiles under JDK can be considered a Plain Old Java Object. Follow the Bean convention of getFoo (or isFoo for booleans) and setFoo methods for a mutable attribute named foo leave off the setFoo if foo is immutable.Java Beans must meet the following requirements: Introduction to POJO (Plain Old Java Object)Ī POJO is just a plain, old Java Bean with the restrictions removed.What is Plain Old Java Object?Īlso if you have below questions then you are at right location: In this tutorial I’m going to 1st write simple Java POJO and will perform all Java Reflection Examples on POJO. Best programmatically way to get all Declared Class Fields in Java using Reflection APIs
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