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How to ignore a class during the serialization using Jackson in Java?

raja
raja
Updated on 06-Jul-2020 3K+ Views

The Jackson @JsonIgnoreType annotation can be used to ignore a class during the serialization process and it can mark all the properties or fields of a class to be ignored while serializing and deserializing a JSON object.Syntax@Target(value={ANNOTATION_TYPE, TYPE}) @Retention(value=RUNTIME) public @interface JsonIgnoreTypeExampleimport com.fasterxml.jackson.annotation.*; import com.fasterxml.jackson.core.*; import com.fasterxml.jackson.databind.*; import java.io.*; public class JsonIgnoreTypeTest { public static void main(String args[]) throws IOException { Employee emp = new Employee(); ObjectMapper mapper = new ObjectMapper(); String jsonString = mapper.writerWithDefaultPrettyPrinter().writeValueAsString(emp); ...

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How to add a JSON string to an existing JSON file in Java?

raja
raja
Updated on 06-Jul-2020 7K+ Views

A Gson is a json library for Java and it can be used to generate a JSON. In the initial step, we can read a JSON file and parsing to a Java object then need to typecast the Java object to a JSonObject and parsing to a JsonArray. Then iterating this JSON array to print the JsonElement. We can create a JsonWriter class to write a JSON encoded value to a stream, one token at a time. Finally, a new JSON string can be written to an existing json file. Exampleimport java.io.*; import java.util.*; import com.google.gson.*; import com.google.gson.stream.*; import com.google.gson.annotations.*; public class JSONFilewriteTest {   ...

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How to ignore the multiple properties of a JSON object in Java?

raja
raja
Updated on 06-Jul-2020 4K+ Views

The @JsonIgnoreProperties Jackson annotation can be used to specify a list of properties or fields of a class to ignore. The @JsonIgnoreProperties annotation can be placed above the class declaration instead of above the individual properties or fields to ignore.Syntax@Target(value={ANNOTATION_TYPE, TYPE, METHOD, CONSTRUCTOR, FIELD}) @Retention(value=RUNTIME) public @interface JsonIgnorePropertiesExampleimport java.io.*; import com.fasterxml.jackson.annotation.*; import com.fasterxml.jackson.databind.*; public class JsonIgnorePropertiesTest { public static void main(String[] args) throws IOException { Customer customer = new Customer("120", "Ravi", "Hyderabad"); System.out.println(customer); ObjectMapper mapper = new ObjectMapper(); ...

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When to use @JsonAutoDetect annotation in Java?

raja
raja
Updated on 06-Jul-2020 6K+ Views

The @JsonAutoDetect annotation can be used at the class level to override the visibility of the properties of a class during serialization and deserialization. We can set the visibility with the properties like "creatorVisibility", "fieldVisibility", "getterVisibility", "setterVisibility" and "isGetterVisibility". The JsonAutoDetect class can define public static constants that are similar to Java class visibility levels like "ANY", "DEFAULT", "NON_PRIVATE", "NONE", "PROTECTED_AND_PRIVATE" and "PUBLIC_ONLY".Exampleimport com.fasterxml.jackson.annotation.*; import com.fasterxml.jackson.databind.*; import java.io.*; public class JsonAutoDetectTest { public static void main(String[] args) throws IOException { Address address = new Address("Madhapur", "Hyderabad", "Telangana"); ...

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How to serialize a JSON string to an Output Handler in Java?

raja
raja
Updated on 06-Jul-2020 682 Views

The Flexjson is a lightweight library for serializing and deserializing Java objects into and from JSON format. A JSONSerializer is the main class for performing serialization of Java objects to JSON. We can serialize a JSON string to an Output Handler using the WriterOutputHandler class and it implements the OutputHandler interface.Syntaxpublic class WriterOutputHandler extends Object implements OutputHandlerExampleimport java.io.*; import flexjson.JSONSerializer; import flexjson.OutputHandler; import flexjson.WriterOutputHandler; public class JsonOutputHandlerTest { public static void main(String[] args) { JSONSerializer serializer = new JSONSerializer().prettyPrint(true); // pretty print JSON Employee emp ...

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How to auto-increment the property of a JSONObject in Java?

raja
raja
Updated on 06-Jul-2020 2K+ Views

A JSONObject is an unordered collection of name/value pairs and parses text from a String to produce a map-like object. However, we can auto-increment the property of a JSONObject using the increment() method of JSONObject class. If there is no such property, create one with a value of 1. If there is such a property and if it is an Integer, Long, Double or Float then add one to it.Syntaxpublic JSONObject increment(java.lang.String key) throws JSONExceptionExampleimport org.json.JSONException; import org.json.JSONObject; public class IncrementJSONObjectTest { public static void main(String[] args) throws JSONException { JSONObject ...

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How to concatenate strings in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 06-Jul-2020 384 Views

Concatenation can be done by using paste function in R.Example> paste("I", "Love", "R", sep=" ") [1] "I Love R"If we want to specify characters between words then it can be done by using collapse argument as follows −> x x [1] "I" "Love" "R" > paste(x, collapse="-") [1] "I-Love-R"

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How to find mode(s) of a vector in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 06-Jul-2020 423 Views

Just like mean and median there is no in-built function in R to find the mode.We can make use of the following user created function for this purpose> Modes

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How to create a frequency table of a vector that contains repeated values in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 06-Jul-2020 791 Views

We can do this by using table function with as.data.frameExample> X as.data.frame(table(X)) X Freq 1 21 2 2 23 3 3 24 1 4 25 5 5 34 2 6 47 2 7 64 1 8 69 5 9 70 1

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How to change the column names and row names of a data frame in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 06-Jul-2020 660 Views

We can colnames function to change the column names and rownames function to change the row names.Example> df df ID Salry 1 1 10000 2 2 30000 3 3 22000 4 4 27000 5 5 18000 > colnames(df) df EmployeeID Salary 1 1 10000 2 2 30000 3 3 22000 4 4 27000 5 5 18000 > rownames(df)

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