Category: avro

AVRO – Serialization ”; Previous Next Data is serialized for two objectives − For persistent storage To transport the data over network What is Serialization? Serialization is the process of translating data structures or objects state into binary or textual form to transport the data over network or to store on some persisten storage. Once the data is transported over network or retrieved from the persistent storage, it needs to be deserialized again. Serialization is termed as marshalling and deserialization is termed as unmarshalling. Serialization in Java Java provides a mechanism, called object serialization where an object can be represented as a sequence of bytes that includes the object”s data as well as information about the object”s type and the types of data stored in the object. After a serialized object is written into a file, it can be read from the file and deserialized. That is, the type information and bytes that represent the object and its data can be used to recreate the object in memory. ObjectInputStream and ObjectOutputStream classes are used to serialize and deserialize an object respectively in Java. Serialization in Hadoop Generally in distributed systems like Hadoop, the concept of serialization is used for Interprocess Communication and Persistent Storage. Interprocess Communication To establish the interprocess communication between the nodes connected in a network, RPC technique was used. RPC used internal serialization to convert the message into binary format before sending it to the remote node via network. At the other end the remote system deserializes the binary stream into the original message. The RPC serialization format is required to be as follows − Compact − To make the best use of network bandwidth, which is the most scarce resource in a data center. Fast − Since the communication between the nodes is crucial in distributed systems, the serialization and deserialization process should be quick, producing less overhead. Extensible − Protocols change over time to meet new requirements, so it should be straightforward to evolve the protocol in a controlled manner for clients and servers. Interoperable − The message format should support the nodes that are written in different languages. Persistent Storage Persistent Storage is a digital storage facility that does not lose its data with the loss of power supply. Files, folders, databases are the examples of persistent storage. Writable Interface This is the interface in Hadoop which provides methods for serialization and deserialization. The following table describes the methods − S.No. Methods and Description 1 void readFields(DataInput in) This method is used to deserialize the fields of the given object. 2 void write(DataOutput out) This method is used to serialize the fields of the given object. Writable Comparable Interface It is the combination of Writable and Comparable interfaces. This interface inherits Writable interface of Hadoop as well as Comparable interface of Java. Therefore it provides methods for data serialization, deserialization, and comparison. S.No. Methods and Description 1 int compareTo(class obj) This method compares current object with the given object obj. In addition to these classes, Hadoop supports a number of wrapper classes that implement WritableComparable interface. Each class wraps a Java primitive type. The class hierarchy of Hadoop serialization is given below − These classes are useful to serialize various types of data in Hadoop. For instance, let us consider the IntWritable class. Let us see how this class is used to serialize and deserialize the data in Hadoop. IntWritable Class This class implements Writable, Comparable, and WritableComparable interfaces. It wraps an integer data type in it. This class provides methods used to serialize and deserialize integer type of data. Constructors S.No. Summary 1 IntWritable() 2 IntWritable( int value) Methods S.No. Summary 1 int get() Using this method you can get the integer value present in the current object. 2 void readFields(DataInput in) This method is used to deserialize the data in the given DataInput object. 3 void set(int value) This method is used to set the value of the current IntWritable object. 4 void write(DataOutput out) This method is used to serialize the data in the current object to the given DataOutput object. Serializing the Data in Hadoop The procedure to serialize the integer type of data is discussed below. Instantiate IntWritable class by wrapping an integer value in it. Instantiate ByteArrayOutputStream class. Instantiate DataOutputStream class and pass the object of ByteArrayOutputStream class to it. Serialize the integer value in IntWritable object using write() method. This method needs an object of DataOutputStream class. The serialized data will be stored in the byte array object which is passed as parameter to the DataOutputStream class at the time of instantiation. Convert the data in the object to byte array. Example The following example shows how to serialize data of integer type in Hadoop − import java.io.ByteArrayOutputStream; import java.io.DataOutputStream; import java.io.IOException; import org.apache.hadoop.io.IntWritable; public class Serialization { public byte[] serialize() throws IOException{ //Instantiating the IntWritable object IntWritable intwritable = new IntWritable(12); //Instantiating ByteArrayOutputStream object ByteArrayOutputStream byteoutputStream = new ByteArrayOutputStream(); //Instantiating DataOutputStream object DataOutputStream dataOutputStream = new DataOutputStream(byteoutputStream); //Serializing the data intwritable.write(dataOutputStream); //storing the serialized object in bytearray byte[] byteArray = byteoutputStream.toByteArray(); //Closing the OutputStream dataOutputStream.close(); return(byteArray); } public static void main(String args[]) throws IOException{ Serialization serialization= new Serialization(); serialization.serialize(); System.out.println(); } } Deserializing the Data in Hadoop The procedure to deserialize the integer type of data is discussed below − Instantiate IntWritable class by wrapping an integer value in it. Instantiate ByteArrayOutputStream class. Instantiate DataOutputStream class and pass the object of ByteArrayOutputStream class to it. Deserialize the data in the object of DataInputStream using readFields() method of IntWritable class. The deserialized data will be stored in the object of IntWritable class. You can retrieve this data using get() method of this class. Example The following example shows how to deserialize the data of integer type in Hadoop − import java.io.ByteArrayInputStream; import java.io.DataInputStream; import org.apache.hadoop.io.IntWritable; public class Deserialization { public void deserialize(byte[]byteArray) throws Exception{ //Instantiating the IntWritable class IntWritable intwritable =new IntWritable(); //Instantiating ByteArrayInputStream object ByteArrayInputStream InputStream = new ByteArrayInputStream(byteArray); //Instantiating DataInputStream object

AVRO – Overview ”; Previous Next To transfer data over a network or for its persistent storage, you need to serialize the data. Prior to the serialization APIs provided by Java and Hadoop, we have a special utility, called Avro, a schema-based serialization technique. This tutorial teaches you how to serialize and deserialize the data using Avro. Avro provides libraries for various programming languages. In this tutorial, we demonstrate the examples using Java library. What is Avro? Apache Avro is a language-neutral data serialization system. It was developed by Doug Cutting, the father of Hadoop. Since Hadoop writable classes lack language portability, Avro becomes quite helpful, as it deals with data formats that can be processed by multiple languages. Avro is a preferred tool to serialize data in Hadoop. Avro has a schema-based system. A language-independent schema is associated with its read and write operations. Avro serializes the data which has a built-in schema. Avro serializes the data into a compact binary format, which can be deserialized by any application. Avro uses JSON format to declare the data structures. Presently, it supports languages such as Java, C, C++, C#, Python, and Ruby. Avro Schemas Avro depends heavily on its schema. It allows every data to be written with no prior knowledge of the schema. It serializes fast and the resulting serialized data is lesser in size. Schema is stored along with the Avro data in a file for any further processing. In RPC, the client and the server exchange schemas during the connection. This exchange helps in the communication between same named fields, missing fields, extra fields, etc. Avro schemas are defined with JSON that simplifies its implementation in languages with JSON libraries. Like Avro, there are other serialization mechanisms in Hadoop such as Sequence Files, Protocol Buffers, and Thrift. Comparison with Thrift and Protocol Buffers Thrift and Protocol Buffers are the most competent libraries with Avro. Avro differs from these frameworks in the following ways − Avro supports both dynamic and static types as per the requirement. Protocol Buffers and Thrift use Interface Definition Languages (IDLs) to specify schemas and their types. These IDLs are used to generate code for serialization and deserialization. Avro is built in the Hadoop ecosystem. Thrift and Protocol Buffers are not built in Hadoop ecosystem. Unlike Thrift and Protocol Buffer, Avro”s schema definition is in JSON and not in any proprietary IDL. Property Avro Thrift & Protocol Buffer Dynamic schema Yes No Built into Hadoop Yes No Schema in JSON Yes No No need to compile Yes No No need to declare IDs Yes No Bleeding edge Yes No Features of Avro Listed below are some of the prominent features of Avro − Avro is a language-neutral data serialization system. It can be processed by many languages (currently C, C++, C#, Java, Python, and Ruby). Avro creates binary structured format that is both compressible and splittable. Hence it can be efficiently used as the input to Hadoop MapReduce jobs. Avro provides rich data structures. For example, you can create a record that contains an array, an enumerated type, and a sub record. These datatypes can be created in any language, can be processed in Hadoop, and the results can be fed to a third language. Avro schemas defined in JSON, facilitate implementation in the languages that already have JSON libraries. Avro creates a self-describing file named Avro Data File, in which it stores data along with its schema in the metadata section. Avro is also used in Remote Procedure Calls (RPCs). During RPC, client and server exchange schemas in the connection handshake. General Working of Avro To use Avro, you need to follow the given workflow − Step 1 − Create schemas. Here you need to design Avro schema according to your data. Step 2 − Read the schemas into your program. It is done in two ways − By Generating a Class Corresponding to Schema − Compile the schema using Avro. This generates a class file corresponding to the schema By Using Parsers Library − You can directly read the schema using parsers library. Step 3 − Serialize the data using the serialization API provided for Avro, which is found in the package org.apache.avro.specific. Step 4 − Deserialize the data using deserialization API provided for Avro, which is found in the package org.apache.avro.specific. Print Page Previous Next Advertisements ”;

AVRO – Deserialization By Generating Class ”; Previous Next As described earlier, one can read an Avro schema into a program either by generating a class corresponding to the schema or by using the parsers library. This chapter describes how to read the schema by generating a class and Deserialize the data using Avro. Deserialization by Generating a Class The serialized data is stored in the file emp.avro. You can deserialize and read it using Avro. Follow the procedure given below to deserialize the serialized data from a file. Step 1 Create an object of DatumReader interface using SpecificDatumReader class. DatumReader<emp>empDatumReader = new SpecificDatumReader<emp>(emp.class); Step 2 Instantiate DataFileReader for emp class. This class reads serialized data from a file. It requires the Dataumeader object, and path of the file where the serialized data is existing, as a parameters to the constructor. DataFileReader<emp> dataFileReader = new DataFileReader(new File(“/path/to/emp.avro”), empDatumReader); Step 3 Print the deserialized data, using the methods of DataFileReader. The hasNext() method will return a boolean if there are any elements in the Reader. The next() method of DataFileReader returns the data in the Reader. while(dataFileReader.hasNext()){ em=dataFileReader.next(em); System.out.println(em); } Example – Deserialization by Generating a Class The following complete program shows how to deserialize the data in a file using Avro. import java.io.File; import java.io.IOException; import org.apache.avro.file.DataFileReader; import org.apache.avro.io.DatumReader; import org.apache.avro.specific.SpecificDatumReader; public class Deserialize { public static void main(String args[]) throws IOException{ //DeSerializing the objects DatumReader<emp> empDatumReader = new SpecificDatumReader<emp>(emp.class); //Instantiating DataFileReader DataFileReader<emp> dataFileReader = new DataFileReader<emp>(new File(“/home/Hadoop/Avro_Work/with_code_genfile/emp.avro”), empDatumReader); emp em=null; while(dataFileReader.hasNext()){ em=dataFileReader.next(em); System.out.println(em); } } } Browse into the directory where the generated code is placed. In this case, at home/Hadoop/Avro_work/with_code_gen. $ cd home/Hadoop/Avro_work/with_code_gen/ Now, copy and save the above program in the file named DeSerialize.java. Compile and execute it as shown below − $ javac Deserialize.java $ java Deserialize Output {“name”: “omar”, “id”: 1, “salary”: 30000, “age”: 21, “address”: “Hyderabad”} {“name”: “ram”, “id”: 2, “salary”: 40000, “age”: 30, “address”: “Hyderabad”} {“name”: “robbin”, “id”: 3, “salary”: 35000, “age”: 25, “address”: “Hyderabad”} Print Page Previous Next Advertisements ”;

AVRO – Schemas ”; Previous Next Avro, being a schema-based serialization utility, accepts schemas as input. In spite of various schemas being available, Avro follows its own standards of defining schemas. These schemas describe the following details − type of file (record by default) location of record name of the record fields in the record with their corresponding data types Using these schemas, you can store serialized values in binary format using less space. These values are stored without any metadata. Creating Avro Schemas The Avro schema is created in JavaScript Object Notation (JSON) document format, which is a lightweight text-based data interchange format. It is created in one of the following ways − A JSON string A JSON object A JSON array Example − The following example shows a schema, which defines a document, under the name space Tutorialspoint, with name Employee, having fields name and age. { “type” : “record”, “namespace” : “Tutorialspoint”, “name” : “Employee”, “fields” : [ { “name” : “Name” , “type” : “string” }, { “name” : “Age” , “type” : “int” } ] } In this example, you can observe that there are four fields for each record − type − This field comes under the document as well as the under the field named fields. In case of document, it shows the type of the document, generally a record because there are multiple fields. When it is field, the type describes data type. namespace − This field describes the name of the namespace in which the object resides. name − This field comes under the document as well as the under the field named fields. In case of document, it describes the schema name. This schema name together with the namespace, uniquely identifies the schema within the store (Namespace.schema name). In the above example, the full name of the schema will be Tutorialspoint.Employee. In case of fields, it describes name of the field. Primitive Data Types of Avro Avro schema is having primitive data types as well as complex data types. The following table describes the primitive data types of Avro − Data type Description null Null is a type having no value. int 32-bit signed integer. long 64-bit signed integer. float single precision (32-bit) IEEE 754 floating-point number. double double precision (64-bit) IEEE 754 floating-point number. bytes sequence of 8-bit unsigned bytes. string Unicode character sequence. Complex Data Types of Avro Along with primitive data types, Avro provides six complex data types namely Records, Enums, Arrays, Maps, Unions, and Fixed. Record A record data type in Avro is a collection of multiple attributes. It supports the following attributes − name − The value of this field holds the name of the record. namespace − The value of this field holds the name of the namespace where the object is stored. type − The value of this attribute holds either the type of the document (record) or the datatype of the field in the schema. fields − This field holds a JSON array, which have the list of all of the fields in the schema, each having name and the type attributes. Example Given below is the example of a record. { ” type ” : “record”, ” namespace ” : “Tutorialspoint”, ” name ” : “Employee”, ” fields ” : [ { “name” : ” Name” , “type” : “string” }, { “name” : “age” , “type” : “int” } ] } Enum An enumeration is a list of items in a collection, Avro enumeration supports the following attributes − name − The value of this field holds the name of the enumeration. namespace − The value of this field contains the string that qualifies the name of the Enumeration. symbols − The value of this field holds the enum”s symbols as an array of names. Example Given below is the example of an enumeration. { “type” : “enum”, “name” : “Numbers”, “namespace”: “data”, “symbols” : [ “ONE”, “TWO”, “THREE”, “FOUR” ] } Arrays This data type defines an array field having a single attribute items. This items attribute specifies the type of items in the array. Example { ” type ” : ” array “, ” items ” : ” int ” } Maps The map data type is an array of key-value pairs, it organizes data as key-value pairs. The key for an Avro map must be a string. The values of a map hold the data type of the content of map. Example {“type” : “map”, “values” : “int”} Unions A union datatype is used whenever the field has one or more datatypes. They are represented as JSON arrays. For example, if a field that could be either an int or null, then the union is represented as [“int”, “null”]. Example Given below is an example document using unions − { “type” : “record”, “namespace” : “tutorialspoint”, “name” : “empdetails “, “fields” : [ { “name” : “experience”, “type”: [“int”, “null”] }, { “name” : “age”, “type”: “int” } ] } Fixed This data type is used to declare a fixed-sized field that can be used for storing binary data. It has field name and data as attributes. Name holds the name of the field, and size holds the size of the field. Example { “type” : “fixed” , “name” : “bdata”, “size” : 1048576} Print Page Previous Next Advertisements ”;

AVRO – Deserialization Using Parsers ”; Previous Next As mentioned earlier, one can read an Avro schema into a program either by generating a class corresponding to a schema or by using the parsers library. In Avro, data is always stored with its corresponding schema. Therefore, we can always read a serialized item without code generation. This chapter describes how to read the schema using parsers library and Deserializing the data using Avro. Deserialization Using Parsers Library The serialized data is stored in the file mydata.txt. You can deserialize and read it using Avro. Follow the procedure given below to deserialize the serialized data from a file. Step 1 First of all, read the schema from the file. To do so, use Schema.Parser class. This class provides methods to parse the schema in different formats. Instantiate the Schema.Parser class by passing the file path where the schema is stored. Schema schema = new Schema.Parser().parse(new File(“/path/to/emp.avsc”)); Step 2 Create an object of DatumReader interface using SpecificDatumReader class. DatumReader<emp>empDatumReader = new SpecificDatumReader<emp>(emp.class); Step 3 Instantiate DataFileReader class. This class reads serialized data from a file. It requires the DatumReader object, and path of the file where the serialized data exists, as a parameters to the constructor. DataFileReader<GenericRecord> dataFileReader = new DataFileReader<GenericRecord>(new File(“/path/to/mydata.txt”), datumReader); Step 4 Print the deserialized data, using the methods of DataFileReader. The hasNext() method returns a boolean if there are any elements in the Reader . The next() method of DataFileReader returns the data in the Reader. while(dataFileReader.hasNext()){ em=dataFileReader.next(em); System.out.println(em); } Example – Deserialization Using Parsers Library The following complete program shows how to deserialize the serialized data using Parsers library − public class Deserialize { public static void main(String args[]) throws Exception{ //Instantiating the Schema.Parser class. Schema schema = new Schema.Parser().parse(new File(“/home/Hadoop/Avro/schema/emp.avsc”)); DatumReader<GenericRecord> datumReader = new GenericDatumReader<GenericRecord>(schema); DataFileReader<GenericRecord> dataFileReader = new DataFileReader<GenericRecord>(new File(“/home/Hadoop/Avro_Work/without_code_gen/mydata.txt”), datumReader); GenericRecord emp = null; while (dataFileReader.hasNext()) { emp = dataFileReader.next(emp); System.out.println(emp); } System.out.println(“hello”); } } Browse into the directory where the generated code is placed. In this case, it is at home/Hadoop/Avro_work/without_code_gen. $ cd home/Hadoop/Avro_work/without_code_gen/ Now copy and save the above program in the file named DeSerialize.java. Compile and execute it as shown below − $ javac Deserialize.java $ java Deserialize Output {“name”: “ramu”, “id”: 1, “salary”: 30000, “age”: 25, “address”: “chennai”} {“name”: “rahman”, “id”: 2, “salary”: 35000, “age”: 30, “address”: “Delhi”} Print Page Previous Next Advertisements ”;

AVRO – Serialization Using Parsers ”; Previous Next One can read an Avro schema into a program either by generating a class corresponding to a schema or by using the parsers library. In Avro, data is always stored with its corresponding schema. Therefore, we can always read a schema without code generation. This chapter describes how to read the schema by using parsers library and to serialize the data using Avro. Serialization Using Parsers Library To serialize the data, we need to read the schema, create data according to the schema, and serialize the schema using the Avro API. The following procedure serializes the data without generating any code − Step 1 First of all, read the schema from the file. To do so, use Schema.Parser class. This class provides methods to parse the schema in different formats. Instantiate the Schema.Parser class by passing the file path where the schema is stored. Schema schema = new Schema.Parser().parse(new File(“/path/to/emp.avsc”)); Step 2 Create the object of GenericRecord interface, by instantiating GenericData.Record class as shown below. Pass the above created schema object to its constructor. GenericRecord e1 = new GenericData.Record(schema); Step 3 Insert the values in the schema using the put() method of the GenericData class. e1.put(“name”, “ramu”); e1.put(“id”, 001); e1.put(“salary”,30000); e1.put(“age”, 25); e1.put(“address”, “chennai”); Step 4 Create an object of DatumWriter interface using the SpecificDatumWriter class. It converts Java objects into in-memory serialized format. The following example instantiates SpecificDatumWriter class object for emp class − DatumWriter<emp> empDatumWriter = new SpecificDatumWriter<emp>(emp.class); Step 5 Instantiate DataFileWriter for emp class. This class writes serialized records of data conforming to a schema, along with the schema itself, in a file. This class requires the DatumWriter object, as a parameter to the constructor. DataFileWriter<emp> dataFileWriter = new DataFileWriter<emp>(empDatumWriter); Step 6 Open a new file to store the data matching to the given schema using create() method. This method requires the schema, and the path of the file where the data is to be stored, as parameters. In the example given below, schema is passed using getSchema() method and the data file is stored in the path /home/Hadoop/Avro/serialized_file/emp.avro. empFileWriter.create(e1.getSchema(), new File(“/home/Hadoop/Avro/serialized_file/emp.avro”)); Step 7 Add all the created records to the file using append( ) method as shown below. empFileWriter.append(e1); empFileWriter.append(e2); empFileWriter.append(e3); Example – Serialization Using Parsers The following complete program shows how to serialize the data using parsers − import java.io.File; import java.io.IOException; import org.apache.avro.Schema; import org.apache.avro.file.DataFileWriter; import org.apache.avro.generic.GenericData; import org.apache.avro.generic.GenericDatumWriter; import org.apache.avro.generic.GenericRecord; import org.apache.avro.io.DatumWriter; public class Seriali { public static void main(String args[]) throws IOException{ //Instantiating the Schema.Parser class. Schema schema = new Schema.Parser().parse(new File(“/home/Hadoop/Avro/schema/emp.avsc”)); //Instantiating the GenericRecord class. GenericRecord e1 = new GenericData.Record(schema); //Insert data according to schema e1.put(“name”, “ramu”); e1.put(“id”, 001); e1.put(“salary”,30000); e1.put(“age”, 25); e1.put(“address”, “chenni”); GenericRecord e2 = new GenericData.Record(schema); e2.put(“name”, “rahman”); e2.put(“id”, 002); e2.put(“salary”, 35000); e2.put(“age”, 30); e2.put(“address”, “Delhi”); DatumWriter<GenericRecord> datumWriter = new GenericDatumWriter<GenericRecord>(schema); DataFileWriter<GenericRecord> dataFileWriter = new DataFileWriter<GenericRecord>(datumWriter); dataFileWriter.create(schema, new File(“/home/Hadoop/Avro_work/without_code_gen/mydata.txt”)); dataFileWriter.append(e1); dataFileWriter.append(e2); dataFileWriter.close(); System.out.println(“data successfully serialized”); } } Browse into the directory where the generated code is placed. In this case, at home/Hadoop/Avro_work/without_code_gen. $ cd home/Hadoop/Avro_work/without_code_gen/ Now copy and save the above program in the file named Serialize.java. Compile and execute it as shown below − $ javac Serialize.java $ java Serialize Output data successfully serialized If you verify the path given in the program, you can find the generated serialized file as shown below. Print Page Previous Next Advertisements ”;

AVRO Tutorial PDF Version Quick Guide Resources Job Search Discussion Apache Avro is a language-neutral data serialization system, developed by Doug Cutting, the father of Hadoop. This is a brief tutorial that provides an overview of how to set up Avro and how to serialize and deserialize data using Avro. Audience This tutorial is prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a successful Hadoop developer. It will be a handy resource for enthusiasts who want to use Avro for data serialization and deserialization. Prerequisites Before you start proceeding with this tutorial, we assume that you are already aware of Hadoop”s architecture and APIs, and you have experience in writing basic applications, preferably using Java. Print Page Previous Next Advertisements ”;

Discuss AVRO ”; Previous Next Apache Avro is a language-neutral data serialization system, developed by Doug Cutting, the father of Hadoop. This is a brief tutorial that provides an overview of how to set up Avro and how to serialize and deserialize data using Avro. Print Page Previous Next Advertisements ”;

AVRO – Useful Resources ”; Previous Next The following resources contain additional information on AVRO. Please use them to get more in-depth knowledge on this. Useful Video Courses Advance Big Data Analytics using Hive & Sqoop Best Seller 51 Lectures 4 hours Navdeep Kaur More Detail Big Data Crash Course 68 Lectures 9 hours DataCouch More Detail Big Data For Architects 61 Lectures 7.5 hours DataCouch More Detail Learn Advanced Apache Kafka from Scratch Featured 154 Lectures 9 hours Learnkart Technology Pvt Ltd More Detail Kafka Streams with Spring Cloud Stream 49 Lectures 7 hours Packt Publishing More Detail AWS Athena – Interactive SQL Interface 17 Lectures 1 hours Jim Macaulay More Detail Print Page Previous Next Advertisements ”;

AVRO – Environment Setup ”; Previous Next Apache software foundation provides Avro with various releases. You can download the required release from Apache mirrors. Let us see, how to set up the environment to work with Avro − Downloading Avro To download Apache Avro, proceed with the following − Open the web page Apache.org. You will see the homepage of Apache Avro as shown below − Click on project → releases. You will get a list of releases. Select the latest release which leads you to a download link. mirror.nexcess is one of the links where you can find the list of all libraries of different languages that Avro supports as shown below − You can select and download the library for any of the languages provided. In this tutorial, we use Java. Hence download the jar files avro-1.7.7.jar and avro-tools-1.7.7.jar. Avro with Eclipse To use Avro in Eclipse environment, you need to follow the steps given below − Step 1. Open eclipse. Step 2. Create a project. Step 3. Right-click on the project name. You will get a shortcut menu. Step 4. Click on Build Path. It leads you to another shortcut menu. Step 5. Click on Configure Build Path… You can see Properties window of your project as shown below − Step 6. Under libraries tab, click on ADD EXternal JARs… button. Step 7. Select the jar file avro-1.77.jar you have downloaded. Step 8. Click on OK. Avro with Maven You can also get the Avro library into your project using Maven. Given below is the pom.xml file for Avro. <project xmlns=”http://maven.apache.org/POM/4.0.0″ xmlns:xsi=” http://www.w3.org/2001/XMLSchema-instance” xsi:schemaLocation=”http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd”> <modelVersion>4.0.0</modelVersion> <groupId>Test</groupId> <artifactId>Test</artifactId> <version>0.0.1-SNAPSHOT</version> <build> <sourceDirectory>src</sourceDirectory> <plugins> <plugin> <artifactId>maven-compiler-plugin</artifactId> <version>3.1</version> <configuration> <source>1.7</source> <target>1.7</target> </configuration> </plugin> </plugins> </build> <dependencies> <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro</artifactId> <version>1.7.7</version> </dependency> <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro-tools</artifactId> <version>1.7.7</version> </dependency> <dependency> <groupId>org.apache.logging.log4j</groupId> <artifactId>log4j-api</artifactId> <version>2.0-beta9</version> </dependency> <dependency> <groupId>org.apache.logging.log4j</groupId> <artifactId>log4j-core</artifactId> <version>2.0-beta9</version> </dependency> </dependencies> </project> Setting Classpath To work with Avro in Linux environment, download the following jar files − avro-1.77.jar avro-tools-1.77.jar log4j-api-2.0-beta9.jar og4j-core-2.0.beta9.jar. Copy these files into a folder and set the classpath to the folder, in the ./bashrc file as shown below. #class path for Avro export CLASSPATH=$CLASSPATH://home/Hadoop/Avro_Work/jars/* Print Page Previous Next Advertisements ”;