In this Spark article, you will learn how to convert Avro file to JSON file format with Scala example, In order to convert first, we will read an Avro file into DataFrame and write it in a JSON file.
1. What is Apache Avro
Apache Avro is an open-source, row-based, data serialization and data exchange framework for Hadoop projects, originally developed by databricks as an open-source library that supports reading and writing data in Avro file format. it is mostly used in Apache Spark especially for Kafka-based data pipelines. When Avro data is stored in a file, its schema is stored with it, so that files may be processed later by any program.
It has build to serialize and exchange big data between different Hadoop based projects. It serializes data in a compact binary format and schema is in JSON format that defines the field names and data types.
2. Avro Advantages
- Supports complex data structures like Arrays, Map, Array of map and map of array elements.
- A compact, binary serialization format which provides fast while transferring data.
- row-based data serialization system.
- Support multi-languages, meaning data written by one language can be read by different languages.
- Code generation is not required to read or write data files.
- Simple integration with dynamic languages.
3. Read Avro File
avro() function is not provided in Spark DataFrameReader hence, we should use DataSource format as “avro” or “org.apache.spark.sql.avro” and load() is used to read the Avro file.
// Read avro file
val df = spark.read.format("avro")
.load("src/main/resources/zipcodes.avro")
df.show()
df.printSchema()
In case, if you have Avro data partitioned, use where() function to load a specific partition, below snippet loads an Avro file with Zipcode 19802
spark.read
.format("avro")
.load("zipcodes_partition.avro")
.where(col("Zipcode") === 19802)
.show()
If you want to read more on Avro, I would recommend checking how to Read and Write Avro file with a specific schema along with the dependencies it needed.
4. Spark Convert Avro to JSON file
In the previous section, we have read the Avro file into DataFrame now let’s convert it to JSON by saving it to JSON file format.
// Convert to json
df.write.mode(SaveMode.Overwrite)
.json("/tmp/json/zipcodes.json")
Alternatively, you can also write
df.write
.json("/tmp/json/zipcodes.json")
If you want to read more on JSON, I would recommend checking how to Read and Write JSON file with a specific schema.
5. Complete Example of convert Avro file to JSON file format
package com.sparkbyexamples.spark.dataframe
import org.apache.spark.sql.{SaveMode, SparkSession}
object AvroToJson extends App {
val spark: SparkSession = SparkSession.builder()
.master("local[1]")
.appName("SparkByExample")
.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
// Read avro file
val df = spark.read.format("avro")
.load("src/main/resources/zipcodes.avro")
df.show()
df.printSchema()
// Convert to json
df.write.mode(SaveMode.Overwrite)
.json("/tmp/json/zipcodes.json")
}
Conclusion
In this Spark article, you have learned how to convert an Avro file to a JSON file format with Scala examples. Though we literally don’t convert from Avro format to JSON straight, first we convert it to DataFrame and then DataFrame can be saved to any format Spark supports.
Happy Learning !!
