CODEWITHSUNDEEP
scalaapache-sparkbinarydecoderspark-avro
Vicky
Vicky

Reputation: 11

How to read Avro Binary(Base64) Encoded data in Spark Scala

I am trying to read avro file which is encoded in Binary(Base64) and snappy compressed Hadoop cat on the avro file looks like:

Objavro.schema? 
{"type":"record","name":"ConnectDefault","namespace":"xyz.connect.avro","fields": 
[{"name":"service","type":"string"},{"name":"timestamp","type":"long"}, 
{"name":"count","type":"int"},{"name":"encoderKey","type":{"type":"map","values":"string"}}, 
{"name":"schema","type":"string"},{"name":"data","type":"string"}]}>??n]

I need to extract and read the "schema" and "data" from the above file. The "schema" is assosiated with the "data" which has multiple fileds

I tried below steps:

1.Reading the binary file

val binaryFilesRDD = sc.binaryFiles("file+0+00724+00731.avro").map { x => ( x._2.toArray) }
binaryFilesRDD: org.apache.spark.rdd.RDD[Array[Byte]] = MapPartitionsRDD[1] at map at 
<console>:24
  1. Converting the RDD[Array[Byte]] into Array[Byte] 
scala> val newArray = binaryFilesRDD.collect().flatten 
    newArray: Array[Byte] = Array(17, 18, 16, 51, 24, 22, 17, 18, 117, 151, 76, 105, 95, 124....
  1. Calling the following method using newArray (i.e. Array[Byte] ) to get Records from Bytes
    def getGenericRecordfromByte(inputData:Array[Byte], inputDataSchema: Schema): GenericRecord = 
    {
        val datareader = new GenericDatumReader[GenericRecord](inputDataSchema)
        val datadecoder = DecoderFactory.get.binaryDecoder(inputData, null)
        datareader.read(null, datadecoder) 
    }

But I am getting following errors.

    scala> val newDataRecords = getGenericRecordfromByte(newArray,inputDataSchema)
    org.apache.avro.AvroRuntimeException: Malformed data. Length is negative: -40
  at org.apache.avro.io.BinaryDecoder.doReadBytes(BinaryDecoder.java:336)
  at org.apache.avro.io.BinaryDecoder.readString(BinaryDecoder.java:263)
  at org.apache.avro.io.ResolvingDecoder.readString(ResolvingDecoder.java:201)
  at org.apache.avro.generic.GenericDatumReader.readString(GenericDatumReader.java:363)
  at org.apache.avro.generic.GenericDatumReader.readString(GenericDatumReader.java:355)
  at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:157)
  at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:193)
  at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:183)
  at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:151)
  at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:142)

Please advice

Upvotes: 1

Views: 2827

Answers (2)

Marcela Romero
Marcela Romero

Reputation: 81

For 2.3.x and older versions (https://github.com/databricks/spark-avro/blob/branch-4.0/README-for-old-spark-versions.md):

spark-shell --packages com.databricks:spark-avro_2.11:4.0.0 ...

then, in your code:

val avro = spark.read.format("com.databricks.spark.avro").load(/path/)

Upvotes: 0

Vitaly Olegovitch
Vitaly Olegovitch

Reputation: 3547

You start the spark-shell like this:

spark-shell --packages org.apache.spark:spark-avro_2.11:2.4.4

Or maybe like this:

spark2-shell --packages org.apache.spark:spark-avro_2.11:2.4.4

An then you do:

spark.read.format("com.databricks.spark.avro").load("/file/path")

Upvotes: 1

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