I'm trying to use the spark-avro
package as described in Apache Avro Data Source Guide.
When I submit the following command:
val df = spark.read.format("avro").load("~/foo.avro")
I get an error:
java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.sql.avro.AvroFileFormat could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:43)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.filterImpl(TraversableLike.scala:247)
at scala.collection.TraversableLike$class.filter(TraversableLike.scala:259)
at scala.collection.AbstractTraversable.filter(Traversable.scala:104)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:630)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:194)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
... 49 elided
Caused by: java.lang.NoSuchMethodError: org.apache.spark.sql.execution.datasources.FileFormat.$init$(Lorg/apache/spark/sql/execution/datasources/FileFormat;)V
at org.apache.spark.sql.avro.AvroFileFormat.<init>(AvroFileFormat.scala:44)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:380)
... 62 more
I've tried different versions of the org.apache.spark:spark-avro_2.12:2.4.0
package (2.4.0, 2.4.1, and 2.4.2), and I currently use Spark 2.4.1, but neither worked.
I start my spark-shell with the following command:
spark-shell --packages org.apache.spark:spark-avro_2.12:2.4.0
Avro Viewer is a free online tool to view Avro files.
Going from Avro to Pandas DataFrame is also a three-step process: Create a list to store the records — This list will store dictionary objects you can later convert to Pandas DataFrame. Read and parse the Avro file — Use fastavro. reader() to read the file and then iterate over the records.
tl;dr Since Spark 2.4.x+ provides built-in support for reading and writing Apache Avro data, but the spark-avro module is external and not included in spark-submit or spark-shell by default, you should make sure that you use the same Scala version (ex. 2.12) for the spark-shell
and --packages
.
The reason for the exception is that you use spark-shell
that is from Spark built against Scala 2.11.12 while --packages
specifies a dependency with Scala 2.12 (in org.apache.spark:spark-avro_2.12:2.4.0
).
Use --packages org.apache.spark:spark-avro_2.11:2.4.0
and you should be fine.
just incase if some one is interested for pyspark 2.7 and spark 2.4.3
below package works
bin/pyspark --packages org.apache.spark:spark-avro_2.11:2.4.3
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