How can I read/write data from Azurite using Spark? - scala

Question

I have tried to read/write Parquet files from/to Azurite using Spark like this:
import com.holdenkarau.spark.testing.DatasetSuiteBase
import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode
import org.scalatest.WordSpec
class SimpleAzuriteSpec extends WordSpec with DatasetSuiteBase {
val AzuriteHost = "localhost"
val AzuritePort = 10000
val AzuriteAccountName = "devstoreaccount1"
val AzuriteAccountKey = "Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw=="
val AzuriteContainer = "container1"
val AzuriteDirectory = "dir1"
val AzuritePath = s"wasb://$AzuriteContainer#$AzuriteAccountName.blob.core.windows.net/$AzuriteDirectory/"
override final def conf: SparkConf = {
val cfg = super.conf
val settings =
Map(
s"spark.hadoop.fs.azure.storage.emulator.account.name" -> AzuriteAccountName,
s"spark.hadoop.fs.azure.account.key.${AzuriteAccountName}.blob.core.windows.net" -> AzuriteAccountKey
)
settings.foreach { case (k, v) =>
cfg.set(k, v)
}
cfg
}
"Spark" must {
"write to/read from Azurite" in {
import spark.implicits._
val xs = List(Rec(1, "Alice"), Rec(2, "Bob"))
val inputDs = spark.createDataset(xs)
inputDs.write
.format("parquet")
.mode(SaveMode.Overwrite)
.save(AzuritePath)
val ds = spark.read
.format("parquet")
.load(AzuritePath)
.as[Rec]
ds.show(truncate = false)
val actual = ds.collect().toList.sortBy(_.id)
assert(actual == xs)
}
}
}
case class Rec(id: Int, name: String)
I have tried both Azurite 3.9.0 and Azurite 2.7.0 (both in Docker). I can transfer files to/from Azurite using az (dockerized as well).
The test above runs on the Docker host. Azurite is reachable from the Docker host.
I am using Spark 2.4.5, Hadoop 2.10.0, and this dependency:
libraryDependencies += "org.apache.hadoop" % "hadoop-azure" % "2.10.0"
When using az, this connection string works:
AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://azurite-3.9.0:10000/devstoreaccount1;QueueEndpoint=http://azurite-3.9.0:10001/devstoreaccount1;"
yet I do not know how to configure this in Spark.
My question: How can I configure the host, the port, credentials etc. (in the path or in SparkConf)?

Answer 1

Yes, that's possible, but azurite should be accessible via 127.0.0.1:10000 for wasb (so if it runs on another machine then port forwarding will help) and then specify following spark args as example:
./pyspark --conf "spark.hadoop.fs.defaultFS=wasb://container#azurite" --conf "spark.hadoop.fs.azure.storage.emulator.account.name=azurite"
Then default file system will be backed up by your instance of azurite.

Answer 2

Don't use Azurite, just add these Jars to your Spark Dockerfile:
# Set JARS env
ENV JARS=${SPARK_HOME}/jars/azure-storage-${AZURE_STORAGE_VER}.jar,${SPARK_HOME}/jars/hadoop-azure-${HADOOP_AZURE_VER}.jar,${SPARK_HOME}/jars/jetty-util-ajax-${JETTY_VER}.jar,${SPARK_HOME}/jars/jetty-util-${JETTY_VER}.jar
RUN echo "spark.jars ${JARS}" >> $SPARK_HOME/conf/spark-defaults.conf
Set your configuration:
spark = SparkSession.builder.config(conf=sparkConf).getOrCreate()
spark.sparkContext._jsc.hadoopConfiguration().set(f"fs.azure.account.key.{ os.environ['AZURE_STORAGE_ACCOUNT'] }.blob.core.windows.net", os.environ['AZURE_STORAGE_KEY'])
Then you can read it:
val df = spark.read.parquet("wasbs://<container-name>#<storage-account-name>.blob.core.windows.net/<directory-name>")