将delta lake写入AWS S3（无数据块）

Question

# Creating PySpark Object
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("XMLParser").getOrCreate()
sc=spark.sparkContext
hadoop_conf=sc._jsc.hadoopConfiguration()
hadoop_conf.set("fs.s3n.impl", "org.apache.hadoop.fs.s3native.NativeS3FileSystem")
hadoop_conf.set("fs.s3n.awsAccessKeyId", aws_key)
hadoop_conf.set("fs.s3n.awsSecretAccessKey", aws_secret)

然后我就可以从我的s3存储桶中使用以下代码读取该文件

df = spark.read.format("xml").options(rootTag='returnResult', rowTag="query").load("s3n://bucketName/folder/file.xml")

但是当我试图使用delta-lake（拼花文件）使用以下代码写回s3时

df.write.format("delta").mode('overwrite').save("s3n://bucket/folder/file")

我得到了这个错误

    Py4JJavaError: An error occurred while calling o778.save.
: java.io.IOException: The error typically occurs when the default LogStore implementation, that
 is, HDFSLogStore, is used to write into a Delta table on a non-HDFS storage system.
 In order to get the transactional ACID guarantees on table updates, you have to use the
 correct implementation of LogStore that is appropriate for your storage system.
 See https://docs.delta.io/latest/delta-storage.html " for details.

    at org.apache.spark.sql.delta.DeltaErrors$.incorrectLogStoreImplementationException(DeltaErrors.scala:157)
    at org.apache.spark.sql.delta.storage.HDFSLogStore.writeInternal(HDFSLogStore.scala:73)
    at org.apache.spark.sql.delta.storage.HDFSLogStore.write(HDFSLogStore.scala:64)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$doCommit$1.apply$mcJ$sp(OptimisticTransaction.scala:434)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$doCommit$1.apply(OptimisticTransaction.scala:416)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$doCommit$1.apply(OptimisticTransaction.scala:416)
    at org.apache.spark.sql.delta.DeltaLog.lockInterruptibly(DeltaLog.scala:152)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$class.doCommit(OptimisticTransaction.scala:415)
    at org.apache.spark.sql.delta.OptimisticTransaction.doCommit(OptimisticTransaction.scala:80)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$commit$1.apply$mcJ$sp(OptimisticTransaction.scala:326)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$commit$1.apply(OptimisticTransaction.scala:284)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$$anonfun$commit$1.apply(OptimisticTransaction.scala:284)
    at com.databricks.spark.util.DatabricksLogging$class.recordOperation(DatabricksLogging.scala:77)
    at org.apache.spark.sql.delta.OptimisticTransaction.recordOperation(OptimisticTransaction.scala:80)
    at org.apache.spark.sql.delta.metering.DeltaLogging$class.recordDeltaOperation(DeltaLogging.scala:103)
    at org.apache.spark.sql.delta.OptimisticTransaction.recordDeltaOperation(OptimisticTransaction.scala:80)
    at org.apache.spark.sql.delta.OptimisticTransactionImpl$class.commit(OptimisticTransaction.scala:284)
    at org.apache.spark.sql.delta.OptimisticTransaction.commit(OptimisticTransaction.scala:80)
    at org.apache.spark.sql.delta.commands.WriteIntoDelta$$anonfun$run$1.apply(WriteIntoDelta.scala:67)
    at org.apache.spark.sql.delta.commands.WriteIntoDelta$$anonfun$run$1.apply(WriteIntoDelta.scala:64)
    at org.apache.spark.sql.delta.DeltaLog.withNewTransaction(DeltaLog.scala:188)
    at org.apache.spark.sql.delta.commands.WriteIntoDelta.run(WriteIntoDelta.scala:64)
    at org.apache.spark.sql.delta.sources.DeltaDataSource.createRelation(DeltaDataSource.scala:134)
    at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:81)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.hadoop.fs.UnsupportedFileSystemException: fs.AbstractFileSystem.s3n.impl=null: No AbstractFileSystem configured for scheme: s3n
    at org.apache.hadoop.fs.AbstractFileSystem.createFileSystem(AbstractFileSystem.java:160)
    at org.apache.hadoop.fs.AbstractFileSystem.get(AbstractFileSystem.java:249)
    at org.apache.hadoop.fs.FileContext$2.run(FileContext.java:334)
    at org.apache.hadoop.fs.FileContext$2.run(FileContext.java:331)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:422)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)
    at org.apache.hadoop.fs.FileContext.getAbstractFileSystem(FileContext.java:331)
    at org.apache.hadoop.fs.FileContext.getFileContext(FileContext.java:448)
    at org.apache.spark.sql.delta.storage.HDFSLogStore.getFileContext(HDFSLogStore.scala:47)
    at org.apache.spark.sql.delta.storage.HDFSLogStore.writeInternal(HDFSLogStore.scala:70)
    ... 53 more

我试图跟踪stacktrace中给出的链接，但不知道如何解决此问题。任何帮助都会被拒绝

Answer 1

我发现它不需要对S3位置做任何特殊的操作。我只需要将增量格式的文件写入S3中不存在的文件夹。如果它已经存在并且里面有对象，我会得到与OP相同的错误

以下是我的spark会话创建代码：

spark = glueContext.spark_session.builder \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
.getOrCreate()