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Gidon Gershinsky commented on PARQUET-2193: ------------------------------------------- Hmm, looks like this method runs over all columns, projected and not projected: org.apache.parquet.hadoop.ParquetRecordReader.checkDeltaByteArrayProblem(ParquetRecordReader.java:191) Please check if setting "parquet.split.files" to "false" solves this problem. > Encrypting only one field in nested field prevents reading of other fields in > nested field without keys > ------------------------------------------------------------------------------------------------------- > > Key: PARQUET-2193 > URL: https://issues.apache.org/jira/browse/PARQUET-2193 > Project: Parquet > Issue Type: New Feature > Components: parquet-mr > Affects Versions: 1.12.0 > Reporter: Vignesh Nageswaran > Priority: Major > > Hi Team, > While exploring parquet encryption, it is found that, if a field in nested > column is encrypted , and If I want to read this parquet directory from other > applications which does not have encryption keys to decrypt it, I cannot read > the remaining fields of the nested column without keys. > Example > ` > {code:java} > case class nestedItem(ic: Int = 0, sic : Double, pc: Int = 0) > case class SquareItem(int_column: Int, square_int_column : Double, > partitionCol: Int, nestedCol :nestedItem) > `{code} > In the case class `SquareItem` , `nestedCol` field is nested field and I want > to encrypt a field `ic` within it. > > I also want the footer to be non encrypted , so that I can use the encrypted > parquet file by legacy applications. > > Encryption is successful, however, when I query the parquet file using spark > 3.3.0 without having any configuration for parquet encryption set up , I > cannot non encrypted fields of `nestedCol` `sic`. I was expecting that only > `nestedCol` `ic` field will not be querable. > > > Reproducer. > Spark 3.3.0 Using Spark-shell > Downloaded the file > [parquet-hadoop-1.12.0-tests.jar|https://repo1.maven.org/maven2/org/apache/parquet/parquet-hadoop/1.12.0/parquet-hadoop-1.12.0-tests.jar] > and added it to spark-jars folder > Code to create encrypted data. # > > {code:java} > sc.hadoopConfiguration.set("parquet.crypto.factory.class" > ,"org.apache.parquet.crypto.keytools.PropertiesDrivenCryptoFactory") > sc.hadoopConfiguration.set("parquet.encryption.kms.client.class" > ,"org.apache.parquet.crypto.keytools.mocks.InMemoryKMS") > sc.hadoopConfiguration.set("parquet.encryption.key.list","key1a: > BAECAwQFBgcICQoLDA0ODw==, key2a: BAECAAECAAECAAECAAECAA==, keyz: > BAECAAECAAECAAECAAECAA==") > sc.hadoopConfiguration.set("parquet.encryption.key.material.store.internally","false") > val encryptedParquetPath = "/tmp/par_enc_footer_non_encrypted" > valpartitionCol = 1 > case class nestedItem(ic: Int = 0, sic : Double, pc: Int = 0) > case class SquareItem(int_column: Int, square_int_column : Double, > partitionCol: Int, nestedCol :nestedItem) > val dataRange = (1 to 100).toList > val squares = sc.parallelize(dataRange.map(i => new SquareItem(i, > scala.math.pow(i,2), partitionCol,nestedItem(i,i)))) > squares.toDS().show() > squares.toDS().write.partitionBy("partitionCol").mode("overwrite").option("parquet.encryption.column.keys", > > "key1a:square_int_column,nestedCol.ic;").option("parquet.encryption.plaintext.footer",true).option("parquet.encryption.footer.key", > "keyz").parquet(encryptedParquetPath) > {code} > Code to read the data trying to access non encrypted nested field by opening > a new spark-shell > > {code:java} > val encryptedParquetPath = "/tmp/par_enc_footer_non_encrypted" > spark.sqlContext.read.parquet(encryptedParquetPath).createOrReplaceTempView("test") > spark.sql("select nestedCol.sic from test").show(){code} > As you can see that nestedCol.sic is not encrypted , I was expecting the > results, but > I get the below error > > {code:java} > Caused by: org.apache.parquet.crypto.ParquetCryptoRuntimeException: > [square_int_column]. Null File Decryptor > at > org.apache.parquet.hadoop.metadata.EncryptedColumnChunkMetaData.decryptIfNeeded(ColumnChunkMetaData.java:602) > at > org.apache.parquet.hadoop.metadata.ColumnChunkMetaData.getEncodings(ColumnChunkMetaData.java:348) > at > org.apache.parquet.hadoop.ParquetRecordReader.checkDeltaByteArrayProblem(ParquetRecordReader.java:191) > at > org.apache.parquet.hadoop.ParquetRecordReader.initializeInternalReader(ParquetRecordReader.java:177) > at > org.apache.parquet.hadoop.ParquetRecordReader.initialize(ParquetRecordReader.java:140) > at > org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.$anonfun$buildReaderWithPartitionValues$1(ParquetFileFormat.scala:375) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:209) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:270) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) > at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:364) > at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:365) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:329) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > at org.apache.spark.scheduler.Task.run(Task.scala:136) > at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551) > at > java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) > at > java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) > at java.base/java.lang.Thread.run(Thread.java:833){code} -- This message was sent by Atlassian Jira (v8.20.10#820010)