thamindur / spark-privacy-preserver Goto Github PK

I met the following problems when running this demo(). Can you help me solve them？

Environment: window10, spark==2.4.5, python=3.7

ERROR:

Py4JJavaError Traceback (most recent call last)
C:\Users\ADMINI~1\AppData\Local\Temp/ipykernel_26432/2800914202.py in
21 dfn = Preserver.k_anonymize(
22 df, k, feature_columns, sensitive_column, categorical, schema)
---> 23 dfn.show()

D:\installFile\Anaconda3\envs\spark\lib\site-packages\pyspark\sql\dataframe.py in show(self, n, truncate, vertical)
378 """
379 if isinstance(truncate, bool) and truncate:
--> 380 print(self._jdf.showString(n, 20, vertical))
381 else:
382 print(self._jdf.showString(n, int(truncate), vertical))

D:\installFile\Anaconda3\envs\spark\lib\site-packages\py4j\java_gateway.py in call(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:

D:\installFile\Anaconda3\envs\spark\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()

D:\installFile\Anaconda3\envs\spark\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(

Py4JJavaError: An error occurred while calling o63.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0 (TID 16, localhost, executor driver): java.lang.IllegalArgumentException
at java.nio.ByteBuffer.allocate(Unknown Source)
at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)
at org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)
at org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)
at org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)
at org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)
at org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.IllegalArgumentException
at java.nio.ByteBuffer.allocate(Unknown Source)
at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)
at org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)
at org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)
at org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)
at org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)
at org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
... 1 more

Thanks for consolidating all these great privacy methods into one package. This may not be an issue but rather a misunderstanding about the intention behind non categorical fields.

It seems that agg_numerical_column, which groups partitions with numerical fields, creates labels based on the last two digits of the min and max of a series. That, in turn, creates surprising results for fields with a range above 100, e.g. zip codes.

Concretely, a partition with range 120-190 gets the same label as one with range 220-290. Is that the intended behaviour?

hi @ThaminduR! Thank you for your work here!

I'm trying to repeat the examples using jupyter/pyspark-notebook:spark-2 docker container with PySpark 2.4.5 and Python 3.7.6 (as required in the readme) but have no success. I tried many things to run it but I got errors again and again.

Is there a way to have step by step guide or docker container for test the code?

What I did:

# Run container
docker run --rm -it --entrypoint /bin/bash jupyter/pyspark-notebook:spark-2

apt update && apt install -y git vim
pip install -U pip

# Install dependencies manually
pip install -U pandas>=1.1 pyarrow diffprivlib==0.2.1 tabulate==0.8.7 mypy>=0.770 kmodes

# Install `spark-privacy-preserver`
git clone https://github.com/ThaminduR/spark-privacy-preserver
cd spark-privacy-preserver
pip install --no-deps .
pyspark
# Run the code from mondrian_preserver demo.ipynb

The line:

dfn = Preserver.k_anonymize(df, k, feature_columns, sensitive_column, categorical, schema)
dfn.show()

Output:

ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 13) java.lang.IllegalArgumentException

Could you please help with environment setup and runnning? Thanks!

I am facing the issue when i am trying to install spark_privacy_preserver package and using python 3.7.3 version.
I have tried installing by pip install spark_privacy_preserver and through cloning process too. I will attach the screenshots for more info.
Please help me. Thanks in Advance.