[ 
https://issues.apache.org/jira/browse/PARQUET-1633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17338998#comment-17338998
 ] 

ASF GitHub Bot commented on PARQUET-1633:
-----------------------------------------

eadwright commented on a change in pull request #902:
URL: https://github.com/apache/parquet-mr/pull/902#discussion_r625792180



##########
File path: 
parquet-hadoop/src/main/java/org/apache/parquet/hadoop/ParquetFileReader.java
##########
@@ -1464,7 +1464,7 @@ protected PageHeader readPageHeader(BlockCipher.Decryptor 
blockDecryptor, byte[]
      */
     private void verifyCrc(int referenceCrc, byte[] bytes, String 
exceptionMsg) {
       crc.reset();
-      crc.update(bytes);
+      crc.update(bytes, 0, bytes.length);

Review comment:
       Changed to adopt a Java 8 API, to be consistent with the pom




-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


> Integer overflow in ParquetFileReader.ConsecutiveChunkList
> ----------------------------------------------------------
>
>                 Key: PARQUET-1633
>                 URL: https://issues.apache.org/jira/browse/PARQUET-1633
>             Project: Parquet
>          Issue Type: Bug
>          Components: parquet-mr
>    Affects Versions: 1.10.1
>            Reporter: Ivan Sadikov
>            Priority: Major
>
> When reading a large Parquet file (2.8GB), I encounter the following 
> exception:
> {code:java}
> Caused by: org.apache.parquet.io.ParquetDecodingException: Can not read value 
> at 0 in block -1 in file 
> dbfs:/user/hive/warehouse/demo.db/test_table/part-00014-tid-1888470069989036737-593c82a4-528b-4975-8de0-5bcbc5e9827d-10856-1-c000.snappy.parquet
> at 
> org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:251)
> at 
> org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:207)
> at 
> org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:40)
> at 
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.getNext(FileScanRDD.scala:228)
> ... 14 more
> Caused by: java.lang.IllegalArgumentException: Illegal Capacity: -212
> at java.util.ArrayList.<init>(ArrayList.java:157)
> at 
> org.apache.parquet.hadoop.ParquetFileReader$ConsecutiveChunkList.readAll(ParquetFileReader.java:1169){code}
>  
> The file metadata is:
>  * block 1 (3 columns)
>  ** rowCount: 110,100
>  ** totalByteSize: 348,492,072
>  ** compressedSize: 165,689,649
>  * block 2 (3 columns)
>  ** rowCount: 90,054
>  ** totalByteSize: 3,243,165,541
>  ** compressedSize: 2,509,579,966
>  * block 3 (3 columns)
>  ** rowCount: 105,119
>  ** totalByteSize: 350,901,693
>  ** compressedSize: 144,952,177
>  * block 4 (3 columns)
>  ** rowCount: 48,741
>  ** totalByteSize: 1,275,995
>  ** compressedSize: 914,205
> I don't have the code to reproduce the issue, unfortunately; however, I 
> looked at the code and it seems that integer {{length}} field in 
> ConsecutiveChunkList overflows, which results in negative capacity for array 
> list in {{readAll}} method:
> {code:java}
> int fullAllocations = length / options.getMaxAllocationSize();
> int lastAllocationSize = length % options.getMaxAllocationSize();
>       
> int numAllocations = fullAllocations + (lastAllocationSize > 0 ? 1 : 0);
> List<ByteBuffer> buffers = new ArrayList<>(numAllocations);{code}
>  
> This is caused by cast to integer in {{readNextRowGroup}} method in 
> ParquetFileReader:
> {code:java}
> currentChunks.addChunk(new ChunkDescriptor(columnDescriptor, mc, startingPos, 
> (int)mc.getTotalSize()));
> {code}
> which overflows when total size of the column is larger than 
> Integer.MAX_VALUE.
> I would appreciate if you could help addressing the issue. Thanks!
>  



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

Reply via email to