When multiple threads are calling append against the same file, the file can get corrupt. The root of the problem is that a stale file stat may be used for append in DFSClient. If the file size changes between getFileStatus() and namenode.append(), DataStreamer will get confused about how to align data to the checksum boundary and break the assumption made by data nodes.
When it happens, datanode may not write the last checksum. On the next append attempt, datanode won't be able to reposition for the partial chunk, since the last checksum is missing. The append will fail after running out of data nodes to copy the partial block to.
However, if there are more threads that try to append, this leads to a more serious situation. In a few minutes, a lease recovery and block recovery will happen. The block recovery truncates the block to the ack'ed size in order to make sure to keep only the portion of data that is checksum-verified. The problem is, during the last successful append, the last data node verified the checksum and ack'ed before writing data and wrong metadata to the disk and all data nodes in the pipeline wrote the same wrong metadata. So the ack'ed size contains the corrupt portion of the data.
Since block recovery does not perform any checksum verification, the file sizes are adjusted and after commitBlockSynchronization(), another thread will be allowed to append to the corrupt file. This latent corruption may not be detected for a very long time.
The first failing append() would have created a partial copy of the block in the temporary directory of every data node in the cluster. After this failure, it is likely under replicated, so the file will be scheduled for replication after being closed. Before
HDFS-6948, replication didn't work until a node is added or restarted because of the temporary file being on all data nodes. As a result, the corruption could not be detected by replication. After HDFS-6948, the corruption will be detected after the file is closed by lease recovery or subsequent append-close.