Users of Hadoop rightly expect that no data will be lost or corrupted during storage or processing. However, since every I/O operation on the disk or network carries with it a small chance of introducing errors into the data that it is reading or writing, when the volumes of data flowing through the system are as large as the ones Hadoop is capable of handling, the chance of data corruption occurring is high.
The usual way of detecting corrupted data is by computing a checksum for the data when it first enters the system, and again whenever it is transmitted across a channel that is unreliable and hence capable of corrupting the data. The data is deemed to be corrupt if the newly generated checksum doesn’t exactly match the original. This technique doesn’t offer any way to fix the data merely error detection. (And this is a reason for not using low-end hardware; in particular, be sure to use ECC memory.) Note that it is possible that it’s the checksum that is corrupt, not the data, but this is very unlikely, since the checksum is much smaller than the data.
A commonly used error-detecting code is CRC-32 (cyclic redundancy check), which computes a 32-bit integer checksum for input of any size.
Data Integrity in HDFS
HDFS transparently checksums all data written to it and by default verifies checksums when reading data. A separate checksum is created for every io.bytes.per.checksum bytes of data. The default is 512 bytes, and since a CRC-32 checksum is 4 bytes long,the storage overhead is less than 1%.
Datanodes are responsible for verifying the data they receive before storing the data and its checksum. This applies to data that they receive from clients and from other datanodes during replication. A client writing data sends it to a pipeline of datanodes(as explained in Chapter The Hadoop Distributed File System), and the last datanode in the pipeline verifies the checksum. If it detects an error, the client receives a ChecksumException, a subclass of IOExcep tion, which it should handle in an application-specific manner, by retrying the operation,for example.
When clients read data from datanodes, they verify checksums as well, comparing them with the ones stored at the datanode. Each datanode keeps a persistent log of checksum verifications, so it knows the last time each of its blocks was verified. When a clientsuccessfully verifies a block, it tells the datanode, which updates its log. Keeping statistics such as these is valuable in detecting bad disks.
Aside from block verification on client reads, each datanode runs a DataBlockScanner in a background thread that periodically verifies all the blocks stored on the datanode. This is to guard against corruption due to “bit rot” in the physical storage media. See “Datanode block scanner” for details on how to access the scanner reports.
Since HDFS stores replicas of blocks, it can “heal” corrupted blocks by copying one of the good replicas to produce a new, uncorrupt replica. The way this works is that if a client detects an error when reading a block, it reports the bad block and the datanode it was trying to read from to the namenode before throwing a ChecksumException. The namenode marks the block replica as corrupt, so it doesn’t direct clients to it, or try to copy this replica to another datanode. It then schedules a copy of the block to be replicated on another datanode, so its replication factor is back at the expected level. Once this has happened, the corrupt replica is deleted.
It is possible to disable verification of checksums by passing false to the setVerify Checksum() method on FileSystem, before using the open() method to read a file. The same effect is possible from the shell by using the -ignoreCrc option with the -get or the equivalent -copyToLocal command. This feature is useful if you have a corrupt file that you want to inspect so you can decide what to do with it. For example, you might want to see whether it can be salvaged before you delete it.
The Hadoop LocalFileSystem performs client-side checksumming. This means that when you write a file called filename, the filesystem client transparently creates a hidden file, .filename.crc, in the same directory containing the checksums for each chunk of the file. Like HDFS, the chunk size is controlled by the io.bytes.per.checksum property, which defaults to 512 bytes. The chunk size is stored as metadata in the .crc file, so thefile can be read back correctly even if the setting for the chunk size has changed. Checksums are verified when the file is read, and if an error is detected, LocalFileSystem throws a ChecksumException.
Checksums are fairly cheap to compute (in Java, they are implemented in native code), typically adding a few percent overhead to the time to read or write a file. For most pay for data integrity. It is, however, possible to disable checksums: typically when the underlying filesystem supports checksums natively. This is accomplished by using RawLocalFileSystem in place of Local FileSystem. To do this globally in an application, it suffices to remap the implementation for file URIs by setting the property fs.file.impl to the value org.apache.hadoop.fs.RawLocalFileSystem. Alternatively, you can directly create a Raw LocalFileSystem instance, which may be useful if you want to disable checksum verification for only some reads; for example:
LocalFileSystem uses ChecksumFileSystem to do its work, and this class makes it easy to add checksumming to other (nonchecksummed) filesystems, as Checksum FileSystem is just a wrapper around FileSystem. The general idiom is as follows:
The underlying filesystem is called the raw filesystem, and may be retrieved using the getRawFileSystem() method on hecksumFileSystem. ChecksumFileSystem has a few more useful methods for working with checksums, such as getChecksumFile() for getting the path of a checksum file for any file. Check the documentation for the others.
If an error is detected by ChecksumFileSystem when reading a file, it will call its reportChecksumFailure() method. The default implementation does nothing, but LocalFileSystem moves the offending file and its checksum to a side directory on the same device called bad_files. Administrators should periodically check for these bad files and take action on them.
Hadoop Related Interview Questions
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The Hadoop Distributed Filesystem
Developing A Mapreduce Application
How Mapreduce Works
Mapreduce Types And Formats
Setting Up A Hadoop Cluster
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