Friday, August 28, 2009

HDFS and Erasure Codes (HDFS-RAID)

The Hadoop Distributed File System has been great in providing a cloud-type file system. It is robust (when administered correctly :-)) and highly scalable. However, one of the main drawbacks of HDFS is that each piece of data is replicated in three places. This is acceptable because disk storage is cheap and is becoming cheaper by the day; this isn't a problem if you have a relatively small to medium size cluster. The price difference (in absolute terms) is not much whether you use 15 disks or whether you use 10 disks. If we consider the cost of $1 per GByte, the price difference between fifteen 1 TB disk and ten 1 TB disk is only $5K. But when the total size of your cluster is 10 PBytes, then the costs savings in storing the data in two places versus three is a huge ten million dollars!

The reason HDFS stores disk blocks in triplicate is because it uses commodity hardware and there is non-negligible probability of a disk failure. It has been observed that a replication factor of 3 and the fact the HDFS aggressively detects failures and immediately replicates failed -block-replicas is sufficient to never lose any data in practice. The challenge now is to achieve an effective replication factor of 3 while keeping the real physical replication factor at close to 2! How best to do it than by using Erasure Codes.

I heard about this idea called DiskReduce from the folks at CMU. The CMU PDL Labs has been a powerhouse of research in file systems and it is no surprise that they proposed a elegant way of implementing erasure codes in HDFS. I borrowed heavily from their idea in my implementation of Erasure Codes in HDFS described in HDFS-503. One of the main motivation of my design is to keep the HDFS Erasure Coding as a software layer above HDFS rather than inter-twining it inside of HDFS code. The HDFS code is complex by itself and it is really nice to not have to make it more complex and heavyweight.

Distributed Raid File System consists of two main software components. The first component is the RaidNode, a daemon that creates parity files from specified HDFS files. The second component "raidfs" is a software that is layered over a HDFS client and it intercepts all calls that an application makes to the HDFS client. If the HDFS client encounters corrupted data while reading a file, the raidfs client detects it; it uses the relevant parity blocks to recover the corrupted data (if possible) and returns the data to the application. The application is completely transparent to the fact that parity data was used to satisfy it's read request. The Distributed Raid File System can be configured in such a way that a set of data blocks of a file are combined together to form one or more parity blocks. This allows one to reduce the replication factor of a HDFS file from 3 to 2 while keeping the failure probabilty relatively same as before.

I have seen that using a stripe size of 10 blocks decreases the physical replication factor of a file to 2.2 while keeping the effective replication factor of a file at 3. This typically results in saving 25% to 30% of storage space in a HDFS cluster.

One of the shortcoming of this implementation is that we need a parity file for every file in HDFS. This potentially increases the number of files in the NameNode. To alleviate this problem, I will enhance this implementation (in future) to use the Hadoop Archive feature to archive all the parity files together in larger containers so that the NameNode does not have to support additional files when the HDFS Erasure Coding is switched on. This works reasonably well because it is a very very rare case that the parity files are ever used to satisfy a read request.

I am hoping that this feature becomes part of Hadoop 0.21 release scheduled for September 2009!