Duplicacy erasure coding1/1/2024 ![]() ![]() Read the data from source nodes: Input data is read in parallel from source nodes using a dedicated thread pool. This process is similar to how replicated blocks are re-replicated on failure. The recovery task is passed as a heartbeat response. Failed EC blocks are detected by the NameNode, which then chooses a DataNode to do the recovery work. Upon failures, it issues additional read requests for decoding.ĭataNode Extensions - The DataNode runs an additional ErasureCodingWorker (ECWorker) task for background recovery of failed erasure coded blocks. It then issues read requests in parallel. On the input / read path, DFSStripedInputStream translates a requested logical byte range of data as ranges into internal blocks stored on DataNodes. A coordinator takes charge of operations on the entire block group, including ending the current block group, allocating a new block group, and so forth. The streamers mostly work asynchronously. On the output / write path, DFSStripedOutputStream manages a set of data streamers, one for each DataNode storing an internal block in the current block group. This allows management at the level of the block group rather than the block.Ĭlient Extensions - The client read and write paths were enhanced to work on multiple internal blocks in a block group in parallel. The ID of a block group can be inferred from the ID of any of its internal blocks. To reduce NameNode memory consumption from these additional blocks, a new hierarchical block naming protocol was introduced. NameNode Extensions - Striped HDFS files are logically composed of block groups, each of which contains a certain number of internal blocks. ![]() See the design doc and discussion on HDFS-7285 for more information. In the future, HDFS will also support a contiguous EC layout. ![]() To better support small files, in this first phase of work HDFS supports EC with striping. In typical HDFS clusters, small files can account for over 3/4 of total storage consumption. This greatly simplifies file operations such as deletion, quota reporting, and migration between federated namespaces. Second, it naturally distributes a small file to multiple DataNodes and eliminates the need to bundle multiple files into a single coding group. Online EC also enhances sequential I/O performance by leveraging multiple disk spindles in parallel this is especially desirable in clusters with high end networking. First, it enables online EC (writing data immediately in EC format), avoiding a conversion phase and immediately saving storage space. In the context of EC, striping has several critical advantages. But with EC (6 data, 3 parity) deployment, it will only consume 9 blocks of disk space. As an example, a 3x replicated file with 6 blocks will consume 6*3 = 18 blocks of disk space. Integrating EC with HDFS can improve storage efficiency while still providing similar data durability as traditional replication-based HDFS deployments. The error on any striping cell can be recovered through decoding calculation based on surviving data and parity cells. For each stripe of original data cells, a certain number of parity cells are calculated and stored – the process of which is called encoding. In the rest of this guide this unit of striping distribution is termed a striping cell (or cell). RAID implements EC through striping, which divides logically sequential data (such as a file) into smaller units (such as bit, byte, or block) and stores consecutive units on different disks. In storage systems, the most notable usage of EC is Redundant Array of Inexpensive Disks (RAID). It is always 1 and cannot be changed via -setrep command. Replication factor of an EC file is meaningless. In typical Erasure Coding (EC) setups, the storage overhead is no more than 50%. ![]() Therefore, a natural improvement is to use Erasure Coding (EC) in place of replication, which provides the same level of fault-tolerance with much less storage space. However, for warm and cold datasets with relatively low I/O activities, additional block replicas are rarely accessed during normal operations, but still consume the same amount of resources as the first replica. Replication is expensive – the default 3x replication scheme in HDFS has 200% overhead in storage space and other resources (e.g., network bandwidth). Running Applications in runC Containers.Running Applications in Docker Containers. ![]()
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