Does HDFS use Erasure Coding?

Therefore, HDFS uses Erasure Coding in place of replication to provide the same level of fault tolerance with storage overhead to be not more than 50\%. A replication factor of an Erasure Coded file is always one, and we cannot change it.

Which programming language has HDFS implemented?

Java
Explanation: HDFS is implemented in Java and any computer which can run Java can host a NameNode/DataNode on it.

What is Erasure Coding in storage?

Erasure coding (EC) is a method of data protection in which data is broken into fragments, expanded and encoded with redundant data pieces and stored across a set of different locations or storage media.

How does Hadoop handle fault tolerance?

HDFS is highly fault-tolerant. Before Hadoop 3, it handles faults by the process of replica creation. HDFS also maintains the replication factor by creating a replica of data on other available machines in the cluster if suddenly one machine fails. Hadoop 3 introduced Erasure Coding to provide Fault Tolerance.

What is Hadoop erasure coding?

Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50\% compared to replication while maintaining the same durability guarantees. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from.

Why is erasure coding better than RAID?

Erasure coding is parity-based, which means the data is broken into fragments and encoded, so it can be stored anywhere. This makes it well-suited to protecting cloud storage. Erasure coding also uses less storage capacity than RAID, and allows for data recovery if two or more parts of a storage system fail.

What are supported programming languages for Hadoop MapReduce?

Hadoop is capable of running MapReduce programs written in various languages: Java, Ruby, Python, and C++. The programs of Map Reduce in cloud computing are parallel in nature, thus are very useful for performing large-scale data analysis using multiple machines in the cluster.

What is erasure coding in Hadoop?

Why is erasure coding needed?

Erasure coding provides an answer, for very large datasets and for applications such as object and software-defined storage. This makes it well-suited to protecting cloud storage. Erasure coding also uses less storage capacity than RAID, and allows for data recovery if two or more parts of a storage system fail.

What makes the HDFS fault-tolerant?

HDFS is fault-tolerant because it replicates data on different DataNodes. By default, a block of data is replicated on three DataNodes. The data blocks are stored in different DataNodes. If one node crashes, the data can still be retrieved from other DataNodes.

How does Hadoop HDFS ensure that data is not lost and can be retrieved quickly?

Data Integrity in Hadoop is achieved by maintaining the checksum of the data written to the block. Whenever data is written to HDFS blocks , HDFS calculate the checksum for all data written and verify checksum when it will read that data. The seperate checksum will create for every dfs.

What is the difference between Erasure Coding and HDFS?

On the other hand, with Erasure Coding, a file with six blocks will consume only nine blocks of disk space (6 data, 3 parity). Thus requires only 50\% storage overhead. HDFS Erasure Coding uses EC algorithms to calculate the parity for each data block (cell).

How many failures can HDFS tolerate?

For example, the three-way replication scheme typically used in HDFS tolerates up to two failures with a storage efficiency of one-third (alternatively, 200\% overhead). Erasure coding (EC) is a branch of information theory which extends a message with redundant data for fault tolerance.

What is the best buffer size for Hadoop?

For five or more concurrent threads, a 1024KB buffer size yielded the best performance. For this reason, the default EC policies of HDFS are all configured to have a cell size of 1024 KB. A set of DFSIO (Hadoop’s distributed I/O benchmark) tests were run to compare the throughput of 3x replication and EC.

Why is HDFS EC so CPU-intensive?

The codec that performs the erasure coding calculations can be an important accelerator of HDFS EC. Encoding and decoding are very CPU-intensive and can be a bottleneck for read/write paths. HDFS EC uses the Reed-Solomon (RS) algorithm, by default the RS (6,3) schema.