Ensuring data integrity is basic necessity or back bond in big data processing environment to achieve accurate outcome. Of course, same is applicable while executing any data moving operations with traditional data storage systems (RDBMS, Document Repository etc) through various applications. Data transportation over the network, device to device transfer, ETL process, and many more. In two words, data integrity can be defined as assurance of the accuracy and consistency of data throughout the entire life cycle.
In big data processing environment, data(rest) gets persisted in a distributed manner because of huge volume. So, achieving data integrity on top of it is challenging. Hadoop Distributed File Systems (HDFS) has been efficiently built/developed to store any type of data in a distributed manner in the form of data block (breaks down huge volume of data into a set of individual blocks) with data integrity commitment. There might be multiple reasons to get corrupt data blocks in HDFS, starting from IO operation on the system disk, network failure etc.
Internally, HDFS smartly utilizes checksum for data integrity. A checksum is a small-sized datum derived from a block of digital data for the purpose of detecting errors. HDFS calculates/computes checksums for each data block and eventually store in a separate hidden file in the same HDFS namespace. HDFS uses 32-bit Cyclic Redundancy Check (CRC32) as default checksum algorithm because of 4 bytes long and less than 1% storage overhead. Similarly verifies checksums while reading data from the data nodes. If there is a discrepancy or error in the checksum value, the exception “CheckSumException” will be thrown to the client during retrieval of data for processing. 512 bytes is the default value.
Once data blocks are received by data nodes in the multi-node cluster, they compute and store checksum as well before storing the data in disk. And would be compared with stored one in the data nodes while client read the data. Besides, every individual data node maintains a persistent log of checksum verification to keep track when last verification occurred on each data block. Programmatically we can disable the checksum verification while submitting a job to the cluster. Using Apache Hadoop 3.1, comparison of checksums of a file stored in hdfs can be done with locally stored file. Please read at https://issues.apache.org/jira/browse/HDFS-13056.
Can be reached for real-time POC development and hands-on technical training at [email protected] Besides, to design, develop just as help in any Hadoop/Big Data handling related task. Gautam is a advisor and furthermore an Educator as well. Before that, he filled in as Sr. Technical Architect in different technologies and business space across numerous nations.
He is energetic about sharing information through blogs, preparing workshops on different Big Data related innovations, systems and related technologies.