The number of blocks depends on the initial size of the file. All but the last block are the same size MB , while the last one is what remains of the file. For example, an MB file is broken up into seven data blocks. Six of the seven blocks are MB, while the seventh data block is the remaining 32 MB. It is recommended to have at least three replicas, which is also the default setting.
The master node stores them onto separate DataNodes of the cluster. The state of nodes is closely monitored to ensure the data is always available.
To ensure high accessibility, reliability, and fault-tolerance, developers advise setting up the three replicas using the following topology:. HDFS has a master-slave architecture. The master node is the NameNode , which manages over multiple slave nodes within the cluster, known as DataNodes.
Hadoop 2. This novelty was quite significant since having a single master node with all the information within the cluster posed a great vulnerability. While the first one deals with all client-operations within the cluster, the second one keeps in-sync with all its work if there is a need for failover.
The active NameNode keeps track of the metadata of each data block and its replicas. This includes the file name, permission, ID, location, and number of replicas. It keeps all the information in an fsimage , a namespace image stored on the local memory of the file system. Additionally, it maintains transaction logs called EditLogs , which record all changes made on the system.
Note: Whenever a new block is added, replicated, or deleted, it gets recorded in the EditLogs. The main purpose of the Stanby NameNode is to solve the problem of the single point of failure. It reads any changes made to the EditLogs and applies it to its NameSpace the files and the directories in the data. If the master node fails, the Zookeeper service carries out the failover allowing the standby to maintain an active session.
DataNodes are slave daemons that store data blocks assigned by the NameNode. As mentioned above, the default settings ensure each data block has three replicas. You can change the number of replicas, however, it is not advisable to go under three. To learn more, read What is Bare Metal Cloud. Manages big data. HDFS is excellent in handling large datasets and provides a solution that traditional file systems could not.
It does this by segregating the data into manageable blocks which allow fast processing times. It exposes file system access similar to a traditional file system. However, the file is split into many parts in the background and distributed on the cluster for reliability and scalability.
In the next article, we will discuss the map-reduce program and see how to leverage this data structure and storage paradigm. View All. Menish Gupta Updated date Jul 08, As we saw in the prior article, every machine works on its own portion of data.
Next Recommended Reading. Windows 10 Vs Windows Visual Studio Vs Visual Studio Understanding Matplotlib With Examples. Understanding Numpy With Examples. C Evolution. If there exists a replica on the same rack as the reader node, then that replica is preferred to satisfy the read request. On startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state.
A Blockreport contains the list of data blocks that a DataNode is hosting. Each block has a specified minimum number of replicas. A block is considered safely replicated when the minimum number of replicas of that data block has checked in with the NameNode.
After a configurable percentage of safely replicated data blocks checks in with the NameNode plus an additional 30 seconds , the NameNode exits the Safemode state. It then determines the list of data blocks if any that still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.
The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog.
The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk.
It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future. It stores each block of HDFS data in a separate file in its local file system.
The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately.
It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport.
It talks the ClientProtocol with the NameNode. The primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions. A network partition can cause a subset of DataNodes to lose connectivity with the NameNode.
The NameNode detects this condition by the absence of a Heartbeat message. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased.
The HDFS architecture is compatible with data rebalancing schemes. A scheme might automatically move data from one DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster.
These types of data rebalancing schemes are not yet implemented. It is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace. When a client retrieves file contents it verifies that the data it received from each DataNode matches the checksum stored in the associated checksum file.
If not, then the client can opt to retrieve that block from another DataNode that has a replica of that block. A corruption of these files can cause the HDFS instance to be non-functional. This synchronous updating of multiple copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support.
However, this degradation is acceptable because even though HDFS applications are very data intensive in nature, they are not metadata intensive.
If the NameNode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the NameNode software to another machine is not supported. Snapshots support storing a copy of data at a particular instant of time. One usage of the snapshot feature may be to roll back a corrupted HDFS instance to a previously known good point in time. HDFS does not currently support snapshots but will in a future release.
HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds.
HDFS supports write-once-read-many semantics on files. A client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. The NameNode inserts the file name into the file system hierarchy and allocates a data block for it.
The NameNode responds to the client request with the identity of the DataNode and the destination data block. Then the client flushes the block of data from the local temporary file to the specified DataNode. When a file is closed, the remaining un-flushed data in the temporary local file is transferred to the DataNode. The client then tells the NameNode that the file is closed.
At this point, the NameNode commits the file creation operation into a persistent store. If the NameNode dies before the file is closed, the file is lost.
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