This corruption can occur because of faults in the storage device, a bad network or buggy software. When a client creates a HDFS file, it computes a checksum of each block on 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 a Datanode satisfies the checksum stored in the 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 entire cluster to be non-functional. For this reason, the Namenode can be configured to support multiple copies of the FsImage and EditLog.
This synchronous updating of multiple EditLog may degrade the rate of namespace transactions per second that a Namenode can support. But this degradation is acceptable because HDFS applications are very data intensive in nature; they are not metadata intensive. If a 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 cluster to a previously known good point in time. HDFS current does not support snapshots but it will be supported it in future release. HDFS is designed to support large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write the data only once; they read the data one or more times and require that reads are 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, the HDFS client caches the file data into a temporary local file. An application-write is 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 s and the destination data block.
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 instructs 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.
The above approach has been adopted after careful consideration of target applications that run on HDFS. Applications need streaming writes to files. If a client writes to a remote file directly without any client side buffering, the network speed and the congestion in the network impacts throughput considerably.
This approach is not without precedence either. Earlier distributed file system, e. AFS have used client side caching to improve performance. When a client is writing data to a HDFS file, its data is first written to a local file as explained above.
Suppose the HDFS file has a replication factor of three. When the local file accumulates a block of user data, the client retrieves a list of Datanodes from the Namenode.
This list represents the Datanodes that will host a replica of that block. The client then flushes the data block to the first Datanode. The first Datanode starts receiving the data in small portions 4 KB , writes each portion to its local repository and transfers that portion to the second Datanode in the list. The second Datanode, in turn, starts receiving each portion of the data block, writes that portion to its repository and then flushes that portion to the third Datanode.
The third Datanode writes the data to its local repository. A Datanode could be receiving data from the previous one in the pipeline and at the same time it could be forwarding data to the next one in the pipeline.
Thus, the data is pipelined from one Datanode to the next. HDFS can be accessed by application by many different ways. HDFS allows user data to be organized in the form of files and directories. The syntax of this command set is similar to other shells e. Here are some sample commands:. The command syntax for DFSShell is targeted for applications that need a scripting language to interact with the stored data.
The DFSAdmin command set is used for administering a dfs cluster. These are commands that are used only by a HDFS administrator.
When a file is deleted by a user or an application, it is not immediately removed from HDFS. The deletion of the file causes the blocks associated with the file to be freed. There could be an appreciable time delay between the time a file is deleted by a user and the time of the corresponding increase in free space in HDFS.
The current default policy is to delete files that are older than 6 hours. In future, this policy will be configurable through a well defined interface. When the replication factor of a file is reduced, the Namenode selects excess replicas that can be deleted.
The next Heartbeat transfers this information to the Datanode. The Datanode then removes the corresponding blocks and the corresponding free space appears in the cluster. The point to note here is that there might be a time delay between the completion of the setReplication API and the appearance of free space in the cluster.
Download the HDFS source code. Mailing Lists. Nightly Build. Related Projects. Hardware Failure Hardware Failure is the norm rather than the exception. Moving computation is cheaper than moving data A computation requested by an application is most optimal if the computation can be done near where the data is located.
Portability across Heterogeneous Hardware and Software Platforms HDFS should be designed in such a way that it is easily portable from one platform to another.
Replica Placement. The implementation of the above policy is work-in-progress. SafeMode On startup, the Namenode enters a special state called Safemode. Data Correctness It is possible that a block of data fetched from a Datanode is corrupted.
Snapshots Snapshots support storing a copy of data at a particular instant of time. Staging A client-request to create a file does not reach the Namenode immediately. You can config the interval between each round, the interval is set by dfs. When encounter unexpected exceptions, it will try several times before stoping the service, which is set by dfs. A HDFS cluster can recognize the topology of racks where each nodes are put. It is important to configure this topology in order to optimize the data capacity and usage.
For more detail, please check the rack awareness in common document. During start up the NameNode loads the file system state from the fsimage and the edits log file. It then waits for DataNodes to report their blocks so that it does not prematurely start replicating the blocks though enough replicas already exist in the cluster. During this time NameNode stays in Safemode. Safemode for the NameNode is essentially a read-only mode for the HDFS cluster, where it does not allow any modifications to file system or blocks.
Normally the NameNode leaves Safemode automatically after the DataNodes have reported that most file system blocks are available. NameNode front page shows whether Safemode is on or off. A more detailed description and configuration is maintained as JavaDoc for setSafeMode. HDFS supports the fsck command to check for various inconsistencies.
It is designed for reporting problems with various files, for example, missing blocks for a file or under-replicated blocks. Unlike a traditional fsck utility for native file systems, this command does not correct the errors it detects.
Normally NameNode automatically corrects most of the recoverable failures. By default fsck ignores open files but provides an option to select all files during reporting. For command usage, see fsck. HDFS supports the fetchdt command to fetch Delegation Token and store it in a file on the local system. This token can be later used to access secure server NameNode for example from a non secure client.
For command usage, see fetchdt command. Typically, you will configure multiple metadata storage locations. Then, if one storage location is corrupt, you can read the metadata from one of the other storage locations.
However, what can you do if the only storage locations available are corrupt? In this case, there is a special NameNode startup mode called Recovery mode that may allow you to recover most of your data. When in recovery mode, the NameNode will interactively prompt you at the command line about possible courses of action you can take to recover your data. This option will force recovery mode to always select the first choice. Normally, this will be the most reasonable choice.
Because Recovery mode can cause you to lose data, you should always back up your edit log and fsimage before using it. When Hadoop is upgraded on an existing cluster, as with any software upgrade, it is possible there are new bugs or incompatible changes that affect existing applications and were not discovered earlier.
HDFS allows administrators to go back to earlier version of Hadoop and rollback the cluster to the state it was in before the upgrade. HDFS can have one such backup at a time. The following briefly describes the typical upgrade procedure:. Most of the time, cluster works just fine. Once the new HDFS is considered working well may be after a few days of operation , finalize the upgrade.
Note that until the cluster is finalized, deleting the files that existed before the upgrade does not free up real disk space on the DataNodes. If the NameNode encounters a reserved path during upgrade, it will print an error like the following:. Please rollback and delete or rename this path, or upgrade with the -renameReserved [key-value pairs] option to automatically rename these paths during upgrade. Specifying -upgrade -renameReserved [optional key-value pairs] causes the NameNode to automatically rename any reserved paths found during startup.
For example, to rename all paths named. If no key-value pairs are specified with -renameReserved , the NameNode will then suffix reserved paths with. There are some caveats to this renaming process. This is because data inconsistency can result if an edit log operation refers to the destination of an automatically renamed file.
Datanode supports hot swappable drives. The following briefly describes the typical hot swapping drive procedure:. The user updates the DataNode configuration dfs.
Once the reconfiguration task has completed, the user can safely umount the removed data volume directories and physically remove the disks. The file permissions are designed to be similar to file permissions on other familiar platforms like Linux. Currently, security is limited to simple file permissions. Future versions of HDFS will support network authentication protocols like Kerberos for user authentication and encryption of data transfers.
The details are discussed in the Permissions Guide. Hadoop currently runs on clusters with thousands of nodes. The PoweredBy Wiki page lists some of the organizations that deploy Hadoop on large clusters.
Currently the total memory available on NameNode is the primary scalability limitation. On very large clusters, increasing average size of files stored in HDFS helps with increasing cluster size without increasing memory requirements on NameNode. The default configuration may not suite very large clusters.
This user guide is a good starting point for working with HDFS. While the user guide continues to improve, there is a large wealth of documentation about Hadoop and HDFS. These nodes manage the data storage of their system.
They also perform operations such as block creation, deletion, and replication according to the instructions of the namenode. Generally the user data is stored in the files of HDFS.
These file segments are called as blocks. Therefore HDFS should have mechanisms for quick and automatic fault detection and recovery. Especially where huge datasets are involved, it reduces the network traffic and increases the throughput. Arnab Chakraborty. Pranjal Srivastava. Pari Margu.
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