本篇文章为你整理了HDFS Architecture Guide（）的详细内容，包含有 HDFS Architecture Guide，希望能帮助你了解 HDFS Architecture Guide。
Hardware failure is the norm rather than the exception. An HDFS instance may consist of hundreds or thousands of server machines,
each storing part of the file system s data. The fact that there are a huge number of components and that each component has
a non-trivial probability of failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick,
automatic recovery from them is a core architectural goal of HDFS.
Streaming Data Access
Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run
on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on
high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements that are not needed for
applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates.
Large Data Sets
Applications that run on HDFS have large data sets. A typical file in HDFS is gigabytes to terabytes in size. Thus, HDFS is tuned to
support large files. It should provide high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It should support
tens of millions of files in a single instance.
Simple Coherency Model
HDFS applications need a write-once-read-many access model for files. A file once created, written, and closed need not be changed.
This assumption simplifies data coherency issues and enables high throughput data access. A MapReduce application or a web crawler
application fits perfectly with this model. There is a plan to support appending-writes to files in the future.
Moving Computation is Cheaper than Moving Data
A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true
when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The
assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where
the application is running. HDFS provides interfaces for applications to move themselves closer to where the data is located.
Portability Across Heterogeneous Hardware and Software Platforms
HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a
platform of choice for a large set of applications.
HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file
system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node
in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows
user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes.
The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also
determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file
system s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode.
The NameNode and DataNode are pieces of software designed to run on commodity machines. These machines typically run a
GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case.
The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode.
The File System Namespace
HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside
these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and
remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas. HDFS
does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features.
The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is
recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by
HDFS. The number of copies of a file is called the replication factor of that file. This information is stored by the NameNode.
HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence
of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance.
The block size and replication factor are configurable per file. An application can specify the number of replicas of a file.
The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and
have strictly one writer at any time.
The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport
from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A
Blockreport contains a list of all blocks on a DataNode.
Replica Placement: The First Baby Steps
The placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes
HDFS from most other distributed file systems. This is a feature that needs lots of tuning and experience. The purpose
of a rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization.
The current implementation for the replica placement policy is a first effort in this direction. The short-term goals of
implementing this policy are to validate it on production systems, learn more about its behavior, and build a foundation
to test and research more sophisticated policies.
Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication
between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines
in the same rack is greater than network bandwidth between machines in different racks.
The NameNode determines the rack id each DataNode belongs to via the process outlined in
Hadoop Rack Awareness.
A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack
fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in
the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of
writes because a write needs to transfer blocks to multiple racks.
For the common case, when the replication factor is three, HDFS s placement policy is to put one replica
on one node in the local rack, another on a node in a different (remote) rack, and the last on a different node in the
same remote rack. This policy cuts the inter-rack write traffic which generally improves write performance. The
chance of rack failure is far less than that of node failure; this policy does not impact data reliability and availability
guarantees. However, it does reduce the aggregate network bandwidth used when reading data since a block is
placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute
across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third
are evenly distributed across the remaining racks. This policy improves write performance without compromising
data reliability or read performance.
The current, default replica placement policy described here is a work in progress.
Replica Selection
To minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica
that is closest to the reader. If there exists a replica on the same rack as the reader node, then that replica is
preferred to satisfy the read request. If angg/ HDFS cluster spans multiple data centers, then a replica that is
resident in the local data center is preferred over any remote replica.
Safemode
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. The NameNode receives Heartbeat and Blockreport messages
from the DataNodes. 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 Persistence of File System Metadata
The HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog
to persistently record every change that occurs to file system metadata. For example, creating a new file in
HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the
replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file
in its local host OS file system to store 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 FsImage is stored as
a file in the NameNode s local file system too.
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.
The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files.
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.
All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to
a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients.
Robustness
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.
Data Disk Failure, Heartbeats and Re-Replication
Each DataNode sends a Heartbeat message to the NameNode periodically. 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. The NameNode marks DataNodes without recent Heartbeats as dead and
does not forward any new IO requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. 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.
Cluster Rebalancing
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.
Data Integrity
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. The HDFS client software
implements checksum checking on the contents of HDFS files. 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.
Metadata Disk Failure
The FsImage and the EditLog are central data structures of HDFS. A corruption of these files can
cause the HDFS instance to be non-functional. For this reason, the NameNode can be configured
to support maintaining multiple copies of the FsImage and EditLog. Any update to either the FsImage
or EditLog causes each of the FsImages and EditLogs to get updated synchronously. 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. When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use.
The NameNode machine is a single point of failure for an HDFS cluster. 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
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 typical block size used by HDFS is 64 MB. Thus,
an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different DataNode.
Staging
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. When the local file accumulates data worth over one HDFS block size, the
client contacts the NameNode. 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.
The above approach has been adopted after careful consideration of target applications that run on
HDFS. These 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 precedent. Earlier distributed file systems,
e.g. AFS, have used client side caching to improve performance. A POSIX requirement has been relaxed to achieve higher performance of data uploads.
Replication Pipelining
When a client is writing data to an HDFS file, its data is first written to a local file as explained
in the previous section. Suppose the HDFS file has a replication factor of three. When the local
file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode.
This list contains 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. Finally, the third DataNode writes the
data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline
and at the same time forwarding data to the next one in the pipeline. Thus, the data is pipelined from
one DataNode to the next.
HDFS can be accessed from applications in many different ways. Natively, HDFS provides a
Java API for applications to
use. A C language wrapper for this Java API is also available. In addition, an HTTP browser
can also be used to browse the files of an HDFS instance. Work is in progress to expose
HDFS through the WebDAV protocol.
FS Shell
HDFS allows user data to be organized in the form of files and directories. It provides a commandline
interface called FS shell that lets a user interact with the data in HDFS. The syntax of this command
set is similar to other shells (e.g. bash, csh) that users are already familiar with. Here are some sample
action/command pairs:
View the contents of a file named /foodir/myfile.txt
bin/hadoop dfs -cat /foodir/myfile.txt
FS shell is targeted for applications that need a scripting language to interact with the stored data.
DFSAdmin
The DFSAdmin command set is used for administering an HDFS cluster. These are commands that are
used only by an HDFS administrator. Here are some sample action/command pairs:
A typical HDFS install configures a web server to expose the HDFS namespace through
a configurable TCP port. This allows a user to navigate the HDFS namespace and view
the contents of its files using a web browser.
When a file is deleted by a user or an application, it is not immediately removed from HDFS. Instead,
HDFS first renames it to a file in the /trash directory. The file can be restored quickly
as long as it remains in /trash. A file remains in /trash for a configurable
amount of time. After the expiry of its life in /trash, the NameNode deletes the file from
the HDFS namespace. The deletion of a file causes the blocks associated with the file to be freed.
Note that 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.
A user can Undelete a file after deleting it as long as it remains in the /trash directory.
If a user wants to undelete a file that he/she has deleted, he/she can navigate the /trash
directory and retrieve the file. The /trash directory contains only the latest copy of the file
that was deleted. The /trash directory is just like any other directory with one special
feature: HDFS applies specified policies to automatically delete files from this directory. The current
default policy is to delete files from /trash that are more than 6 hours old. In the future,
this policy will be configurable through a well defined interface.
Decrease Replication Factor
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. Once again, there might be a time delay
between the completion of the setReplication API call and the appearance of free space in the cluster.
以上就是HDFS Architecture Guide（）的详细内容，想要了解更多 HDFS Architecture Guide的内容，请持续关注盛行IT软件开发工作室。
郑重声明：本文由网友发布，不代表盛行IT的观点，版权归原作者所有，仅为传播更多信息之目的，如有侵权请联系，我们将第一时间修改或删除，多谢。