HDFS-5841. Update HDFS caching documentation with new changes. (wang)
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1562649 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Andrew Wang
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c96d078033
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@ -559,6 +559,8 @@ Release 2.3.0 - UNRELEASED
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HDFS-5788. listLocatedStatus response can be very large. (Nathan Roberts
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via kihwal)
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HDFS-5841. Update HDFS caching documentation with new changes. (wang)
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OPTIMIZATIONS
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HDFS-5239. Allow FSNamesystem lock fairness to be configurable (daryn)
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@ -620,7 +620,7 @@ public class CacheAdmin extends Configured implements Tool {
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"directives being added to the pool. This can be specified in " +
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"seconds, minutes, hours, and days, e.g. 120s, 30m, 4h, 2d. " +
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"Valid units are [smhd]. By default, no maximum is set. " +
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"This can also be manually specified by \"never\".");
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"A value of \"never\" specifies that there is no limit.");
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return getShortUsage() + "\n" +
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"Add a new cache pool.\n\n" +
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listing.toString();
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@ -22,110 +22,140 @@ Centralized Cache Management in HDFS
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%{toc|section=1|fromDepth=2|toDepth=4}
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* {Background}
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* {Overview}
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Normally, HDFS relies on the operating system to cache data it reads from disk.
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However, HDFS can also be configured to use centralized cache management. Under
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centralized cache management, the HDFS NameNode itself decides which blocks
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should be cached, and where they should be cached.
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<Centralized cache management> in HDFS is an explicit caching mechanism that
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allows users to specify <paths> to be cached by HDFS. The NameNode will
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communicate with DataNodes that have the desired blocks on disk, and instruct
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them to cache the blocks in off-heap caches.
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Centralized cache management has several advantages. First of all, it
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prevents frequently used block files from being evicted from memory. This is
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particularly important when the size of the working set exceeds the size of
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main memory, which is true for many big data applications. Secondly, when
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HDFS decides what should be cached, it can let clients know about this
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information through the getFileBlockLocations API. Finally, when the DataNode
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knows a block is locked into memory, it can provide access to that block via
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mmap.
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Centralized cache management in HDFS has many significant advantages.
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[[1]] Explicit pinning prevents frequently used data from being evicted from
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memory. This is particularly important when the size of the working set
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exceeds the size of main memory, which is common for many HDFS workloads.
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[[1]] Because DataNode caches are managed by the NameNode, applications can
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query the set of cached block locations when making task placement decisions.
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Co-locating a task with a cached block replica improves read performance.
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[[1]] When block has been cached by a DataNode, clients can use a new ,
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more-efficient, zero-copy read API. Since checksum verification of cached
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data is done once by the DataNode, clients can incur essentially zero
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overhead when using this new API.
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[[1]] Centralized caching can improve overall cluster memory utilization.
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When relying on the OS buffer cache at each DataNode, repeated reads of
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a block will result in all <n> replicas of the block being pulled into
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buffer cache. With centralized cache management, a user can explicitly pin
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only <m> of the <n> replicas, saving <n-m> memory.
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* {Use Cases}
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Centralized cache management is most useful for files which are accessed very
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often. For example, a "fact table" in Hive which is often used in joins is a
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good candidate for caching. On the other hand, when running a classic
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"word count" MapReduce job which counts the number of words in each
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document, there may not be any good candidates for caching, since all the
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files may be accessed exactly once.
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Centralized cache management is useful for files that accessed repeatedly.
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For example, a small <fact table> in Hive which is often used for joins is a
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good candidate for caching. On the other hand, caching the input of a <
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one year reporting query> is probably less useful, since the
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historical data might only be read once.
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Centralized cache management is also useful for mixed workloads with
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performance SLAs. Caching the working set of a high-priority workload
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insures that it does not contend for disk I/O with a low-priority workload.
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* {Architecture}
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[images/caching.png] Caching Architecture
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With centralized cache management, the NameNode coordinates all caching
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across the cluster. It receives cache information from each DataNode via the
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cache report, a periodic message that describes all the blocks IDs cached on
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a given DataNode. The NameNode will reply to DataNode heartbeat messages
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with commands telling it which blocks to cache and which to uncache.
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In this architecture, the NameNode is responsible for coordinating all the
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DataNode off-heap caches in the cluster. The NameNode periodically receives
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a <cache report> from each DataNode which describes all the blocks cached
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on a given DN. The NameNode manages DataNode caches by piggybacking cache and
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uncache commands on the DataNode heartbeat.
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The NameNode stores a set of path cache directives, which tell it which files
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to cache. The NameNode also stores a set of cache pools, which are groups of
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cache directives. These directives and pools are persisted to the edit log
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and fsimage, and will be loaded if the cluster is restarted.
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The NameNode queries its set of <cache directives> to determine
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which paths should be cached. Cache directives are persistently stored in the
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fsimage and edit log, and can be added, removed, and modified via Java and
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command-line APIs. The NameNode also stores a set of <cache pools>,
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which are administrative entities used to group cache directives together for
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resource management and enforcing permissions.
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Periodically, the NameNode rescans the namespace, to see which blocks need to
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be cached based on the current set of path cache directives. Rescans are also
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triggered by relevant user actions, such as adding or removing a cache
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directive or removing a cache pool.
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Cache directives also may specific a numeric cache replication, which is the
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number of replicas to cache. This number may be equal to or smaller than the
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file's block replication. If multiple cache directives cover the same file
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with different cache replication settings, then the highest cache replication
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setting is applied.
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The NameNode periodically rescans the namespace and active cache directives
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to determine which blocks need to be cached or uncached and assign caching
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work to DataNodes. Rescans can also be triggered by user actions like adding
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or removing a cache directive or removing a cache pool.
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We do not currently cache blocks which are under construction, corrupt, or
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otherwise incomplete. If a cache directive covers a symlink, the symlink
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target is not cached.
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Caching is currently done on a per-file basis, although we would like to add
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block-level granularity in the future.
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Caching is currently done on the file or directory-level. Block and sub-block
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caching is an item of future work.
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* {Interface}
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* {Concepts}
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The NameNode stores a list of "cache directives." These directives contain a
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path as well as the number of times blocks in that path should be replicated.
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** {Cache directive}
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Paths can be either directories or files. If the path specifies a file, that
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file is cached. If the path specifies a directory, all the files in the
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directory will be cached. However, this process is not recursive-- only the
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direct children of the directory will be cached.
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A <cache directive> defines a path that should be cached. Paths can be either
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directories or files. Directories are cached non-recursively, meaning only
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files in the first-level listing of the directory.
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** {hdfs cacheadmin Shell}
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Directives also specify additional parameters, such as the cache replication
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factor and expiration time. The replication factor specifies the number of
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block replicas to cache. If multiple cache directives refer to the same file,
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the maximum cache replication factor is applied.
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Path cache directives can be created by the <<<hdfs cacheadmin
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-addDirective>>> command and removed via the <<<hdfs cacheadmin
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-removeDirective>>> command. To list the current path cache directives, use
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<<<hdfs cacheadmin -listDirectives>>>. Each path cache directive has a
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unique 64-bit ID number which will not be reused if it is deleted. To remove
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all path cache directives with a specified path, use <<<hdfs cacheadmin
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-removeDirectives>>>.
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The expiration time is specified on the command line as a <time-to-live
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(TTL)>, a relative expiration time in the future. After a cache directive
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expires, it is no longer considered by the NameNode when making caching
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decisions.
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Directives are grouped into "cache pools." Each cache pool gets a share of
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the cluster's resources. Additionally, cache pools are used for
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authentication. Cache pools have a mode, user, and group, similar to regular
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files. The same authentication rules are applied as for normal files. So, for
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example, if the mode is 0777, any user can add or remove directives from the
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cache pool. If the mode is 0644, only the owner can write to the cache pool,
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but anyone can read from it. And so forth.
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** {Cache pool}
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Cache pools are identified by name. They can be created by the <<<hdfs
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cacheAdmin -addPool>>> command, modified by the <<<hdfs cacheadmin
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-modifyPool>>> command, and removed via the <<<hdfs cacheadmin
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-removePool>>> command. To list the current cache pools, use <<<hdfs
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cacheAdmin -listPools>>>
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A <cache pool> is an administrative entity used to manage groups of cache
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directives. Cache pools have UNIX-like <permissions>, which restrict which
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users and groups have access to the pool. Write permissions allow users to
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add and remove cache directives to the pool. Read permissions allow users to
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list the cache directives in a pool, as well as additional metadata. Execute
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permissions are unused.
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Cache pools are also used for resource management. Pools can enforce a
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maximum <limit>, which restricts the number of bytes that can be cached in
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aggregate by directives in the pool. Normally, the sum of the pool limits
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will approximately equal the amount of aggregate memory reserved for
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HDFS caching on the cluster. Cache pools also track a number of statistics
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to help cluster users determine what is and should be cached.
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Pools also can enforce a maximum time-to-live. This restricts the maximum
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expiration time of directives being added to the pool.
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* {<<<cacheadmin>>> command-line interface}
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On the command-line, administrators and users can interact with cache pools
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and directives via the <<<hdfs cacheadmin>>> subcommand.
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Cache directives are identified by a unique, non-repeating 64-bit integer ID.
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IDs will not be reused even if a cache directive is later removed.
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Cache pools are identified by a unique string name.
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** {Cache directive commands}
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*** {addDirective}
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Usage: <<<hdfs cacheadmin -addDirective -path <path> -replication <replication> -pool <pool-name> >>>
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Usage: <<<hdfs cacheadmin -addDirective -path <path> -pool <pool-name> [-force] [-replication <replication>] [-ttl <time-to-live>]>>>
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Add a new cache directive.
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*--+--+
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\<path\> | A path to cache. The path can be a directory or a file.
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*--+--+
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\<pool-name\> | The pool to which the directive will be added. You must have write permission on the cache pool in order to add new directives.
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*--+--+
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-force | Skips checking of cache pool resource limits.
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*--+--+
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\<replication\> | The cache replication factor to use. Defaults to 1.
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*--+--+
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\<pool-name\> | The pool to which the directive will be added. You must have write permission on the cache pool in order to add new directives.
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\<time-to-live\> | How long the directive is valid. Can be specified in minutes, hours, and days, e.g. 30m, 4h, 2d. Valid units are [smhd]. "never" indicates a directive that never expires. If unspecified, the directive never expires.
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*--+--+
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*** {removeDirective}
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@ -150,7 +180,7 @@ Centralized Cache Management in HDFS
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*** {listDirectives}
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Usage: <<<hdfs cacheadmin -listDirectives [-path <path>] [-pool <pool>] >>>
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Usage: <<<hdfs cacheadmin -listDirectives [-stats] [-path <path>] [-pool <pool>]>>>
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List cache directives.
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@ -159,10 +189,14 @@ Centralized Cache Management in HDFS
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*--+--+
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\<pool\> | List only path cache directives in that pool.
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*--+--+
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-stats | List path-based cache directive statistics.
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*--+--+
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** {Cache pool commands}
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*** {addPool}
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Usage: <<<hdfs cacheadmin -addPool <name> [-owner <owner>] [-group <group>] [-mode <mode>] [-weight <weight>] >>>
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Usage: <<<hdfs cacheadmin -addPool <name> [-owner <owner>] [-group <group>] [-mode <mode>] [-limit <limit>] [-maxTtl <maxTtl>>>>
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Add a new cache pool.
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@ -175,12 +209,14 @@ Centralized Cache Management in HDFS
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*--+--+
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\<mode\> | UNIX-style permissions for the pool. Permissions are specified in octal, e.g. 0755. By default, this is set to 0755.
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*--+--+
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\<weight\> | Weight of the pool. This is a relative measure of the importance of the pool used during cache resource management. By default, it is set to 100.
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\<limit\> | The maximum number of bytes that can be cached by directives in this pool, in aggregate. By default, no limit is set.
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*--+--+
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\<maxTtl\> | The maximum allowed time-to-live for directives being added to the pool. This can be specified in seconds, minutes, hours, and days, e.g. 120s, 30m, 4h, 2d. Valid units are [smhd]. By default, no maximum is set. A value of \"never\" specifies that there is no limit.
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*--+--+
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*** {modifyPool}
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Usage: <<<hdfs cacheadmin -modifyPool <name> [-owner <owner>] [-group <group>] [-mode <mode>] [-weight <weight>] >>>
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Usage: <<<hdfs cacheadmin -modifyPool <name> [-owner <owner>] [-group <group>] [-mode <mode>] [-limit <limit>] [-maxTtl <maxTtl>]>>>
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Modifies the metadata of an existing cache pool.
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@ -193,7 +229,9 @@ Centralized Cache Management in HDFS
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*--+--+
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\<mode\> | Unix-style permissions of the pool in octal.
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*--+--+
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\<weight\> | Weight of the pool.
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\<limit\> | Maximum number of bytes that can be cached by this pool.
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*--+--+
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\<maxTtl\> | The maximum allowed time-to-live for directives being added to the pool.
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*--+--+
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*** {removePool}
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@ -208,11 +246,13 @@ Centralized Cache Management in HDFS
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*** {listPools}
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Usage: <<<hdfs cacheadmin -listPools [name] >>>
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Usage: <<<hdfs cacheadmin -listPools [-stats] [<name>]>>>
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Display information about one or more cache pools, e.g. name, owner, group,
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permissions, etc.
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*--+--+
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-stats | Display additional cache pool statistics.
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*--+--+
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\<name\> | If specified, list only the named cache pool.
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*--+--+
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@ -244,10 +284,12 @@ Centralized Cache Management in HDFS
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* dfs.datanode.max.locked.memory
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The DataNode will treat this as the maximum amount of memory it can use for
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its cache. When setting this value, please remember that you will need space
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in memory for other things, such as the Java virtual machine (JVM) itself
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and the operating system's page cache.
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This determines the maximum amount of memory a DataNode will use for caching.
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The "locked-in-memory size" ulimit (<<<ulimit -l>>>) of the DataNode user
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also needs to be increased to match this parameter (see below section on
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{{OS Limits}}). When setting this value, please remember that you will need
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space in memory for other things as well, such as the DataNode and
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application JVM heaps and the operating system page cache.
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*** Optional
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