Enterprises are using Hadoop for interactive real-time data processing via projects such as the Stinger Initiative. We describe two new HDFS features – Centralized Cache Management and Heterogeneous Storage – that allow applications to effectively use low latency storage media such as Solid State Disks and RAM. In the first part of this talk, we discuss Centralized Cache Management to coordinate caching important datasets and place tasks for memory locality. HDFS deployments today rely on the OS buffer cache to keep data in RAM for faster access. However, the user has no direct control over what data is held in RAM or how long it?s going to stay there. Centralized Cache Management allows users to specify which data to lock into RAM. Next, we describe Heterogeneous Storage support for applications to choose storage media based on their performance and durability requirements. Perhaps the most interesting of the newer storage media are Solid State Drives which provide improved random IO performance over spinning disks. We also discuss memory as a storage tier which can be useful for temporary files and intermediate data for latency sensitive real-time applications. In the last part of the talk we describe how administrators can use quota mechanism extensions to manage fair distribution of scarce storage resources across users and applications.

1. © Hortonworks Inc. 2011
Interactive Hadoop via Flash and
Memory
Arpit Agarwal
aagarwal@hortonworks.com
@aagarw
Chris Nauroth
cnauroth@hortonworks.com
@cnauroth
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HDFS Reads
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HDFS Short-Circuit Reads
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HDFS Short-Circuit Reads
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Shortcomings of Existing RAM Utilization
• Lack of Control
– Kernel decides what to retain in cache and what to evict based on observations of
access patterns.
• Sub-optimal RAM Utilization
– Tasks for multiple jobs are interleaved on the same node, and one task’s activity
could trigger eviction of data that would have been valuable to retain in cache for
the other task.
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Centralized Cache Management
• Provides users with explicit control of which HDFS file paths to keep
resident in memory.
• Allows clients to query location of cached block replicas, opening
possibility for job scheduling improvements.
• Utilizes off-heap memory, not subject to GC overhead or JVM tuning.
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Using Centralized Cache Management
• Pre-Requisites
– Native Hadoop library required, currently supported on Linux only.
– Set process ulimit for maximum locked memory.
– Configure dfs.datanode.max.locked.memory in hdfs-site.xml, set to the amount of
memory to dedicate towards caching.
• New Concepts
– Cache Pool
– Contains and manages a group of cache directives.
– Has Unix-style permissions.
– Can constrain resource utilization by defining a maximum number of cached bytes or a
maximum time to live.
– Cache Directive
– Specifies a file system path to cache.
– Specifying a directory caches all files in that directory (not recursive).
– Can specify number of replicas to cache and time to live.
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Using Centralized Cache Management
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Using Centralized Cache Management
• CLI: Adding a Cache Pool
> hdfs cacheadmin -addPool common-pool
Successfully added cache pool common-pool.
> hdfs cacheadmin -listPools
Found 1 result.
NAME OWNER GROUP MODE LIMIT MAXTTL
common-pool cnauroth cnauroth rwxr-xr-x unlimited never
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Using Centralized Cache Management
• CLI: Adding a Cache Directive
> hdfs cacheadmin -addDirective
-path /hello-amsterdam
-pool common-pool
Added cache directive 1
> hdfs cacheadmin -listDirectives
Found 1 entry
ID POOL REPL EXPIRY PATH
1 common-pool 1 never /hello-amsterdam
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Using Centralized Cache Management
• CLI: Removing a Cache Directive
> hdfs cacheadmin -removeDirective 1
Removed cached directive 1
> hdfs cacheadmin -removeDirectives
-path /hello-amsterdam
Removed cached directive 1
Removed every cache directive with path /hello-amsterdam
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Using Centralized Cache Management
• API: DistributedFileSystem Methods
public void addCachePool(CachePoolInfo info)
public RemoteIterator<CachePoolEntry> listCachePools()
public long addCacheDirective(CacheDirectiveInfo info)
public RemoteIterator<CacheDirectiveEntry>
listCacheDirectives(CacheDirectiveInfo filter)
public void removeCacheDirective(long id)
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Centralized Cache Management Behind the
Scenes
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Centralized Cache Management Behind the
Scenes
• Block files are memory-mapped into the DataNode process.
> pmap `jps | grep DataNode | awk '{ print $1 }'` |
grep blk
00007f92e4b1f000 124928K r--s- /data/dfs/data/current/BP-
1740238118-127.0.1.1-
1395252171596/current/finalized/blk_1073741827
00007f92ecd21000 131072K r--s- /data/dfs/data/current/BP-
1740238118-127.0.1.1-
1395252171596/current/finalized/blk_1073741826
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Centralized Cache Management Behind the
Scenes
• Pages of each block file are 100% resident in memory.
> vmtouch /data/dfs/data/current/BP-1740238118-127.0.1.1-
1395252171596/current/finalized/blk_1073741826
Files: 1
Directories: 0
Resident Pages: 32768/32768 128M/128M 100%
Elapsed: 0.001198 seconds
> vmtouch /data/dfs/data/current/BP-1740238118-127.0.1.1-
1395252171596/current/finalized/blk_1073741827
Files: 1
Directories: 0
Resident Pages: 31232/31232 122M/122M 100%
Elapsed: 0.00172 seconds
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HDFS Zero-Copy Reads
• Applications read straight from direct byte buffers, backed by the
memory-mapped block file.
• Eliminates overhead of intermediate copy of bytes to buffer in user
space.
• Applications must change code to use a new read API on
DFSInputStream:
public ByteBuffer read(ByteBufferPool factory, int maxLength,
EnumSet<ReadOption> opts)
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Heterogeneous Storages for HDFS
Architecting the Future of Big Data
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Goals
• Extend HDFS to support a variety of Storage Media
• Applications can choose their target storage
• Use existing APIs wherever possible
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Interesting Storage Media
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Cost Example Use case
Spinning Disk (HDD) Low High volume batch data
Solid State Disk (SSD) 10x of HDD HBase Tables
RAM 100x of HDD Hive Materialized Views
Your custom Media ? ?

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HDFS Storage Architecture - Before
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HDFS Storage Architecture - Now
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Storage Preferences
• Introduce Storage Type per Storage Medium
• Storage Hint from application to HDFS
–Specifies application’s preferred Storage Type
• Advisory
• Subject to available space/quotas
• Fallback Storage is HDD
–May be configurable in the future
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Storage Preferences (continued)
• Specify preference when creating a file
–Write replicas directly to Storage Medium of choice
• Change preference for an existing file
–E.g. to migrate existing file replicas from HDD to SSD
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Quota Management
• Extend existing Quota Mechanisms
• Administrators ensure fair distribution of limited
resources
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File Creation with Storage Types
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Move existing replicas to target Storage
Type
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Transient Files (Planned feature)
• Target storage type is Memory
–Writes will go to RAM
–Allow short circuit writes equivalent to Short circuit reads to
local in-memory block replicas
• Checkpoint files to disk by changing storage type
• Or discard
• High performance writes For Low volume transient
data
–e.g. Hive Materialized Views
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References
• http://hortonworks.com/blog/heterogeneous-storages-hdfs/
• HDFS-2832 – Heterogeneous Storages phase 1 – DataNode as a
collection of storages
• HDFS-5682 – Heterogeneous Storages phase 2 – APIs to expose
Storage Types
• HDFS-4949 – Centralized cache management in HDFS
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