Evolving HDFS to a Generalized Distributed Storage Subsystem

Evolving HDFS to a Generalized Storage Subsystem Sanjay RadiaJitendra Pandey

2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Speakers

Sanjay RadiaChief Architect, Founder, Hortonworks

Part of the original Hadoop team at Yahoo! since 2007Chief Architect of Hadoop Core at Yahoo!Apache Hadoop PMC and Committer

PriorStartup, Sun Microsystems, Inria …Ph.D., University of Waterloo

Jitendra PandeyDirector of Engineering, HortonworksLead for HDFS development at Hortonworks

Member of Apache Software FoundationPMC member, Committer for HadoopCommitter for Hive

Page 2Architecting the Future of Big Data


Overview

HDFS – Evolution in past and motivations for the futureScaling HDFS• Where we do well (# of clients/cluster size, raw storage)• Where we have challenges (Small files and blocks)• Solution

• Partial namespace (Briefly)• Block Containers - But we are generalizing the storage layer to support this

Storage Containers to Generalize the Storage Layer


Background: HDFS Layering

DN 1 DN 2 DN m.. .. ..

NS1

Foreign NS n

... ... NS k

Block Management Layer

Block Pool nBlock Pool kBlock Pool 1

NN-1 NN-k NN-n

Common Storage

Bloc

k St

orag

eN

ames

pace


Security in virtualized compute env

HDFS Dimensions Large # of compute clients: 100K cores

Reliability Reliability Reliability, Disk/DN FTHA, DR, Snapshots ….

PBs of Data (Big Data)Horizontal Scaling

Bad AppsMulti-tenancy Resource Mgt/Isolation, Audit

Large number of files and blocks

Beyond files: optimized storage

Heterogeneous storage

Erasure codes (In Beta)

Performance

File co-location

Fat DataNodes BRs

TransparentEncryption


ScalabilityThe Problems and the Solutions


Scalability – What HDFS Does Well

• HDFS NN stores all namespace metadata in memory (as per GFS)• Scales to large clusters (5K) since all metadata in memory

– 60K-100K tasks can share the Namenode– Low latency

• Large data if files are large • Proof points of large data and large clusters

– Single Organizations have over 600PB in HDFS– Single clusters with over 200PB using federation– Large clusters over 4K multi-core nodes bombarding a single NN

Metadata in memory the strength of the original GFS and HDFS designBut also its weakness in scaling number of files and blocks


Scalability - The Challenges

• Challenges• Large number of files (> 350 million)• NN’s strength has become a limitation

• Number of File operations• Need to improve concurrency move to multiple name servers

HDFS Federation is the current solution• Add NameNodes to scale number of files & operations• Deployed at Twitter

• Cluster with three NameNodes > 5000 node cluster (Plans to grow to 10,000 nodes)

• Back ported and used at Facebook to scale HDFS


Scaling Files and Blocks

1. Scale Namespace• Keep only partial namespace in memory - the workingSet

• Of last 3-5 years data only small portion is actively used – the working set metadata fits in memory

- Do not want to page the working set =>still large NN memory to scale to 100K tasks

2. Scale Block Management• Keeping only part of the BlockMap in mem does not work• Solution: Containers of blocks (2GB-16GB+)

• Will reduce BlockMap• Reduce Number of Block/Container reports

But extend DN to support generalized Storage Container


Big Picture A Brief Interlude on Partial Namespace + Volumes

Partial Namespace in Memory is not focus of this talk


Partial Namespace - Briefly

• Has been prototyped• Benchmarks so that model works well• Most file systems keep only partial namespace in memory but not at this scale

– Hence Cache replacement policies of working-set is important

• Work in progress to get it into HDFS

• Namespace Volumes – a better way to Federate the Namespace service• Partial Namespace in Memory will allow multiple namespace volumes• Scale both namespace and number of operations using multiple servers• BTW Nameservers can run on DataNodes if you prefer …

12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved© Hortonworks Inc. 2013 - Confidential

Big Picture on HDFS Namespace + Volumes .. Only WorkingSet of namespace in memory

› Scale beyond memory of NN NameServer – Containers for namespaces

› More namespace volumes– Chosen per user/tenant/DBs– Management policies (quota, …)– Mount tables for unified namespace

• Can be managed by a central volume server

Number of NameServers = › Sum of (Namespace working set) +› Sum of (Namespace throughput)› Move namespace for balancing

› N+K Failover amongst NameServers

12

Datanode Datanode…

…

NameServers as Containers of Namespaces

Storage Layer


Storage Containers: Better HDFS and Beyond


DataNodes

Big PictureSupport multiple data layout structures• Indexing• Caching• Use cases

• HDFS Block Container (scale blocks) + Co-location• Object Store Container• Local replica + S3 replica• HBase

Common Shared Infrastructure for• Replication • Consistency • Cluster membership• Container location

Other Container Benefits• Place to put in protocol enhancements• Smaller riskier features

BlockContainer

Object StoreContainer

HBaseContainer

TableContainer

ClusterMembership

ReplicationManagement

ContainerLocation Service

Container Management Services

(Runs on DataNodes)

HBaseObject Store

Metadata

Applications

HDFS

Physical Storage - Shared


HDFS on Containers

DNDN DN DNClient

Data Path

Namenode(File System Metadata)

SCM(Storage Container

Manager)

• Container reports• HeartbeatsContainer LookupFile System

operations


Storage Container Manager

Manages cluster membership– Collects heartbeats from the Data Nodes

Maintains location of each container– Collects container reports– Assigns Data Nodes for containers

Does NOT maintain the state of each container Does NOT manage the consistency or recovery for containers

– Consistency management is completely within the containers


Current vs New World (Storage Containers)Current• Namespace (in NameNode)

• File=BlockIds[]

• BlockManager (In NameNode)• BlockMap: BlockId->locations

• PipeLine repair• Replication management

• BlockData in DataNode• BlockId->Data

New World• Namespace (in NameNode)

• File=BlockIds[] (but BlockId=ContainerId+LocalBid)

• ContainerManager (logically central)• ContainerMap: ContainerId->locations

• Cluster membership

• Containers (in DataNode)• Container’s BlockMetadata + Data

• Key (e.g. BlockId)->Data

• Replication using RAFT protocol• Replication and consistency


Storage Container Contains data for many blocks with different block ids

Recall how the client will perform the mapping:–file blockId[] (NN)–blockId ->ContainerLocation (Container Manager)–Container maps the blockId to data (DataNode)

A container can be viewed as a local key-value store.–Block Id is the key and Block data is the value

Storage Container Prototype using LevelDB –An embeddable key-value store–BlockId is the key and filename of local file is value–Optimizations

–Small blocks (< 1MB) can be stored directly in rocksDB–Other compaction for block data to avoid lots of files

– But this can be evolved over time Page 18


ContainerIndex

Chunk data file

Chunk data file

Chunk data file

Chunk data file

Key 1LSMe.g

LevelDB/RocksDB

Key N

Chunk Data File Name Offset Length

Container Structure


Replication: Possible Approaches Data pipeline

– Data pipeline as a form of chain replication has been successfully used for data– However, its correctness depended on central coordinator– Needs to be extended for block metadata, but hard to get it right given no

central coordinator

Use RAFT replication instead of data pipeline, for both data and metadata

– Proven to be correct. We have implemented a RAFT library and we plan to incubate it in Apache.

– Has been primarily used for small updates and transactions, fits well for metadata

– Could be performance concerns for large streaming writes, prototype in progress

Hybrid: RAFT + Pipeline– Hybrid approach: It can be viewed as if central coordinator is replaced by RAFT– Data pipeline approach for the data + the raft protocol -- under discussion

Page 20


Storage Containers: Additional Use Cases Block Store

– Block store can be thought of as an external Disk Volume mounted via SCSI, or other OS mechanisms.

– Mount a block volume as a block device under Ext4– Many use cases require POSIX capabilities including random write– E.g. Persisted local storage in virtualized environments

– Local Storage for YARN containers

HDFS as Caching for Cloud Storage– Enable Hadoop Ecosystem for Data in the Cloud– Better performance– Seamless migration from on-prem to cloud– Proposal to store blocks externally in the cloud by Chris Douglas et al in HDFS-

9806Page 21


CBlock: Block Store Interface

Volume: A block device or a mountable disk in the system Blocks are 4K size: A block is a physically contiguous disk space in the volume. Block is the unit of read/write/update

– Get/Put Block API is sufficient– Sounds very similar to an object store (Its actually simpler)

Not to confuse with HDFS blocks – HDFS Blocks are much larger– Hdfs blocks can only be appended– In this context blocks are unit of IO operations by native file systems.


CBlock Prototype


CBlock Prototype

cBlock server allows creation and management of SCSI volumes. cBlock server state will be replicated via RAFT.

cBlock Server

cBlock iSCSI Server Running on each data node, allows clients to easily mount the volumes. Provides a data plane that allows clients to read and write to containers. Scalable since we have one or more iSCSI servers per node, and iSCSI servers are stateless.


Next steps• Block container (to support tens of billions of blocks)

• 2-4gb block containers initially => reduction of 40-80 in BR and block map

• Reduce BR pressure in on NN

• Partial Namespace (to billions of files per volume)• Will take us to 2B files initially and then more as we gain experience on file-working-set management

• Volumes + N+K failover • Scale both ops and namespace + operational improvement for HA

• Other containers• Local Replica & Cloud storage (e.g. S3) replica (Caching Mount)

• Object store, HBase …..


Summary

• HDFS scale proven in real production systems• 4K+ clusters• >200PB in single federated NN cluster and >30PB in non-federated clusters• But very large number of small files is a challenge

• Important Area of Current Focus: Scaling # Files and Blocks• Partial Namespace: initially scale to 2B files, later 5-10B files per volume + multiple volumes• Block containers: initially scale to 6B-12B blocks, later to 100B+ blocks

– However we are implementing this to extend the storage layer

• Restructuring storage layer to support generalized storage containers• Support storage needs beyond HDFS: Block Store, Object Store, better HBase support, etc.


Q&A

Thank You

Technology

Evolving HDFS to a Generalized Distributed Storage Subsystem