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HDFS What’s New and Future

HDFS What’s New and Future. Suresh Srinivas @ suresh_m_s Sanjay Radia @ srr. About Me. Architect & Founder at Hortonworks Long time Apache Hadoop committer and PMC member Designed and developed many key Hadoop features Experience in supporting many clusters

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HDFS What’s New and Future

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  1. HDFS What’s New and Future Suresh Srinivas @suresh_m_s Sanjay Radia @srr

  2. About Me • Architect & Founder at Hortonworks • Long time Apache Hadoop committer and PMC member • Designed and developed many key Hadoop features • Experience in supporting many clusters • Including some of the world’s largest Hadoop clusters Architecting the Future of Big Data

  3. Agenda • HDFS – What’s new • New features in release 2.0 • Future • Short term and long term features • Major architectural directions Architecting the Future of Big Data

  4. We have been hard at work… • Progress is being made in many areas • Scalability • Performance • Enterprise features • Ongoing operability improvements • Expand Hadoop ecosystem to more platforms and use cases • 2192 commits in Hadoop in the last year • Almost a million lines of changes • ~150 contributors • Lot of new contributors - ~80 with < 3 patches • 350K lines of changes in HDFS and common Architecting the Future of Big Data

  5. Building on Rock-solid Foundation • Original design choices - simple and robust • Single Namenode metadata server – all state in memory • Fault Tolerance: multiple replicas, active monitoring • Storage: Rely on OS’s file system not raw disk • Reliability • Over 7 9’s of data reliability, less than 0.38 failures across 25 clusters • Operability • Small teams can manage large clusters • An operator per 3K node cluster • Fast Time to repair on node or disk failure • Minutes to an hour Vs. RAID array repairs taking many long hours • Scalable - proven by large scale deployments not bits • > 100 PB storage, > 400 million files, > 4500 nodes in a single cluster • ~ 100 K nodes of HDFS in deployment and use Architecting the Future of Big Data

  6. Current Hadoop Releases • Release 1.x stream • Current stable release 1.2.0 • Only critical bug fixes, improvements, and feature ported back • Release 2.x stream • Close to trunk • Current release 2.1.0-beta – almost ready • Has the new features • YARN • MapReduce 2 • New HDFS features • Expected to be stable and GA by September Architecting the Future of Big Data

  7. Federation – Many Namespaces .. .. NN-n NN-k NN-1 Foreign NS n • Block Storage as generic storage service • DNs store blocks in Block Poolsfor all the Namespace Volumes • Multiple independent Namenodes and Namespace Volumes in a cluster • Scalability by adding more namenodes/namespaces • Isolation – separating applications to their own namespaces • Client side mount tables/ViewFS for integrated views Namespace NS k NS1 ... ... Pool k Pool 1 Pool n Block Pools Block Storage DN 2 DN m DN 1 .. Common Storage Architecting the Future of Big Data

  8. High Availability – No SPOF • Support standby namenode and failover • Planned downtime • Unplanned downtime • Release 1.1 • Cold standby • Uses NFS as shared storage • Standard HA frameworks as failover controller • Linux HA and VMWareVSphere • Suitable for small clusters up to 500 nodes Architecting the Future of Big Data

  9. High Availability – Release 2.0 • Support for Hot Standby • Manual and automatic failover • Automatic failover with Failover Controller • Active NN election and failure detection using ZooKeeper • Periodic NN health check • Failover on NN failure • Removed shared storage dependency • Quorum Journal Manager • 3 to 5 Journal Nodes for storing journal • Edit must be written to quorum number of Journal Nodes • Replay cache for correctness & transparent failovers • In progress – will be in 2.1.0-beta Architecting the Future of Big Data

  10. ZK ZK ZK JN JN JN Heartbeat Heartbeat FailoverController Active FailoverController Standby Cmds NN Active NN Standby Shared NN state through Quorum of JournalNodes Monitor Health of NN. OS, HW Monitor Health of NN. OS, HW Namenode HA has no external dependency Architecting the Future of Big Data Block Reports to Active & Standby DN fencing: only obey commands from active DN DN DN DN

  11. Snapshots (HDFS-2802) • Support for read-only COW snapshots • Design allows read-write snapshots • Namenode only operation – no data copy made • Metadata in namenode - no complicated distributed mechanism • Datanodes have no knowledge • Snapshot entire namespace or sub directories • Nested snapshots allowed • Managed by Admin • Users can take snapshots of directories they own • Efficient • Instantaneous creation • Memory used is highly optimized • Does not affect regular HDFS operations Architecting the Future of Big Data

  12. Snapshot Design • A large number of snapshots supported • State proportional to the changes between the snapshots • Supports millions of snapshots • Based on Persistent Data Structures • Maintains changes in the diff list at the Inodes • Tracks creation, deletion, and modification • Snapshot state Sn = current - ∆n ∆n ∆n-1 ∆0 Current Sn Sn-1 S0 Architecting the Future of Big Data

  13. Snapshot – APIs and CLIs • All commands & APIs can use snapshot path • /<path>/.snapshot/<snapshot_name>/file.txt • cp /from/.snpashot/snap1/file.txt /to/file.txt • CLIs • Admin allows snapshots • Snapshottable Directories • Users can create/delete/rename snapshots • Tool to print diff between snapshots • Admin tool to print all snapshottable directories and snapshots • Status • Work is complete – available in 2.1.0-beta Architecting the Future of Big Data

  14. Performance Improvements • Many Improvements • SSE4.2 CRC32C – ~3x less CPU on read path • Read path improvements - fewer memory copies • Short-circuit read for 2-3x faster random reads (local reads) • Unix domain socket based local reads • Simpler to configure and generic for many applications • I/O improvements using posix_fadvise() • libhdfsimprovements for zero copy reads • Significant improvements – I/O 2.5x to 5x faster • Lot of improvements back ported to release 1.x Architecting the Future of Big Data

  15. NFS Support (HDFS-4750) • NFS Gateway provides NFS access to HDFS • File browsing, Data download/upload, Data streaming • No client-side library • Better alternative to Hadoop + Fuse based solution • Better consistency guarantees • Supports NFSv3 • Stateless Gateway • Simpler design, easy to handle failures • Future work • High Availability for NFS Gateway • NFSv4 support? Architecting the Future of Big Data

  16. Stronger Compatibility • Hadoop RPC on the wire encryption uses protobuf • Post 2.1.0 beta stronger compatibility • Java API • Wire protocol • Both forward and backward compatibility • Simplifies migrating to newer releases • Rolling upgrades • Enabled by strong wire protocol compatibility • Security improvements • Negotiation of authentication • Multiplex secure sessions in a connection Architecting the Future of Big Data

  17. Other Features • New append pipeline • Improvements for other projects • Stale Node to improve HBase MTTR • Block placement enhancements • Better support for other topologies such as VMs and Cloud • Expanding ecosystem, platforms and applicability • Native support for Windows • File and Block IDs are unique • Major architectural step • Allows caches, layered file systems • A key requirement for archives and disaster recovery Architecting the Future of Big Data

  18. Enterprise Readiness • Storage fault-tolerance – built into HDFS  • 100% data reliability • High Availability  • Standard Interfaces  • WebHdfs(REST), Fuse, NFS, HTTPFS, libwebhdfsand libhdfs • Wire protocol compatibility  • Protocol buffers • Rolling upgrades  • Snapshots  • Disaster Recovery  • Distcpfor parallel and incremental copies across cluster • Apache Ambari and HDP for automated management Architecting the Future of Big Data

  19. HDFS Futures Architecting the Future of Big Data

  20. From Batch to Real-time • New latency sensitive real-time use cases • Interactive queries - Stinger/Tez • HBase • Heterogeneous (HDFS-2832) • Storage properties were hidden • Datanode abstraction from single storage to collection of storages • Memory, SSDs, Disks as storage types • Hierarchical storage tiers • Enables memory cache • Work in progress • Datanode Cache (HDFS-4949) • Enable fast zero copy access to data cached/pinned in datanode memory • Co-ordinated cache management • Future – more sophisticated caching policies • Cache partial blocks based on access pattern • LRU, LRU-2 Architecting the Future of Big Data

  21. Storage Abstraction • Fundamental storage abstraction improvements • Short Term (Post 2.x GA) • Heterogeneous storage • Block level APIs for direct access to fault tolerant storage • Apps & Services can by-pass file system interface • Granular block placement policies • Co-locate related data/blocks in specific nodes • Policy/tools to migrate related data together • Long Term • Explore support for objects/key-value store and APIs • Serving from Datanodes optimized based on file structure Architecting the Future of Big Data

  22. Higher Scalability • Even higher scalability of namespace • Only working set in Namenode memory • Namenode as container of namespaces • Support large number of namespaces • Explore new types of namespaces • Further scale the block storage • Block management to Datanodes • Block collection/Mega block group abstraction Architecting the Future of Big Data

  23. High Availability • Further enhancements to HA • Expand Full stack HA to include other dependent services • Support multiple standby nodes • Use standby for reads • Simplify management – eliminate special daemons for journals • Move Namenode metadata to HDFS Architecting the Future of Big Data

  24. Q & A • Myths and misinformation • Not reliable (was never true) • Namenode dies all state is lost (was never true) • Hard to operate • Slow and not performant • Namenode is a single point of failure • Needs shared NFS storage • Does not have point in time recovery • Does not support disaster recovery Thank You! Architecting the Future of Big Data

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