Hadoop Open Platform-as-a-Service (Hops)

Transcription

1 Hadoop Open Platform-as-a-Service (Hops) Academics: PostDocs: PhDs: R/Engineers: Jim Dowling, Seif Haridi Gautier Berthou (SICS) Salman Niazi, Mahmoud Ismail, Kamal Hakimzadeh, Ali Gholami Stig Viaene (SICS), Steffen Grohschmeidt MSc Students: Theofilos Kakantousis, Nikolaos Stangios, Sri Srijeyanthan, Vangelos Savvidis, Seçkin Savaşçı.

2 What is Systems Research?* Systems research is the scientific study, analysis, modeling and engineering of effective software platforms. Its challenge is to provide dependable, powerful, performant, secure and scalable solutions within an increasingly complex IT environment. *Drushel et al, Fostering Systems Research in Europe, A White Paper by EuroSys, 2006

3 Why is Big Data Important? In a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research. More data trumps better algorithms * * The Unreasonable Effectiveness of Data [Halevey, Norvig et al 09]

4 Bill Gates biggest product regret*

5 Windows Future Storage (WinFS*) WinFS was an attempt to bring the benefits of schema and relational databases to the Windows file system. The WinFS effort was started around 1999 as the successor to the planned storage layer of Cairo and died in 2006 after consuming many thousands of hours of efforts from really smart engineers. - [Brian Welcker]* *

6 Background: Hadoop Filesystem and MapRed 6

7 HDFS: Hadoop Filesystem write /crawler/bot/jd.io/1 Name node Under-replicated blocks Heartbeats Rebalance Re-replicate blocks Data nodes Data nodes

8 Big Data Processing with No Data Locality Job( /genomes/jim.bam ) submit Workflow Manager Compute Grid Node Job This doesn t scale. Bandwidth is the bottleneck

9 MapReduce Data Locality Job( /genomes/jim.bam ) submit Job Tracker Task Task Task Task Task Task Tracker Tracker Tracker Tracker Tracker Tracker Job Job Job Job Job Job DN DN DN DN DN DN R R = resultfile(s) R R

10 MapReduce Programming Model join Scan filter Sort Batch Sequential Processing With Fault Tolerance

11 The NameNode 11

12 HDFS NameNode Stores Mappings: path_component -> inode inode -> {block} block -> {replica1,replica2,replica3} External API to HDFS Clients - Internal API to DataNodes Monitors Datanodes for failures, corrupted data Manages Leases, Quotas, (re-)replication Must do all this in a single JVM - Spotify have a 90GB Heap storing references to 300m files 12

13 High Availability for the NameNode HDFS 2.x Agreement on the Active Master ZK ZK ZK DOESN T SCALEOUT! JN JN JN Master-Slave Replication of NN State. NN Active Shared NN log stored in NN quorum of journal nodes NN Standby Faster Recovery, Cut Journal Log Checkpt NN DN DN DN DN

14 The Evolution of the NamNode HDFS (2006) - In-memory metadata HDFS 0.07 (2006) - WAL (EditLog) - FSImage HDFS 0.21 (2009) - Weaken Global Lock They reinvented the Database for the NameNode! HDFS 2.0 (2011) - Eventually Consistent Replication: HA-NameNode

15 Databases had these features long ago Oracle v6 (1988) - Redo and Undo Logs - Rollback Segments Oracle V7.1 (1994) - Symmetric Replication and have continued to evolve.. Oracle 9i RAC (2001) - Shared State Replication

16 The end of the One-size-fits-All Database Columnar Databases - Vertica, Hana NewSQL Databases - MySQL Cluster, VoltDB, Memstore, AtlasDB, FoundationDB Graph Databases - Neo4J RDBMSes - MySQL, Postgres, DB2, Oracle, SQLServer In-Memory Stores - Memcached, Redis Key-Value Stores - Dynamo, Cassandra, MongoDB, Riak Petabyte Databases - BigQuery (Google), RedShift (Amazon), Impala (Cloudera) Stonebraker et al, One Size Fits All: An Idea Whose Time Has Come and Gone,

17 MySQL Cluster (NDB) Shared Nothing DB SQL API NDB API 30+ million update transactions/second on a 30-node cluster Distributed, In-memory 2-Phase Commit - Replicate DB, not the Log! Real-time - Low TransactionInactive timeouts Commodity Hardware Scales out - Millions of transactions/sec - TB-sized datasets (48 nodes) Split-Brain solved with Arbitrator Pattern SQL and Native Blocking/Non- Blocking APIs 17

18 18 HopsFS

19 HopsFS Customizable and Scalable Metadata High throughput for read and write operations NameNode failover time 5 seconds (vs ~1 minute for HDFS)

20 Request Handling (Apache HDFS vs HopsFS) Apache HDFS NameNode Request Handling HopsFS NameNode Request Handling

21 Fine-Grained Locking, Transactional Updates NDB gives us READ_COMMITTED isolation-level, not strong enough. We implemented Serializability for FS operations using implicit locking in the DAG and row-level locking in NDB. [Hakimzadeh, Peiro, Dowling, Scaling HDFS with a Strongly Consistent Relational Model for Metadata, DAIS 2014] 21

22 Preventing Deadlocks and Starvation read mv /user/jdowling/dna.bam block_report Solution: all request threads for inode operations traverse the FS hierarchy in the same order, acquiring locks in the same order. Block-level operations have to follow the same order. 22

23 Per Transaction Cache Experimentation revealed many roundtrips to the database per transaction. Cache intermediate transaction results at NameNodes. We also use Memcached at each NameNode to cache mappings of: path->{inode/blocks/replicas}

24 Sometimes, Transactions Just ain t Enough Large Subtree Operations with millions of Inodes can t be executed in a single Transaction, due to the low timeouts for Transactions (real-time). Subtree Operations: 4-phase Protocol Sacrifices Atomicity, but keeps Isolation and Consistency. Batch operations and multithreading for performance. Failed NameNodes handled transparently. Leases used to handle failed clients. 24

25 Leader Election using the Database (NDB) We need a leader NameNode to coordinate replication and lease management Use NDB as shared memory for Leader Election. No more Zookeeper, yay! 25

26 HopsFS Internal Protocol Scalability On 100PB+ clusters, internal protocols make up most of the network traffic for HDFS Block Reporting and Exiting Safe Mode - Batching and work stealing.

27 HopsFS Write Performance 1 Gbit Network, Nodes: 12-core Xeon 2.8 Ghz. 2-Node NDB Cluster. 27

28 HopsFS Read Performance 1 Gbit Network, Nodes: 12-core Xeon 2.8 Ghz. 2-Node NDB Cluster. 28

29 HopsFS Erasure Coding HDFS 2.x Triple Replication (300%) 2x Replication + XOR (220%) Reed-Solomon (140%)

30 HopsFS Erasure Coding Data durability with Triple Replication Data durability with Reed-Solomon 30

31 Comparison with HDFS-RAID

32 HopsFS Snapshots Read-Only Root-Level Single Snapshot - Support rollback on unsuccessful software upgrades - Prototype developed, ongoing work on integration - Snapshot rollback order-of-growth is O(N)

33 We did the same for YARN 33

34 Apache Hadoop Yarn HA/Scaleout Limitations Clients Zookeeper Primary RM Standby RM NM NM NM NM NM The Resource Manager (RM) is a bottleneck. Zookeeper throughput not high enough to persist all RM state Standby resource manager can only recover partial state All running jobs must be restarted. RM state not queryable. 34

35 Hops Yarn. Client NDB NDB NDB RM RM NM NM NM NM NM The RM is a State-Machine. Almost no session state to manage. Transparent failover working. 35

36 Hops Yarn FIFO Scheduler Capacity Scheduler Fair Scheduler Distributed Resource Tracker Service (ongoing) Make YARN more interactive (ongoing) - Reduce NodeManager Heartbeat Time 36

37 Hops-Hadoop NDB NDB NDB NDB NN NN NN RM RM RM HDFS HDFS YARN YARN DN NM DN NM DN NM DN NM DN NM DN NM DN NM DN NM DN NM DN NM DN NM DN NM Exabyte-Scale Hadoop

38 The Hops Stack Continued 38

39 Bringing Data People Together Data Owners - Metadata, Ingestion - Non-programmers Data Scientists - Data analysts - Programmers HopsHub Spark Flink Adam Cuneiform Hops-YARN Hops-HDFS Karamel/PaaS 39

40 Perimeter Security and Multi-Tenancy HopsHub - Project-level RBAC Hadoop trusted proxy - Analytics Plugin Framework Adam, Cuneiform, Spark, Flink, MR - REST APIs Network Isolation Kerberos LIMS Related Hadoop Security Projects Knox, Sentry, Rhino LDAP 40

41 HopsHub Two-Factor Authentication 41

42 Projects for Multi-Tenancy; Activity Trails Project Global Activity Trail 42

43 Project Membership 43

44 HDFS Files File Browser (Iceberg)

45 Upload Data Overcome 3 GB browser upload limit Apache Flume Automated Ingestion of Data 45

46 Run Cuneiform Workflows on YARN FastQ files Align BAM file Variant Calling VCF file (~250 GB) (~10 GB) (~5 MB) Annotate Results 46

47 Ongoing MSc Projects Realizing the meta-data dream of WinFS - Vangelis Optimizing YARN s Resource Tracker Service (interactive YARN) - Sri Interactive Data Analytics (Zeppelin-EE) - Seckin 47

48 PaaS support with Chef/Karamel Support for EC2, Vagrant, Bare Metal. 48

49 Conclusions Hops will be the first European distribution of Hadoop when released. - First beta release coming in Q Lots of ideas for future work - Tighter Spark, Flink integration - BiobankCloud support NGS Hadoop Workshop Feb 19-20, Stockholm - Signup at 49