Distributed File Systems
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1 Distributed File Systems Mauro Fruet University of Trento - Italy 2011/12/19 Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 1 / 39
2 Outline 1 Distributed File Systems 2 The Google File System (GFS) 3 The Hadoop Distributed File System (HDFS) 4 Conclusions 5 Bibliography Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 2 / 39
3 Distributed File Systems Introduction Introduction Definition of DFS File system that allows access to files from multiple hosts via a computer network Features Share files and storage resources on multiple machines No direct access to data storage for clients Clients interact using a protocol Access restrictions to file system Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 3 / 39
4 Distributed File Systems Introduction DFS Goals Access transparency Location transparency Concurrent file updates File replication Hardware and software hetereogenity Fault tolerance Consistency Security Efficiency Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 4 / 39
5 Distributed File Systems Most Important DFS Most Important DFS NFS (Network File System), developed by Sun Microsystems in 1984 Small number of clients, very small number of servers Any machine can be a client and/or a server Stateless file server with few exceptions (file locking) High performance AFS (Andrew File System), developed by the Carnegie Mellon University Support sharing on a large scale (up to 10,000 users) Client machines have disks (cache files for long periods) Most files are small Reads more common than writes Most files read/written by one user Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 5 / 39
6 Distributed File Systems Most Important DFS Need for Large Streams of Data Standard DFS not adequate to manage large streams of data: Facebook: 25 TB/day for logs CERN: 40 TB/day - 15 PB/year Such workloads need different assumptions and goals. Proposed solutions: The Google File System (GFS) Hadoop Distributed File System (HDFS) by Apache Software Foundation Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 6 / 39
7 The Google File System (GFS) Introduction The Google File System (GFS) [1] Proprietary DFS, only key ideas available Developed and implemented by Google in 2003 Shares many goals with standard DFS (performance, scalability, reliability, availability,...) Needed to manage data-intensive applications Provides fault tolerance Provides high performance Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 7 / 39
8 The Google File System (GFS) Design Overview Assumptions Composed of inexpensive commodity components that often fail Hundreds of thousands of commodity machines Modest number of huge files (many GBs) Large streaming reads and small random reads Append to files rather than overwrite Files seldom modified again Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 8 / 39
9 Architecture The Google File System (GFS) Design Overview Figure: GFS Architecture Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 9 / 39
10 The Google File System (GFS) Design Overview Architecture Master has file system metadata: Namespace Access control information Mapping from files to chunks Current locations of chunks Master controls: Chunk lease management Garbage collection Chunk migration Periodical HeartBeat messages between master and chunkservers Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 10 / 39
11 The Google File System (GFS) Design Overview Metadata All metadata in main memory of the master Mutations logged to an operation log No persistent record of replicas locations Operation Log: Contains critical metadata changes Defines order of concurrent operations Replicated on multiple remote machines Used to recover the master Checkpoint of master if log beyond certain size: Master switches to a new log Checkpoint created with all mutations before switch Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 11 / 39
12 The Google File System (GFS) Design Overview Consistency Model File namespace mutations are atomic Namespace locking guarantees atomicity and correctness Mutations applied in same order on all replicas Chunk version numbers used to detect any stale replica (missed mutations while chunkserver was down) Stale replicas are garbage collected Checksum for detection of data corruption Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 12 / 39
13 The Google File System (GFS) System Interactions Leases and Mutation Order Use of leases to maintain consistent mutation order Master grants a chunk lease to a replica, called primary Primary chooses serial order Lease grant order defined by the master Order within a lease defined by the primary Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 13 / 39
14 The Google File System (GFS) Control Flow of a Write System Interactions Figure: Write Control and Data Flow Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 14 / 39
15 Data Flow The Google File System (GFS) System Interactions Separated flow of data and control Goals: 1 Fully use outgoing bandwidth of each machine: Data pushed in a chain of chunkservers 2 Avoid network bottlenecks and high-latency links: Each machine forwards data to closest machine 3 Minimize latency: Once a chunkserver receives data, it starts forwarding immediately Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 15 / 39
16 Snapshots The Google File System (GFS) System Interactions Used to: 1 Quickly create copies of huge data sets 2 Checkpoint current state The master: 1 Revokes leases on chunks of the files it is about to snapshot 2 Logs the operation to disk 3 Duplicates the corresponding metadata New snapshot file points to same chunk C of source file New chunk C created for first write after snapshot Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 16 / 39
17 The Google File System (GFS) System Interactions Namespace Management and Locking No per-directory data structure No hard or symbolic links Namespace maps full pathnames to metadata Each master operation acquires a set of locks before it runs Read lock on internal nodes and read/write lock on the leaf Allowed concurrent mutations in the same directory Read lock on directory prevents its deletion, renaming or snapshot Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 17 / 39
18 The Google File System (GFS) System Interactions Replica Placement Hundreds of chunkservers across many racks Chunkservers accessed from hundreds of clients Goals: Maximize data reliability and availability Maximize network bandwidth utilization Replicas spread across machines and racks Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 18 / 39
19 The Google File System (GFS) System Interactions Creation, Re-replication and Rebalancing Creation Place new replicas on chunkservers with below-average disk usage Limit number of recent creations on each chunkservers Re-replication When number of available replicas falls below a user-specified goal Prioritization: based on how far it is from its replication goal Rebalancing Periodically, for better disk utilization and load balancing Distribution of replicas is analyzed Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 19 / 39
20 The Google File System (GFS) System Interactions Garbage Collection File deletion logged by master File renamed to a hidden name with deletion timestamp Master regularly deletes files older than 3 days (configurable) Until then, hidden file can be read and undeleted When a hidden file is removed, its in-memory metadata is erased Orphaned chunks identified, corresponding metadata erased Safety against accidental irreversible deletion Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 20 / 39
21 The Google File System (GFS) System Interactions Stale Replica Detection Need to distinguish between up-to-date and stale replicas Chunk version number: Increased when master grants new lease on the chunk Not increased if replica is unavailable Stale replicas deleted by master in regular garbage collection Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 21 / 39
22 The Google File System (GFS) Fault Tolerance and Diagnosis High Availability Fast Recovery Master and chunkservers have to restore their state and start in seconds no matter how they terminated Master Replication Master state replicated for reliability on multiple machines When master fails: It can restart almost instantly A new master process is started elsewhere shadow (not mirror) master provides only read-only access to file system when primary master is down Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 22 / 39
23 The Google File System (GFS) Fault Tolerance and Diagnosis Data Integrity Checksums to detect corruption of stored data Integrity verified by each chunkserver Corruptions not propagated to other chunkservers Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 23 / 39
24 The Hadoop Distributed File System (HDFS) Introduction The Hadoop Distributed File System (HDFS) [2] Sub-project of Apache Hadoop project Primary storage system used by Hadoop applications Creates multiple replicas of data blocks Distributes replicas on nodes Highly fault-tolerant Designed to run on commodity hardware Suitable for applications for large data sets Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 24 / 39
25 The Hadoop Distributed File System (HDFS) Introduction Powered By HDFS used by some of the world s biggest Web sites Controls the top search engines Powered By Adobe since 2008: 30 nodes; clusters from 5 to 14 nodes; plan on a 80 nodes cluster ebay: 532 nodes cluster; 8*532 cores, 5.3 PB Facebook: one 1,100 nodes cluster with 8,800 cores and 12 PB; one 300 nodes cluster with 2,400 cores and 3 PB Twitter: 12 TB/day Yahoo!: more than 100,000 CPUs in more than 40,000 PCs; biggest cluster: 4,500 nodes Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 25 / 39
26 The Hadoop Distributed File System (HDFS) Writing Data in HDFS Cluster Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 26 / 39
27 The Hadoop Distributed File System (HDFS) Writing Data in HDFS Cluster Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 27 / 39
28 The Hadoop Distributed File System (HDFS) Writing Data in HDFS Cluster Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 28 / 39
29 The Hadoop Distributed File System (HDFS) Reading Data in HDFS Cluster Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 29 / 39
30 The Hadoop Distributed File System (HDFS) Types of Faults and Their Detection Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 30 / 39
31 The Hadoop Distributed File System (HDFS) Types of Faults and Their Detection Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 31 / 39
32 The Hadoop Distributed File System (HDFS) Writing and Reading Data Handling Reading and Writing Failures Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 32 / 39
33 The Hadoop Distributed File System (HDFS) Handling DataNode Failures Writing and Reading Data Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 33 / 39
34 The Hadoop Distributed File System (HDFS) Replica Placement Strategy Replica Placement Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 34 / 39
35 The Hadoop Distributed File System (HDFS) Replica Placement Cluster Rebalancing Balanced cluster: no under-utilized or over-utilized DataNodes Common reason: addition of new DataNodes Requirements No block loss No change of the number of replicas No reduction of the number of racks a block resides in No saturation of the network Rebalancing issued by an administrator Rebalancing decisions made by a Rebalancing Server Goal of each iteration: reduce imbalance in over/under-utilized DataNodes Source and destination selection based on some priorities Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 35 / 39
36 The Hadoop Distributed File System (HDFS) Checkpoint and Backup Persistence of File System Metadata To maintain file system namespace: fsimage: latest checkpoint of the namespace edits: log of changes to namespace since last checkpoint When NameNode starts up, it merges fsimage and edits Then, it overwrites fsimage with the new HDFS state and begins a new edits log Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 36 / 39
37 The Hadoop Distributed File System (HDFS) Checkpoint and Backup Checkpoint Node and Backup Node CheckPoint Node Periodically creates checkpoints of the namespace Usually runs on a different machine wrt NameNode Multiple CheckPoint Nodes may be used simultaneously Backup Node As Checkpoint Node, but it applies edits to its own namespace copy Only one Backup Node at a time, no Checkpoint Nodes allowed Work in progress: concurrently use multiple Backup Nodes Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 37 / 39
38 Conclusions Conclusions GFS Proprietary file system All the details in the paper of 2003 HDFS Open-source project Inspired by the Google File System Used by many big companies Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 38 / 39
39 Bibliography References Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, SOSP 03, pages 29 43, New York, NY, USA, ACM. The Apache Software Foundation. Hadoop distributed file system Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 39 / 39
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