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Lecture XIII: Replication-II

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  1. Lecture XIII: Replication-II CMPT 401 Summer 2007 Dr. Alexandra Fedorova

  2. Outline • Google File System • A real replicated file system • Paxos • A consensus algorithm used in real systems • Harp • A replicated research file system

  3. Google File System • A real massive distributed file system • Hundreds of servers and clients • The largest cluster has >1000 storage nodes, over 300 TB of disk storage, hundreds of clients • Metadata replication • Data replication • Design driven by application workload and technological environment • Avoided many of difficulties traditionally associated with replication by designing for a specific use case

  4. Specifics of the Google Environment • FS is built of hundreds of storage machines, built of inexpensive commodity parts • Component failures are a norm • Application and OS bugs • Human errors • Hardware failures: disks, memory, network, power supplies • Millions of files, each 100 MB or larger • Multi-GB files are common • Applications are written for GFS • Allows co-design of the file system and applications

  5. Specifics of the Google Workload • Most files are mutated by appending new data – large sequential writes • Random writes are very uncommon • Files are written once, then they are only read • Reads are sequential • Large streaming reads and small random reads • High bandwidth is more important than low latency • Google applications: • Data analysis programs that scan through data repositories • Data streaming applications • Archiving • Applications producing (intermediate) search results

  6. GFS Architecture

  7. GFS Architecture (cont.) • Single master • Multiple chunk servers • Multiple clients • Each is a commodity Linux machine, a server is a user-level process • Files are divided into chunks • Each chunk has a handle (an ID assigned by the master) • Each chunk is replicated (on three machines by default) • Master stores metadata, manages chunks, does garbage collection, etc. • Clients communicate with master for metadata operations, but with chunkservers for data operations • No additional caching (besides the Linux in-memory buffer caching)

  8. Client/GFS Interaction • Client: • Takes file and offset • Translates it into the chunk index within the file • Sends request to master, containing file name and chunk index • Master: • Replies with the corresponding chunk handle and location of the replicas (the master must know where the replicas are) • Client: • Caches this information • Contacts one of the replicas (i.e., a chunkserver) for data

  9. Master • Stores metadata • The file and chunk namespaces • Mapping from files to chunks • Locations of each chunk’s replicas • Interacts with clients • Creates chunk replicas • Orchestrates chunk modifications across multiple replicas • Ensures atomic concurrent appends • Locks concurrent operations • Deletes old files (via garbage collection)

  10. Metadata On Master • Metadata – data about the data: • File names • Mapping of file names to chunk IDs • Chunk locations • Metadata is kept in memory • File names and chunk mappings are also kept persistent in an operation log • Chunk locations are kept in memory only • It will be lost during the crash • The master asks chunk servers about their chunks at startup – builds a table of chunk locations

  11. Why Keep Metadata In Memory? • To keep master operations fast • Master can periodically scan its internal state in the background, in order to implement: • Garbage collection • Re-replication (in case of chunk server failures) • Chunk migration (for load balancing) • But the file system size is limited by the amount of memory on the master? • This has not been a problem for GFS – metadata is compact

  12. Why Not Keep Chunk Locations Persistent? • Chunk location – which chunk server has a replica of a given chunk • Master polls chunk servers for that information on startup • Thereafter, master keeps itself up-to-date: • It controls all initial chunk placement, migration and re-replication • It monitors chunkserver status with regular HeartBeat messages • Motivation: simplicity • Eliminates the need to keep master and chunkservers synchronized • Synchronization would be needed when chunkservers: • Join and leave the cluster • Change names • Fail and restart

  13. Operation Log • Historical record of metadata changes • Maintains logical order of concurrent operations • Log is used for recovery – the master replays it in the event of failures • Master periodically checkpoints the log • Checkpoint is a B-tree data structure • Can be loaded into memory • Used for namespace lookup without extra parsing • Checkpoint can be done on the background

  14. Data Consistency in GFS • Loose data consistency – applications are designed for it • Applications may see inconsistent data – data is different on different replicas • Applications may see data from partially completed writes – undefined file region • On successful modification the file region is consistent • A write may leave the region undefined – if the client reads the file before another client’s write is complete • Replicas are not guaranteed to be bytewise identical (we’ll see why later, and how clients deal with this)

  15. Data Consistency in GFS (cont.) • Failures: • A modification may fail at one or more replicas • On modification failure, file region is inconsistent • Successes: • Modifications are applied to a chunk in the same order on all replicas • After a number of successful modifications, the file region is guaranteed to be defined: • All replicas have the same data • All replicas contain all the data written by all the write operations

  16. Implications of Loose Data Consistency For Applications • Applications are designed to handle loose data consistency • Example 1: a file is generated from beginning to end • An application creates a file with a temporary name • Atomically renames the file • May periodically checkpoint the file while it is written • File is written via appends – more resilient to failures than random writes • Example 2: producer-consumer file • Many writers concurrently append to one file (for merged results) • Each record is self-validating (contains a checksum) • Client filters out padding and duplicate records

  17. Updates of Replicated Data • Each mutation (modification) is performed at all the replicas • Modifications are applied in the same order across all replicas • Master grants a chunk lease to one replica – i.e., the primary • The primary picks a serial order for all mutations to the chunk • The client pushes data to all replicas • The primary tells the replicas in which order they should apply modifications

  18. Updates of Replicated Data (cont.) • Client asks master for replica locations • Master responds • Client pushes data to all replicas; replicas store it in a buffer cache • Client sends a write request to the primary (identifying the data that had been pushed) • Primary forwards request to the secondaries (identifies the order) • The secondaries respond to the primary • The primary responds to the client

  19. Failure Handling During Updates • If a write fails at the primary: • The primary may report failure to the client – the client will retry • If the primary does not respond, the client retries from Step 1 by contacting the master • If a write succeeds at the primary, but fails at several replicas • The client retries several times (Step 3-7)

  20. Data Flow • Data flow is decoupled from control flow • Data is pushed linearly across all chunkservers in a pipelined fashion (not necessarily from client to primary and from primary to secondary) • Client forwards data to the closest replica; that replica forwards to the next closest replica, etc. • Pipelined fashion: while the data is incoming, the server begins forwarding it to the next replica • This design ensures good network utilization

  21. Atomic Record Appends • Atomic append is a write – but GFS (the primary replica) chooses the offset where the append happens; returns appends to the client • This way GFS can decide on serial order of concurrent appends without client synchronization • If an append fails at some replicas – the client retries • As a result, the file may contain multiple copies of the same record, plus replicas may be bytewise different • But after a successful update all replicas will be defined – they will all have the data written by the client at the same offset

  22. Non-Identical Replicas • Because of failed and retried record appends, replicas may be non-identical bytewise • Some replicas may have duplicate records (because of failed and retried appends) • Some replicas may have padded file space (empty space filled with junk) – if the master chooses record offset higher than the first available offset at a replica • Clients must deal with it: they write self-identifying records so they can distinguish valid data from junk • If the cannot tolerate duplicates, they must insert version numbers in records • GFS pushes complexity to the client; without this, complex failure recovery scheme would need to be in place

  23. Snapshot • Copy of a file or a directory tree – used by applications for fast copies of data sets and for checkpointing • Steps involved to snapshot directory A: • Master revokes leases on directory A • Logs the operation to disk, copies metadata for A to A’ in its memory: both A and A’ point to the same files on disk • When a client wants to write to chunk C in A, master defers replying to the client; creates a new chunk handle C’ • Master asks each chunkserver that has replica C to create a copy in chunk C’ – this ensures that copies are created locally, not over the network • All new clients modifications go to chunk C’

  24. Namespace Management and Locking • Each file or directory has an associated read/write lock • Each operation on a master acquires a set of read/write locks before it runs • Read locks are acquired on all files/directories that are being accessed, i.e., each intermediate directory in /d1/d2/…/dn • Write locks are acquired on • Snapshots (to prevent creation of new files in a directory during the snapshot) • File names – when that file is created • No write lock on directory is needed on file creation – no directory inode to modify; multiple file creations can be done concurrently

  25. Garbage Collection • File deletion is not done immediately – space from deleted files is garbage collected lazily • When a file is deleted – the master logs the operation and renames it to a hidden name • During regular metadata scan the master deletes that file’s metadata (after at least three days) • During regular scan of chunk namespace, the master identifies orphaned chunks, deletes that metadata • Master tells chunk replicas to delete orphaned chunks

  26. Load Balancing • Goals: • Maximize data availability and reliability • Maximize network bandwidth utilization • Google infrastructure: • Cluster consists of hundreds of racks • Each rack has a dozen machines • Racks are connected by network switches • A rack is on a single power circuit • Must balance load across machines and across racks

  27. Creation, Re-replication, Rebalancing • Creation (initial replica placement): • On chunk servers with low disk space utilization • Limit the number of recent creations on each chunkserver – recent creations mean heavy write traffic • Spread replicas across racks • Re-replication • When the number of replicas falls below the replication target • When a chunkserver becomes unavailable • When a replica becomes corrupted • A new replica is copied directly from an existing one • Re-balancing • Master periodically examines replica distribution and moves them to meet load-balancing criteria

  28. Fault Tolerance • Fast recovery • No distinction between normal and abnormal shutdown • Servers are routinely restarted by “killing” a server process • Servers are designed for fast recovery – all state can be recovered from the log • Chunk replication • Master replication • Data integrity • Diagnostic tools

  29. Chunk Replication • Each chunk is replicated on multiple chunkservers on different racks • Users can specify different replication levels for different parts of the file namespace (default is 3) • The master clones existing replicas as needed to keep each chunk fully replicated

  30. Single Master • Simplifies design • Master can make sophisticated load-balancing decisions involving chunk placement using global knowledge • To prevent master from becoming the bottleneck • Clients communicate with master only for metadata • Master keeps metadata in memory • Clients cache metadata • File data is transferred from chunkservers

  31. Master Replication • Master state is replicated on multiple machines, so a new server can become master if the old master fails • What is replicated: operation logs and checkpoints • A modification is considered successful only after it has been logged on all master replicas • A single master is in charge; if it fails, it restarts almost instantaneously • If a machine fails and the master cannot restart itself, a failure detector outside GFS starts a new master with a replicated operation log (no master election) • Master replicas are master’s shadows – they operate similarly to the master w.r.t. updating the log, the in-memory metadata, polling the chunkservers

  32. Data Integrity • Disks often fail – may cause data corruption • Detect corrupt replicas by comparing with other chunk servers? • Not a good idea – divergent replicas may be legal • Each chunkserver verifies its own replicas using checksums • Checksums are kept in memory and stored persistently in the log • Small effect on read performance – checksums are kept in memory, checksum computation can be overlapped with I/O • Write performance: checksum computation optimized for appends • Checksum can be computed incrementally for a checksum block (64KB) • If corruption is detected, the master creates new replicas using data from correct chunks • During idle periods chunkservers scan inactive chunks for corruption

  33. Detecting Stale Replicas • A replica may become stale if it misses a modification while the chunkserver was down • Each chunk has a version number, version numbers are used to detect stale replicas • A stale replica will never be given to the client as a chunk location, and will never participate in mutation • A client may read from a stale replica (because the client caches metadata) • But this window is limited, because cache entries time out

  34. Diagnostic Tools • GFS servers perform diagnostic logging • Helps debugging and performance analysis • Diagnostic logs record: • Chunk servers going up and down • All RPC requests and replies • RPC requests and responses from different machine logs can be collated and analyzed to determine exact interaction between machines • Logs are also used for load testing and performance analysis

  35. GFS Summary • Real replicated file system • Uses commodity hardware – hundreds of commodity PCs and disks • Two levels of replication: • Metadata is replicated via replicated masters • Data is replicated on replicated chunkservers • Designed for specific use case – for Google applications • And applications are designed for GFS • This is why it is simple and it actually works

  36. GFS Summary (cont.) • Design philosophy: • A replicated FS can’t do all things right and all things well: • Strong data consistency? • Identical replicas? • Fast concurrent operations? • That’s too hard… • So make several operations fast, make them common case • Common case operations – atomic appends • Client deal with weak consistency • Write self-identifying records • Deal with duplicate records and padding • Something to learn: if generic design is hard, design for your use case – that’s your only hope!

  37. Outline • Google File System • A real replicated file system • Paxos • A consensus algorithm used in real systems • Used in Chubby – Google’s distributed lock service • Why a consensus algorithm? Many replicated FS use consensus algorithms • Harp • A replicated research file system

  38. The Consensus Problem • A collection of processes can propose values • Only a single of the proposed values must be chosen • Three classes of agents: • Proposers (propose the values) • Acceptors (accept the values) • Learners (learn the chosen values) • System model • Asynchronous system • Failstop failures

  39. Acceptors • Naïve solution: • A single acceptor • Accepts the first proposed value it receives • Problem: algorithm cannot terminate if the acceptor fails • Let’s have multiple acceptors • A value is chosen if the majority of acceptors accept it • We want a value to be chosen even if only one value has been proposed, so we have a requirement: P1:An acceptor must accept the first proposal that it receives

  40. Accepting More than One Proposal • P1:An acceptor must accept the first proposal that it receives • There is a problem: • multiple proposers propose different values • each acceptor has accepted a value • no single value is accepted by the majority • So we must allow for acceptor to accept more than one proposal • We distinguish proposals by numbers: number value n v

  41. Choosing a Value • A value is considered chosen when it has been accepted by a majority of acceptors • But acceptors may accept many different proposals! • We must ensure that all accepted proposals have the same value! 1 X A1 A1 4 X 5 A3 X

  42. Same Value for All Proposals • We must ensure that all accepted proposals have the same value! So we have another requirement: P2:If a proposal with a value v is chosen, then every higher-numbered proposal issued by any proposal has value v • This ensures that even if acceptors accept different proposals, the values will be the same

  43. Same Value for All Proposals Accepted proposal numbers Accepted values Proposal numbers Proposed values A1 P1 2 X A2 1 X P2 1 X 1 X A3 2 X P3 How does P3 learn X?

  44. Learning The “Right” Value for Proposal • A proposer decides to issue a proposal numbered n • A proposer must learn the value of the highest numbered proposal less than n, such that: • That proposal has been accepted in the pas, or • That proposal will be accepted in the future • Learning the proposals accepted thus far is easy – just ask around • Predicting the future (which proposals will be accepted?) is hard • So the proposer controls the future! • Makes the acceptors promise not to accept any proposals numbered less than n

  45. Proposer-Acceptor Dialogue I accepted X, with proposal #5 Hey, what value have you accepted so far? acceptor proposer Ok, do me afavour, don’t accept any other proposals numbered < 5. You got it!

  46. Algorithm at the Proposer • A proposer chooses request number n, sends a prepare request to some set of acceptors, asking to respond with: • The highest-numbered proposal <n that it has accepted • A promise to never accept another proposal numbered <n • The proposer may receive responses from a majority of acceptors - it chooses the value v for its new proposal n and send it to everyone • The proposer may receive responses saying that acceptors accepted no proposals - it chooses any value v and issues proposal n • Once v is chosen the proposer sends an accept request with a new v

  47. Algorithm at the Acceptor • An acceptor responds to a prepare request • An acceptor responds to an accept request nonly if it had not responded to a request >n • Several optimizations: • An acceptor does not respond to prepare request n if it has already responded to a prepare request >n (because it will not accept proposal n anyway) • An acceptor ignores prepare request n if it has already accepted a proposal >n

  48. The Entire Algorithm • Phase 1: • A proposer selects a proposal number n and sends a prepare request with number n to a majority of acceptors • An acceptor responds to the request (unless it knows to ignore it) with: • A promise not to accept lower-numbered request • The highest-numbered request it has accepted so far • Phase 2: • If the proposer receives responses to its prepare request, it learns (or chooses) the right v and sends accept request to acceptors • If an acceptor receives an accept request n it accepts the value unless it has promised to another proposer not to accept proposal with that number

  49. Let’s Play Paxos • We have two proposers p1 and p2 • We have k acceptors a1, …, ak • Each person in class is either a proposer or an acceptor; I orchestrate the actions of proposers/acceptors • We will use the following notation: • PR(i) – prepare request for proposal i • respPR(i, v) – respond to PR(i) with previously accepted value v • respPR(i, - ) – respond to PR(i) if no proposal had been accepted • AR(i, v) – accept request for proposal i, value v • respAR(i, v) – respond accepting value v

  50. Ensuring Different Proposal Numbers • Each new proposal must have a different proposal number • How do different proposers ensure that they do not use the same numbers? • They each draw from different number sets: • E.g., one uses even numbers another one odd numbers, etc.