EECS 498 Introduction to Distributed Systems

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1 EECS 498 Introduction to Distributed Systems Fall 2017 Harsha V. Madhyastha

2 Dynamo Recap Consistent hashing 1-hop DHT enabled by gossip Execution of reads and writes Coordinated by first available successor of key Read/write from N available successors Declare success if R reads or W writes succeed Use vector clocks to identify causal precedence Vector of (coordinator, count) pairs November 8, 2017 EECS 498 Lecture 16 2

3 Dealing with Failures Permanent failures: Identified and marked manually Discovered via gossip Temporary failures: Handled by sloppy quorums What if less than W of N replicas are up? How does a node catch up once it recovers? November 8, 2017 EECS 498 Lecture 16 3

4 Hinted Handoff Suppose coordinator doesn t receive W replies from N successors Could return failure But, want to maximize availability Coordinator tries writing to subsequent nodes (beyond N successors of key) Coordinator informs recipient of intended node November 8, 2017 EECS 498 Lecture 16 4

5 Hinted Handoff: Example Say N = 3, W = 3 Key K Key K Hinted Handoff: B writes to itself, D and E E points to node C F G A B C Coordinator Nodes B, C and D store keys in range (A,B) including K. When C is available again E forwards the replicated data back to C E D November 8, 2017 EECS 498 Lecture 16 5

6 Removing threats to durability Hinted handoff node may crash before it replicates data to appropriate node Need to ensure that each key-value pair is replicated N times Solution: Replica synchronization Nodes nearby on ring periodically gossip»compare the (k, v) pairs they hold»copy any missing keys the other has How to efficiently sync between two nodes? November 8, 2017 EECS 498 Lecture 16 6

7 How to sync, quickly? B tells A: highest timestamp for every node Version vector e.g., X 30, Y 40 A What if every node stores only (key, value) map? How to efficiently sync? B -,10, X -,10, X -,20, Y -,20, Y -,30, X -,30, X -,40, X -,40, Y November 8, 2017 EECS 498 Lecture 16 7

8 Efficient synchronization with Merkle trees Merkle tree hierarchically summarizes the keyvalue pairs a node holds Leaf node = hash of one key s value Internal node = hash of concatenation of children Compare roots;; if match, values match If they don t match, compare children Iterate this process down the tree November 8, 2017 EECS 498 Lecture 16 8

9 Merkle tree reconciliation B is missing orange key;; A is missing green one Exchange and compare hash nodes from root downwards, pruning when hashes match A s values: [0, ) [0, ) [2 127, ) B s values: [0, ) [0, ) [2 127, ) November 8, 2017 EECS 498 Lecture 16 9

10 Load Balancing What needs to happen to bootstrap N72? N105 N120 N10 N32 Copy (k,v) pairs from N90 to N72 N90 N72 N60 November 8, 2017 EECS 498 Lecture 16 10

11 Initializing New Server kv-store map[string]string InitServer(nodeID int64) { s = get_successor(hash(id)) call(s, Transfer, ID, &kv-store) } November 8, 2017 EECS 498 Lecture 16 11

12 Handing Off to New Server kv-store map[string]string Transfer(ID int64, shard map[str]str) { for key, val := range kv-store { if hash(key) < hash(id) { shard[key] = value delete(kv-store, key) } } } November 8, 2017 EECS 498 Lecture 16 12

13 Revisiting Consistent Hashing What needs to happen to bootstrap N72? Copy (k,v) pairs from N90 to N72 Consistent hashing determines both partitioning and placement N32 Need to scan (k,v) map Node at addition/removal N90 affects N90 both how data is partitioned Worse with and virtual where nodes partitions are placed Need to recompute Merkle trees N120 N105 Root cause of problems: N72 N10 N60 November 8, 2017 EECS 498 Lecture 16 13

14 Decoupling Partitioning and Placement Partition hash space statically into fixed number of equal sized shards Place shard on first N virtual nodes after its end Need to identify only which shards to hand off Maintain Merkle tree per shard November 8, 2017 EECS 498 Lecture 16 14

15 Dynamo Summary Scalability and low latency: 1-hop DHT enabled by inter-node gossip High availability: Sloppy quorums, hinted handoff Eventual consistency: Vector clocks, Merkle trees Load balancing: Decoupling partitioning and placement of data November 8, 2017 EECS 498 Lecture 16 15

16 Impact of Dynamo NoSQL systems Eventual consistency popularized by Dynamo Better scalability than strongly consistent systems Frustration over application development complexity to offer intuitive user experience Recent trend: Scalable strong consistency Example: Google s Spanner November 8, 2017 EECS 498 Lecture 16 16

17 Impact of Partitioning State Why consistent hashing or DHT? Enable load balancing across partitions Accommodate state too big for one server What if operation touches multiple partitions? Examples: Add meeting to calendars of two participants Transfer money from one account to another What about looking up balance of two accounts? Need distributed transactions November 8, 2017 EECS 498 Lecture 16 17

18 Executing Transactions To ensure atomicity, execute one transaction at a time Problem? Desired property: Serializability Despite concurrent execution, externally visible effects equivalent to some serial order of execution November 8, 2017 EECS 498 Lecture 16 18

19 Example of Serializability Concurrent execution of transactions: T1: Transfer $10 from Alice to Bob T2: Read balance in Alice s and Bob s accounts Initial balance of $100 in both accounts Permissible outputs for T2? (Alice: $100, Bob: $100) or (Alice: $90, Bob: $110) Invalid outputs for T2: (Alice: $90, Bob: $100) or (Alice: $100: Bob: $110) November 8, 2017 EECS 498 Lecture 16 19

20 Example Scenario Many students are each looking to host a party Each host invites a subset of other students Party is on only if all invitees can make it Consensus among all students Slow to identify all parties that are on How can each host decide whether his/her party is on without coordinating all students? November 8, 2017 EECS 498 Lecture 16 20

21 Achieving Serializability Client submits transaction to coordinator (TC) TC acquires locks on all data involved Once locks acquired, execute transaction and release locks November 8, 2017 EECS 498 Lecture 16 21

22 Two Phase Locking TC P1 Lock Commit P2 P3 November 8, 2017 EECS 498 Lecture 16 22

23 Two Phase Locking TC P1 Lock Abort P2 P3 November 8, 2017 EECS 498 Lecture 16 23

24 Two Phase Locking TC acquires locks on all necessary shards before attempting transaction Disjoint transactions can execute concurrently Related transactions wait on each other How to ensure fault-tolerance? November 8, 2017 EECS 498 Lecture 16 24

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