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Transaction chains: achieving serializability with
low-latency in geo-distributed storage systems
Yang Zhang Russell Power Siyuan Zhou
Yair Sovran *Marcos K. Aguilera Jinyang Li
New York University
*Microsoft Research Silicon Valley
Why geo-distributed storage?
Large-scale Web applications
Geo-distributed storage
Replication
Geo-distribution is hard
Low latency:
O(Intra-datacenter RTT)
Strong semantics:
relational tables w/
transactions
Prior work
Strict
serializable
High latency
Spanner [OSDI’12]
Serializable
Provably high latency
according to CAP
Various
non-serializable
Our work
Walter [SOSP’11]
COPS [SOSP’11]
?
Eiger [NSDI’13]
Low latency
Dynamo [SOSP’07]
Eventual
Key/value
only
Limited forms of
transaction
General
transaction
Our contributions
1. A new primitive: transaction chain
– Allow for low latency, serializable transactions
1. Lynx geo-storage system: built with chains
– Relational tables
– Secondary indices, materialized join views
Talk Outline
•
•
•
•
Motivation
Transaction chains
Lynx
Evaluation
Why transaction chains?
Auction service
Items
Bids
Bidder
Alice
Item
Book
Price
$100
Seller Item
Highest bid
Alice
iPhone
$20
Bob
Book
$20
Bob
Camera
$100
Bob
Alice
Datacenter-1
Datacenter-2
Why transaction chains?
Operation: Alice bids on Bob’s camera
1. Insert bid to Alice’s Bids
2. Update highest bid on Bob’s Items
Alice’s Bids
Alice
Book
$100
Bob
Bob’s Items
Alice
Bob
Datacenter-1
Camera
Datacenter-2
$100
Why transaction chains?
Operation: Alice bids on Bob’s camera
1. Insert bid to Alice’s Bids
2. Update highest bid on Bob’s Items
Alice’s Bids
Alice
Book
$100
Bob
Bob’s Items
Alice
Bob
Datacenter-1
Camera
Datacenter-2
$100
Low latency with first-hop return
Alice
bid on Bob’s camera
Alice’s Bids
Alice
Book
$100
Alice
Camera
$500
Bob
Bob’s Items
Bob
Datacenter-1
Camera
Datacenter-2
$100
$500
Problem: what if chains fail?
1. What if servers fail after executing first-hop?
2. What if a chain is aborted in the middle?
Solution: provide all-or-nothing atomicity
1. Chains are durably logged at first-hop
– Logs are replicated to another closest data center
– Chains are re-executed upon recovery
2. Chains allow user-aborts only at first hop
• Guarantee: First hop commits  all hops
eventually commit
Problem: non-serializable interleaving
• Concurrent chains ordered inconsistently at different hops
Not serializable!
T1
Server-X: T1 < T2
X=1
T2
Y=1
T2
X=2
Server-Y: T2 < T1
T1
Y=2
Time
• Traditional 2PL+2PC prevents non-serializable interleaving
at the cost of high latency
Solution: detect non-serializable
interleaving via static analysis
• Statically analyze all chains to be executed
– Web applications invoke fixed set of operations
T1
X=1
Y=1
Conflict?
T2
X=2
Y=2
A SC-cycle has both
red and blue edges
Serializable if no SC-cycle [Shasha et. al TODS’95]
Outline
•
•
•
•
Motivation
Transaction chains
Lynx’s design
Evaluation
How Lynx uses chains
• User chains: used by programmers to
implement application logic
• System chains: used internally to maintain
– Secondary indexes
– Materialized join views
– Geo-replicas
Example: secondary index
Bids (secondary index)
Bids (base table)
Bidder Item
Price
Bidder Item
Price
Alice
Camera $100
Alice
Camera
$100
Bob
iPhone
Bob
Car
$20
Alice Book
Alice iPhone
$20
$20
$100
Bob Car
Bob
$20
Camera
$100
Example user and system chain
Alice
bid on Bob’s camera
Alice
Book
$100
Alice
Camera
$100
Bob
Bob
Datacenter-1
Camera
Datacenter-2
$100
Lynx statically analyzes all chains beforehand
Read-bids
Read
Bids table
Put-bid
Insert to
Bids table
Update
Items table
Put-bid
Insert to
Bids table
Update
Items table
SC-cycle
Read-bids Read
Bids table
One solution: execute chain as
a distributed transaction
SC-cycle source #1:
false conflicts in user chains
Put-bid
Insert to
Bids table
Update
Items table
False conflict because
max(bid, current_price)
commutes
Put-bid
Insert to
Bids table
Update
Items table
Solution: users annotate commutativity
Update
Items table
commutes
Put-bid
Insert to
Bids table
Put-bid
Insert to
Bids table
Update
Items table
SC-cycle source #2: system chains
Put-bid
Put-bid
Insert to Insert to
Bids table Bids-secondary
Insert to Insert to
Bids table Bids-secondary
…
…
SC-cycle
Solution: chains provide origin-ordering
• Observation: conflicting system chains originate at the
same first hop server.
T1
T2
Insert to
Bids table
Insert to
Bids table
Insert to
Bids-secondary
Insert to
Bids-secondary
Both write
the same row
of Bids table
• Origin-ordering: if chains T1 < T2 at same first hop, then
T1 < T2 at all subsequent overlapping hops.
– Can be implemented cheaply  sequence number vectors
Limitations of Lynx/chains
1. Chains are not strictly serializable, only serializable.
2. Programmers can abort only at first hop
• Our application experience: limitations are managable
Outline
•
•
•
•
Motivation
Transaction chains
Lynx’s design
Evaluation
Simple Twitter Clone on Lynx
Tweets
Geo-replicated
Author Tweet
Alice
New York rocks
Bob
Time to sleep
Eve
Hi there
Follow-Graph
Geo-replicated
From
To
Tweets JOIN Follow-Graph (Timeline)
Author
(=to)
Follow-Graph (secondary)
To
From
Alice
Bob
Bob
Alice
Alice
Eve
Bob
Clark
From
Tweet
Bob
Alice Time to sleep
Eve
Alice
Hi there
Experimental setup
europe
Lynx protoype:
• In-memory database
• Local disk logging only.
us-west
us-east
Returning on first-hop allows low latency
Chain completion
300
252
Latency (ms)
250
200
174
150
100
First hop return
50
3.2
0
Follow-user
Post-tweet
Follow-user
3.1
3.1
Post-tweet Read-timeline
Applications achieve good throughput
1.6
1.35
Million ops/sec
1.4
1.2
1
0.8
0.6
0.4
0.2
0.184
0.173
Follow-User
Post-Tweet
0
Read-Timeline
Related work
• Transaction decomposition
– SAGAS [SIGMOD’96], step-decomposed transactions
• Incremental view maintenance
– Views for PNUTS [SIGMOD’09]
• Various geo-distributed/replicated storage
– Spanner[OSDI’12], MDCC[Eurosys’13],
Megastore[CIDR’11], COPS [SOSP’11], Eiger[NSDI’13],
RedBlue[OSDI’12].
Conclusion
• Chains support serializability at low latency
– With static analysis of SC-cycles
• Key techniques to reduce SC-cycles
– Origin ordering
– Commutative annotation
• Chains are useful
– Performing application logic
– Maintaining indices/join views/geo-replicas
Limitations of Lynx/chains
1. Chains are not strict serializable
Time
Serializable
Strict serializable
Remedies:
– Programmers can wait for chain completion
– Lynx provides read-your-own-writes
2. Programmers can only abort at first hop
• Our application experience shows the limitations are managable
2PC and chains
The easy way
T1
T2
T1
R(A)
W(A)
R(A)
W(B)
T2
T2
T1
W(A)
R(A)
W(B)
T1
R(A)
2PC-W(AB)
2PC and chains
The hard way
T1
T2
R(A)
R(B)
W(A)
W(B)
R(A)
T1
T2
T2
W(A)
R(B)
2PC-W(AB)
W(B)
T1
T1
R(A)
R(B)
R(A)
R(B)
2PC and chains
The hard way
Chain
A
B
C
D
DC1
DC2
DC3
DC4
Parallel
unlock
2PC
retry
Lynx is scalable
3000
2770
2500
QPS (K/s)
2000
1500
Follow
1350
Tweet
1000
Timeline
586
500
374 356
265
48 42
93 86
184 173
0
1
2
4
#Servers per DC
8
Challenge of static analysis: false conflict
T1
T2
1. Insert bid into bid history
2. Update max price on item
Conflict on
bid history
Conflict on
item
1. Insert bid into bid history
2. Update max price on item
SC-cycle  Not serializable
Solution: communitivity annotations
T1
1. Insert bid into bid history
No real conflict
because bid ids
are unique
T2
Conflict on
Commutative
bid history
operation
1. Insert bid into bid history
2. Update max price on item
Updating max
commutes
Conflict on
Commutative
operation
item
2. Update max price on item
No SC-cycle  Serializable
ACID: all-or-nothing atomicity
• Chain’s failure guarantee:
– If the first hop of a chain commits, then all hops
eventually commit
• Users are only allowed to abort a chain in the first hop
• Achievable with low latency:
– Log chains durably at the first hop
• Logs replicated to a nearby datacenter
– Re-execute stalled chains upon failure recovery
ACID: serializability
• Serializability
– Execution result appears as if obey a serial order
for all transactions
– No restrictions on the serial order
Transactions
Ordering 1
Ordering 2
Problem #2: unsafe interleaving
• Serializability
– Execution result appears as if obey a serial order
for all transactions
– No restrictions on the serial order
Transactions
Ordering 1
Ordering 2
Chains are not linearizable
• Serializability
• Linearability  a total ordering of chains
a total ordering of chains
& total order obeys the issue order
Transactions
Time
Ordering 1
Linearizable
Ordering 2
Transaction chains: recap
• Chains provide all-or-nothing atomicity
• Chains ensure serializability via static analysis
• Practical challenges:
– How to use chains?
– How to avoid SC-cycles?
Example user chain
Items
Bids
Bidder
Alice
Item
Seller
Price
Camera
100
Item
Bob
Camera
Highest
100
Alice
1. Insert bid into Alice’s bid history
Bob
2. Update max price on Bob’s camera
Lynx implementation
• 5000 lines C++ and 3500 lines RPC library
• Uses an in-memory key/value store
• Support user chains in Javascript (via V8)
Geo-distributed storage is hard
• Applications demand simplicity & performance
– Friendly programming model
• Relational tables
• Transactions
– Fast response
• Ideally, operation latency = O(intra-datacenter RTT)
• Geo-distribution leads to high latency
– Coordinate data access across datacenters
• Operation latency = O(inter-datacenter RTT) = O(100ms)

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