One-Copy Serializability with Snapshot Isolation under

One-Copy Serializability with Snapshot Isolation
under the Hood
Mihaela A. Bornea 1 , Orion Hodson 2 , Sameh Elnikety 2 , Alan Fekete 3
Athens U. of Econ and Business,
Microsoft Research,
University of Sydney
With careful design and engineering, SI engines can be
replicated to get even higher performance with a replicated
form of SI such as Generalized Snapshot Isolation (GSI) [13],
which is also not serializable. The objective of this paper is
to provide a concurrency control algorithm for replicated SI
databases that achieves almost the same performance as GSI
while providing global one-copy-serializability (1SR).
To guarantee serializability, concurrency control algorithms
typically require access to data read and written in update
transactions. Accessing this data is especially difficult in a
replicated database system since built-in facilities inside a
centralized DBMS, like the lock manager, are not available
at a global level. Moreover, replicated systems face the additional challenge of how this data is extracted, represented
and communicated among the replicas. Notice that serializability algorithms for a centralized system, such as two-phase
locking (2PL) and serializable snapshot isolation (SSI) [7],
are not sufficient in replicated database systems: while every
replica enforces serializability locally for local transactions,
the replicated database system does not provide 1SR.
We propose a new concurrency control algorithm, called
Serializable Generalized Snapshot Isolation (SGSI), for middleware replicated database systems. Each replica uses snapshot isolation, while the replication middleware applies SGSI
to ensure one-copy serializability. SGSI validates both the
writeset and readset of update transactions.
Typically, the readset is much larger than the writeset and
it is challenging to identify and represent. Our work is the
first to address this challenge in a replicated setting. We
introduce a technique for extracting the readset by applying
automatic query transformation for each SQL statement in
an update transaction. We describe how readsets are certified
and prove certification correctness. Surprisingly, we find that
readset certification supersedes writeset certification: Writeset
certification is no longer needed. Contrary to prior work,
writesets are extracted for update propagation and durability,
not for preventing global write-write conflicts through writeset
We show how to implement SGSI in the replication middleware, providing several practical advantages. Changing the
replication middleware is easier than changing the database
engine, which is more complex consisting of millions source
code lines and could be closed-source such as Microsoft SQL
Server and Oracle. The middleware implementation is easier
to deploy; It can be used with engines from different vendors,
and does not have to be changed with engine upgrades.
Abstract—This paper presents a method that allows a replicated database system to provide a global isolation level stronger
than the isolation level provided on each individual database
replica. We propose a new multi-version concurrency control algorithm called, serializable generalized snapshot isolation (SGSI),
that targets middleware replicated database systems. Each replica
runs snapshot isolation locally and the replication middleware
guarantees global one-copy serializability. We introduce novel
techniques to provide a stronger global isolation level, namely
readset extraction and enhanced certification that prevents readwrite and write-write conflicts in a replicated setting. We prove
the correctness of the proposed algorithm, and build a prototype replicated database system to evaluate SGSI performance
experimentally. Extensive experiments with an 8 replica database
system under the TPC-W workload mixes demonstrate the
practicality and low overhead of the algorithm.
In many server systems replication is used to achieve higher
performance and availability than a centralized server. Replication in database systems is, however, particularly challenging
because the transactional semantics have to be maintained.
The effects of an update transaction at one replica have to be
efficiently propagated and synchronized at all other replicas,
while maintaining consistency for all update and read-only
transactions. This challenge has long been recognized [18],
leading to several replication protocols that explicitly tradeoff consistency to achieve higher performance: A replicated
database system may provide a lower isolation level than a
centralized database system.
We show, contrary to common belief, that a replicated
database system can efficiently provide a global isolation
level stronger than the isolation level provided by the constituent replicas. We focus here on snapshot isolated database
systems and introduce a concurrency control algorithm that
guarantees global one-copy serializability (1SR), while each
replica guarantees snapshot isolation (SI), which is weaker
than serializability. We support this claim by proposing an algorithm, proving its correctness, implementing it, and building
a prototype replicated database to evaluate it experimentally.
Database engines such as PostgreSQL and Oracle support SI
as it provides attractive performance for an important class of
transactional workloads that have certain properties, e.g., dominance of read-only transactions, short updates and absence of
write hot-spots. To take advantage of this performance gain,
some database engines support SI in addition to traditional
locking schemes. For example, Microsoft SQL Server supports
both SI and 2PL. The performance gain of using SI comes at
a correctness cost: SI is not serializable.
The main contributions of this work are the following:
• We propose SGSI, a novel concurrency control algorithm
for replicated databases with SI replicas. SGSI ensures
1SR, an isolation level stronger than the level of individual components. We formally prove SGSI correctness;
• We are the first to address the problem of readset management in a replicated setting. We introduce a framework which provides techniques to extract, represent and
communicate the readset among replicas in the system;
• We show how to implement SGSI and build a prototype
middleware replicated system. We provide concrete solutions for extracting the readset of update transaction and
for its certification in the relational model;
• We conduct extensive experiments showing SGSI is practical guaranteeing correctness at a low performance cost.
The paper is structured as follows. Section II provides
background information on snapshot isolation and its anomalies. We define SGSI in Section III while in Section IV we
show how SGSI is used in a replicated system. We present
the readset certification framework for a relational DBMS in
Section V and readset certification in Section VI. We discuss
the prototype implementation and experimental evaluation in
Section VII. The paper ends with related work in Section VIII
and conclusions in Section IX.
B. Serialization Anomalies Under SI
Since SI is weaker than serializability, it allows certain
anomalies. These anomalies are also allowed by GSI since
SI is a special case of GSI. SGSI prevents all serialization
Write Skew Anomaly. The following scenario can introduce
the write skew anomaly [5]. Assume two transactions T1 and
T2 that withdraw money from two bank accounts X and Y .
The bank enforces the constraint that the sum of X and Y is
positive. Transactions T1 and T2 are executed concurrently on
a database where each account, X and Y , contains an initial
balance of 50. The following history is not serializable, but
can be generated under SI:
h1 = R1 (X0 , 50), R1 (Y0 , 50), R2 (X0 , 50), R2 (Y0 , 50),
W1 (X1 , −40), W2 (Y1 , −40). At the end of this history the
constraint imposed by the bank is violated.
Phantom Anomaly. Phantoms [14] are caused by data
item insertions or deletions. The effect of this type of
anomaly is shown in the following non-serializable history:
h2 = R1 (X0 , 50), R1 (Y0 , 50), R2 (X0 , 50), R2 (Y0 , 50),
W2 (Z0 , −20), W1 (X2 , −40). X and Y are accounts belonging to the same group on which the bank should enforce
a positive balance. While transaction T1 withdraws 90 from
account X, transaction T2 creates a new account Z with a fee
of 20. These operations result in a negative group balance.
h3 = R2 (X0 , 0), R2 (Y0 , 0), R1 (Y0 , 0), W1 (Y1 , 20), C1 ,
R3 (X0 , 0), R3 (Y1 , 20), C3 , W2 (X2 , −11), C2 . Transaction T1
deposits 20 in a savings account Y , transaction T2 withdraws
10 from checking account and pays 1 as overdraft penalty.
Transaction T3 just outputs the balance for the client. The
anomaly in this example is that the user sees values 0 and 20
while the final values are -11 and 20. Such values would not
be reported in a serializable history.
In this section we present a brief introduction to SI, GSI,
1SR and provide examples showing non-serializable executions. SGSI prevents all such anomalies.
A. Concurrency Control
Snapshot Isolation (SI). Snapshot isolation (SI) [5] provides
each transaction with a snapshot of the database at the time
of transaction start. Snapshot isolation is available in several
database engines, such as Oracle, PostgreSQL, and Microsoft
SQL Server. Moreover, in some systems that do not implement
two-phase locking (2PL) schemes, including Oracle and PostgreSQL, SI is the strongest available isolation level. Snapshot
isolation has attractive performance properties when compared
to two-phase locking (2PL), particularly for read dominated
workloads: Under SI, read-only transactions can neither block,
nor abort, and they do not block concurrent update transactions. However, SI allows non-serializable behavior and may
introduce inconsistencies.
Generalized Snapshot Isolation (GSI). Generalized Snapshot
Isolation (GSI) [13] extends SI to replicated databases. GSI
allows transactions to use local snapshots of the database on
each replica and provides the same desirable non-blocking and
non-aborting properties as SI for read-only transactions. GSI
has the same serializability anomalies as SI.
One-Copy Serializability (1SR). The main correctness criterion for replicated databases is One-Copy Serializability
(1SR) [6]. The effect is that transactions performed on the
database replicas have an ordering which is equivalent to
an ordering obtained when the transactions are performed
sequentially in a single centralized database.
Here we define SGSI and show that it guarantees serializability. We first introduce a multi-version concurrency
control model to formally define SGSI and then we prove
its correctness.
A. Database and Transaction Model
We assume that a database is a collection of uniquely
identified data items. Several versions of each data item may
co-exist simultaneously in the database, but there is a total
order among the versions of each data item. A snapshot of
the database is a committed state of the database.
A transaction Ti is a sequence of read and write operations
on data items, followed by either a commit or an abort. We
denote Ti ’s write on item X by Wi (Xi ). If Ti executes Wi (Xi )
and commits, then a new version of X, denoted by Xi , is
added to the database. Moreover, we denote Ti ’s read on item
Y by Ri (Yj ), which means that Ti reads the version of item Y
produced by transaction Tj . Ti ’s commit or abort is denoted
by Ci or Ai , respectively. To simplify the presentation, we
assume that transactions do not contain redundant operations1 :
A transaction reads any item at most once and writes any item
at most once, and if a transaction writes an item, it does not
read that item afterwards.
A transaction is read-only if it contains no write operation,
and is update otherwise. The readset of transaction Ti , denoted
readset(Ti ), is the set of data items that Ti reads. Similarly,
the writeset of transaction Ti , denoted writeset(Ti ), is the set
of data items that Ti writes. We add additional information to
the writesets to include the old and new values of the written
data items.
A history h over a set of transactions T = {T1 , T2 , ..., Tn }
is a partial order ≺ such that (a) h contains the operations of
each transaction in T ; (b) for each Ti ∈ T , and all operations
Oi and Oi0 in Ti : if Oi precedes Oi0 in Ti , then Oi ≺ Oi0 in h;
and (c) if Ti reads X from Tj , then Wj (Xj ) ≺ Ri (Xj ) in h
[6]. To simplify definitions, we assign a distinct time to each
database operation, resulting in a total order consistent with
the partial order ≺.
In SGSI, each transaction Ti observes a snapshot of the
database that is taken at some time, denoted snapshot(Ti ).
This snapshot includes the updates of all transactions that
have committed before snapshot(Ti ). To argue about the
timing relationships among transactions, we use the following
definitions for transaction Ti :
• snapshot(Ti ): the time when Ti ’s snapshot is taken.
• commit(Ti ): the time of Ci , if Ti commits.
We define the relation impacts for update transactions.
• Tj write-impacts Ti iff
writeset(Ti ) ∩ writeset(Tj ) 6= ∅, and
snapshot(Ti ) < commit(Tj ) < commit(Ti ).
• Tj read-impacts Ti iff
readset(Ti ) ∩ writeset(Tj ) 6= ∅, and
snapshot(Ti ) < commit(Tj ) < commit(Ti ).
Only committed update transactions may impact update
transaction Ti . Read-only transactions and uncommitted transactions cannot impact Ti . When committing an active update
transaction Ti , we say “Tj impacts Ti ” to mean that if Ti were
to commit now, then Tj would impact Ti .
R2. (SGSI No Write Impact Rule)
∀ Ti , Tj such that Ci , Cj ∈ h :
4- ¬(Tj write-impacts Ti ).
R3. (SGSI No Read Impact Rule)
∀ Ti , Tj such that Ci , Cj ∈ h :
5- ¬(Tj read-impacts Ti ).
The read rule R1 ensures that each transaction reads only
committed data, that is, each transaction observes a committed
snapshot of the database. This snapshot could be any snapshot
that has been taken before the transaction starts, which can be
provided efficiently in a distributed system.
Rules R2 and R3 limit which transactions can commit.
This process is called certification and we will see in the
next section that they require communicating readsets and
writesets. The no-write-impact rule R2 prevents any update
transaction Ti from committing if it is write-impacted by
another committed update transaction. Rule R3 is similar,
preventing Ti from committing if it is read-impacted.
C. SGSI Correctness
Theorem 1: (Serializability.) R1, R2 and R3 ensure onecopy serializability.
Proof: Let h be a history that satisfies R1, R2, and R3.
We show that whenever there is a dependency p → q in h,
where p is an operation in Ti and q is an operation in Tj
on the same data item, and where Ti and Tj both commit,
then commit(Ti ) precedes commit(Tj ). This implies that h
is view serializable, with the serialization order given by the
order of commit events [31] (see also Theorem 3.13 of [32]).
Consider the possible dependency types.
1) wr true-dependency. Assume for some item X, p =
Wi (Xi ) and q = Rj (Xi ). R1 gives directly that
commit(Ti ) < snapshot(Tj ) and snapshot(Tj ) is
before commit(Tj ). Thus the whole execution interval
of Ti comes before the execution interval of Tj , and in
particular commit(Ti ) < commit(Tj ).
2) ww output-dependency. Assume for some item X,
p = Wi (Xi ), q = Wj (Xj ) and Xi precedes Xj in
the version order on X. Since X ∈ writeset(Ti ) ∩
writeset(Tj ), the intersection of writesets is nonempty. Since R2 says that ¬(Tj write-impacts Ti ), we
see that either commit(Tj ) < snapshot(Ti ) or else
commit(Ti ) < commit(Tj ). In the former case we have
that the whole execution interval of Tj comes before the
execution interval of Ti , which contradicts the version
order placing Xi before Xj . Thus we are left with the
latter case, that is, commit(Ti ) < commit(Tj ).
3) rw anti-dependency. Assume for some item X, p =
Ri (Xk ), q = Wj (Xj ) and Xk precedes Xj in the
version order on X. Since X ∈ readset(Ti ) ∩
writeset(Tj ), the intersection of readset(Ti ) with
writeset(Tj ) is non-empty. Since R3 says that
¬(Tj read-impacts Ti ), we see that commit(Tj ) <
snapshot(Ti ) or commit(Ti ) < commit(Tj ). In the
former case we have (by rule R1 and the fact that
B. SGSI Definition
SGSI has three rules: R1 regulates read operations, while R2
and R3 regulate commit operations. For any history h created
by SGSI, the following properties hold (indices i, j, and k are
• R1. (SGSI Read Rule)
∀ Ti , Xj such that Ri (Xj ) ∈ h :
1- Wj (Xj ) ∈ h and Cj ∈ h;
2- commit(Tj ) < snapshot(Ti );
3- ∀ Tk such that Wk (Xk ), Ck ∈ h :
[commit(Tk ) < commit(Tj ) or
snapshot(Ti ) < commit(Tk )].
1 This assumption is not restrictive: These redundant operations can be
added to the model, but they complicate the presentation.
Ri (Xk ) ∈ h) that commit(Tj ) < commit(Tk ) which
contradicts the version ordering placing Xk before
Xj . Thus we are left with the latter case, that is,
commit(Ti ) < commit(Tj ).
been executed at other replicas and have been certified. These
are applied directly to the database. For client transactions,
the proxy applies the SQL statement inside each transaction
to the database, and sends the response to the client via
the load balancer. For update statements, the proxy extracts
the partial writeset of the transaction for early certification
[11]. It checks whether this partial writeset conflicts with
any pending writeset of refresh transactions to prevent the
hidden deadlock problem [33]. In the case of conflict, the
client’s update transaction is aborted. When the client requests
to commit a transaction, the proxy commits immediately
if the transaction is read-only. For update transactions, the
proxy sends a certification request to the certifier and awaits
a decision. When certifier’s decision is received, the proxy
commits (or aborts) the transaction to the database and sends
the outcome to the client.
Fault Tolerance. The system assumes the standard crashrecovery failure model [2]. In this failure model, a host may
crash independently and subsequently recover. The certifier
is lightweight and deterministic, and may be replicated for
availability [11] using the state machine approach [27]. The
load balancer is lightweight because it maintains only a small
amount of soft-state and a failover (standby load balancer)
may be used for availability. The hard (persistent) state in the
system is maintained by database replicas, and this state is
orders of magnitude larger than the load balancer’s state. After
a failure, the failed component can recover using standard
approaches as discussed in prior work [11]. This design has
no single point of failure as each component is replicated.
In our implementation, the database replicas are replicated
for higher performance. We use a single load balancer and
a certifier, which both can also be replicated for higher
availability. As shown in prior work [10], [11], [12], a single
load balancer and a single certifier are sufficient to match a
replicated database system of up to 16 replicas.
Update and Read-Only
Fig. 1.
Certification Requests
for Update Transactions
SGSI Replicated Database System Architecture.
SGSI is defined in the previous section. Here we focus
on how to use SGSI in distributed system. We introduce a
distributed system model and present the replicated database
architecture. We propose a certification algorithm to enforce
SGSI rules R2 and R3. We defer the implementation aspects
for relational database engines to the next section.
A. Distributed System Model
We assume an asynchronous distributed system composed
of a set of database sites Replica1 . . . Replican which communicate with a reliable message passing protocol. No assumptions are made regarding the time taken for messages to
be transmitted or subsequently processed. Each site has a full
copy of the database.
C. SGSI Certification Algorithm
B. Replicated System Architecture
We present the SGSI certification algorithm for the asynchronous distributed model. The objective of the algorithm
is to commit transactions when they satisfy SGSI rules R2
and R3. In order to detect and prevent conflicts, the certifier
manages the writesets produced by the committed transactions
together with the commit order. Roughly speaking, to commit
an update transaction, the replica sends a certification request
containing the transaction readset and writeset. The certifier
ensures that there is no committed update transaction which
is read-impacting (i.e., the readset is still valid) or writeimpacting (i.e., there is no write-write conflict).
Database Versioning. When an update transaction commits,
it creates a new version of the database, identified by a version
number assigned by the certifier forming the global commit
order. Initially, the database starts at version 0. A replica
evolves from version Vi to version Vi+1 by applying the
writeset of update transaction T that commits at version i + 1.
Replacing timestamps. For transaction T , both snapshot(T )
and commit(T ) have been defined in the transactional model
The system architecture (Figure 1) is comprised of three
component types: load balancer, replica and certifier. The
design is consistent with the state-of-the-art for middlewarebased replicated database systems [9], [11], [20], [21], [24].
Load Balancer. The load balancer receives transactions from
client applications and passes them to the replicas using a
load balancing strategy such as round robin or least number
of connections. It also relays responses from replicas to clients.
Certifier. The certifier performs the following tasks: (a) detects
and prevents system-wide conflicts, and assigns a total order to
update transactions that commit, (b) ensures the durability of
its decisions and committed transactions, and (c) forwards the
writeset of every committed update transaction to the replicas
in form the of refresh transactions.
Replica. Each replica consists of a proxy and a standalone
DBMS employing snapshot isolation. The proxy receives
client transactions from the load balancer and refresh transactions from the certifier. Refresh transactions are those that have
Algorithm 1 Certification Algorithm.
equivalent theorem, Theorem 3: Certification in the relational
model, as it is more relevant to our implementation in the
relational model.
1- When Replicaj sends (VRepj , writeset(T ), readset(T )),
Certif ier receives and executes:
if validate(VRepj , writeset(T ), readset(T )) == false then
send (abort,-,-) to Replicaj
Vmaster ← Vmaster + 1
h ← T ; manage writeset(T )
send (commit, Vmaster ) to Replicaj
send refresh(Vmaster , writeset(T )) to Replicai , i 6= j.
2- When ready to commit T , Replicai executes:
send (VRepi , writeset(T ), readset(T )) to Certif ier
wait until receive (result, Vnew ) from Certif ier
if result == commit then
db-apply-writesets(writeset(T ))
Vi ← Vnew
db-commit(T )
db-abort(T ).
3- When Certif ier sends refresh(Vmaster , writeset(Tj )),
Replicak receives and executes:
db-apply-writesets(writeset(Tj ))
VRepk ← Vmaster .
A. Transaction Model and Query Language
We employ a relational transaction model in which each
transaction starts with a BEGIN statement and ends with a
COMMIT or ABORT. Read and write operations are expressed
through SQL queries. We support a large subset of SQL
queries in a transaction. The notations used in the SQL
transaction model are summarized in the Table I.
SQL Queries inside a transaction
A. SELECT expr list FROM Ri WHERE pred(Ri )
C. UPDATE Ri SET attrj = valuej WHERE pred(Ri )
E. SELECT AGG(attr) FROM Ri WHERE pred(Ri )
GROUP BY group attr
HAVING pred(AGG(attr))
F. SELECT attr list
FROM R1 ...Ri ...Rn
WHERE pred(R1 ) LOP ... LOP pred(Ri )
LOP ... LOP pred(Rn )
LOP join pred(Ri , Rj )
in terms of timestamps. We cannot use timestamps in the
asynchronous distributed model because it requires access to
global time. Instead, we use versions, as follows:
• snapshot(T ): the version of database that T observes.
• commit(T ): the commit version of T .
Algorithm Description. As presented in Algorithm 1, the
certifier receives a certification request containing the writeset,
the readset and the version of replica snapshot. Given a
certification request from Replicaj for a transaction T with
snapshot(T ) = VRepj , the certifier accesses all committed
writesets with a version greater than snapshot(T ). Writesets
with version number smaller or equal to snapshot(T ) belong
to transactions that committed before the snapshot of T was
taken and transaction T sees their effects. For each accessed
writeset item X, the certifier checks if X ∈ writeset(T ) or
X ∈ readset(T ), in which case T is aborted since its commit
would introduce an impacted transaction in the history and an
abort message is sent to Replicaj . If certification is successful,
the transaction commits and the certifier assigns a global order
to the transaction which becomes the version of the database.
A commit response message is sent to Replicaj , and a refresh
message containing the newly added writeset is sent to all
other replicas in the system.
The second part of Algorithm 1 describes the replica’s
actions when it receives a request to commit an update
transaction T . The replica snapshot version, the writeset and
the readset are sent to the certifier. The replica waits for the
certification response to commit or abort T . In the third part
of Algorithm 1, each replica applies the writeset received in a
refresh message to its local database.
We state the following theorem for algorithm correctness.
Theorem 2: (Certification with versions.) The certification
algorithm (which uses versions) satisfies SGSI rules R2 and
R3 (which are expressed in timestamps).
We omit the proof of Theorem 2, but show the proof of an
expr list
pred(Ri )
join pred(Ri , Rj )
group attr
Relation belonging to the database schema
List of projected attributes and expressions as used
in SELECT SQL statement
List of attribute values as used in the INSERT SQL
attribute of relation R
SQL selection predicate on attributes of
relation Ri
SQL join predicate on the join attributes
of Ri and Rj
Primary key
Primary key value
applied on attribute attr of relation R
Attribute of relation R included in the
GROUP BY clause
a general predicate on the value of the aggregate
included in the HAVING clause
a logical operator: OR, AND, NOT
Our model includes basic SQL statements accessing one
relation: SELECT, UPDATE, DELETE, and INSERT. Grouping and aggregates (AGG i.e. MIN, MAX, AVG, SUM ) are part
of our model. We also support queries involving more than
one relation. Finally, our query language also incorporates
subqueries as well as UNION, INTERSECT, EXCEPT, ORDER
BY and DISTINCT, the details of which are omitted due to
the lack of space.
B. Writesets
Writesets are used for certification and for update propagation. We assume that each tuple in the database is identified
by its primary key value. Tuples in the writeset can be
introduced by UPDATE, INSERT or DELETE SQL statements
in a transaction.
A writeset is a list of tuples, including the old and new
values for each attribute in the tuple.
If certification is successful, the certifier adds both the old
and new values of the writeset to its database, and sends the
new values of the writeset to replicas for update propagation.
The writeset of a transaction is not certified. As we show in
Section VI-B it is sufficient to certify the readset.
There are several approaches to extract writesets, including
triggers, log sniffing or direct support from the database engine
(e.g., in Oracle) [11], [21]. In our prototype the writeset is
extracted by applying the predicates of the update statements
on the replica database since we parse SQL statements to
extract the readset. We show in the next section that readset
certification subsume writeset certification.
is orders of magnitude smaller than the size of the database at
each replica. The certifier periodically garbage collects its data
structures, maintaining a small size for CertDB. Old writesets
are removed from the log file to limit its size. When the
certifier receives a certification request for a transaction with
a snapshot version that is older than CertDB (i.e., when older
versions have been purged from CertDB), the transaction is
aborted. This happens rarely since CertDB maintains enough
versions to make this event unlikely.
Readset certification is one of the key contributions in
this work. The readset is extracted by applying an automatic
query transformation to each SQL statement inside an update transaction. The transformation creates the certification
queries which are evaluated during the certification process.
First we introduce basic certification and next the enhanced
certification. We present certification correctness proof for
each type of transformation. In addition, we show that readset
certification subsumes writeset certification.
We certify the readsets for update transactions. Read-only
transactions do not need certification. Roughly speaking, to
certify the readset of a transaction we want to ensure that if
the transaction executes on the latest version it would read
the same values; that is, no concurrent update transaction
committed writes into the readset.
We observe that the INSERT, UPDATE and DELETE statements have readsets since each SQL statement in our model
has a predicate that defines the readset.
In order to perform the query transformation and extract
the readset, the replica identifies the predicate of each statement. The certification query resulted from the transformation
includes the predicate of the original query as well as a a
version predicate which restricts certification to concurrent
transactions. The certifier executes the certification queries of
a given transaction T on CertDB. The result of the certification
queries forms the conflict set, denoted CS(T ), of transaction
T . If CS(T ) 6= φ, then transaction T aborts; otherwise, there
is no impacting transaction and T commits.
Theorem 3: (Certification in the relational model.) When
certifying transaction T with conflict set CS(T ), if there is a
read-impacting or write-impacting transaction in the history,
then CS(T ) 6= φ.
We prove the theorem for each statement type and present
the certification queries.
C. Readsets
Readset identification is challenging and, as far as we know,
this work is the first to consider this problem in a replicated
setting. In contrast to writesets, identifying readsets using the
tuples that are read by a transaction based on the primary keys
is a poor choice: First, the readset of a transaction is typically
much larger than the writeset, and it is, therefore, expensive
to send the set of rows read in a transaction from the replica
to the certifier. Second, without capturing predicates in the
readset, phantoms might be introduced, obviating 1SR.
Our approach is based on the observation that the readset
of a SQL query is defined by its predicate. The readset is a
list of predicates expressed as SQL queries. We explain how
to extract the readset in Section VI.
D. Writeset Management at Certifier
The certifier manages two data structures: a persistent log
(hard-state) and a main memory database (soft-state). The
writesets of all committed update transactions are stored in the
log. After a crash, certifier uses the log to recover its state.
The content of recent committed writesets is maintained
in an in-memory database named certification database
(CertDB). The CertDB is used to certify update transactions
by validating their writesets and readsets, and is not durable
as its contents can be recovered from the log file. CertDB has
a schema similar to that of the replicated database, augmented
with a version attribute in each relation. After a successful
commit, each tuple in the transaction’s writeset is extended
with the commit version of the transaction and is inserted in
the corresponding relation in CertDB. CertDB contains both
the old and the new values of an updated tuple in order to
support predicate-based certification.
Certification must be executed quickly since it is required
for each update transaction. This is achieved by keeping
CertDB small and in-memory. The amount of space needed
by the CertDB is small because it is proportional to the sum
over all active update transactions multiplied by the number
of database elements that the transaction updates. CertDB size
A. SELECT Queries
The readset of a SELECT statement includes any tuple that
matches the selection predicate pred(Ri ) on the attributes
of relation Ri . The replica constructs the certification query
using the original SQL statement by combining the following
components: (a) SELECT * FROM; (b) the target table of
the SQL query; (c) the content of WHERE clause, and (d) the
version predicate.
Certification Queries: SELECT
version > snapshot(T )
ORDER BY and projections are ignored because they do not
influence the predicate operation.
Proof of Theorem 3: Let Ti denote an update transaction
to be certified, with its snapshot version snapshot(Ti ) and
readset(Ti ) captured in the above certification query. Let Tj
be a read-impacting transaction that created database version
Vj . We show that CS(Ti ) 6= φ, which causes Ti to abort.
Since Tj read-impacts Ti , Tj writes a tuple that matches
the predicate of the SELECT statement if Ti would be executed on version Vj . Let t ∈ R be a this tuple. In this
case t ∈ CertDB and Vj > snapshot(T ). When the
certifier executes the certification queries of Ti , t matches
both the selection predicate and the version predicate. Thus
t ∈ CS(Ti ).
B. Update Statements
C. Groups and Aggregates
In this section we show how to extract and certify the readset
of update statements. One important result of this section is
that certifying the readset of update statements also detects
ww conflicts and it is, therefore, not necessary to certify the
writeset. This claim is supported by the proof of Theorem 3.
The readset of an UPDATE SQL statement includes any tuple that matches the predicate on the target table. Similarly, the
readset of a DELETE statement contains any tuple that matches
the deletion predicate. The readset of an INSERT statement is
identified by the primary key of the new inserted tuples, based
on the fact that the database checks the uniqueness of the
primary key. These conditions are captured by the following
certification queries:
Aggregate queries have predicates specified by the WHERE
clause. Such predicates determine the readset. When present
in an aggregate, the HAVING clause restricts the aggregation
groups that appear in the query results. We remove it from
the certification query in order to validate the results for all
groups. Aggregate queries are transformed as follows:
Aggregates Certification
version > snapshot(t)
The outcome of aggregates like AVG and SUM depends
on the value of each tuple contained in the set over which
they are evaluated. However, the outcome of MIN and MAX
is determined by the value of one tuple; we introduce an
optimization for this case. Consider the MAX aggregate where
the GROUP BY statement is missing. In order to determine
the maximum value, the replica DBMS reads all values of the
relation. If concurrent (remote) transaction modifies any of the
relation tuples, it causes the aggregate to abort during readset
certification even if a transaction modifies tuples that do not
influence the outcome of MAX. At the time when the certification queries are built the value of the MAX aggregate over
attr is already known and we assume it is equal to max val.
Moreover, the result of the aggregate changes if concurrent
transactions write a tuples of Ri with attr ≥ max val. Based
on the previous observation, the certification query can be rewritten as:
Certification Queries: Update Queries
version > snapshot(T )
version > snapshot(T )
version > snapshot(T )
Proof of Theorem 3: We show that when certifying Ti , if
Tj write-impacts Ti , then CS(Ti ) 6= φ. Consider tuple t ∈ R
such that t ∈ writeset(Ti ) and t ∈ writeset(Tj ). We
consider all cases that cause t ∈ writeset(Ti ):
Since t ∈ writeset(Tj ), Tj modifies tuple t. Using
the same reasoning as in the previous case, we state that
the value of t in the snapshot of Tj also satisfies the
delete predicate pred(R). Thus, tuple t is in CertDB and
the value of its version attribute is Vj > snapshot(Ti ).
Moreover, after the certification queries of Ti are executed
at the certifier, t ∈ CS(Ti ) and Ti aborts.
INSERT statement. In this case transaction Ti tries to
insert tuple t in the database. The certification query for
Ti contains a predicate on the primary key of t. Let
Tj be a write-impacting transaction that also inserted
(or modified) t in the database at version Vj . Since Tj
committed, t exists in CertDB and its version attribute
is Vj > snapshot(Ti ). t triggers the certification queries
and t ∈ CS(Ti ) and Ti aborts.
UPDATE statement. In this case transaction Ti modifies
tuple t which satisfies the update predicate pred(R).
Since t ∈ writeset(Tj ), Tj modifies tuple t. The
value of t in the snapshot of Tj also satisfies the update
predicate pred(R). (Otherwise there is another writeimpacting transaction that modifies t from a value that
satisfies pred(R) to a value that does not. This transaction
commits after the snapshot of Ti was taken and before the
snapshot of Tj was taken). Thus, tuple t is in CertDB and
the value of its version attribute is Vj > snapshot(Ti ).
Moreover, after the certification queries of Ti are executed
at the certifier, t ∈ CS(Ti ) and Ti aborts.
DELETE statement. In this case transaction Ti modifies
tuple t which satisfies the delete predicate pred(R).
Aggregates Certification
attr ≥ max val AND version > snapshot(T )
D. Joins
Joins have SELECT queries involving several target relations Ri , i = 1..n. There are two types of predicates,
combined by conjunctive and/or disjunctive logical operators,
that define the readset. First, the selection predicates involve
only attributes of a single relation Ri and are denoted by
pred(Ri ). Second, the join predicates involve attributes that
define the join between two pairs of relations Ri and Rj and
are denoted by join pred(Ri , Rj ).
So far, the certifier maintains CertDB, which contains the
writesets of recently committed transactions. CertDB is not
enough to evaluate all predicates and to certify the readset.
We present an approximation here and in the next subsection
we show how to enhance the certifier to perform refined
certification for joins.
Certifying a superset of the readset. The certifier can
check for updates in each individual relation that participates
in the join using the following certification queries:
that are updated after the snapshot of T was taken, the certifier
checks that VStart > snapshot(T ) OR VEnd > snapshot(T ).
We call this the update predicate, denoted upd(RiC ), and we
employ it when accessing any relation RiC at the certifier.
In order to detect tuples that are visible to transaction T , the
certifier checks that NOT(VEnd < snapshot(T ) AND VEnd 6=
−1). We call this the visibility predicate, denoted vis(RiC ),
and we use it to access to any relation RiC at the certifier.
All SQL queries can be certified on the certifier copy of the
database in order to reduce the superset of the readset.
Join Certification. We show below the extended certification
queries for joins. Certification is successful if there is no tuple
returned, showing the absence of any impacting transaction.
Join Certification
for each relation Ri :
F. SELECT * FROM Ri WHERE version > snapshot(T )
Certification Query: Joins
F. SELECT * FROM R1C ...RiC ...RnC
WHERE (query pred)
AND (upd(R1C ) ... OR upd(RiC ) ... OR upd(RnC ))
It is easy to prove that these certification queries guarantee
correctness since they match any committed writesets belonging to any of the Ri target relations.
AND (vis(R1C ) ... AND vis(RiC ) ... AND vis(RnC ))
E. Extended Certification
Proof of Certification Completeness:
Let Ti be a transaction that needs to be certified and its
readset is identified by the previous certification query. We
show that if there is no impacting transaction, the certification
request always succeeds.
Assume this transaction is aborted while there is no impacting transaction. If the transaction is aborted, the certification
query returns at least one tuple. Consider the set of instances
tRi ∈ Ri that satisfy query pred and produce this tuple
in the join result. This implies that there is at least one
tuple instance tR ∈ R in this set that matches the version
predicate upd(R). Thus, we have VStart > snapshot(Ti ) or
VEnd > snapshot(Ti ). This further implies that there is a
transaction that committed after the snapshot of Ti was taken
and it modified tuple tR . Since all the components tRi that
satisfy the query pred are visible to Ti , our initial assumption
that there is no impacting transaction is contradicted.
Data Managed at Certifier. Certification should ideally
satisfy two properties: soundness (correctness, i.e., when a
transaction is certified successfully, there is no read-impacting
or write-impacting committed transaction), and completeness
(accuracy, i.e., if there is no read-impacting or write impacting
transaction, the certification request always succeeds).
The certifier maintains soundness at all times, but completeness is a function of the amount of data maintained at the
certifier. At one end of the spectrum, maintaining the recent
writesets at the certifier, may require using a superset of the
query readset. This case can lead to an unnecessary abort. At
the other end of the spectrum, maintaining the full database
at the certifier achieves completeness but has the maximum
Unnecessary aborts may happen when the certifier does
not maintain enough information to evaluate the readset join
predicates. One alternative would be to have the replica send
this information to the certifier; this approach is, however, not
practical as it is likely too expensive to ship the needed data
from the replica to the certifier.
Another alternative is to store and maintain the data required
to evaluate the join predicates at the certifier. In essence, this
is similar to maintaining a materialized view in a database
system. Here the certifier manages a copy of the relations
referenced in the query and we denote these relation by
R1C . . . RnC . Relations used in transactions that are aborted
frequently are good candidates. The main cost of maintaining
a relation in CertDB is the memory needed to store the relation
as well as an increase in the processing of certification queries.
To maintain a relation at the certifier, we extend it with start
and end versions (VStart and VEnd ) to determine the visibility
of each tuple, similar to the way versions are managed in a
multi-version row storage engine, for example as in Postgres
[29]. The latest version of a tuple has VEnd = −1.
To certify transaction T containing a query with an arbitrary
predicate query pred over the target relations R1 ...Rn , we
introduce two additional predicates. In order to detect tuples
Proof of Certification Soundness:
Let Ti be a transaction that needs to be certified and its
readset is identified by the previous certification query. Let
transaction Tj be an impacting transaction that modifies tuple
tR ∈ R and it creates version Vj .
We show that the certification queries for Ti report at least
one result and transaction Ti aborts.
Since the update of tuple tR in transaction Tj causes the
modification of an item read by transaction Ti , there is a set
of tuples tRi ∈ Ri , i = 1..n and tR influences the ability
of this set to satisfy the predicate query pred. This further
implies that all tuples tRi are visible to the transaction Ti .
Moreover, tR can influence the outcome of the query pred
evaluation if: (a) it was part of a tuple set that matched the
predicate and it was modified by Tj , or (b) it is a new instance
created by Tj that completes a set of tuples such that they
satisfy the predicate. The same set of tuples mentioned above
satisfies the query pred and the visibility predicates vis(Ri ),
i = 1..n of the certification queries when they are evaluated
at the certifier copy of the database. However, in case (a),
tuple tR has VEnd = Vj while in case (b), VStart = Vj .
Vj > snapshot(Ti ) and in both cases the update predicate
upd(R) is also satisfied. Thus, the certification query has at
least one response and Ti aborts.
We build a replicated system for experimental evaluation.
System architecture is described in Section IV. We extend the
prototype with a web server front-end.
has an Intel Core 2 Duo Processor running at 2.2GHz with
2GB DDR2 memory, and a 7200RPM 250GB SATA drive.
The load generator and the web front-end (and load balancer) run on Dell T3500 with Intel Xeon E5520 processors
and 4GB of memory running Windows Server 2008. The
load generator, web front-end and load balancer are well
provisioned so that they do not become the bottleneck in
any experiment. All machines have a single gigabit ethernet
interface and are connected to one gigabit ethernet switch.
A. Implementation
C. TPC-W Benchmark
Each replica runs a multithreaded C# application as the
proxy and runs a local instance of Microsoft SQL Server 2008
as the database. SQL Server is configured to run transactions
at snapshot isolation level with row-level locking. Replicas
receive client transactions via the web front-end and extract the
readset and writeset of each transaction. The readset is automatically identified by the replica from the text of SQL queries.
We parse this text and locate the target relations as well as the
predicates of the query. Then, we build the certification queries
as explained in Sections VI, including extended certification in
Subsection VI-E. The writeset is extracted from the UPDATE,
DELETE and INSERT statements. We extract the predicate
of UPDATE and DELETE statements and generate queries to
capture the writesets. Notice that in the case of UPDATE, we
capture both the new and old values.
The certifier is implemented as a C# application. It uses
an in-memory SQLite.NET database for conflict detection.
SQLite.NET constrains the implementation to a single connection to the in-memory database, serializing conflict detection.
The certifier batches commits to the in-memory database to
mitigate the commit costs. Experimentally we found committing after batches of 32 certifications provides better throughput and scaling. The certifier also periodically cleans old
state from the in-memory database to constrain memory usage
and improve performance. In the measurements presented the
cleaning period was every 5000 certifications and records older
than 1000 versions are expunged.
The prototype implements session consistency [8] where
each client sees increasing versions of the database system,
and after a client commits an update transaction, the next client
transaction observes the effects of this update.
The web front-end and load balancer component is a C#
ASP.NET application running under IIS 7.0. The load generator emulates clients that drive the front end. Each emulated
client is bound to a session and generates requests as it is
driven through the client model. The requests are forwarded
to replicas with round-robin load balancing policy.
The load generator implements the browser emulator described in the TPC-W specification [30] and is unmodified
from prior research projects [11], [13].
TPC-W models an e-commerce site, in the form of an
online bookstore. The TPC-W specification requires 14 different interactions, each of which must be invoked with
a particular frequency. Of these interactions, eight initiate
update transactions, whereas the other six generate read-only
transactions. Each interaction may also involve requests for
multiple embedded images for items in the inventory.
Mixes. TPC-W has three workload mixes that vary in the
fraction of update transactions. The browsing mix workload
has 5% updates, the shopping mix workload has 20% updates
and the ordering mix workload has 50% updates. The shopping
mix is the main mix but we have explored all mixes in
our experiments. During the update propagation, the average
writeset size is about 300 Bytes.
Database Size. In our experiments, the TPC-W database
scaling parameters are 200 EBS (emulated browsers) and
10000 items. Each experiment begins with the same 850 MB
initial database. The database fits within the memory available
in each replica (2GB). This choice maximizes the demands on
processor resources at the replica and at the certifier. A larger
database would have a greater I/O component and pose less
of a performance challenge.
Measurement Interval. Each experiment ran with a 3-minute
warm up period followed by a 5 minute measurement interval.
D. SmallBank Benchmark
The SmallBank benchmark [3] models a simple banking
database. It consists from three tables: Account (name, customerID), Saving (customerID, balance) and Checking (customerID, balance). It contains five transaction types for balance (Bal), deposit-checking (DC), withdraw-from-checking
(WC), transfer-to-savings (TS) and amalgamate (Amg) which
are assigned uniformly to clients. The Bal transaction is readonly while DC, WC, TS and Amg are update transactions.
SmallBank is not serializable under SI and GSI.
We use a database containing 1000 randomly generated
customers and their checking and savings accounts(as employed in prior work[7]). The test driver runs the five possible
transactions with uniform distribution, creating a workload
with 80% update transactions. The driver uses one thread per
client. The running time for each experiment is 300 seconds.
Each experiment is repeated 10 times and the error bars in
Figure 8 show the standard deviation.
B. Hardware Components
The machines used for the certifier and database replicas are
Dell Optiplex 755 running 64-bit Windows Server 2008. Each
1-replica SGSI
1-replica GSI
2-replica SGSI
2-replica GSI
4-replica SGSI
4-replica GSI
8-replica SGSI
8-replica GSI
8-replica GSI
8-replica SGSI
Response Time, ms
Throughput, TPM
Number of Clients
Fig. 2.
Throughput of TPC-W Shopping Mix (20% updates).
Fig. 4.
Scalability of TPC-W Shopping Mix (20% updates).
8-replica GSI
8-replica SGSI
Certifier CPU Utilization, %
1-replica SGSI
1-replica GSI
2-replica SGSI
2-replica GSI
4-replica SGSI
4-replica GSI
8-replica SGSI
8-replica GSI
Response Time, ms
Throughput, TPM
Number of Clients
Fig. 3.
Throughput, TPM
Resp. Time of TPC-W Shopping Mix (20% updates).
Fig. 5.
Certifier CPU util., TPC-W Shopping(20% updates).
against response time for an 8-replica system using the TPCW Shopping Mix for both SGSI and GSI certification.
The SGSI curve is slightly below GSI, showing that they
have almost the same performance. To explain the difference
between SGSI and GSI curves, we study the overhead of SGSI
certification, in terms of certifier response time and abort rate.
The CPU is the bottleneck resource at the certifier. Figure 5
shows that certifier CPU utilization is under 60% and 43% for
SGSI and GSI respectively. Certifier response time for SGSI
varies from 7-20 ms and for GSI varies from 4-6 ms as the
load at the certifier varies from 8000-51000 TPM.
The abort rates we observe for SGSI in our TPC-W experiments are low: the Shopping Mix has a peak of 8% of
transactions aborting. For all mixes, we see the number of
aborts increase with the rate of certifications. A more detailed
study of abort rates is presented in Section VII-F.
In Figures 2 and 3, the performance of GSI and SGSI are
shown for different degrees of replication. The performance of
SGSI and GSI are almost identical in throughput and response
time for all degrees of replication. We conclude that SGSI
introduces a small overhead when compared to GSI due to
extra processing needed to guarantee one-copy serializability.
3) Sensitivity to Update Transaction Ratio: We next turn
to studying how SGSI scales under different ratios of update
transactions. If the workload is read-only, replication should
yield linear scalability. For update dominated workloads the
E. TPC-W Performance Evaluation
Our objective is to show that SGSI is a practical replication
algorithm with competitive performance. We investigate its
scalability and overhead. We use the TPC-W benchmark to
assess SGSI performance as it is widely used to evaluate
replicated database systems [11], [22], [33], [20].
1) Scaling of SGSI with Replication Degree: We use the
main workload of TPC-W, the Shopping Mix (20% updates)
to assess system scalability under SGSI in Figures 2 and 3. We
vary the number of clients from 150 to 1050 along the X-axis,
and we plot four curves for 1, 2, 4 and 8 replica SGSI systems
(and we discuss GSI curves in next subsection). The Y-axis is
either throughput in TPM (transactions per minute) or average
response time in ms (milliseconds). Replication increases the
peak throughput, in particular the 8-replica system reaches
51000 TPM, which is 4X the single replica performance.
Replication is also used to reduce average response time; at
the 1050 clients, response time is reduced from 4000 ms at
1 replica to 230 ms at 8 replicas. We conclude that the SGSI
system provides good scalability with the number of replicas
to increase throughput or reduce average response time.
2) Comparing SGSI to GSI: The performance of GSI
represents an upper bound on the performance for SGSI. SGSI
certifies readsets and writesets to provide serializability, whilst
GSI only certifies writesets. Figure 4 shows the throughput
8-replica TPC-W Browsing Mix
8-replica TPC-W Shopping Mix
8-replica TPC-W Ordering Mix
ww conflicts
rw conflicts
Conflicts, %
Throughput, TPM
Fig. 6.
Multi-Programming Level
Number of Clients
SGSI Throughput of TPC-W Mixes.
Fig. 8.
Conflicts with SmallBank (80% updates).
8-replica TPC-W Browsing Mix
8-replica TPC-W Shopping Mix
8-replica TPC-W Ordering Mix
Figure 8 shows the increase in abort rates as the multiprogramming level increases from 10 to 300. SGSI and
GSI both abort ww conflicts, whereas rw conflicts are only
aborted under SGSI to guarantee one-copy serializability. The
additional aborts from rw conflicts under SGSI are strictly
lower than baseline ww conflict aborts. This suggests that
SGSI has a slight effect on the observed abort rates. This is
important since workloads of SI engines have low abort rates.
When examining the rw conflicts, the issue arises as to the
number leading to serialization errors. Testing for serializability is a well-known NP-complete problem, and all practical
serializable concurrency control techniques abort transactions
that could be serialized. Figure 8 shows the number of pivots
as an upper bound on potential serialization errors. A pivot
has an incoming and an outgoing rw-dependency in the
serializability graph [7] indicating a dangerous structure that
could be unserializable [16]. Our results show up to 2.5% of
rw conflicts are pivots and suggest that the majority of rw
conflicts would be serializable were we to build the multiversion serializability graph and check for cycle absence.
Response Time, ms
Number of Clients
Fig. 7.
SGSI Response Time of TPC-W Mixes.
replication may give little performance improvement, or even
a degradation, as the number of replicas increases, since the
updates need to be certified and applied at every replica.
We use all three TPC-W mixes to characterize the performance of SGSI from read dominated workloads to update
intensive workloads. Figures 6 and 7 show throughput and
average response times for an 8-replica SGSI system as the
number of TPC-W clients varies. The Browsing Mix (5%
updates) is read dominated and the figures show that its
throughput scales almost linearly up to 88000 TPM, which
is 7X the single replica performance. Under the Shopping
Mix (20% updates) throughput reaches a peak of 51000 TPM:
4X the single replica performance. The Ordering Mix (50%
updates) reaches a throughput of 19500 TPM at 8 replicas,
which is 2X the throughput of a single replica. As expected,
the benefits of replication depends on the ratio of updates.
The related work on Snapshot Isolation (SI) and Generalized
Snapshot Isolation (GSI) has been discussed in Section II-A.
Here we present related work on serializability and replication.
Serializability. Serializability [6], [23] is the most common
database correctness criteria. Researchers have investigated
guaranteeing serializability in centralized systems both when
using weaker isolation levels and when using application
specific knowledge. Adya et al. [1] have provided a theoretical
foundation to formally specify practical isolation levels for
commercial databases. Atluri et al. [4] have studied the serializability of weaker isolation levels such as the ANSI SQL
isolation levels for centralized databases. Shasha et al. [28]
have presented the conditions that allow a transaction to be
divided into smaller sub-transactions that release locks earlier
than the original transaction under traditional locking policies.
Serializability under SI. Several researchers [15], [16], [13]
have recently demonstrated that, under certain conditions on
the workload, transactions executing on a database with SI
F. Abort Analysis via SmallBank
We analyze the impact SGSI on abort rates under varying
levels of multiprogramming. The abort rates reported for TPCW in Section VII-E2 are low and others have reported similar
findings [10], [33]. We use the SmallBank benchmark as its
workload consists of 80% updates and it supports a variable
number of clients. SmallBank was used to evaluate making SI
serializable both within, and outside of, SI engines [3], [7].
produce serializable histories. Serializable Snapshot Isolation
(SSI) [7] is a concurrency control mechanism which enforces
serializability by correcting anomalies allowed by SI. SSI is
implemented inside the database engine and it detects and
prevents the anomalies at run time.
Replicated Database Systems. Gray et al. [18] have classified database replication into two schemes: eager and lazy.
Eager replication provides consistency at the cost of limited
scalability. Lazy replication increases performance by allowing
replicas to diverge, exposing clients to inconsistent states of
the database. The prototype we use has features from both lazy
(e.g., in update propagation) and eager (e.g., no reconciliation
is ever needed) systems.
SI Replicated Database Systems. Kemme et al. implement
Postgres-R [19], and integrate the replica management with
the database concurrency control [21], [33] in Postgres-R(SI).
Plattner et al. [24] present Ganymed, a master-slave scheme
for replicating snapshot isolated databases in clusters of machines. Tashkent [11], [12] is a replicated database system that
provides GSI and Pangea [22] uses eager update propagation.
None of these systems guarantees serializability.
SI Federated Database Systems. Schenkel et al. [26] discuss
using SI in federated databases where global transactions
access data distributed across multiple sites. Their protocols
guarantee SI at the federation level rather than serializability.
In later work [25], a ticketing algorithm ensures serializability
in federated SI databases. Global update transactions are executed sequentially in ticket order. In contrast, SGSI addresses
the problem of replication rather than federation, and update
transactions execute concurrently on replicas.
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This research investigates providing a global isolation level
in replicated database systems stronger than the level provided
locally by individual replicas. We introduce SGSI, a concurrency control technique that ensures one-copy serializability in
replicated systems in which each replica is snapshot isolated.
We employ novel techniques to extract transaction readsets and
perform enhanced certification. We implement SGSI and build
a prototype replicated system and evaluate its performance to
show that SGSI is practical. The performance results under
the TPC-W workload mixes show that the replicated system
performance scales with the number of replicas.
We are grateful to Konstantinos Krikellas for his contributions to our experimental system, Tim Harris for his insightful
comments, Zografoula Vagena who participated in the early
stages of this work, and M. Tamer Özsu for his suggestions.
Mohammad Alomari provided us with the small bank benchmark. Fernando Pedone and Willy Zwaenepoel suggested the
possibility of building a GSI system that provides 1SR.
[1] A. Adya, B. Liskov, and P. E. O’Neil. Generalized Isolation Level
Definitions. In ICDE 2000.
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