stasis-aries-wal/doc/paper/LLADD-Freenix.tex

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\title{\Large \bf LLADD: An Extensible Transactional Storage Layer\\
  \normalsize{(yaahd)}}

\author{
Russell Sears  and  Eric Brewer\\
{\em UC Berkeley}\\
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{\normalsize \{sears,brewer\}@cs.berkeley.edu, http://lladd.sourceforge.net} \\
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\subsection*{Abstract}

Although many systems provide transactionally consistent data management,
existing implementations are generally monolithic and tied to a higher-level DBMS, limiting the scope of their usefulness to a single application
or a specific type of problem. As a result, many systems are forced
to ``work around'' the data models provided by a transactional storage
layer. Manifestations of this problem include ``impedence mismatch''
in the database world and the limited number of data models provided
by existing libraries such as Berkeley DB. In this paper, we describe
a light-weight, easily extensible library, LLADD, that allows application
developers to develop scalable and transactional application-specific
data structures. We demonstrate that LLADD is simpler than prior systems,
is very flexible and performs favorably in a number of
micro-benchmarks. We also describe, in simple and concrete terms,
the issues inherent in the design and implementation of robust, scalable
transactional data structures. In addition to the source code, we
have also made a comprehensive suite of unit-tests, API documentation,
and debugging mechanisms publicly available.%
\footnote{http://lladd.sourceforge.net/%
}


\section{Introduction}

Changes in data models, consistency requirements, system scalibility,
communication models and fault models require changes to the storage
and recovery subsystems of modern applications. 

For applications that are willing to store all of their data in a
DBMS, and access it only via SQL, existing databases are just fine and
LLADD has little to offer.  However, for those applications that need
more direct management of data, LLADD offers a layered architecture
that enables simple but robust data management.\footnote{A large class
of such applications are deemed ``navigational'' in the database
vocabulary, as they directly navigate data structures rather than
perform set operations.}
We also believe that LLADD is applicable in
the context of new, special-purpose database systems such as XML databases,
streaming databases, and extensible/semantic file systems~\cite{reiser, semantic}.  These form a 
fruitful area of current research,~\cite{newTypes} but existing monolithic database systems tend to be a poor fit for these new areas.

The basic approach of LLADD, taken from ARIES~\cite{aries}, is to build
\emph{transactional pages}, which enables recovery on a page-by-page
basis, despite support for high concurrency and the minimization of
disk seeks during commit (by using a log).  We show how to build a variety
of useful data managers on top of this layer, including persistent
hash tables, lightweight recoverable virtual memory (LRVM)~\cite{lrvm}, and simple
databases.  We also cover the details of crash recovery,
application-level support for transaction abort and commit, and latching for multithreaded applications.
Finally, we discuss the shortcomings of common applications, and explain
why LLADD provides an appropriate solution to these problems.

%[more coverage of kinds of apps?  imap, lrvm, cht, file system, database]

Many implementations of transactional pages exist in industry and
in the literature. Unfortunately, these algorithms tend either to
be straightforward and unsuitable for real-world deployment, or are
robust and scalable, but achieve these properties by relying upon
intricate sets of internal and often implicit interactions. The
ARIES algorithm falls into the second category, and has been extremely
sucessful as part of the IBM DB2 database system.
It provides performance and reliability that is comparable to that of current
commercial and open-source products. Unfortunately, while the algorithm
is conceptually simple, many subtleties arise in its implementation.
We chose ARIES as the basis of LLADD, and have made a significant
effort to document these interactions. Although  a complete discussion
of the ARIES algorithm is beyond the scope of this paper, we will
provide a brief overview, and explain the details that are relevant
to developers that wish to extend LLADD. 

By documenting the interface between ARIES and higher-level primitives
such as data structures and by structuring LLADD to make this
interface explicit in both the library and its extensions, we hope to
make it easy to produce correct and efficient durable data
structures. In existing systems (and indeed, in earlier versions of
LLADD), the implementation of such structures is extremely
complicated, and subject to the introduction of incredibly subtle
errors that would only be evident during crash recovery or at other
inconvenient times.  Thus there is great value in reusing these lower
layers.

Finally, by approaching this problem by implementing a number of simple
modules that ``do one thing and do it well'', we believe that
LLADD can provide competitive performance while making future improvements
to its core implementation significantly easier. In order to achieve
this goal, LLADD has been split into a number of modules forming a
{\em core library}, and a number of extensions called {\em operations} that
build upon the core library. Since each of these modules exports a
stable interface, they can be independently improved.


\subsection{Prior Work\label{sub:Prior-Work}}

An extensive amount of prior work covers the algorithms presented in
this paper.  Most fundamentally, systems that provide transactional
consistency to their users generally include a number of common
modules.  Figure~\ref{cap:DB-Architecture} presents a high-level overview of a typical system.

\begin{figure}
\includegraphics[%
  width=1.0\columnwidth]{DB-Architecture.pdf}


\caption{\label{cap:DB-Architecture}Conceptual view of a modern
transactional application.  Current systems include high-level
functionality, such as indices and locking, but are not designed to
allow developers to replace this functionality with
application-specific modules.}
\end{figure}

Many systems make use of transactional storage that is
designed for a specific application, or set of applications.  LLADD
provides a flexible substrate that allows such systems to be
developed easily.  The complexity of existing systems varies widely, as do
the applications for which these systems are designed.  

On the database side of things, relational databases excel in areas
where performance is important, but where the consistency and
durability of the data are crucial.  Often, databases significantly
outlive the software that uses them, and must be able to cope with
changes in business practices, system architectures, etc.~\cite{relational}

Object-oriented databases are more focused on facilitating the
development of complex applications that require reliable storage and
may take advantage of less-flexible, more efficient data models,
as they often only interact with a single application, or a handful of
variants of that application.~\cite{lamb}

Databases are designed for circumstances where development time may
dominate cost, many users must share access to the same data, and
where security, scalability, and a host of other concerns are
important.  In many, if not most, circumstances these issues are less
important, or even irrelevant.  Therefore, applying a database in
these situations is likely overkill, which may partially explain the
popularity of MySQL~\cite{mysql}, which allows some of these constraints to be
relaxed at the discretion of a developer or end user.

Still, there are many applications where MySQL is too
inflexible.  In order to serve these applications, a host of software
solutions have been devised.  Some are extremely complex, such as
semantic file systems, where the file system understands the contents
of the files that it contains, and is able to provide services such as
rapid search, or file-type specific operations such as thumbnailing,
automatic content updates, and so on.  Others are simpler, such as
Berkeley~DB,~\cite{berkeleyDB, bdb} which provides transactional storage of data in unindexed
form, in indexed form using a hash table or tree.  LRVM is a version
of malloc() that provides transacational memory, and is similar to an
object-oriented database, but is much lighter weight, and more
flexible~\cite{lrvm}.

Finally, some applications require incredibly simple, but extremely
scalable storage mechanisms.  Cluster Hash Tables are a good example
of the type of system that serves these applications well, due to
their relative simplicity, and extremely good scalability
characteristics.  Depending on the fault model on which a cluster hash table is
implemented, it is quite plausible that key portions of
the transactional mechanism, such as forcing log entries to disk, will
be replaced with other durability schemes, such as in-memory
replication across many nodes, or multiplexing log entries across
multiple systems.  This level of flexibility would be difficult to
retrofit into existing transactional applications, but is often appropriate
in the environments in which these applications are deployed.

We have only provided a small sampling of the many applications that
make use of transactional storage.  Unfortunately, it is extremely
difficult to implement a correct, efficient and scalable transactional
data store, and we know of no library that provides low-level access
to the primitives of such a durability algorithm.  These algorithms
have a reputation of being complex, with many intricate interactions,
which prevent them from being implemented in a modular, easily
understandable, and extensible way.  

Because of this, many applications that would benefit from
transactional storage, such as CVS and many implementations of IMAP,
either ignore the problem, leaving the burden of recovery to system
administrators or users, or implement ad-hoc solutions that employ
complex, application-specific storage protocols in order to ensure
the consistency of their data.  This increases the complexity of such
applications, and often provides only a partial solution to the
transactional storage problem, resulting in erratic and unpredictable
application behavior.

In addition to describing a flexible implementation of ARIES, a well-tested
``industrial strength'' algorithm for transactional storage, this paper
outlines the most important interactions that we discovered (that
is, the ones that could not or should not be encapsulated within our
implementation), and gives the reader a sense of how to use the
primitives the library provides.

%Many plausible lock managers, can do any one you want.
%too much implemented part of DB; need more 'flexible' substrate.

\section{ARIES from an Operation's Perspective}

Instead of providing a comprehensive discussion of ARIES, we will
focus upon those features of the algorithm that are most relevant
to a developer attempting to add a new set of operations. Correctly
implementing such extensions is complicated by concerns regarding
concurrency, recovery, and the possibility that any operation may
be rolled back at runtime.

We first sketch the constraints placed upon operation implementations,
and then describe the properties of our implementation that
make these constraints necessary. Because comprehensive discussions of
write ahead logging protocols and ARIES are available elsewhere,~\cite{haerder, aries} we
only discuss those details relevant to the implementation of new
operations in LLADD.


\subsection{Properties of an Operation\label{sub:OperationProperties}}

A LLADD operation consists of some code that manipulates data that has 
been stored in transactional pages.  These operations implement the high-level
actions that are composed into transactions.  They are implemented at
a relatively low level, and have full access to the ARIES algorithm.
Applications are implemented on top of the interfaces provided 
by an application-specfic set of (potentially reusable) operations.  This allows the the application,
the operation, and LLADD itself to be independently improved.

Since transactions may be aborted,
the effects of an operation must be reversible. Furthermore, aborting
and comitting transactions may be interleaved, and LLADD does not
allow cascading aborts,%
\footnote{That is, by aborting, one transaction may not cause other transactions
to abort. To understand why operation implementors must worry about
this, imagine that transaction A split a node in a tree, transaction
B added some data to the node that A just created, and then A aborted.
When A was undone, what would become of the data that B inserted?%
} so in order to implement an operation, we must implement some sort
of locking, or other concurrency mechanism that isolates transactions
from each other. LLADD only provides physical consistency; due to the variety of locking systems available, and their interaction with application workload,~\cite{multipleGenericLocking} we leave
it to the application to decide what sort of transaction isolation is
appropriate.  

For example, it is relatively easy to
build a strict two-phase locking lock manager~\cite{hierarcicalLocking} on top of LLADD, as
needed by a DBMS, or a simpler lock-per-folder approach that would
suffice for an IMAP server.  Thus, data dependencies among
transactions are allowed, but we still must ensure the physical
consistency of our data structures, such as operations on pages or locks.

Also, all actions performed by a transaction that committed must be
restored in the case of a crash, and all actions performed by aborting
transactions must be undone. In order for LLADD to arrange for this
to happen at recovery, operations must produce log entries that contain
all information necessary for undo and redo.

An important concept in ARIES is the ``log sequence number'' or LSN.
An LSN is essentially a virtual timestamp that goes on every page; it
marks  the last log entry that is reflected on the page, and
implies that all previous log entries are also reflected. Given the
LSN, LLADD calculates where to start playing back the log to bring the page
up to date.  The LSN goes on the page so that it is always written to
disk atomically with the data on the page.

ARIES (and thus LLADD) allows pages to be {\em stolen}, i.e. written
back to disk while they still contain uncommitted data.  It is
tempting to disallow this, but to do so has serious consequences such as
a increased need for buffer memory (to hold all dirty pages). Worse,
as we allow multiple transactions to run concurrently on the same page
(but not typically the same item), it may be that a given page {\em
always} contains some uncommitted data and thus could never be written
back to disk.  To handle stolen pages, we log UNDO records that
we can use to undo the uncommitted changes in case we crash.  LLADD
ensures that the UNDO record is durable in the log before the
page is written back to disk and that the page LSN reflects this log entry.

Similarly, we do not force pages out to disk every time a transaction
commits, as this limits performance.  Instead, we log REDO records
that we can use to redo the change in case the committed version never
makes it to disk.  LLADD ensures that the REDO entry is durable in the
log before the transaction commits.  REDO entries are physical changes
to a single page (``page-oriented redo''), and thus must be redone in
the exact order.

One unique aspect of LLADD, which
is not true for ARIES, is that {\em normal} operations use the REDO
function; i.e. there is no way to modify the page except via the REDO
operation.  This has the great property that the REDO code is known to
work, since even the original update is a ``redo''.
In general, the LLADD philosophy is that you
define operations in terms of their REDO/UNDO behavior, and then build
the actual update methods around those.

Eventually, the page makes it to disk, but the REDO entry is still
useful: we can use it to roll forward a single page from an archived
copy.  Thus one of the nice properties of LLADD, which has been
tested, is that we can handle media failures very gracefully: lost
disk blocks or even whole files can be recovered given an old version
and the log.  

\subsection{Normal Processing}

Operation implementors follow the pattern in Figure \ref{cap:Tset},
and need only implement a wrapper function (``Tset()'' in the figure,
and register a pair of redo and undo functions with LLADD.
The Tupdate function, which is built into LLADD, handles most of the
runtime complexity.  LLADD uses the undo and redo functions
during recovery in the same way that they are used during normal
processing.  

The complexity of the ARIES algorithm lies in determining
exactly when the undo and redo operations should be applied.  LLADD 
handles these details for the implementors of operations.


\subsubsection{The buffer manager}

LLADD manages memory on behalf of the application and prevents pages
from being stolen prematurely. Although LLADD uses the STEAL policy
and may write buffer pages to disk before transaction commit, it still
must make sure that the UNDO log entries have been forced to disk
before the page is written to disk. Therefore, operations must inform
the buffer manager when they write to a page, and update the LSN of
the page. This is handled automatically by the write methods that LLADD 
provides to operation implementors (such as writeRecord()). However,
it is also possible to create your own low-level page manipulation
routines, in which case these routines must follow the protocol.


\subsubsection{Log entries and forward operation\\ (the Tupdate() function)\label{sub:Tupdate}}

In order to handle crashes correctly, and in order to undo the
effects of aborted transactions, LLADD provides operation implementors
with a mechanism to log undo and redo information for their actions.
This takes the form of the log entry interface, which works as follows.
Operations consist of a wrapper function that performs some pre-calculations
and perhaps acquires latches. The wrapper function then passes a log
entry to LLADD. LLADD passes this entry to the logger, {\em and then processes
it as though it were redoing the action during recovery}, calling a function
that the operation implementor registered with
LLADD. When the function returns, control is passed back to the wrapper
function, which performs any post processing (such as generating return
values), and releases any latches that it acquired. %
\begin{figure}
\begin{center}
\includegraphics[%
  width=0.70\columnwidth]{TSetCall.pdf}
\end{center}

\caption{\label{cap:Tset}Runtime behavior of a simple operation. Tset() and redoSet() are
extensions that implement a new operation, while Tupdate() is built in. New operations
need not be aware of the complexities of LLADD.}
\end{figure}


This way, the operation's behavior during recovery's redo phase (an
uncommon case) will be identical to the behavior during normal processing,
making it easier to spot bugs. Similarly, undo and redo operations take
an identical set of parameters, and undo during recovery is the same 
as undo during normal processing.  This makes recovery bugs more obvious and allows redo
functions to be reused to implement undo. 

Although any latches acquired by the wrapper function will not be
reacquired during recovery, the redo phase of the recovery process
is single threaded. Since latches acquired by the wrapper function
are held while the log entry and page are updated, the ordering of
the log entries and page updates associated with a particular latch
will be consistent. Because undo occurs during normal operation, 
some care must be taken to ensure that undo operations obtain the 
proper latches.


\subsection{Recovery}

In this section, we present the details of crash recovery, user-defined logging, and atomic actions that commit even if their enclosing transaction aborts.

\subsubsection{ANALYSIS / REDO / UNDO}

Recovery in ARIES consists of three stages, analysis, redo and undo. 
The first, analysis, is
implemented by LLADD, but will not be discussed in this
paper. The second, redo, ensures that each redo entry in the log 
will have been applied to each page in the page file exactly once.
The third phase, undo, rolls back any transactions that were active
when the crash occured, as though the application manually aborted
them with the {}``abort'' function call.
  
After the analysis phase, the on-disk version of the page file
is in the same state it was in when LLADD crashed. This means that
some subset of the page updates performed during normal operation
have made it to disk, and that the log contains full redo and undo
information for the version of each page present in the page file.%
\footnote{Although this discussion assumes that the entire log is present, the
ARIES algorithm supports log truncation, which allows us to discard
old portions of the log, bounding its size on disk.%
} Because we make no further assumptions regarding the order in which
pages were propogated to disk, redo must assume that any
data structures, lookup tables, etc. that span more than a single
page are in an inconsistent state. Therefore, as the redo phase re-applies
 the information in the log to the page file, it must address all pages directly. 

This implies that the redo information for each operation in the log
must contain the physical address (page number) of the information
that it modifies, and the portion of the operation executed by a single
redo log entry must only rely upon the contents of the page that the 
entry refers to. Since we assume that pages are propagated to disk
atomically, the REDO phase may rely upon information contained within
a single page.

Once redo completes, we have applied some prefix of the run-time log.
Therefore, we know that the page file is in
a physically consistent state, although it contains portions of the
results of uncomitted transactions. The final stage of recovery is
the undo phase, which simply aborts all uncomitted transactions. Since
the page file is physically consistent, the transactions may be aborted
exactly as they would be during normal operation. 


\subsubsection{Physical, Logical and Phisiological Logging.}

The above discussion avoided the use of some common terminology 
that should be presented here. {\em Physical logging } 
is the practice of logging physical (byte-level) updates
and the physical (page number) addresses to which they are applied.

{\em Physiological logging } is what LLADD recommends for its redo 
records. The physical address (page number) is stored, but the byte offset
and the actual difference are stored implicitly in the parameters
of the redo or undo function. These parameters allow the function to 
update the page in a way that preserves application semantics.
One common use for this is {\em slotted pages}, which use an on-page level of 
indirection to allow records to be rearranged within the page; instead of using the page offset, redo 
operations use a logical offset to locate the data. This allows data within
a single page to be re-arranged at runtime to produce contiguous
regions of free space. LLADD generalizes this model; for example, the parameters passed to the function may utilize application specific properties in order to be significantly smaller than the physical change made to the page.~\cite{physiological}

{\em Logical logging } can only be used for undo entries in LLADD,
and is identical to physiological logging, except that it stores a
logical address (the key of a hash table, for instance) instead of
a physical address. This allows the location of data in the page file
to change, even if outstanding transactions may have to roll back
changes made to that data. Clearly, for LLADD to be able to apply
logical log entries, the page file must be physically consistent,
ruling out use of logical logging for redo operations.

LLADD supports all three types of logging, and allows developers to
register new operations, which is the key to its extensibility. After
discussing LLADD's architecture, we will revisit this topic with a
concrete example.


\subsection{Concurrency and Aborted Transactions}

Section~\ref{sub:OperationProperties} states that LLADD does not
allow cascading aborts, implying that operation implementors must
protect transactions from any structural changes made to data structures
by uncomitted transactions, but LLADD does not provide any mechanisms
designed for long-term locking. However, one of LLADD's goals is to
make it easy to implement custom data structures for use within safe,
multi-threaded transactions. Clearly, an additional mechanism is needed.

The solution is to allow portions of an operation to ``commit'' before
the operation returns.\footnote{We considered the use of nested top actions, which LLADD could easily
support. However, we currently use the slightly simpler (and lighter-weight)
mechanism described here. If the need arises, we will add support
for nested top actions.}
An operation's wrapper is just a normal function, and therefore may
generate multiple log entries. First, it writes an undo-only entry
to the log. This entry will cause the \emph{logical} inverse of the
current operation to be performed at recovery or abort, must be idempotent,
and must fail gracefully if applied to a version of the database that
does not contain the results of the current operation. Also, it must
behave correctly even if an arbitrary number of intervening operations
are performed on the data structure.

Next, the operation writes one or more redo-only log entries that may perform structural
modifications to the data structure. These redo entries have the constraint that any prefix of them must leave the database in a consistent state, since only a prefix might execute before a crash.  This is not as hard as it sounds, and in fact the
$B^{LINK}$ tree~\cite{blink} is an example of a B-Tree implementation
that behaves in this way, while the linear hash table implementation
discussed in Section~\ref{sub:Linear-Hash-Table} is a scalable 
hash table that meets these constraints.

%[EAB: I still think there must be a way to log all of the redoes
%before any of the actions take place, thus ensuring that you can redo
%the whole thing if needed. Alternatively, we could pin a page until
%the set completes, in which case we know that that all of the records
%are in the log before any page is stolen.]

\subsection{Summary}

This section presented a relatively simple set of rules and patterns
that a developer must follow in order to implement a durable, transactional
and highly-concurrent data structure using LLADD:

\begin{itemize}
\item Pages should only be updated inside of a redo or undo function.
\item An update to a page should update the LSN. 
\item If the data read by the wrapper function must match the state of
the page that the redo function sees, then the wrapper should latch
the relevant data.
\item Redo operations should address pages by their physical offset,
while Undo operations should use a more permanent address (such as
index key) if the data may move between pages over time.
\item An undo operation must correctly update a data structure if any
prefix of its corresponding redo operations are applied to the
structure, and if any number of intervening operations are applied to
the structure.
\end{itemize}
Because undo and redo operations during normal operation and recovery
are similar, most bugs will be found with conventional testing
strategies.  It is difficult to verify the final property, although a
number of tools could be written to simulate various crash scenarios,
and check the behavior of operations under these scenarios.  Of course, 
such a tool could easily be applied to existing LLADD operations.

Note that the ARIES algorithm is extremely complex, and we have left
out most of the details needed to understand how ARIES works, or to 
implement it correctly.
Yet, we believe we have covered everything that a programmer needs
 to know in order to implement new data structures using the 
functionality that ARIES provides. This was possible due to the encapsulation
of the ARIES algorithm inside of LLADD, which is the feature that
most strongly differentiates LLADD from other, similar libraries.
We hope that this will increase the availability of transactional
data primitives to application developers.


\section{LLADD Architecture}

%
\begin{figure}
~~\includegraphics[%
  width=1.0\columnwidth]{LLADD-Arch3.pdf}

\caption{\label{cap:LLADD-Architecture}Simplified LLADD Architecture: The
core of the library places as few restrictions on the application's
data layout as possible. Custom {}``operations'' implement the client's
desired data layout. The separation of these two sets of modules makes
it easy to improve and customize LLADD.}
\end{figure}
LLADD is a toolkit for building transaction managers.
It provides user-defined redo and undo behavior, and has an extendible
logging system with 19 types of log entries so far (not counting those
internal to LLADD, such as ``begin'', ``abort'', and ``clr''). Most of these
extensions deal with data layout or modification, but some deal with
other aspects of LLADD, such as extensions to recovery semantics (Section
\ref{sub:Two-Phase-Commit}). LLADD comes with some default page layout
schemes, but allows its users to redefine this layout as is appropriate.
Currently LLADD imposes two requirements on page layouts. The first
32 bits must contain an LSN for recovery purposes,
and the second 32 bits must contain the page type (since we allow multple page formats).

Although it ships with basic operations that support variable-length
records, hash tables and other common data types, our goal is to
decouple all decisions regarding data format from the implementation
of the logging and recovery systems. Therefore, the preceeding section
is essentially documentation for users of the library, while
the purpose of the performance numbers in our evaluation section are
not to validate our hash table, but to show that the underlying architecture
is able to efficiently support interesting data structures.

Despite the complexity of the interactions among its modules, the
basic ARIES algorithm itself is quite simple. Therefore, in order to
keep LLADD simple, we started with a set of modules, and iteratively
refined the boundaries among these modules. Figure~\ref{cap:LLADD-Architecture} presents the resulting architecture.  The
core of the LLADD library is quite small at 2218 lines of code, 2155
lines of implementations of operations and other extensions, and 408
lines of installable header files.\footnote{These counts were generated using David
A. Wheeler's {\tt SLOCCount}.} The code has been documented extensively,
and we hope that we have exposed most of the subtle interactions
among internal modules in the online documentation.

As LLADD has evolved, many of its sub-systems have been incrementally
improved, and we believe that the current set of modules is amenable
to the addition of new functionality. For instance, the logging module
interface encapsulates all of the details regarding its on disk format,
which allows for some of the exotic logging and replication techniques mentioned above.
Similarly, the interface encodes the dependencies
between the logger and other subsystems.%
\footnote{For example, the buffer manager must ensure that the logger has forced the appropriate
log entries to disk before writing a dirty page to disk. Otherwise,
it would be impossible to undo the changes that had been made to the
page.%
}

The buffer manager is another potential area for extension.
Because the interface between the buffer manager and LLADD is simple,
we would like to support transactional access to resources beyond
simple page files. Some examples include transactional updates of
multiple files on disk, transactional groups of program executions
or network requests, or even leveraging some of the advances being
made in the Linux and other modern OS kernels. For example,
ReiserFS recently added support for atomic file-system operations.
This could be used to provide variable-sized pages
to LLADD.  We revisit these ideas when we discuss existing systems 
such as CVS and IMAP, although they are applicible in many other 
circumstances.

From the testing point of view, the advantage of LLADD's division
into subsystems with simple interfaces is obvious. We are able to
use standard unit-testing techniques to test each of LLADD's subsystems
independently, and have documented both external and internal interfaces,
making it easy to add new tests and debug old ones. Furthermore, by
adding a ``simulate crash'' operation to a few of the key components,
we can simulate application level crashes by clearing LLADD's internal
state, re-initializing the library and verifying that recovery was
successful. These tests currently cover approximately 
90\%\footnote{generated using ``gcov'', and ``lcov,''}
of the code. We have not yet developed a mechanism that models hardware failures, but plan to develop a test harness that verifies operation behavior in exceptional circumstances.

LLADD's performance requirements vary wildly depending on the workload
with which it is presented. Its performance on a large number of small,
sequential transactions will always be limited by the amount of time
required to flush a page to disk. To some extent, compact logical
and physiological log entries improve this situation. On the other
hand, long running transactions only rarely force-write to disk and
become CPU bound. Standard profiling techniques of the overall library's
performance and microbenchmarks of crucial modules handle such situations
nicely. 

Each module of LLADD is reentrant, and a
C preprocessor directive allows the entire library to be instrumented
in order to profile latching behavior, which aids in perfomance
tuning and debugging. A thread that is not involved in
an I/O request never needs to wait for a latch held by a thread that
is waiting for I/O.%
\footnote{Strictly speaking, this statement is only true for LLADD's core.
However, there are variants of most popular data structures that allow
us to preserve these invariants.%
}

There are a number of performance optimizations that are specific
to multithreaded operations that we do not perform. The most glaring
omission is log bundling; if multiple transactions commit at once,
LLADD must still force the log to disk once per transaction. This problem
is not fundamental, but simply has not been addressed by current code
base. Similarly, as page eviction requires a force-write if the
full ARIES recovery algorithm is in use, we could implement a thread
that asynchronously maintained a set of free buffer pages. We plan to 
implement such optimizations in the future.


\section{Sample Operations}

In order to validate LLADD's architecture, and to show that it simplifies
the creation of efficient data structures, we have have implemented
a number of simple extensions. In this section, we describe their
design, and provide some concrete examples of our experiences extending
LLADD.  We would like to emphasize that this discussion reflects a 
``worst case'' scenario; if LLADD extensions apprpriate for an application 
already exist, the process detailed in this section is unnecessary.  If an 
application does not require concurrent, multithreaded applications, then 
physical logging can be used, allowing for the extremly simple 
implementation of new operations.


\subsection{Linear Hash Table\label{sub:Linear-Hash-Table}}

Linear hash tables are hash tables that are able to extend their bucket
list incrementally at runtime. They work as follows. Imagine that
we want to double the size of a hash table of size $2^{n}$, and that
the hash table has been constructed with some hash function $h_{n}(x)=h(x)\, mod\,2^{n}$.
Choose $h_{n+1}(x)=h(x)\, mod\,2^{n+1}$ as the hash function for
the new table. Conceptually we are simply prepending a random bit
to the old value of the hash function, so all lower order bits remain
the same. At this point, we could simply block all concurrent access
and iterate over the entire hash table, reinserting values according
to the new hash function. 

However, because of the way we chose $h_{n+1}(x),$ we know that the
contents of each bucket, $m$, will be split betwen bucket $m$ and
bucket $m+2^{n}$. Therefore, if we keep track of the last bucket
that was split, we can split a few buckets at a time, resizing the
hash table without introducing long pauses while we reorganize the
hash table~\cite{lht}. We can handle overflow using standard techniques;
LLADD's linear hash table uses linked lists of overflow buckets.

The bucket list must be addressible as though it was an expandable array. We have implemented
this functionality as a separate module reusable by applications, but will not discuss it here.

For the purposes of comparison, we provide two linear hash implementations.
The first is straightforward, and is layered on top of LLADD's standard
record setting operation, Tset(), and therefore performs physical
undo. This implementation provided a stepping stone to the more sophisticated
version which employs logical undo, and uses an identical on-disk
layout. As we discussed earlier, logical undo provides more opportunities
for concurrency, while decreasing the size of log entries. In fact,
the physical-undo implementation of the linear hash table cannot support
concurrent transactions, while threads utilizing the logical-undo 
implementation never hold locks on more than two buckets.%
\footnote{However, only one thread may expand the hashtable at once.  In order to amortize the overhead of initiating an expansion, and to allow concurrent insertions, the hash table is expanded in increments of a few thousand buckets.}
We see some performance improvement due to logical logging in Section~\ref{sec:eval}.

\begin{figure}
\begin{center}
\includegraphics[%
  width=0.70\columnwidth]{LinkedList.pdf}
\end{center}

\caption{\label{cap:Linear-Hash-Table}Linear Hash Table Bucket operations.}
\end{figure}


From our point of view, the linked list management portion of the hash 
table algorithm is particularly iteresting.  It is straightforward in the
physical case, but must be performed in a specific order in the logical
case. See Figure \ref{cap:Linear-Hash-Table} for a sequence of steps
that safely implement the necessary linked list operations. Note that
in the first two cases, the portion of the linked list that is visible
from LLADD's point of view is always logically consistent. This is important
for crash recovery; it is possible that LLADD will crash before the
entire sequence of operations has been completed. The logging protocol
guarantees that some prefix of the log will be available. Therefore,
because the run-time version of the hash table is always consistent,
we know that the version of the hash table produced by the REDO phase 
of recovery will also be consistent.  Note that we have to worry about ordering because the buffer
manager only provides atomic updates of single pages, but our linked list may span pages.

The third case, where buckets are split as the bucket list is expanded,
is a bit more complicated. We must maintain consistency between two
linked lists, and a page at the begining of the hash table that contains
the last bucket that we successfully split. Here, we use the undo
entry to ensure proper crash recovery, not by undoing the split, but 
by actually redoing it; this is a perfectly valid ``undo'' strategy for some operations.
Our bucket split algorithm
is idempotent, so it may be applied an arbitrary number of times to
a given bucket with no ill-effects. Also note that in this case
there is not a good reason to undo a bucket split, so we can safely
apply the split whether or not the current transaction commits.

First, we write an ``undo'' record that checks the hash table's metadata and
redoes the split if necessary (this record has no effect 
unless we crash during this bucket split). Second, we write (and execute) a series
of redo-only records to the log. These encode the bucket split, and follow
the linked list protocols listed above. Finally, we write a redo-only
entry that updates the hash table's metadata.%
\footnote{Had we been using nested top actions, we would not need the special
undo entry, but we would need to store {\em physical} undo information for
each of the modifications made to the bucket, since any subset of the pages may have been stolen.%
}

We allow pointer aliasing at this step so that a given key can be
present for a short period of time in both buckets. If we crash before
the undo entry is written, no harm is done. If we crash after the
entire update makes it to log, the redo stage will set the hash's
metadata appropriately, and the undo record becomes a no-op. If
we crash in the middle of the bucket split, we know that the current
transaction did not commit, and that recovery will execute the undo
record. It will see that the bucket split is still pending and finish
splitting the bucket. Therefore, the hash table is correctly restored. 

Note that there is a point during the undo phase where the bucket
is in an inconsistent physical state.  Normally the redo phase
brings the page file to a fully consistent physical state.
We handle this by obtaining a lock on the bucket during normal
operation. This blocks any attempt to write log entries
that alter a bucket while it is being split.  Therefore, the log 
cannot contain any entries that will accidentally attempt to
access an inconsistent bucket.

Since the second implementation of the linear hash table uses logical
undo, we are able to allow concurrent updates to different portions
of the table. This is not true in the case of the implementation that
uses pure physical logging, as physical undo cannot generally tolerate
concurrent structural modifications to data structures.


\subsection{Two-Phase Commit\label{sub:Two-Phase-Commit}}

The two-phase commit protocol is used in clustering applications where
multiple, well maintained, well connected computers must agree upon
a set of successful transactions. Some of the systems could crash,
or the network could fail during operation, but we assume that such
failures are temporary. Two-phase commit designates a single computer
as the coordinator of a given transaction. This computer contacts
the other systems participating in the transaction, and asks them
to prepare to commit. If a subordinate system sees
that an error has occurred, or the transaction should be aborted for
some other reason, then it informs the coordinator. Otherwise, it
enters the \emph{prepared} state, and tells the coordinator that it
is ready to commit. At some point in the future the coordinator will
reply, telling the subordinate to commit or abort. From LLADD's point
of view, the interesting portion of this algorithm is the \emph{prepared}
state, since it must be able to commit a prepared transaction if it
crashes before the coordinator responds, but cannot commit before
hearing the response, since it may be asked to abort the transaction.

Implementing the prepare state on top of the ARIES algorithm constists
of writing a special log entry that informs the undo portion of the
recovery phase that it should stop rolling back the current transaction
and instead add it to the list of active transactions.%
\footnote{Also, any locks that the transaction obtained should be restored,
which is outside of the scope of LLADD, although a LLADD operation could 
easily implement this functionality on behalf of an external lock manager.%
} Due to LLADD's extendible logging system, and the simplicity
of its recovery code, it took an afternoon for a programmer to become familiar with LLADD's
architecture and add the prepare operation.  This implementation of prepare allows
LLADD to support applications that require two-phase commit.  A preliminary 
implementation of a cluster hash table that employs two-phase
commit is included in LLADD's CVS repository.

\subsection{Other Applications}

Previously, we mentioned a few systems that we think would benefit
from LLADD.  Here we sketch the process of implementing such
applications.  LRVM implements a transactional version of malloc() \cite{lrvm}.  It
employs the operating system's virtual memory system to generate page
faults if the application accesses a portion of memory that have not
been swapped in.  These page faults are intercepted and processed by a
transactional storage layer which loads the corresponding pages from
disk.  A few simple functions such as abort() and commit() are
provided to the application, and allow it to control the duration of
its transactions.  LLADD provides such a layer and the necessary
calls, reducing the LRVM implementation to an implementation of the
page fault handling code.  The performance of the transactional
storage system is crucial for this sort of application, and the
variable length, keyed access, and higher levels of abstraction
provided by existing libraries impose a severe performance penalty.  LLADD could easily
be extended so that it employs an appropriate on-disk structure that
provides efficient, offset based access to aligned, fixed length
blocks of data.  Furthermore, LRVM requires a set\_range() operation
that efficiently updates a range of a record, saving logging overhead.
All of these features could easily added to LLADD, providing a simple,
fast version of LRVM that would benefit from the infrastructure
surrounding LLADD.

CVS provides version control over large sets of files.  Multiple users
may concurrently update the repository of files, and CVS attempts to
merge conflicts, and maintain the consistency of the file tree.  By
adding the ability to perform file system manipulations to LLADD, we
could easily support applications with requirements similar to those
of CVS.  Furthermore, we could combine the file-system manipulation
with record-oriented storage to store application-level logs, and
other important metadata.  This would allow a single mechanism to
support applications such as CVS, simplifying fault tolerance, and
improving the scalibility of such applications.

IMAP is similar to CVS, but benefits further since it uses a simple,
folder-based locking protocol, which would be extremely easy to
implement using LLADD.

These last two examples highlight some of the potential advantages of
extending LLADD to manipulate the file system, although it is possible
that LLADD's page file would provide improved performance over the
file system, at the expense of the transparency
of file-system based storage mechanisms.

%[cite j2ee in next paragraph]

Another area of interest is in transactional serialization mechanisms
for programming languages.  Existing solutions are often complex, or
are layered on top of a relational database, or other system that uses
a data format that is different than the representation the
programming language uses.  J2EE implementations and the wide variety of 
other persistance mechanisms
available for Java provide a nice survey of the potential design
choices and tradeoffs.  Since LLADD can easily be adapted to an
application's desired data format, we believe that it is a good match
for such persistance mechanisms.

\section{\label{sec:eval} Performance}

We hope that the preceeding sections have given the reader an idea
of the usefulness and extensibility of the LLADD library. In this
section we focus on performance evaluation.

In order to evaluate the physical and logical hashtable implementations,
we first ran a test that inserts some tuples into the database. For
this test, we chose fixed-length (key, value) pairs of integers. For
simplicity, our hashtable implementations currently only support fixed-length
keys and values, so this this test puts us at a significant advantage.
It also provides an example of the type of workload that LLADD handles
well; LLADD is designed to support application
specific transactional data structures.  For comparison, we also ran 
``Record Number'' trials, named after the Berkeley DB access method.  
In this case, data is essentially stored in a large on-disk array.  This test provides a measurement of the speed of the 
lowest level primitive supported by Berkeley DB, and the corresponding LLADD extension. 

%
\begin{figure*}
\begin{center}
\includegraphics[%
  width=0.75\textwidth]{INSERT.pdf}
\end{center}

\caption{\label{cap:INSERTS}The final data points for LLADD's and Berkeley
DB's record number based storage are 7.4 and 9.5 seconds, respectively.
LLADD's hash table is significantly faster than Berkeley DB in this
test, but provides less functionality than the Berkeley DB hash. Finally,
the logical logging version of LLADD's hash table is faster than the
physical version, and handles the multi-threaded test well. The threaded
test spawned 200 threads and split its workload into 200 separate transactions.}
\end{figure*}

%
\begin{figure*}
\begin{center}
\includegraphics[%
  width=0.75\textwidth]{THREADS-sparse.pdf}
\end{center}
\caption{\label{cap:THREADS}The time required to perform a fixed
amount of processing, split across various numbers of threads.  This
test was run agains the highly concurrent Logical Logging version of
the linear hash table.  No significant performance degradation was
seen within the range measured.  The inserts were done in serial, and
the lookups were performed in parallel.}
\end{figure*}


The times included in Figure \ref{cap:INSERTS} include page file
and log creation, insertion of the tuples as a single transaction,
and a clean program shutdown. We used the ``transapp.cs'' program from
the Berkeley DB 4.2 tutorial to run the Berkeley DB tests, and hardcoded
it to use integers instead of strings. We used the Berkeley DB {}``DB\_HASH''
index type for the hashtable implementation, and {}``DB\_RECNO''
in order to run the {}``Record Number'' test.  

Since LLADD addresses records as \{Page, Slot, Size\} triples, which
is a lower level interface than Berkeley DB exports, we used the expandable
array that supports the hashtable implementation to run the {}``LLADD
Record Number'' test.

One should not look at Figure \ref{cap:INSERTS}, and conclude {}``LLADD
is almost five times faster than Berkeley DB,'' since we chose a
hash table implementation that is tuned for fixed-length data. Instead,
the conclusions we draw from this test are that, first, LLADD's primitive
operations are on par, perforance wise, with Berkeley DB's, which
we find very encouraging. Second, even a highly tuned implementation
of a ``simple'' general-purpose data structure is not without overhead,
and for applications where performance is important a special purpose
structure may be appropriate.

%Also, the multithreaded test run shows that the library is capable of
%handling a large number of threads. The performance degradation
%associated with running 200 concurrent threads was negligible.  Figure
%TODO expands upon this point by plotting the time taken for various
%numbers of threads to perform a total of 500,000 (TODO-CHECK) read operations.  The
%logical logging version of LLADD's hashtable outperformed the physical
The logical logging version of LLADD's hashtable outperformed the physical
logging version for two reasons.  First, since it writes fewer undo
records, it generates a smaller log file.  Second, in order to
emphasize the performance benefits of our extension mechanism, we use
lower level primitives for the logical logging version.  The logical
logging version implements locking at the bucket level, so many
mutexes that are acquired by LLADD's default mechanisms are redundant.
The physical logging version of the hashtable serves as a rough proxy
for an implementation on top of a non-extendible system.  Therefore,
it uses LLADD's default mechanisms, which include the redundant
acquisition of locks.

As a final note on our first performance graph, we would like to address
the fact that LLADD's hashtable curve is non-linear. LLADD currently
uses a fixed-size in-memory hashtable implementation in many areas,
and it is possible that we exceeded the fixed-size of this hashtable
on the larger test sets. Also, LLADD's buffer manager is currently
fixed size. Regardless of the cause of this non-linearity, we do not
believe that it is fundamental to our design.

The multithreaded test run in the first figure shows that the library
is capable of handling a large number of threads. The performance
degradation associated with running 200 concurrent threads was
negligible.  Figure~\ref{cap:THREADS} expands upon this point by plotting the time
taken for various numbers of threads to perform a total of 500,000
 read operations.  The performance of LLADD in this figure
is essentially flat, showing only a negligable slowdown up to 250
threads.  (Our test system prevented us from spawning more than 250
simultaneous threads, and we suspect that LLADD would easily scale to more than 250 threads.  This test was
performed on a uniprocessor machine, so we did not expect to see a
significant speedup when we moved from a single thread to multiple
threads. 

Unfortunately, when ran this test on a multi-processor machine, we saw
a degradation in performance instead of the expected speed up.
The problem seems to be the additional overhead incurred by
multi-threaded applications running on SMP machines under Linux 2.6,
as the single thread test spent a small amount of time in the Linux
kernel, while even the two-thread version of the test spent a
significant time in kernel code.  We suspect that the large number of
briefly-held latches that LLADD acquires caused this problem.  We plan
to investigate this problem further, adopting LLADD to a more advanced
threading package with user-level latches~\cite{capriccio}, or providing an ``SMP Mode''  compile-time option that
decreases the number of latches that LLADD acquires at the expense of
opportunities for concurrency.

\section{Future Work}

LLADD is an extendible implementation of the ARIES algorithm. This
allows application developers to incorporate transactional recovery
into a wide range of systems. We have a few ideas along these lines,
and also have some ideas for extensions to LLADD itself.

LLADD currently relies upon its buffer manager for page-oriented storage.
Although we did not have space to discuss it in this paper, we have
a blob implementation that stores large data outside of the page file.
This concept could be extended to arbitrary primitives, such as transactional
updates to file system directory trees, or integration of networking
or other operations directly into LLADD transactions. Doing this would
allow LLADD to act as a sort of ``glue code'' among various systems,
ensuring data integrity and adding database-style functionality, such
as continuous backup to systems that currently do not provide such
mechanisms. We believe that there is quite a bit of room for the developement
of new software systems in the space between the high-level, but sometimes
inappropriate interfaces exported by existing transactiona storage systems, 
and the unsafe, low-level primitives provided supported by current file systems.

Currently, although we have implemented a two-phase commit algorithm,
LLADD really is not very network aware. If we provided a clean abstraction
that allowed LLADD extensions and operations to cross network boundaries,
then we could provide a wider range of network consistency algorithms,
and cleanly support the implementation of operations that perform
well in networked and in local environments.

Although LLADD is re-entrant, its latching mechanisms only provide physical
consistency. Traditionally, lock managers, which provide higher levels
of consistency, have been tightly coupled with transactional page implementations.
Generally, the semantics of undo and redo operations provided by the
transactional page layer and its associated data structures determine
the level of concurrency that is possible. Since prior systems provide
a monolithic set of primitives to their users, these systems typically had complex interactions among the lock manager, on-disk formats and the transactional
page layer. Due to the clean interfaces that LLADD provides between on-disk formats and its transactional page layer, and because of its extensible log entries, the implementation of general purpose, modular lock managers on top of LLADD seems to be straightforward.  We plan to investigate this in the future, as it would provide significant opportunities for code reuse, and for the implementation of extremely flexible transactional systems.

\section{Conclusion}

We have outlined the design and implementation of a library for the
development of transactional storage systems.  By decoupling the
on-disk format from the transactional storage system, we provide
applications with customizable, high-performance, transactional
storage.  By summarizing and documenting the interactions between
these customizations and the storage system, we make it easy to
implement such customizations.  

Current applications generally must choose between high-level,
general-purpose libraries that impose severe performance penalties,
and ad-hoc ``from scratch'' atomicity and durability mechanisms.  By
bridging this gap, allowing applications to make use of high-level,
efficient, and special-purpose transactional storage, we hope to make
it easy to implement efficient systems that make use of specialized, reliable storage mechanisms.  Today, such applications typically have to choose between efficiency, reliable storage, and ease of development.  As a result such applications are often complext, or fail to meet their users requirements.

By releasing LLADD to the community, we hope that we will be able to
provide a toolkit that aids in the development of real-world
applications, and is flexible enough for use as a research platform.

Because of the interface between operation extensions and the
underlying implementation of the ARIES algorithm, we allow operation
extensions and the implementation of the library to evolve
independently, allowing applications to adopt to advanced replication
techniques as the circumstances in which they are deployed changes.

\section{Acknowledgements}

We would like to thank Jason Bayer, Jim Blomo and Jimmy
Kittiyachavalit for their implementation work and contributions to
earlier versions of LLADD.  Joe Hellerstein and Mike Franlin provided
us with invaluable advice.  Rob von Behren provided us with some last
minute assistance during the benchmarking process.

\section{Availability}

LLADD is free software, available at:

\begin{center}
{\tt http://www.sourceforge.net/projects/lladd}\\
\end{center}

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\end{document}