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<head>
<title>Challenges in Embedded Database System Administration</title>
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<body bgcolor=white>
<center>
<h1>Challenges in Embedded Database System Administration</h1>
<h3>Margo Seltzer, Harvard University</h3>
<h3>Michael Olson, Sleepycat Software, Inc.</h3>
<em>{margo,mao}@sleepycat.com</em>
</center>
<p>
Database configuration and maintenance have historically been complex tasks,
often
requiring expert knowledge of database design and application
behavior.
In an embedded environment, it is not feasible to require such
expertise and ongoing database maintenance.
This paper discusses the database administration
challenges posed by embedded systems and describes how the
Berkeley DB architecture addresses these challenges.

<h2>1. Introduction</h2>

Embedded systems provide a combination of opportunities and challenges
in application and system configuration and management.
As an embedded system is most often dedicated to a single application or
small set of tasks, the operating conditions of the system are
typically better understood than those of general purpose computing
environments.
Similarly, as embedded systems are dedicated to a small set of tasks,
one would expect that the software to manage them should be small
and simple.
On the other hand, once an embedded system is deployed, it must
continue to function without interruption and without administrator
intervention.
<p>
Database administration consists of two components,
initial configuration and ongoing maintenance.
Initial configuration consists of database design, manifestation,
and tuning.
The instantiation of the design includes decomposing the design
into tables, relations, or objects and designating proper indices
and their implementations (e.g., Btrees, hash tables, etc.).
Tuning a design requires selecting a location for the log and
data files, selecting appropriate database page sizes, specifying
the size of in-memory caches, and specifying the limits of 
multi-threading and concurrency.
As embedded systems define a specific environment and set of tasks,
requiring expertise during the initial system
configuration process is acceptable, and we focus our efforts on
the ongoing maintenance of the system.
In this way, our emphasis differs from other projects such as
Microsoft's AutoAdmin project <a href="#Chaud982">[3]</a>, and the "no-knobs"
administration that is identified as an area of important future
research by the Asilomar authors<a href="#Bern98">[1]</a>.
<p> 
In this paper, we focus on what the authors
of the Asilomar report call "gizmo" databases <a href="#Bern98"> [1]</a>,
databases
that reside in devices such as smart cards, toasters, or telephones.
The key characteristics of such databases are that their
functionality is completely transparent to users, no one ever
performs explicit database operations or
database maintenance, the database may crash at any time and
must recover instantly, the device may undergo a hard reset at
any time, requiring that the database return to its initial
state, and the semantic integrity of the database must be maintained
at all times.
In Section 2, we provide more detail on the sorts of tasks
typically performed by database administrators (DBAs) that must
be automated in an embedded system.
<p>
The rest of this paper is structured as follows.
In Section 2, we outline the requirements for embedded database support.
In Section 3, we discuss how Berkeley DB
is conducive to the hands-off management
required in embedded systems.
In Section 4, we discuss novel features that 
enhance Berkeley
DB's suitability for the embedded applications.
In Section 5, we discuss issues of footprint size.
In Section 6 we discuss related work, and we conclude
in Section 7.

<h2>2. Embedded Database Requirements</h2>
Historically, much of the commercial database industry has been driven
by the requirements of high performance online transaction
processing (OLTP), complex query processing, and the industry
standard benchmarks that have emerged (e.g., TPC-C <a href="#TPCC">[9]</a>,
TPC-D <a href="#TPCD">[10]</a>) to
allow for system comparisons.
As embedded systems typically perform fairly simple queries,
such metrics are not nearly as relevant for embedded database
systems as are ease of maintenance, robustness, and small footprint.
Of these three requirements, robustness and ease of maintenance
are the key issues.
Users must trust the data stored in their devices and must not need
to manually perform anything resembling system administration in order
to get their unit to work properly.
Fortunately, ease of use and robustness are important side
effects of simplicity and good design.
These, in turn, lead to a small size, providing the third
requirement of an embedded system.
<h3>2.1 The User Perspective</h3>
<p>
In the embedded database arena, it is the ongoing maintenance tasks
that must be automated, not necessarily the initial system configuration.
There are five tasks
that are traditionally performed by DBAs,
but must be performed automatically
in embedded database systems.
These tasks are
log archival and reclamation,
backup,
data compaction/reorganization,
automatic and rapid recovery, and
reinitialization from scratch.
<P>
Log archival and backup are tightly coupled.
Database backups are part of any
large database installation, and log archival is analogous to incremental
backup.
It is not clear what the implications of backup and archival are in
an embedded system.
Consumers do not back up their VCRs or refrigerators, yet they do
(or should) back up their personal computers or personal digital
assistants.
For the remainder of this paper, we assume that backups, in some form,
are required for gizmo databases (imagine having to reprogram, manually,
the television viewing access pattern learned by some set-top television
systems today).
Furthermore, we require that those backups are nearly instantaneous or
completely transparent,
as users should not be aware that their gizmos are being backed up
and should not have to explicitly initiate such backups.
<p>
Data compaction or reorganization has traditionally required periodic
dumping and restoration of
database tables and the recreation of indices.
In an embedded system, such reorganization must happen automatically.
<p>
Recovery issues are similar in embedded and traditional environments
with a few exceptions.
While a few seconds or even a minute recovery is acceptable
for a large server installation, no one is willing to wait
for their telephone or television to reboot.
As with archival, recovery must be nearly instantaneous in an embedded product.
Secondly, it is often the case that a system will be completely
reinitialized, rather than simply rebooted.
In this case, the embedded database must be restored to its initial
state, freeing all its resources.
This is not typically a requirement of large server systems.
<h3>2.2 The Developer Perspective</h3>
<p>
In addition to the maintenance-free operation required of the
embedded systems, there are a number of requirements that fall
out of the constrained resources typically found in the "gizmos"
using gizmo databases. These requirements are:
small footprint,
short code-path,
programmatic interface for tight application coupling and
to avoid the overhead (in both time and size) of 
interfaces such as SQL and ODBC,
application configurability and flexibility,
support for complete memory-resident operation (e.g., these systems
must run on gizmos without file systems), and
support for multi-threading.
<p>
A small footprint and short code-path are self-explanatory, however
what is not as obvious is that the programmatic interface requirement
is the logical result of them.
Traditional interfaces such as ODBC and SQL add significant
size overhead and frequently add multiple context/thread switches
per operation, not to mention several IPC calls.
An embedded product is less likely to require the complex
query processing that SQL enables.
Instead, in the embedded space, the ability for an application
to configure the database for the specific tasks in question
is more important than a general query interface.
<p>
As some systems do not provide storage other than RAM and ROM,
it is essential that an embedded database work seemlessly
in memory-only environments.
Similarly, many of today's embedded operating systems provide a
single address space architecture, so a simple, multi-threaded
capability is essential for application requiring any concurrency.
<p>
In general, embedded applications run on gizmos whose native
operating system support varies tremendously.
For example, the embedded OS may or may
not support user-level processing or multi-threading.
Even if it does, a particular embedded
application may or may not need it.
Not all applications need more than one thread of control.
An embedded database must provide mechanisms to developers
without deciding policy.
For example, the threading model in an application is a matter of policy,
and depends
not on the database software, but on the hardware, operating
system, and the application's feature set.
Therefore, the data manager must provide for the use of multi-threading,
but not require it.

<h2>3. Berkeley DB: A Database for Embedded Systems</h2>
Berkeley DB is the result of implementing database functionality 
using the UNIX tool-based philosophy.
The current Berkeley DB package, as distributed by Sleepycat
Software, is a descendant of the hash and btree access methods
distributed with 4.4BSD and its descendents.
The original package (referred to as DB-1.85),
while intended as a public domain replacement for dbm and
its followers (e.g., ndbm, gdbm, etc), rapidly became widely
used as an efficient, easy-to-use data store. 
It was incorporated into a number of Open Source packages including
Perl, Sendmail, Kerberos, and the GNU C-library.
<p>
Versions 2.X and higher are distributed by Sleepycat Software and
add functionality for concurrency, logging, transactions, and
recovery.
Each piece of additional functionality is implemented as an independent
module, which means that the subsystems can be used outside the
context of Berkeley DB.  For example, the locking subsystem can
easily be used to implement locking for a non-DB application and
the shared memory buffer pool can be used for any application
caching data in main memory.
This subsystem design allows a designer to pick and choose
the functionality necessary for the application, minimizing
memory footprint and maximizing performance.
This addresses the small footprint and short code-path criteria
mentioned in the previous section.
<p>
As Berkeley DB grew out of a replacement for dbm, its primary
implementation language has always been C and its interface has
been programmatic.  The C interface is the native interface,
unlike many database systems where the programmatic API is simply
a layer on top of an already-costly query interface (e.g. embedded
SQL).
Berkeley DB's heritage is also apparent in its data model; it has
none.
The database stores unstructured key/data pairs, specified as
variable length byte strings.
This leaves schema design and representation issues the responsibility
of the application, which is ideal for an embedded environment.
Applications retain full control over specification of their data
types, representation, index values, and index relationships.
In other words, Berkeley DB provides a robust, high-performance,
keyed storage system, not a particular database management system.
We have designed for simplicity and performance, trading off
complex, general purpose support that is better encapsulated in
applications.
<p>
Another element of Berkeley DB's programmatic interface is its
customizability; applications can specify Btree comparison and
prefix compression functions, hash functions, error routines,
and recovery models.
This means that embedded applications can tailor the underlying
database to best suit their data demands.
Similarly, the utilities traditionally bundled with a database
manager (e.g., recovery, dump/restore, archive) are implemented
as tiny wrapper programs around library routines.  This means
that it is not necessary to run separate applications for the
utilities.  Instead, independent threads can act as utility
daemons, or regular query threads can perform utility functions.
Many of the current products built on Berkeley DB are bundled as
a single large server with independent threads that perform functions
such as checkpoint, deadlock detection, and performance monitoring.
<p>
As mentioned earlier, living in an embedded environment requires
flexible management of storage.
Berkeley DB does not require any preallocation of disk space
for log or data files.
While many commercial database systems take complete control
of a raw device, Berkeley DB uses a normal file system, and
can therefore, safely and easily share a data space with other
programs.
All databases and log files are native files of the host environment,
so whatever utilities are provided by the environment can be used
to manage database files as well.
<p>
Berkeley DB provides three different memory models for its
management of shared information.
Applications can use the IEEE Std 1003.1b-1993 (POSIX) <tt>mmap</tt>
interface to share
data, they can use system shared memory, as frequently provided
by the shmget family of interfaces, or they can use per-process
heap memory (e.g., malloc).
Applications that require no permanent storage and do not provide
shared memory facilities can still use Berkeley DB by requesting
strictly private memory and specifying that all databases be
memory-resident.
This provides pure-memory operation.
<p>
Lastly, Berkeley DB is designed for rapid startup -- recovery can
happen automatically as part of system initialization.
This means that Berkeley DB works correctly in environments where
gizmos are suddenly shut down and restarted.

<h2>4. Extensions for Embedded Environments </h2>
While the Berkeley DB library has been designed for use in
embedded systems, all the features described above are useful
in more conventional systems as well.
In this section, we discuss a number of features and "automatic
knobs" that are specifically geared
toward the more constrained environments found in gizmo databases.

<h3>4.1 Automatic compression</h3>
Following the programmatic interface design philosophy, we 
support application-specific (or default) compression routines.
These can be geared toward the particular data types present
in the application's dataset, thus providing better compression
than a general purpose routine.
Note that the application could instead specify an encryption
function and create encrypted databases instead of compressed ones.
Alternately, the application might specify a function that performs
both compression and encryption.
<p>
As applications are also permitted to specify comparison and hash
functions, the application can chose to organize its data based
either on uncompressed and clear-text data or compressed and encrypted
data.
If the application indicates that data should be compared in its
processed form (i.e., compressed and encrypted), then the compression
and encryption are performed on individual data items and the in-memory
representation retains these characteristics.
However, if the application indicates that data should be compared in
its original form, then entire pages are transformed upon being read
into or written out of the main memory buffer cache.
These two alternatives provide the flexibility to trade space
and security for performance.

<h3>4.2 In-memory logging & transactions</h3>
One of the four key properties of transaction systems is durability.
This means that transaction systems are designed for permanent storage
(most commonly disk).  However, as mentioned above, embedded systems
do not necessarily contain any such storage.
Nevertheless, transactions can be useful in this environment to
preserve the semantic integrity of the underlying storage.
Berkeley DB optionally provides logging functionality and
transaction support regardless of whether the database and logs
are on disk or in memory.

<h3>4.3 Remote Logs</h3>
While we do not expect users to backup their television sets and
toasters, it is conceivable that a set-top box provided by a
cable carrier should, in fact, be backed up by that cable carrier.
The ability to store logs remotely can provide "information appliance"
functionality, and can also be used in conjunction with local logs
to enhance reliability.
Furthermore, remote logs provide for catastrophic recovery, e.g., loss
of the gizmo, destruction of the gizmo, etc.

<h3>4.4 Application References to Database Buffers</h3>

Typically, when data is returned to the user, it must be copied
from the data manager's buffer cache (or data page) into the
application's memory.
However, in an embedded environment, the robustness of the
total software package is of paramount importance, not the
isolation between the application and the data manager.
As a result, it is possible for the data manager to avoid
copies by giving applications direct references to data items
in a shared memory cache.
This is a significant performance optimization that can be
allowed when the application and data manager are tightly
integrated.

<h3>4.5 Recoverable database creation/deletion</h3>

In a conventional database management system, the creation of
database tables (relations) and indices are heavyweight operations
that are not recoverable.
This is not acceptable in a complex embedded environment where
instantaneous recovery and robust operation in the face of
all types of database operations is essential.
While Berkeley DB files can be removed using normal file system
utilities, we provide transaction protected utilities that
allow us to recover both database creation and deletion.

<h3>4.6 Adaptive concurrency control</h3>
The Berkeley DB package uses page-level locking by default.
This trades off fine grain concurrency control for simplicity
during recovery. (Finer grain concurrency control can be
obtained by reducing the page size in the database.)
However, when multiple threads/processes perform page-locking
in the presence of writing operations, there is the
potential for deadlock.
As some environments do not need or desire the overhead of
logging and transactions, it is important to provide the
ability for concurrent access without the potential for
deadlock.
<p>
Berkeley DB provides an option to perform coarser grain,
deadlock-free locking.
Rather than locking on pages, locking is performed at the
interface to the database.
Multiple readers or a single writer are allowed to be
active in the database at any instant in time, with
conflicting requests queued automatically.
The presence of cursors, through which applications can both
read and write data, complicates this design.
If a cursor is currently being used for reading, but will later
be used to write, the system will be deadlock prone if no
special precautions are taken.
To handle this situation, we require that, when a cursor is
created, the application specify any future intention to write.
If there is an intention to write, the cursor is granted an
intention-to-write lock which does not conflict with readers,
but does conflict with other intention-to-write locks and write
locks.
The end result is that the application is limited to a single
potentially writing cursor accessing the database at any point
in time.
<p>
Under periods of low contention (but potentially high throughput),
the normal page-level locking provides the best overall throughput.
However, as contention rises, so does the potential for deadlock.
As some cross-over point, switching to the less concurrent, but
deadlock-free locking protocol will result in higher throughput
as operations must never be retried.
Given the operating conditions of an embedded database manager,
it is useful to make this change automatically as the system
itself detects high contention.

<h3>4.7 Adaptive synchronization</h3>

In addition to the logical locks that protect the integrity of the
database pages, Berkeley DB must synchronize access to shared memory
data structures, such as the lock table, in-memory buffer pool, and
in-memory log buffer.
Each independent module uses a single mutex to protect its shared
data structures, under the assumption that operations that require
the mutex are very short and the potential for conflict is
low.
Unfortunately, in highly concurrent environments with multiple processors
present, this assumption is not always true.
When this assumption becomes invalid (that is, we observe significant
contention for the subsystem mutexes), we can switch over to a finer-grained
concurrency model for the mutexes.
Once again, there is a performance trade-off.  Fine-grain mutexes
impose a penalty of approximately 25% (due to the increased number
of mutexes required for each operation), but allow for higher throughput.
Using fine-grain mutexes under low contention would cause a decrease
in performance, so it is important to monitor the system carefully,
so that the change can be executed only when it will increase system
throughput without jeopardizing latency.

<h2>5. Footprint of an Embedded System</h2>
While traditional systems compete on price-performance, the
embedded players will compete on price, features, and footprint.
The earlier sections have focused on features; in this section
we focus on footprint.
<p>
Oracle reports that Oracle Lite 3.0 requires 350 KB to 750 KB
of memory and approximately 2.5 MB of hard disk space <a href="#Oracle">[7]</a>.
This includes drivers for interfaces such as ODBC and JDBC.
In contrast, Berkeley DB ranges in size from 75 KB to under 200 KB,
foregoing heavyweight interfaces such as ODBC and JDBC and
providing a variety of deployed sizes that can be used depending
on application needs.  At the low end, applications requiring
a simple single-user access method can choose from either extended
linear hashing, B+ trees, or record-number based retrieval and
pay only the 75 KB space requirement.
Applications requiring all three access methods will observe the
110 KB footprint.
At the high end, a fully recoverable, high-performance system
occupies less than a quarter megabyte of memory.
This is a system you can easily incorporate in your toaster oven.
Table 1 shows the per-module break down of the entire Berkeley DB
library.  Note that this does not include memory used to cache database
pages.

<table border>
<tr><th colspan=4>Object sizes in bytes</th></tr>
<tr><th align=left>Subsystem</th><th align=center>Text</th><th align=center>Data</th><th align=center>Bss</th></tr>
<tr><td>Btree-specific routines</td><td align=right>28812</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td>Recno-specific routines</td><td align=right>7211</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td>Hash-specific routines</td><td align=right>23742</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td colspan=4></td></tr>
<tr><td>Memory Pool</td><td align=right>14535</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td>Access method common code</td><td align=right>23252</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td>OS compatibility library</td><td align=right>4980</td><td align=right>52</td><td align=right>0</td></tr>
<tr><td>Support utilities</td><td align=right>6165</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td colspan=4></td></tr>
<tr><th>All modules for Btree access method only</th><td align=right>77744</td><td align=right>52</td><td align=right>0</td></tr>
<tr><th>All modules for Recno access method only</th><td align=right>84955</td><td align=right>52</td><td align=right>0</td></tr>
<tr><th>All modules for Hash access method only</th><td align=right>72674</td><td align=right>52</td><td align=right>0</td></tr>
<tr><td colspan=4></td></tr>
<tr><th align=left>All Access Methods</th><td align=right>108697</td><td align=right>52</td><td align=right>0</td></tr>
<tr><td colspan=4><br></td></tr>
<tr><td>Locking</td><td align=right>12533</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td colspan=4></td></tr>
<tr><td>Recovery</td><td align=right>26948</td><td align=right>8</td><td align=right>4</td></tr>
<tr><td>Logging</td><td align=right>37367</td><td align=right>0</td><td align=right>0</td></tr>
<tr><td colspan=4></td></tr>
<tr><th align=left>Full Package</th><td align=right>185545</td><td align=right>60</td><td align=right>4</td></tr>
<tr><br></tr>
</table>

<h2>6. Related Work</h2>

Every three to five years, leading researchers in the database
community convene to identify future directions in database
research.
They produce a report of this meeting, named for the year and
location of the meeting.
The most recent of these reports, the 1998 Asilomar report,
identifies the embedded database market as one of the
high growth areas in database research <a href="#Bern98">[1]</a>.
Not surprisingly, market analysts identify the embedded database
market as a high-growth area in the commercial sector as well <a href="#Host98">
[5]</a>.
<p>
The Asilomar report identifies a new class of database applications, which they
term "gizmo" databases, small databases embedded in tiny mobile
appliances, e.g., smart-cards, telephones, personal digital assistants.
Such databases must be self-managing, secure and reliable.
Thus, the idea is that gizmo databases require plug and play data
management with no database administrator (DBA), no human settable
parameters, and the ability to adapt to changing conditions.
More specifically, the Asilomar authors claim that the goal is
self-tuning, including defining the physical DB design, the
logical DB design, and automatic reports and utilities <a href="#Bern98">[1]</a>
To date,
few researchers have accepted this challenge, and there is a dearth
of research literature on the subject.
<p>
Our approach to embedded database administration is fundamentally
different than that described by the Asilomar authors.
We adopt their terminology, but view the challenge in supporting
gizmo databases to be that of self-sustenance <em>after</em> initial
deployment.  Therefore, we find it, not only acceptable, but
desirable to assume that application developers control initial
database design and configuration.  To the best of our knowledge,
none of the published work in this area addresses this approach.
<p>
As the research community has not provided guidance in this
arena, most work in embedded database administration has fallen
to the commercial vendors.
These vendors fall into two camps, companies selling databases
specifically designed for embedding or programmatic access
and the major database vendors (e.g., Oracle, Informix, Sybase).
<p>
The embedded vendors all acknowledge the need for automatic
administration, but fail to identify precisely how their
products actually accomplish this.
A notable exception is Interbase whose white paper
comparison with Sybase and Microsoft's SQL servers
explicitly address features of maintenance ease.
Interbase claims that as they use no log files, there is
no need for log reclamation, checkpoint tuning, or other
tasks associated with log management.  However, Interbase
uses Transaction Information Pages, and it is unclear
how these are reused or reclaimed <a href="#Interbase">[6]</a>.
Additionally, with a log-free system, they must use
a FORCE policy (write all pages to disk at commit),
as defined by Haerder and Reuter <a href="#Haerder">[4]</a>.  This has
serious performance consequences for disk-based systems.
The approach described in this paper does use logs and
therefore requires log reclamation,
but provides hooks so the application may reclaim logs
safely and programmatically.
While Berkeley DB does require checkpoints, the goal of
tuning the checkpoint interval is to bound recovery time.
Since the checkpoint interval in Berkeley DB can be expressed
by the amount of log data written, it requires no tuning.
The application designer sets a target recovery time, and
selects the amount of log data that can be read in that interval
and specifies the checkpoint interval appropriately.  Even as
load changes, the time to recover does not.
<p>
The backup approaches taken by Interbase and Berkeley DB
are similar in that they both allow online backup, but
rather different in their affect on transactions running
during backup.  As Interbase performs backups as transactions
<a href="#Interbase">[6]</a>, concurrent queries can suffer potentially long
delays.  Berkeley DB uses native operating system system utilities
and recovery for backups, so there is no interference with
concurrent activity, other than potential contention on disk
arms.
<p>
There are a number of database vendors selling in
the embedded market (e.g., Raima, 
Centura, Pervasive, Faircom), but none highlight
the special requirements of embedded database
applications.
On the other end of the spectrum, the major vendors,
Oracle, Sybase, Microsoft, are all becoming convinced
of the importance of the embedded market.
As mentioned earlier, Oracle has announced its
Oracle Lite server for embedded use.
Sybase has announced its UltraLite platform for "application-optimized,
high-performance, SQL database engine for professional
application developers building solutions for mobile and embedded platforms."
<a href="#Sybase">[8]</a>.
We believe that SQL is incompatible with the
gizmo database environment or truly embedded systems for which Berkeley
DB is most suitable.
Microsoft research is taking a different approach, developing
technology to assist in automating initial database design and
index specification <a href="#Chaud98">[2]</a><a href="#Chaud982">[3]</a>.
As mentioned earlier, we believe that such configuration is, not only
acceptable in the embedded market, but desirable so that applications
can tune their database management for the target environment.
<h2>7. Conclusions</h2>
The coming wave of embedded systems poses a new set of challenges
for data management.
The traditional server-based, big footprint systems designed for
high performance on big iron are not the right approach in this
environment.
Instead, application developers need small, fast, versatile systems
that can be tailored to a specific environment.
In this paper, we have identified several of the key issues in
providing these systems and shown how Berkeley DB provides
many of the characteristics necessary for such applications.

<h2>8. References</h2>
<p>
[1] <a name="Bern98"> Bernstein, P., Brodie, M., Ceri, S., DeWitt, D., Franklin, M.,
Garcia-Molina, H., Gray, J., Held, J., Hellerstein, J.,
Jagadish, H., Lesk, M., Maier, D., Naughton, J.,
Pirahesh, H., Stonebraker, M., Ullman, J.,
"The Asilomar Report on Database Research,"
SIGMOD Record 27(4): 74-80, 1998.
</a>
<p>
[2] <a name="Chaud98"> Chaudhuri, S., Narasayya, V.,
"AutoAdmin 'What-If' Index Analysis Utility,"
<em>Proceedings of the ACM SIGMOD Conference</em>, Seattle, 1998.
</a>
<p>
[3] <a name="Chaud982"> Chaudhuri, S., Narasayya, V.,
"An Efficient, Cost-Driver Index Selection Tool for Microsoft SQL Server,"
<em>Proceedings of the 23rd VLDB Conference</em>, Athens, Greece, 1997.
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<p>
[4] <a name="Harder"> Haerder, T., Reuter, A.,
"Principles of Transaction-Oriented Database Recovery,"
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</a>
<p>
[5] <a name="Host98"> Hostetler, M., "Cover Is Off A New Type of Database,"
Embedded DB News,
http://www.theadvisors.com/embeddeddbnews.htm,
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</a>
<p>
[6] <a name="Interbase"> Interbase, "A Comparison of Borland InterBase 4.0
Sybase SQL Server and Microsoft SQL Server,"
http://web.interbase.com/products/doc_info_f.html.
</a>
<p>
[7] <a name="Oracle"> Oracle, "Oracle Delivers New Server, Application Suite
to Power the Web for Mission-Critical Business,"
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</a>
<p>
[8] <a name="Sybase"> Sybase, Sybase UltraLite, http://www.sybase.com/products/ultralite/beta.
</a>
<p>
[9] <a name="TPCC"> Transaction Processing Council, "TPC-C Benchmark Specification,
Version 3.4," San Jose, CA, August 1998.
</a>
<p>
[10] <a name="TPCD"> Transaction Processing Council, "TPC-D Benchmark Specification,
Version 2.1," San Jose, CA, April 1999.
</a>
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