Hard Disk Storage and Basic File Structure

Introduction
The glorious history of the Hard Disk Drive begins back in 1956 when IBM introduced
RAMAC [LH57], a magnetic storage media with an enormous, for the time, total capacity
of 5 megabytes. Fifty years since the ¯rst commercial drive, the disk drive is the prevailing
storage media in almost every computer system to date. Surprisingly, despite the signi¯-
cant technological improvements in storage capacity and operational performance, modern
disk drives are based on the same physical and electromagnetic properties that were ¯rst
introduced by the RAMAC.www.laptoonz.com
In this chapter we perform a gentle introduction to the most important properties of
storage media in general and disk drives in particular, and the way information is organized
internally in these storage devices. Data-oriented applications such as Database Manage-
ment Systems (DBMSs) are based on the low-level organization of data to create more
complex constructs, like ¯les and database relations. Therefore, the low-level operational
characteristics of storage devices play an important role on the design of complex data man-
agement software and has a direct impact on performance. The importance of the low-level
properties of storage media is compounded by the wide variety of storage media present in
the memory hierarchy of a computer system that have diverse characteristics in terms of
access speed and storage capacity.
0-8493-8597-0/01/$0.00+$1.50
°c 2007 by CRC Press, LLC 19-1
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Furthermore, we examine in more detail hard disk drives and the higher-level disk based
organization of data that has been adopted by modern DBMSs into ¯les, pages, and records.
We look at a variety of data storage strategies that enable e±cient handling of processing
tasks that require data to be moved across the various levels of the memory hierarchy
continuously, and are, hence, greatly a®ected by the access speed of hard drives that is
orders of magnitude slower than main memory.
Despite the technological improvements achieved today in single-disk storage systems, the
disk is still a serious performance bottleneck in I/O-dominated applications. An important
direction towards alleviating this bottleneck is to exploit several disk drives concurrently,
enabling parallelism when possible. Towards this goal, the disk array has been introduced,
which uses multiple disk devices to store information. Since by increasing the number of disk
devices, disk failures may occur at much higher rates, the disk array needs to be enhanced
with appropriate mechanisms towards fault tolerance in order to increase data availability.
We also discuss in detail various techniques for guaranteeing various levels of fault tolerance
in disk arrays.
Finally, we present the three prevailing technologies for connecting hard disk drives and
other secondary and tertiary storage devices to a computer system. In particular, we elab-
orate on the di®erences between directly attaching storage devices to a computer system
versus accessing information over the network using network attached storage.
The rest of the chapter is organized as follows. Section 19.2 brie°y describes the char-
acteristics of storage media and illustrates the memory hierarchy that modern computer
systems use for processing information. Section 19.3 focuses on the hard disk drive and
discusses its basic operational characteristics and properties. Data are organized by means
of records and ¯les. These issues are discussed in Section 19.4, whereas bu®ering is studied
in Section 19.5. Next, we discuss e±ciency and data availability issues and we give the
most important properties of disk arrays in Section 19.6. Section 19.7 brie°y illustrates the
prevailing approaches of connecting storage devices to computer systems. Finally, Section
19.8 concludes the chapter.
19.2 Storage Media
With the introduction of the stored-program computer, ¯rst detailed by John Von Neu-
mann in 1945, came the advent of modern general purpose computers. The Von Neumann
architecture, as it is commonly referred to, ties the central processing unit with a storage
structure | the computer's memory | that stores both data and program instructions,
implementing, as a consequence, a computer that is a Universal Turing Machine. Hence,
memory became an inseparable component of almost every computer system to date. Cur-
rent state-of-the-art computers utilize a memory hierarchy consisting of various types of
storage devices, each of which has characteristics that make it suitable for a wide range of
applications.
Computer memory can be classi¯ed into various categories, according to its read/write
capabilities, support for random or sequential access, volatility, capacity, access speed, cost
per byte, portability, longevity, reliability, and many other facets. Memory is also classi¯ed
according to its proximity to the central processing unit, more speci¯cally into primary,
secondary and tertiary storage.
Intuitively, for performance considerations, the closer the memory is to the CPU the
faster the data access speed that needs to be supported. As data is stored further away
from the processor, capacity rather then speed plays a more important role and, secondarily,
cost and longevity. With today's technology, faster and more reliable memories that are
Disk Storage and Basic File Structure 19-3
TertiarySecondaryPriOmpatriycal DisHkMFasTrl a(dRaaiDs nCepDhV geaM iMi csDsDkhet-ee mreRDmirvsoOroerisrMvyye, CD-ROM) FIGURE 19.1: The memory hierarchy.
based on semiconductors are quite expensive to manufacture, hence their cost per byte
tends to be high. On the other hand, slower electro-mechanical and optical storage devices
o®er signi¯cantly smaller cost per byte, much larger capacities, and permanent storage
of information without the need for continuous power supply. For that reason, primary
memory tends to be very small in size, and typically the closer the memory is to the CPU
the smaller the size. On the other hand, secondary memory, being considerably cheaper,
features sizes many orders of magnitude larger than primary memory, but with a signi¯cant
impact on performance. The size of tertiary storage is virtually unlimited since it can always
be extended with very small cost.
A typical memory hierarchy of a modern computer system is shown in Figure 19.1. Pri-
mary memory consists of CPU registers, cache, and main memory which are all semicon-
ductor based media. Secondary memory consists of magnetic disk drives. Tertiary memory,
which is used for o²ine storage of information, consists of tape drives, optical devices (like
DVD-ROMS) and °ash memories. Database systems need to make e±cient use of the mem-
ory hierarchy in order to guarantee good performance. In order for the database system to
operate on the data, the data needs to be placed either in cache or main memory. Straight-
forwardly, since the size of a typical database is expected to exceed the capacity of available
primary memory, transferring data between secondary and primary storage is unavoidable.
For that reason, the design of database algorithms is heavily in°uenced by this bottleneck,
trying to minimize the total cost of transferring data between various levels of the memory
hierarchy. The following sections describe in more detail the major types of storage media
in use today, as well as optimization strategies for e±cient use of the memory hierarchy.
19.2.1 Primary Memory
At the top level of the memory hierarchy are the processor registers which provide the
fastest possible read/write access to data (usually one CPU cycle) and have sizes of only
up to a few bytes. Processor registers are used for storing and retrieving data that is being
accessed directly by currently executing instructions.
Next is the processor cache which is a small and very fast read/write memory used
for storing data that has been recently accessed from slower storage devices. The cache
memory is further layered into two (and occasionally three) levels, once more for balancing
19-4
the fundamental trade-o® between latency and cost. Level 1 cache (L1) is build into the
processor core and thus can be accessed in just a few CPU cycles. Typical sizes are in
the range of tens of kilobytes. For example, the AMD Athlon 64 FX processor has a total
of 128 kilobytes L1 cache per core, 64 kilobytes for data and 64 kilobytes for instructions.
Level 2 cache (L2) is larger than L1 with typical sizes ranging from a few hundred kilobytes
up to, for example, 4 megabytes for the Intel Core 2 Duo processor. The main di®erence
between L1 and L2 cache is that the L2 cache is always situated outside the processor core,
hence having much larger latency (from two up to ten times larger than L1). Modern cache
memories are implemented using the Static Random Access Memory (SRAM) technology.
SRAMs use memory cells consisting of transistors only (instead of a capacitor), meaning
that they don't have to be dynamically refreshed on regular time intervals in order to
retain their contents. This makes them extremely e±cient in terms of access times but also
expensive to produce. The primary characteristic of random access memories is that any
memory location can be accessed independently and uniformly, in constant time.
The slowest primary memory in the hierarchy is main memory, which is used for storing
the instructions and data of running programs. The size of main memory is typically
several orders of magnitude larger than the cache. Computer systems with 4 gigabytes of
main memory are not uncommon. Usually, the cheaper Dynamic Random Access Memory
(DRAM) technology is used in this case. DRAM stores a single bit of information using
one capacitor and one transistor only, and therefore has much higher storage density in
comparison to SRAM. Given that main memory is separated from the CPU by the address
bus, and since DRAM is designed for higher density rather than speed, main memory
typically has one order of magnitude slower access time than cache memory (about 30 CPU
cycles for fetching one memory location). Another drawback of memories based on DRAM
is that since real-world capacitors are not perfect, they have to be regularly refreshed to
preserve their contents. This is one more reason that makes DRAM slower than SRAM.
19.2.2 Secondary Storage
Up to this point we discussed solid state memory devices only, which o®er true random
access read/write capabilities. Nevertheless, these memories require continuous supply of
power in order to preserve their contents. Thus, they are not suitable for long-term storage
of information. Furthermore, they have a high cost per byte and small storage density
and, hence, they are not economical for storing large quantities of data. For these reasons,
electro-mechanical devices have been introduced.
The most important device of this class is the Hard Disk Drive (HDD) , which is based
on magnetic principles for permanently storing information. HDDs have cost per byte at
least two orders of magnitude smaller than that of DRAM, making them suitable for storing
vast amounts of data. In addition, they have random access capability, and relatively fast
access times and transfer rates (the actual access time for a datum depends on its exact
location on the medium). All these characteristics make them very appealing candidates for
on-line storage and retrieval of information and, hence, hard drives are used as secondary
memory in most computer systems. Nevertheless, even though signi¯cant technological
improvements have taken place the last few years relating to the mechanical properties of
hard drives, there is still a six orders of magnitude access time gap between DRAM and
HDDs. The importance of the low-level operational principles of the hard disk drive in the
design of e±cient database management systems and algorithms will be discussed in Section
19.3.
Disk Storage and Basic File Structure 19-5
19.2.3 Tertiary Storage
A storage device that has gained wide acceptance as a backup medium in the past decade
is the optical disk drive (based on laser technology), e.g., CD-ROM and DVD-ROM drives.
A DVD disk is a non-volatile medium that can store up to 4.7 gigabytes of data, has access
speed one order of magnitude slower than a typical hard drive, and approximately half the
cost per byte of stored data. The advantage of optical disks it that they can be distributed
and stored easily, taking up very little space. Therefore, optical disks are commonly used
for backup storage of information that does not need to be accessed very frequently. Latest
advances in technology gave rise to Blue Ray DVD, a new standard in optical devices.
A Blue Ray disk can store up to 200 gigabytes of data in a single medium. Currently
commercially available disks store up to 25 gigabytes and have approximately the same cost
per byte as HDDs, but this cost is expected to drop sharply in the next few years, as Blue
Ray becomes the standard.
A backup storage medium based entirely on solid state circuits is the non-volatile Read
Only Memory and Flash memory. The main characteristic of ROMs and Flash memories is
that they can retain the stored information even without a continuous power supply, due to
an ingenious design called the °oating gate transistor. The drawback of these memories is
that, even though they can be reprogrammed repeatedly, the actual number of erase-write
operations supported is limited. The most common ROM in use today is the EEPROM
technology which can support up to one million erase-write operations during its lifetime.
It also supports reprogramming as well as random access of any single memory location
individually. Flash memories cost far less than EEPROMs, but support fewer erase-write
operations overall and can be reprogrammed only on a per block basis. This reduces the
expected erase-write capabilities ever further. An attractive quality of °ash memories is
their kinetic shock resistance, which makes them suitable for portable devices. Furthermore,
they can withstand extremely high pressure and are even water resistant, making them
suitable for niche applications. Digital memories based on °ash technology that can store
up to 1 gigabyte of data are currently becoming a standard.
Finally, a tertiary storage medium most commonly used for archival purposes of vast
quantities of data is the tape drive. Tape drives allow only sequential access to the data
(the tape has to be wound up before accessing any particular segment of data). Nevertheless,
modern tape drives exhibit data transfer rates close to those of mid-range HDDs (once the
position on the tape for reading or writing has been located). They also o®er longer archival
stability then both hard drives and optical disks. Their storage capacity reaches up to 80
gigabytes per cartridge for half the cost per byte of HDDs. Given the introduction of Blue
Ray DVDs it is expected that within a few years, as the price of Blue Ray disks decreases,
tape drives will become obsolete. In addition, given the increasing capacity of HDDs, as
well as the decrease in physical size and constantly decreasing cost per byte, large RAID
enclosures of HDDs are becoming more appealing than tape silos. Like tapes, HDDs can be
kept o²ine in order to maximize their life expectancy. In addition, RAID technology can
be used to provide fault tolerance (RAID will be discussed in more detail in Section 19.6).
19.3 The Hard Disk Drive
The hard disk drive is by far the most common secondary storage device in use today, and
since many design and architectural choices behind database systems are in°uenced by the
inner mechanics of disks, in this section we examine in detail how a disk drive works.
Database systems use hard disk drives to permanently store the data composing a database.
Data is stored in the form of ¯les , where every database is a collection of ¯les, and every
19-6
Arm Heads Sector Track CTyralicnkder
SpindlePlater Actuator FIGURE 19.2: A typical disk.
¯le contains a number of records . Files are stored on the disk as a number of consecu-
tive pages where every page occupies a certain number of sectors . Sectors are the smaller
addressable unit of information on the disk (as will be explained shortly), consisting of a
¯xed number of bytes. When the database system needs to load a certain record in main
memory for processing, ¯rst it has to identify the ¯le and page that contains the record,
and then issue a request to load that page from the disk. The ¯le and page number have
to be translated into a physical address on the disk. The disk controller is responsible for
locating and transferring into main memory all the sectors composing the requested page.
The most important characteristic of accessing information stored on the disk is that the
actual placement of the requested sectors, as well as access patterns between consecutive
requests, play a major role in the overall cost of such operations. The reason for this is
directly related to the design and operational characteristics of the disk.
19.3.1 Design and Operation
A simpli¯cation of the internal view of a typical hard drive is illustrated in Figure 19.2. The
hard drive consists of three major components | the platters, the heads, and the actuator.
The platters is were the actual information is stored. Each platter has two surfaces, and
every surface can store several million bits of information. This information is hierarchically
grouped into cylinders , tracks , and sectors , as shown in the ¯gure. Every cylinder is a
collection of tracks (the tracks with the same radii) and every track is a collection of sectors.
The heads are responsible for reading and writing data on the surfaces of the platters and,
thus, there is one head per surface. The actuator is a specially designed motor for accurately
placing the heads over the precise track that needs to be read or written. For simplicity, all
heads are rigidly mounted on the actuator arms and move only as a unit over exactly the
same track on each platter.
The three components, spindle, heads and actuator, must be tightly and precisely coupled
together for the hard drive to work correctly. Here is a brief overview of the operations
Disk Storage and Basic File Structure 19-7
that need to take place in order to read a single byte from the disk. First, the address of
the byte in terms of the disk's geometry is determined in the form of a cylinder, head, and
sector. Then, the disk controller has to determine if the requested information is already
contained in the disk bu®er, and if yes the read operation terminates. If not, then the
controller has to activate the spindle motor to spin the drive to operating speed (if it is
not spinning already), and instruct the actuator to move the heads on top of the requested
cylinder on the platters of the disk. When the heads are in place, the controller activates
the appropriate head to read the requested sector. The controller waits until the requested
sector rotates underneath the head and immediately instructs the head to start reading the
contents of the sector into the bu®er. In what follows we describe in more detail these three
major components of a hard drive.
Platters. The main disk assembly consists of a spindle upon which a number of circular
platters are ¯rmly attached one on top of the other. The surface of the platters is were all
the data is stored, using a thin media layer composed of magnetic material. A 0 or 1 bit is
represented by orienting the magnetic material in one or the other direction. The bulk of
the platter, the substrate, consists of a rigid and magnetically inert material upon which the
media layer is deposited. Older drives used iron oxide (commonly referred to as rust) as the
media layer, and substrates composed of aluminum alloy, which is inexpensive, lightweight
and abundant. In newer drives the media layer is composed of special magnetic coatings
referred to as thin ¯lm media which can support higher data density. Glass composites are
used for the substrate, which provide a much smoother surface.
Every platter has two active surfaces, and every surface can store a number of bits,
dictated by the maximum data density supported by the magnetic material. The bits
stored on a surface are organized into tracks , which form concentric circles upon the platter.
Every track is divided into a number of sectors (an angular section of a circle), interleaved
with gaps. Sectors are the smallest addressable unit of storage on the disk. Every read or
write operation on the disk a®ects at least one sector at a time. In the rest we refer to a
read or write operation as a disk I/O . All sectors contain a preset number of bytes (512
bytes for most disk drives). Gaps are necessary for separating consecutive sectors and also
storing other information, like error correcting codes, synchronization and identi¯cation
information, etc. In older drives all tracks consisted of the same number of sectors. Clearly,
given that tracks with smaller radius have shorter circumference, the inner most tracks can
hold much fewer bits than the outer tracks. Hence, a constant number of sectors per track
means that outer tracks remain highly underutilized, not reaching their maximum data
density potential. Newer drives employ a geometry referred to as zoned bit recording which
allows a variable number of sectors per group of tracks. This design maximizes the amount
of data that can be stored on the disk but, on the other hand, necessitates designing more
complex hard disk controllers that can support varying disk geometries.
Heads. The heads of the disk are by far the most sophisticated component of the hard
drive. They are suspended at the edge of the actuator arms, ridding as close as possible
to the platters, on a very thin layer of air. For that purpose it is imperative that platters
are as smooth as possible in order to avoid head crashes which would damage the surface of
the disk. The essential operation of a disk head is to transform magnetic °uctuations into
electrical signals for reading, and vice versa for writing. The accuracy of the disk heads is
one of the major factors that determine the maximum capacity that can be supported by a
hard drive, limiting the maximum data density per track and track density per surface.
Actuator. The actuator assembly is responsible for placing the heads over the correct track
on the platters. For simplicity, all heads are mounted together on the actuator arms and
19-8
move as a unit, being positioned over exactly the same track on their respective platters at
the same time. Conceptually, the group of tracks at the same position on all platters is said
to form a cylinder . Modern actuators use a device based on electromagnetic attraction and
repulsion for moving the arms, and a closed-loop feedback system that dynamically places
the heads over the correct location. The feedback is provided by the heads themselves,
which read special synchronization information stored on the platters by the manufacturer
for locating the requested cylinder. The overhead of this information is not trivial and
cannot be ignored. Substantial e®ort has been spent to try and reduce the amount of
information needed for adjusting the heads. In older hard drives a whole surface was
devoted for storing synchronization and identi¯cation information. In current technology
this information is dispersed on the disk.
19.3.2 Physical and Operational Characteristics
Based on the previous discussion, the basic characteristics of a modern hard drive can be
grouped into the physical properties of the platters, that de¯ne the maximum capacity of
the drive, and the operational characteristics that de¯ne its performance in practice.
The physical characteristics can be summarized as follows:
² Number of Platters . The number of platters is directly proportional to the
overall capacity of the disk. Drives with one up to twelve platters can be found
today, but a typical disk has about four platters, with each platter packing as
much as 200 gigabytes of data.
² Track Density (TPI) . Increasing the number of tracks that can ¯t in a single
surface is important for reducing the number of platters required to reach a
certain capacity. More platters mean more heads, which are very expensive,
and a larger disk pro¯le as well as weight. A typical hard drive can have as
many as 100,000 Tracks Per Inch (TPI), but disks with 200,000 TPI have been
manufactured.
² Linear Density (BPI). The linear density of a disk refers to the total number
of bits that can be packed consecutively in one inch of a track. Currently used
magnetic materials and read/write heads can support up to 900,000 Bits Per Inch
(BPI).
The basic operational characteristics are:
² Seek Time (ms) . Seek time is the time it takes the drive to position the heads
over the requested cylinder. It can vary from 0 ms if the heads are already over
the right cylinder, and up to 15 to 20 ms for a full stroke, i.e., the time needed
to move the heads over the entire width of the platters. The most commonly
used metric is the average seek time needed to move from a random cylinder to
another. A typical drive features an average seek time of less than 11 ms, but
high-end drives with average seek times of less than 4 ms are currently being
manufactured.
² Rotational Speed (RPM) . The rotational speed of the spindle of a hard drive
plays an important role on the transfer rate of the disk, since it directly a®ects
latency . Since the rotation of the platters is independent of the head move-
ment, after the actuator has positioned the heads over the correct cylinder the
requested sector might not be directly under the head. The time it takes for the
sector to move into position is called latency. Latency can be 0 ms in the best
case, and almost a full rotation of the platters in the worst. Clearly, the higher
Disk Storage and Basic File Structure 19-9
the rotational speed of the spindle, the smaller the average latency. A typical
rotational speed for a modern hard drive is 7,200 Rounds Per Minute (RPM),
i.e., it takes approximately 8 ms for a full rotation. In that case, assuming that
on average the requested sector will be half way around the track after the head
is positioned properly, the average latency is expected to be close to 4 ms. Drives
featuring as high as 15,000 RPM do exist today.
² Internal Transfer Rate (Mb/s) . The transfer rate of a drive refers to the overall
speed with which data is transferred from the drive to main memory and vice
versa. The transfer rate can be broken down into the internal media transfer
rate and the interface transfer rate. Due to the mechanical nature of the internal
transfer operation that is based on the rotation of the spindle, the internal transfer
rate is what limits the overall data transfer rate of the drive. The internal transfer
rate is the time it takes the drive to read one track in full. The transfer rate for a
given track can be computed directly by using the total number of bits stored in
the track and the rotational speed of the spindle. The more data that is stored in
a track and the faster the rotational speed, the higher the internal data transfer
rate. Since the number of bits per track varies depending on the radius, the
internal data transfer rate depends on the position of the requested data on the
surface of the disk. Typical hard drives feature average internal transfer rates
close to 1,000 Megabits Per Second (MB/s).
² Sustained Transfer Rate (Mb/s). Essentially, the internal transfer rate refers to
the time it takes to read a single track of the disk. This rate does not take into
account the head movement for repositioning the heads in case that the requested
data does not ¯t in a single track. In practice, a single disk I/O needs to access
data that spans a large number of tracks. For that reason, the sustained transfer
rate is another measure that characterizes the performance of a hard drive for
large sequential reads that have to scan multiple tracks. Typical sustained rates
for today's hard drives are 650 Mb/s.
Let an imaginary hard drive contain four platters, 3.75 inches in diameter, with 100,000
TPI, 600,000 BPI, 512 bytes per sector, and feature a rotational speed of 7,200 RPM and
an average seek time of 10 ms. Lets assume that the usable surface of the platter is between
0.2 and 3.7 inches. In that case, on average a track can hold approximately 449 kilobytes
(3:7 ¢¼ ¢ 600; 000 = 851 kilobytes for the outer most track and 0:2 ¢¼ ¢ 600; 000 = 46 kilobytes
for the inner most track). Thus, the drive can store 449 ¢ 100; 000 ¢ 3:5 = 150 gigabytes per
surface, since it has an active surface of 3:7 ¡ 0:2 = 3:5 inches and 100,000 tracks per inch.
Hence, the drive has a total capacity of 150 ¢ 8 = 1; 200 gigabytes (for a total of 8 surfaces),
including the space used for head placement information, sector gaps, error correcting codes,
and other overhead. Since the drive supports 7,200 RPM the spindle makes a full rotation
every 8 ms. Given the rotational speed of the drive and the average number of bytes per
track, the drive can read on average 1; 000 ¢ 449=8 = 438 Mb/s. Notice that the maximum
transfer rate when data is read from the outer most tracks is much larger in practice. An
important consideration when computing the actual transfer rate of data is the average
seek time for positioning the heads. Let's assume that an 8 kilobyte block of data needs
to be accessed from the disk. On average, we need 10 ms to position the heads and 4 ms
latency to ¯nd the ¯rst of 16 sectors containing the data. If all sectors are consecutive on
the same track, given the average transfer rate of the disk, it takes another 0.1 ms to read
all 16 sectors, for a total of 14.1 ms to complete the transfer. On the other hand, in the
worst case all sectors are scattered randomly on the disk, thus necessitating a total of 16
random seeks, costing 14 ms each. Obviously, the seek time dominates the overall cost of
19-10
the transfer. For that reason, e±cient data storage organization is essential for improving
performance.
19.3.3 E±cient Data Storage Organization
As the previous example illustrates, the organization of data on disk plays an important
role on the performance of certain tasks. Ideally, we could take advantage of the operating
principles of a hard drive to improve the performance and overall throughput of a database
system. Given a typical hard drive, in an average of 14 ms per data access a modern CPU
can execute several million instructions. In most practical situations, processing the data
read from or written to the disk in one fetch operation requires at most a few thousand
instructions. Evidently, the disk I/O cost becomes a dominant factor during the execution
of an algorithm. For that reason it is crucial to design algorithms that minimize the total
number of disk I/O requests. In addition, we would like to organize data on disk in a way
that minimizes the total number of random seeks that are needed for completing all I/O
requests, taking into account that multiple user request might be pending at any given
time. For that reason, the best algorithms for main memory processing are not necessarily
e±cient when considering that data is also stored in secondary storage.
Since the amount of data processed in a typical database system far exceed main memory
sizes in most practical applications, almost all database query and update algorithms are
designed with these principles in mind. In addition, since most database systems have to
serve multiple user requests simultaneously, e®ective scheduling algorithms for satisfying
I/O requests that do not exhibit locality and refer to data that is potentially dispersed on
the disk, need to be developed. Notice here that these issues are not limited to secondary
storage only. Similar problems arise when considering all levels of the memory hierarchy.
Given the access time gap between di®erent levels of cache memory and main memory, by
designing cache-conscious algorithms that use data structures with small memory footprints,
operating mostly within the cache, the performance of a variety of database operations can
improve even further.
Sequential Versus Random I/O
Many database operations, like sorting large relations on a given attribute, require continu-
ous reading and writing of large amounts of related data. The e±ciency of such operations
can be substantially improved by strategically placing data that is read or written in tandem,
in neighboring locations on the disk. Ideally, we would like to minimize the total movement
of the heads for executing all the necessary I/O request for completing the operation, as
was illustrated in Section 19.3.2.
The simplest way to guarantee minimal random head movements is to store the data
sequentially on the disk, starting from a given sector of a given track and storing the data
continuously on consecutive tracks of the same surface of the disk. Essentially, after one
random seek for locating the ¯rst sector containing the requested data, we can read the the
rest of the data by only performing sequential track-to-track seeks which are much faster
than random seeks. In addition, we can also reduce the rotational latency in-between track-
to-track seeks by using a strategy called cylinder skew . Suppose that we start storing the
data on sector 1 of track 1. After track 1 is full, we continue storing the data on sector 2
of track 2, and so on. Clearly, after one full rotation of the disk, having read or written
track 1 completely, a track-to-track seek will ideally place the heads over track 2 exactly
before sector 2 is in position, resulting in minimal latency (see Figure 19.3). In practice,
cylinder skew is much larger than one sector per track. Given current rotational speeds
Disk Storage and Basic File Structure 19-11
1 23 FIGURE 19.3: An example of cylinder skew. A block of data is stored in consecutive
tracks, where the ¯rst sector per track is skewed with respect to the previous track in order
to reduce rotational latency.
and track-to-track head seek times, by the time the actuator places the heads over the next
cylinder the platter might have performed as much as 20% of a full rotation. The principle
though remains the same.
To speed-up sequential reads even further, we could organize the data in sequential cylin-
ders instead of tracks, to take advantage of all disk heads simultaneously and reduce the
track-to-track head movements as well. If we assume that disk heads can read in paral-
lel then we minimize both track-to-track seeks and rotational latency by storing the data
starting from the same sector on all tracks of each cylinder. Otherwise, if we are limited
to reading from every head sequentially, we can reduce the rotational latency for reading
consecutive tracks of the same cylinder by using a strategy similar to cylinder skew, referred
to as head skew . If the beginning of the data is stored in sector 1 of track 1 on head 1, then
we continue storing the data on sector 2 of track 1 on head 2, and so on. After reading the
track under head 1, by the time the controller switches to head 2, ideally sector 2 of track 1
will be located under head 2. Furthermore, a combination of head skew and cylinder skew
can be employed for reading consecutive cylinders.
Finally, another approach for minimizing random seeks is to mirror the data using mul-
tiple disks. The advantage of mirroring is, ¯rst, redundancy in case of disk failure and,
second, that data can be read in parallel from all disks, hence reducing the overall I/O cost
by a factor equal to the number of disks. The drawback is that this solution is much more
expensive. A more detailed description of mirroring appears in Section 19.6.
Disk Scheduling Algorithms
Beyond large database operations that require sequential access to data, typical DBMS
workload involves multiple, simultaneous user request, possibly on a variety of relations. In
that case, the typical workload of a disk drive involves data that are dispersed on the disk,
and almost all of the time result in a large number of random I/Os. Intuitively, in order
to maximize system throughput it is necessary to schedule disk I/O request appropriately,
depending on the speci¯c location of each request on disk, such that head movement is
minimized.
The naive scheduling algorithm is called First Come First Served (FCFS) . In FCFS no
19-12
e®ort is made to minimize head movement. A better approach for lowering the average
response time per request is to use Shortest Seek Time First (SSTF) . Given a queue of I/O
requests, SSTF selects from the queue the request that results in the least amount of head
movement, given the current location of the heads. Obviously, this strategy favors requests
that exhibit locality. A disadvantage is that it might cause starvation . Requests that refer
to data located far away from the current head position will experience heavy delays if a
large number of requests that exhibit locality exist and are being serviced ¯rst, due to the
position of the heads.
A di®erent approach that avoids starvation is commonly referred to as the elevator algo-
rithm or SCAN . The elevator algorithm does not perform any random head movements at
all. Instead, it continuously moves the heads across the disk from the inner cylinders to the
outer and vice versa, and serves the next I/O request in the direction of the head movement.
If no requests are pending on the current direction, the direction is immediately reversed. In
the best case, a request that just arrived is serviced next. In the worst case a request for the
outer most or inner most cylinder arrives right after the heads reversed direction and there
exist a request for the other far end of the platter. The elevator algorithm does not have
optimal throughput nor minimum average waiting time, but yields very good performance
in practice for heavy workloads. As the number of requests increases, it is expected that
these requests will be uniformly distributed over all cylinders of the disk resulting in perfect
utilization of the head movement | at least one request per track-to-track seek.
Notice that with the elevator algorithm the cylinders in the middle of the platters are
accessed twice as much as those at the inner and outer edges. A simple modi¯cation of the
elevator algorithm is to allow the heads to move to one direction only. When the heads
move to the end of the platters, they immediately seek back to the other end and start
servicing requests in the same direction again. This has the advantage that all cylinders
get treated fairly.
Table 19.3.3 shows an example of how these di®erent scheduling algorithms work in
practice. Let a hard drive with track-to-track seek time equal to 1 ms, and absolute seek
time proportional to the number of tracks traveled (e.g., moving directly from track 1 to
track 10 requires 10 ms). Assume that one request arrives every 1 ms, initially the heads
are positioned over track 1, and the average rotational latency is 1 ms. The FCFS strategy
processes requests in their arrival order and yields a total of 32 ms for servicing all requests.
The SSTF strategy processes request 5 ¯rst, performing a random seek from track 1 to track
5 in 4 ms, plus 1 ms latency. By the time request 5 has been serviced, requests for tracks 6,
1, 10 and 2 have arrived since 5 ms have elapsed already. SSTF chooses to serve request 2
next, breaking a tie in terms of shortest head movement between 2 and 10. Next, it proceeds
to track 1 and ¯nally track 10 for a total of 24 ms. The SCAN algorithm begins servicing
requests sequentially from track 1. A request for track 5 arrives ¯rst and is serviced directly
in 5 ms including the rotational delay. The next request in the same direction is for track 6,
continuing to track 10 and then reversing direction and servicing track 2 and ¯nally track
1 for a total of 23 ms. Notice that if SSTF has chosen to service track 10 instead of 2 when
breaking the tie, the schedule of SSTF would have been the same as SCAN. SCAN does not
always result in the minimum total access time. It is not hard to construct examples where
SSTF works much better than SCAN. SCAN does not minimize the individual, per request
access time either. Notice that request 1 is serviced last, even though it arrives before 2
and 10.
19.4 Files and Records
Disk Storage and Basic File Structure 19-13
Time 0 ms 1 ms 2 ms 3 ms 4 ms
Track 5 6 1 10 2
FCFS 5, 5 ms 6, 2 ms 1, 6 ms 10, 10 ms 2, 9 ms 32 ms
SSTF 5, 5 ms 6, 2 ms 2, 5 ms 1, 2 ms 10, 10 ms 24 ms
SCAN 5, 5 ms 6, 2 ms 10, 5 ms 2, 9 ms 1, 2 ms 23 ms
As we have already seen, relational and object oriented databases consist of a large number
of relations with a well de¯ned schema. Every relation consists of a collection of records or
objects, where every record or object consists of a number of attributes. These attributes
are assigned to speci¯c data types, like strings, dates and real numbers. The DBMS stores a
relation in secondary storage as a ¯le. A ¯le consists of several pages , where every page spans
a ¯xed number of sectors (e.g., a typical page size is 8 kilobytes for a total of 16 sectors
per page). Each record of the relation is stored in one (or occasionally several) pages.
Straightforwardly, each record is associated with a unique physical address on secondary
storage, which is a combination of the ¯le identi¯er corresponding to the relation that the
record belongs to, a page within this ¯le, and an o®set within this page. The physical
address is also called the record identi¯er .
In the previous section we saw essentially how the DBMS stores ¯les and pages, such
that fetching a single page or a single ¯le from disk to memory (and vice versa) takes
advantage of the low-level operational characteristics of disks. Nevertheless, at a higher
level the DBMS treats a ¯le or a page as a collection of records and, hence, has to also
manage how records are organized into ¯les and pages. In the simplest form, a ¯le is an
ordered collection of pages, where every page points to the location of the next page in the
¯le. This ¯le structure is commonly referred to as a heap ¯le . When a record needs to be
processed, (inserted, deleted, updated or moved) a disk I/O is performed for fetching the
entire page containing the requested record into main memory. The appropriate page that
needs to be accessed is deduced from the identi¯er of the requested record.
It is clear that the DBMS faces a number of problems when organizing records into ¯les
and pages, the most important of which are:
² E®ectively representing records with a multiplicity of data types.
² Packing records into pages by minimizing unused space.
² Organizing records within a page for handling record updates e±ciently.
² Handling records that span multiple pages.
² Organizing pages within ¯les.
The rest of this section presents various approaches used by modern DBMSs for addressing
these issues.
19.4.1 Physical Representation of Records
Consider the following SQL statement:
CREATE TABLE Employees (
ID INTEGER PRIMARY KEY,
Name VARCHAR(30),
Address VARCHAR(255),
BirthDate DATE
);
19-14
((ab))001112311454A212la3n4 Tu60ri6n g23 19123L4o1An4dlaonn ,T Eunrignlgand25London, England4020869 23 1912297 FIGURE 19.4: An example of ¯xed-length and variable-length records.
This statement creates a table containing records that can be represented either using
¯xed-length or variable-length encodings . In a ¯xed-length record representation every
attribute within the record is represented using a ¯xed number of bytes. For instance, the
above database schema shows that although some ¯elds may have variable lengths, their
maximum potential size is known in advance. Even though di®erent employees may have
addresses with di®erent lengths, no employee will have an address that needs size larger
than 255 bytes (which is either common knowledge or the fact that the DBMS will truncate
longer addresses automatically). Therefore a ¯xed-length representation is to always assign
255 bytes to an Address. Hence, we can encode a record belonging to the Employee relation
as a byte array by simply concatenating all attributes one after the other. In our example
a record will be encoded using 297 consecutive bytes; four bytes for the ID, 30 bytes for
the Name, 255 bytes for the Address, and 8 bytes for the BirthDate. Parsing the record
is straightforward since we already know the exact size of every attribute and, thus, we
know exactly where the data of every attribute begins and ends inside the byte array. The
size of each attribute for a given relation is deduced by the database schema which can be
stored in the system catalog , and accessed by the DBMS on demand. Although simple,
the ¯xed-length representation wastes space. Even if a record has a short address which
occupies only a few bytes, 255 bytes will be used.
In a variable-length record representation the number of bytes that an attribute value
occupies depends on the actual size of this attribute within the speci¯c record. Therefore
a short address will occupy fewer bytes than a long address. In that case, delimiting two
adjacent ¯elds is made possible by using a special delimiter symbol (which is reserved and
cannot appear in the actual data). An alternative approach is to keep some \header"
information at the beginning of the record, which stores the actual length or the o®set
of the beginning of every attribute. With this information, by reading the header of the
record we can immediately deduce the beginning and the end of all attributes. The header
information also provides a natural way for representing NULL values , simply by setting
the length of a NULL attribute to 0. In that case it might be bene¯cial to store in the
header the length of the ¯xed-length attributes as well. That way, not only can we encode
NULL values easily for all attributes, but we will not have to consult the system catalog in
order to parse the record as well.
An example of a ¯xed-length record is shown in Figure 19.4(a). Here, no header infor-
mation is needed for delimiting between two consecutive attributes, since the size of each
attribute is known through the database schema. The total size of the record is 297 bytes.
A variable-length record is shown in Figure 19.4(b). In this example, the record stores
a small header in the ¯rst few bytes, then all the ¯xed-length attributes, and ¯nally the
variable-length attributes. The header stores two values. The ¯rst one is the length of the
Name attribute. The second one is the length of the Address attribute. Since we already
know the length of the ID and BirthDate ¯elds, we can deduce the o®sets of the remaining
Disk Storage and Basic File Structure 19-15
attributes easily. Using this representation a record with n variable-length attributes needs
only n entries in the header. The total size of the record in this case is 40 bytes.
With a ¯xed-length representation updating any of the attribute values is straightforward;
the old value is simply replaced by the new value. Nevertheless, in the variable-length
representation when updating the variable-length attributes, two cases arise. In the ¯rst
case the new value has length smaller than the old value, meaning that we need to compact
the record by shifting all subsequent attributes to the left. In the second case the new
value has length larger than the old value, and we need to expand the record by shifting
all subsequent attributes to the right. In both cases, we also need to update the header
with the length of the new value. Clearly, even though variable-length records result in
storage space savings, they are more costly to maintain in the presence of record updates.
To complicate matters even further, when multiple records are packed into a page and a
record gets updated, not only do we need to shift the attributes within this record but,
potentially, all subsequent records within the page need to be shifted as well. The following
section discusses these issues.
19.4.2 Organizing Records Into Pages
Since the record size is typically much smaller than the page size, a page can hold several
records. Many interesting issues arise when records need to be organized into pages in the
presence of insertions, deletions and updates, especially when records need to be maintained
in a particular order (e.g., in sorted order of the primary key).
1101. . . .
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header
rec1 rec2 rec3
FIGURE 19.5: Page layout for ¯xed-length records.
Figure 19.5 is a simpli¯ed illustration of a disk page that stores ¯xed-length records. The
page can be thought of as containing a number of slots with ¯xed size. The header of the
page contains the number of records currently stored in the page and a bitmap signifying
whether a slot holds a record or not. The total number of records is useful for quickly
determining whether a page has empty slots or not. In the example, only slots 1, 2 and
4 are occupied. Initially, all bitmap entries are o®. To insert a record, we locate the ¯rst
available slot by scanning the bitmap. Then, we copy the record data into the corresponding
slot and turn the appropriate bit on. To delete a record we simply switch the corresponding
bit o®. With this approach, a record can be identi¯ed using the page number and the index
of the bit corresponding to the slot containing the record data. This scheme is also called
an unpacked page . An alternative version called packed page exists, where no empty slots
are allowed to exist in-between records. If a record is deleted, the last record in the page
is moved into the newly freed slot. With this scheme there is no need to store a bitmap in
the header. However, the deletion cost is higher.
Both these schemes are generally useful only for applications that do not need to keep
track of individual record identi¯ers , since records change slots arbitrarily when deletions
occur, and dangling pointers to records might be introduced. If some application is ex-
ternally pointing to some record through its record identi¯er and the record is relocated
19-16
to a new slot, the record identi¯er becomes invalid since it points to an incorrect slot. A
similar situation occurs when the record is deleted by some application, but there still exist
other applications pointing to this records through its record identi¯er. Then, either these
pointers refer to a deleted and, thus, invalid record or to a di®erent, newer record that
happened to replace the deleted entry in the freed slot. A more sophisticated record orga-
nization policy can be used to alleviate these problems, which is also suitable for handling
variable-length records.
. . . . . . . .
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N free size, offset size, offset rec1 rec2
header
FIGURE 19.6: Page layout for variable-length records.
Figure 19.6 illustrates a disk page that stores variable-length records. In this case, since
records have arbitrary sizes, whenever a new record needs to be inserted in the page we need
to locate a slot with space large enough to store the new record. As records get inserted
and deleted dynamically, we need to keep track of the empty space available inside each
page. Moreover, we also need to be able to move existing records in order to keep most of
the empty space contiguous at the end of the page, which will help for storing large records
later on and also for minimizing the unused space in-between consecutive records. Another
problem that arises when using variable-length records is the space lost whenever a record
gets updated and the new values either decrease or increase the overall size of the record. If
the record decreases in size, unused space is introduced in-between two consecutive records.
If the record expands, then it might be necessary to relocate the record in case it does not
¯t in its current slot. A sophisticated record organization policy can be used to address all
these issues and also to avoid problems with dangling pointers and pointers to records that
have been relocated, as already discussed.
The solution is to keep a record directory in the header of the page. The directory
contains one entry per record, where every entry stores the length of the record and the
o®set to the beginning of the record's data. The free space within the page starts after
the end of the last record in the directory. A pointer to the beginning of the free space
is stored in the header as well. The identi¯er of every record is the combination of the
page number and the corresponding index of the record in the directory. When a new
record needs to be inserted, a new entry is appended at the end of the directory and the
record is stored at the beginning of the free space. If the free space is not large enough
to contain the record, then we need to compact the existing entries in order to reclaim
the space lost by deletions and updates. In that case we need to shift all existing records
such that their data appear contiguously at the beginning of the page (after the header)
and all free space contiguously at the end. Notice that relocating the record data within
the page does not a®ect record identi¯ers since their corresponding index in the directory
remains unchanged. For the same reason, a record deletion does not remove the entry of
the deleted record from the directory but simply sets the length of the record to zero, which
is a special °ag denoting that the record corresponding to this directory entry has been
deleted. Removing the entry from the directory would result in a®ecting the indices of
Disk Storage and Basic File Structure 19-17
all subsequent directory entries and, as a side-e®ect, the corresponding record identi¯ers.
Zeroed out directory entries can be reclaimed by re-using them for newly inserted records.
Nevertheless, allowing directory entries to be reclaimed introduces once again problems
with dangling pointers to deleted records, allowing the pointers to refer to either obsolete
information, or information belonging to a newer record.
Since the record directory always increases in size as insertions and deletions occur (es-
pecially when entries for deleted records are not reclaimed) it is possible for the directory
size to exceed the total size preallocated for the header. A solution to this problem is to
use an over°ow page to store the directory, and store only a pointer to the over°ow page in
the header of the original page. Another solution is to store the header at the end of the
page instead of the beginning, expanding the directory into the free space of the page as it
grows.
Another problem that might arise is if a record cannot ¯t in a page even after the page
has been compacted. In that case we can either allocate an over°ow page to store the new
record or store the record in a sibling page. In both cases we need to leave a forwarding
address for that record in the directory of the original page. The forwarding address is
simply a pointer to the over°ow or sibling page.
Finally, another special situation arises for very large records that do not ¯t in a single
page or occupy almost one page by themselves. In this case allocating a set number of
pages per record might be space ine±cient. Consider for example the case where a record
occupies a little more than half the size of a page. In this case, only one record can ¯t per
page, and almost half of the space within the page remains unused. Clearly, better space
utilization will result by fragmenting entries between consecutive pages, in order to use the
dead space. Fragmenting records can be handled easily by keeping with every fragment a
pointer to the next fragment in the form of a forwarding address, as mentioned earlier. The
lack of a forwarding address for a fragment signi¯es that the record is complete.
19.4.3 Organizing Pages Into Files
A DBMS stores data into ¯les, where each ¯le is a set of pages. A ¯le can grow or shrink in
size as data is inserted or deleted, and hence is a dynamic data structure that needs to be
maintained e±ciently. As already mentioned, the simplest ¯le organization, the heap ¯le ,
is an ordered collection of pages. File consistency is achieved either by linking every page
to the next in a linked list fashion by using pointers, or by maintaining a page directory
structure that keeps track of pages within the disk. Notice that the pages can be randomly
scattered on disk, or sequentially stored for e±ciency. More complex ¯le organizations can
be designed in order to be able to allocate free pages on demand whenever needed (especially
when sequential ¯le storage is necessary), and to be able to ¯nd pages with enough free
space to hold a given record. For example, the page directory structure is a good candidate
for storing per page information on available free space. By consulting the page directory,
we can determine which page has enough space to store a given record directly, without the
need to sequentially scan all pages in the ¯le. Sophisticated ¯le organizations are especially
important when records need to be maintained in a speci¯c order, e.g., sorted by some given
attribute. The advanced indexing techniques that will be discussed in the following chapter
are specially designed ¯le storage organizations to speci¯cally serve this purpose.
19.5 The Bu®er Manager
19-18
The DBMS processes records by initially fetching the corresponding pages from the disk
into main memory. Given that a disk access is orders of magnitude slower than a memory
access, it is imperative to limit as much as possible the number of disk I/O requests that
need to be performed in order to complete a given task. An elegant idea for limiting the total
number of disk accesses is to bu®er pages into available main memory, such that subsequent
operations on the same page do not trigger additional disk I/O. For that reason, all modern
database management systems implement their own sophisticated bu®er manager . ¤
19.5.1 The Concept of Bu®ering
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memory buffer
disk
FIGURE 19.7: Illustration of bu®ering.
Figure 19.7 illustrates the concept of bu®ering. All records are stored on the disk as
paginated ¯les, as previously discussed. The job of the bu®er manager is to maintain copies
of several pages in a main memory bu®er as well. Upon request of a page, the bu®er
manager checks if that page is already contained in the bu®er. If it is, the disk I/O can be
avoided. If not, the page is loaded from disk directly into the bu®er. In the latter case two
situations arise. In the ¯rst case the bu®er has enough space to hold the new page. In the
other case, the bu®er is full and, hence, an old page needs to be evicted. Modern DBMSs
implement various replacement policies for evicting pages from the bu®er, which will be
discussed in the next section. Assuming that a page is chosen for replacement uniformly
at random, since that page might have been modi¯ed while residing in the bu®er, before
being discarded it might be necessary to ¯rst write it back to the disk. A modi¯ed page
is called dirty . An unmodi¯ed page is called clean . The bu®er manager associates every
page with a dirty bit . If the bit is set, then the page has been modi¯ed and, thus, needs
to be written back to the disk upon eviction. Otherwise, the page is clean and the bu®er
copy can be discarded directly.
Every page residing in the bu®er is also associated with a pin count . A page can be
chosen for replacement only if its pin count is equal to zero. An application can pin a
¤The operating system on which the DBMS is running on, also has a bu®er management component.
However, a DBMS usually implements its own bu®er manager for better performance and °exibility.
Disk Storage and Basic File Structure 19-19
page in memory by incrementing its pin count by one. After a page is no longer needed, the
application that pinned the page should unpin it by decreasing its pin count by one. Pinning
pages in the bu®er is useful when an application knows that this page will be accessed very
frequently in the future and as a consequence should not be discarded unless if it is absolutely
necessary. For example, such cases arise when processing the page directory structure of
a ¯le, a page that is being compacted, or when reorganizing various data structures and
indices, like the B-tree, that will be discussed in the following chapter.
19.5.2 Replacement Policies
In general when the DBMS needs to pick a bu®er page for replacement there are multiple
strategies that can be used. Every strategy has advantages and disadvantages, depending
on the data access distribution for a speci¯c workload, the ease of implementation, the
cost of managing bu®ered pages, and other factors. The most commonly used replacement
policies are: First In First Out (FIFO), Least Recently Used (LRU), Most Recently Used
(MRU), and Least Frequently Used (LFU).
FIFO replaces the page that entered the bu®er before all other pages.
LRU is the most widely used policy. It replaces the page which has not been used for the
longest time. LRU can be implemented simply by maintaining the bu®er as a linked list and
whenever a page is accessed, by moving it to the beginning of the bu®er. Then we always
pick the page at the end of the bu®er for replacement. Table 19.1 shows a running example
of the FIFO and LRU replacement policies. Let a bu®er have a capacity of three pages and
be initially empty. After the ¯rst three pages 0, 1 and 5 are loaded from disk, the bu®er is
full. (Here we enclose a page identi¯er in parentheses if the page needs to be loaded from
disk.) To access page 0 in step 2, no disk I/O is needed for either FIFO nor LRU. However,
we can observe a small di®erence. The LRU policy moves the most recently accessed page,
which is 0 in this case, to the head of the queue. In Step 3, both policies need to load page
2 from the disk. Because the bu®ers are full, in both cases a page needs to be replaced.
While FIFO replaces page 0 which entered the bu®er ¯rst, LRU does not, because 0 has
recently been used. Instead, LRU replaces page 1 which is the least recently used. In step
4, both schemes access page 5 in the bu®er and therefore do not trigger a disk I/O. Once
again in LRU page 5 is moved to the head of queue. Step 5 shows a di®erence in the I/O
performance of the two schemes. In FIFO, page 0 needs to be loaded from disk, while in
LRU page 0 is already present in bu®er. Step 6 accesses page 1 which requires a disk I/O in
both cases. Then in step 7, again by using LRU a disk I/O is avoided. Clearly, depending
on the data access distribution the two policies can work equally well. Nevertheless, when
data accesses exhibit locality of reference , where several pages are accessed many times in
a row, the LRU policy is by far superior to FIFO.
Step Data FIFO bu®er LRU bu®er
1 access 0, 1, 5 (0) (1) (5) (0) (1) (5)
2 access 0 0 1 5 1 5 0
3 access 2 1 5 (2) 5 0 (2)
4 access 5 1 5 2 0 2 5
5 access 0 5 2 (0) 2 5 0
6 access 1 2 0 (1) 5 0 (1)
7 access 5 0 1 (5) 0 1 5
TABLE 19.1 Bu®ering comparison of FIFO and LRU when accessing pages 0, 1, 5, 0, 2, 5, 0, 1, 5.
19-20
Contrary to LRU, MRU replaces the most recently accessed page. MRU works best under
a scenario where many long sequential scans of pages occur. In such cases LRU may su®er
from sequential °ooding . Consider, for example, a bu®er with 9 pages and a ¯le with 10
pages. If we sequentially scan the ¯le multiple times, after the bu®er is full for the ¯rst
time, the LRU scheme will result in every single page access performing a disk I/O. MRU
is much better in this case.
LFU counts the number of times that a page stored in the bu®er has been used, and
picks for replacement the one that has been used the least. This policy has the drawback
that very frequently accessed pages will remain in the bu®er forever, potentially causing
starvation. To overcome this problem, a modi¯cation of this approach is compute the access
frequency of every page (i.e., the number of accesses divided by the time the page has stayed
in bu®er), instead of simply the number of accesses.
19.5.3 Bu®ering Structured Pages
In this section we describe an interesting implementation issue relating to how we can bu®er
pages that store records with a known user-de¯ned structure more e±ciently. While a page
can be viewed simply as an array of bytes on the disk in low-level, in reality it is typically
understood by higher-level DBMS components or user applications as a collection of user-
de¯ned structures, e.g., a relational tuple or an object in object relational DBMSs. There are
various advantages in injecting higher-level knowledge of the speci¯c structure of the entries
contained within pages at the bu®er manager level. If the bu®er manager understands
pages simply as byte arrays, then every time a page entry needs to be accessed, it will
have to be converted into its corresponding structured representation, which results into
two severe e±ciency problems. The ¯rst is the processing cost of converting an entry, which
unnecessarily wastes processing power. The other is the fact that two copies of essentially
the same data need to be kept in main memory, one unstructured within the bu®er, and the
other structured within the memory space of the application using the data. A better choice
is to bu®er each page entry directly as a pointer to its user-de¯ned structure, the moment
the page is inserted into the bu®er. That way every entry is converted into a structured
representation only once, and all higher-level components of the DBMS access directly the
same copy of the structured data through the bu®er. A drawback of this technique is
that it breaks the data encapsulation and decoupling between the low-level bu®er manager
component of the DBMS and the higher-level components. In addition, using this technique
it is fairly hard to support arbitrary user-de¯ned data types dynamically. Issues related with
this technique are discussed in more detail in [Zha05].
19.6 Disk Arrays
Single disk storage systems face some problems that require robust solutions towards two
main directions: 1. e±ciency ; and 2. availability . Regarding e±ciency, although bu®ering
o®ers a great boost in performance by keeping part of the data in fast storage, disk accesses
are still a bottleneck in I/O bounded applications (e.g., multimedia databases, scienti¯c
databases). Therefore, more e±cient secondary storage systems are required. Regarding
availability, a single disk system is vulnerable to a number of threats such as data loss
or unavailability of the whole system due to storage failure. Both cases are considered
catastrophic, taking into account that data is priceless and system failure is not an option
for systems that should be operational 24 hours a day, 7 days a week. Even though an
automated backup solution could alleviate the problem to a certain extend, data loss is still
Disk Storage and Basic File Structure 19-21
possible, especially for frequently updated databases. Essentially, availability re°ects the
percentage of time the data is available. If the disks fail, the time interval required to either
repair or substitute the disk has a signi¯cant impact on availability. The more time the
data remains o®-line the more the availability of the storage system is impacted. Taking
into account the previous observations, the question is if there is a mechanism that tackles
both problems (e±ciency and availability). The answer is a±rmative and one of the tools
to achieve this is the disk array.
19.6.1 E±ciency and Availability
The disk array has been introduced in [PGK88], under the term RAID (Redundant Array
of Inexpensive Disks) . In this section, we brie°y discuss e±ciency and availability issues,
which motivated the development of the disk array technology.
Assume that a computer system is supported by a single disk device. This means that
every I/O request is directed to a single queue and will be served by a single disk, according
to the scheduling technique supported. Without loss of generality, assume that FCFS is
used to serve the I/O requests. Figure 19.8(a) shows the contents of the I/O queue at a
given time instance. Let r1, r2, r3, r4, r5 and r6 be the pending requests, waiting for service,
where the request ri asks for the disk page pi. Moreover, let T(ri) denote the time required
for request ri to be served. Evidently, the time T1disk
total required to serve all requests is given
by:
T1disk
total =
X6
i=1
T(ri)
This means that request r6 will have to wait all previous requests to be served ¯rst. The
question is if the utilization of more disk devices can help in reducing the service time.
Assume that the data are distributed in three disks, such that disk pages p1, p2 reside on
the ¯rst disk, pages p3, p4 reside on the second disk, and pages p5, p6 reside on the third
disk. An I/O subsystem with three disks is illustrated in Figure 19.8(b). In this setting,
each disk maintains its own queue of requests. Therefore, a request for a speci¯c page is
directed to the appropriate disk queue and it is served by the corresponding disk device.
The time required to serve all requests is given by:
p1
p2
p3
p5
p6
p4
r6
r5
r4
r3
r2
r1
p1
p2
p3
p4
p5
p6
r2
r1
r4
r3
r6
r5
(a) I/O sybsystem with one disk (b) I/O sybsystem with three disks
FIGURE 19.8: I/O subsystems with one and three disks.
19-22
T3disks
total = maxfT(r1) + T(r2); T(r3) + T(r4); T(r5) + T(r6)g
The previous equation states that the time required to serve all six requests is dominated
by the \slowest" disk. The optimal speed-up with this approach would be T3disks
total = T1disk
total
3 .
Although it is hard to achieve such an ideal performance improvement, we expect that
the time required to serve all requests will decrease by increasing the number of disks.
Evidently, such an improvement is possible only if the requests are directed to di®erent disk
devices. For example, if all requests involve data that reside on the ¯rst disk only, then no
improvement is possible. However, by carefully distributing the data to the available disks,
signi¯cant performance improvements may be achieved. To achieve this, knowledge of data
access distribution is required in order to perform e®ective data placement.
Although the distribution of the data to more than one disks helps in improving perfor-
mance, it does not help in increasing availability. Data availability is strongly related to
fault tolerance , which measures the ability of a system to continue its normal operation even
if one (or more) of its components is non-operational. By using more than one disks, not
only fault tolerance is not being improved, but the probability that the disk array as a whole
will fail increases. An important disk parameter is the Mean Time To Failure (MTTF)
which is given in the disk speci¯cations provided by the manufacturer. This parameter
measures the average time interval which guarantees no hardware failure of the disk device.
The larger the MTTF the more reliable the disk is. The problem arises when more than one
disk is used. In such a case, if any of the disks fails the whole array has a major problem.
More formally, if MTTF(1) denotes the MTTF of a single disk and MTTF(N) denotes the
MTTF of an array of N identical disks, then the following holds:
MTTF(N) =
MTTF(1)
N
For example, if a disk device has an MTTF of 500,000 hours and 100 such disks are
used in an array, the MTTF of the array drops to 5,000 hours which is about 200 days of
operation. This simple example demonstrates that availability is seriously a®ected if we just
use multiple disks to solve the e±ciency problem. What we need is to improve e±ciency
without sacri¯cing data availability. This can be achieved by providing more intelligent
disk arrays, equipped with sophisticated mechanisms towards increased fault tolerance, by
surviving hardware failures. The primary target is to be able to access the data even if one
of the disks is non-operational.
19.6.2 RAID Levels
Research in disk array technology has focused on attacking the availability problem, by
incorporating sophisticated mechanisms. The result of this research is a number of proposals
which di®er with respect to data distribution across multiple disk devices. Therefore, a
number of di®erent RAID levels have appeared. The simplest approach is to just use
multiple disk devices where each disk hosts di®erent parts of the data. This approach is
termed RAID level 0 , and as it has been demonstrated in the previous section, it does not
tackle the availability problem at all, since it does not provide any fault tolerance (if a disk
fails, the corresponding data is not accessible). By using RAID level 0, only the e±ciency
issue is handled. Under this scheme, each data ¯le is split into a number of blocks, and the
blocks are distributed to the available disks without using any redundancy. This technique
is also known as data striping at the block level. An example is illustrated in Figure 19.9.
Disk Storage and Basic File Structure 19-23
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
D
1
D
2
D
3
D
4
FIGURE 19.9: RAID level 0.
The ¯rst RAID level which attacks the availability problem is the RAID level 1 , also
known as disk mirroring . In this approach, disks appear in pairs. Each pair contains exactly
the same data. Therefore, if one of the disks fails, the data are still available through its
mirror disk. Figure 19.10 illustrates a disk array based on RAID level 1. Data are written
in disks D and D0. It is evident that the con¯guration can survive only up to one disk
failure. Usually, RAID level 1 is combined with RAID level 0 to get larger con¯gurations
of mirrored groups of disks. The major advantage of this approach is its simplicity, since
no specialized hardware is required to perform the mirroring operation. However, this is
an expensive solution since essentially the number of disks is doubled, leading to increased
operational costs.
A
B
C
D
A
B
C
D
D D'
FIGURE 19.10: RAID level 1.
The ¯rst RAID level which uses striping enriched with redundancy information is the
RAID level 2 , also known as memory-style ECC . Data are split at the bit level, and
the bits are distributed across the number of available data disks. Additionally, a set of
parity disks is used hosting redundant information towards data recovery. The redundant
bits are calculated using Hamming codes, a form of Error Correcting Code (ECC). An
example disk array based on RAID level 2 is depicted in Figure 19.11. RAID level 2 is
not used in modern systems. It is an expensive solution which requires many disk devices.
Moreover, the controller required is complex and expensive to produce. In addition, the
error correction scheme provided is available today in single disk systems. Finally, the bit-
level striping utilized is not the best solution for transaction-oriented systems, since all disks
should be accessed to synthesize a part of the data, and therefore multiple disk accesses are
not possible.
Based on the fact that if a disk sector fails the disk itself can recognize the failing sector,
the majority of the redundant information maintained by RAID level 2 is not required.
19-24
A0
B0
C0
D0
A1
B1
C1
D1
A2
B2
C2
D2
A3
B3
C3
D3
D1 D2 D3 D4
ECC/Ax
ECC/Bx
ECC/Cx
ECC/Dx
ECC/Ay
ECC/By
ECC/Cy
ECC/Dy
ECC/Az
ECC/Bz
ECC/Cz
ECC/Dz
RD1 RD2 RD3
data disks redundant (parity) disks
FIGURE 19.11: RAID level 2.
This observation motivated the development of RAID level 3 , which is also known as bit-
interleaved parity scheme , as an attempt to reduce the number of parity disks used. Parity
information is maintained in a single redundant disk, which is reserved speci¯cally for this
purpose. An example con¯guration of a RAID level 3 array is illustrated in Figure 19.12.
The data block A is striped across disks (parts a1 through a7). The parity information of
stripes is generated while the block is written to the disks and it is stored on the redundant
disk. When a read request is performed the parity is checked to ensure that data are correct
(no errors occurred). In case of a disk failure the data can be recovered by using the data
from the other data disks and the parity information stored on the redundant disk.
a0
b0
c0
d0
D1 D2 D3 D4 RD
data disks redundant (parity) disk
a4
b4
c4
d4
a1
b1
c1
d1
a5
b5
c5
d5
a2
b2
c2
d2
a6
b6
c6
d6
a3
b3
c3
d3
a7
b7
c7
d7
Parity a0-3
Parity b0-3
Parity c0-3
Parity d0-3
Parity a4-7
Parity b4-7
Parity c4-7
Parity d4-7
FIGURE 19.12: RAID level 3.
The next level, RAID level 4 , also known as block-interleaved parity scheme , follows the
same procedure as in RAID level 3 to store parity information | Each ¯le is partitioned
into blocks and the parity information is stored on a single disk. However, each block is
not striped across disks. Instead, whole data blocks are distributed to the available disks.
Parity information is generated during write operations and veri¯ed during read operations.
By utilizing whole data blocks instead of striping individual bits RAID level 4 improves the
performance of random access operations in comparison to RAID level 3. This is because
locality of reference is better preserved due to the decomposition into blocks which leads
to multiple read requests being served by di®erent disks. However, the single parity disk
may become a bottleneck when there is a large number of write requests. An example of a
RAID level 4 array is depicted in Figure 19.13. The data ¯le A is decomposed into blocks
Disk Storage and Basic File Structure 19-25
A0 through A3 and these blocks are distributed across the data disks. The same applies for
data ¯les B, C and D.
A0
B0
C0
D0
A1
B1
C1
D1
A2
B2
C2
D2
A3
B3
C3
D3
D1 D2 D3 D4
Parity A
Parity B
Parity C
Parity D
RD
data disks redundant (parity) disk
FIGURE 19.13: RAID level 4.
The next RAID level, RAID level 5 , known as block-interleaved distributed parity scheme
, di®ers from levels 2, 3 and 4 in that parity information is also distributed across data disks.
Therefore, no dedicated parity disk is being used. Striping is performed at the block level
(like RAID level 4) but instead of using a dedicated parity disk, parity information is stored
on the data disks. Again, parity is generated during write requests and it is veri¯ed during
read requests. This method improves the performance of random write operations, since no
single parity disk is being used. Fault tolerance is maintained by ensuring that the parity
information is stored in a di®erent disk than the disk containing the corresponding data.
Therefore, the array can survive the failure of a single disk device, since the data that has
been lost can be reconstructed by using the operational disks and parity information stored
therein. RAID level 5 is one of the most popular disk array con¯gurations. An example
RAID level 5 con¯guration with ¯ve disks is illustrated in Figure 19.14.
A0
B0
C0
D0
A1
B1
C1
Parity D
A2
B2
Parity C
D1
A3
Parity B
C2
D2
D1 D2 D3 D4
Parity A
B3
C3
D3
D5
Parity E E0 E1 E2 E3
FIGURE 19.14: RAID level 5.
RAID level 6 is an extension of RAID level 5 towards better fault tolerance. RAID
levels 2, 3, 4 and 5 can survive up to one disk failure, whereas RAID level 6 can survive
two disk failures. To achieve this, dual parity information is utilized, which is based on
Reed-Solomon codes (p+q parity). This is why the RAID scheme is called p+q redundancy
19-26
scheme as well . Generating the dual parity information during writes results in a small
performance degradation. However, this con¯guration is slightly more e±cient during reads
in some cases. An example RAID level 6 con¯guration is depicted in Figure 19.15.
A0
B0
C0
A1
B1
Parity Cp
A2
Parity Bp Parity Bq
Parity Cq
Parity Ap
C1
D1
D1 D2 D3 D4
Parity Aq
B2
C2
D2
D5
Parity Dp Parity Dq D0
FIGURE 19.15: RAID level 6.
19.6.3 Combinations of RAID Levels
In addition to the aforementioned fundamental RAID levels, some combinations have been
proposed to combine the power of more than one levels, towards better performance. Such
a combined (or nested) RAID scheme is termed \RAID X+Y", where X is the RAID level
applied ¯rst and Y is the level applied on top. Because of the nesting applied, these RAID
level combinations are also known as nested RAID schemes .
The most commonly combined level is RAID level 0, which is put together to work with
levels 1, 3 and 5. This way, the increased performance of RAID level 0 is combined with
the increased fault tolerance provided by the other schemes. The result is a powerful disk
array with increased e±ciency and data availability.
Two examples are given in Figure 19.16 and 19.17. In the ¯rst scheme (Figure 19.16),
RAID level 0 is applied ¯rst (inner scheme) and then RAID level 1 is applied on top of it
(outer scheme). In the second scheme (Figure 19.17) RAID level 1 is applied ¯rst, and then
RAID level 0 is applied on top. Both con¯gurations o®er excellent overall performance by
combining the e±ciency contributed by RAID level 0 and data redundancy provided by the
mirroring scheme of RAID level 1. Other important RAID combinations that are frequently
used include RAID 0+3, RAID 3+0, RAID 0+5, RAID 5+0, RAID 1+5 and RAID 5+1.
19.6.4 Summary of RAID Schemes
Selecting the appropriate disk array scheme for a speci¯c application is not an easy task.
A RAID level which performs well in a particular application, does not mean that it will
perform equally well in another. Moreover, the choice should be based on other criteria as
well, such as the operational cost, the degree of availability o®ered, the number of disks
allowed, etc. Table 19.2 presents a qualitative comparison that summarizes the aforemen-
tioned RAID schemes (except level 2 which is not commercially supported), and helps in
identifying fundamental di®erences between RAID levels. An extensive comparison of RAID
levels can be found in [CLG+94, ASV+02].
Disk Storage and Basic File Structure 19-27
A
B
C
D
E
F
G
H
I
J
K
L
D
1
D
2
D
3
A
B
C
D
E
F
G
H
I
J
K
L
D
1
' D
2
' D
3
'
RAID level 0 RAID level 0
RAID level 1
FIGURE 19.16: RAID level 0+1.
A
B
C
D
E
F
G
H
I
J
K
L
D
1
D
2
D
3
A
B
C
D
E
F
G
H
I
J
K
L
D
1
' D
2
' D
3
'
RAID level 0
RAID level 1 RAID level 1 RAID level 1
FIGURE 19.17: RAID level 1+0.
characteristic 0 1 3 4 5 6 0+1 1+0
min #disks 2 2 3 3 3 4 4 4
fault tolerance none ² ² ²² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ²² ² ² ²²
availability ² ² ² ²² ² ² ²² ² ² ²² ² ² ²² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ²
read e±ciency ² ² ²² ² ² ² ² ² ² ² ² ²² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ² ²
write e±ciency ² ² ²² ² ² ² ² ²² ²² ² ² ² ²² ² ² ²²
cost ² ²² ²² ²² ²² ² ² ² ² ² ² ² ² ²
TABLE 19.2 Qualitative comparison of RAID schemes.
19.7 Storage Systems
Connecting storage devices to computer systems is an important issue that needs to be
handled appropriately, since the scheme used may impact storage capacity, performance,
scalability, availability and other factors as well. There are three basic storage technologies
used in modern computer systems: 1. Direct-Attached Storage (DAS); 2. Network-Attached
Storage (NAS); and 2. Storage Area Network (SAN).
Figure 19.18 depicts how storage is connected to a computer system by using DAS, NAS
or SAN. In the sequel we brie°y discuss the most important properties and di®erentiations
of these schemes.
DAS technology is the simplest and the most fundamental scheme. Storage devices (e.g.,
19-28
(a) DAS
LAN
NAS storage
(b) NAS
LAN
NAS storage
disks
tapes
(c) SAN
IDC
server
LAN
FDDI
SCSI bus
disks tapes
IDC
workstation workstation workstation server workstation workstation
IDC
server
IDC
server
disks
optical
FIGURE 19.18: Connecting storage devices to computer systems.
disks, tapes) are directly connected (attached) to a server, by using an I/O interconnection
interface (e.g., IDE, SCSI), as it is illustrated in Figure 19.18(a). The strong connection
between the server and storage devices has a direct impact on the way clients access the
data. Workstations that require data access have to connect to the appropriate server ¯rst
and then retrieve (or store) the data. Every data exchange operation between a client and
a storage device has to be processed by the server. This simple scheme, although widely
used due to its relatively low cost, has two major disadvantages:
1. If the server is non-operational due to a failure, data are not accessible at all,
which has a direct impact on data availability.
2. The server in addition to providing access to the stored data has the responsibility
of running applications. Therefore, performance degradation may be observed,
especially when the demands for data access are high.
To alleviate the aforementioned problems, another storage technology has been proposed
which is based on dedicated machines for ¯le sharing. NAS technology is a simple and cost-
e®ective alternative for enterprises that require data sharing at the ¯le level. A NAS device
is composed of both management software and storage devices. In contrast to DAS, a NAS
device is a dedicated server whose responsibility is just to provide access to the stored data
over a network. Applications are supported by other dedicated servers of the network. This
scheme o®ers better utilization of processing and storage resources, since these operations
are supported by di®erent machines. Moreover, scalability is easily achieved either by adding
more storage devices on a NAS device, or by installing more NAS devices on the network.
An example con¯guration composed of two workstations and two NAS devices is illustrated
in Figure 19.18(b).
The major disadvantage of NAS is that since NAS devices are attached to the Local Area
Network (LAN) network utilization may degrade when a large amount of information has
to be exchanged among servers. This is not uncommon in bandwidth intensive applications
such as multimedia processing. To attack the problem, SAN has been proposed, which
o®ers excellent performance and data availability. A SAN is a dedicated storage network
with the ability to e±ciently transfer data among storage devices and servers at the block
level (in contrast to DAS and NAS which transfer data at the ¯le level). This is achieved
by using a ¯ber ring (separately from the LAN) which is a gigabit network allowing data
Disk Storage and Basic File Structure 19-29
transfers at very high rates. The key issue in this scheme is that data transfers between
a server and a storage device do not interfere with other data traveling through the LAN.
SAN o®ers signi¯cantly better network utilization, scalability and data availability than
any other storage technology. Despite the great advantages of SAN over DAS and NAS,
several enterprises do not adopt it due its high cost. The main reason for the increased cost
is the use of the ¯ber ring. Each device installed in the SAN requires a ¯ber interface. An
example SAN con¯guration is depicted in Figure 19.18(c).
19.8 Conclusions
Storage media are of vital importance in order for computer systems to store and organize
information e®ectively and e±ciently. Storage media compose a hierarchy (the memory hi-
erarchy) with primary storage (registers, cache and main memory) lying at the top, tertiary
storage (tapes, optical disks and °ash memory) lying at the bottom and secondary stor-
age (hard disks) bridging the gap. In this chapter we brie°y described all members of the
memory hierarchy and primarily focused on the hard disk drive, which is still a ubiquitous
secondary storage media found in almost every computer system to date.
The performance of a database management system is greatly a®ected by how the data
is organized and represented in secondary storage, as well as by the strategies used to
transfer the data in and out of main memory for processing. In this chapter we explored
the low-level operational characteristics of the hard drive and saw how we can exploit this
knowledge to store information in ways that maximize the transfer rate between disks and
main memory. At a higher level, we saw how to e®ectively represent data using ¯xed-
length and variable-length records, in order to support e±cient data updates, insertions
and deletions, by minimizing the unused space within pages and ¯les. Finally, we discussed
bu®ering techniques that make e±cient use of available main memory, in order to minimize
the number of disk accesses required to complete a given task.
Aside from using a single data storage device e®ectively, we also discussed a number
of architectures suitable for storing data in multiple storage devices concurrently, thus
exploiting possible parallelism when accessing the data. Additionally, we discussed a number
of strategies that can be used to increase data availability and storage reliability, using
replication and error correcting codes. Finally, we discussed the prevailing technologies for
connecting secondary and tertiary storage devices to a computer system, which is a factor
that can greatly a®ect data availability and storage performance in heavily utilized server
systems.