Chapter 13: Data Storage Structures

Database System Concepts, 7th Ed.

©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
 File Organization
 The database is stored as a collection of files. Each file is a
sequence of records. A record is a sequence of fields.
 One approach
• Assume record size is fixed
• Each file has records of one particular type only
• Different files are used for different relations
This case is easiest to implement; will consider variable length
records later
 We assume that records are smaller than a disk block
.

Database System Concepts - 7th Edition 13.2 ©Silberschatz, Korth and Sudarshan
 Fixed-Length Records

 Simple approach:
• Store record i starting from byte n ∗ (i – 1), where n is the size of
each record.
• Record access is simple but records may cross blocks
 Modification: do not allow records to cross block boundaries

Database System Concepts - 7th Edition 13.3 ©Silberschatz, Korth and Sudarshan
 Fixed-Length Records

 Deletion of record i: alternatives:

• move records i + 1, . . ., n to i, . . . , n – 1
• move record n to i
• do not move records, but link all free records on a free list
Record 3 deleted

Database System Concepts - 7th Edition 13.4 ©Silberschatz, Korth and Sudarshan
 Fixed-Length Records

 Deletion of record i: alternatives:

• move records i + 1, . . ., n to i, . . . , n – 1
• move record n to i
• do not move records, but link all free records on a free list
Record 3 deleted and replaced by record 11

Database System Concepts - 7th Edition 13.5 ©Silberschatz, Korth and Sudarshan
 Fixed-Length Records

 Deletion of record i: alternatives:

• move records i + 1, . . ., n to i, . . . , n – 1
• move record n to i
• do not move records, but link all free records on a free list

Database System Concepts - 7th Edition 13.6 ©Silberschatz, Korth and Sudarshan
 Variable-Length Records

 Variable-length records arise in database systems in several ways:

• Storage of multiple record types in a file.
• Record types that allow variable lengths for one or more fields such
as strings (varchar)
• Record types that allow repeating fields (used in some older data
models).
 Attributes are stored in order
 Variable length attributes represented by fixed size (offset, length), with
actual data stored after all fixed length attributes
 Null values represented by null-value bitmap

Database System Concepts - 7th Edition 13.7 ©Silberschatz, Korth and Sudarshan
 Variable-Length Records: Slotted Page Structure

 Slotted page header contains:

• number of record entries
• end of free space in the block
• location and size of each record
 Records can be moved around within a page to keep them contiguous
with no empty space between them; entry in the header must be
updated.
 Pointers should not point directly to record — instead they should
point to the entry for the record in header.

Database System Concepts - 7th Edition 13.8 ©Silberschatz, Korth and Sudarshan
 Storing Large Objects
 E.g. blob/clob types
 Records must be smaller than pages
 Alternatives:
• Store as files in file systems
• Store as files managed by database
• Break into pieces and store in multiple tuples in separate relation
 PostgreSQL TOAST

Database System Concepts - 7th Edition 13.9 ©Silberschatz, Korth and Sudarshan
 Organization of Records in Files

 Heap – record can be placed anywhere in the file where there is

space
 Sequential – store records in sequential order, based on the value
of the search key of each record
 In a multitable clustering file organization records of several
different relations can be stored in the same file
• Motivation: store related records on the same block to
minimize I/O
 B+-tree file organization
• Ordered storage even with inserts/deletes
• More on this in Chapter 14
 Hashing – a hash function computed on search key; the result
specifies in which block of the file the record should be placed
• More on this in Chapter 14
Database System Concepts - 7th Edition 13.10 ©Silberschatz, Korth and Sudarshan
 Heap File Organization
 Records can be placed anywhere in the file where there is free space
 Records usually do not move once allocated
 Important to be able to efficiently find free space within file
 Free-space map
• Array with 1 entry per block. Each entry is a few bits to a byte,
and records fraction of block that is free
• In example below, 3 bits per block, value divided by 8 indicates
fraction of block that is free

• Can have second-level free-space map

• In example below, each entry stores maximum from 4 entries of
first-level free-space map

 Free space map written to disk periodically, OK to have wrong (old)

values for some entries (will be detected and fixed)
Database System Concepts - 7th Edition 13.11 ©Silberschatz, Korth and Sudarshan
 Sequential File Organization
 Suitable for applications that require sequential processing of
the entire file
 The records in the file are ordered by a search-key

Database System Concepts - 7th Edition 13.12 ©Silberschatz, Korth and Sudarshan
 Sequential File Organization (Cont.)
 Deletion – use pointer chains
 Insertion –locate the position where the record is to be inserted
• if there is free space insert there
• if no free space, insert the record in an overflow block
• In either case, pointer chain must be updated
 Need to reorganize the file
from time to time to restore
sequential order

Database System Concepts - 7th Edition 13.13 ©Silberschatz, Korth and Sudarshan
 Multitable Clustering File Organization
Store several relations in one file using a multitable clustering
file organization

department

instructor

multitable clustering
of department and
instructor

Database System Concepts - 7th Edition 13.14 ©Silberschatz, Korth and Sudarshan
 Multitable Clustering File Organization (cont.)

 good for queries involving department ⨝ instructor, and for

queries involving one single department and its instructors
 bad for queries involving only department
 results in variable size records
 Can add pointer chains to link records of a particular relation

Database System Concepts - 7th Edition 13.15 ©Silberschatz, Korth and Sudarshan
 Partitioning
 Table partitioning: Records in a relation can be partitioned into
smaller relations that are stored separately
 E.g. transaction relation may be partitioned into
transaction_2018, transaction_2019, etc.
 Queries written on transaction must access records in all partitions
• Unless query has a selection such as year=2019, in which case
only one partition in needed
 Partitioning
• Reduces costs of some operations such as free space
management
• Allows different partitions to be stored on different storage
devices
 E.g. transaction partition for current year on SSD, for older
years on magnetic disk

Database System Concepts - 7th Edition 13.16 ©Silberschatz, Korth and Sudarshan
 Data Dictionary Storage
The Data dictionary (also called system catalog) stores
metadata; that is, data about data, such as
 Information about relations
• names of relations
• names, types and lengths of attributes of each relation
• names and definitions of views
• integrity constraints
 User and accounting information, including passwords
 Statistical and descriptive data
• number of tuples in each relation
 Physical file organization information
• How relation is stored (sequential/hash/…)
• Physical location of relation
 Information about indices (Chapter 14)

Database System Concepts - 7th Edition 13.17 ©Silberschatz, Korth and Sudarshan
 Relational Representation of System Metadata

 Relational
representation on
disk
 Specialized data
structures
designed for
efficient access, in
memory

Database System Concepts - 7th Edition 13.18 ©Silberschatz, Korth and Sudarshan
 Storage Access
 Blocks are units of both storage allocation and data transfer.
 Database system seeks to minimize the number of block transfers
between the disk and memory. We can reduce the number of disk
accesses by keeping as many blocks as possible in main memory.
 Buffer – portion of main memory available to store copies of disk
blocks.
 Buffer manager – subsystem responsible for allocating buffer space
in main memory.

Database System Concepts - 7th Edition 13.19 ©Silberschatz, Korth and Sudarshan
 Buffer Manager
 Programs call on the buffer manager when they need a block from
disk.
• If the block is already in the buffer, buffer manager returns the
address of the block in main memory
• If the block is not in the buffer, the buffer manager
 Allocates space in the buffer for the block
• Replacing (throwing out) some other block, if required, to
make space for the new block.
• Replaced block written back to disk only if it was modified
since the most recent time that it was written to/fetched
from the disk.
 Reads the block from the disk to the buffer, and returns the
address of the block in main memory to requester.

Database System Concepts - 7th Edition 13.20 ©Silberschatz, Korth and Sudarshan
 Buffer Manager
 Buffer replacement strategy (details coming up!)
 Pinned block: memory block that is not allowed to be written back to disk
• Pin done before reading/writing data from a block
• Unpin done when read /write is complete
• Multiple concurrent pin/unpin operations possible
 Keep a pin count, buffer block can be evicted only if pin count = 0
 Shared and exclusive locks on buffer
• Needed to prevent concurrent operations from reading page contents
as they are moved/reorganized, and to ensure only one
move/reorganize at a time
• Readers get shared lock, updates to a block require exclusive lock
• Locking rules:
 Only one process can get exclusive lock at a time
 Shared lock cannot be concurrently with exclusive lock
 Multiple processes may be given shared lock concurrently

Database System Concepts - 7th Edition 13.21 ©Silberschatz, Korth and Sudarshan
 Buffer-Replacement Policies
 Most operating systems replace the block least recently used (LRU
strategy)
• Idea behind LRU – use past pattern of block references as a
predictor of future references
• LRU can be bad for some queries
 Queries have well-defined access patterns (such as sequential
scans), and a database system can use the information in a user’s
query to predict future references
 Mixed strategy with hints on replacement strategy provided
by the query optimizer is preferable
 Example of bad access pattern for LRU: when computing the join of 2
relations r and s by a nested loops
for each tuple tr of r do
for each tuple ts of s do
if the tuples tr and ts match …

Database System Concepts - 7th Edition 13.22 ©Silberschatz, Korth and Sudarshan
 Buffer-Replacement Policies (Cont.)
 Toss-immediate strategy – frees the space occupied by a block as soon
as the final tuple of that block has been processed
 Most recently used (MRU) strategy – system must pin the block
currently being processed. After the final tuple of that block has been
processed, the block is unpinned, and it becomes the most recently used
block.
 Buffer manager can use statistical information regarding the probability
that a request will reference a particular relation
• E.g., the data dictionary is frequently accessed. Heuristic: keep
data-dictionary blocks in main memory buffer
 Operating system or buffer manager may reorder writes
• Can lead to corruption of data structures on disk
 E.g. linked list of blocks with missing block on disk
 File systems perform consistency check to detect such situations
• Careful ordering of writes can avoid many such problems

Database System Concepts - 7th Edition 13.23 ©Silberschatz, Korth and Sudarshan
 Optimization of Disk Block Access (Cont.)

 Buffer managers support forced output of blocks for the purpose of recovery
(more in Chapter 19)
 Nonvolatile write buffers speed up disk writes by writing blocks to a non-
volatile RAM or flash buffer immediately
• Writes can be reordered to minimize disk arm movement
 Log disk – a disk devoted to writing a sequential log of block updates
• Used exactly like nonvolatile RAM
 Write to log disk is very fast since no seeks are required
 Journaling file systems write data in-order to NV-RAM or log disk
• Reordering without journaling: risk of corruption of file system data

Database System Concepts - 7th Edition 13.24 ©Silberschatz, Korth and Sudarshan
 Column-Oriented Storage
 Also known as columnar representation
 Store each attribute of a relation separately

Database System Concepts - 7th Edition 13.25 ©Silberschatz, Korth and Sudarshan
 Columnar Representation
 Benefits:
• Reduced IO if only some attributes are accessed
• Improved CPU cache performance
• Improved compression
• Vector processing on modern CPU architectures
 Drawbacks
• Cost of tuple reconstruction from columnar representation
• Cost of tuple deletion and update
• Cost of decompression
 Columnar representation found to be more efficient for decision
support than row-oriented representation
 Traditional row-oriented representation preferable for transaction
processing
 Some databases support both representations
• Called hybrid row/column stores
Database System Concepts - 7th Edition 13.26 ©Silberschatz, Korth and Sudarshan
 Columnar File Representation
 ORC and Parquet: file
formats with columnar
storage inside file
 Very popular for big-data
applications
 Orc file format shown on
right:

Database System Concepts - 7th Edition 13.27 ©Silberschatz, Korth and Sudarshan
 Storage Organization in
Main-Memory Databases
 Can store records directly in
memory without a buffer
manager
 Column-oriented storage can be
used in-memory for decision
support applications
• Compression reduces
memory requirement

Database System Concepts - 7th Edition 13.28 ©Silberschatz, Korth and Sudarshan
 End of Chapter 13

Database System Concepts - 7th Edition 13.29 ©Silberschatz, Korth and Sudarshan