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CS 440 Database Management Systems

RDBMS Architecture and Data Storage

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Announcements

• Normal form and FD practice session on Feb 4th in the class.

• Assignment 1 due on Feb 7th – Submission through TEACH.

• Project progress report due on Feb 11th

– 1 – 2 pages of status report– Submission through TEACH

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Database Implementation

Conceptual Design

Physical Storage

Schema

Entity Relationship(ER)

Model

Relational Model Files and Indexes

User Requirements

SQL

Data

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The big advantage of RDBMS

• It separates logical level (schema) from physical level (implementation).

• Physical data independence– Users do not worry about how their data is stored and

processes on the physical devices.– It is all SQL!– Their queries work over (almost) all RDBMS

deployments.

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Issues in logical level

• Data models – Relational, XML , …

• Query language• Data quality– normalization

• Usability• ...

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Issues on physical level

• Processor: 100 – 1000 MIPS• Main memory: 1μs – 1 ns• Secondary storage: higher capacity and durability• Disk random access : Seek time + rotational

latency + transfer time– Seek time: 4 ms - 15 ms!– Rotational latency: 2 ms – 7 ms!– Transfer time: around 1000 Mb/ sec– Read, write in blocks.

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Storage capacity versus access time

10-9 10-6 10-3 10-0 103

access time (sec)

1015

1013

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107

105

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cache

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from Gray & Reuterupdated in 2002

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Storage cost versus access time

10-9 10-6 10-3 10-0 103

access time (sec)

104

102

100

10-2

10-4

cache

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opticaldisks

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from Gray & Reuter

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Gloomy future: Moor’s law

• Speed of processors and cost and maximum capacity of storage increase exponentially over time.

• But storage (main and secondary) access time grows much more slowly.

• This is why managing and analyzing big data is hard.

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Issues in physical level

Three things are important in the database systems: performance, performance, and performance! ( Bruce Lindsay, co-creator of System R)

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Issues in physical level

• Other things also matter– Reliability when it comes to transactions.–…

• But performance is still a big deal.

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Is it easy to achieve good performance?

• Let’s build an RDBMS.• It supports core SQL.• No stored procedure for this version!

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Storing Data

• Store each relation in an ASCII file:

Person (SSN, Name, Age)

person.txt: 111222333 - John - 24 444222111 - Charles - 43

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Storing Data

• Store schema information in a catalogue relation:

Catalogue (AttrName, Type, RelName, Position)

catalogue.txt: SSN - String – Person - 1 Name - String - Person - 2 Age – Integer – Person - 3

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SQL Support

• SQL compiler • Like any other computer language compiler.

SELECT SSN FROM Person;

SSN 111222333 444222111

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Query Execution: Selection

1. Find the selection attribute position from the catalogue.

2. Scan the file that contains the relation.

3. Show the tuples that satisfy the condition.

SELECT * FROM Person WHERE SSN = 111222333;

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Query Execution: Join1. Read the catalogue to find the info on join

attributes.

2. Read the first relation, for each tuple: a. Read the second relation, for each tuple:

b. Assemble the join tuple

c. Output if they satisfy the condition.

SELECT *FROM Person, PersonAddrWHERE Person.SSN = PersonAddr.SSNand Person.SSN = 111222333;

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Performance Issues: Storing Data

• Update John to Sheldon– Rewrite the whole file very slow– Type conversion slow

• Delete the tuple with SSN of 111222333.

Person (SSN, Name, Age)

person.txt: 111222333 - John - 24 444222111 - Charles - 43

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Performance Issues: Selection

• We have to scan the whole relation to select some tuples very slow

• We can use an index to find the tuples much fasters.

SELECT * FROM Person WHERE SSN = 111222333;

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Performance Issues: Selection

• Read tuples one by one–Much faster if we read a whole bunch of them

together: caching

SELECT * FROM Person WHERE SSN = 111222333;

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Performance Issues: Join

• Quadratic I/O access– Very slow for large relations

SELECT * FROM Person, PersonAddr WHERE Person.SSN = PersonAddr.SSN and Person.SSN = 111222333;

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Performance Issues: Query Execution

• Two ways of executing the query– First join, then select– First select, then join much faster

• Query (execution) optimization.

SELECT * FROM Person, PersonAddr WHERE Person.SSN = PersonAddr.SSN

and Person.SSN = 111222333;

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Reliability

• Update the name in person– Power outage is the operation done? – Disk crash

Update Person SET Name = Smith WHERE Person.SSN = 111222333;

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Probably not that many people download our RDBMS

• Let’s redesign the components of our RDBMS

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Database Implementation

Conceptual Design

Physical Storage

Schema

Entity Relationship(ER)

Model

Relational Model Files and Indexes

User Requirements Data storage

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Random access versus sequential access

• Disk random access : Seek time + rotational latency + transfer time.

• Disk sequential access: reading blocks next to each other

• No seek time or rotational latency • Much faster than random access

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Units of data on physical device

• Fields: data items• Records• Blocks• Files

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Fields

• Fixed size– Integer, Boolean, …

• Variable length– Varchar, …– Null terminated– Size at the beginning of the string

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Records: Sets of Fields

• Schema– Number of fields, types of fields, order, …

• Fixed format and length– Record holds only the data items

• Variable format and length– Record holds fields and their size, type, …

information

• Range of formats in between

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Record Header

• Pointer to the record schema ( record type)• Record size• Timestamp• Other info …

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Blocks

• Collection of records• Reduces number of I/O access• Different from OS blocks–Why should RDBMS manage its own blocks?– It knows the access pattern better than OS.

• Separating records in a block– Fixed size records: no worry!–Markers between records– Keep record size information in records or block

header.

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Spanned versus un-spanned

• Unspanned– Each records belongs to only one block

• Spanned– Records may store across multiple records– Saves space– The only way to deal with large records and fields:

blob, image, …

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Heap versus Sorted Files

• Heap files– There is not any order in the file– New blocks (records) are inserted at the end of file.

• Sorted files– Order blocks (and records) based on some key.– Physically contiguous or using links to the next

blocks.

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Average Cost of Data Operations

• Insertion – Heap files are more efficient.– Overflow areas for sorted files.

• Search for a record– Sorted files are more efficient.

• Search for a range of records– Sorted files are more efficient.

• Deletion– Heap files are more efficient – Although we find the record faster in the sorted file.

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Indirection

• The address of a record on the disk• Physical address – Device ID, Cylinder #, Track #, …

• Map physical addresses to logical addresses– Flexible in moving records for insertion and deletion– Costly lookup–Many options in between, tradeoff

Rec ID Physical Address

Logical address

Physical address on disk

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Block Header

• Data about block• File, relation, DB IDs • Block ID and type• Record directory• Pointer to free space• Timestamp• Other info …

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Row and Column Stores

• We have talked about row store– All fields of a record are stored together.

SSN1 Name1 Age1 Salary1SSN2 Name2 Age2 Salary2SSN3 Name3 Age3 Salary3

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Row and Column Stores

• We can store the fields in columns.–We can store SSNs implicitly.

SSN1 Name1SSN2 Name2SSN3 Name3

SSN1 Age1SSN2 Age2SSN3 Age3

SSN1 Salary1SSN2 Salary2SSN3 Salary3

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Row versus column store

• Column store– Compact storage– Faster reads on data analysis and mining operations

• Row store– Faster writes – Faster reads for record access (OLTP)

• Further reading–Mike Stonebreaker, et al, “C-Store, a column oriented

DBMS”, VLDB’05.