CS 540 Database Management Systems

CS 540 Database Management Systems RDBMS Architecture and Data Storage

Database Implementation User Requirements SQL Physical Storage Conceptual Design Schema Data Relational Model Entity Relationship(ER) Model Files and Indexes

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.

Issues in logical level • Data models • Relational, XML , … • Query language • Data quality • normalization • Usability • ...

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.

Storage capacity versus access time offline tape nearline tape & optical disks 1015 1013 magnetic optical disks 1011 electronic secondary online tape 109 typical capacity (bytes) electronic main 107 from Gray & Reuter updated in 2002 105 cache 103 103 10-9 10-6 10-3 10-0 access time (sec)

Storage cost versus access time from Gray & Reuter 104 cache electronic main online tape 102 electronic secondary magnetic optical disks nearline tape & optical disks dollars/MB 100 10-2 offline tape 10-4 103 10-9 10-6 10-3 10-0 access time (sec)

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.

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

Issues in physical level • Other things also matter • Reliability when it comes to transactions. • … • But performance is still a big deal.

Is it easy to achieve good performance? • Let’s build an RDBMS. • It supports core SQL. • No stored procedure for this version!

Storing Data • Store each relation in an ASCII file: Person (SSN, Name, Age) person.txt: 111222333 - John - 24 444222111 - Charles - 43

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

SQL Support • SQL compiler • Like any other computer language compiler. SELECT SSN FROM Person; SSN 111222333 444222111

Query Execution: Selection • Find the selection attribute position from the catalogue. • Scan the file that contains the relation. • Show the tuples that satisfy the condition. SELECT * FROM Person WHERE SSN = 111222333;

Query Execution: Join • Read the catalogue to find the info on join attributes. • Read the first relation, for each tuple: • Read the second relation, for each tuple: • Assemble the join tuple • Output if they satisfy the condition. SELECT * FROM Person, PersonAddr WHERE Person.SSN = PersonAddr.SSN and Person.SSN = 111222333;

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

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;

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;

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;

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;

Reliability • Update the name in person • Power outage  is the operation done? • Disk crash Update Person SET Name = Smith WHERE Person.SSN = 111222333;

Probably not that many people download our RDBMS  • Let’s redesign the components of our RDBMS

Database Implementation Data storage User Requirements Physical Storage Conceptual Design Schema Relational Model Entity Relationship(ER) Model Files and Indexes

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

Units of data on physical device • Fields: data items • Records • Blocks • Files

Fields • Fixed size • Integer, Boolean, … • Variable length • Varchar, … • Null terminated • Size at the beginning of the string

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

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

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.

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, …

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.

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.

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 Physical address on disk Logical address

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

Row and Column Stores • We have talked about row store • All fields of a record are stored together.

Row and Column Stores • We can store the fields in columns. • We can store SSNs implicitly.

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.

CS 540 Database Management Systems