Comp 305-001/488-305: Database Administration

Spring 2016: Tuesdays, 4:15-6:45, Corboy L08

Week 1

Read in Elmasri & Navathe (EN). Chapter numberings are (7th edition)/(6th edition)
Course work:

Tables
DBA
Installation
History/Maps/Terminology
NoSQL
Simple queries
Joins
Keys and Foreign Keys

What is a database?

Mainly, a set of related tables. Each table is a set of records (rows, items) of similar type.
Example: the company database. Let's start with company.pdf.

The term "database" sometimes refers to the software package, eg MySQL or Postgres, but most modern databases also use it to refer to a particular collection of related tables. Thus Postgres might support multiple databases, each with different names. The company database might be one; the university database might be another.

We interact with databases through queries, using the SQL language. Here are a few very simple read-only (data access) queries, involving only the employee table.

select * from employee e where e.ssn = '333445555';    -- one unique result
select e.fname, e.lname, e.salary from employee e where e.super_ssn = '333445555';    -- multiple results
select e.lname, e.ssn, e.salary from employee e where e.salary >= 30000;   

We will consistently use table-alias variables, like e above, although SQL does not absolutely require them. We'll return to SQL below, but for now note that each query has three parts:
The queries above involved just one table. The most interesting queries involve joins of two or more tables. The join condition defines a relationship between the tables.

list each employee with their department name:
select e.fname, e.lname, d.dname from employee e join department d on e.dno = d.dnumber; 

We used the explicit join notation above, in which the from clause lists a single, joined table. The implicit join notation would be as follows; the join condition is now moved to the where clause. It is deprecated and should not be used in this class.

select e.fname, e.lname, d.dname from employee e, department d where e.dno = d.dnumber; 

Why didn't we just put the department name in the employee table?

list employees working on project 2, with hours:
select e.fname, e.lname, w.hours, w.pno from employee e join works_on w on e.ssn = w.essn where w.pno = 2;

Why didn't we just put the employee's projects in the employee table? This has a very different answer!

Here's a join involving three tables. How would you describe it in English?

select e.fname, e.lname, p.pname
from employee e join works_on w on e.ssn = w.essn join project p on w.pno = p.pnumber
where w.hours >= 10;

Why don't we put everything into one table? Why do we have multiple tables at all? Here's a query that would give us the employee, works_on and project tables in one large table (with some columns edited out)

select e.fname, e.lname, e.salary, w.hours, p.pname, p.plocation
from employee e join works_on w on e.ssn = w.essn join project p on w.pno = p.pnumber;

Suppose somebody tells you "we can just keep one spreadsheet with the above information", and that therefore you don't need a database with multiple tables! What could possibly go wrong?

What if we want to change the name of ProductX to FooMatic? What if we want to give Franklin Wong a raise? What if Reorganization moves from Houston to Stafford? In each case, we need to update multiple records to make one change. What if we miss a record?

Division of a database into multiple "independent" tables is a very important practice. The goal of the decomposition is to retain all the original information while at the same time avoiding the data-consistency problem of having multiple locations that should have the same value, but might not. We "reconnect" separate tables using the join operation. Unfortunately, if we have a lot of data, joins aren't always as efficient as we'd like.

What does a DBA (Database Administrator) do?

Here's a traditional, somewhat Oracle-centric list:

We might add a couple broader missions to this list:

Our goal in this course is to cover the fundamentals of database operation, focusing primarily but not exclusively on the relational model, and addressing both theoretical and practical concerns. We will, in the process of doing this, cover a fair bit of SQL.

The Oracle Database Administrator's Guide lists the following steps as central for DB administration:

Task 1: Evaluate the Database Server Hardware
Task 2: Install the Oracle Database Software
Task 3: Plan the Database
Task 4: Create and Open the Database
Task 5: Back Up the Database
Task 6: Enroll System Users
Task 7: Implement the Database Design
Task 8: Back Up the Fully Functional Database
Task 9: Tune Database Performance
Task 10: Download and Install Patches
Task 11: Roll Out to Additional Hosts

This is a somewhat more "mechanical" list of tasks than mine.

Here is a rather simple division of database applications into two categories:
The traditional Relational Database Management System (RDBMS): like Oracle and MySQL. Data is stored in rows; one disk fetch is needed for one row. The Structured Query Language (SQL) is used to access and update the data.

Problems with traditional RDBMS:
There are many alternatives to traditional RDBMSs; see below. These typically are developed to address RDBMS difficulties with massive data stores, although often people then try to apply the new models to ordinary-sized data stores.

The RDBMS-still-central contrarian view: www.mongodb-is-web-scale.com (warning: some (ok, a lot of) rude language, and some cluelessness regarding agriculture (for example, most "bulls" are castrated as calves, not as adults))


Installation assignment:

    Install PostgreSQL on your own machine

Oracle v MySQL
Terms; demo; SQL
Consistency
Relational "lists"


Oracle v MySQL v PostgreSQL: three general-purpose databases.

Oracle has many proprietary extensions. Some are there more to lock you into Oracle than because they are a good idea. That said, Oracle does do well with big, transactional DBs. But Oracle is expensive to license.

Note that, with Oracle's purchase of Sun Microsystems, Oracle now owns MySQL. Some people think this is why they bought Sun. (Others think it was for Java.)

PostgreSQL has always tried to support (close to) the full SQL standard, and to support it fairly strictly. In this it resembles Oracle, without the latter's proprietary extensions.

Performance used to be an issue in Postgres. But in the last decade a great deal of progress has been made; Postgres is now very fast. Postgres is also much more similar to Oracle than MySQL (which is a little ironic because Oracle now owns MySQL, though the usual assumption is that Oracle doesn't want MySQL to compete with the family jewels).

Traditionally, MySQL often omitted support for some features. Transactions are one, though this is now beginning to appear in MySQL. Another is key constraints, and foreign key constraints in particular. However, consider the following:
MySQL is dual-licensed: as open-source (which means your additions must also be open-source, if you release them) and as a proprietary product (meaning you can develop and sell proprietary extensions to MySQL, provided you pay the licensing fee).


History

Relational databases came out of theoretical work of Edgar Codd, in a 1970 paper titled A Relational Model of Data for Large Shared Data Banks. At the time, the approach was seen as too computationally expensive. But by 1980, both IBM DB2 and (early version of) Oracle were out, and it was becoming generally recognized that relational databases were the wave of the future.

Codd's relational approach solved a major data consistency problem, by eliminating redundancy in data storage. We looked at that above.

While there are now other types of databases, notably object-oriented databases, it is fair to say that no later development has offered a convincing solution to a general organizational problem that is intractable with the relational model. That said, relational databases sometimes have serious scaling problems, and there is a significant No-SQL movement.

SEQUEL / SQL

In the early 1970's, IBM introduced SEQUEL: Structured English Query Language. For trademark reasons, the name had to be changed; IBM chose SQL (Structured Query Language). To this day, many people prefer to pronounce SQL as "sequel" rather than as "ess queue ell"; note that in this case the "sequel" pronunciation actually came first.

The "official" pronunciation for MySQL is "My ess queue ell", but they tolerate "mysequel". The following is from the MySQL reference manual, §1.3.2:

MySQL is named after co-founder Monty Widenius's daughter, My.

Widenius's youngest daughter is Maria. Widenius is currently working on MariaDB, a drop-in replacement for MySQL.

SQL is fundamentally command-line. It is also a rather non-procedural language!

Wrapping a good Human-Computer Interface (HCI) around SQL is good practice (well-nigh essential); generally this is done in some high-level language with a SQL interface (eg JDBC). However, actual queries are still done in SQL.

Maps

Underlying all databases is the Map data structure. A Map object has a key field, say of type K, and a data field, say of type D, with the following operations:
insert(K key, D data);    // inserts or updates a ⟨key,data⟩ record
D lookup(K key);            // retrieves the data portion given the key
There are some minor variants; for example, sometimes insert() requires that the key not already be present, and an update(K key, D data) is provided when the key is already present. Sometimes there are separate methods to look up to see if a key is present.

From this perspective, a database is a collection of Map structures, with the following additions:

Terminology

University database, EN p 6

A record is a list of data fields. The fields do not have to have the same type, and are usually referenced by name rather than by position (or number). Example: ⟨'Peter', 'Dordal', '123561234', 32020, 15.7, true, 37⟩. The fields may also be called columns or attributes.

A table is a set of records. In some object-oriented databases we might identify a record with a class type, and a single table might consist of records from a parent class and from multiple base classes. However, in relational databases all records of a table have the same type: all records have the same fields, in the same order.

If we imagine a table laid out graphically, with records as rows, the use of the word column to describe a field makes more sense.

A database is a set of tables. The database may impose some constraints between tables.

A key field is a field we can use for searching a table, that is guaranteed to return at most a single record. That is, the SSN field of an Employee database is a key (we hope!), but the FirstName field is probably not. Given a key field, a database might maintain an index to speed up lookups on that key; non-key fields can also have indexes however, and without an index we can always use linear search.
In the University database of EN, here are the keys:
Tables with two columns as key often serve to represent relationships; the GRADE_REPORT table identifies the "has-taken-the-class" relationship between STUDENTs and SECTIONs: Peter has-taken-the-class COMP-305-001.

We can use the four tables here to provide a list of what students have taken what courses in what semesters, and what grades they earned. This sort of mixing and matching of data from multiple tables is the hallmark of the relational database approach.

Abstractly, a relation is a set of lists/rows/tuples that are all of the same type: there are the same number of fields in each row, and each field consists of elements of a single type. This corresponds to a subset of the cartesian product A1×A2×...×An of the underlying attribute sets. Relational databases almost always enforce this restriction: rows of a given table must be homogeneous: all the rows must be of the same length and with the same respective field types.

Mathematically, however, one can have a relation that is a subset of A×B, where B is a union of a base class C and two derived classes C×D and C×E: C ∪ C×D ∪ C×E. This would allow the relation to have heterogeneous tuples like ⟨a,c⟩ ⟨a,c,d⟩ and ⟨a,c,e⟩, though these might be more accurately written as ⟨a,c⟩ ⟨a,⟨c,d⟩⟩ and ⟨a,⟨c,e⟩⟩. Some object-oriented databases do in fact provide support for constructions such as this, although relational databases usually do not.

Another hallmark of the relational approach is that all fields are supposed to be atomic types; that is, they are not lists or sub-relations (strings, however, are allowed, even with substring operations). For example, suppose we want to store the student name, id, and list of courses:

    ⟨'peter', address, 23456, [343, 346, 353]⟩
    ⟨'paul', address2, 45678, [343, 372]⟩

Lists are not atomic; a strict relational approach would decompose this into two tables, one of ⟨name, address, student_id⟩ data, indexed by student_id, and a second table of ⟨student_id, coursenum⟩, indexed by both fields together and containing

23456
 343
23456
 346
23453
 353
45678
 343
45678
 372

Note that doing this allows us to recover the original lists of courses on a per-student_id basis, by mixing and matching from the two new tables. Note also that the atomic-types rule would disallow the C ∪ C×D ∪ C×E example above, as union types are not atomic.

Sometimes the above decomposition is too inefficient. To get the list of Peter's courses, we have to search a rather large secondary table. Postgres does allow us to include lists as fields, violating the strict-relational model.

Relational DBs, SQL and NoSQL

We will define the concept of a relational database more below, though the rules above are a start.

The SQL language is designed as a way to ask questions about a relational database without specifying exactly how the data is to be retrieved. Consider the following from http://www.aosabook.org/en/nosql.html by Adam Marcus:

SQL is a declarative language for querying data. A declarative language is one in which a programmer specifies what they want the system to do, rather than procedurally defining how the system should do it. A few examples include: find the record for employee 39, project out only the employee name and phone number from their entire record, filter employee records to those that work in accounting, count the employees in each department, or join the data from the employees table with the managers table.

That sounds dandy: you just tell the computer what to do, not how to do it. But there is a downside:

To a first approximation, SQL allows you to ask these questions without thinking about how the data is laid out on disk, which indices to use to access the data, or what algorithms to use to process the data. A significant architectural component of most relational databases is a query optimizer, which decides which of the many logically equivalent query plans to execute to most quickly answer a query. These optimizers are often better than the average database user, but sometimes they do not have enough information or have too simple a model of the system in order to generate the most efficient execution.

It turns out to be surprisingly easy to write inefficient queries, particularly if complex "inner-query" searches are needed (though [even] MySQL is making progress on these). SQL allows you to search on any field, not just a key field for which there is an index; for very large datasets, this is ill-advised. SQL allows, in effect, any query regardless of how efficient it is. Some restrictions imposed by the NoSQL world (or, more accurately, imposed by the real world and implemented by the NoSQL world) are:
These limitations are essentially always done for the sake of efficiency and performance.

You can get something like the same effect by using a relational database, and just promising yourself not to use it in inefficient ways.

NoSQL example

The native database used by the Asterisk phone switch consists of tables (actually called "families") that are ⟨key,value⟩ pairs. Neither the key nor the value need be atomic, or for that matter the same type as other entries. (This is done for simplicity, not because the DB is huge.)

The MS Windows registry is a similar example.

Basic SQL operations: querying & updating

SQL:
select * from STUDENT
select * from STUDENT where Student_number = 17;
select * from SECTION where 101 <= Section_identifier and Section_identifier <= 115;  

The last query above is somewhat misleading, as Section_identifiers are usually not intended for linear comparison.

Figure 2.1 on EN p 32: a schema for the students database

In the queries above, the * means "all columns of the selected rows"; sometimes we want fewer:

select name from student;
select name, major from student where student_number = 17;
select course_number, semester from section where 101<= section_identifier and section_identifier <= 115;

Finally, when we are working with multiple tables, it is excellent practice to name the tables, and then qualify all column names with the table names, as follows. We will use this style consistently throughout the semester; it is particularly useful when the where clause involves multiple tables (as in joins).

select s.name from student s;
select s.name, s.major from student s where s.student_number = 17;

Table Joins

Suppose in the university database we want to know the names of everyone in section 112. (A peculiarity of the specific data given as example is that no section has more than one student!) The GRADE_REPORT table has only student numbers; we need to match these up with names from the STUDENT table. This operation, of matching corresponding rows of different tables, is known as the join. Here is the SQL for the query, where the join condition is in bold:

select s.name from student s, grade_report gr
where s.student_number = gr.student_number and gr.section_identifier = 112;

We are retrieving records from two tables here, but restricting attention to pairs of records that "match up" according to the join condition.

The join can also be done with the following alternative syntax:

select s.name from student s join grade_report gr
on s.student_number = gr.student_number
where gr.section_identifier = 112;

Finally, maybe we want the name and the grade:

select s.name, gr.grade from student s, grade_report gr
where s.student_number = gr.student_number and gr.section_identifier = 112;

The join operation was once derided as introducing too much inefficiency. Technical advances in the 1980's made this issue less important, but the rise of huge datasets in this century has made this again relevant.


Here are two "classic" joins:

1. Names and grades for everyone in Section 112. The grade_report table has two-column key ⟨student_number,section_identifier⟩. The join is on the student_number attribute, which is the key to the student table. The join can be viewed as taking each grade_report record with section_identifier=112 and using the student table to look up the student number and find the corresponding name.

select s.name , gr.grade
from student s join grade_report gr on s.student_number = gr.student_number
where gr.section_identifier = 112;

2. Names of courses for all courses taught in Fall 08. Both tables involved have single-column keys; course_number for course and section_identifier for section. The join involves the key field of the course table, so the join can be viewed as taking each suitable section record and looking up the course number in the course table to find the name.

select c.course_name
from course c join section s on s.course_number = c.course_number
where s.year=2008 and s.semester='Fall';

Joins "look" symmetrical, but in most cases one table is used as a direct map-type lookup for values appearing in the other table. Reversing the roles here (eg taking each name, and searching the grade_report table for the grades of all matching students in section 112), is a very different kind of operation.

Most joins involve a primary-key attribute from one table joined to an attribute on the other table that has a foreign-key constraint referencing the primary key of the first table. The primary keys in the examples above are student.student_number and course.course_number.

In both the examples above, we used the shorthand s and gr. These are sometimes called table aliases or tuple variables or (in the SQL standard) correlation names. Their use greatly enhances readability, as it makes clear which table a given column comes from. I recommend their use in all queries. See E&N6, p 101.

Sometimes the keyword "as" is inserted: select s.name, gr.grade from student as s, grade_report as gr ....

Sometimes it is helpful to view a table alias as a cursor variable: a variable ranging over each record in the corresponding table. Thus:

    for gr in grade_report:
        for s in student:
               if s.student_number = gr.student_number and gr.section_identifier = 112
               then: print record

This is an oversimplification of how joins are actually implemented, however; it is too inefficient for large tables.

Keys and Foreign Keys

Except in usual cases, every table will have a primary key. For example, the primary key of table 'employee' is the ssn attribute, the primary key of table 'department' is the dnumber attribute, and the primary key of table 'works_on' is the pair of attributes (essn,pno).

The database enforces the rule that we cannot have two different records with the same primary-key value. This is a constraint, not a description. It happens to be the case that no two employees have the same lname (or same fname), but lname is not a key field  as we can easily add Samuel Wong. But if we try to add Samuel Wong with ssn 333445555, the insertion (or update) will fail.

As we will see later, generally an index is created on the primary-key attribute. To enforce the primary-key constraint, every time a primary-key value is inserted or updated the index is used to see if another record has the same key value.

Several of the Company and University tables also have foreign-key constraints. Such constraints are quite different, and don't involve a key constraint at all. The classic example is employee.dno, which is intended as a reference to a row in the department table that contains the full information for that department. We don't want to allow an employee to have dno=6, because over in the department table there is no department with dnumber=6.

Another way to put this is to say that employee.dno has a foreign-key constraint which references department.dnumber. The constraint here is on employee.dno: any value for employee.dno must appear somewhere in the dnumber column of department. Usually the referenced attribute is a key for its table, as is department.dnumber here. The attribute employee.dno is sometimes said to be the foreign key, because it must match a value in a "foreign" table that is, in that table, a key.

To enforce this foreign-key constraint, the database system must check whenever a new employee is inserted, or an employee.dno value is updated. It must also check whenever a department is deleted, or a department.dnumber value is updated.

The Company database has several other foreign-key constraints:

Sometimes foreign-key constraints lead to annoying circularities. When the tables employee and department are created initially, both are empty. We cannot add the first employee until the corresponding department already exists. Similarly, due to the first foreign-key constraint in the bulleted list above, we cannot add the first department until after the department-manager employee already exists.

First Look at ER Diagrams

Programmers often use Booch diagrams or UML diagrams to display object relationships visually. DBAs usually use Entity-Relationship, or ER, diagrams. Entities are the "physical objects" represented by the database, drawn with rectangles. Relationships are between entities; the easiest relationships are the binary ones. In the Company database the entities are

The diagram can be seen in ER.html.

The most interesting relationship is works_on, between employee and project, because it is many-to-many (one employee can work on many projects, and one project can have many employees). There are also several one-to-many relationships:

All these ended up without their own table. Where do we encode each of these three?

There's also a one-to-one manages relationship between departments and employees. Every department has exactly one manager, and every employee manages at most one department. Most employees don't manage any department, but that doesn't change the one-to-one rule. We'll come back to this one later.