An algebra is a formal structure consisting of sets and operations on those sets.
Relational algebra is a formal system for manipulating relations.
For the set operations on relations, both operands must have the same scheme, and the result has that same scheme.
Selects tuples from a relation whose attributes meet the selection criteria, which is normally expressed as a predicate.
R2 = select(R1,P)
That is, from R1 we create a new relation R2 containing those tuples from R1 that satisfy (make true) the predicate P.
A predicate is a boolean expression whose operators are the logical connectives (and, or, not) and arithmetic comparisons (LT, LE, GT, GE, EQ, NE), and whose operands are either domain names or domain constants.
select(Workstation,Room=633) =
Name Room Mem Proc Monitor ==================================== coke 633 16384 SP4 color17 bass 633 8124 SP2 color19 bashful 633 8124 SP1 b/w
select(User,Status=UG and Idle<1:00) =
Login Name Status Idle Shell Sever ================================================ jli J. Inka UG 0:00 bsh UG
Chooses a subset of the columns in a relation, and discards the rest.
R2 = project(R1,D1,D2,...Dn)
That is, from the tuples in R1 we create a new relation R2 containing only the domains D1,D2,..Dn.
project(Server,Name,Status) =
Name Status ============== diamond up emerald up graphite down ruby up frito up
project(select(User,Status=UG),Name,Status) =
Name Status ================== A. Cohn UG J. Inka UG R. Kemp UG
Combines attributes of two relations into one.
R3 = join(R1,D1,R2,D2)
Given a domain from each relation, join considers all possible pairs of tuples from the two relations, and if their values for the chosen domains are equal, it adds a tuple to the result containing all the attributes of both tuples (discarding the duplicate domain D2).
Natural join: If the two relations being joined have exactly one attribute (domain) name in common, then we assume that the single attribute in common is the one being compared to see if a new tuple will be inserted in the result.
Assuming that we've augmented the domain names in our lab database so that we use MachineName, PrinterName, ServerName, and UserName in place of the generic domain "Name", then
join(Workstations,Printers)
is a natural join, on the shared attribute name Room.
The result is a relation of all workstation/printer attribute
pairs that are in the same room.
Find all workstations in a room with a printer.
R1 R2 R3 Name Room Name Room WName Pname Room ============ ============ ==================== coke 633 chaucer 737 coke uglab 633 bass 633 keats 706 bass uglab 633 bashful 633 poe 707 bashful uglab 633 tab 628 dali 737 crush 628 uglab 633
To implement R1 U R2 (while eliminating duplicates) we can
If we allow duplicates in union (and remove them later) we can
If we have an index and don't want duplicates we can
Intersection and set difference have corresponding implementations.
To implement projection we must
In the absence of an index we
Given an index, and a predicate that uses the index key, we
A nested loop join on relations R1 (with N domains) and R2 (with M domains), considers all |R1| x |R2| pairs of tuples.
R3= join(R1,Ai,R2,Bj)
for each tuple t in R1 do
for each tuple s in R2 do
if t.Ai = s.Bj then
insert(R3, t.A1, t.A2, ..., t.AN,
s.B1, ..., s.B(j-1), s.B(j+1), ..., s.BM)
This implementation takes time O(|R1|*|R2|).
An index join exploits the existence of an index for one of the domains used in the join to find matching tuples more quickly.
R3= join(R1,Ai,R2,Bj)
for each tuple t in R1 do
for each tuple s in R2 at index(t.Ai) do
insert(R3, t.A1, t.A2, ..., t.AN,
s.B1, ..., s.B(j-1), s.B(j+1), ..., s.BM)
We could choose to use an index for R2, and reverse the order of the loops.
The decision on which index to use depends on the number of tuples in each relation.
If we don't have an index for a domain in the join, we can still improve on the nested-loop join using sort join.
R3= join(R1,Ai,R2,Bj)
Comparison
Relational algebra is an unambiguous notation (or formalism) for expressing queries.
Queries are simply expressions in relational algebra.
Expressions can be manipulated symbolically to produce simpler expressions according to the laws of relational algebra.
Expression simplification is an important query optimization technique, which can affect the running time of queries by an order of magnitude or more.
Commutativity (assuming order of columns doesn't matter)
join(R1, Ai, R2, Bj) = join(R2, Bj, R1, Ai)
Nonassociativity
join (join(R1, Ai, R2, Bj),Bj,R3,Ck)
Commutativity
select(select(R1,P1),P2) = select(select(R1,P2),P1)
Selection pushing
Selection Splitting (where P = A and B)
select(R,P) = select(select(R,B),A)
Consider the following 4 relation database
Implement the query "Where is Amy at Noon on Monday?"
Let P be (Name="Amy" and Day="Monday" and Hour="Noon")
We can use a brute-force approach that joins all the data in the relations into a single large relation, selects those tuples that meet the query criteria, and then isolates the answer field using projection.
project(select(join(join(join(CSG,SNAP),CDH),CR),P),Room)
The selection uses only Name, Day, and Hour attributes (and not Course or Room), so we can push the selection inside the outermost join.
We cannot push selection further, because the predicate involves attributes from both operands of the next innermost join (R1,CDH).
We can split the selection into two, one based on Name, and the other based on Day-Hour.
Now we can push the first selection inside the join, since it involves only attributes from the CDH relation.
Similarly we can push the second selection inside the preceding join, since it involves only attributes from R1 (ie, Name).
Continuing to push the second select inside the first join
To push a projection operation inside a join requires that the result of the projection contain the attributes used in the join.
In this case, we know that the domains in the projection will exist in the relation that results from the join.
In performing projection first (on the two join relations)
Let PDR = {D|D domain in R, D in {D1...Dn}} U Ai
Let PDS = {D|D domain in S, D in {D1...Dn}} U Bi
Implement the query "Where is Amy at Noon on Monday?"
This approach carries along unnecessary attributes every step of the way.
We use projection pushing to eliminate unnecessary attributes early in the implementation.
Note that R5 is unnecessary, since the domains in the projection are all the domains of CR.
Implement the query "Where is Amy at Noon on Monday?"
We can continue pushing the projection on Course below the join for R4.
We can continue pushing the projection on Course for R4 below the join for R2.