Nested Sets and Materialized Path SQL Trees
by Rampant author Vadim Tropashko
Relational databases are universally conceived of as an
advance over their predecessors network and
hierarchical models. Superior in every querying respect,
they turned out to be surprisingly incomplete when modeling
transitive dependencies. Almost every couple of months a
question about how to model a tree in the database pops up
at the comp.database.theory newsgroup. In this article I'll
investigate two out of four well known approaches to
accomplishing this and show a connection between them. We'll
discover a new method that could be considered as a "mixin"
between materialized path and nested sets.
Adjacency List
Tree structure is a special case of Directed Acyclic Graph
(DAG). One way to represent DAG structure is:
create table emp (
ename
varchar2(100),
mgrname
varchar2(100)
);
Each record of the emp table identified by ename is
referring to its parent mgrname. For example, if JONES
reports to KING, then the emp table contains <ename='JONES',
mgrname='KING'> record. Suppose, the emp table also includes
<ename='SCOTT', mgrname='JONES'>. Then, if the emp table
doesn't contain the <ename='SCOTT', mgrname='KING'> record,
and the same is true for every pair of adjoined records,
then it is called adjacency list. If the opposite is
true, then the emp table is a transitively closed
relation.
A typical hierarchical query would ask if SCOTT indirectly
reports to KING. Since we don't know the number of levels
between the two, we can't tell how many times to selfjoin
emp, so that the task can't be solved in traditional SQL. If
transitive closure tcemp of the emp table is known, then the
query is trivial:
select 'TRUE' from tcemp
where ename =
'SCOTT' and mgrname = 'KING'
The ease of querying comes at the expense of
transitive closure maintenance.
Alternatively, hierarchical queries can be answered with SQL
extensions: either SQL3/DB2 recursive query
with
tcemp as (
select
ename,mgrname from tcemp
union
select tcemp.ename,emp.mgrname
from tcemp,emp
where
tcemp.mgrname = emp.ename
) select 'TRUE' from
tcemp
where ename = 'SCOTT' and mgrname =
'KING';
that calculates tcemp as an intermediate relation, or Oracle
proprietary connectby syntax
select 'TRUE' from (
select
ename from emp
connect by prior
mgrname = ename
start with ename = 'SCOTT'
) where ename = 'KING';
in which the inner query "chases the pointers" from the
SCOTT node to the root of the tree, and then the outer query
checks whether the KING node is on the path.
Adjacency list is arguably the most intuitive tree model.
Our main focus, however, would be the following two methods.
Materialized Path
In this approach each record stores the whole path to the
root. In our previous example, lets assume that KING is a
root node. Then, the record with
ename =
'SCOTT'
is connected to the root via the path SCOTT>JONES>KING.
Modern databases allow representing a list of nodes as a
single value, but since materialized path has been invented
long before then, the convention stuck to plain character
string of nodes concatenated with some separator; most often
'.' or '/'. In the latter case, an analogy to pathnames in
UNIX file system is especially pronounced.
In more compact variation of the method, we use sibling
numerators instead of node's primary keys within the path
string. Extending our example:
ENAME

PATH

KING 
1 
JONES 
1.1 
SCOTT 
1.1.1 
ADAMS 
1.1.1.1

FORD 
1.1.2 
SMITH 
1.1.2.1

BLAKE 
1.2 
ALLEN 
1.2.1 
WARD 
1.2.2 
CLARK 
1.3 
MILLER 
1.3.1 
Path
1.1.2
indicates that FORD is the second child of the parent JONES.
Let's write some queries.
1. An employee FORD and chain of his supervisors:
select e1.ename from emp e1, emp e2
where
e2.path like e1.path  '%'
and e2.name = 'FORD'
2. An employee JONES and all his (indirect) subordinates:
select e1.ename from emp e1, emp e2
where
e1.path like e2.path  '%'
and e2.name = 'JONES'
Although both queries look symmetrical, there is a
fundamental difference in their respective performances. If
a subtree of subordinates is small compared to the size of
the whole hierarchy, then the execution where database
fetches e2 record by the name primary key, and then performs
a range scan of e1.path, which is guaranteed to be
quick.
On the other hand, the "supervisors" query is roughly
equivalent to
select e1.ename from emp e1, emp e2
where
e2.path > e1.path and e2.path < e1.path  'Z'
and e2.name = 'FORD'
Or, noticing that we essentially know
e2.path,
it can further be reduced to
select e1.ename from emp e1
where
e2path > e1.path and e2path < e1.path  'Z'
Here, it is clear that indexing on path doesn't work (except
for "accidental" cases in which e2path happens to be near
the domain boundary, so that predicate e2path > e1.path is
selective).
The obvious solution is that we don't have to refer to the
database to figure out all the supervisor paths! For
example, supervisors of
1.1.2
are 1.1
and 1.
A simple recursive string parsing function can extract those
paths, and then the supervisor names can be answered by
select e1.ename from emp where e1.path
in ('1.1','1')
which should be executed as a fast concatenated plan.
Nested Sets
Both the materialized path and
Joe Celko's nested sets provide the capability to answer
hierarchical queries with standard SQL syntax. In both
models, the global position of the node in the
hierarchy is "encoded" as opposed to an adjacency list of
which each link is a local connection between
immediate neighbors only. Similar to materialized path, the
nested sets model suffers from supervisors query performance
problem:
select p2.emp from Personnel p1, Personnel p2
where p1.lft between p2.lft and p2.rgt
and p1.emp = 'Chuck'
(Note: This query is borrowed from the
previously cited Celko article). Here, the problem is
even more explicit than in the case of a materialized path:
we need to find all the intervals that cover a given point.
This problem is known to be difficult. Although there are
specialized indexing schemes like RTree, none of them is as
universally accepted as BTree. For example, if the
supervisor's path contains just 10 nodes and the size of the
whole tree is 1000000, none of indexing techniques could
provide 1000000/10=100000 times performance increase. (Such
a performance improvement factor is typically associated
with index range scan in a similar, very selective, data
volume condition.)
Unlike a materialized path, the trick by which we computed
all the nodes without querying the database doesn't work for
nested sets.
Another — more fundamental — disadvantage of nested sets is
that nested sets coding is volatile. If we insert a
node into the middle of the hierarchy, all the intervals
with the boundaries above the insertion point have to be
recomputed. In other words, when we insert a record into the
database, roughly half of the other records need to be
updated. This is why the nested sets model received only
limited acceptance for static hierarchies.
Nested sets are intervals of integers. In an attempt to make
the nested sets model more tolerant to insertions, Celko
suggested we give up the property that each node always has
(rgtlft+1)/2 children. In my opinion, this is a halfstep
towards a solution: any gap in a
nested set model with large gaps and spreads in the
numbering still could be covered with intervals leaving
no space for adding more children, if those intervals are
allowed to have boundaries at discrete points (i.e.,
integers) only. One needs to use a dense domain like
rational, or real numbers instead.
Nested
Intervals
Nested intervals generalize nested sets. A node [clft, crgt]
is an (indirect) descendant of [plft, prgt] if:
plft <=
clft
and
crgt >= prgt
The domain for interval boundaries is not limited by
integers anymore: we admit rational or even real numbers, if
necessary. Now, with a reasonable policy, adding a child
node is never a problem. One example of such a policy would
be finding an unoccupied segment [lft1, rgt1] within a
parent interval [plft, prgt] and inserting a child node
[(2*lft1+rgt1)/3, (rgt1+2*lft)/3]:
After insertion, we still have two more unoccupied segments
[lft1,(2*lft1+rgt1)/3] and [(rgt1+2*lft)/3,rgt1] to add more
children to the parent node.
We are going to amend this naive policy in the following
sections.
Partial Order
Let's look at two dimensional picture of nested intervals.
Let's assume that rgt is a horizontal axis x, and lft is a
vertical one  y. Then, the nested intervals tree looks like
this:
Each node [lft, rgt] has its descendants bounded within the
twodimensional cone y >= lft & x <= rgt. Since the right
interval boundary is always less than the left one, none of
the nodes are allowed above the diagonal y = x.
The other way to look at this picture is to notice that a
child node is a descendant of the parent node whenever a set
of all points defined by the child cone y >= clft & x <=
crgt is a subset of the parent cone y >= plft & x <= prgt. A
subset relationship between the cones on the plane is a
partial order.
Now that we know the two constraints to which tree nodes
conform, I'll describe exactly how to place them at the xy
plane.
The Mapping
Tree root choice is completely arbitrary: we'll assume the
interval [0,1] to be the root node. In our geometrical
interpretation, all the tree nodes belong to the lower
triangle of the unit square at the xy plane.
We'll describe further details of the mapping by induction.
For each node of the tree, let's first define two important
points at the xy plane. The depthfirst convergence point
is an intersection between the diagonal and the vertical
line through the node. For example, the depthfirst
convergence point for <x=1,y=1/2> is <x=1,y=1>. The
breadthfirst convergence point is an
intersection between the diagonal and the horizontal line
through the point. For example, the breadthfirst
convergence point for <x=1,y=1/2> is <x=1/2,y=1/2>.
Now, for each parent node, we define the position of the
first child as a midpoint halfway between the parent point
and depthfirst convergence point. Then, each sibling is
defined as a midpoint halfway between the previous sibling
point and breadthfirst convergence point:
For example, node 2.1 is positioned at x=1/2, y=3/8.
Now that the mapping is defined, it is clear which dense
domain we are using: it's not rationals, and not reals
either, but binary fractions (although, the former two would
suffice, of course).
Interestingly, the descendant subtree for the parent node
"1.2" is a scaled down replica of the subtree at node "1.1."
Similarly, a subtree at node 1.1 is a scaled down replica of
the tree at node "1." A structure with selfsimilarities is
called a fractal.
Normalization
Next, we notice that x and y are not completely independent.
We can tell what are both x and y if we know their sum.
Given
the numerator
and
denominator
of the rational number representing the sum of the node
coordinates, we can calculate x and y coordinates back as:
function x_numer( numer integer, denom
integer )
RETURN integer IS
ret_num
integer;
ret_den integer;
BEGIN
ret_num := numer+1;
ret_den := denom*2;
while
floor(ret_num/2) = ret_num/2
loop
ret_num :=
ret_num/2;
ret_den := ret_den/2;
end
loop;
RETURN ret_num;
END;
function x_denom( numer integer, denom
integer )
...
RETURN ret_den;
END;
in which function x_denom body differs from x_numer in the
return variable only. Informally, numer+1 increment
would move the ret_num/ret_den point vertically up to
the diagonal, and then x coordinate is half of the value, so
we just multiplied the denominator by two. Next, we reduce
both numerator and denominator by the common power of two.
Naturally, y coordinate is defined as a complement to the
sum:
function y_numer( numer integer, denom integer
)
RETURN integer IS
num
integer;
den integer;
BEGIN
num := x_numer(numer, denom);
den := x_denom(numer, denom);
while
den < denom loop
num :=
num*2;
den := den*2;
end loop;
num := numer  num;
while floor(num/2) = num/2 loop
num := num/2;
den :=
den/2;
end loop;
RETURN num;
END;
function y_denom( numer integer, denom integer
)
...
RETURN den;
END;
Now, the test (where
39/32
is the node 1.3.1):
select x_numer(39,32)'/'x_denom(39,32),
y_numer(39,32)'/'y_denom(39,32) from dual
5/8
19/32
select 5/8+19/32, 39/32 from dual
1.21875
1.21875
I don't use a floating point to represent rational numbers,
and wrote all the functions with integer ariphmetics
instead. To put it bluntly, the floating point number
concept in general, and the IEEE standard in particular, is
useful for rendering 3Dgame graphics only. In the last
test, however, we used a floating point just to verify that
5/8
and
19/32,
returned by the previous query, do indeed add to
39/32.
We'll store two integer numbers — numerator and
denominator of the sum of the coordinates x and y — as
an encoded node path. Incidentally, Celko's nested sets use
two integers as well. Unlike nested sets, our mapping is
stable: each node has a predefined placement at the xy
plane, so that the queries involving node position in the
hierarchy could be answered without reference to the
database. In this respect, our hierarchy model is
essentially a materialized path encoded as a rational
number.
Finding Parent Encoding and
Sibling Number
Given a child node with
numer/denom
encoding, we find the node's parent like this:
function parent_numer( numer integer, denom
integer )
RETURN integer IS
ret_num integer;
ret_den integer;
BEGIN
if numer=3 then
return NULL;
end if;
ret_num := (numer1)/2;
ret_den := denom/2;
while floor((ret_num1)/4) =
(ret_num1)/4 loop
ret_num := (ret_num+1)/2;
ret_den := ret_den/2;
end loop;
RETURN ret_num;
END;
function parent_denom( numer integer, denom
integer )
...
RETURN ret_den;
END;
The idea behind the algorithm is the following: If the node
is on the very top level — and all these nodes have a
numerator equal to 3 — then the node has no parent.
Otherwise, we must move vertically down the xy plane at a
distance equal to the distance from the depthfirst
convergence point. If the node happens to be the first
child, then that is the answer. Otherwise, we must move
horizontally at a distance equal to the distance from the
breadthfirst convergence point until we meet the parent
node.
Here is the test of the method (in which
27/32
is the node 2.1.2, while
7/8
is 2.1):
select parent_numer(27,32)'/'parent_denom(27,32)
from dual
7/8
In the previous method, counting the steps when navigating
horizontally would give the sibling number:
function sibling_number( numer integer, denom
integer )
RETURN integer IS
ret_num integer;
ret_den integer;
ret integer;
BEGIN
if numer=3 then
return NULL;
end if;
ret_num := (numer1)/2;
ret_den := denom/2;
ret := 1;
while floor((ret_num1)/4) = (ret_num1)/4 loop
if ret_num=1 and ret_den=1 then
return ret;
end
if;
ret_num :=
(ret_num+1)/2;
ret_den :=
ret_den/2;
ret
:= ret+1;
end loop;
RETURN ret;
END;
For a node at the very first level a special stop condition,
ret_num=1 and ret_den=1 is needed.
The test:
select
sibling_number(7,8) from dual
1
Calculating Materialized Path and Distance between nodes
Strictly speaking, we don't have to use a materialized path,
since our encoding is an alternative. On the other hand, a
materialized path provides a much more intuitive
visualization of the node position in the hierarchy, so that
we can use the materialized path for input and output of the
data if we provide the mapping to our model.
Implementation is a simple application of the methods from
the previous section. We print the sibling number, jump to
the parent, then repeat the above two steps until we reach
the root:
function path( numer integer, denom integer
)
RETURN varchar2 IS
BEGIN
if numer is NULL then
return '';
end if;
RETURN path(parent_numer(numer, denom),
parent_denom(numer, denom))

'.'  sibling_number(numer, denom);
END;
select path(15,16) from dual
.2.1.1
Now we are ready to write the main query: given the 2
nodes, P and C, when P is the parent of C? A more
general query would return the number of levels between P
and C if C is reachable from P, and some exception
indicator; otherwise:
function distance( num1 integer, den1 integer,
num2 integer, den2 integer )
RETURN
integer IS
BEGIN
if num1 is
NULL then
return
999999;
end if;
if
num1=num2 and den1=den2 then
return 0;
end if;
RETURN 1+distance(parent_numer(num1, den1),
parent_denom(num1, den1),
num2,den2);
END;
select distance(27,32,3,4) from dual
2
Negative numbers are interpreted as exceptions. If the
num1/den1 node is not reachable from num2/den2, then the
navigation converges to the root, and
level(num1/den1)999999 would be returned (readers are
advised to find a less clumsy solution).
The alternative way to answer whether two nodes are
connected is by simply calculating the x and y coordinates,
and checking if the parent interval encloses the child.
Although none of the methods refer to disk, checking whether
the partial order exists between the points seems much less
expensive! On the other hand, it is just a computer
architecture artifact that comparing two integers is an
atomic operation. More thorough implementation of the method
would involve a domain of integers with a unlimited range
(those kinds of numbers are supported by computer algebra
systems), so that a comparison operation would be iterative
as well.
Our system wouldn't be complete without a function inverse
to the path, which returns a node's numer/denom value once
the path is provided. Let's introduce two auxiliary
functions, first:
function child_numer
( num integer, den
integer, child integer )
RETURN
integer IS
BEGIN
RETURN
num*power(2, child)+3power(2, child);
END;
function child_denom
( num integer,
den integer, child integer )
RETURN
integer IS
BEGIN
RETURN
den*power(2, child);
END;
select
child_numer(3,2,3)  '/' 
child_denom(3,2,3) from dual
19/16
For example, the third child of the node 1 (encoded as 3/2)
is the node 1.3 (encoded as 19/16).
The path encoding function is:
function path_numer( path varchar2 )
RETURN integer IS
num integer;
den integer;
postfix
varchar2(1000);
sibling varchar2(100);
BEGIN
num := 1;
den :=
1;
postfix := '.'  path  '.';
while length(postfix) > 1 loop
sibling := substr(postfix, 2,
instr(postfix,'.',2)2);
postfix := substr(postfix,
instr(postfix,'.',2),
length(postfix)
instr(postfix,'.',2)+1);
num := child_numer(num,den,to_number(sibling));
den := child_denom(num,den,to_number(sibling));
end
loop;
RETURN num;
END;
function path_denom( path varchar2 )
...
RETURN den;
END;
select
path_numer('2.1.3')  '/' 
path_denom('2.1.3') from dual
51/64
The Final Test
Now that the infrastructure is completed, we can test it.
Let's create the hierarchy
create table emps (
name varchar2(30),
numer integer,
denom integer
)
alter table emps
ADD CONSTRAINT
uk_name UNIQUE (name) USING INDEX
(CREATE UNIQUE INDEX name_idx on emps(name))
ADD CONSTRAINT UK_node
UNIQUE
(numer, denom) USING INDEX
(CREATE
UNIQUE INDEX node_idx on emps(numer, denom))
and fill it with some data:
insert into emps values ('KING',
path_numer('1'),path_denom('1'));
insert into emps
values ('JONES',
path_numer('1.1'),path_denom('1.1'));
insert into
emps values ('SCOTT',
path_numer('1.1.1'),path_denom('1.1.1'));
insert into
emps values ('ADAMS',
path_numer('1.1.1.1'),path_denom('1.1.1.1'));
insert
into emps values ('FORD',
path_numer('1.1.2'),path_denom('1.1.2'));
insert into
emps values ('SMITH',
path_numer('1.1.2.1'),path_denom('1.1.2.1'));
insert
into emps values ('BLAKE',
path_numer('1.2'),path_denom('1.2'));
insert into
emps values ('ALLEN',
path_numer('1.2.1'),path_denom('1.2.1'));
insert into
emps values ('WARD',
path_numer('1.2.2'),path_denom('1.2.2'));
insert into
emps values ('MARTIN',
path_numer('1.2.3'),path_denom('1.2.3'));
insert into
emps values ('TURNER',
path_numer('1.2.4'),path_denom('1.2.4'));
insert into
emps values ('CLARK',
path_numer('1.3'),path_denom('1.3'));
insert into
emps values ('MILLER',
path_numer('1.3.1'),path_denom('1.3.1'));
commit;
All the functions written in the previous sections are
conveniently combined in a single view:
create or replace
view hierarchy as
select name, numer, denom,
y_numer(numer,denom) numer_left,
y_denom(numer,denom) denom_left,
x_numer(numer,denom) numer_right,
x_denom(numer,denom) denom_right,
path (numer,denom) path,
distance(numer,denom,3,2) depth
from emps
And, finally, we can create the hierarchical reports.