TOC Previous Next

Trees and Other Hierarchies in MySQL

Graphs and SQL Edge list Edge list tree CTE edge list treewalk Automate tree drawing
Nested sets model of a tree Edge-list model of a network Parts explosions

Most non-trivial data is hierarchical. Customers have orders, which have line items, which refer to products, which have prices. Population samples have subjects, who take tests, which give results, which have sub-results and norms. Web sites have pages, which have links, which collect hits across dates and times. These are hierarchies of tables. The number of tables limits the number of JOINs needed to walk the tree. For such data, standard SQL is excellent.

But when data maps a family tree, or a browsing history, or a bill of materials, data rows relate hierarchically to other rows in the same table. We no longer know how many JOINs we need to walk the tree. We need a different data model.

That model is the graph (Fig 1), which is a set of nodes (vertices) and the edges (lines or arcs) that connect them. This chapter is about how to model and query graphs in a MySQL database.

Graph theory is a branch of topology. It is the study of geometric relations that aren't changed by stretching and compression—rubber sheet geometry, some call it. Graph theory is ideal for modelling hierarchies—like family trees, browsing histories, search trees, Bayesian networks and bills of materials— whose shape and size we can't know in advance.

Let the set of nodes in Fig 1 be N, the set of edges be L, and the graph be G. Then G is the tuple or ordered pair {N,L}:

    N = {A,B,C,D,E,F}
    L = {AC,CD,CF,BE}
    G = {N,L}

If the edges are directed, the graph is a digraph or directed graph. A mixed graph has both directed and undirected edges.

Examples of graphs are organisational charts; itineraries; route maps; parts explosions; massively multiplayer games; language rules; chat histories; network and link analysis in a wide variety of fields, for example search engines, forensics, epidemiology and telecommunications; data mining; models of chemical structure hierarchies; and biochemical processes.

Graph characteristics and models

Nodes and edges : Two nodes are adjacent if there is an edge between them. Two edges are adjacent if they connect to a common node. In a complete graph, all nodes are adjacent to all other nodes.

In a digraph, the number of edges entering a node is its indegree; the number leaving is its outdegree. A node of indegree zero is a root node, a node of outdegree zero is a leaf node.

In a weighted graph, used for example to solve the travelling salesman problem, edges have a weight attribute. A digraph with weighted edges is a network.

Paths and cycles: A connected sequence of edges is a path, its length the number of edges traversed. Two nodes are connected if there is a path between them. If there is a path connecting every pair of nodes, the graph is a connected graph.

A path in which no node repeats is a simple path. A path which returns to its own origin without crossing itself is a cycle or circuit. A graph with multiple paths between at least one pair of nodes is reconvergent. A reconvergent graph may be cyclic or acyclic. A unit length cycle is a loop.

If a graph's edges intersect only at nodes, it is planar. Two paths having no node in common are independent.

Traversing graphs: There are two main approaches, breadth-first and depth-first. Breadth-first traversal visits all a node's siblings before moving on to the next level, and typically uses a queue. Depth-first traversal follows edges down to leaves and back before proceeding to siblings, and typically uses a stack.

Sparsity: A graph where the size of E approaches the maximum N² is dense. When the multiple is much smaller than N, the graph is considered sparse.

Trees: A tree is a connected graph with no cycles. It is also a graph where the indegree of the root node is 0, and the indegree of every other node is 1. A tree where every node is of outdegree <=2 is a binary tree. A forest is a graph in which every connected component is a tree.

Euler paths: A path which traverses every edge in a graph exactly once is an Euler path. An Euler path which is a circuit is an Euler circuit.

If and only if every node of a connected graph has even degree, it has an Euler circuit (which is why the good people of Königsberg cannot go for a walk crossing each of their seven bridges exactly once). If and only if a connected graph has exactly 2 nodes with odd degree, it has a non-circuit Euler path. The degree of an endpoint of a non-cycle Euler path is 1 + twice the number of times the path passes through that node, so it is always odd.

Models for computing graphs

Traditionally, computer science textbooks have offered edge lists, adjacency lists and adjacency matrices as data structures for graphs, with algorithms implemented in languages like C, C++ and Java. More recently other models and tools have been suggested, including query languages customised for graphs.

Edge list: The simplest way to represent a graph is to list its edges: for Fig 1, the edge list is{AC,CD,CF,BE}. It is easy to add an edge to the list; deletion is a little harder.

Table 1
Nodes	Adjacent nodes
A	C
B	E
C	F,D,A
D	C
E	B
F	C

Adjacency list: An adjacency list is a ragged array: for each node it lists all adjacent nodes. Thus it represents a directed graph of n nodes as a list of n lists where list i contains node j if the graph has an edge from node i to node j.

An undirected graph may be represented by having node j in the list for node i, and node i in the list for node j. Table 1 shows the adjacency list of the graph in Fig 1 interpreted as undirected.

Adjacency matrix: An adjacency matrix represents a graph with n nodes as an n x n matrix, where the entry at (i,j) is 1 if there is an edge from node i to node j, or zero if there is not.

An adjacency matrix can represent a weighted graph using the weight as the entry, and can represent an undirected graph by using the same entry in both (i,j) and (j,i), or by using an upper triangular matrix.

There are useful glossaries here and here.

Graphs and SQL

Standard SQL has been cumbersome for the recursive row-to-row logic of graphs. To fix this, DB2, Oracle, SQL Server and PostgreSQL have added recursive Common Table Expressions (CTE s). Before 8.0, MySQL did not have CTEs, so recursive graph traversal required stored routines. MariaDB has had CTEs since version 10.2.2 , MySQL since version 8.0.2. Joe Celko and Scott Stephens, among others, have published general SQL graph problem solutions that are simpler and smaller than equivalent C++, C# or Java code. Here we show how to use such tools.

Beware that in ports of edge list and adjacency list methods to SQL, there has been name slippage. What SQLers often call an adjacency list isn't like the adjacency list shown in Table 1; it's an edge list. Here we'll honour that fact, and mostly call them edge lists, but to keep the peace we’ll sometimes call them edge-adjacency lists.

There are also two newer kinds of models: what Joe Celko calls the nested sets model—an interval model using greater-than/less-than arithmetic to encode tree relationships and modified preorder tree traversal (MPTT) to query them, and Tropashko's materialised path model, where each node is stored with its (denormalised) path to the root. So now we have five main ways to model graphs in MySQL:

edge-adjacency lists: based on an adaptation by EF Codd of the logic of linked lists to table structures and queries,
adjacency matrices,
nested sets for trees simplify some queries, but insertion and deletion are cumbersome, and
materialised paths,
recursive CTEs.

The edge list

The edge list is the simplest possible SQL representation of a graph: minimally, a single edges table where each row specifies one node and its parent (which is NULL for the root node), or to avoid DKNF problems, two tables: one for the nodes, the other to link the edges.

In the real world, the nodes table might be a table of personnel, or assembly parts, or locations on a map. It might have many other columms of data. The edges table might also have additional columns for edge properties. The key integers of both tables might be BIGINTs.

To model Fig 1, though, we keep things as simple as possible:

Listing 1
CREATE TABLE nodes(
  nodeID CHAR(1) PRIMARY KEY
);
CREATE TABLE edges(
  childID CHAR(1) NOT NULL,
  parentID CHAR(1) NOT NULL,
  PRIMARY KEY(childID,parentID)
);
INSERT INTO nodes VALUES('A'), ('B'), ('C'), ('D'), ('E'), ('F');
INSERT INTO edges VALUES ('A','C'), ('C','D'), ('C','F'), ('B','E');
SELECT * FROM edges;
+---------+----------+
| childID | parentID |
+---------+----------+
| A       | C        |
| B       | E        |
| C       | D        |
| C       | F        |
+---------+----------+

Now, without any assumptions about whether the graph is connected, whether it is directed, whether it is a tree, or whatever, how hard is it to write a reachability ("closure") procedure to tell us where we can get to from here, wherever 'here' is? It's easy with a breadth-first search:

Seed the list with the starting node,
Add, but do not duplicate, nodes which are children of nodes in the list,
Add, but do not duplicate, nodes which are parents of nodes in the list,
Repeat steps 2 and 3 until there are no more nodes to add.

Here it is as a MySQL stored procedure. It avoids duplicate nodes by defining reached.nodeID as a primary key and adding reachable nodes with INSERT IGNORE:

Listing 2
DROP PROCEDURE IF EXISTS ListReached;
DELIMITER go
CREATE PROCEDURE ListReached( IN root CHAR(1) )
BEGIN
  DECLARE rows SMALLINT DEFAULT 0;
  DROP TABLE IF EXISTS reached;
  CREATE TABLE reached (
    nodeID CHAR(1) PRIMARY KEY
  ) ENGINE=HEAP;
  INSERT INTO reached VALUES (root );
  SET rows = ROW_COUNT();
  WHILE rows > 0 DO
    INSERT IGNORE INTO reached
      SELECT DISTINCT childID
      FROM edges AS e
      INNER JOIN reached AS p ON e.parentID = p.nodeID;
    SET rows = ROW_COUNT();
    INSERT IGNORE INTO reached
      SELECT DISTINCT parentID
      FROM edges AS e
      INNER JOIN reached AS p ON e.childID = p.nodeID;
    SET rows = rows + ROW_COUNT();
  END WHILE;
  SELECT * FROM reached;
  DROP TABLE reached;
END;
go
DELIMITER ;
CALL ListReached('A');
+--------+
| nodeID |
+--------+
| A      |
| C      |
| D      |
| F      |
+--------+

To make the procedure more versatile, give it input parameters to tell it whether to list child, parent or all connections, and whether to recognise loops (for example C to C).

To give the model referential integrity, use InnoDB and make edges.childID and edges.parentID foreign keys. To add or delete a node, add or delete desired single rows in nodes and edges. To change an edge, edit it. The model does not require the graph to be connected or treelike, and does not presume direction.

The edge list is basic to what SQLers often call the adjacency list model.

Edge-adjacency list model of a tree

Writers in the SQL graph literature often give solutions using single denormalised tables. Denormalisation can cost, big time. The bigger the table, the bigger the cost. You cannot edit nodes and edges separately. Carrying extra node information during edge computation slows performance.

To avoid such difficulties, normalise trees like William Shakespeare's family tree (Fig 2) into two tables, nodes (family) with a row for each individual's information, about individuals, and edges (familytree)with a row for each parent-child link or edge.

Listing 3
-- Base data:
CREATE TABLE family (
  ID smallint unsigned PRIMARY KEY AUTO_INCREMENT,
  name char(20) default '',
  siborder tinyint(4) default NULL,
  born smallint(4) unsigned default NULL,
  died smallint(4) unsigned default NULL
);
INSERT INTO family VALUES (1, 'Richard Shakespeare', NULL, NULL, 1561);
INSERT INTO family VALUES (2, 'Henry Shakespeare', 1, NULL, 1569);
INSERT INTO family VALUES (3, 'John Shakespeare', 2, 1530, 1601);
INSERT INTO family VALUES (4, 'Joan Shakespeare', 1, 1558, NULL);
INSERT INTO family VALUES (5, 'Margaret Shakespeare', 2, 1562, 1563);
INSERT INTO family VALUES (6, 'William Shakespeare', 3, 1564, 1616);
INSERT INTO family VALUES (7, 'Gilbert Shakespeare', 4, 1566, 1612);
INSERT INTO family VALUES (8, 'Joan Shakespeare', 5, 1568, 1646);
INSERT INTO family VALUES (9, 'Anne Shakespeare', 6, 1571, 1579);
INSERT INTO family VALUES (10, 'Richard Shakespeare', 7, 1574, 1613);
INSERT INTO family VALUES (11, 'Edmond Shakespeare', 8, 1580, 1607);
INSERT INTO family VALUES (12, 'Susana Shakespeare', 1, 1583, 1649);
INSERT INTO family VALUES (13, 'Hamnet Shakespeare', 1, 1585, 1596);
INSERT INTO family VALUES (14, 'Judith Shakespeare', 1, 1585, 1662);
INSERT INTO family VALUES (15, 'William Hart', 1, 1600, 1639);
INSERT INTO family VALUES (16, 'Mary Hart', 2, 1603, 1607);
INSERT INTO family VALUES (17, 'Thomas Hart', 3, 1605, 1670);
INSERT INTO family VALUES (18, 'Michael Hart', 1, 1608, 1618);
INSERT INTO family VALUES (19, 'Elizabeth Hall', 1, 1608, 1670);
INSERT INTO family VALUES (20, 'Shakespeare Quiney', 1, 1616, 1617);
INSERT INTO family VALUES (21, 'Richard Quiney', 2, 1618, 1639);
INSERT INTO family VALUES (22, 'Thomas Quiney', 3, 1620, 1639);
INSERT INTO family VALUES (23, 'John Bernard', 1, NULL, 1674);
-- Table which models the tree:
CREATE TABLE familytree (
  childID smallint unsigned NOT NULL,
  parentID smallint unsigned NULL,
  PRIMARY KEY (childID, parentID);
);
INSERT INTO familytree VALUES
  (2, 1), (3, 1), (4, 2), (5, 2), (6, 2), (7, 2), (8, 2), (9, 2),
  (10, 2), (11, 2), (12, 6), (13, 6), (14, 6), (15, 8), (16, 8),
  (17, 8), (18, 8), (19, 12), (20, 14), (21, 14), (22, 14), (23, 19);

(The family PK is auto-increment, but the listing is more reader-friendly when the ID values are shown.)

It will be useful to have a function that returns family.name for a parent or child ID in familytree:

Listing 4
DROP FUNCTION IF EXISTS PersonName;
CREATE FUNCTION PersonName(pid smallint) RETURNS VARCHAR(20) DETERMINISTIC 
RETURN (SELECT name FROM family WHERE ID=pid);
SELECT PersonName( parentID ) AS 'Father of William'
FROM familytree
WHERE childID = 6;
+-------------------+
| Father of William |
+-------------------+
| Henry Shakespeare |
+-------------------+
SELECT PersonName( childID ) AS 'Children of William'
FROM familytree
WHERE parentID = ( SELECT ID FROM family WHERE name = 'William Shakespeare' );
+---------------------+
| Children of William |
+---------------------+
| Susana Shakespeare  |
| Hamnet Shakespeare  |
| Judith Shakespeare  |
+---------------------+
SELECT PersonName(childID) AS child, PersonName(parentID) AS parent
FROM familytree;
+----------------------+---------------------+
| child                | parent              |
+----------------------+---------------------+
| Henry Shakespeare    | Richard Shakespeare |
| John Shakespeare     | Richard Shakespeare |
| Joan Shakespeare     | Henry Shakespeare   |
| Margaret Shakespeare | Henry Shakespeare   |
| William Shakespeare  | Henry Shakespeare   |
| Gilbert Shakespeare  | Henry Shakespeare   |
| Joan Shakespeare     | Henry Shakespeare   |
| Anne Shakespeare     | Henry Shakespeare   |
| Richard Shakespeare  | Henry Shakespeare   |
| Edmond Shakespeare   | Henry Shakespeare   |
| Susana Shakespeare   | William Shakespeare |
| Hamnet Shakespeare   | William Shakespeare |
| Judith Shakespeare   | William Shakespeare |
| William Hart         | Joan Shakespeare    |
| Mary Hart            | Joan Shakespeare    |
| Thomas Hart          | Joan Shakespeare    |
| Michael Hart         | Joan Shakespeare    |
| Elizabeth Hall       | Susana Shakespeare  |
| Shakespeare Quiney   | Judith Shakespeare  |
| Richard Quiney       | Judith Shakespeare  |
| Thomas Quiney        | Judith Shakespeare  |
| John Bernard         | Elizabeth Hall      |
+----------------------+---------------------+

A same-table foreign key can simplify tree maintenance:

Listing 4a
create table edges (
  ID int PRIMARY KEY,
  parentid int,
  foreign key(parentID) references edges(ID) ON DELETE CASCADE ON UPDATE CASCADE
) engine=innodb;
insert into edges(ID,parentID) values (1,null),(2,1),(3,1),(4,2);
select * from edges;
+----+----------+
| ID | parentid |
+----+----------+
|  1 |     NULL |
|  2 |        1 |
|  3 |        1 |
|  4 |        2 |
+----+----------+
delete from edges where id=2;
select * from edges;
+----+----------+
| ID | parentid |
+----+----------+
|  1 |     NULL |
|  3 |        1 |
+----+----------+

Simple queries retrieve basic facts about the tree, for example GROUP_CONCAT() collects parent nodes with their children in correct order:

Listing 5
SELECT parentID AS Parent, GROUP_CONCAT(childID ORDER BY siborder) AS Children
FROM familytree t
JOIN family f ON t.parentID=f.ID
GROUP BY parentID;
+--------+-------------------+
| Parent | Children          |
+--------+-------------------+
|      1 | 3,2               |
|      2 | 4,5,6,7,8,9,10,11 |
|      6 | 12,13,14          |
|      8 | 18,17,16,15       |
|     12 | 19                |
|     14 | 22,21,20          |
|     19 | 23                |
+--------+-------------------+

Iterate over those comma-separated lists with a bit of application code and you have a hybrid treewalk. The paterfamilias is the root node, individuals with no children are the leaf nodes, and queries to retrieve subtree statistics are straightforward:

Listing 6
SELECT
  PersonName(ID) AS Paterfamilias,
  IFNULL(born,'?') AS Born,
  IFNULL(died,'?') AS Died
FROM family AS f
LEFT JOIN familytree AS t ON f.ID=t.childID
WHERE t.childID IS NULL;
+---------------------+------+------+
| Paterfamilias       | Born | Died |
+---------------------+------+------+
| Richard Shakespeare | ?    | 1561 |
+---------------------+------+------+
SELECT
  PersonName(ID) AS Childless,
  IFNULL(born,'?') AS Born,
  IFNULL(died,'?') AS Died
FROM family AS f
LEFT JOIN familytree AS t ON f.ID=t.parentID
WHERE t.parentID IS NULL;
+----------------------+------+------+
| Childless            | Born | Died |
+----------------------+------+------+
| John Shakespeare     | 1530 | 1601 |
| Joan Shakespeare     | 1558 | ?    |
| Margaret Shakespeare | 1562 | 1563 |
| Gilbert Shakespeare  | 1566 | 1612 |
| Anne Shakespeare     | 1571 | 1579 |
| Richard Shakespeare  | 1574 | 1613 |
| Edmond Shakespeare   | 1580 | 1607 |
| Hamnet Shakespeare   | 1585 | 1596 |
| William Hart         | 1600 | 1639 |
| Mary Hart            | 1603 | 1607 |
| Thomas Hart          | 1605 | 1670 |
| Michael Hart         | 1608 | 1618 |
| Shakespeare Quiney   | 1616 | 1617 |
| Richard Quiney       | 1618 | 1639 |
| Thomas Quiney        | 1620 | 1639 |
| John Bernard         | ?    | 1674 |
+----------------------+------+------+
SELECT ROUND(AVG(died-born),2) AS 'Longevity of the childless'
FROM family AS f
LEFT JOIN familytree AS t ON f.ID=t.parentID
WHERE t.parentID IS NULL;
+----------------------------+
| Longevity of the childless |
+----------------------------+
|                      25.86 |
+----------------------------+

In striking contrast with Celko's nested sets model, inserting a new item in this model requires no revision of existing rows. We just add a new family row, then a new familytree row with IDs specifying who is parent to whom. Deletion is also a two-step: delete the familytree row for that child-parent link, then delete the family row for that child.

Walking an edge list tree

Edge list tree traversal is supposed to be difficult. Usually we don't know in advance how many levels the tree has, so the query needs recursion or a logically equivalent loop. Without CTEs (i.e.,, before MySQL 8.0 or MariaDB 10.2.2), that requires a stored procedure. Here is a simple algorithm that just seeds a result table with children of the root node, then adds remaining edges with INSERT IGNORE:

Listing 7
DROP PROCEDURE IF EXISTS famsubtree;
DELIMITER go
CREATE PROCEDURE famsubtree( root INT )
BEGIN
  DROP TABLE IF EXISTS famsubtree;
  CREATE TABLE famsubtree( childID smallint unsigned not null,
    parentID smallint unsigned null, Primary Key(childID,parentID) )
    SELECT childID, parentID, 0 AS level
    FROM familytree
    WHERE parentID = root;
  REPEAT
    INSERT IGNORE INTO famsubtree
      SELECT f.childID, f.parentID, s.level+1
      FROM familytree AS f
      JOIN famsubtree AS s ON f.parentID = s.childID;
  UNTIL Row_Count() = 0 END REPEAT;
END ;
go
DELIMITER ;
call famsubtree(1);       -- from the root you can see forever
SELECT Concat(Space(level),parentID) AS Parent, Group_Concat(childID ORDER BY childID) AS Child
FROM famsubtree
GROUP BY parentID;
+--------+-------------------+
| Parent | Child             |
+--------+-------------------+
| 1      | 2,3               |
|  2     | 4,5,6,7,8,9,10,11 |
|   6    | 12,13,14          |
|   8    | 15,16,17,18       |
|    12  | 19                |
|    14  | 20,21,22          |
|     19 | 23                |
+--------+-------------------+

Simple and quick. The logic ports to any edge list. We can prove that right now by writing a generic version. GenericTree() just needs parameters for the name of the target table, the names of its child and parent ID columns, and the parent ID whose descendants are sought:

Listing 7a: General-purpose edge list tree walker
DROP PROCEDURE IF EXISTS GenericTree;
DELIMITER go
CREATE PROCEDURE GenericTree(
  edgeTable CHAR(64), edgeIDcol CHAR(64), edgeParentIDcol CHAR(64), ancestorID INT
)
BEGIN
  DECLARE r INT DEFAULT 0;
  DROP TABLE IF EXISTS subtree;
  SET @sql = Concat( 'CREATE TABLE subtree  SELECT ',
                     edgeIDcol,' AS childID, ',
                     edgeParentIDcol, ' AS parentID,',
                     '0 AS level FROM ',
                     edgeTable, ' WHERE ', edgeParentIDcol, '=', ancestorID );
  PREPARE stmt FROM @sql;
  EXECUTE stmt;
  DROP PREPARE stmt;
  ALTER TABLE subtree ADD PRIMARY KEY(childID,parentID);
  REPEAT
    SET @sql = Concat( 'INSERT IGNORE INTO subtree SELECT a.', edgeIDcol,
                       ',a.',edgeparentIDcol, ',b.level+1 FROM ',
                       edgeTable, ' AS a JOIN subtree AS b ON a.',edgeParentIDcol, '=b.childID' );
    PREPARE stmt FROM @sql;
    EXECUTE stmt;
    SET r=Row_Count();  -- save row_count() result before DROP PREPARE loses the value
    DROP PREPARE stmt;
  UNTIL r < 1 END REPEAT;
END ;
go
DELIMITER ;

To retrieve details (e.g., names) associated with node IDs, write a frontend query to join the subtree result table with the required detail table(s) ...

CALL GenericTree('familytree','childID','parentID',1);
SELECT Concat(Repeat( ' ', s.level), a.name ) AS Parent, b.name AS Child
FROM subtree s
JOIN family a ON s.parentID=a.ID
JOIN family b ON s.childID=b.ID;
+-----------------------+----------------------+
| Parent                | Child                |
+-----------------------+----------------------+
| Richard Shakespeare   | Henry Shakespeare    |
| Richard Shakespeare   | John Shakespeare     |
|  Henry Shakespeare    | Joan Shakespeare     |
|  Henry Shakespeare    | Margaret Shakespeare |
|  Henry Shakespeare    | William Shakespeare  |
|  Henry Shakespeare    | Gilbert Shakespeare  |
|  Henry Shakespeare    | Joan Shakespeare     |
|  Henry Shakespeare    | Anne Shakespeare     |
|  Henry Shakespeare    | Richard Shakespeare  |
|  Henry Shakespeare    | Edmond Shakespeare   |
|   William Shakespeare | Susana Shakespeare   |
|   William Shakespeare | Hamnet Shakespeare   |
|   William Shakespeare | Judith Shakespeare   |
|   Joan Shakespeare    | William Hart         |
|   Joan Shakespeare    | Mary Hart            |
|   Joan Shakespeare    | Thomas Hart          |
|   Joan Shakespeare    | Michael Hart         |
|    Susana Shakespeare | Elizabeth Hall       |
|    Judith Shakespeare | Shakespeare Quiney   |
|    Judith Shakespeare | Richard Quiney       |
|    Judith Shakespeare | Thomas Quiney        |
|     Elizabeth Hall    | John Bernard         |
+-----------------------+----------------------+

Called for the root node, GenericTree() generates a reachability (closure) table. Is it fast? On standard hardware it walks a 5,000-node tree in less than 0.5 secs—much faster than a comparable nested sets query on the same tree! It has no serious scaling issues. And its logic can be used to prune: call GenericTree() then delete the listed rows. Better still, write a generic tree pruner from Listing 7a and a DELETE command. To insert a subtree, prepare a table of new rows, point its top edge at an existing node as parent, and INSERT it.

The edge list treewalk is logically recursive, so how about coding it recursively? Here is a recursive depth-first PHP treewalk for the familytree and family tables:

Listing 7b: Recursive edge list subtree in PHP
$info = recursivesubtree( 1, $a = array(), 0 );
foreach( $info as $row )
  echo str_repeat( " ", 2*$row[4] ), ( $row[3] > 0 ) ? "<b>{$row[1]}</b>" : $row[1], "<br/>";
function recursivesubtree( $rootID, $a, $level ) {
  $childcountqry = "(SELECT COUNT(*) FROM familytree WHERE parentID=t.childID) AS childcount";
  $qry = "SELECT t.childid,f.name,t.parentid,$childcountqry,$level " .
         "FROM familytree t JOIN family f ON t.childID=f.ID " .
         "WHERE parentid=$rootID ORDER BY childcount<>0,t.childID";
  $res = mysql_query( $qry );
  while( $row = mysql_fetch_row( $res )) {
    $a[] = $row;
    if( $row[3] > 0 ) $a = recursivesubtree( $row[0], $a, $level+1 );    // down before right
  }
  return $a;
}

A query with a subquery, a fetch loop, and a recursive call--that's all there is to it. A nice feature of this algorithm is that it writes result rows in display-ready order. To port this to MySQL, you must have set maximum recursion depth in my.cnf/ini or in your client:

Listing 7c: Recursive edge list subtree in MySQL
SET @@SESSION.max_sp_recursion_depth=25;
DROP PROCEDURE IF EXISTS recursivesubtree;
DELIMITER go
CREATE PROCEDURE recursivesubtree( iroot INT, ilevel INT )
BEGIN
  DECLARE irows,ichildid,iparentid,ichildcount,done INT DEFAULT 0;
  DECLARE cname VARCHAR(64);
  SET irows = ( SELECT COUNT(*) FROM familytree WHERE parentID=iroot );
  IF ilevel = 0 THEN
    DROP TEMPORARY TABLE IF EXISTS _descendants;
    CREATE TEMPORARY TABLE _descendants (
      childID INT, parentID INT, name VARCHAR(64), childcount INT, level INT
  );
  END IF;
  IF irows > 0 THEN
    BEGIN
      DECLARE cur CURSOR FOR
        SELECT
          childid,parentid,f.name,
          (SELECT COUNT(*) FROM familytree WHERE parentID=t.childID) AS childcount
        FROM familytree t JOIN family f ON t.childID=f.ID
        WHERE parentid=iroot
        ORDER BY childcount<>0,t.childID;
      DECLARE CONTINUE HANDLER FOR SQLSTATE '02000' SET done = 1;
      OPEN cur;
      WHILE NOT done DO
        FETCH cur INTO ichildid,iparentid,cname,ichildcount;
        IF NOT done THEN
          INSERT INTO _descendants VALUES(ichildid,iparentid,cname,ichildcount,ilevel );
          IF ichildcount > 0 THEN
            CALL recursivesubtree( ichildid, ilevel + 1 );
          END IF;
        END IF;
      END WHILE;
      CLOSE cur;
    END;
  END IF;
  IF ilevel = 0 THEN
    -- Show result table headed by name that corresponds to iroot:
    SET cname = (SELECT name FROM family WHERE ID=iroot);
    SET @sql = CONCAT('SELECT CONCAT(REPEAT(CHAR(32),2*level),IF(childcount,UPPER(name),name))',
                      ' AS ', CHAR(39),'Descendants of ',cname,CHAR(39),' FROM _descendants');
    PREPARE stmt FROM @sql;
    EXECUTE stmt;
    DROP PREPARE stmt;
  END IF;
END;
go
DELIMITER ;
CALL recursivesubtree(1,0);
+------------------------------------+
| Descendants of Richard Shakespeare |
+------------------------------------+
| HENRY SHAKESPEARE                  |
|   Joan Shakespeare                 |
|   Margaret Shakespeare             |
|   WILLIAM SHAKESPEARE              |
|     SUSANA SHAKESPEARE             |
|       ELIZABETH HALL               |
|         John Bernard               |
|     Hamnet Shakespeare             |
|     JUDITH SHAKESPEARE             |
|       Shakespeare Quiney           |
|       Richard Quiney               |
|       Thomas Quiney                |
|   Gilbert Shakespeare              |
|   JOAN SHAKESPEARE                 |
|     William Hart                   |
|     Mary Hart                      |
|     Thomas Hart                    |
|     Michael Hart                   |
|   Anne Shakespeare                 |
|   Richard Shakespeare              |
|   Edmond Shakespeare               |
| John Shakespeare                   |
+------------------------------------+

In MySQL this recursive treewalk can be up to 100 times slower than GenericTree(). Its slowness is comparable to that of a MySQL version of Kendall Willet's depth-first edge list subtree algorithm:

Listing 7d: Depth-first edge list subtree
CREATE PROCEDURE depthfirstsubtree( iroot INT )
BEGIN
  DECLARE ilastvisited, inxt, ilastord INT;
  SET ilastvisited = iroot;
  SET ilastord = 1;
  DROP TABLE IF EXISTS descendants;
  CREATE TABLE descendants SELECT childID,parentID,-1 AS ord FROM familytree;
  UPDATE descendants SET ord=1 WHERE childID=iroot;
  this: LOOP
    SET inxt = NULL;
    SELECT MIN(childID) INTO inxt FROM descendants   -- go down
    WHERE parentID = ilastvisited AND ord = -1 ;
    IF inxt IS NULL THEN                             -- nothing down, so go right
      SELECT MIN(d2.childID) INTO inxt
      FROM descendants d1
      JOIN descendants d2 ON d1.parentID = d2.parentID AND d1.childID < d2.childID
      WHERE d1.childID = ilastvisited;
    END IF;
    IF inxt IS NULL THEN                             -- nothing right. so go up
      SELECT parentID INTO inxt FROM descendants
      WHERE childID = ilastvisited AND parentID IS NOT NULL;
    END IF;
    UPDATE descendants SET ord = ilastord + 1
    WHERE childID = inxt AND ord = -1;
    IF ROW_COUNT() > 0 THEN
      SET ilastord = ilastord + 1;
    END IF;
    IF inxt IS NULL THEN
      LEAVE this;
    END IF;
    SET ilastvisited = inxt;
  END LOOP;
END;

One reason Willet's is slower is that MySQL does not permit multiple references to a temporary table in a query. When all algorithms are denied temp tables, though, this algorithm is still slower than recursion, and both are much slower than GenericTree().

A simple procedure to retrieve a node's ancestors:

Listing 7e: Find node's ancestors
CREATE PROCEDURE ancestors( pid int )
begin
  drop temporary table if exists _ancestors;
  create temporary table _ancestors(parent int);
  set @id = pid;
  repeat
    select parentID,count(*) into @parent,@y from familytree where childID=@id;
    if @y>0 then
      insert into _ancestors values(@parent);
      set @id=@parent;
    end if;
  until @parent is null or @y=0 end repeat;
  select * from _ancestors order by parent;
end;

Finally, for MariaDB from 10.2.2 and MySQL from 8.0.1, here is a recursive treewalk using a CTE. Such queries (see SELECT / WITH in Chapter 6) have five parts: a WITH clause to declare the derived table; a query to initialise the derived table, in this case with the root node; a UNION command; the recursive join; and a final output SELECT. We built the familytree table without a row for the root, so the initialising SELECT creates it, but it needn't be shown in the result:


Listing 7f: Tree listing using a CTE:
WITH RECURSIVE treewalk AS ( 
  SELECT 
    CAST(1 AS UNSIGNED) AS childID,         -- UNION NEEDS EXACT TYPE MATCH
    CAST(NULL AS UNSIGNED) AS parentID, 
    CAST(0 AS UNSIGNED) AS level,
    0 AS siborder
  UNION ALL 
  SELECT familytree.childID, familytree.parentID, treewalk.level+1 AS level, family.siborder
  FROM familytree 
  JOIN treewalk ON familytree.parentID=treewalk.childID
  JOIN family   ON family.ID=familytree.childID 
)
SELECT  
  Concat( Space(level-1), parentID ) AS Parent, 
  level-1 AS Depth,  
  Group_Concat( childID ORDER BY siborder ) AS Children
FROM treewalk 
WHERE level>0
GROUP BY treewalk.parentID ORDER BY treewalk.parentID;  -- Unset only_full_group_by sql_mode 
+--------+-------+-------------------+
| Parent | Depth | Children          | 
+--------+-------+-------------------+ 
| 1      | 0     | 2,3               |
|  2     | 1     | 4,5,6,7,8,9,10,11 | 
|   6    | 2     | 13,12,14          |
|   8    | 2     | 18,15,16,17       | 
|    12  | 3     | 19                |
|    14  | 3     | 20,21,22          | 
|     19 | 4     | 23                |
+--------+-------+-------------------+

The breadth-first logic is that of Listing 7, but the CTE makes this treewalk about ten times faster, and implements recursion so requires no stored routine. Set sql_mode to not include only_full_group_by. If the graph being traversed is cyclic, avoid an endless loop by changing UNION ALL to UNION DISTINCT. A query can cascade CTEs. Without CTEs such a query probably needed temporary or intermediate tables. With CTEs, no more. This alone may cut query running time by 50% or more—see “Treewalks with CTEs” on our Common Queries page.

To walk a subtree, create a comma-separated cumulative path column and order on it, e.g., for table infotree(id, parentid, name) this retrieves the subtree of node 5:


Listing 7g: Walk a tree with CTE:
set @root=5;                           -- subtree root value 
with recursive treewalk as ( 
  select id, 0 as level, cast( id as char ) as path, name 
  from infotree 
  where id=@root                       -- query for subtree root  
  union 
  select                               -- query for nodes 
    t.id, tw.level+1 as level, 
    concat( path, ',', t.id ) as path, -- the path down to this node 
    t.name 
  from infotree t 
  join treewalk tw on t.parentid=tw.id 
) 
select * from treewalk order by path;

Edge list tree queries perform faster, and are easier to write, than their reputation suggests—especially when CTEs are available. And edge tables are flexible. For a tree describing a parts explosion rather than a family, just add columns for weight, quantity, assembly time, cost, price and so on. Reports need only aggregate column values and sums. We'll revisit this near the end of the chapter.

Enumerating paths in an edge-adjacency list

Path enumeration in an edge list tree is almost as easy as depth-first subtree traversal:

create a table for paths,
seed it with paths of unit length from the tree table,
iteratively add paths till there are no more to add.

MySQL's INSERT IGNORE command simplifies the code by removing the need for a NOT EXISTS(...) clause in the INSERT ... SELECT statement. Since adjacencies are logically symmetrical, we make path direction the caller's choice, UP or DOWN:

Listing 8
DROP PROCEDURE IF EXISTS ListAdjacencyPaths;
DELIMITER go
CREATE PROCEDURE ListAdjacencyPaths( IN direction CHAR(5) )
BEGIN
  DROP TABLE IF EXISTS paths;
  CREATE TABLE paths(
    start SMALLINT,
    stop SMALLINT,
    len SMALLINT,
    PRIMARY KEY(start,stop)
  ) ENGINE=HEAP;
  IF direction = 'UP' THEN
    INSERT INTO paths
      SELECT childID,parentID,1
      FROM familytree;
  ELSE
    INSERT INTO paths
      SELECT parentID,childID,1
      FROM familytree;
  END IF;
  WHILE ROW_COUNT() > 0 DO
    INSERT IGNORE INTO paths
      SELECT DISTINCT
        p1.start,
        p2.stop,
        p1.len + p2.len
      FROM paths AS p1 INNER JOIN paths AS p2 ON p1.stop = p2.start;
  END WHILE;
  SELECT start, stop, len
  FROM paths
  ORDER BY start, stop;
  DROP TABLE paths;
END;
go
DELIMITER ;

To find the paths from just one node, seed the paths table with paths from the starting node, then iteratively search a JOIN of familytree and paths for edges which will extend existing paths in the user-specified direction:

Listing 8a
DROP PROCEDURE IF EXISTS ListAdjacencyPathsOfNode;
DELIMITER go
CREATE PROCEDURE ListAdjacencyPathsOfNode( IN node SMALLINT, IN direction CHAR(5) )
BEGIN
  TRUNCATE paths;
  IF direction = 'UP' THEN
    INSERT INTO paths
      SELECT childID,parentID,1
      FROM familytree
      WHERE childID = node;
  ELSE
    INSERT INTO paths
      SELECT parentID,childID,1
      FROM familytree
      WHERE parentID = node;
  END IF;
  WHILE ROW_COUNT() > 0 DO
    IF direction = 'UP' THEN
      INSERT IGNORE INTO paths
        SELECT DISTINCT
          paths.start,
          familytree.parentID,
          paths.len + 1
        FROM paths
          INNER JOIN familytree ON paths.stop = familytree.childID;
    ELSE
      INSERT IGNORE INTO paths
        SELECT DISTINCT
          paths.start,
          familytree.childID,
          paths.len + 1
        FROM paths
          INNER JOIN familytree ON paths.stop = familytree.parentID;
    END IF;
  END WHILE;
    SELECT start, stop, len
    FROM paths
    ORDER BY start, stop;
END;
go
DELIMITER ;
CALL ListAdjacencyPathsOfNode(1,'DOWN');
+-------+------+------+
| start | stop | len  |
+-------+------+------+
|     1 |    2 |    1 |
|     1 |    3 |    1 |
|     1 |    4 |    2 |
|     1 |    5 |    2 |
|     1 |    6 |    2 |
|     1 |    7 |    2 |
|     1 |    8 |    2 |
|     1 |    9 |    2 |
|     1 |   10 |    2 |
|     1 |   11 |    2 |
|     1 |   12 |    3 |
|     1 |   13 |    3 |
|     1 |   14 |    3 |
|     1 |   15 |    3 |
|     1 |   16 |    3 |
|     1 |   17 |    3 |
|     1 |   18 |    3 |
|     1 |   19 |    4 |
|     1 |   20 |    4 |
|     1 |   21 |    4 |
|     1 |   22 |    4 |
|     1 |   23 |    5 |
+-------+------+------+

With CTEs, path queries are far simpler, and they run much faster, e.g., find an individual’s ancestors:


Listing 8b: List an individual's ancestors (path to root):
WITH RECURSIVE ctepath AS (
  SELECT parentID FROM familytree WHERE childID=23             -- INDIVIDUAL’S FATHER
  UNION ALL
  SELECT f.parentID FROM familytree f                          -- AND THAT INDIVIDUAL’S FATHER ETC
  JOIN ctepath ON f.childID=ctepath.parentID
)
SELECT Group_Concat(parentID) As AncestorsOf23 FROM ctepath;   -- RETURNS 19,12,6,2,1

These algorithms don't bend the brain. They perform acceptably with large trees, faster with CTEs. Querying edge-adjacency lists for subtrees and paths is less daunting than their reputation suggests.

Automate tree drawing!

Tables of numbers may be the most boring objects on earth. How to bring them alive? The Google Visualization API library has an ‘OrgChart’ module that can make edge list trees look like Fig 2, but each instance needs fifty or so lines of specific JavaScript code, plus an additional line of code for each row of data in the tree. Could we autogenerate that code? Mais oui! The module needs child node and parent node columns of data, and accepts an optional third column for info that pops up when the mouse hovers. Here is such a query for the Shakespeare family tree ...

Listing 9a
select concat( node.ID,' ', node.name) as node,
       if( edges.parentID is null, '', concat(parent.ID, ' ',parent.name)) as parent,
       if( node.born is null, 'Birthdate unknown', concat( 'Born ', node.born )) as tooltip
from      family     as node
left join familytree as edges  on node.ID=edges.childID
left join family     as parent on edges.parentID=parent.ID;

and here is a PHP function to generate HTML and JavaScript that paints an OrgChart for any tree query returning string columns for node, parent and optionally tooltips:

Listing 9b
function orgchart( $conn, $qry ) {
  $cols = array(); $rows = array();
  $res = mysqli_query( $conn, $qry ) or exit( mysqli_error($conn) );
  $colcount = mysqli_num_fields( $res );
  if( $colcount < 2 ) exit( "Org chart needs two or three columns" );
  $rowcount = mysqli_num_rows( $res );
  for( $i=0; $i<$colcount; $i++ ) $cols[] = mysqli_fetch_field( $res, $i );
  while( $row = mysqli_fetch_row($res) ) $rows[] = $row;
  echo "<html>\n<head>\n",
       "  <script type='text/javascript' src='https://www.google.com/jsapi'></script>\n",
       "  <script type='text/javascript'>\n",
       "    google.load('visualization', '1', {'packages':['orgchart']});\n",
       "    google.setOnLoadCallback(drawChart);\n",
       "    function drawChart() {\n",
       "      var data = new google.visualization.DataTable();\n";
  for( $i=0; $i<$colcount; $i++ ) echo "      data.addColumn('string','{$cols[$i]->name}')\n";
  echo "      data.addRows([\n";
  for( $j=0; $j<$rowcount; $j++ ) {
    $row = $rows[$j];
    $c = (( $j < $rowcount-1 ) ? "," : "" );
    echo "        ['{$row[0]}','{$row[1]}','{$row[2]}']$c\n";
  }
  echo "      ]);\n",
       "      var chart = new google.visualization.OrgChart(document.getElementById('chart_div'));\n",
       "      var options = {'size':'small','allowHtml':'true','allowCollapse':'true'};\n",
       "      chart.draw(data, options);\n",
       "    }\n",
       "  </script>\n/head>\n<body>\n",
       "  <div id='chart_div'></div>\n",
       "</body>\n</html>";
}

Nested sets model of a tree

Imagine an oval drawn round every leaf and every subtree in Fig 2, and a final oval round the entire tree. The tree is a set. Each subtree is a subset. That's the basic idea of the nested sets model.

The advantage of the nested sets model is that root, leaves, subtrees, levels, tree height, ancestors, descendants and paths can be retrieved without recursion or application language code. The disadvantages are:

initial setup of the tree table can be difficult,
queries for parents (immediate superiors) and children (immediate subordinates) are more complicated than with an edge list model,
insertion, updates and deletion are extremely cumbersome since they may require updates to much of the tree.

The nested sets model depends on using a modified preorder tree traversal (MPTT) depth-first algorithm to assign each node left and right integers which define the node's tree position. All nodes of a subtree have

left values greater than the subtree parent's left value, and
right values smaller than that of the subtree parent's right value.

so queries for subtrees are dead simple. If the numbering scheme is integer-sequential as in Fig 3, the root node receives a left value of 1 and a right value equal to twice the item count.

To see how to code nested sets using MPTT, trace the ascending integers in Fig 3, starting with 1 on the left side of the root node (Richard Shakespeare). Following edges downward and leftward, the left side of each box gets the next integer. When you reach a leaf (Joan, left=3), the right side of that box gets the next integer (4). If there is another node to the right on the same level, continue in that direction; otherwise continue up the right side of the subtree you just descended. When you arrive back at the root on the right side, you're done. Down, right and up.

A serious problem with this scheme jumps out right away: after you've written the Fig 3 tree to a table, what if historians discover an older brother or sister of Henry and John? Every row in the tree table must be updated!

Celko and others have proposed alternative numbering schemes to get round this problem, but the logical difficulty remains: inserts and updates can invalidate many or all rows, and no SQL CHECK or CONSTRAINT can prevent it. The nested sets model is not good for trees which require frequent updates, and is pretty much unsupportable for large updatable trees that will be accessed by many concurrent users. But as we'll see in a moment, it can be very useful indeed for reporting a tree.

How to build a nested sets representation from an edge list

Obviously, numbering a tree by hand would be error-prone, seriously impractical for large trees, so it's usually best to code the tree initially as an edge list, then use a stored procedure to translate the edge list representation to nested sets. Celko 's depth-first pushdown stack method will translate any edge list tree into a nested sets tree, though slowly:

Create a table nestedsettree for the tree: node, leftedge, rightedge, and a stack pointer (top),
Seed that table with the root node of the edge list tree,
Set a nextedge counter to 1 plus the left value of the root node, i.e. 2,
While that counter is less than the rightedge value of the root node ...
- insert a row for this parent's smallest unwritten child, and drop down a level, or
- if we're out of children, increment rightedge , write it to the current row, and back up a level.

This version has been improved to handle edge list trees with or without a row containing the root node and its NULL parent:

Listing 10
DROP PROCEDURE IF EXISTS EdgeListToNestedSet;
DELIMITER go
CREATE PROCEDURE EdgeListToNestedSet( edgeTable CHAR(64), idCol CHAR(64), parentCol CHAR(64) )
BEGIN
  DECLARE maxrightedge, rows SMALLINT DEFAULT 0;
  DECLARE trees, current SMALLINT DEFAULT 1;
  DECLARE nextedge SMALLINT DEFAULT 2;
  DECLARE msg CHAR(128);
  -- create working tree table as a copy of edgeTable
  DROP TEMPORARY TABLE IF EXISTS tree;
  CREATE TEMPORARY TABLE tree( childID INT, parentID INT );
  SET @sql = CONCAT( 'INSERT INTO tree SELECT ', idCol, ',', parentCol, ' FROM ', edgeTable );
  PREPARE stmt FROM @sql; EXECUTE stmt; DROP PREPARE stmt;
  -- initialise result table
  DROP TABLE IF EXISTS nestedsettree;
  CREATE TABLE nestedsettree (
    top SMALLINT, nodeID SMALLINT, leftedge SMALLINT, rightedge SMALLINT,
    KEY(nodeID,leftedge,rightedge)
  ) ENGINE=HEAP;
  -- root is child with NULL parent or parent which is not a child
  SET @nulls = ( SELECT Count(*) FROM tree WHERE parentID IS NULL );
  IF @nulls>1 THEN SET trees=2;
  ELSEIF @nulls=1 THEN
    SET @root = ( SELECT childID FROM tree WHERE parentID IS NULL );
    DELETE FROM tree WHERE childID=@root;
  ELSE
    SET @sql = CONCAT( 'SELECT Count(DISTINCT f.', parentcol, ') INTO @roots FROM ', edgeTable,
                       ' f LEFT JOIN ', edgeTable, ' t ON f.', parentCol, '=', 't.', idCol,
                       ' WHERE t.', idCol, ' IS NULL' );
    PREPARE stmt FROM @sql; EXECUTE stmt; DROP PREPARE stmt;
    IF @roots <> 1 THEN SET trees=@roots;
    ELSE
      SET @sql = CONCAT( 'SELECT DISTINCT f.', parentCol, ' INTO @root FROM ', edgeTable,
                         ' f LEFT JOIN ', edgeTable, ' t ON f.', parentCol, '=', 't.',
                         idCol, ' WHERE t.', idCol, ' IS NULL' );
      PREPARE stmt FROM @sql; EXECUTE stmt; DROP PREPARE stmt;
    END IF;
  END IF;
  IF trees<>1 THEN
    SET msg = IF( trees=0, "No tree found", "Table has multiple trees" );
    SELECT msg AS 'Cannot continue';
  ELSE    -- build nested sets tree
    SET maxrightedge = 2 * (1 + (SELECT + COUNT(*) FROM tree));
    INSERT INTO nestedsettree VALUES( 1, @root, 1, maxrightedge );
    WHILE nextedge < maxrightedge DO
      SET rows=(SELECT Count(*) FROM nestedsettree s JOIN tree t ON s.nodeID=t.parentID AND s.top=current);
      IF rows > 0 THEN
        BEGIN
          INSERT INTO nestedsettree
            SELECT current+1, MIN(t.childID), nextedge, NULL
            FROM nestedsettree AS s
            JOIN tree AS t ON s.nodeID = t.parentID AND s.top = current;
          DELETE FROM tree
          WHERE childID = (SELECT nodeID FROM nestedsettree WHERE top=(current+1));
          SET nextedge = nextedge + 1, current = current + 1;
         END;
       ELSE
         UPDATE nestedsettree SET rightedge=nextedge, top = -top WHERE top=current;
         SET nextedge=nextedge+1, current=current-1;
      END IF;
    END WHILE;
    -- show result
    IF (SELECT COUNT(*) FROM tree) > 0 THEN
      SELECT 'Orphaned rows remain' AS 'Error';
    END IF;
    DROP TEMPORARY TABLE tree;
  END IF;
END;
go
DELIMITER ;
CALL EdgeListToNestedSet( 'familytree', 'childID', 'parentID' );
SELECT
  nodeID, PersonName(nodeID) AS Name,
  ABS(top) AS 'Tree Level', leftedge AS 'Left', rightedge AS 'Right'
FROM nestedsettree
ORDER BY nodeID;
+--------+----------------------+------------+------+-------+
| nodeID | Name                 | Tree Level | Left | Right |
+--------+----------------------+------------+------+-------+
|      1 | Richard Shakespeare  |          1 |    1 |    46 |
|      2 | Henry Shakespeare    |          2 |    2 |    43 |
|      3 | John Shakespeare     |          2 |   44 |    45 |
|      4 | Joan Shakespeare     |          3 |    3 |     4 |
|      5 | Margaret Shakespeare |          3 |    5 |     6 |
|      6 | William Shakespeare  |          3 |    7 |    24 |
|      7 | Gilbert Shakespeare  |          3 |   25 |    26 |
|      8 | Joan Shakespeare     |          3 |   27 |    36 |
|      9 | Anne Shakespeare     |          3 |   37 |    38 |
|     10 | Richard Shakespeare  |          3 |   39 |    40 |
|     11 | Edmond Shakespeare   |          3 |   41 |    42 |
|     12 | Susana Shakespeare   |          4 |    8 |    13 |
|     13 | Hamnet Shakespeare   |          4 |   14 |    15 |
|     14 | Judith Shakespeare   |          4 |   16 |    23 |
|     15 | William Hart         |          4 |   28 |    29 |
|     16 | Mary Hart            |          4 |   30 |    31 |
|     17 | Thomas Hart          |          4 |   32 |    33 |
|     18 | Michael Hart         |          4 |   34 |    35 |
|     19 | Elizabeth Hall       |          5 |    9 |    12 |
|     20 | Shakespeare Quiney   |          5 |   17 |    18 |
|     21 | Richard Quiney       |          5 |   19 |    20 |
|     22 | Thomas Quiney        |          5 |   21 |    22 |
|     23 | John Bernard         |          6 |   10 |    11 |
+--------+----------------------+------------+------+-------+

Verify the function with a query that generates an edge list tree from a nested sets tree:

Listing 10a:
SELECT a.nodeID, b.nodeID AS parent
FROM nestedsettree AS a
LEFT JOIN nestedsettree AS b ON b.leftedge = (
  SELECT MAX( leftedge )
  FROM nestedsettree AS t
  WHERE a.leftedge > t.leftedge AND a.leftedge < t.rightedge
)
ORDER BY a.nodeID;

For how to keep multiple trees in one table, see “Multiple trees in one table” on our Common Queries page.

Finding a node's parent and children

In an edge list, the parent of a node is the row's parentID, and its children are the rows where that nodeID is parentID. What could be simpler? In comparison, nested sets queries for parents and their children are tortuous and slow. One way to fetch the child nodes of a given node is to INNER JOIN the nested sets tree table AS parent to itself AS child ON child.leftedge BETWEEN parent.leftedge AND parent.rightedge, then scope on the target row's leftedge and rightedge values. In the resulting list, child.nodeID values one level down occur once and are children, grandkids are two levels down and occur twice, and so on:

Listing 11
SELECT PersonName(child.nodeID) AS 'Descendants of William', COUNT(*) AS Generation
FROM nestedsettree AS parent
JOIN nestedsettree AS child ON child.leftedge BETWEEN parent.leftedge AND parent.rightedge
WHERE parent.leftedge > 7 AND parent.rightedge < 24       -- William’s leftedge, rightedge
GROUP BY child.nodeID;
+------------------------+------------+
| Descendants of William | Generation |
+------------------------+------------+
| Susana Shakespeare     |          1 |
| Hamnet Shakespeare     |          1 |
| Judith Shakespeare     |          1 |
| Elizabeth Hall         |          2 |
| Shakespeare Quiney     |          2 |
| Richard Quiney         |          2 |
| Thomas Quiney          |          2 |
| John Bernard           |          3 |
+------------------------+------------+

Therefore HAVING COUNT(t2.nodeID)=1 scopes listed descendants to the children:

Listing 11a
SELECT PersonName(child.nodeID) AS 'Children of William'
FROM nestedsettree AS parent
JOIN nestedsettree AS child ON child.leftedge BETWEEN parent.leftedge AND parent.rightedge
WHERE parent.leftedge > 7 AND parent.rightedge < 24
GROUP BY child.nodeID
HAVING COUNT(child.nodeID)=1
+---------------------+
| Children of William |
+---------------------+
| Susana Shakespeare  |
| Hamnet Shakespeare  |
| Judith Shakespeare  |
+---------------------+

Retrieving a subtree or a subset of parents requires yet another join:

Listing 11b
SELECT Parent, Group_Concat(Child ORDER BY Child) AS Children
FROM (
  SELECT master.nodeID AS Parent, child.nodeID AS Child
  FROM nestedsettree AS master
  JOIN nestedsettree AS parent
  JOIN nestedsettree AS child ON child.leftedge BETWEEN parent.leftedge AND parent.rightedge
  WHERE parent.leftedge > master.leftedge AND parent.rightedge < master.rightedge
  GROUP BY master.nodeID, child.nodeID
  HAVING COUNT(*)=1
) AS tmp
WHERE parent in(6,8,12,14)
GROUP BY Parent;
+--------+-------------------+
| Parent | Children          |
+--------+-------------------+
|      6 | 12,13,14          |
|      8 | 15,16,17,18       |
|     12 | 19                |
|     14 | 20,21,22          |
+--------+-------------------+

This takes hundreds of times longer than a query for the same info from an edge list! An aggregating version of Listing 19 is easier to write, but is an even worse performer:

Listing 11c
SELECT p.nodeID AS Parent, Group_Concat(c.nodeID) AS Children
FROM nestedsettree AS p
JOIN nestedsettree AS c
  ON p.leftedge = (SELECT MAX(s.leftedge) FROM nestedsettree AS s
                   WHERE c.leftedge > s.leftedge AND c.leftedge < s.rightedge)
GROUP BY Parent;
+--------+-------------------+
| Parent | Children          |
+--------+-------------------+
|      1 | 2,3               |
|      2 | 5,6,7,8,9,10,11,4 |
|      6 | 12,13,14          |
|      8 | 15,16,17,18       |
|     12 | 19                |
|     14 | 20,21,22          |
|     19 | 23                |
+--------+-------------------+

Logic that is reciprocal to that of Listing 11a gets us the parent of a node:

retrieve its leftedge and rightedge values,
compute its level,
find the node which is one level up and has edge values outside the node's leftedge and rightedge values.

Listing 12
DROP PROCEDURE IF EXISTS ShowNestedSetParent;
DELIMITER go
CREATE PROCEDURE ShowNestedSetParent( node SMALLINT )
BEGIN
  DECLARE level, thisleft, thisright SMALLINT DEFAULT 0;
  -- find node edges
  SELECT leftedge, rightedge
    INTO thisleft, thisright
  FROM nestedsettree
  WHERE nodeID = node;
  -- find node level
  SELECT COUNT(n1.nodeid)
    INTO level
  FROM nestedsettree AS n1
    INNER JOIN nestedsettree AS n2
    ON n2.leftedge BETWEEN n1.leftedge AND n1.rightedge
  WHERE n2.nodeid = node
  GROUP BY n2.nodeid;
  -- find parent
  SELECT
    PersonName(n2.nodeid) AS Parent
  FROM nestedsettree AS n1
    INNER JOIN nestedsettree AS n2
    ON n2.leftedge BETWEEN n1.leftedge AND n1.rightedge
  WHERE n2.leftedge < thisleft AND n2.rightedge > thisright
  GROUP BY n2.nodeid
  HAVING COUNT(n1.nodeid)=level-1;
END;
go
DELIMITER ;
CALL ShowNestedSetParent(6);
+-------------------+
| Parent            |
+-------------------+
| Henry Shakespeare |
+-------------------+

Other queries

For some query problems, edge list and nested sets queries are equivalently simple. For example to find the tree root and leaves, compare Listing 6 with:

Listing 13
SELECT
  name AS Paterfamilias,
  IFNULL(born,'?') AS Born,
  IFNULL(died,'?') AS Died
FROM nestedsettree AS t
INNER JOIN family AS f ON t.nodeID=f.ID
WHERE leftedge = 1;
+---------------------+------+------+
| Paterfamilias       | Born | Died |
+---------------------+------+------+
| Richard Shakespeare | ?    | 1561 |
+---------------------+------+------+
SELECT
  name AS 'Childless Shakespeares',
  IFNULL(born,'?') AS Born,
  IFNULL(died,'?') AS Died
FROM nestedsettree AS t
INNER JOIN family AS f ON t.nodeID=f.ID
WHERE rightedge = leftedge + 1;
+------------------------+------+------+
| Childless Shakespeares | Born | Died |
+------------------------+------+------+
| Joan Shakespeare       | 1558 | ?    |
| Margaret Shakespeare   | 1562 | 1563 |
| John Bernard           | ?    | 1674 |
| Hamnet Shakespeare     | 1585 | 1596 |
| Shakespeare Quiney     | 1616 | 1617 |
| Richard Quiney         | 1618 | 1639 |
| Thomas Quiney          | 1620 | 1639 |
| Gilbert Shakespeare    | 1566 | 1612 |
| William Hart           | 1600 | 1639 |
| Mary Hart              | 1603 | 1607 |
| Thomas Hart            | 1605 | 1670 |
| Michael Hart           | 1608 | 1618 |
| Anne Shakespeare       | 1571 | 1579 |
| Richard Shakespeare    | 1574 | 1613 |
| Edmond Shakespeare     | 1580 | 1607 |
| John Shakespeare       | 1530 | 1601 |
+------------------------+------+------+

Finding subtrees in a nested sets model requires no twisted code, no stored procedure. To retrieve the nestedsettree nodes in William's subtree, just ask for nodes whose leftedge values are greater, and whose rightedge values are smaller than William's:

Listing 14
SELECT PersonName(t.nodeID) AS Descendant
FROM nestedsettree AS s
  JOIN nestedsettree AS t ON s.leftedge < t.leftedge AND s.rightedge > t.rightedge
  JOIN family f ON s.nodeID = f.ID
WHERE f.name = 'William Shakespeare';

Finding a single path in the nested sets model is about as complicated as edge list path enumeration (Listings 8, 9):

Listing 15
SELECT
  t2.nodeID AS Node,
  PersonName(t2.nodeID) AS Person,
  (SELECT COUNT(*)
   FROM nestedsettree AS t4
   WHERE t4.leftedge BETWEEN t1.leftedge AND t1.rightedge
     AND t2.leftedge BETWEEN t4.leftedge AND t4.rightedge
   ) AS Path
FROM nestedsettree AS t1
  INNER JOIN nestedsettree AS t2 ON t2.leftedge BETWEEN t1.leftedge AND t1.rightedge
  INNER JOIN nestedsettree AS t3 ON t3.leftedge BETWEEN t2.leftedge AND t2.rightedge
WHERE t1.nodeID=(SELECT ID FROM family WHERE name='William Shakespeare')
  AND t3.nodeID=(SELECT ID FROM family WHERE name='John Bernard');
+------+---------------------+------+
| Node | Person              | Path |
+------+---------------------+------+
|    6 | William Shakespeare |    1 |
|   12 | Susana Shakespeare  |    2 |
|   19 | Elizabeth Hall      |    3 |
|   23 | John Bernard        |    4 |
+------+---------------------+------+

Displaying the tree

Here the nested sets model shines. The arithmetic that was used to build the tree makes short work of summary queries. For example to retrieve a node list which preserves all parent-child relations, we need just two facts:

listing order is the order taken in the node walk that created the tree, i.e. leftedge,
a node's indentation depth is simply the JOIN (edge) count from root to node:

Listing 16
SELECT
  CONCAT( SPACE(2*COUNT(parent.nodeid)-2), PersonName(child.nodeid) )
  AS 'The Shakespeare Family Tree'
FROM nestedsettree AS parent
  INNER JOIN nestedsettree AS child
  ON child.leftedge BETWEEN parent.leftedge AND parent.rightedge
GROUP BY child.nodeid
ORDER BY child.leftedge;
+-----------------------------+
| The Shakespeare Family Tree |
+-----------------------------+
| Richard Shakespeare         |
|   Henry Shakespeare         |
|     Joan Shakespeare        |
|     Margaret Shakespeare    |
|     William Shakespeare     |
|       Susana Shakespeare    |
|         Elizabeth Hall      |
|           John Bernard      |
|       Hamnet Shakespeare    |
|       Judith Shakespeare    |
|         Shakespeare Quiney  |
|         Richard Quiney      |
|         Thomas Quiney       |
|     Gilbert Shakespeare     |
|     Joan Shakespeare        |
|       William Hart          |
|       Mary Hart             |
|       Thomas Hart           |
|       Michael Hart          |
|     Anne Shakespeare        |
|     Richard Shakespeare     |
|     Edmond Shakespeare      |
|   John Shakespeare          |
+-----------------------------+

To retrieve only a subtree, add a query clause which restricts nodes to those whose edges are within the range of the parent node's left and right edge values, for example for William and his descendants...

WHERE parent.leftedge >= 7 AND parent.rightedge <=24

Node insertions, updates and deletions

Nested sets arithmetic also helps with insertions, updates and deletions, but the problem remains that changing just one node can require changing much of the tree.

Inserting a node in the tree requires at least two pieces of information: the nodeID to insert, and the nodeID of its parent. The model is normalised so the nodeID first must have been added to the parent (family) table. The algorithm for adding the node to the tree is:

increment leftedge by 2 in nodes where the rightedge value is greater than the parent's rightedge,
increment rightedge by 2 in nodes where the leftedge value is greater than the parent's leftedge,
insert a row with the given nodeID, leftedge = 1 + parent's leftedge, rightedge = 2 + parent's leftedge.

That's not difficult, but all rows will have to be updated!

Listing 17
DROP PROCEDURE IF EXISTS InsertNestedSetNode;
DELIMITER go
CREATE PROCEDURE InsertNestedSetNode( IN node SMALLINT, IN parent SMALLINT )
BEGIN
  DECLARE parentleft, parentright SMALLINT DEFAULT 0;
  SELECT leftedge, rightedge
    INTO parentleft, parentright
  FROM nestedsettree
  WHERE nodeID = parent;
  IF FOUND_ROWS() = 1 THEN
    BEGIN
      UPDATE nestedsettree
        SET rightedge = rightedge + 2
      WHERE rightedge > parentleft;
      UPDATE nestedsettree
        SET leftedge = leftedge + 2
      WHERE leftedge > parentleft;
      INSERT INTO nestedsettree
        VALUES ( 0, node, parentleft + 1, parentleft + 2 );
    END;
  END IF;
END;
go
DELIMITER ;

"Sibline" or horizontal order is obviously significant in family trees, but may not be significant in other trees. Listing 17 adds the new node at the left edge of the sibline. To specify another position, modify the procedure to accept a third parameter for the nodeID which is to be to the left or right of the insertion point.

Updating a node in place requires nothing more than editing nodeID to point at a different parent row.

Deleting a node raises the problem of how to repair links severed by the deletion. In tree models of parts explosions, the item to be deleted is often replaced by a new item, so it can be treated like a simple node update. In organisational boss-employee charts, though, does a colleague move over, does a subordinate get promoted, does everybody in the subtree move up a level, or does something else happen? No formula can catch all the possibilities. Listing 18 illustrates how to handle two common scenarios, move everyone up, and move someone over. All possibilities except simple node replacement of require changes elsewhere in the tree.

Listing 18
DROP PROCEDURE IF EXISTS DeleteNestedSetNode;
DELIMITER go
CREATE PROCEDURE DeleteNestedSetNode( IN mode CHAR(7), IN node SMALLINT )
BEGIN
  DECLARE thisleft, thisright SMALLINT DEFAULT 0;
  SELECT leftedge, rightedge
    INTO thisleft, thisright
  FROM nestedsettree
  WHERE nodeID = node;
  IF mode = 'PROMOTE' THEN
    BEGIN                                                         -- Ian Holsman found these two bugs
      DELETE FROM nestedsettree
      WHERE nodeID = node;
      UPDATE nestedsettree
        SET leftedge = leftedge - 1, rightedge = rightedge - 1    -- rather than = thisleft
      WHERE leftedge BETWEEN thisleft AND thisright;
      UPDATE nestedsettree
        SET rightedge = rightedge - 2
      WHERE rightedge > thisright;
      UPDATE nestedsettree
        SET leftedge = leftedge - 2
      WHERE leftedge > thisright;                                 -- rather than > thisleft
    END;
  ELSEIF mode = 'REPLACE' THEN
    BEGIN
      UPDATE nestedsettree
        SET leftedge = thisleft - 1, rightedge = thisright
      WHERE leftedge = thisleft + 1;
      UPDATE nestedsettree
        SET rightedge = rightedge - 2
      WHERE rightedge > thisleft;
      UPDATE nestedsettree
        SET leftedge = leftedge - 2
      WHERE leftedge > thisleft;
      DELETE FROM nestedsettree
      WHERE nodeID = node;
    END;
  END IF;
END;
go
DELIMITER ;

Nested set model summary

Some nested sets queries are quicker than their edge list counterparts. Some are slower. None are faster than edge list queries using recursive CTEs. Given the concurrency nightmare that nested sets impose for inserts and deletions, it is reasonable to reserve the nested sets model for fairly static trees whose users are mostly interested in querying subtrees.

If you will be using the nested sets model, you may be converting back and forth with edge list models, so here is a simple query to build an edge list from a nested sets tree:

Listing 19
SELECT
  p.nodeID AS parentID,
  c.nodeID AS childID
FROM nestedsettree AS p
  INNER JOIN nestedsettree AS c
  ON p.leftedge = (SELECT MAX(s.leftedge)
                   FROM nestedsettree AS s
                   WHERE c.leftedge > s.leftedge
                     AND c.leftedge < s.rightedge)
ORDER BY p.nodeID;

Edge list model of a non-tree graph

Many graphs are not trees. Imagine a small airline which has just acquired licences for flights no longer than 6,000 km between Los Angeles (LAX), New York (JFK), Heathrow in London, Charles de Gaulle in Paris, Amsterdam-Schiphol, Arlanda in Sweden, and Helsinki-Vantaa. You have been asked to compute the shortest possible one-way routes that do not deviate more than 90° from the direction of the first hop—roughly, one-way routes and no circuits.

Airports are nodes, flights are edges, routes are paths. We will need three tables.

Airports (nodes)

To identify an airport we need its code, location name, latitude and longitude. Latitude and longitude are usually given as degrees, minutes and seconds, north or south of the equator, east or west of Greenwich. To hide details that aren't directly relevant to nodes and edges, code latitude and longitude as simple reals where longitudes west of Greenwich and latitudes south of the equator are negative, whilst longitudes east of Greenwich and latitudes north of the equator are positive:

Listing 20
CREATE TABLE airports (
  code char(3) NOT NULL,
  city char(100) default NULL,
  latitude float NOT NULL,
  longitude float NOT NULL,
  PRIMARY KEY (code)
) ;
INSERT INTO airports VALUES ('JFK', 'New York, NY', 40.75, -73.97);
INSERT INTO airports VALUES ('LAX', 'Los Angeles, CA', 34.05, -118.22);
INSERT INTO airports VALUES ('LHR', 'London, England', 51.5, -0.45);
INSERT INTO airports VALUES ('HEL', 'Helsinki, Finland', 60.17, 24.97);
INSERT INTO airports VALUES ('CDG', 'Paris, France', 48.86, 2.33);
INSERT INTO airports VALUES ('STL', 'St Louis, MO', 38.63, -90.2);
INSERT INTO airports VALUES ('ARN', 'Stockholm, Sweden', 59.33, 18.05);

Flights (edges)

The model attaches two weights to flights: distance and direction.

We need a method of calculating the Great Circle Distance—the geographical distance between any two cities - another natural job for a stored function. The distance calculation

converts to radians the degree coordinates of any two points on the earth's surface,
calculates the angle of the arc subtended by the two points, and
converts the result, also in radians, to surface (circumferential) kilometres (1 radian=6,378.388 km).

Listing 21
SET GLOBAL log_bin_trust_function_creators=TRUE;   -- since 5.0.16
DROP FUNCTION IF EXISTS GeoDistKM;
DELIMITER go
CREATE FUNCTION GeoDistKM( lat1 FLOAT, lon1 FLOAT, lat2 FLOAT, lon2 FLOAT ) RETURNS float
BEGIN
  DECLARE pi, q1, q2, q3 FLOAT;
  SET pi = PI();
  SET lat1 = lat1 * pi / 180;
  SET lon1 = lon1 * pi / 180;
  SET lat2 = lat2 * pi / 180;
  SET lon2 = lon2 * pi / 180;
  SET q1 = COS(lon1-lon2);
  SET q2 = COS(lat1-lat2);
  SET q3 = COS(lat1+lat2);
  SET rads = ACOS( 0.5*((1.0+q1)*q2 - (1.0-q1)*q3) );
  RETURN 6378.388 * rads;
END;
go
DELIMITER ;

That takes care of flight distances. Flight direction is, approximately, the arctangent (ATAN) of the difference between flights.depart and flights.arrive latitudes and longitudes. Now we can seed the airline's flights table with one-hop flights up to 6,000 km long:

Listing 22
CREATE TABLE flights (
  id INT PRIMARY KEY AUTO_INCREMENT,
  depart CHAR(3),
  arrive CHAR(3),
  distance DECIMAL(10,2),
  direction DECIMAL(10,2)
) ;
INSERT INTO flights
  SELECT
    NULL,
    depart.code,
    arrive.code,
    ROUND(GeoDistKM(depart.latitude,depart.longitude,arrive.latitude,arrive.longitude),2),
    ROUND(DEGREES(ATAN(arrive.latitude-depart.latitude,arrive.longitude-depart.longitude)),2)
  FROM airports AS depart
  INNER JOIN airports AS arrive ON depart.code <> arrive.code
  HAVING Km <= 6000;
SELECT * FROM flights;
+----+--------+--------+----------+-----------+
| id | depart | arrive | distance | direction |
+----+--------+--------+----------+-----------+
|  1 | LAX    | JFK    | 3941.18  | 8.61      |
|  2 | LHR    | JFK    | 5550.77  | -171.68   |
|  3 | CDG    | JFK    | 5837.46  | -173.93   |
|  4 | STL    | JFK    | 1408.11  | 7.44      |
|  5 | JFK    | LAX    | 3941.18  | -171.39   |
|  6 | STL    | LAX    | 2553.37  | -170.72   |
|  7 | JFK    | LHR    | 5550.77  | 8.32      |
|  8 | HEL    | LHR    | 1841.91  | -161.17   |
|  9 | CDG    | LHR    | 354.41   | 136.48    |
| 10 | ARN    | LHR    | 1450.12  | -157.06   |
| 11 | LHR    | HEL    | 1841.91  | 18.83     |
| 12 | CDG    | HEL    | 1912.96  | 26.54     |
| 13 | ARN    | HEL    | 398.99   | 6.92      |
| 14 | JFK    | CDG    | 5837.46  | 6.07      |
| 15 | LHR    | CDG    | 354.41   | -43.52    |
| 16 | HEL    | CDG    | 1912.96  | -153.46   |
| 17 | ARN    | CDG    | 1545.23  | -146.34   |
| 18 | JFK    | STL    | 1408.11  | -172.56   |
| 19 | LAX    | STL    | 2553.37  | 9.28      |
| 20 | LHR    | ARN    | 1450.12  | 22.94     |
| 21 | HEL    | ARN    | 398.99   | -173.08   |
| 22 | CDG    | ARN    | 1545.23  | 33.66     |
+----+--------+--------+----------+-----------+

The distances agree approximately with public information sources for flight lengths. For a pair of airports A and B not very near the poles, the error in calculating direction using ATAN(), is small. To remove that error, instead of ATAN() use a formula from spherical trigonometry (for example one of the formulas at http://www.dynagen.co.za/eugene/where/formula.html).

Routes (paths)

A route is a path along one or more of these edges, so flights:routes is a 1:many relationship. For simplicity it's efficient to denormalise representation of routes with a variation of the materialised path model to store all the hops of one route as a list of flights in one routes column. The column routes.route is the sequence of airports, from first departure to final arrival, the column routes.hops is the number of hops in that route, and the column routes.direction is the direction:

Listing 23
CREATE TABLE routes (
  id INT PRIMARY KEY AUTO_INCREMENT,
  depart CHAR(3),
  arrive CHAR(3),
  hops SMALLINT,
  route CHAR(50),
  distance DECIMAL(10,2),
  direction DECIMAL(10,2)
) ;

Starting with an empty routes table, how do we populate it with the shortest routes between all ordered pairs of airports?

Insert all 1-hop flights from the flights table.
Add in the set of shortest multi-hop routes for all pairs of airports which don't have rows in the flights table.

For 1-hop flights we just write

Listing 24
INSERT INTO routes
  SELECT
    NULL,
    depart,
    arrive,
    1,
    CONCAT(depart,',',arrive),
    distance,
    direction
  FROM flights;

NULL being the placeholder for the auto-incrementing id column.

For multi-hop routes, we iteratively add in sets of all allowed 2-hop, 3-hop, ... n-hop routes, replacing longer routes by shorter routes as we find them, until there is nothing more to add or replace. That also breaks down to two logical steps: add hops to build the set of next allowed routes, and update longer routes with shorter ones.

Next allowed routes

The set of next allowed routes is the set of shortest routes that can be built by adding, to existing routes, flights that leave from the last arrival airport of an existing route, arrive at an airport not yet in the given route, and stay within ± 90° of the route's initial compass direction. So every new route is a JOIN between routes and flights in which

depart = routes.depart,
arrive = flights.arrive,
flights.depart = routes.arrive,
distance = MIN(routes.distance + flights.distance),
LOCATE( flights.arrive,routes.route) = 0,
flights.direction+360 > routes.direction+270 AND flights.direction+360 < routes.direction+450

This looks like a natural logical unit of work for a View:

Listing 25
CREATE OR REPLACE VIEW nextroutes AS
  SELECT
    routes.depart, flights.arrive, routes.hops+1 AS hops,
    CONCAT(routes.route, ',', flights.arrive) AS route,
    MIN(routes.distance + flights.distance) AS distance, routes.direction
  FROM routes 
  JOIN flights ON routes.arrive = flights.depart AND LOCATE(flights.arrive,routes.route) = 0
  WHERE flights.direction BETWEEN routes.direction-90 AND routes.direction+90
  GROUP BY depart,arrive;

How to add these new hops to routes? In standard SQL, this variant on a query by Scott Stephens should do it...

Listing 26
INSERT INTO routes
  SELECT NULL,depart,arrive,hops,route,distance,direction FROM nextroutes
  WHERE (nextroutes.depart,nextroutes.arrive) NOT IN (
    SELECT depart,arrive FROM routes
  );

but MySQL does not yet support update table subqueries. No worries, rewriting the subquery as a join speeds it up:

Listing 27
INSERT INTO routes
  SELECT
    NULL,
    nextroutes.depart,
    nextroutes.arrive,
    nextroutes.hops,
    nextroutes.route,
    nextroutes.distance,
    nextroutes.direction
  FROM nextroutes
  LEFT JOIN routes ON nextroutes.depart = routes.depart
        AND nextroutes.arrive = routes.arrive
  WHERE routes.depart IS NULL AND routes.arrive IS NULL;

Running that code right after the initial seeding from flights gives ...

SELECT * FROM routes;
+----+--------+--------+------+-------------+----------+-----------+
| id | depart | arrive | hops | route       | distance | direction |
+----+--------+--------+------+-------------+----------+-----------+
|  1 | LAX    | JFK    |    1 | LAX,JFK     | 3941.18  | 8.61      |
|  2 | LHR    | JFK    |    1 | LHR,JFK     | 5550.77  | -171.68   |
|  3 | CDG    | JFK    |    1 | CDG,JFK     | 5837.46  | -173.93   |
|  4 | STL    | JFK    |    1 | STL,JFK     | 1408.11  | 7.44      |
|  5 | JFK    | LAX    |    1 | JFK,LAX     | 3941.18  | -171.39   |
|  6 | STL    | LAX    |    1 | STL,LAX     | 2553.37  | -170.72   |
|  7 | JFK    | LHR    |    1 | JFK,LHR     | 5550.77  | 8.32      |
|  8 | HEL    | LHR    |    1 | HEL,LHR     | 1841.91  | -161.17   |
|  9 | CDG    | LHR    |    1 | CDG,LHR     | 354.41   | 136.48    |
| 10 | ARN    | LHR    |    1 | ARN,LHR     | 1450.12  | -157.06   |
| 11 | LHR    | HEL    |    1 | LHR,HEL     | 1841.91  | 18.83     |
| 12 | CDG    | HEL    |    1 | CDG,HEL     | 1912.96  | 26.54     |
| 13 | ARN    | HEL    |    1 | ARN,HEL     | 398.99   | 6.92      |
| 14 | JFK    | CDG    |    1 | JFK,CDG     | 5837.46  | 6.07      |
| 15 | LHR    | CDG    |    1 | LHR,CDG     | 354.41   | -43.52    |
| 16 | HEL    | CDG    |    1 | HEL,CDG     | 1912.96  | -153.46   |
| 17 | ARN    | CDG    |    1 | ARN,CDG     | 1545.23  | -146.34   |
| 18 | JFK    | STL    |    1 | JFK,STL     | 1408.11  | -172.56   |
| 19 | LAX    | STL    |    1 | LAX,STL     | 2553.37  | 9.28      |
| 20 | LHR    | ARN    |    1 | LHR,ARN     | 1450.12  | 22.94     |
| 21 | HEL    | ARN    |    1 | HEL,ARN     | 398.99   | -173.08   |
| 22 | CDG    | ARN    |    1 | CDG,ARN     | 1545.23  | 33.66     |
| 23 | ARN    | JFK    |    2 | ARN,LHR,JFK | 7000.89  | -157.06   |
| 24 | CDG    | LAX    |    2 | CDG,JFK,LAX | 9778.64  | -173.93   |
| 25 | CDG    | STL    |    2 | CDG,JFK,STL | 7245.57  | -173.93   |
| 26 | HEL    | JFK    |    2 | HEL,LHR,JFK | 7392.68  | -161.17   |
| 27 | JFK    | ARN    |    2 | JFK,LHR,ARN | 7000.89  | 8.32      |
| 28 | JFK    | HEL    |    2 | JFK,LHR,HEL | 7392.68  | 8.32      |
| 29 | LAX    | CDG    |    2 | LAX,JFK,CDG | 9778.64  | 8.61      |
| 30 | LAX    | LHR    |    2 | LAX,JFK,LHR | 9491.95  | 8.61      |
| 31 | LHR    | LAX    |    2 | LHR,JFK,LAX | 9491.95  | -171.68   |
| 32 | LHR    | STL    |    2 | LHR,JFK,STL | 6958.88  | -171.68   |
| 33 | STL    | CDG    |    2 | STL,JFK,CDG | 7245.57  | 7.44      |
| 34 | STL    | LHR    |    2 | STL,JFK,LHR | 6958.88  | 7.44      |
+----+--------+--------+------+-------------+----------+-----------+

... adding 12 two-hop rows.

Replace longer routes with shorter ones

As we build routes with more hops, it is logically possible that the nextroutes view will find shorter routes for an existing routes pair of depart and arrive. Standard SQL for replacing existing routes rows with nextroutes rows that match (depart, arrive) and have shorter distance values would be ...

Listing 28
UPDATE routes SET (hops,route,distance,direction) = (
  SELECT hops, route, distance, direction
  FROM nextroutes
  WHERE nextroutes.depart = routes.depart AND nextroutes.arrive = routes.arrive
)
WHERE (depart,arrive) IN (
  SELECT depart,arrive FROM nextroutes
  WHERE nextroutes.distance < routes.distance
);

... but MySQL does not support SET(col1,...) syntax, and as with Listing 7, MySQL does not yet accept subqueries referencing the table being updated, so we have to write more literal SQL:

Listing 29
UPDATE routes, nextroutes
SET
  routes.hops=nextroutes.hops,
  routes.route=nextroutes.route,
  routes.distance=nextroutes.distance,
  routes.direction=nextroutes.direction
WHERE routes.arrive=nextroutes.arrive
  AND routes.depart=nextroutes.depart
  AND nextroutes.distance < routes.distance;

Running this code right after the first run of Listing 27 updates no rows. To test the logic of iteration, continue running Listings 27 and 29 until no rows are being added or changed. The final result is:

SELECT * FROM ROUTES;
+----+--------+--------+------+-----------------+----------+-----------+
| id | depart | arrive | hops | route           | distance | direction |
+----+--------+--------+------+-----------------+----------+-----------+
|  1 | LAX    | JFK    |    1 | LAX,JFK         | 3941.18  | 8.61      |
|  2 | LHR    | JFK    |    1 | LHR,JFK         | 5550.77  | -171.68   |
|  3 | CDG    | JFK    |    1 | CDG,JFK         | 5837.46  | -173.93   |
|  4 | STL    | JFK    |    1 | STL,JFK         | 1408.11  | 7.44      |
|  5 | JFK    | LAX    |    1 | JFK,LAX         | 3941.18  | -171.39   |
|  6 | STL    | LAX    |    1 | STL,LAX         | 2553.37  | -170.72   |
|  7 | JFK    | LHR    |    1 | JFK,LHR         | 5550.77  | 8.32      |
|  8 | HEL    | LHR    |    1 | HEL,LHR         | 1841.91  | -161.17   |
|  9 | CDG    | LHR    |    1 | CDG,LHR         | 354.41   | 136.48    |
| 10 | ARN    | LHR    |    1 | ARN,LHR         | 1450.12  | -157.06   |
| 11 | LHR    | HEL    |    1 | LHR,HEL         | 1841.91  | 18.83     |
| 12 | CDG    | HEL    |    1 | CDG,HEL         | 1912.96  | 26.54     |
| 13 | ARN    | HEL    |    1 | ARN,HEL         | 398.99   | 6.92      |
| 14 | JFK    | CDG    |    1 | JFK,CDG         | 5837.46  | 6.07      |
| 15 | LHR    | CDG    |    1 | LHR,CDG         | 354.41   | -43.52    |
| 16 | HEL    | CDG    |    1 | HEL,CDG         | 1912.96  | -153.46   |
| 17 | ARN    | CDG    |    1 | ARN,CDG         | 1545.23  | -146.34   |
| 18 | JFK    | STL    |    1 | JFK,STL         | 1408.11  | -172.56   |
| 19 | LAX    | STL    |    1 | LAX,STL         | 2553.37  | 9.28      |
| 20 | LHR    | ARN    |    1 | LHR,ARN         | 1450.12  | 22.94     |
| 21 | HEL    | ARN    |    1 | HEL,ARN         | 398.99   | -173.08   |
| 22 | CDG    | ARN    |    1 | CDG,ARN         | 1545.23  | 33.66     |
| 23 | ARN    | JFK    |    2 | ARN,LHR,JFK     | 7000.89  | -157.06   |
| 24 | CDG    | LAX    |    2 | CDG,JFK,LAX     | 9778.64  | -173.93   |
| 25 | CDG    | STL    |    2 | CDG,JFK,STL     | 7245.57  | -173.93   |
| 26 | HEL    | JFK    |    2 | HEL,LHR,JFK     | 7392.68  | -161.17   |
| 27 | JFK    | ARN    |    2 | JFK,LHR,ARN     | 7000.89  | 8.32      |
| 28 | JFK    | HEL    |    2 | JFK,LHR,HEL     | 7392.68  | 8.32      |
| 29 | LAX    | CDG    |    2 | LAX,JFK,CDG     | 9778.64  | 8.61      |
| 30 | LAX    | LHR    |    2 | LAX,JFK,LHR     | 9491.95  | 8.61      |
| 31 | LHR    | LAX    |    2 | LHR,JFK,LAX     | 9491.95  | -171.68   |
| 32 | LHR    | STL    |    2 | LHR,JFK,STL     | 6958.88  | -171.68   |
| 33 | STL    | CDG    |    2 | STL,JFK,CDG     | 7245.57  | 7.44      |
| 34 | STL    | LHR    |    2 | STL,JFK,LHR     | 6958.88  | 7.44      |
| 35 | ARN    | LAX    |    3 | ARN,LHR,JFK,LAX | 10942.07 | -157.06   |
| 36 | ARN    | STL    |    3 | ARN,LHR,JFK,STL | 8409.00  | -157.06   |
| 37 | HEL    | LAX    |    3 | HEL,LHR,JFK,LAX | 11333.86 | -161.17   |
| 38 | HEL    | STL    |    3 | HEL,LHR,JFK,STL | 8800.79  | -161.17   |
| 39 | LAX    | ARN    |    3 | LAX,JFK,CDG,ARN | 10942.07 | 8.61      |
| 40 | LAX    | HEL    |    3 | LAX,JFK,CDG,HEL | 11333.86 | 8.61      |
| 41 | STL    | ARN    |    3 | STL,JFK,CDG,ARN | 8409.00  | 7.44      |
| 42 | STL    | HEL    |    3 | STL,JFK,CDG,HEL | 8800.79  | 7.44      |
+----+--------+--------+------+-----------------+----------+-----------+

All that's left to do is to assemble the code in a stored procedure:

Listing 30
DROP PROCEDURE IF EXISTS BuildRoutes;
DELIMITER go
CREATE PROCEDURE BuildRoutes()
BEGIN
  DECLARE rows INT DEFAULT 0;
  TRUNCATE routes;
  -- STEP 1, LISTING 24: SEED ROUTES WITH 1-HOP FLIGHTS
  INSERT INTO routes (depart, arrive, hops, route, distance, direction  )
    SELECT depart, arrive, 1, CONCAT(depart,',',arrive), distance, direction
  FROM flights;
  SET rows = ROW_COUNT();
  WHILE (rows > 0) DO
    -- STEP 2, LISTINGS 25, 27: ADD NEXT SET OF ROUTES
    INSERT INTO routes (depart, arrive, hops, route, distance, direction  )
      SELECT nextroutes.depart, nextroutes.arrive, nextroutes.hops,
        nextroutes.route, nextroutes.distance, nextroutes.direction
      FROM nextroutes
      LEFT JOIN routes ON nextroutes.depart=routes.depart AND nextroutes.arrive=routes.arrive
      WHERE routes.ID IS NULL;
      SET rows = ROW_COUNT();
    -- STEP 3, UPDATE SHORTER ROUTES IF ANY
    UPDATE routes
    JOIN nextroutes USING(arrive,depart)
    SET routes.hops=nextroutes.hops, routes.route=nextroutes.route,
        routes.distance=nextroutes.distance, routes.direction=nextroutes.direction
    WHERE nextroutes.distance < routes.distance;
  END WHILE;
END;
go
DELIMITER ;

The procedure looks like a candidate for translation a CTE, but the update command and the two joins to the table being written to (one in the nextroutes View, one in the insert loop) defeat the CTE engines in both MariaDB and PostgreSQL.

Route queries

Route queries are straightforward. How do we check that the algorithm produced no duplicate depart-arrive pairs? The following query should yield zero rows ...

Listing 31
SELECT depart, arrive, COUNT(*)
FROM routes
GROUP BY depart,arrive
HAVING COUNT(*) > 1;

... and does. Reachability queries are just as simple, for example where can we fly to from Helsinki?

Listing 32
SELECT *
FROM routes
WHERE depart='HEL'
ORDER BY distance;
+----+--------+--------+------+-----------------+----------+-----------+
| id | depart | arrive | hops | route           | distance | direction |
+----+--------+--------+------+-----------------+----------+-----------+
| 21 | HEL    | ARN    |    1 | HEL,ARN         | 398.99   | -173.08   |
|  8 | HEL    | LHR    |    1 | HEL,LHR         | 1841.91  | -161.17   |
| 16 | HEL    | CDG    |    1 | HEL,CDG         | 1912.96  | -153.46   |
| 26 | HEL    | JFK    |    2 | HEL,LHR,JFK     | 7392.68  | -161.17   |
| 38 | HEL    | STL    |    3 | HEL,LHR,JFK,STL | 8800.79  | -161.17   |
| 37 | HEL    | LAX    |    3 | HEL,LHR,JFK,LAX | 11333.86 | -161.17   |
+----+--------+--------+------+-----------------+----------+-----------+

An extended edge list model is simple to implement, gracefully accepts extended attributes for nodes, edge and paths, does not unduly penalise updates, and responds to queries with reasonable speed.

Parts explosions

A bill of materials for a house would include the cement block, lumber, shingles, doors, wallboard, windows, plumbing, electrical system, heating system, and so on. Each subassembly also has a bill of materials; the heating system has a furnace, ducts, and so on. A bill of materials implosion links component pieces to a major assembly. A bill of materials explosion breaks apart assemblies and subassemblies into their component parts.

Which graph model best handles a parts explosion? Combining edge list and "nested sets" algorithms seems a natural solution.

Imagine a new company that plans to make variously sized bookcases, either packaged as do-it-yourself kits of, or assembled from sides, shelves, shelf brackets, backboards, feet and screws. Shelves and sides are cut from planks. Backboards are trimmed from laminated sheeting. Feet are machine-carved from readycut blocks. Screws and shelf brackets are purchased in bulk. Here are the elements of one bookcase ...

  1 backboard, 2 x 1 m
    1 laminate
    8 screws
  2 sides 2m x 30 cm
    1 plank length 4m
    12 screws
  8 shelves 1 m x 30 cm (incl. top and bottom)
    2 planks
    24 shelf brackets
  4 feet 4cm x 4cm
    4 cubes
    16 screws

... which may be packaged in a box for sale at one price, or assembled as a finished product at a different price. At any time we need to be able to answer questions like

Do we have enough parts to make the bookcases on order?
What assemblies or packages would be most profitable to make given the current inventory?

There is no reason to break the normalising rule that item detail belongs in a nodes table, and graph logic belongs in an edges table. Edges also require a quantity attribute, for example a shelf includes four shelf brackets. Nodes and edges may also have costs and prices:

item purchase cost,
item assembly cost,
assembly cost,
assembly selling price.

In many parts problems like this one, items occur in multiple assemblies and subassemblies. The graph is not a tree. Also, it is often desirable to model multiple graphs without the table glut that would arise from giving each graph its own edges table. A simple way to solve this problem is to represent multiple graphs (assemblies) in the edges table by giving every row not only childID and parentID pointers, but a pointer which identifies the root itemID of the graph to which the row belongs.

So the data model is just two tables, for items (nodes and for product graphs or assemblies (edges). Assume that the company begins with a plan to sell the 2m x 1m bookcase in two forms, assembled and kit, and that the purchasing department has bought quantities of raw materials (laminate, planks, shelf supports, screws, wood cubes, boxes). Here are the nodes (items) and edges (assemblies):

Listing 33
CREATE TABLE items (
  itemID INT PRIMARY KEY AUTO_INCREMENT,
  name CHAR(20) NOT NULL,
  onhand INT NOT NULL DEFAULT 0,
  reserved INT NOT NULL DEFAULT 0,
  purchasecost DECIMAL(10,2) NOT NULL DEFAULT 0,
  assemblycost DECIMAL(10,2) NOT NULL DEFAULT 0,
  price DECIMAL(10,2) NOT NULL DEFAULT 0
);
CREATE TABLE assemblies (
 assemblyID INT NOT NULL,
 assemblyroot INT NOT NULL,
 childID INT NOT NULL,
 parentID INT NOT NULL,
 quantity DECIMAL(10,2) NOT NULL,
 assemblycost DECIMAL(10,2) NOT NULL,
 PRIMARY KEY(assemblyID,childID,parentID)
);
INSERT INTO items VALUES -- inventory
 (1,'laminate',40,0,4,0,8),
 (2,'screw',1000,0,0.1,0,.2),
 (3,'plank',200,0,10,0,20),
 (4,'shelf bracket',400,0,0.20,0,.4),
 (5,'wood cube',100,0,0.5,0,1),
 (6,'box',40,0,1,0,2),
 (7,'backboard',0,0,0,3,0),
 (8,'side',0,0,0,8,0),
 (9,'shelf',0,0,0,4,0),
 (10,'foot',0,0,0,1,0),
 (11,'bookcase2x30',0,0,0,10,0),
 (12,'bookcase2x30 kit',0,0,0,2,0);
INSERT INTO assemblies VALUES
 (1,11,1,7,1,0), -- laminate to backboard
 (2,11,2,7,8,0), -- screws to backboard
 (3,11,3,8,.5,0), -- planks to side
 (4,11,2,8,6,0), -- screws to side
 (5,11,3,9,0.25,0), -- planks to shelf
 (6,11,4,9,4,0), -- shelf brackets to shelf
 (7,11,5,10,1,0), -- wood cubes to foot
 (8,11,2,10,1,0), -- screws to foot
 (9,11,7,11,1,0), -- backboard to bookcase
 (10,11,8,11,2,0), -- sides to bookcase
 (11,11,9,11,8,0), -- shelves to bookcase
 (12,11,10,11,4,0), -- feet to bookcase
 (13,12,1,7,1,0), -- laminate to backboard
 (14,12,2,7,8,0), -- screws to backboard
 (15,12,3,8,0.5,0), -- planks to side
 (16,12,2,8,6,0), -- screws to sides
 (17,12,3,9,0.25,0), -- planks to shelf
 (18,12,4,9,4,0), -- shelf brackets to shelves
 (19,12,5,10,1,0), -- wood cubes to foot
 (20,12,2,10,1,0), -- screws to foot
 (21,12,7,12,1,0), -- backboard to bookcase kit
 (22,12,8,12,2,0), -- sides to bookcase kit
 (23,12,9,12,8,0), -- shelves to bookcase kit
 (24,12,10,12,4,0), -- feet to bookcase kit
 (25,12,6,12,1,0); -- container box to bookcase kit

Now, we want a parts list, a bill of materials, which will list show parent-child relationships and quantities, and sum the costs. Could we adapt the depth-first "nested sets" treewalk algorithm (Listing 10) to this problem even though our graph is not a tree and our sets are not properly nested? Yes: touch up the treewalk to handle multiple parent nodes for any child node, and add code to percolate costs and quantities up the graph. Navigation remains simple using leftedge and rightedge values. This is just the sort of problem the Celko algorithm is good for: reporting!

Listing 34
CREATE PROCEDURE ShowBOM( IN root INT )
BEGIN
  DECLARE thischild, thisparent, rows, maxrightedge INT DEFAULT 0;
  DECLARE thislevel, nextedgenum INT DEFAULT 1;
  DECLARE thisqty, thiscost DECIMAL(10,2);
  -- Create and seed intermediate table:
  DROP TABLE IF EXISTS edges;
  CREATE TABLE edges (
    childID smallint NOT NULL,
    parentID smallint NOT NULL,
    PRIMARY KEY (childID, parentID)
  ) ENGINE=HEAP;
  INSERT INTO edges
    SELECT childID,parentID
    FROM assemblies
    WHERE assemblyRoot = root;
  SET maxrightedge = 2 * (1 + (SELECT COUNT(*) FROM edges));
  -- Create and seed result table:
  DROP TABLE IF EXISTS bom;
  CREATE TABLE bom (
    level SMALLINT,
    nodeID SMALLINT,
    parentID SMALLINT,
    qty DECIMAL(10,2),
    cost DECIMAL(10,2),
    leftedge SMALLINT,
    rightedge SMALLINT
  ) ENGINE=HEAP;
  INSERT INTO bom VALUES( thislevel, root, 0, 0, 0, nextedgenum, maxrightedge );
  SET nextedgenum = nextedgenum + 1;
  WHILE nextedgenum < maxrightedge DO
    -- How many children of this node remain in the edges table?
    SET rows = (
      SELECT COUNT(*)
      FROM bom AS p
      JOIN edges AS c ON p.nodeID=c.parentID AND p.level=thislevel
    );
    IF rows > 0 THEN
      -- Child edge exists. Compute qty & cost, insert in bom, delete from edges.
      BEGIN
        -- Alas MySQL nulls MIN(t.childid) when we combine the next two queries
        SET thischild = (
          SELECT MIN(c.childID)
          FROM bom AS p
          INNER JOIN edges AS c ON p.nodeID=c.parentID AND p.level=thislevel
        );
        SET thisparent = (
          SELECT DISTINCT c.parentID
          FROM bom AS p
          INNER JOIN edges AS c ON p.nodeID=c.parentID AND p.level=thislevel
        );
        SET thisqty = (
          SELECT quantity FROM assemblies
          WHERE assemblyroot = root
            AND childID = thischild
            AND parentID = thisparent
        );
        SET thiscost = (
          SELECT thisqty * ( a.assemblycost + i.purchasecost + i.assemblycost )
          FROM assemblies AS a
          JOIN items AS i ON a.childID = i.itemID
          WHERE assemblyroot = root
            AND a.parentID = thisparent
            AND a.childID = thischild
        );
        INSERT INTO bom
          VALUES(thislevel+1, thischild, thisparent, thisqty, thiscost, nextedgenum, NULL);
        DELETE FROM edges WHERE childID=thischild AND parentID=thisparent;
        SET thislevel = thislevel + 1, nextedgenum = nextedgenum + 1;
      END;
    ELSE
      BEGIN
        -- Set rightedge, remove item from edges
        UPDATE bom
        SET rightedge=nextedgenum, level = -level
        WHERE level = thislevel;
        SET thislevel = thislevel – 1, nextedgenum = nextedgenum + 1;
      END;
    END IF;
  END WHILE;
  SET rows := ( SELECT COUNT(*) FROM edges );
  IF rows > 0 THEN
    SELECT 'Orphaned rows remain';
  ELSE
    BEGIN
      SET thiscost = (SELECT SUM(cost*qty) FROM bom);
      UPDATE bom SET qty=1, cost=thiscost WHERE nodeID = root;
      SELECT
        CONCAT(Space(Abs(level)*2), ItemName(nodeid,root)) AS Item,
        ROUND(qty,1) AS Qty,
        ROUND(cost,2) AS Cost
      FROM bom
      ORDER BY leftedge;
    END;
  END IF;
END;
go
DELIMITER ;
-- Function used by ShowBOM() to retrieve bom item names: 
DROP FUNCTION IF EXISTS ItemName; 
SET GLOBAL log_bin_trust_function_creators=TRUE; 
DELIMITER go 
CREATE FUNCTION ItemName( id INT, root INT ) RETURNS CHAR(20) 
BEGIN 
  DECLARE s CHAR(20) DEFAULT ''; 
  SELECT name INTO s FROM items WHERE itemid=id; 
  RETURN IF( id = root, UCASE(s), s ); 
END; 
go 
DELIMITER ; 
CALL SHOWBOM(11);
+---------------------+------+--------+
| Item                | Qty  | Cost   |
+---------------------+------+--------+
|   BOOKCASE2X30      |  1.0 | 327.93 |
|     backboard       |  1.0 |   3.00 |
|       laminate      |  1.0 |   4.00 |
|       screw         |  8.0 |   0.80 |
|     side            |  2.0 |  16.00 |
|       screw         |  6.0 |   0.60 |
|       plank         |  0.5 |   5.00 |
|     shelf           |  8.0 |  32.00 |
|       plank         |  0.3 |   2.50 |
|       shelf bracket |  4.0 |   0.80 |
|     foot            |  4.0 |   4.00 |
|       screw         |  1.0 |   0.10 |
|       wood cube     |  1.0 |   0.50 |
+---------------------+------+--------+

With ShowBOM() in hand, it's easy to compare costs of assemblies and subassemblies. By adding price columns, we can do the same for prices and profit margins. And now that MySQL has re-enabled prepared statements in stored procedures, it will be relatively easy to write a more general version of ShowBOM(). We leave that to you.

Shorter and sweeter

But ShowBOM() is not the small, efficient bit of nested sets reporting code we were hoping for. There is a simpler solution: hide graph cycles from the edges table by making them references to rows in a nodes table, so we can treat the edges table like a tree; then apply a breadth-first edge-list subtree algorithm to generate the Bill of Materials. Again assume a cabinetmaking company making bookcases (with a different costing model). For clarity, skip inventory tracking for now. An items table ww_nodes tracks purchased and assembled bookcase elements with their individual costs, and an assemblies/edges ww_edges table tracks sets of edges that combine to make products.

Listing 35: DDL for a simpler parts explosion
DROP TABLE IF EXISTS ww_nodes;
CREATE TABLE ww_nodes (
 nodeID int,
 description CHAR(50),
 cost decimal(10,2)
);
INSERT INTO ww_nodes VALUES (1,'finished bookcase',10);
INSERT INTO ww_nodes VALUES (2,'backboard2x1',1);
INSERT INTO ww_nodes VALUES (3,'laminate2x1',8);
INSERT INTO ww_nodes VALUES (4,'screw',.10);
INSERT INTO ww_nodes VALUES (5,'side',4);
INSERT INTO ww_nodes VALUES (6,'plank',20);
INSERT INTO ww_nodes VALUES (7,'shelf',4);
INSERT INTO ww_nodes VALUES (8,'shelf bracket',.5);
INSERT INTO ww_nodes VALUES (9,'feet',1);
INSERT INTO ww_nodes VALUES (10,'cube4cmx4cm',1);
INSERT INTO ww_nodes VALUES (11,'bookcase kit',2);
INSERT INTO ww_nodes VALUES (12,'carton',1);
DROP TABLE IF EXISTS ww_edges;
CREATE TABLE ww_edges (
 rootID INT,
 nodeID int,
 parentnodeID int,
 qty decimal(10,2)
);
INSERT INTO ww_edges VALUES (1,1,null,1);
INSERT INTO ww_edges VALUES (1,2,1,1);
INSERT INTO ww_edges VALUES (1,3,2,1);
INSERT INTO ww_edges VALUES (1,4,2,8);
INSERT INTO ww_edges VALUES (1,5,1,2);
INSERT INTO ww_edges VALUES (1,6,5,1);
INSERT INTO ww_edges VALUES (1,4,5,12);
INSERT INTO ww_edges VALUES (1,7,1,8);
INSERT INTO ww_edges VALUES (1,6,7,.5);
INSERT INTO ww_edges VALUES (1,8,7,4);
INSERT INTO ww_edges VALUES (1,9,1,4);
INSERT INTO ww_edges VALUES (1,10,9,1);
INSERT INTO ww_edges VALUES (1,4,9,1);
INSERT INTO ww_edges VALUES (11,11,null,1);
INSERT INTO ww_edges VALUES (11,2,11,1);
INSERT INTO ww_edges VALUES (11,3,2,1);
INSERT INTO ww_edges VALUES (11,4,2,8);
INSERT INTO ww_edges VALUES (11,5,11,2);
INSERT INTO ww_edges VALUES (11,6,5,1);
INSERT INTO ww_edges VALUES (11,4,5,12);
INSERT INTO ww_edges VALUES (11,7,11,8);
INSERT INTO ww_edges VALUES (11,6,7,.5);
INSERT INTO ww_edges VALUES (11,8,7,4);
INSERT INTO ww_edges VALUES (11,9,11,4);
INSERT INTO ww_edges VALUES (11,10,9,1);
INSERT INTO ww_edges VALUES (11,4,9,11);
INSERT INTO ww_edges VALUES (11,12,11,1);

Here is an adaptation of the breadth-first edge list algorithm to retrieve a Bill of Materials for a product identified by a rootID:

1. Initialise a level-tracking variable to zero.

2. Seed a temp reporting table with the rootID of the desired product.

3. While rows are retrieved, increment level and add rows to the temp table whose parentnodeIDs are nodes at the current level.

4. Print the BOM ordered by path with indentation proportional to tree level.

Listing 36: A simpler parts explosion
DROP PROCEDURE IF EXISTS ww_bom;
DELIMITER go
CREATE PROCEDURE ww_bom( root INT )
BEGIN
 DECLARE lev INT DEFAULT 0;
 DECLARE totalcost DECIMAL( 10,2);
 DROP TABLE IF EXISTS temp;
 CREATE TABLE temp -- initialise temp table with root node
 SELECT
  e.nodeID AS nodeID,
  n.description AS Item,
  e.parentnodeID,
  e.qty,
  n.cost AS nodecost,
  e.qty * n.cost AS cost,
  0 as level, -- tree level
  CONCAT(e.nodeID,'') AS path -- path to this node as a string
 FROM ww_nodes n
 JOIN ww_edges e USING(nodeID) -- root node
 WHERE e.nodeID = root AND e.parentnodeID IS NULL;
 WHILE FOUND_ROWS() > 0 DO 
BEGIN
 SET lev = lev+1; -- increment level
 INSERT INTO temp -- add children of this level
 SELECT 
  e.nodeID,
  n.description AS Item,
  e.parentnodeID,
  e.qty,
  n.cost AS nodecost,
  e.qty * n.cost AS cost,
  lev, 
  CONCAT(t.path,',',e.nodeID)
 FROM ww_nodes n
 JOIN ww_edges e USING(nodeID)
 JOIN temp t ON e.parentnodeID = t.nodeID
 WHERE e.rootID = root AND t.level = lev-1;
 END;
END WHILE;
 WHILE lev > 0 DO -- percolate costs up the graph
  BEGIN
   SET lev = lev - 1;
   DROP TABLE IF EXISTS tempcost;
   CREATE TABLE tempcost -- compute child cost
   SELECT p.nodeID, SUM(c.nodecost*c.qty) AS childcost
   FROM temp p 
   JOIN temp c ON p.nodeid=c.parentnodeid
   WHERE c.level=lev
   GROUP by p.nodeid;
   UPDATE temp JOIN tempcost USING(nodeID) -- update parent item cost
    SET nodecost = nodecost + tempcost.childcost;
   UPDATE temp SET cost = qty * nodecost -- update parent cost
   WHERE level=lev-1;
  END;
 END WHILE;
 SELECT -- list BoM
  CONCAT(SPACE(level*2),Item) AS Item,
  ROUND(nodecost,2) AS 'Unit Cost',
  ROUND(Qty,0) AS Qty,ROUND(cost,2) AS Cost 
 FROM temp
 ORDER by path; 
END;
go
DELIMITER ;
CALL ww_bom( 1 );
+-------------------+-----------+------+--------+
| Item              | Unit Cost | Qty  |  Cost  |
+-------------------+-----------+------+--------+
| finished bookcase | 206.60    | 1.0  | 206.60 |
| backboard2x1      | 9.80      | 1.0  | 9.80   |
| laminate2x1       | 8.00      | 1.0  | 8.00   |
| screw             | 0.10      | 8.0  | 0.80   |
| side              | 25.20     | 2.0  | 50.40  |
| screw             | 0.10      | 12.0 | 1.20   |
| plank             | 20.00     | 1.0  | 20.00  |
| shelf             | 16.00     | 8.0  | 128.00 |
| plank             | 20.00     | 0.5  | 10.00  |
| shelf bracket     | 0.50      | 4.0  | 2.00   |
| foot              | 2.10      | 4.0  | 8.40   |
| cube4cmx4cm       | 1.00      | 1.0  | 1.00   |
| screw             | 0.10      | 1.0  | 0.10   |
+-------------------+-----------+------+--------+

Summary

Stored procedures, stored functions and Views make it possible to implement edge list graph models, nested sets graph models, and breadth-first and depth-first graph search algorithms since MySQL 5. Common Table Expressions in MariaDB since version 10.2.2, and in MySQL since version 8.0.1, greatly improve edge list query performance. .