I have a table which defines a child-parent relationship between nodes:
CREATE TABLE node ( ' pseudo code alert
id INTEGER PRIMARY KEY,
parentID INTEGER, ' should be a valid id.
)
If parentID always points to a valid existing node, then this will naturally define a tree structure.
If the parentID is NULL then we may assume that the node is a root node.
How would I:
Find all the nodes which are decendents of a given node?
Find all the nodes under a given node to a specific depth?
I would like to do each of these as a single SQL (I expect it would necessarily be recursive) or two mutually recursive queries.
I'm doing this in an ODBC context, so I can't rely on any vendor specific features.
Edit
No tables are written yet, so adding extra columns/tables is perfectly acceptable.
The tree will potentially be updated and added to quite often; auxillary data structures/tables/columns would be possible, though need to be kept up-to-date.
If you have any magic books you reach for for this kind of query, I'd like to know.
Many thanks.
This link provides a tutorial on both the Adjacency List Model (as described in the question), and the Nested Set Model. It is written as part of the documentation for MySQL.
What is not discussed in that article is insertion/delection time, and maintenance cost of the two approaches. For example:
a dynamically grown tree using the Nested Set Model would seem to need some maintenance to maintain the nesting (e.g. renumbering all left and right set numbers)
removal of a node in the adjacency list model would require updates in at least one other row.
If you have any magic books you reach for for this kind of query, I'd like to know.
Celko's Trees and Hierarchies in SQL For Smarties
Store the entire "path" from the root node's ID in a separate column, being sure to use a separator at the beginning and end as well. E.g. let's say 1 is the parent of 5, which is the parent of 17, and your separator character is dash, you would store the value -1-5-17- in your path column.
Now to find all children of 5 you can simply select records where the path includes -5-
The separators at the ends are necessary so you don't need to worry about ID's that are at the leftmost or rightmost end of the field when you use LIKE.
As for your depth issue, if you add a depth column to your table indicating the current nesting depth, this becomes easy as well. You look up your starting node's depth and then you add x to it where x is the number of levels deep you want to search, and you filter out records with greater depth than that.
Related
i am trying to design a table which contains sections and each section contains tasks and each task contains sub tasks and so on. I would like to do it under one table. Please let me know the best single table approach which is scalable. I am pretty new to database design. Also please suggest if single table is not the best approach then what could be the best approach to do this. I am using db2.
Put quite simply, I would say use 1 table for tasks.
In addition to all its various other attributes, each task should have a primary identifier, and another column to optionally contain the identifier of its parent task.
If you are using DB2 for z/OS, then you will use a recursive query with a common table expression. Otherwise you you can use a hierarchical recursive query in DB2 for i, or possibly in DB2 for LUW (Linux, Unix, Windows).
Other designs requiring more tables, each specializing in a certain part of the task:subtask relationship, may needlessly introduce issues or limitations.
There are a few ways to do this.
One idea is to use two tables: Sections and Tasks
There could be a one to many relationship between the two. The Task table could be designed as a tree with a TaskId and a ParentTaksId which means you can have Tasks that go n-levels deep (sub tasks of sub tasks og sub tasks etc). Every Task except for the root task will have a parent.
I guess you can also solve this by using a single table where you just add a section column to the Task table I described above.
If you are going to put everything into one table although convenient will be inefficient in the long run. This would mean you will be storing unnecessary repeated groups of data in your database which would not be processor and memory friendly at all. It would in fact violate the Normalization rules and to be more specific the 1st Normal Form which says that there should be no repeating groups that could be found in your table. And it would actually also violate the 3rd Normal Form which means there will be no (transitional) dependency of a non-primary key to another non-primary key.
To give you an illustration, I will put your design into one table. Although I will be guessing on the possible fields but just bear with it because this is for the sake of discussion. Look at the graphics below:
If you look the graphics above (although this is rather small you could download the image and see it closer for yourself), the SectionName, Taskname, TaskInitiator, TaskStartDate and TaskEndDate are unnecessary repeated which as I mentioned earlier a violation of the 1st Normal Form.
Secondly, Taskname, TaskInitiator, TaskStartDate and TaskEndDate are functionally dependent on TaskID which is not a primary key instead of SectionID which in this case should be the primary key (if on a separate table). This is violation of 3rd Normal Form which says that there should be no Transitional Dependence or non-primary key should be dependent on
another non-primary key.
Although there are instances that you have to de-normalized but I believe this one should be normalized. In my own estimation there should be three tables involved in your design, namely, Sections,Tasks and SubTasks that would like the one below.
Section is related to Tasks, that is, a section could have many Tasks.
And Task is related to Sub-Tasks, that is, a Task could have many Sub-tasks.
If I understand correctly the original poster does not know, how many levels of hierarchy will be needed (hence "and so on"). His problem is to create a design that can hold a structure of any depth.
Imho that is a complex issue that does not have a single answer. When implementing such a design you need to count such factors as:
Will the structure be fairly constant? (How many writes?)
How often will this structure be read?
What operations will need to be possible? (Get all children objects of a given object? Get the parent object? Get the direct children?)
If the structure will be constant You could use the nested set model (http://en.wikipedia.org/wiki/Nested_set_model)
In this way the table has a 'left' and 'right' column. The parent object has its left and right column encompasing the values of any of its children object.
In that way you can list all the children of an object using a query like this:
SELECT child.id
FROM table AS parent
JOIN table AS child
ON child.left BETWEEN parent.left AND parent.right
AND child.right BETWEEN parent.left AND parent.right
WHERE
parent.id = #searchId
This design can be VERY fast to read, but is also EXTREMELY costly when the structure changes (for example when adding a child to any object You will have to update any object with a 'right' value that is higher than the inserted one).
If you need to be able to make changes to structure in real time you should probably use a design with two tables - one holding the objects, the second the structure (something like parentId, childId, differenceInHierarchyLevels).
I'm trying to store the data in a binary space partitioning tree in a relational database. The tricky part about this data structure is it has two different types of nodes. The first type, which we call a data node, simply holds a certain number of items. We define the maximum number of items able to be held as t. The second type, which we refer to as a container node, holds two other child nodes. When an item is added to the tree, the nodes are recursed until a data node is found. If the number of items in the data node are less than t, then the item is inserted into the data node. Otherwise the data node is split into two other data nodes, and is replaced by one of the container nodes. When an element is deleted, a reverse process must happen.
I'm a little bit lost. How am I supposed to make this work using a relational model?
Why not have two tables, one for nodes and one for items? (Note that I used the term "leaf" instead of "data" nodes below when I wrote my answer; a "leaf" node has data items, a non-"leaf" node contains other nodes.)
The node table would have columns like this: id primary key, parentid references node, leaf boolean and in addition some columns to describe the spatial bounaries of the node and how it will/has been split. (I don't know if you're working in 2D or 3D so I haven't given details on the geometry.)
The data table would have id primary key, leafid references node and whatever data.
You can traverse the tree downward by issuing SELECT * FROM node WHERE parentid = ?queries at each level and checking which child to descend into. Adding a data item to a leaf is a simple INSERT. Splitting a node requires unsetting the leaf flag, inserting two new leaf nodes, and updating all the data items in the node to point to the appropriate child node by changing their leafid values.
Note that SQL round trips can be expensive, so if you're looking to use this for a real application, consider using a relatively large t in the DB constructing a finer-grained tree in memory of the leaves you are interested in after you have the data items.
I have a table (id, parent_id, data) where parent_id points to another row in same table (or is null).
Is there a standard way to query (1) all the ancestors of a certain id and (2) all the descendants of a certain id?
I'm also doing this in DBIx::Class, so if there's a most convenient way to do it with that module (or some other), I'd love to hear about that as well.
EDIT: clarify - all parents = all ancestors, all children = all descendants.
This highly depends on the flavor of SQL you are using.
In Oracle, you can use the START WITH id = yourid CONNECT BY PRIOR id = parent_id construct. In PostgreSQL, you can use a function connectby('tablename', 'id', 'parent_id', 'id', value, 0).
In many cases, it makes sense to represent trees
differently, by defining a column which will hold,
for every node, a complete path from the root element
to this node.
There are plenty examples of this technique
to be found on the Internet, the most recent one I saw,
which also deals with DBIx::Class, can be found here: http://blogs.perl.org/users/ovid/2010/05/threaded-forum-sql.html
It looks like we're going to go with DBIx::Class::Tree::AdjacencyList at the moment. It does almost everything I was looking for (no ancestors resultset, unfortunately - but we can work around that by approaching the questions we need to ask from the other direction).
However, #Grrrr's answer got me thinking, and we may add a separate table + module (id, record_type, record_ancestors) that would attach to our models that have a parent_id column and provide an ancestors resultset (basically by doing a search_rs where the id is in the split of the relevant ancestors row by w/e delimiter we pick). That's a fair bit of work just to get such a result set, so we'll probably only go there if we find questions where it's really impractical to ask "is this a child of parent x" and really need "is this a parent of child x"?
EDIT: or maybe we'll use DBIx::Class::Tree::Mobius - though it looks like viewing the table raw would be incomprehensible.
I am looking at quite a monstrosity of an application that uses several tables to represent a reporting hierarchy, but eacvh of these tables is identical. The table at the bottom of the hierarchy has the most records, each of which has a ParentID to the rollup table above it, eventually all adding up to only one total in the top rollup table.
I am plagued to insanity by gargantuan 'if' blocks of code with hard-coded joins and table names, and I am trying hard to recognise some sane reason for not using a single table, with a levelID in each row, instead of one table for each level, for all these levels, or at least several views on the same table. The latter because the database was designed to be used in MSAccess, which doesn't allow aliased sub-queries AFAIK.
Are the tables all the same in their semantics or just in their form? If the tables are identical in both form and semantics, then a single table solution is probably far superior, but I don't know enough about your case to say for sure.
Representing a hierarchy in an SQL table can be quite a challenge. Fortunately, reporting hierarchies are generally small enough and stable enough so that a variety of techniques will work.
The simplest technique goes by the name "adjacency list" model. In this model, there are two columns one of which refers to the other. I'll call them MyTable(ID, ParentID). In a real case, Mytable will have other columns. ParentID references ID in a different row of the same table. This is easy to implement, and easy to update. It can be a pain to do rollups.
Another technique goes by the name "nested sets". Call it MyTable (ID, lft, rgt, Level). (Level is redundant, and often omitted). Here we have two columns (lft and rgt) that show where the row fits into the hierarchy, because lgt and rgt are nested inside the lft and rgt of all the ancestors of the node in question. This technique is hard to update. It's easy to do rollups, and to find subtrees, and ancestor paths, and lots of other types of queries.
A third way to "flatten the hierarchy". In this technique, each level of the hierarchy has a named column of its own, and each row displays its entire ancestry all the way back to the apex of the hierarchy. Here we have MyTable (ID, Division, Department, Group, Team). Division, Department, Group and Team are all levels of the hierarchy. This is ultimately easy for users who access the data via a point and click drill down interface, because there's nothing for them to learn, if the column names are chosen well. It requires a name for each level. It does not adapt well to indefinite levels of hierarchy. It's got a lot of redundancy. In general flattened hiearachies are generated automatically from a table that stores hierarchies in adajency list form or nested set form.
Any one of these are a good alternative to separate tables for every level in the hierarchy.
excellent solutions are suggested here already. I would add one to use a hierarchical database such as hypergraph.
How would you get tree-structured data from a database with the best performance? For example, say you have a folder-hierarchy in a database. Where the folder-database-row has ID, Name and ParentID columns.
Would you use a special algorithm to get all the data at once, minimizing the amount of database-calls and process it in code?
Or would you use do many calls to the database and sort of get the structure done from the database directly?
Maybe there are different answers based on x amount of database-rows, hierarchy-depth or whatever?
Edit: I use Microsoft SQL Server, but answers out of other perspectives are interesting too.
It really depends on how you are going to access the tree.
One clever technique is to give every node a string id, where the parent's id is a predictable substring of the child. For example, the parent could be '01', and the children would be '0100', '0101', '0102', etc. This way you can select an entire subtree from the database at once with:
SELECT * FROM treedata WHERE id LIKE '0101%';
Because the criterion is an initial substring, an index on the ID column would speed the query.
Out of all the ways to store a tree in a RDMS the most common are adjacency lists and nested sets. Nested sets are optimized for reads and can retrieve an entire tree in a single query. Adjacency lists are optimized for writes and can added to with in a simple query.
With adjacency lists each node a has column that refers to the parent node or the child node (other links are possible). Using that you can build the hierarchy based on parent child relationships. Unfortunately unless you restrict your tree's depth you cannot pull the whole thing in one query and reading it is usually slower than updating it.
With the nested set model the inverse is true, reading is fast and easy but updates get complex because you must maintain the numbering system. The nested set model encodes both parentage and sort order by enumerating all of the nodes using a preorder based numbering system.
I've used the nested set model and while it is complex for read optimizing a large hierarchy it is worth it. Once you do a few exercises in drawing out the tree and numbering the nodes you should get the hang of it.
My research on this method started at this article: Managing Hierarchical Data in MySQL.
In the product I work on we have some tree structures stored in SQL Server and use the technique mentioned above to store a node's hierarchy in the record. i.e.
tblTreeNode
TreeID = 1
TreeNodeID = 100
ParentTreeNodeID = 99
Hierarchy = ".33.59.99.100."
[...] (actual data payload for node)
Maintaining the the hierarchy is the tricky bit of course and makes use of triggers. But generating it on an insert/delete/move is never recursive, because the parent or child's hierarchy has all the information you need.
you can get all of node's descendants thusly:
SELECT * FROM tblNode WHERE Hierarchy LIKE '%.100.%'
Here's the insert trigger:
--Setup the top level if there is any
UPDATE T
SET T.TreeNodeHierarchy = '.' + CONVERT(nvarchar(10), T.TreeNodeID) + '.'
FROM tblTreeNode AS T
INNER JOIN inserted i ON T.TreeNodeID = i.TreeNodeID
WHERE (i.ParentTreeNodeID IS NULL) AND (i.TreeNodeHierarchy IS NULL)
WHILE EXISTS (SELECT * FROM tblTreeNode WHERE TreeNodeHierarchy IS NULL)
BEGIN
--Update those items that we have enough information to update - parent has text in Hierarchy
UPDATE CHILD
SET CHILD.TreeNodeHierarchy = PARENT.TreeNodeHierarchy + CONVERT(nvarchar(10),CHILD.TreeNodeID) + '.'
FROM tblTreeNode AS CHILD
INNER JOIN tblTreeNode AS PARENT ON CHILD.ParentTreeNodeID = PARENT.TreeNodeID
WHERE (CHILD.TreeNodeHierarchy IS NULL) AND (PARENT.TreeNodeHierarchy IS NOT NULL)
END
and here's the update trigger:
--Only want to do something if Parent IDs were changed
IF UPDATE(ParentTreeNodeID)
BEGIN
--Update the changed items to reflect their new parents
UPDATE CHILD
SET CHILD.TreeNodeHierarchy = CASE WHEN PARENT.TreeNodeID IS NULL THEN '.' + CONVERT(nvarchar,CHILD.TreeNodeID) + '.' ELSE PARENT.TreeNodeHierarchy + CONVERT(nvarchar, CHILD.TreeNodeID) + '.' END
FROM tblTreeNode AS CHILD
INNER JOIN inserted AS I ON CHILD.TreeNodeID = I.TreeNodeID
LEFT JOIN tblTreeNode AS PARENT ON CHILD.ParentTreeNodeID = PARENT.TreeNodeID
--Now update any sub items of the changed rows if any exist
IF EXISTS (
SELECT *
FROM tblTreeNode
INNER JOIN deleted ON tblTreeNode.ParentTreeNodeID = deleted.TreeNodeID
)
UPDATE CHILD
SET CHILD.TreeNodeHierarchy = NEWPARENT.TreeNodeHierarchy + RIGHT(CHILD.TreeNodeHierarchy, LEN(CHILD.TreeNodeHierarchy) - LEN(OLDPARENT.TreeNodeHierarchy))
FROM tblTreeNode AS CHILD
INNER JOIN deleted AS OLDPARENT ON CHILD.TreeNodeHierarchy LIKE (OLDPARENT.TreeNodeHierarchy + '%')
INNER JOIN tblTreeNode AS NEWPARENT ON OLDPARENT.TreeNodeID = NEWPARENT.TreeNodeID
END
one more bit, a check constraint to prevent a circular reference in tree nodes:
ALTER TABLE [dbo].[tblTreeNode] WITH NOCHECK ADD CONSTRAINT [CK_tblTreeNode_TreeNodeHierarchy] CHECK
((charindex(('.' + convert(nvarchar(10),[TreeNodeID]) + '.'),[TreeNodeHierarchy],(charindex(('.' + convert(nvarchar(10),[TreeNodeID]) + '.'),[TreeNodeHierarchy]) + 1)) = 0))
I would also recommend triggers to prevent more than one root node (null parent) per tree, and to keep related nodes from belonging to different TreeIDs (but those are a little more trivial than the above.)
You'll want to check for your particular case to see if this solution performs acceptably. Hope this helps!
Celko wrote about this (2000):
http://www.dbmsmag.com/9603d06.html
http://www.intelligententerprise.com/001020/celko1_1.jhtml;jsessionid=3DFR02341QLDEQSNDLRSKHSCJUNN2JVN?_requestid=32818
and other people asked:
Joining other tables in oracle tree queries
How to calculate the sum of values in a tree using SQL
How to store directory / hierarchy / tree structure in the database?
Performance of recursive stored procedures in MYSQL to get hierarchical data
What is the most efficient/elegant way to parse a flat table into a tree?
finally, you could look at the rails "acts_as_tree" (read-heavy) and "acts_as_nested_set" (write-heavy) plugins. I don't ahve a good link comparing them.
There are several common kinds of queries against a hierarchy. Most other kinds of queries are variations on these.
From a parent, find all children.
a. To a specific depth. For example, given my immediate parent, all children to a depth of 1 will be my siblings.
b. To the bottom of the tree.
From a child, find all parents.
a. To a specific depth. For example, my immediate parent is parents to a depth of 1.
b. To an unlimited depth.
The (a) cases (a specific depth) are easier in SQL. The special case (depth=1) is trivial in SQL. The non-zero depth is harder. A finite, but non-zero depth, can be done via a finite number of joins. The (b) cases, with indefinite depth (to the top, to the bottom), are really hard.
If you tree is HUGE (millions of nodes) then you're in a world of hurt no matter what you try to do.
If your tree is under a million nodes, just fetch it all into memory and work on it there. Life is much simpler in an OO world. Simply fetch the rows and build the tree as the rows are returned.
If you have a Huge tree, you have two choices.
Recursive cursors to handle the unlimited fetching. This means the maintenance of the structure is O(1) -- just update a few nodes and you're done. However fetching is O(n*log(n)) because you have to open a cursor for each node with children.
Clever "heap numbering" algorithms can encode the parentage of each node. Once each node is properly numbered, a trivial SQL SELECT can be used for all four types of queries. Changes to the tree structure, however, require renumbering the nodes, making the cost of a change fairly high compared to the cost of retrieval.
If you have many trees in the database, and you will only ever get the whole tree out, I would store a tree ID (or root node ID) and a parent node ID for each node in the database, get all the nodes for a particular tree ID, and process in memory.
However if you will be getting subtrees out, you can only get a subtree of a particular parent node ID, so you either need to store all parent nodes of each node to use the above method, or perform multiple SQL queries as you descend into the tree (hope there are no cycles in your tree!), although you can reuse the same Prepared Statement (assuming that nodes are of the same type and are all stored in a single table) to prevent re-compiling the SQL, so it might not be slower, indeed with database optimisations applied to the query it could be preferable. Might want to run some tests to find out.
If you are only storing one tree, your question becomes one of querying subtrees only, and the second answer applied.
Google for "Materialized Path" or "Genetic Trees"...
In Oracle there is SELECT ... CONNECT BY statement to retrieve trees.
I am a fan of the simple method of storing an ID associated with its parentID:
ID ParentID
1 null
2 null
3 1
4 2
... ...
It is easy to maintain, and very scalable.
This article is interesting as it shows some retrieval methods as well as a way to store the lineage as a derived column. The lineage provides a shortcut method to retrieve the hierarchy without too many joins.
Not going to work for all situations, but for example given a comment structure:
ID | ParentCommentID
You could also store TopCommentID which represents the top most comment:
ID | ParentCommentID | TopCommentID
Where the TopCommentID and ParentCommentID are null or 0 when it's the topmost comment. For child comments, ParentCommentID points to the comment above it, and TopCommentID points to the topmost parent.