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The author fails to mention another very common approach that does not suffer (as badly) from the nested set approach. This strategy is called "materialized pat

The author fails to mention another very common approach that does not suffer (as badly) from the nested set approach. This strategy is called "materialized path". In the materialized path strategy, each node/row has its full path in the tree stored in a column and that path is "materialized" or "derived" from a depth-first traversal to that node. In conjunction with the adjacency list approach this could yield much better results in most situations than a nested set model. Taking the tree used in the article, the paths would look like (using '/' for path delimiter but you can use whatever you like): electronics electronics/televisions electronics/televisions/tube electronics/televisions/lcd electronics/televisions/plasma electronics/portable electronics electronics/portable electronics/mp3 players electronics/portable electronics/mp3 players/flash electronics/portable electronics/cd players electronics/portable electronics/2 way radios Alternatively it would make more sense to base the materialized path off of an immutable field such as the PKey of the record (in the same order as the paths above, from the article): 1 1/2 1/2/4 1/2/5 1/2/6 1/3 1/3/7 1/3/7/10 1/3/8 1/3/9 This has the following advantages: * Node insertion/deletion overhead is low / does not require updating other nodes unlike nested set model * Query for subnodes is easy: "select * from tree where path like 'electronics/televisions'". To get only direct subnodes add a parent_id specifier. * Getting the full path for a node is easy: it's built into the node itself * Node depth is easy: count the number of path "segments" Downsides: * Moving sub-tree requires updating all sub-node paths Hope this helps someone! Cheers, -Zach

turtle4 17y ago
Helpful, yes. Thank you.
NyxWulf 17y ago
Materialized path is a good tool for trees that won't be deeply nested, and for tables that won't grow particularly large. However, because the path is stored in a string, read operations are not particularly efficient. It's a good tool to know, but it doesn't scale as well as Nested Set to very large datasets. That being set, it's much less complicated to implement than Nested Sets and is more efficient than adjacency list for many types of data.
- snprbob86 17y ago
  How are reads not particularly efficient? If you use rooted paths in your lookup, then even a "LIKE '/a/b/c%'" query for all decedents will effectively utilize the index. I think that this would be good for deeply nested trees also. As Zach implied, the down side of this approach is moving subtrees. Unless you have a very volatile tree structure, this should be perfect for most uses.
  - NyxWulf 17y ago
    Because B-Tree indexes perform orders of magnitude better on smaller lookup values, like say an integer, than they do on large (and even worse variable length) strings. There are a number of factors that contribute to this, but two big ones are the raw computation time it takes to compare two strings is much larger than the comparison of an integer that matches the register size of the machine. Second, the depth of the B-Tree is dependent on the key size used for the lookup. As I said above, if you are using the materialized path for the type of problem it's best at solving, the speed differences won't matter so much. But that's primarily because the tree's aren't particularly deeply nested and/or that tree table itself isn't overly large. So in essence you are trading computational complexity for ease of use on smaller sets of data. In many cases that's exactly what's needed. On the other hand, if you will be modeling very large trees, or will have a huge number of them, nested sets are more efficient in terms of encoding and storage, as well as lookups and retrievals. The down side is that nested sets are more complex to setup and work with, and make understanding the structure of your trees more difficult. IMHO, it's important not to fall into the trap that one technology/tool/solution/data structure will solve all of your problems. It's good to know the pro's and con's of different solutions and which problems they are most efficient at solving.
- crux_ 17y ago
  I'm not so sure about inefficiency; in particular, most databases that I've seen can use an index for string-prefix queries, which means its performance ought to remain acceptable even for large datasets. (Assumption: subtree queries are the ones you care about making fast.) Also: inserts are practically free compared to the linked article, where they require updating the left and right numbering for every following node! Another nice property: if you make entries in the materialized tree column constant-width (e.g. by zero-padding), an alphabetical sort by that column will give you a depth-first dump of the tree -- the exact order you'd like for, say, a comment thread or a table of contents. When I've implemented materialized paths in the past, I have run into issues with the maximum allowed length of indexable string types (which limits the tree depth), but this was in the long ago late 90s. :) I think it's a very nice albeit imperfect way of storing certain types of trees, especially ones that are mostly insert+query-only.
johnrob 17y ago
I'm not the only person who uses this! I wish I knew this had a name when I proposed it; everybody I was working with thought it was strange. I think it's a good mix of performance and query-ability: you can do quite a bit here using LIKE matches. Of course, the main assumptions are that the dataset isn't huge, and the hierarchy isn't changing often.
TweedHeads 17y ago
Interesting. For short trees and in-memory management another option would be: electronics /televisions //tube //lcd //plasma /portable electronics //mp3 players ///flash //cd players //2 way radios Where depth levels are represented by slashes only. Replace with indexes and you have a shorter version. Let's see what else we can come up with just for fun...
- jerf 17y ago
  You seem to be assuming a record order; SQL doesn't really have that. (You can hack stuff together but you're still building on an essentially orderless foundation.) That's a fine text document format, though tabs or spaces would be more traditional in that role.
  - TweedHeads 17y ago
    record: n int, val varchar2 Manage and sort in-memory and store already ordered when done (no matter if it is physically unordered). That's why I said, for small trees and in-memory management.

turtle4 17y ago

Helpful, yes. Thank you.

NyxWulf 17y ago

Materialized path is a good tool for trees that won't be deeply nested, and for tables that won't grow particularly large. However, because the path is stored in a string, read operations are not particularly efficient. It's a good tool to know, but it doesn't scale as well as Nested Set to very large datasets. That being set, it's much less complicated to implement than Nested Sets and is more efficient than adjacency list for many types of data.

snprbob86 17y ago
How are reads not particularly efficient? If you use rooted paths in your lookup, then even a "LIKE '/a/b/c%'" query for all decedents will effectively utilize the index. I think that this would be good for deeply nested trees also. As Zach implied, the down side of this approach is moving subtrees. Unless you have a very volatile tree structure, this should be perfect for most uses.
- NyxWulf 17y ago
  Because B-Tree indexes perform orders of magnitude better on smaller lookup values, like say an integer, than they do on large (and even worse variable length) strings. There are a number of factors that contribute to this, but two big ones are the raw computation time it takes to compare two strings is much larger than the comparison of an integer that matches the register size of the machine. Second, the depth of the B-Tree is dependent on the key size used for the lookup. As I said above, if you are using the materialized path for the type of problem it's best at solving, the speed differences won't matter so much. But that's primarily because the tree's aren't particularly deeply nested and/or that tree table itself isn't overly large. So in essence you are trading computational complexity for ease of use on smaller sets of data. In many cases that's exactly what's needed. On the other hand, if you will be modeling very large trees, or will have a huge number of them, nested sets are more efficient in terms of encoding and storage, as well as lookups and retrievals. The down side is that nested sets are more complex to setup and work with, and make understanding the structure of your trees more difficult. IMHO, it's important not to fall into the trap that one technology/tool/solution/data structure will solve all of your problems. It's good to know the pro's and con's of different solutions and which problems they are most efficient at solving.
crux_ 17y ago
I'm not so sure about inefficiency; in particular, most databases that I've seen can use an index for string-prefix queries, which means its performance ought to remain acceptable even for large datasets. (Assumption: subtree queries are the ones you care about making fast.) Also: inserts are practically free compared to the linked article, where they require updating the left and right numbering for every following node! Another nice property: if you make entries in the materialized tree column constant-width (e.g. by zero-padding), an alphabetical sort by that column will give you a depth-first dump of the tree -- the exact order you'd like for, say, a comment thread or a table of contents. When I've implemented materialized paths in the past, I have run into issues with the maximum allowed length of indexable string types (which limits the tree depth), but this was in the long ago late 90s. :) I think it's a very nice albeit imperfect way of storing certain types of trees, especially ones that are mostly insert+query-only.

johnrob 17y ago

I'm not the only person who uses this! I wish I knew this had a name when I proposed it; everybody I was working with thought it was strange. I think it's a good mix of performance and query-ability: you can do quite a bit here using LIKE matches. Of course, the main assumptions are that the dataset isn't huge, and the hierarchy isn't changing often.

TweedHeads 17y ago

Interesting. For short trees and in-memory management another option would be: electronics /televisions //tube //lcd //plasma /portable electronics //mp3 players ///flash //cd players //2 way radios Where depth levels are represented by slashes only. Replace with indexes and you have a shorter version. Let's see what else we can come up with just for fun...

jerf 17y ago
You seem to be assuming a record order; SQL doesn't really have that. (You can hack stuff together but you're still building on an essentially orderless foundation.) That's a fine text document format, though tabs or spaces would be more traditional in that role.
- TweedHeads 17y ago
  record: n int, val varchar2 Manage and sort in-memory and store already ordered when done (no matter if it is physically unordered). That's why I said, for small trees and in-memory management.