As we know, the tree index is the most well-known indexing technique that is used in database systems and file systems. I need to know if there is an update operation in the B-tree technique because all websites talk about insert, delete or search operations. The picture I attached has a question that calcifies what I am looking for.
Question about balanced trees and updates
Balanced tree is one of the data access techniques in database. How does it work? How
data can be inserted in? How data can be deleted from it; how data can be updated? And
how data can be reviewed? By giving an example explain all of them.
Related
I am currently working with java spring and postgres.
I have a query on a table, many filters can be applied to the query and each filter needs many joins.
This query is very slow, due to the number of joins that must be performed, also because there are many elements in the table.
Foreign keys and indexes are correctly created.
I know one approach could be to keep duplicate information to avoid doing the joins. By this I mean creating a new table called infoSearch and keeping it updated via triggers. At the time of the query, perform search operations on said table. This way I would do just one join.
But I have some doubts:
What is the best approach in postgres to save item list flat?
I know there is a json datatype, could I use this to hold the information needed for the search and use jsonPath? is this performant with lists?
I also greatly appreciate any advice on another approach that can be used to fix this.
Is there any software that can be used to make this more efficient?
I'm wondering if it wouldn't be more performant to move to another style of database, like graph based. At this point the only problem I have is with this specific table, the rest of the problem is simple queries that adapt very well to relational bases.
Is there any scaling stat based on ratios and number of items which base to choose from?
Denormalization is a tried and true way to speed up queries/reports/searching processes for relational databases. It uses a standard time vs space tradeoff to reduce the time of query, at the cost of duplicating the data and increasing write/insert time.
There are third party tools that are specifically designed for this use-case, including search tools (like ElasticSearch, Solr, etc) and other document-centric databases. Graph databases are probably not useful in this context. They are focused on traversing relationships, not broad searches.
In a SQL database, we generally have related information stored in different tables in a database. From what I read from RocksDB document, there's really no clear or 'right' way to represent this kind of structure. So I'm wondering what is the practice to categorize information?
Say I have three types of information, Customer, Product, and Employee. And I want to implement these in RocksDB. Should I use prefix of the key, different column families, or different databases?
Thanks for any suggestion.
You can do it by coming up with some prefix which will mean such table, such column, such id. You could for simplicity store in one column family and definetely in one db since you have atomic operations, snapshots and so on. The better question why would you want to store relational data in nosql db unless you are building something higher level.
By the way, checkout linqdb which is an example of higher-level db where you can store entities, perform linq-style operations and it uses rocksdb underneath.
The way data is organized in key-value store is up the the implementation. There is no "one good way to go" it depends on the underlying key-value store features (is it key ordered in particular).
The same normalization/denormalization technics applies.
I think the piece you are missing about key-value store application design is the concept of key composition. Quickly, is the practice of building keys in such a way that they are amenable to querying. If the database is ordered then it will also also for prefix/range/scan queries and next/previous navigation. This will lead you to build key prefixes in such a way that querying is fast ie. that doesn't require a full table scan.
Expand your research to other key value stores like bsddb or leveldb here on Stack Overflow.
I'm coding a new NoSQL database, and had what I thought was a novel idea (for me anyways) regarding the hashing mechanism used to locate nodes for a given key.
I'm using object keys that incorporate a timestamp. A hash will be used to determine the node(s) holding the data. Pretty common so far.
The (possible) twist lies in that a map will record the times at which nodes have been added to the cluster. That way I can determine for any given object which nodes were present in the cluster when that object was added (and therefore which nodes hold the object's data).
I'm thinking that in this way growing the cluster wont require any data to be transferred. Objects always live on the same node...for ever.
Has anyone tried something like this? Any potential problems that anyone can foresee?
I am trying to use Django to create a recursive relationship, which gives users a folder-like hierarchical structure in which to place resources.
What would be the best way to achieve this?
I know I could use treebeard or mptt to create a nested set but I have read that making changes to the tree structure (something that would be happening a lot in this case) can be quite an intensive operation as a lot of fields have to be updated.
On the other hand, I could folder model with a ForeignKey to self but how do I manage the top level folders with no foreign key value? Will Django complain if I just set this value to be NULL?
Any advice appreciated.
Thanks.
Treebeard actually supports three different tree implementations, just choose the one that will suite your needs.
Adjacency List (fast writes at the cost of slow reads)
Materialized Path (probably the fastest way of working with trees in SQL)
Nested Sets (very efficient reads at the cost of high maintenance on write/delete operations)
Docs are here: https://tabo.pe/projects/django-treebeard/docs/tip/
I'm starting a project and I'm in the designing phase: I.e., I haven't decided yet on which db framework I'm going to use. I'm going to have code that creates a "forest" like structure. That is, many trees, where each tree is a standard: nodes and edges. After the code creates these trees I want to save them in the db. (and then pull them out eventually)
The naive approach to representing the data in the db is a relational db with two tables: nodes and edges. That is, the nodes table will have a node id, node data, etc.. And the edges table will be a mapping of node id to node id.
Is there a better approach? Or given the (limited) assumptions I'm giving this is the best approach? How about if we add an assumption that the trees are relatively small - is it better to save the whole tree as a blob in the db? Which type of db should I use in that case? Please comment on speed/scalability.
Thanks
I showed a solution similar to your nodes & edges tables, in my answer to the StackOverflow question: What is the most efficient/elegant way to parse a flat table into a tree? I call this solution "Closure Table".
I did a presentation on different methods of storing and using trees in SQL, Models for Hierarchical Data with SQL and PHP. I demonstrated that with the right indexes (depending on the queries you need to run), the Closure Table design can have very good performance, even over large collections of edges (about 500K edges in my demo).
I also covered the design in my book, SQL Antipatterns Volume 1: Avoiding the Pitfalls of Database Programming.
Be sure to use some sort of low level-coding for the entity being treed to prevent looping. The entity might be a part, subject, folder, etc.
With an Entity file and and Entity-Xref file you can loop through one of say two relationships between the two files, a parent and a child relation.
A level is the level an entity found in a tree. A low-level-code for the entity is the lowest level an entity is found in any tree anywhere. Check to make sure the low level code of the entity you want to make a child is less than or equal to prevent a loop. after adding an entity as a child it will become at least one level lower.