What are horizontal and vertical partitions in database and what is the difference?
I read that
SELECT is a horizontal partition of the relation into two set of tuples.
PROJECT is a vertical partition of the relation into two relations.
However, I don't understand what that means. Can you explain it in layman's terms?
Not a complete answer to the question but it answers what is asked in the question title. So the general meaning of horizontal and vertical database partitioning is:
Horizontal partitioning involves putting different rows into different tables. Perhaps customers with ZIP codes less than 50000 are stored in CustomersEast, while customers with ZIP codes greater than or equal to 50000 are stored in CustomersWest. The two partition tables are then CustomersEast and CustomersWest, while a view with a union might be created over both of them to provide a complete view of all customers.
Vertical partitioning involves creating tables with fewer columns and using additional tables to store the remaining columns. Normalization also involves this splitting of columns across tables, but vertical partitioning goes beyond that and partitions columns even when already normalized.
See more details here.
What are horizontal and vertical partitions in database and what is , Horizontal partitioning involves putting different rows into different tables. Perhaps customers with ZIP codes less than 50000 are stored in - Horizontal partitioning partitions or segments rows into multiple tables with the same columns. On the other hand, vertical partitioning segments columns into multiple tables containing the same rows. - E.g. of horizontal partitioning :- customers with pin codes less than 50000 are stored in CustomersEast, while customers with pin codes
A projection creates a subset of attributes in a relation hence a "vertical partition"
A selection creates a subset of the tuples in a relation hence a "horizontal partition"
Given a table
a : b : c : d : e ----------------- 1 : 2 : 3 : 4 : 5 1 : 2 : 3 : 4 : 5 2 : 2 : 3 : 4 : 5 2 : 2 : 3 : 4 : 5
An expression such as
PROJECT a, b (SELECT a=1 (r)) -- SELECT a, b FROM r WHERE a=1
a : b | c : d : e ----------------- 1 : 2 | 3 : 4 : 5 1 : 2 | 3 : 4 : 5 ================= < -- horizontal partition (by SELECTION) 2 : 2 | 3 : 4 : 5 2 : 2 | 3 : 4 : 5 ^ -- vertical partition (by PROJECTION)
a : b ------ 1 : 2 1 : 2
Difference between horizontal and vertical partitioning of data , Horizontal partitioning of data refers to storing different rows into different tables. E.g.: Students with their first name starting from A-M are stored in table A.. vertically. In vertical partitioning the table is divided on the. basis of columns . In horizontal partitioning the table is divided. on the basis of rows .
Necromancing. I think the existing answers are too abstract.
So here my attempts at a more practical explanation:
Partitioning form a developer's point of view is all about performance. More exactly, it's about what happens when you have large amounts of data in your tables, and you still want to query the data fast.
Here some excerpts from slides by Bill Karwin about what exactly horizontal partitioning is all about:
The above is bad, because:
Horizontal partitioning divides a table into multiple tables. Each table then contains the same number of columns, but fewer rows.
The difference: Query Performance and simplicity
Now, on the difference between horizontal and vertical partitioning:
"Tribbles" can also accumulate in columns. Example:
The solution to that problem is VERTICAL PARTITIONING Proper normalization is ONE form of vertical partitioning
To quote technet
Vertical partitioning divides a table into multiple tables that contain fewer columns.
The two types of vertical partitioning are normalization and row splitting:
Normalization is the standard database process of removing redundant columns from a table and putting them in secondary tables that are linked to the primary table by primary key and foreign key relationships.
Row splitting divides the original table vertically into tables with fewer columns. Each logical row in a split table matches the same logical row in the other tables as identified by a UNIQUE KEY column that is identical in all of the partitioned tables. For example, joining the row with ID 712 from each split table re-creates the original row. Like horizontal partitioning, vertical partitioning lets queries scan less data. This increases query performance. For example, a table that contains seven columns of which only the first four are generally referenced may benefit from splitting the last three columns into a separate table. Vertical partitioning should be considered carefully, because analyzing data from multiple partitions requires queries that join the tables.
Vertical partitioning also could affect performance if partitions are very large.
That sums it up nicely.
Now on SELECT vs. PROJECT:
This SO post describes the difference as such:
Select Operation : This operation is used to select rows from a table (relation) that specifies a given logic, which is called as a
predicate. The predicate is a user defined condition to select rows of user's choice.
Project Operation : If the user is interested in selecting the values of a few attributes, rather than selection all attributes of the Table (Relation), then one should go for
SELECT is an actual SQL operation (statement), while PROJECT is a term used in relational algebra.
Judging from you posting this on SO and not on MathOverflow, I would suggest you don't read relational algebra books if you just want to learn SQL for developing applications.
If you are in dire need of a recommendation for a good book about (advanced) SQL, here is one
SQL Antipatterns: Avoiding the Pitfalls of Database Programming Bill Karwin ISBN-13: 978-1934356555 ISBN-10: 1934356557
That's the one book about SQL worth reading. Most other books about SQL that I've seen out there can be summed up by this cynical statement about photoshop books:
There are more books about photoshop than people actually using photoshop.
Data Partitioning: Vertical Partitioning, Horizontal Partitioning, and , Another example where vertical partitioning is a great option is when you have different types of data in your database, such as names, dates, Partitioning: splitting the datas of a table in several tables. Vertical partitioning: Splitting the data by columns. (less accessed columns may be stored on storage having slower access, allowing more accessed columns to be more easily cached) Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys. Allow lighter joins.
Consider a single table in a database, it has some rows and columns.
There are two ways your could pick data: You could pick some rows, or you could pick some columns (well ok, three ways, you could pick some rows, and within that pick some columns.)
You can think of select as picking some rows - that's horizontal (and not picking the rest, hence partitioning)
You can think of project as picking some columns - that's vertical (and not picking the rest)
Horizontal vs Vertical Database Partitioning, Although Normalization and partitioning both produce a rearrangement of the columns between tables they have very different purposes. Normalization is first Figure 2: Example of vertical partitioning . Another example where vertical partitioning is a great option is when you have different types of data in your database, such as names, dates, and pictures. You could keep the string values in SQL DB, and pictures in an Azure Blob.
The distinction of horizontal vs vertical comes from the traditional tabular view of a database. A database can be split vertically — storing different tables & columns in a separate database or horizontally — storing rows of a same table in multiple database nodes.
Horizontal partitioning is often referred as Database Sharding.
# Example of vertical partitioning fetch_user_data(user_id) -> db["USER"].fetch(user_id) fetch_photo(photo_id) -> db["PHOTO"].fetch(photo_id) # Example of horizontal partitioning fetch_user_data(user_id) -> user_db[user_id % 2].fetch(user_id)
Find more details here: https://medium.com/@jeeyoungk/how-sharding-works-b4dec46b3f6
Database partitioning - Horizontal and Vertical sharding, For example, I might shard my customer database using CustomerId as a shard key – I'd store ranges 0-10000 in one shard and 10001-20000 in a different shard. Horizontal partitioning (often called sharding). In this strategy, each partition is a separate data store, but all partitions have the same schema. Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers. Vertical partitioning. In this strategy, each partition holds a subset of the fields for items in the data store.
Horizontal vs Vertical Partitioning – Analyticscosm, Once you understand horizontal and vertical partitioning, you can streamline how you store and distribute data from your SQL Server databases. Vertical partitioning, aka row splitting, uses the same splitting techniques as database normalization, but ususally the term (vertical / horizontal) data partitioning refers to a physical optimization whereas normalization is an optimization on the conceptual level. Since you ask for a simple demonstration - assume you have a table like this:
Horizontal and Vertical Partitioning, The fact that modern database systems support different ways of horizontal partitioning, such as range or hash partitioning, only adds to this combinatorial. In a database world horizontal-scaling is often based on the partitioning of the data i.e. each node contains only part of the data, in vertical-scaling the data resides on a single node and
[PDF] Integrating Vertical and Horizontal Partitioning , A database can be split vertically — storing different tables & columns in a An illustrated example of vertical and horizontal partitioning Vertical Partitioning in SQL Server helps users in columns of tables of database which are required to be placed in two or more databases. Thus, the resulting partition becomes more manageable and easy to use, improving the performance of SQL server up to a large extent by enhancing and maximizing the number of I/O operations of Queries.