For those who have studied ERD-lore, it's not new that a relational database is very much like a graph. But it has taken SQL, more than 30 years since it became a standard and almost half a century since its inception to incorporate construct that will allow a DataBase to be treated as a Graph, DBaaG. This is surprising given that SQL was developed as language for relational databases which are modeled using ER diagrams. Better late than never. SQL/PGQ has arrived as 16th part of SQL:2023.
Entity Relationship Diagram, ERD in short, is a tool to model and visualize a database as entity types (which classify the things of interest) and relationships that can exist between them. Entity types and the relationships both map to relations in a Relational DataBase Management System (RDBMS in short). The rows in the relations represent entities (instances of entity types) and relationship between entities respectively. Fig. 1 below shows an ERD for a hypothetical shop.
This diagram very much looks like a graph with entity types represented as nodes and relationships represented by edges. That's exactly what SQL/PGQ is about. It adds language constructs to SQL to present underlying database as a "Property Graph". For example, property graph definition corresponding to the above ERD would look like
CREATE PROPERTY GRAPH shop
VERTEX TABLES (
CreditCard label Payment,
BankAccount label Payment,
Person label Customer,
Company label Customer,
Trust label Customer,
Wishlist label ProdLink,
Order label ProdLink,
Product)
EDGE TABLES (
CCOwns label Owns
BAHolds lable Owns,
CustOrders label CustLink,
CustWishlist label CustLink,
CompanyOrders label CustLink,
CompanyWishlist label CustLink,
TrustOrders label CustLink,
TrustWishlist label CustLink,
OrderCCPayment label OrderPayment,
OrderBAPayment label OrderPayment,
OrderItems label ItemLink,
WishlistItems label ItemLink);
Clever readers may have noticed that some of the entity types have some commonality. CreditCard and BankAccount are both Payment methods. Person, Company and Trust all can be considered as "Customers" by the shop. In a graph entities with commonalities will be represented by visual annotations like colors in Fig. 2. SQL/PGQ chooses to represents them by labels. Columns of the underlying tables are exposed through properties of labels. Traditionally the labels may be implemented as table inheritance or through tables abstracting commonalities. But it may not be necessary anymore.
Augmented with query constructs in SQL/PGQ they make it easy to write queries, especially analytical. Imagine a query to find all the products paid via credit card. There will be tons of JOIN and UNIONs over those joins. That's almost like "implementing" a logic in SQL. You would ask which parts do I JOIN before UNION, and which parts do I UNION before JOIN and so on. That's against the "imperative" spirit of SQL which should allow you to tell "what" you want and leave "how" for the DBMS to figure out. With SQL/PGQ you tell the DBaaG which paths in the graph to traverse. How to traverse them is the system's responsibility. So the SQL/PGQ query looks like below. Much simpler than joins and unions. In fact, it allows me not to mention edge tables at all in the query.
SELECT distinct name FROM
GRAPH_TABLE (shop
MATCHES
(o IS Orders)->(py IS Payment WHERE py.type = 'CC')<-(c IS Customer)->(o IS Order)->(p is Product)
COLUMNS (p.name));
I must note that the query looks more like a mathematical equation than SQL which till now followed natural language syntax. But well, there it is.
What those () mean? What about various literals in it? How to specify properties? I am sure I have roused more questions than those answered here. I plan to write more about it in future. This blog has gone longer than I initially intended it to be, but I hope it has aroused your interest in SQL/PGQ nonetheless.
Oh! but before I end, please note that we are working on implementing SQL/PGQ in PostgreSQL. If you are interested and want to contribute, please follow and respond on pgsql-hackers thread.
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