We have covered a lot of territory in GSQL 102:
Showed how to invoke GSQL Pattern Matching syntax
Explained how Pattern Matching extends the classic FROM clause grammar:
Each hop can be a choice of multiple, individually directed edge types
The Kleene star and a min...max range enable each hop to be repeated.
GSQL automatically finds the shortest paths that satisfy a variable length path.
A virtual match table has a column for each vertex or edge alias in a multi-hop path, and a row for each graph path that satisfies the pattern.
The ACCUM clause iterates on each row in the match table.
A POST-ACCUM clause iterates on one vertex alias; a query can have multiple POST-ACCUM clauses.
Described the improvements to Pattern Matching in TigerGraph 3.0:
The source (leftmost) vertex set can be specified with the same flexibility as the other vertex sets: a vertex type, an alteration of types, or omitted. Explicit seed sets are no longer needed
Restrictions on which vertex aliases may be used in the ACCUM clause have been lifted.
Described three major advanced options:
The PER <vertex alias> clause enables users to fine tune the ACCUM iteration.
Data modification (insert, update, delete) are now supported.
Conjunctive Pattern Matching let users express a complex pattern as a set of path patterns which must all be satisfied.
Provided best practices for writing queries, especially pattern matching queries:
Put the smaller vertex set on the left end.
Specify all vertex and edge types explicitly.
Use the PER clause to reduce the match table size
Provided numerous examples and the full set of LDBC Social Network benchmark queries.
With a little practice, you will be writing GSQL pattern matching queries to efficiently solve real-world problems.