AgensGraph is a multi-model database that supports graphs, relational data, and documents. It provides a graph query language and SQL support while maintaining ACID properties. The developer presentation discusses AgensGraph's goals of providing a graph engine, graph query language, and high performance. It also covers challenges in implementing features like variable length relationships, label hierarchies, and writing queries.

1. Propel your performance:
AgensGraph,
the multi-model database
AgensGraph from the perspective of the developer
Junseok Yang
Senior Research Engineer, Bitnine Global
jsyang@bitnine.net
https://github.com/bitnine-oss/agensgraph

2. What We Want

3. What We Want
Graph * Relational * Document

4. What We Want
Graph * Relational * Document
Graph Query Language * SQL

5. What We Want
Graph * Relational * Document
Graph Query Language * SQL
ACID Properties

6. Don't Reinvent the Wheel
ACID Properties
JSON
SQL
Relational Data Model

7. Development Goal
Graph Engine
Graph Query Language
Performance

8. Vertex and Edge - Storage
1
{ name: 'Tom' } { name: 'Jerry' }
2
3
FRIEND_OF
CHARACTER CHARACTER

9. Vertex and Edge - Storage
{ name: 'Tom' }1 2 { name: 'Jerry' }3 1 2 { }
I P I S E P I P
1
{ name: 'Tom' } { name: 'Jerry' }
2
3
FRIEND_OF
CHARACTER CHARACTER

10. Label - Storage
{ name: 'Tom' }1
2 { name: 'Jerry' }
CHARACTER
3 1 2 { }
FRIEND_OF

11. Label Hierarchy - Storage
{ name: 'Tom' }1
2 { name: 'Jerry' }
CHARACTER
3 1 2 { }
FRIEND_OF
{ name: 'Tom' }1 2 { name: 'Jerry' }
CAT MOUSE
CHARACTER

12. Graph - Storage
Tom
Jerry
Spike
...
graphgraph
database

13. We Need a Graph Query Language
WITH RECURSIVE friends(path, last, len) AS (
-- non-recursive term
SELECT ARRAY[start], end, 1
FROM FRIEND_OF
WHERE start = 1 -- starting point
UNION
-- recursive term
SELECT n.path || n.last, f.end, n.len + 1
FROM friends AS n JOIN FRIEND_OF AS f ON n.last = f.start
WHERE n.len < 2 AND -- change this to set different length
f.start <> ALL (n.path) -- edge uniqueness check
)
SELECT path FROM friends;
An example query to retrieve friend and friend-of-friend relationship using SQL
Using SQL to retrieve graph data is painful!

14. Candidate Languages
Cypher
Alternative

15. The Language We Chose
MATCH (:CHARACTER {name: 'Tom'})-[:FRIEND_OF*1..2]->(f)
RETURN f;
g.V().hasLabel('CHARACTER').has('name', 'Tom')
.outE().hasLabel('FRIEND_OF').inV.loop('f'){it.loops < 2}
Cypher
Alternative

16. The Language We Chose
MATCH (:CHARACTER {name: 'Tom'})-[:FRIEND_OF*1..2]->(f)
RETURN f;
g.V().hasLabel('CHARACTER').has('name', 'Tom')
.outE().hasLabel('FRIEND_OF').inV.loop('f'){it.loops < 2}
Cypher
Alternative

17. Cypher * SQL
SELECT n.name
FROM history,
(MATCH (n) RETURN n) AS emp
WHERE history.year > n.year::int AND
history.event = 'AgensGraph';
Cypher in SQL
MATCH (n)
WHERE n.year::int <
(SELECT year
FROM history
WHERE event = 'AgensGraph')
RETURN n.name;
SQL in Cypher
name
---------
Junseok
(1 row)
year event
1996 PostgreSQL
2016 AgensGraph
history
Junseok
2015
Ryan
2016

18. Write Queries - Challenges
MATCH (n)
CREATE (n)-[r]->(m);
v1
v2
MATCH CREATE
n

19. Write Queries - Challenges
MATCH (n)
CREATE (n)-[r]->(m);
v2
MATCH CREATE
n
v1

20. Write Queries - Challenges
MATCH (n)
CREATE (n)-[r]->(m);
v2
MATCH CREATE
n

21. Write Queries - Challenges
MATCH (n)
CREATE (n)-[r]->(m);
MATCH CREATE
n
v2

22. Write Queries - Challenges
MATCH (n)
CREATE (n)-[r]->(m);
GraphWrite
n

23. Scan on Parent Label - Challenges
Scan on "CHARACTER"
Scan on "CAT"
Scan on "MOUSE"
Append
MATCH (n:CHARACTER) WHERE id(n) = ?
RETURN n;
CHARACTER
CAT MOUSE
Label Hierarchy
planning

24. Scan on Parent Label - Challenges
Scan on "CHARACTER"
Scan on "CAT"
Scan on "MOUSE"
Append
MATCH (n:CHARACTER) WHERE id(n) = ?
RETURN n;
CHARACTER
CAT MOUSE
Label Hierarchy
planning
scan

25. Scan on Parent Label - Challenges
Scan on "CHARACTER"
Scan on "CAT"
Scan on "MOUSE"
Append
MATCH (n:CHARACTER) WHERE id(n) = ?
RETURN n;
CHARACTER
CAT MOUSE
Label Hierarchy
planning
2 1
2
Scan
1
3

26. Scan on Parent Label - Challenges
Scan on "CHARACTER"
Scan on "CAT"
Scan on "MOUSE"
Append
MATCH (n:CHARACTER) WHERE id(n) = ?
RETURN n;
CHARACTER
CAT MOUSE
Label Hierarchy
planning
2 1
2
1
3
Skip scan

27. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;

28. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;
Step 1

29. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;
Step 1
Step 2

30. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;
Level 1
Parent Node

31. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;
Level 2
Level 1
Parent Node

32. Variable Length Relationships - Challenges
MATCH (n)-[r*1..2]->(m)
RETURN m;
Level 2
Level 1
Parent Node

33. LDBC Benchmark
http://www.ldbcouncil.org/
Members
Oracle Labs, IBM, Huawei, SAP, Sparsity, OpenLink Software, Ontotext, Neo Technology
Graphalytics Benchmark
Semantic Publishing Benchmark
Social Network Benchmark

34. Performance Comparisons - LDBC Benchmark
* We had optimized the two database as much as we can.
* The benchmark results can vary according to configurations.
10x faster
(appx.)
Compet
itor
(x)

35. Performance Comparisons - LDBC Benchmark
* We had optimized the two database as much as we can.
* The benchmark results can vary according to configurations.
20x faster
(appx.)
Compet
itor
(x)

36. Performance Comparisons - LDBC Benchmark
* We had optimized the two database as much as we can.
* The benchmark results can vary according to the configurations.
Compet
itor
(x)

37. Performance Comparisons - LDBC Benchmark
50x faster
(appx.)
* We had optimized the two database as much as we can.
* The benchmark results can vary according to configurations.
Compet
itor
(x)

38. Cybercrime Analytic System - Use Case

39. Faster than the Fastest
● We are working on
○ Improving the performance of shortestpath() function
○ Providing more catalog tables

40. agensgraph.com
https://github.com/bitnine-oss/agensgraph
Apache License, Version 2.0
Thank you!