Graph Gurus Episode 12: Tiger Graph v2.3 Overview

Graph Gurus
Episode 12
Introducing TigerGraph 2.3:
Overview and Demo

© 2019 TigerGraph. All Rights Reserved
Developer Edition Available
We now offer Docker versions and VirtualBox versions of the TigerGraph
Developer Edition, so you can now run on
● MacOS
● Windows 10
● Linux
Developer Edition Download https://www.tigergraph.com/developer/
2
Version 2.3
Available Now

© 2019 TigerGraph. All Rights
Reserved
Today's Gurus
3
Victor Lee
Director of Product Management
● BS in Electrical Engineering and Computer Science
from UC Berkeley, MS in Electrical Engineering from
Stanford University
● PhD in Computer Science from Kent State University
focused on graph data mining
● 15+ years in tech industry
Benyue (Emma) Liu
Senior Product Manager
● BS in Engineering from Harvey Mudd College, MS in
Engineering Systems from MIT
● Prior work experience at Oracle and MarkLogic
● Focus - Cloud, Containers, Enterprise Infra, Monitoring,
Management, Connectors

What's New in TigerGraph 2.3?
4
● Kafka Loader
○ integration with data streams
● GraphStudio enhancements
○ Better query workflow, display improvements
● GSQL enhancements
○ Primary Key options, Loading options, SHOW catalog
● Graph Algorithm Library improvements
○ More algorithms, improved performance

GraphStudio - Key 2.3 Enhancements
5
● Friendlier Query Development Workflow
○ ✅ Authoring: See graph schema while writing GSQL queries.
○ ✅ Deploying: After a query is compiled, show its REST endpoint.
● Friendlier Display
○ ✅ For directed edges, show only forward edges
○ ✅ Adjustable text size

GSQL Enhancements
6
✅ Schema Options: Vertex Attribute as PRIMARY_KEY
● In classic GSQL, vertex Primary_ID is a hash key, not an attribute. This minimizes storage space.
● New option to treat the Primary_ID as a regular attribute (e.g. use in WHERE conditions).
✅ Loading Option: User can choose how to handle edges preceding their vertices.
● Default: If either the source id or target id of a new edge refers to a nonexistent vertex,
then the system will create the necessary vertices with default values.
● New option: If the source or target vertex doesn't exist, don't create the edge.
✅ Catalog Display: Enhanced SHOW command
● Show vertices, edges, jobs, or queries.
● Accepts a regEx or glob pattern argument to show only selected items
✅ MultiGraph: Different graphs can use the same query names and job names.

Primary Key Options
7
Classic syntax:
# primary_id is used for indexing only
# Pro: Uses less memory/storage. Con: Cannot reference it in an expression.
If you do want to use the primary id in expressions, then you can duplicate it, such as
where the same value is inserted into the id and the pid fields.
CREATE VERTEX movie (PRIMARY_ID id INT, name STRING, year UINT)
CREATE VERTEX movie (PRIMARY_ID id INT, pid INT, name STRING, year UINT)

Primary Key Options
8
Classic syntax:
# primary_id is used for indexing only
# Pro: Uses less memory/storage. Con: Cannot reference it in an expression.
New syntax options:
# primary_id can now be used as an attribute, e.g.
# SELECT m FROM movie:m WHERE m.id=101
# Same meaning as above; follows SQL syntax.
# This version not supported in GraphStudio yet
USING primary_id_as_attribute="true"
CREATE VERTEX movie (id INT PRIMARY KEY, name STRING, year UINT)

Loading Options
9
Classic Functionality:
You can load an edge before loading its source and target vertices.
When the edge is created, default vertices are created.
Using loading job:
Example: First data line is for an edge. It creates 3 entities:
# Creates vertex Person(1,"","","")
# Creates vertex Person(2,"","","")
# Creates edge Related(1, 2, "Mother of")
Note: The vertices are valid, so we could stop there.
E,1,2,"Mother of"
LOAD f
TO EDGE Related VALUES ($1,$2,$3) WHERE $0 == "E"
TO VERTEX Person VALUES ($1, $2, $3, $4) WHERE $0 == "V"

Loading Options
10
Classic Functionality:
You can load an edge before loading its source and target vertices.
When the edge is created, default vertices are created.
Example: First data line is for an edge.
# Creates vertex Person(1,_,_)
# Creates vertex Person(2,_,_)
# Creates edge Related(1, 2, "Mother of")
Then next data lines are for the vertices:
# Update: Person(2, "Kylo","Ren","Supreme Leader")
# Update: Person(1,"Leia","Organa","General")
V,2,"Kylo","Ren","Supreme Leader"
V,1,"Leia","Organa","General"
E,1,2,"Mother of"

Loading Options
11
New Option for Loading Functionality:
USING VERTEXMUSTEXIST="true":
If an edge's two vertices have not already been loaded, then the edge is rejected.
Example: First data line is for an edge. This line is rejected as invalid.
E,1,2,"Mother of"
LOAD f
TO EDGE Related ($1,$2,$3) WHERE $0 == "E"
TO VERTEX Person ($1, $2, $3, $4) WHERE $0 == "V"
USING VERTEXMUSTEXIST="true"

Graph Algorithm Library Improvements
12
docs.tigergraph.com/graph-algorithm-library
● Similarity (NEW category): Jaccard, Cosine
○ For Recommendation, Entity Resolution
● Centrality: Personalized PageRank
○ Which entities are important to me? Random walk with home page(s)
● Path: more variations of Shortest Path → Degrees of separation
→ Weighted edges: "Shortest" can mean "cheapest" or "most likely"
→ Optimized versions for different cost models
● Community: faster Louvain modularity
○ parallel processing

NEW: Kafka Loader
● Increase Data Availability and Accelerate Time to Value
• Load streaming and batched data from user's Kafka server
• Consistent with GSQL file loading syntax and MultiGraph support
● Embrace Benefits of Kafka Ecosystem
• Scalable data loading and Built-in fault tolerance
• Data buffer - Kafka is in a separate cluster
• Extensible - open up data pipeline from many other data sources
13
Kafka Loader

DEMO
14

Speed to Value from Real-time Streaming Data
- Native Integration with Kafka Platform
16
• Reduce Data Availability Gap and Accelerate
Time to Value
• Native Integration with Real-time Streaming Data
and Batch Data
• Enables Real-time Graph Feature Updates with
Streaming Data in Machine Learning Use Cases
• Decrease Learning Curve With Familiar Syntax
• GSQL Support with Consistent Data Loading Syntax
• Maintain Separation of Control for Data Loading
• Designed with Built-in MultiGraph Support

Extensible and Flexible Data Pipeline
- Leverage Kafka Benefits & Ecosystem
17
• Industry Standard
Pub-Sub
Framework
• Extensible -> Open
Up Data Pipeline
From Many Data
Sources

Scalable and Fault Tolerant for Your Streaming
Data Pipeline
Leverage Benefits of Kafka
• Built-in Fault Tolerance and
Scalability
• Data Buffer - Kafka is in a
Separate Cluster
18
Reference:https://data-flair.training/blogs/advantages-and-disadvantages-of-kafka/

Kafka and TigerGraph Data Pipeline
Static
Data
Sources
Streaming
Data
Sources
Kafka
Loader

Kafka Loader High Level Architecture
• Connect to External Kafka Cluster
• User Commands Through GSQL
Server
• Configuration Files:
• Config 1: Kakfa Cluster
Configuration
• Config 2: Topic/Partition/Offset
Info
20

Kafka Loader : Three Steps
21
Consistent with GSQL Data Loading Steps
Step 1: Define the Data Source
Step 2: Create a Loading Job
Step 3: Run the Loading Job

Prerequisites: Kafka Configuration Files
22
Connect to External
Kafka Data Source
Through
Kafka Cluster
Configuration file
In Step 1
(Kafka broker's domain
name and port)
Define Kafka Data
Source Structure
Through
Kafka Topic/Partition
Configuration File
In Step 2
(Kafka topic, partition list,
and start offset)

Manage Loading Job
• SHOW LOADING STATUS
• ABORT LOADING JOB
• RESUME LOADING JOB
23
Consistent with GSQL Data Loading Syntax

Example Scripts
24
USE GRAPH test_graph
DROP JOB load_person
DROP DATA_SOURCE k1
#create data_source kafka k1 = "kafka_config.json" for graph test_graph
CREATE DATA_SOURCE KAFKA k1 FOR GRAPH test_graph
SET k1 = "kafka_config.json"
# define the loading jobs
CREATE LOADING JOB load_person FOR GRAPH test_graph {
DEFINE FILENAME f1 = "$k1:topic_partition_config.json";
LOAD f1
TO VERTEX Person VALUES ($2, $0, $1),
TO EDGE Person2Comp VALUES ($2, $2, $1)
USING SEPARATOR=",";
}
# load the data
RUN LOADING JOB load_person

DEMO: Kafka Loader
25

LIVE DEMO
26
1. Prep Work:
a. External Kafka cluster is setup
b. Two configuration files are provided on TigerGraph Cluster
c. Graph is created in TigerGraph: ComputerNet
2.
Step 1: Define the Data Source
Step 2: Create a Loading Job
Step 3: Run the Loading Job
SHOW LOADING STATUS
ABORT LOADING JOB
RESUME LOADING JOB
Manage OptionsInitial Loading Steps

Summary
• TigerGraph 2.3 is Released
• Enhancements in GraphStudio and GSQL
• Graph Algorithms Library is Expanded
• Native Integration with Kafka Loader
27
Version 2.3
Available Now

Q&A
Please send your questions via the Q&A menu in Zoom
28

Additional Resources
29
New Developer Portal
https://www.tigergraph.com/developers/
Download the Developer Edition or Enterprise Free Trial
https://www.tigergraph.com/download/
Guru Scripts
https://github.com/tigergraph/ecosys/tree/master/guru_scripts
Join our Developer Forum
https://groups.google.com/a/opengsql.org/forum/#!forum/gsql-users
@TigerGraphDB youtube.com/tigergraph facebook.com/TigerGraphDB linkedin.com/company/TigerGraph

Graph Gurus Episode 12: Tiger Graph v2.3 Overview

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