Roadmap y Novedades de producto

Neo4j Inc. All rights reserved 2023
Roadmap y Novedades de
producto
Jesús Barrasa
Head of Solutions Architecture EMEA
1

Neo4j Inc. All rights reserved 2023 2
…conectando con la intro de Ivan

The Property Graph Model
3
Employee City
Company
Nodes represent
objects (nouns)
Relationships are directional
Relationships connect nodes are
represent actions (verbs)
Relationships can have properties
(name/value pairs)
Nodes can have
properties (name/value
pairs)
name: Emil Eifrem
date_of_birth: 1979-03-01
employee_ID: 1
:HAS_CEO
start_date: 2008-01-20
:LOCATED_IN

Cypher (GQL): Pattern Based
4
MATCH (p:Employee {employee_ID: 1})-[r:WORKS_AT*..3]-(c:Company)
RETURN c.name as company, count(*) as score
ORDER BY score DESC
Node
Pattern
Relationship
Pattern

GQL Standard
5
Project agreed back in September 2019
First version of the full CRUD graph query
language in four-year window
39075 GQL submitted for a DIS (March 24)
Expected publication Q4 2023
Cypher includes GQL features since v.5
DIS = Draft International Standard = technically stable
IS = International Standard.
https://www.iso.org/standard/76120.html

Neo4j for applications
6
const movies = await driver
.executeQuery(
'match (p:Employee)-[r:WORKS_AT*..3]-(c:Company) return c',{limit: 10})
.then((result) => { return result.records.map( (r) => r.get('c') ) })
.catch((error) => { console.error(error) })

Neo4j for analytics
7
from graphdatascience import GraphDataScience
gds = GraphDataScience(DB_ULR, auth=(DB_USER, DB_PASS))
gds.louvain.write(G,writeProperty='community')

Neo4j: Native graph architecture
8
Native Graph
Storage
Native Graph
Processing
Autonomous
Clustering
Composite
Databases
• No mismatch
• Data integrity / ACID
• Schema ﬂexible
• 1000x faster than
relational
• K-Hop now 10-1000x faster
than version 4
• Elastic scale-out for
high throughput
• 100s of machines
across clusters
• Federation of scaled
out shards
• Instant composite
database
Data is connected
as it is stored
Lightning-fast retrieval of data and
relationships via pointer chasing

© 2023 Neo4j, Inc. All rights reserved.
New in Neo4j 5 -
recap
9

Graph Schema: New constraints on nodes,
relationships and properties: Unique relationship
property, Relationship key, Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
10

Graph Pattern Matching: Improved
expressivity of graph navigation with
quantiﬁed path patterns. More powerful and
performant syntax to navigate and traverse
your graph.
11

MATCH path=(a:Account)-[rel:TRANSACTION*3..6]->(a)
WHERE size(apoc.coll.toSet(nodes(path))) = size(nodes(path)) - 1
AND ALL(idx in range(0, size(rel)-2)
WHERE (rel[idx]).date < (rel[idx+1]).date
AND (rel[idx+1].amount / rel[idx].amount) * 100 <= 20
)
RETURN path
Graph Pattern Matching Example
12
Pre-QPP

MATCH path=(a:Account)-[:PERFORMS]->(first_tx)
((tx_i)-[:BENEFITS_TO]->(a_i)-[:PERFORMS]->(tx_j)
WHERE tx_i.date < tx_j.date
AND 0.80 <= tx_i.amount / tx_j.amount <= 1.00
){3,6}
(last_tx)-[:BENEFITS_TO]->(a)
WHERE size(apoc.coll.toSet([a]+a_i)) = size([a]+a_i)
RETURN path
Graph Pattern Matching Example
13
QPP

Call in Transactions: for batched import, control
transaction sizes from Cypher.
Incremental Importer: Ultra-high speed method of
loading data incrementally (10-100x faster than
transactional).
14

Backup & Recovery: Existing full backups can be
updated with differential data instead of
recreating a new full backup, saving a large
amount of storage. Administrators can now
execute point-in-time restore. API to ease
operability
Neo4j Ops Manager: To monitor Neo4j
deployments
15

Luis nos va a dar todos los detalles en su
presentación
Vector Index
16

Parallel Runtime
17

Parallel Runtime: Speed up analytical query up to 100x
18

Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU cores
19
More cores
Faster
Queries

Change Data
Capture (CDC)
20

Change Data Capture: Automated Real-Time Change Tracking
21

Integration Google
Dataflow
22

GCP Dataflow integration
23
https://neo4j.com/docs/dataﬂow-google-cloud/
https://neo4j.com/docs/dataﬂow-bigquery/

Looking Ahead:
Auto-Sharding
24

Fabric Auto-Sharding
25
Coming 2024+
Mapping
Sharding
Rules

Fabric Auto-Sharding
26
Coming 2024+
Mapping
Sharding
Rules
● Leverage customer hints to create sharding strategy
● Enable creation of schema/metadata for sharding
● Automatically increase shards based on customer
conﬁguration
● Create Fabric planner to push data and queries to
right shards
● Overtime support distributed transactions & proxy
nodes
● Make it available in Aura

Graph Data
Science News
27

Edge embeddings
Find missing relationships
● Users generate edge embeddings
using DGL, PyKeen, or PyTorch
Geometric
● Bring them into Neo4j Graph Data
Science or AuraDS stored as a
property.
● Use similarity functions to identify
possible relationship candidates
and check node pairs.
● Create relationships
Help predict missing
relationships between entities
Infer new relationships
Improve semantic
understanding and reasoning
28
How it Helps You

Longest Path Algo
Find the path of maximum
length between two nodes in
a directed acyclic graph

Longest Path
Once a directed acyclic graph
is built, use either Graph Data
Science or the Python client to
call the Longest Path
Algorithm.
Find the path of maximum
length between two nodes in a
graph.
Help solve critical scheduling and
optimization problems by
understanding longest paths
How it Helps You

Topological Sort Algo
Sort nodes in a directed
acyclic graph by
traversing them in order
of dependency
Identify dependencies

Identify
Dependencies
Once a directed acyclic graph
is built, use either Graph Data
Science or the Python client to
call the topological sort
algorithm.
Find dependencies in the
graph.
Ensure that dependent tasks are
performed in the correct order
How it Helps You

Thank
You!
See you 9th April 2024 at our
next GraphSummit Madrid! 33

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