New Features in Neo4j 3.4 / 3.3 - Graph Algorithms, Spatial, Date-Time & Visualization

Release Demo:
Neo4j 3.4 & Bloom
Michael Hunger (@mesirii)
Developer Relations Neo4j
CPH- June 2018
3

Graph
Transactions
Graph
Analytics
Data Integration
Development
& Admin
Analytics
Tooling
Drivers & APIs Discovery & Visualization
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
Native Graph Platform: Tools for Many Users

Neo4j: Enabling the Connected Enterprise
Consumers of Connected Data
AI & Graph Analytics
• Sentiment analysis
• Customer
segmentation
• Machine learning
• Cognitive computing
• Community detection
Transactional Graphs
• Fraud detection
• Real-time recommendations
• Network and IT operations
management
• Knowledge Graphs
• Master Data Management
Discovery & Visualization
• Fraud detection
• Network and IT
operations
• Product information
management
• Risk and portfolio analysisData
Scientists
Business
Users
Applications

Neo4j 3.3 and 3.4
Latest Innovations

Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers & APIs
AI
Neo4j Database 3.3
• 50% faster writes
• Real-time transactions
and traversal applications
Review: The Neo4j Graph Platform, Fall 2017
Neo4j Desktop, the developers’
mission control console
• Free, registered local license
of Enterprise Edition
• APOC library installer
• Algorithm library installer
Data Integration
• Neo4j ETL reveals RDBMS
hidden relationships upon
importing to graph
• Data Importer for fast data
ingestion
Graph Analytics
• Graph Algorithms support
Community Detection,
Centrality and Path Finding
• Cypher for Apache Spark
from openCypher.org
supports graph composition
(sub-graphs) and algorithm
chaining
Discovery & Visualization
• Integration with popular
visualization vendors
• Neo4j Browser and custom
visualizations allow graph
exploration
Bolt with GraphQL and more
• Secure, Causal Clustering
• High-speed analytic processing
• On-prem, Docker & cloud delivery

“Least Connected” load balancing
Faster & more memory efficient runtime
Batch generation of IDs
Schema operations now take local locks
Page cache metadata moved off heap
Native GB+ Tree numeric indexes
Bulk importer paging & memory improvements
Dynamically reload config settings without restarting
Neo4j Admin & Config
Storage & Indexing
Memory Management
Kernel & Transactions
Cypher Engine
Drivers & Bolt Protocol
Neo4j 3.3 Performance Improvements

Concurrent/Transactional Write Performance
25000
20000
15000
10000
5000
0
Neo4j 2.2 Neo4j 2.3 Neo4j 3.0 Neo4j 3.1 Neo4j 3.2 Neo4j 3.3
69%
31%
59%
38%
55%
(Simulates Real-World Workloads)

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READ REPLICAS
London
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READ REPLICAS
New York
Multi-Data Center Clustering with Secure Transit

Neo4j Desktop 1.0
• Mission control for developers
• Connect to both local and remote
Neo4j servers
• Free with registration
• Includes development license for Neo4j Enterprise Edition
• Graph Apps
• Keeps you up to date with latest versions, plugins, etc.
• https://neo4j.com/download

Finds the optimal path
or evaluates route
availability and quality
• Single Source Short Path
• All-Nodes SSP
• Parallel paths
Evaluates how a
group is clustered or
partitioned
• Label Propagation
• Union Find
• Strongly Connected
Components
• Louvain
• Triangle-Count
Determines the
importance of distinct
nodes in the network
• PageRank
• Betweeness
• Closeness
• Degree
Data Science Algorithms

Project Goals
• high performance graph algorithms
• user friendly (procedures)
• support graph projections
• augment OLTP with OLAP
• integrate efficiently with live Neo4j
database
(read, write, Cypher)
• common Graph API to write your
own

Usage
1. Call as Cypher procedure

Usage
2. Pass in specification (Label, Prop, Query) and
configuration

Usage
configuration
3. ~.stream variant returns (a lot) of results
CALL algo.<name>.stream('Label','TYPE',{conf})
YIELD nodeId, score

Usage
configuration
YIELD nodeId, score
1. non-stream variant writes results to graph and
returns statistics
CALL algo.<name>('Label','TYPE',{conf});

Usage
configuration
YIELD nodeId, score
1. non-stream variant writes results to graph and
returns statistics
CALL algo.<name>('Label','TYPE',{conf});
1. Cypher projection:
pass in Cypher for node and relationship lists
CALL algo.<name>(
'MATCH ... RETURN id(n)',
'MATCH (n)-->(m) RETURN id(n), id(m)',
{ graph:'cypher' }
)

Architecture
1. Load Data in parallel from
Neo4j
2. Store in efficient data
structure
3. Run graph algorithm in
parallel using Graph API
4. Write data back in parallel Neo4j
1, 2
Algorithm
Datastructures
4
3
Graph API

TheAlgorithms
Centralities
• PageRank (baseline)
• Betweeness
• Closeness
• Degree

Example: PageRank
CALL algo.pageRank('Page', 'LINKS', {
iterations:20,
dampingFactor:0.85,
write: true,
writeProperty:"pagerank"}
)
YIELD nodes, iterations, loadMillis,
computeMillis, writeMillis,
dampingFactor, write, writeProperty

Clustering
• Label Propagation
• Louvain (Phase2)
• Union Find / WCC
• Strongly Connected
Components
• Triangle-
Count/Clustering CoeffTheAlgorithms

CALL algo.unionFind('User', 'FRIEND', {
write:true,
partitionProperty:'partition'}
)
YIELD nodes, setCount, loadMillis,
computeMillis, writeMillis
Example: UnionFind (CC)

Path-Expansion / Traversal
• Single Short Path
• All-Nodes SSP
• Parallel BFS / DFS
TheAlgorithms

MATCH(start:Location {name:'A'})
CALL algo.deltaStepping.stream(start, 'cost', 3.0)
YIELD nodeId, distance
RETURN nodeId, distance ORDER LIMIT 20
Example: Delta Stepping

Reading Material
• Thanks to Amy Hodler
• 13 Top Resources on Graph Theory
• neo4j.com/blog/
top-13-resources-graph-theory-algorithms/

GraphAware NLP
Comprehensive Suite of NLP
Tools for Neo4j

Feature Matrix
github.com/graphaware/neo4j-nlp

Trump Twitter Analysis
(M. David Allen)

● Import Trump Twitter Archive
● Extract Hashtags and Mentions
● Some Analytics Queries
● Use NLP (Caution)
● for Entities and Sentiment
● Some more queries
Trump is Dooming the World one Tweet(storm) at a Time
https://medium.com/@david.allen_3172/using-nlp-in-neo4j-ac40bc92196f

WITH [ archive.com/data/realdonaldtrump/2018.json',..]
as urls
UNWIND urls AS url
CALL apoc.load.json(url) YIELD value as t
CREATE (tweet:Tweet {
id_str: t.id_str, text: t.text,
created_at: t.created_at, retweets: t.retweet_count,
favorites: t.favorite_count, retweet: t.is_retweet,
reply: t.in_reply_to_user_id_str, source: t.source
}) RETURN count(t);
Importing Data

MATCH (t:Tweet) WHERE t.text contains '#'
WITH t,
apoc.text.regexGroups(t.text, "#([w_]+)") as matches
UNWIND matches as match
WITH t, match[1] as hashtag
MERGE (h:Tag { name: toUpper(hashtag) })
ON CREATE SET h.text = hashtag
MERGE (h)<-[:TAGGED]-(t)
RETURN count(h);
Importing Data - Hashtags

MATCH (t:Tweet) WHERE t.text CONTAINS '@'
WITH t,
apoc.text.regexGroups(t.text, "@([w_]+)") as
matches
UNWIND matches as match
WITH t, match[1] as mention
MERGE (u:User { name: toLower(mention) })
ON CREATE SET u.text = mention
MERGE (u)<-[:MENTIONED]-(t)
RETURN count(u);
Importing Data - Mentions

MATCH (tag:Tag)<-[:TAGGED]-(tw)
RETURN tag.name, count(*) as freq
ORDER BY freq desc LIMIT 10;
MATCH (u:User)<-[:MENTIONED]-(tw)
RETURN u.name, count(*) as freq
ORDER BY freq desc LIMIT 10;
Query Data – Tags / Mentions

NLP Knowledge Studio
by GraphAware

Neo4j 3.4 Videos
youtu.be/mdNJFTUiuA8

Neo4j 3.4 Sandbox
neo4j.com/sandbox

• property storage: local date and time, date & time with timezones
• durations
• indexed
• range scans: $before < event.time < $after
• lots of possible datetime formats including weeks, quarters
• ordering
• Events, Time Tracking, History, Auditing, ...
DateTime
https://neo4j.com/docs/developer-manual/current/cypher/syntax/temporal/

DateTime Types & Functions
Support date Support time Support timezone
Date x
Time x x
LocalTime x
DateTime x x x
LocalDateTime x x

date()
time.transaction()
localtime.statement()
datetime.realtime()
datetime.realtime('Europe/Berlin')
"db.temporal.timezone"
Current date, using statement clock
Current time, using transaction clock
Current local time, using statement clock
Current date and time, using realtime clock
This datetime instance will have the default timezone
of the database
Current date and time, using realtime clock,
in Europe/Berlin timezone
Neo4j setting to configure default timezone*, taking a String, e.g.
"Europe/Berlin".

date("2018-01-01")
localtime("12:00:01.003")
time("12:00:01.003+01:00")
datetime("2018-01-01T12:00:01.003[Europe/Berlin]")

date({year: 2018, month: 1, day: 1})
localtime({hour: 12, minute: 0, second 1, millisecond: 3})
time({hour: 12, minute: 0, second 1, millisecond: 3, timezone: '+01:00'})
datetime({year: 2018, month: 1, day: 1, hour: 12, minute: 0,
second 1, millisecond: 3, timezone: 'Europe/Berlin'})

DateTime Types & Functions - Duration
a = localdatetime("2018-01-01T00:00")
b = localdatetime("2018-02-02T01:00")
duration.between(a, b) -> (1M, 1D, 3600s, 0ns)
duration.inMonths(a, b) -> (1M, 0D, 0s, 0ns)
duration.inDays(a, b) -> (0M, 32D, 0s, 0ns)
duration.inSeconds(a, b) -> (0M, 0D, 2768400s, 0ns)

DateTime Types & Functions - Duration
a = localdatetime("2018-01-01T00:00")
b = localdatetime("2018-02-02T01:00")
duration.between(a, b) -> (1M, 1D, 3600s, 0ns)
duration.inMonths(a, b) -> (1M, 0D, 0s, 0ns)
duration.inDays(a, b) -> (0M, 32D, 0s, 0ns)
duration.inSeconds(a, b) -> (0M, 0D, 2768400s, 0ns)
// Maths
instant + duration
instant – duration
duration * number
duration / number

DateTime Types & Functions - toString
toString
Date 2018-05-07
Time 15:37:20.05+02:00
LocalTime 15:37:20.05
DateTime 2018-05-07T15:37:20.05+02:00[Europe/Stockholm]
LocalDateTime 2018-05-07T15:37:20.05
Duration P12Y5M14DT16H13M10.01S
Prints all temporal types in a format that can be parsed back

DateTime Types & Functions - Pitfalls
• Don’t compare Durations. Add them to the same instant and compare
the results instead.
date + dur < date + dur2
• Don’t subtract instants. Use duration.between.
duration.between(date1, date2)
• Keep predicates simple to leverage indexing.
MATCH (n), (m) WHERE datetime({date: n.date, time: m.time}) = dt
MATCH (n), (m) WHERE n.date = date(dt) AND m.time = time(dt)

Support for Geospatial Search
+
medium.com/neo4j/whats-new-in-neo4j-spatial-features-586d69cda8d0

Geospatial Graph Queries – Coord Systems
Explicit CRS:
• point({x:1, y:2, z:3, crs:'wgs-84-3d'})
• point({x:1, y:2, z:3, srid:4979})
Inferred CRS:
• point({x:1, y:2}) => 'cartesian'
• point({x:1, y:2, z:3}) => 'cartesian-3d'
• point({latitude:2, longitude:1}) => 'wgs-84'
• point({latitude:2, longitude:1, height:1000}) => 'wgs-84-3d'

Geospatial Graph Queries – Index
Neo4j 3.2 introduced the GBPTree:
• Lock-free writes (high insert performance)
• Good read performance (comparable)
Neo4j 3.3 introduced the NativeSchemaNumberIndex:
• And the FusionSchemaIndex
• Allows multiple types to exist in one logical index
Neo4j 3.4 Spatial Index / Date Time Index
• Different indexes for coordinate system
• Hilbert Space Filling Curves

Geospatial Graph Queries
x
y
z

Geospatial Graph Queries
9 index 5 hits

github.com/johnymontana/spacetime-reviews
github.com/johnymontana/spacetime-reviews
spacetime.graph.zone

Neo4j 3.3 Neo4j 3.4
70% Faster
250
500
0
750
1000
Neo4j 3.2
Reads – Neo4j Enterprise Cypher Runtime
10% Faster
Mixed Workload Read Benchmark

Supercharging Graph Writes
One Component:
80% of transactional write overhead (!!!)
?QM ,.
Index Insertion

 ACID
 Optimized for
graph
 Fast Reads
 ~10x Faster Writes
Neo4j GB+ Tree
Index
Label Groupings (Neo4j 3.2)
Numerics (Neo4j 3.3)
Strings (Neo4j 3.4)
QM ,.
External Index
Provider
Native Neo4j Index
Supercharging Graph Writes

68
Writes with Native String Indexes
500%
improvement
Neo4j 3.4 RC1 Performance

Multi-Clustering Support for Global Internet Apps
Horizontally partition graph by domain (country, product, customer, data center)
70
Multi-tenancy
Geo Partitioning
Write Scaling
Driver Support
sa cluster
uk cluster
us_east cluster
hk cluster

LDAP & Active
Directory
User &
Roles
Procedure
Access Controls
Security: Property Blacklisting added
Kerberos SSO
Certs
Intra-Cluster
Encryption
Security
Event Logging
TLS Wire
Encryption
SSN: “043-56-8834”
0000“043-56-8834”
Property
Blacklisting

73
Property Level Security
https://neo4j.com/docs/operations-manual/current/security/authentication-authorization/property-level-security/

Rolling Upgrades
74
3.4 3.4 3.44.0 4.0 4.0
Auto Cache Reheating
Upgrade to new versions
with zero downtime
Store upgrades
may require downtime but
can be done subsequently
For Restarts, Restores, and Cluster Expansion

75
3.4 Features By Edition Community Edition Enterprise Edition
Date / Time data types ■ ■
3D Geospatial data types ■ ■
Performance Improvements
Native String Indexes – up to 5x faster writes ■ ■
2x faster backups ■
Improved Cypher runtime Fast Faster
100B+ object bulk importer ■ Resumable
Enterprise Scaling & Administration
Multi-Clustering (partitioning of clusters) ■
Automatic cache warming ■
Rolling upgrades ■
Resumable copy/restore cluster member ■
New diagnostic metrics and support tools ■
Security: Property blacklisting by user/role ■

Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers & APIs
AI
Neo4j Database 3.4
• 70% faster Cypher
• Native String Indexes
(up to 5x faster writes)
• 100B+ bulk importer
The Neo4j Graph Platform, Summer 2018
Improved Admin Experience
• Rolling upgrades
• 2X faster backups
• Cache Warming on startup
• Improved diagnostics
Morpheus for Apache Spark
• Graph analytics in the data lake
• In-memory Spark graphs from
Apache Hadoop, Hive,
Gremlin and Spark
• Save graphs into Neo4j
• High-speed data exchange
between Neo4j & data lake
• Progressive analysis using
named graphs
Graph Data Science
• High speed graph
algorithms
Neo4j Bloom
• New graph illustration and
communication tool for non-
technical users
• Explore and edit graph
• Search-based
• Create storyboards
• Foundation for graph data
discovery
• Integrated with graph platform
Multi-Cluster routing built into Bolt drivers
• Date/Time data type
• 3-D Geospatial search
• Secure, Horizontal Multi-Clustering
• Property Blacklisting Security

Introducing A Brand New
Graph Exploration Product

Neo4j Bloom
79
• High fidelity
• Scene navigation
• Property views
• Search suggestions
• Saved phrase history
• Property editor
• Schema perspectives
• Bloom chart type
• Visualize
• Communicate
• Discover
• Navigate
• Isolate
• Edit
• Share

80
Neo4j Bloom Features
• Prompted Search
• Property Browser &
editor
• Category icons and
color scheme
• Pan, Zoom & Select

Advancing the Platform
81
Native graph architecture extends
scale, use cases and performance
• Neo4j Database 3.4
Shipping May, 2018
New products for new users
• Neo4j Bloom visualization &
storyboard tool for business
Shipping in June, 2018

New Features in Neo4j 3.4 / 3.3 - Graph Algorithms, Spatial, Date-Time & Visualization

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