Incremental View Maintenance for openCypher Queries

Incremental View Maintenance
for openCypher Queries
Gábor Szárnyas, József Marton
4th openCypher Implementers Meeting

MODEL-DRIVEN ENGINEERING
 Primarily for designing critical systems
 Models are first class citizens during development
o SysML / requirements, statecharts, etc.
o Validation and code generation techniques for correctness
Technology:
Eclipse Modeling Framework (EMF)
 Originally started at IBM as an implementation of the Object
Management Group’s (OMG) Meta Object Facility (MOF).
 i.e. an object-oriented model
 i.e. a property graph-like structure with a metamodel

MODEL VALIDATION
 Implemented with model queries
 Models are typed, attributed graphs
 Typical queries
o Get two components connected by a particular edge
MATCH (r:R)…(s:S) WHERE NOT (r)-[:E]->(s)
o Check if two objects are reachable
MATCH (r:R)…(s:S) WHERE NOT (r)-[:E1|E2*]->(s)
o Property checks
MATCH (r:R)-->(s:S) WHERE r.a = 'x' OR (s:Y)
Complex graph queries

1
switch
sensor C
sensor B
2
sensor A
segment
route
RAILWAY NETWORK MODEL

segment
segment
segmentswitch
sensor C
sensor B
sensor A
route 2
route 1

segment segment
sensor Csensor Bsensor A
route 2route 1
switch
segment
switchPosition
«diverging»
switchPosition
«straight»

:FOLLOWS
:MONITORED_BY
:TARGET:REQUIRES
MATCH (route:Route)
-[:FOLLOWS]->(swP:SwitchPosition)
-[:TARGET]->(sw:Switch)
-[:MONITORED_BY]->(sensor:Sensor)
WHERE NOT (route)-[:REQUIRES]->(sensor)
RETURN route, sensor, swP, sw
sw: Switchsensor: Sensor
route: Route swP: SwitchPosition
G. Szárnyas, B. Izsó, I. Ráth, D. Varró:
The Train Benchmark: cross-technology performance
evaluation of continuous model queries.
Software and Systems Modeling, 2017

segment segment
sensor Csensor Bsensor A
route 2route 1
switch
segment
switchPosition
«diverging»
switchPosition
«straight»

INCREMENTAL VIEW MAINTENANCE (IVM)
In many use cases…
 queries are static
 data changes slowly
-> views can be maintained incrementally
Graph applications
 model validation
 simulation
 recommendation systems
 fraud detection

INGRAPH: IVM ON PROPERTY GRAPHS
Idea: map to relational algebra and use standard IVM techniques
 Challenging aspects
o Property graph data model
o Cypher language
 Formalise the language in relational algebra
 Use nested relational algebra -> closed on operations
Prototype tool: ingraph (OCIM1, OCIM2, GraphConnect talks)
Gábor Szárnyas, József Marton, Dániel Varró:
Formalising openCypher Graph Queries in Relational Algebra.
ADBIS 2017

INGRAPH / GRAPH TO NESTED RELATIONS

INGRAPH / NESTED RELATIONAL ALGEBRA OPS

INGRAPH
 ingraph uses a procedural IVM approach: the Rete algorithm.
o Build caches for each operator
o Maintain caches upon changes
o Supports 15+ out of 25 LDBC BI queries
o Details to be published in a conference paper
o Extensible, but very heavy on memory
 The rest of the talk focuses on the algebraic approach.
Gábor Szárnyas, József Marton et al.:
Incremental View Maintenance on Property Graphs.
arXiv preprint will be available on the 1st week of June

DELTA QUERIES AT A GLANCE
𝐺
evaluate query 𝑄
for each Δ𝐺
evaluate Δ𝑄
changes
Δ𝐺1, Δ𝐺2, …
𝑄(𝐺)
Δ𝑄(Δ𝐺1)
Δ𝑄(Δ𝐺2)
⇒ 𝑄(𝐺 + Δ𝐺1 + Δ𝐺2 + ⋯ )
𝑄 and Δ𝑄 are calculated by the same engine.

IMPLEMENTATION: TRIGGERS IN NEO4J
 Event-driven programming in databases
 Neo4j: TransactionEventHandler interface
o afterCommit(TransactionData data, T state)
o beforeCommit(TransactionData data)
o TransactionData contains Δ𝐺: createdNodes, deletedNodes, …
 Only the updated state of the graph is accessible.
 GraphAware framework: ImprovedTransactionData API
o Get properties and labels/types of deleted elements
Max de Marzi:
Triggers in Neo4j.
2015
Michal Bachman:
Neo4j Improved Transaction Event API.
2014

DERIVING DELTA QUERIES
a b
1 2
3 4
5 6
7 8
a b
1 2
5 6
7 8
𝑅 𝑚
Idea: given query 𝑄, derive delta queries Δ𝑄 and 𝛻𝑄, which
define positive and negative changes, respectively.
But: most IVM techniques are defined for relational algebra.
Notation:
 𝑅 relation
 Δ𝑅 positive changes
 𝛻𝑅 negative changes
 𝑅 𝑚
: maintained relation of 𝑅 ⇒ 𝑅 𝑚
= 𝑅 − 𝛻𝑅 + Δ𝑅
 “−” denotes set minus (∖), “+” denotes set union (∪)
a b
1 2
3 4
5 6
𝑅 𝛻𝑅
Δ𝑅

RELATIONAL ALGEBRA FOR CYPHER
 Query plans in Neo4j ≅ relational algebra + Expand/VarExpand.
 Expand is essentially a natural join.
 Natural join 𝑟 ⋈ 𝑠
 Semijoin 𝑟 ⋉ 𝑠 = 𝜋 𝑅 𝑟 ⋈ 𝑠
 Antijoin 𝑟 ഥ⋉ 𝑠 = 𝑟 ∖ 𝑟 ⋉ 𝑠
 Left outer join 𝑟 ⟕ 𝑠 ≅ 𝑟 ⋈ 𝑠 ∪ 𝑟 ഥ⋉ 𝑠 //plus nulls
Andrés Taylor:
Neo4j Cypher implementation.
First openCypher Implementers Meeting, 2017

v1 v2 v3
1 2 3
1 2 6
RELATIONAL ALGEBRA FOR CYPHER
Natural join: 𝑟 ⋈ 𝑠
MATCH (v1)-[:r]->(v2)-[:s]->(v3)
RETURN *
Semijoin: 𝑟 ⋉ 𝑠
MATCH (v1)-[:r]->(v2)
WHERE (v2)-[:s]->()
Antijoin: 𝑟 ഥ⋉ 𝑠
MATCH (v1)-[:r]->(v2)
WHERE NOT (v2)-[:s]->()
Left outer join: 𝑟 ⟕ 𝑠
MATCH (v1)-[:r]->(v2)
OPTIONAL MATCH (v2)-[:s]->(v3)
1 3
4
2
5 6
:r
:r
:s
:s
v1 v2
1 2
v1 v2
4 5
1 3
4
2
5 6
:r
:r
:s
:s
1 3
4
2
5 6
:r
:r
:s
:s
1 3
4
2
5 6
:r
:r
:s
:s
1 32
6
:r :s
:s
1 2:r
4 5
:r
1 3
4
2
5 6
:r
:r
:s
:s
v1 v2 v3
1 2 3
1 2 6
4 5 null
4 5
v1 v2 v3
1 2 3
1 2 6

T. Griffin, B. Kumar:
Algebraic Change Propagation for Semijoin and Outerjoin Queries.
SIGMOD Record 1998
DERIVING DELTA QUERIES
T. Griffin, L. Libkin:
Incremental Maintenance of Views with Duplicates.
SIGMOD 1995
T. Griffin, L. Libkin, H. Trickey: An Improved Algorithm for the
Incremental Recomputation of Active Relational Expressions.
TKDE 1997
X. Qian, G. Wiederhold:
Incremental Recomputation of Active Relational Expressions.
TKDE 1991

DELTA QUERIES
T. Griffin, L. Libkin:
Incremental Maintenance of Views with Duplicates.
SIGMOD 1995
 The seminal paper
 Δ/𝛻 delta queries
for joins, selections,
projections, etc.
 Bag semantics

DELTA QUERIES
 Semijoins, antijoins,
outer joins
 Set semantics
 Later publications,
e.g. Zhou-Larson’s
ICDE’07 paper
improved these
T. Griffin, B. Kumar:
Algebraic Change Propagation for Semijoin and Outerjoin Queries.
SIGMOD Record 1998

EXAMPLE QUERY #1
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[:c]->(v4)
RETURN v1, v2, v3, v4
Relational algebra expression: 𝑎 ⋈ 𝑏 ⋈ 𝑐
Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐
= 𝑎 ⋈ 𝑏 𝑚
⋈ Δ𝑐 + Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐 𝑚
= 𝑎 ⋈ 𝑏 𝑚
⋈ Δ𝑐 + 𝑎 𝑚
⋈ Δ𝑏 ⋈ 𝑐 𝑚
+ Δ𝑎 ⋈ 𝑏 𝑚
⋈ 𝑐 𝑚
= 𝑎 𝑚
⋈ 𝑏 𝑚
⋈ 𝑐 𝑚
Similarly to 𝛻 𝑎 ⋈ 𝑏 ⋈ 𝑐 .
v1 v2 v3
:b:a
 𝑎 𝑣1, 𝑣2
 𝑏 𝑣2, 𝑣3
 𝑐 𝑣3, 𝑣4
v4
:c

EXAMPLE QUERY #1
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[:c]->(v4)
Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐
= 𝑎 𝑚
⋈ 𝑏 𝑚
⋈ 𝑐 𝑚
UNWIND $pcs AS pc
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[pc]->(v4)
$pcs -> pass lists of nodes/edges as parameters
// This only works in embedded mode, see neo4j/issues/10239
v1 v2 v3
:b:a
 𝑎 𝑣1, 𝑣2
 𝑏 𝑣2, 𝑣3
 𝑐 𝑣3, 𝑣4
v4
:c

POSITIVE DELTA QUERY FOR 𝑎 ⋈ 𝑏 ⋈ 𝑐
// r1 = a⋈b⋈Δc
UNWIND $pcs AS pc
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[pc]->(v4)
UNION ALL
// r2 = a⋈Δb⋈c
UNWIND $pbs AS pb
MATCH (v1)-[:a]->(v2)-[pb]->(v3)-[:c]->(v4)
UNION ALL
// r3 = Δa⋈b⋈c
UNWIND $pas AS pa
MATCH (v1)-[pa]->(v2)-[:b]->(v3)-[:c]->(v4)

POSITIVE DELTA QUERY FOR 𝑎 ⋈ 𝑏 ⋈ 𝑐
Long WITH chains are cumbersome -> patterns+list comprehensions.
WITH [pc IN $pcs | // r1 = a⋈b⋈Δc
[(v1)-[:a]->(v2)-[:b]->(v3)-[pc]->(v4) |
[v1, v2, v3, v4]]]
[pb IN $pbs | // r2 = a⋈Δb⋈c
[(v1)-[:a]->(v2)-[pb]->(v3)-[:c]->(v4) |
[v1, v2, v3, v4]]] +
[pa IN $pas | // r3 = Δa⋈b⋈c
[(v1)-[pa]->(v2)-[:b]->(v3)-[:c]->(v4) |
[v1, v2, v3, v4]]] AS r
RETURN
r[0] AS v1, r[1] AS v2, r[2] AS v3, r[3] AS v4

EXAMPLE QUERY #2
MATCH (route:Route)
-[:FOLLOWS]->(swP:SwitchPosition)
-[:TARGET]->(sw:Switch)
-[:MONITORED_BY]->(sensor:Sensor)
WHERE NOT (route)-[:REQUIRES]->(sensor)
RETURN route, sensor, swP, sw
MATCH (v1)
-[:a]->(v2)
-[:b]->(v3)
-[:c]->(v4)
WHERE NOT (v1)-[:d]->(v4)
v1 v2
v3v4
:b
:c
:d
:a
:FOLLOWS
:MONITORED_BY
:TARGET:REQUIRES
sw: Switchsensor: Sensor
route: Route
swP:
SwitchPosition

NEGATIVE CONDITIONS
v1 v2
v3v4
:b
:c
:d
:a
MATCH (v1)
-[:a]->(v2)
-[:b]->(v3)
-[:c]->(v4)
 𝑎 𝑣1, 𝑣2
 𝑏 𝑣2, 𝑣3
 𝑐 𝑣3, 𝑣4
 𝑑 𝑣1, 𝑣4
⇒ 𝑎 ⋈ 𝑏 ⋈ 𝑐 ഥ⋉ 𝑑

DELTA QUERIES FOR JOINS AND ANTIJOINS
Natural join
 Δ 𝑆 ⋈ 𝑇 = Δ𝑆 ⋈ 𝑇 𝑚
+ 𝑆 𝑚
⋈ Δ𝑇
 𝛻 𝑆 ⋈ 𝑇 = 𝛻𝑆 ⋈ 𝑇 + 𝑆 ⋈ 𝛻𝑇
Antijoin
 Δ 𝑆 ഥ⋉ 𝑇 = 𝑆 − 𝛻𝑆 ⋉ 𝛻𝑇 ഥ⋉ 𝑇 𝑚
+ Δ𝑆 ഥ⋉ 𝑇 𝑚
 𝛻 𝑆 ഥ⋉ 𝑇 = 𝑆 − 𝛻𝑆 ⋉ Δ𝑇 ഥ⋉ 𝑇 + 𝛻𝑆 ഥ⋉ 𝑇
Expression 2Expression 1
Only 𝑆 𝑚 and 𝑇 𝑚
are available.

SUBEXPRESSIONS
1. Δ𝑇 ഥ⋉ 𝑇 = ?
 R1 ഥ⋉ 𝑅2, where 𝑅1 and 𝑅2 both have schema 𝑅.
 𝑅1 ഥ⋉ 𝜃 𝑅2 = 𝑅1 − 𝜋 𝑅 𝑅1 ⋈ 𝜃 𝑅2 = 𝑅1 − 𝑅1 ⋈ 𝜃 𝑅2
 If 𝜃 defines equality on all attributes of 𝑅, the theta join (⋈ 𝜃)
becomes a natural join, which is an intersection for relations
with the same schema.
 𝑅1 ⋈ 𝜃 𝑅2 = 𝑅1 ⋈ 𝑅2 = 𝑅1 ∩ 𝑅2
 𝑅1 − 𝑅1 ⋈ 𝜃 𝑅2 = 𝑅1 − 𝑅1 ∩ 𝑅2 = 𝑅1 − 𝑅2
⇒ ∗ 𝑅1 ഥ⋉ 𝜃 𝑅2 = 𝑅1 − 𝑅2
2. 𝑅 𝑚
= 𝑅 − 𝛻𝑅 + Δ𝑅 ⇒ ∗∗ 𝑅 = 𝑅 𝑚
− Δ𝑅 + 𝛻𝑅

DELTAS FOR ANTIJOINS
Based on Griffin-Kumar’s ’98 paper.
 Δ 𝑆 ഥ⋉ 𝑇 = 𝑆 − 𝛻𝑆 ⋉ 𝛻𝑇 ഥ⋉ 𝑇 𝑚
+ Δ𝑆 ഥ⋉ 𝑇 𝑚
 Δ 𝑆 ഥ⋉ 𝑇 =
∗
𝑆 − 𝛻𝑆 ⋉ 𝛻𝑇 + Δ𝑆 ഥ⋉ 𝑇 𝑚
 Δ 𝑆 ഥ⋉ 𝑇 =
∗∗
𝑆 𝑚
− Δ𝑆 ⋉ 𝛻𝑇 + Δ𝑆 ഥ⋉ 𝑇 𝑚
 Δ 𝑆 ഥ⋉ 𝑇 = 𝑆 𝑚
− Δ𝑆 ⋉ 𝛻𝑇 + Δ𝑆 ഥ⋉ 𝑇 𝑚
 𝛻 𝑆 ഥ⋉ 𝑇 = 𝑆 − 𝛻𝑆 ⋉ Δ𝑇 ഥ⋉ 𝑇 + 𝛻𝑆 ഥ⋉ 𝑇
 𝛻 𝑆 ഥ⋉ 𝑇 =
∗
𝑆 − 𝛻𝑆 ⋉ Δ𝑇 + 𝛻𝑆 ഥ⋉ 𝑇
 𝛻 𝑆 ഥ⋉ 𝑇 =
∗∗
𝑆 𝑚
− Δ𝑆 ⋉ Δ𝑇 + 𝛻𝑆 ഥ⋉ 𝑇 𝑚
− 𝛻𝑇
 𝛻 𝑆 ഥ⋉ 𝑇 = 𝑆 𝑚
− Δ𝑆 ⋉ Δ𝑇 + 𝛻𝑆 ഥ⋉ 𝑇 𝑚
− 𝛻𝑇
∗ 𝑅1 ഥ⋉ 𝜃 𝑅2 = 𝑅1 − 𝑅2
∗∗ 𝑅 = 𝑅 𝑚
− Δ𝑅 + 𝛻𝑅

NEGATIVE CONDITIONS  𝑎 𝑣1, 𝑣2
 𝑏 𝑣2, 𝑣3
 𝑐 𝑣3, 𝑣4
 𝑑 𝑣1, 𝑣4
Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐 ഥ⋉ 𝑑 = 𝑎 ⋈ 𝑏 ⋈ 𝑐 𝑚 − Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐 ⋉ 𝛻𝑑 + Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐 ഥ⋉ 𝑑 𝑚
𝑎 ⋈ 𝑏 ⋈ 𝑐 𝑚
⋉ 𝛻𝑑 − Δ 𝑎 ⋈ 𝑏 ⋈ 𝑐 ⋉ 𝛻𝑑
Pushdown 𝛻𝑑:
𝑎 𝑚 ⋉ 𝛻𝑑 ⋈ 𝑏 𝑚 ⋈ 𝑐 𝑚 ⋉ 𝛻𝑑
𝑎 𝑚 ⋈ 𝑏 𝑚 ⋈ 𝑐 𝑚 ⋉ 𝛻𝑑 𝑎 𝑚 ⋈ 𝑏 𝑚 ⋈ Δ𝑐 + 𝑎 𝑚 ⋈ Δ𝑏 ⋈ 𝑐 𝑚 + Δ𝑎 ⋈ 𝑏 𝑚 ⋈ 𝑐 𝑚
v1 v2
v3v4
:b
:c
:d
:a

NEGATIVE CONDITIONS
Δ ⋈ ⋈ ⋉
⋉ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋈ Δ ⋈ Δ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋈ Δ ⋈ Δ ⋈ ⋈ ⋉
v1 v2
v3v4
:b
:c
:d
:a  ,
 ,
 ,
 ,
⋉ ⋈ ⋈ ⋉
⋉ ⋈ ⋈ Δ ⋉
⋉ ⋈ Δ ⋈ ⋉
Δ ⋉ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋉
⋈ Δ ⋈ ⋉
Δ ⋈ ⋈ ⋉

NEGATIVE CONDITIONS
⋉ ∈ . , where ∩ is a
single vertex, because and represent edges.
Δ ⋈ ⋈ ⋉
⋉ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋈ Δ ⋈ Δ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋈ Δ ⋈ Δ ⋈ ⋈ ⋉
⋉ ∈ .
Δ ⋉ ∈ . Δ
⋉ ∈ .
Δ ⋉ ∈ . Δ
R1
R2
R3
R4
R5
R6
R7
⋉ ⋈ ⋈ ⋉
⋉ ⋈ ⋈ Δ ⋉
⋉ ⋈ Δ ⋈ ⋉
Δ ⋉ ⋈ ⋈ ⋉
⋈ ⋈ Δ ⋉
⋈ Δ ⋈ ⋉
Δ ⋈ ⋈ ⋉
S1
S2
S3
S4
v1 v2
v3v4
:b
:c
:d
:a  ,
 ,
 ,
 ,

WITH
[] AS pes, [] AS nes
WITH
[pe IN pes WHERE type(pe) = 'a'|pe] AS pas,
[pe IN pes WHERE type(pe) = 'b'|pe] AS pbs,
[pe IN pes WHERE type(pe) = 'c'|pe] AS pcs,
[pe IN pes WHERE type(pe) = 'd'|pe] AS pds,
[ne IN nes WHERE type(ne) = 'a'|ne] AS nas,
[ne IN nes WHERE type(ne) = 'b'|ne] AS nbs,
[ne IN nes WHERE type(ne) = 'c'|ne] AS ncs,
[ne IN nes WHERE type(ne) = 'd'|ne] AS nds
WITH
pas, nas, pbs, nbs, pcs, ncs, pds, nds,
[nd IN nds | startNode(nd)] AS nd_v1s,
[nd IN nds | endNode(nd)] AS nd_v4s
// calculating s1s...s4s
// s1s: (𝑎⋉∇𝑑)
UNWIND nd_v1s AS v1
MATCH (v1)-[:a]->(v2)
WITH
nd_v1s, nd_v4s,
collect({v1: v1, v2: v2}) AS s1s
// s2s: (Δ𝑎⋉∇𝑑)
UNWIND pas AS pa
MATCH (v1)-[pa]->(v2)
WHERE v1 IN nd_v1s
WITH
nd_v1s, nd_v4s,
s1s,
// s3s: (𝑐⋉∇𝑑)
UNWIND nd_v4s AS v4
MATCH (v3)-[:c]->(v4)
WITH
nd_v1s, nd_v4s,
s1s, s2s,note
// s4s: (Δ𝑐⋉∇𝑑)
UNWIND pcs AS pc
MATCH (v3)-[pc]->(v4)
WHERE v4 IN nd_v1s
WITH
nd_v1s, nd_v4s,
s1s, s2s, s3s,
// calculating r1...r7
WITH
nd_v1s, nd_v4s, s1s, s2s, s3s, s4s
// r1: (𝑎⋉∇𝑑) ⋈ 𝑏 ⋈ (𝑐⋉∇𝑑)
UNWIND s1s AS s1
UNWIND s3s AS s3
WITH
nd_v1s, nd_v4s, s1s, s2s, s3s, s4s,
s1.v1 AS v1, s1.v2 AS v2, s3.v3 AS v3, s3.v4
AS v4
WHERE (v2)-[:b]->(v3)
WITH
collect([v1, v2, v3, v4]) AS r1
// r2: -(𝑎⋉∇𝑑) ⋈ 𝑏 ⋈ (Δ𝑐⋉∇𝑑)
UNWIND s1s AS s1
UNWIND s4s AS s4
WITH
r1,
AS v4
WHERE (v2)-[:b]->(v3)
WITH
r1,
// r3: -(𝑎⋉∇𝑑) ⋈ Δ𝑏 ⋈ (𝑐⋉∇𝑑)
UNWIND s1s AS s1
UNWIND s3s AS s3
WITH
r1, r2,
AS v4
MATCH (v2)-[b:b]->(v3)
WHERE b IN pbs
WITH
r1, r2,
// r4: -(Δ𝑎⋉∇𝑑) ⋈ 𝑏 ⋈ (𝑐⋉∇𝑑)
UNWIND s2s AS s2
UNWIND s3s AS s3
WITH
r1, r2, r3,
AS v4
MATCH (v2)-[b:b]->(v3)
WHERE b IN pbs
WITH
r1, r2, r3,
// r5: 𝑎 ⋈ 𝑏 ⋈ Δ𝑐 ̅⋉ 𝑑
UNWIND pcs AS pc
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[:pc]->(v4)
WITH
r1, r2, r3, r4,
// r6: 𝑎 ⋈ Δ𝑏 ⋈ 𝑐 ̅⋉ 𝑑
UNWIND pbs AS pb
MATCH (v1)-[:a]->(v2)-[:pb]->(v3)-[:c]->(v4)
WITH
r1, r2, r3, r4, r5,
// r7: Δ𝑎 ⋈ 𝑏 ⋈ 𝑐 ̅⋉ 𝑑
UNWIND pas AS pa
MATCH (v1)-[:pa]->(v2)-[:b]->(v3)-[:c]->(v4)
WITH
r1, r2, r3, r4, r5, r6,
WITH
r1 + r5 + r6 + r7 AS rp,
r2 + r3 + r4 AS rn
RETURN
[r IN rp WHERE NOT r IN rn] AS results
POSITIVE DELTA QUERY FOR 𝑎 ⋈ 𝑏 ⋈ 𝑐 ഥ⋉ 𝑑

POSITIVE DELTA QUERY FOR 𝑎 ⋈ 𝑏 ⋈ 𝑐 ഥ⋉ 𝑑
 Workaround: knowing the change workload helps
o Only consider changes in Δ𝑑 and 𝛻𝑑
o Query is cleaner and much more efficient
UNWIND $nds AS nd
WITH
startNode(nd) AS v1,
endNode(nd) AS v4
MATCH (v1)-[:a]->(v2)-[:b]->(v3)-[:c]->(v4)
 This can outperform recomputing the query from scratch.

LIST OPERATIONS IN CYPHER
Instead of subqueries, use chained queries and combine lists.
WITH [1, 1, 2, 2, 3] AS xs
UNWIND xs AS x
RETURN
collect(DISTINCT x) AS unique
WITH [1, 2, 3] AS xs, [2] AS ys
RETURN
xs + ys AS append,
[x IN xs WHERE NOT x IN ys] AS subtraction,
[x IN xs WHERE x IN ys] AS intersection
WITH [1, 1, 2, 2, 3] AS xs
RETURN
reduce(acc = [], x in xs |
acc + CASE x IN acc
WHEN false THEN [x]
ELSE []
END) AS unique
Get unique list in openCypher

DELTA QUERIES IN CYPHER
Delta queries are complex. Features that would be nice:
 Subqueries //pattern comprehensions go some length
 Named subqueries //help reusability
 Subtracting lists //related: CIR-2017-180
 Use collection elements or function results for matching
These are probably too much to ask.
-> recommended approach: compile directly to query plans.
MATCH (n)
WITH collect(n) AS ns
MATCH (ns[0])
RETURN *
MATCH (n)
WITH collect(n) AS ns
WITH ns[0] AS x
MATCH (x)
RETURN *


CHALLENGES FOR PROPERTY GRAPH QUERIES
Data model
 NF2 (Non-First Normal Form): maps, lists
 No schema (schema-optionality)
 Graph structure
Queries
 Nulls, antijoins and left outerjoins
 Updates on property values
 Aggregates on aggregates, non-distributive functions
 Ordering + skip/limit
 Reachability queries

CHALLENGES FOR PROPERTY GRAPH QUERIES
Data model
 NF2
 No schema
 Graph
Queries
 Nulls
 Updates
 Aggregates
 Ordering
 Reachability
A. Gupta, I. S. Mumick:
Materialized Views.
MIT Press, 1999
R. Chirkova, J. Yang:
Materialized Views.
Foundations and Trends
in Databases, 2012
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Decades of research -> 2 long surveys

OUR SURVEY OF RELATED IVM TECHNIQUES

DBTOASTER
 Shows the scale of the problem
 Relational data model and SQL queries
 R&D for ~5 years @ EPFL, Johns Hopkins, Cornell, etc.
 Approach
o Queries over an algebraic ring
o Higher-order recursive IVM
 Compiler in OCaml
 Backend with code generation for C++, Scala/Spark
Christoph Koch et al.:
DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views.
VLDB Journal 2014

FUTURE DIRECTIONS
 Work out derivation rules for Expand/VarExpand, …
 Automate delta query derivation
 Integrate to Neo4j
 Run performance experiments
o Train Benchmark (set semantics)
o LDBC Social Network Benchmark’s BI workload (bag semantics)

LDBC BENCHMARKS
 Social Network Benchmark
o Business Intelligence workload published
o openCypher reference implementation
o Next goal: full conference paper
 Graphalytics
o Competition is online at graphalytics.org
o Neo4j implementation (using the Graph Algorithms library) WIP
graphalytics-platforms-neo4j/pull/6
Gábor Szárnyas, Arnau Prat-Pérez, Alex Averbuch, József Marton et al.:
An early look at the LDBC Social Network Benchmark’s BI Workload.
GRADES-NDA at SIGMOD, 2018
ldbc/ldbc_snb_implementations

GRAPH ANALYTICS ON THE PANAMA PAPERS
 Network science approach: multidimensional graph metrics
from social network analysis, biology, physics, etc.
 Our work originally targeted software and system models.
 Progress in 2018
o Q1: implemented adapters for Neo4j and CSV
o Q2 goal: analyse Panama papers and using metrics
Gábor Szárnyas, Zsolt Kővári, Ágnes Salánki, Dániel Varró:
Towards the Characterization of Realistic Models:
Evaluation of Multidisciplinary Graph Metrics,
MODELS 2016 ftsrg/model-analyzer

MAPPING CYPHER TO SQL
 Evaluate graph queries in an RDB - similar to ORM
 Approaches
o Cytosm: Cypher to SQL Mapper / gTop: graph topology
o GraphGen – extracting graphs from RDBs
o Ongoing work to map TCK to SQLite
B. A. Steer, A. Alnaimi, M. Lotz, F. Cuadrado, L. Vaquero, J. Varvenne:
Cytosm: Declarative Property Graph Queries Without Data Migration.
GRADES 2017
K. Xirogiannopoulos, V. Srinivas, A. Deshpande:
GraphGen: Adaptive Graph Processing using Relational Databases.
GRADES 2017
cytosm/cytosm
KonstantinosX/graphgen-project

NEO4J APOC LIBRARY
 CSV loader that follows the schema of the neo4j-import tool
 Goal
o Use headers to generate LOAD CSV commands.
o 1st pass: CALL apoc.import.csv.node(file, labels, …)
o 2nd pass: CALL apoc.import.csv.relationship(file, type, …)
 Result
o Many corner cases -> ~700 LOC + tests
o Covers most use cases, but is very slow
o APOC PR pending
neo4j-apoc-procedures/pull/581 neo4j-documentation/pull/121

SCOPING FOR OPENCYPHER
p
p
x
c
c
slizaa-opencypher-xtext/issues/7
 Xtext grammar for the Slizaa software analysis workbench
 Progress in 2018
o Q1: scope analyser implemented for Cypher grammar of M05
o Q2 goal: update to M10

Incremental View Maintenance for openCypher Queries

Incremental View Maintenance for openCypher Queries

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