Graph Databases Marco Serafini COMPSCI 532 Lecture 10 Graph DB - - PowerPoint PPT Presentation

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Graph Databases Marco Serafini COMPSCI 532 Lecture 10 Graph DB - - PowerPoint PPT Presentation

Nov 18, 2023 137 likes •324 views

Graph Databases Marco Serafini COMPSCI 532 Lecture 10 Graph DB Use cases Social network queries E.g. Facebook stores the entire metadata in a social graph Network security Find sequence of steps that lead to intrusion

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Graph Databases

Marco Serafini

COMPSCI 532 Lecture 10

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Graph DB Use cases

  • Social network queries
  • E.g. Facebook stores the entire metadata in a social graph
  • Network security
  • Find sequence of steps that lead to intrusion
  • Fraud detection
  • Find fraud rings
  • Knowledge bases
  • Answer questions, language models
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Resource Description Framework

  • World Wide Web Consortium specification
  • Used for the Semantic Web
  • Web pages define human-readable content
  • Goal: add machine-readable meta-data describing how

pages relate

  • Format to reuse and share data across the Web
  • Examples
  • Wikipedia, census, life sciences, DBPedia
  • Directed labeled multi-graph
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RDF Format

  • Graph is set of triplets = (Subject, Predicate, Object)
  • Subject and predicate are resources
  • Associated with Unique Resource Identifiers (URI)
  • Object can be resource or literal (string)

From S. Decker et al., “Framework for the Semantic Web: An RDF Tutorial”

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Query Language: SPARQL

  • Declarative
  • Defines a query graph
  • RDF store must find all instances in data graph
  • Example
  • “Return friends of user alice01 who live in Paris”

PREFIX sn: http://socialnetwork.com/ontology/ SELECT ?friend WHERE { ?user sn:hasName “alice01”; sn:isFriendOf ?friend. ?friend sn:livesIn sn:Paris. }

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Property Graph Format

  • Vertices and edges can have associated properties
  • Key-value pairs
  • Vertices can be grouped by label
  • Similar to tables, e.g., employees
  • Properties are similar to columns of a table
  • Not a “global” format: no URIs required
  • Typical more compact than RDFs
  • Common is NoSQL graph databases
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Query Languages

  • Cypher
  • Originally used by Neo4j
  • Linear queries
  • Previous example in Cypher

MATCH (u:User)-[:isFriend]->(f:User)–[:livesIn]->(:City {name: ‘Paris’}) WHERE (u.name = ‘Alice’) RETURN f.name

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Relational Representation of Graphs

  • Graphs is a relational DBMS
  • Vertex table, edge table
  • Sometimes edges as triplets
  • Pattern matching
  • Maintain a set of partial matches
  • Extend by edge: self-join on edge table
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Why are Graph Workloads Hard?

  • Many joins: difficult to estimate cardinality
  • Joins require random access
  • Cardinality estimation gets harder at every join
  • Skew: few vertices have very high degree
  • Indexing
  • Adjacency list scans are very frequent
  • Graph-aware databases optimize these
  • Some queries have very low selectivity
  • E.g. triangle closure (potential friends)
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Worst-Case Optimal Joins

  • Worst-Case Optimality
  • O(intermediate results) <= O(final results)
  • Edge-at-a-time approach is not worst-case optimal
  • Number of triangles: O(|E|3/2)
  • Number of wedges: O(|E|2)
  • Vertex-at-a-time (multi-way-joins) are WCO
  • (v1,v2), (v1,v2,v3), (v1,v2,v3, v4), …
  • Will not materialize all wedges
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Subgraph Isomorphism (TurboISO)

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SubTask 1 Match spanning tree from one starting vertex SubTask 2 Match cross-edges

100 100 heavyweight 104 subgraphs * 2 edge lookups v v single starting vertex multiple matching vertices 10 10 10 10 10*10 lightweight 220 edge lookups 10*10

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TurboISO: Flexible Join Order

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Hard to Parallelize

13 Running time (ms)

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Subgraph Enumeration

  • Count all instances of an unlabeled pattern
  • E.g. triangles, squares, cliques
  • Important to rule out permutations
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Reachability Queries

  • Given two vertices v and u
  • Find (and/or rank) paths connecting them
  • Simplest approach: parallel BFS from both vertices
  • Expensive
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Dynamic Graphs

  • Temporal Analysis à Deal with multiple snapshots
  • Real-Time analytics à Work on live graph data
  • Storage implications

UPDATES TRANSACTIONAL SYSTEM ANALYTICAL SYSTEM RESULTS LOAD DYNAMIC DATA STRUCTURE + TRANSACTIONS READ-ONLY DATA STRUCTURE NO TRANSACTIONS E.g.: B-Tree, LSMT E.g.: CSR

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Graph Storage for RT Analytics

  • Sequential adjacency list scan is important
  • CSR: Sequential scan but read-only
  • TEL: LOG-based adjacency list

220 221 222 223 224 225 226 graph scale, V

0.01 0.1 1 10

cache miss/edge

TEL LSMT B+Tree Linked List

220 221 222 223 224 225 226 graph scale, V

0.1 1 10 100

µs/vertex (seeks)

TEL LSMT B+Tree Linked List

Cache misses Seek time Edge scan

220 221 222 223 224 225 226 graph scale, V

10 100 1000

ns/edge (scan)

TEL LSMT B+Tree Linked List

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Open Issues

  • Graph analytics algorithms are diverse
  • Still looking for good APIs
  • There is no “SQL for graphs”
  • Hard to leverage hardware characteristics
  • Scale out to distributed systems: Hard because of edge cut
  • SIMD: hard because of skew and random access
  • Caching: hard because of random access