Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing. During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.

1. © Prof. Dr.-Ing. Wolfgang Lehner |
Challenges in the Design of a Graph Database
Benchmark
FOSDEM‘12 – Graph Processing DevRoom
Marcus Paradies

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> Outline
 Motivation
 Challenges
 Thoughts on Graph Data Generation
 Thoughts on Query Workload
 Summary and Outlook
 Discussion
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> Motivation
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 Graph databases are gaining momentum
 Enterprise corporations are getting interested
 How to compare the available graph database vendors?
 Main issue: Results from benchmarks are not comparable
 Lack of standardization in the data model and query language
 What are “typical“ graph operations?

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>
Challenges
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> Challenge #1: Application Domain
 Graph data is not homogenous
 Graph data from different domains follows different patterns
 Examples:
 Social Network Analysis (SNA)
 Protein Interaction Analysis
 Recommendation Systems
 Supply Chain Management (Vehicle Routing, CRM)
 Fraud Detection in Financial Systems
 …
Challenge: Find an application domain which represents a graph data pattern
common in many different scenarios.
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> Challenge #2: Graph Data Model
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What flavours of graph data models
are commonly used?

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> Challenge #2: Graph Data Model
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Directed Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph Multi Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph Multi Graph
(Plain) Property
Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph Multi Graph
(Plain) Property
Graph
(Structured
Property Graph)

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph Multi Graph
(Plain) Property
Graph
(Structured
Property Graph)
Hyper Graph

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> Challenge #2: Graph Data Model
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Directed Graph
Undirected Graph
Mixed Graph Multi Graph
(Plain) Property
Graph
(Structured
Property Graph)
Hyper Graph
Challenge: Find a graph data model suited for the majority of use cases
from various domains.

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> Challenge #3: Querying Graph Data
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 Large variety in graph processing and manipulation languages
 Each graph database vendor implements own query languages/APIs
 Reason: No standardized graph query language available

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> Challenge #3: Querying Graph Data
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 Large variety in graph processing and manipulation languages
 Each graph database vendor implements own query languages/APIs
 Reason: No standardized graph query language available
Challenge: Find a way to abstract from the zoo of available query languages.

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> Challenge #4: Defining the Workload
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 The workload to be defined is dependent from the underlying
query/manipulation language
 Should complex (algorithmic) operations be part of a database benchmark?
 Which algorithms to pick?
 Social Network Analysis → Find communities
 Supply Chain Management → Find maximal flow
 Web of Data → Find pattern matches
 How are concurrent users represented?
 What about transactionality?

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>
Thoughts on Graph Data Generation
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> Graph Data Generation - Patterns
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 Understanding graph patterns (characteristics) is crucical for a good graph
data generator
 What are distinguishing characteristics of graphs?
 How can we identify graph patterns on large graphs?
 Three main patterns [1]:
 Power law distributed
 Small diameters
 Community Effects
?
=
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> Pattern 1 – Power law distributed
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 Most real-world graph data sets follow a power law distribution
 Examples:
 Internet router graph
 Subsets of the WWW
 Citation Graphs
source: [2] source: [2]

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> Pattern 2 – Small Diameters
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 Effective Diameter (eccentricity): Minimum number of hops, in which a
fraction (e.g. 90%) of all connected pairs of nodes can reach each other
 Other measures exist as well, but are not applicable to disconnected graphs
 In most use cases, diameter is much smaller than the size of the graph
 Examples:
 97% eccentricity of around 16 for path lengths in the WWW
 Average path length around 6 for Epinions social network
source: [1]

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> Pattern 3 – Community Effects
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 Community: A set of nodes, where each node in the set is closer to all other
nodes in the community than to nodes outside the community.
 Communities can be found in many real-world graphs, especially social
networks and collaboration networks
 Clustering Coefficient C: A measure, which qualifies the „clumpiness“ of a
graph

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>
Thoughts on Query Workload
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> Query Workload - Operations
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 Graph Manipulation Operations
 Add/Update/Remove Nodes from the Graph
 Add/Update/Remove Edges from the Graph
 Add/Update/Remove Edge attributes
 Add/Update/Remove Node attributes
 Graph Query Operations
 Retrieve selection of nodes from given filter expression
 Getting the neighbors of a set of nodes (possibly with edge filter constraints)
 Graph Traversals
 Based on basic query operations
 Exploration of neighborhood from a given set of start nodes
 Terminated by the number of steps and/or edge/node filter constraints
 Graph Analytical Operations
 Aggregation operations such as sum, avg, min, max
 Aggregations on node-level and on edge-level

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> Query Workload - Measures
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 Closely related to benchmark capabilities
 Measures from relational benchmarks apply such as
 Average query response time
 Transactions per second (throughput)
 Additional measures for graph traversals
 Traversals per second
 What about distributed scenarios?
 What about concurrent users?

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> Summary and Outlook
 Graph data distribution highly important for graph database benchmark
 Application domains do have very specific graph characteristics
 A graph database benchmark has to provide abstract and high-level graph
operation descriptions
 Feel free to contact me if you want to contribute:
marcus.paradies@gmail.com
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>
Discussion
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> Theses
 A benchmark based on social network data is nice, but might be not be that
representative for large enterprise applications
 Algorithms should NOT be part of a graph database benchmark
 Only support basic operations such as simple lookups and path traversals
 The underlying graph data model should be a simple property graph
 A graph database has to scale in terms of data size as well as number of
concurrent users
 ....
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> References
[1] Graph Mining: Laws, Generators, and Algorithms (2006)
[2] http://konect.uni-koblenz.de/
[3] A Discussion on the Design of Graph Database Benchmarks (2010)
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