challenges in the design of a graph database benchmark

© Prof. Dr.-Ing. Wolfgang Lehner |

Challenges in the Design of a Graph Database Benchmark FOSDEM‘12 – Graph Processing DevRoom

Marcus Paradies

Marcus Paradies | | 1

> 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?


>

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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Directed Graph



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Directed Graph

Undirected Graph



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Directed Graph

Undirected Graph

Mixed Graph



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Directed Graph

Undirected Graph

Mixed Graph Multi Graph



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Directed Graph

Undirected Graph


(Plain) Property Graph



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Directed Graph

Undirected Graph



(Structured Property Graph)



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Directed Graph

Undirected Graph




Hyper Graph



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Directed Graph

Undirected Graph




Hyper Graph

Challenge: Find a graph data model suited for the majority of use cases

from various domains.


> 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 #3: Querying Graph Data

FOSDEM 2012

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.


> 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?


>

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]


> 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]


> 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


>

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


> 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?


> 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:

[email protected]

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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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http://konect.uni-koblenz.de/




challenges in the design of a graph database benchmark

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