Database Reference
In-Depth Information
Manyofthesetoolsserveanextremelyspeciicpurpose,andevenusespeciic
hardware and software (usually using lots of memory and CPU horsepower) to
achieve their tasks, and typically are part of a very different side of the IT architecture.
Graph processing is typically done in batches, in the background, over the course of
severalhours/days/weeksandwouldtypicallynotbewellplacedbetweenaweb
request and a web response. It's a very different kind of ball game.
Simple, aggregate-oriented queries
We mentioned that graphs and graph database management systems are great
for complex queries—things that would make your relational system choke. As a
consequence, simple queries, where write patterns and read patterns align to the
aggregatesthatwearetryingtostore,aretypicallyservedquiteineficientlyina
graph,andwouldbemoreeficientlyhandledbyanaggregate-orientedKey-Value
or Document store. If complexity is low, the advantage of using a graph database
system will be lower too.
Hopefully, this gives you a better view of the things that graph databases are good
and not so good at.
Test questions
Q1.Whichothercategoryofdatabasesbearsthemostresemblancetographdatabases?
1.
Navigational databases.
2.
Relational Databases.
3.
Column-Family stores.
4.
None; graph databases are unique.
Q2. The data model of graph databases is often described as the proprietary graph
data model, containing nodes, relationships, and proprietary elements.
1.
True.
2.
False.
Q3. Simple, aggregate-oriented queries yielding a list of things are a great application
for a graph database.
1.
True.
2.
False.
