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Week 7: Neo4j Indexing and Data Modeling

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COMP5338: Advanced Data Models
Week 7: Neo4j Indexing and Data Modeling

Learning Objectives
In this week, we will use the movie data set from the build-in tutorial to observe various
query execution plans with or without index. We will also practice graph data modeling
by augmenting the original data set with tag and genre information.
Question 1: Neo4j Query plan and Indexing
Neo4j query plan and/or execution statistics can be obtained by adding explain or profile
command in front of any query. The explain command does not execute the query, you
will see a plan with estimated results size at each stage. The profile command displays
the query plan along with actual result size at each stage.
a) This question assumes that there is no index on the Movie Graph. Use the :schema
command to check available indexes and drop any that you may have created in week
6 tutorial. The following three commands illustrates query plans and estimated cost
with full nodes scan and with node label scan. If you want to run the query and get
the actual execution statistics, replace the operator explain with profile.
• Profile a query using full nodes scan
EXPLAIN
MATCH (cloudAtlas {title: “Cloud Atlas”})<-[:DIRECTED]-(directors)
RETURN directors.name
This query finds all directors who have directed something titled “Cloud Atlas”.
Since we did not specify the label of the node, the query starts with a full node
scan. It is followed by a filtering stage to find all nodes with title “Cloud Atlas”
and the nodes are saved in a temporary variable cloudAtlas. The Expand stage
follows the DIRECTED relationship of the cloudAtlas nodes to finds the directors
nodes. The Projection stage extracts only the name property of the directors
nodes.
• Profile a query with node label scan
EXPLAIN
MATCH (bacon:Person {name:”Kevin Bacon”})-[*1..4]-(hollywood)
RETURN DISTINCT hollywood
1
This query finds all things that are within 4 degrees to a Person named “Kevin
Bacon”. The query starts with all nodes with label Person with a stage called
NodeByLabelScan. It is followed by three other stages: Filter, VarLengthExpand(All)
andDistinct. In the first stage, the total number of node scanned is smaller than
that in the previous query.
• Profile a query with at least two node label scan plans
EXPLAIN
MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
WHERE “Neo” in r.roles
RETURN p.name, m.title
This query finds all person that has played the role “Neo” in some movie. The
query may start with all Movie nodes, or with all Person nodes. Because there
are a lot more Person nodes (about 133) than Movie nodes (about 40) in the
graph. The query planner picks the plan starting with all Movie nodes. The
NodeByLabelScan stage is followed by a CacheProperties stage, which reads the
title property of the Movie nodes and cache them in the current row so the
Project stage does not need to read them from store files. This is followed by an
Expand stage, a Filter stage and a Projection stage.
b) Now create the following indexes on Person and Movie nodes:
CREATE INDEX ON :Person(name);
CREATE INDEX ON :Movie(released)
Profile the following two queries to compare their execution plan.
• Profile a query using index
EXPLAIN
MATCH (bacon:Person {name:”Kevin Bacon”})-[*1..4]-(hollywood)
RETURN DISTINCT hollywood
This query is the same as the second query in the last question. The execution cost is quite different because the targeting graph has an index on the name
property now. The query execution starts with a single node having the property.
• Profile a query not using index
EXPLAIN
MATCH (bacon {name:”Kevin Bacon”})-[*1..4]-(hollywood)
RETURN DISTINCT hollywood
This query would get the same result as the previous one. Because the node
label is not specified, the index cannot be used. It starts with a full node scan.
The WHERE sub-clause can also utilize index, for instance, the execution of the following
query start with a NodeIndexSeekByRange stage:
MATCH (m:Movie) WHERE m.released > 2000 RETURN m
2
Now write a query to find out the person that have co-starred the most with Tom Hanks
and inspect the query plan.
Question 2: Neo4j Data Modeling Practice
Neo4j schema design is very similar to domain modeling. In general the main activities
involve making decisions on whether certain piece of data should be modeled as node,
relationship, property or just label/type. In this exercise, you are asked to augment
the original Movie graph in Neo4j tutorial with two additional pieces of information:
• genres: A movie may belong to one or many genres. Table 1 shows sample data of
three movies and their respective genres separated by comma.
Table 1: Sample Genre Data
Title Genres
Sleepless in Seattle Comedy, Drama, Romance
The Da Vinci Code Mystery, Thriller
Apollo 13 Adventure, Drama, History
• tags: Tags can be assigned to movies by any Person. The database needs to keep
the information of who assigns what tag(s) to which movie. Table 3 shows sample
data of tags assigned to three movies. Tags are separated by comma.
Table 2: Sample Tag Data
Person Name Movie Title Tags
Jessica Thompson Sleepless in Seattle Tom Hanks, secret
Mary Sharp Sleepless in Seattle Seattle, Romance
Angela Scope Sleepless in Seattle Tom Hanks
Jessica Thompson Apollo 13 Tom Hanks
Mary Sharp Apollo 13 Space, Tom Hanks
Jessica Thompson The Da Vinci Code Holy Grail,Priory of Sion
Angela Scope The Da Vinci Code Holy Grail, opos dei
The original movie graph contains three reviewers “Jessica Thompson”, “James Thompson” and “Angela Scope”. Each reviewed one or a few movies by giving a rating and a
review summary. You are asked to add the following extra review rating data in the graph
along with the genre and tag data:
Write Cyhper queries to update the graph using the sample data based on your design.
Then write queries to find out
1. all movies with tag “Tom Hanks”
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Table 3: Extra Review Data
Person Name Movie Title Review Rating
Jessica Thompson Sleepless in Seattle 70
Mary Sharp Sleepless in Seattle 80
Mary Sharp Apollo 13 90
Angela Scope The Da Vinci Code 75
2. The number of movies with tag “Tom Hanks”
3. all movies in the “Drama” genre
4. all the users who tagged a movie in “Drama” genre
5. The average rating of all movies in “Drama” genre
6. The average rating of each movie in “Drama” genre
7. The most frequent tag assigned to movie “Apollo 13”
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Week 7: Neo4j Indexing and Data Modeling
$30.00
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