Written by Mark Needham, originally posted on His Blog

An approach to modelling that I often see while working with Neo4j users is creating very generic relationships (e.g. HAS, CONTAINS, IS) and filtering on a relationship property or on a property/label at the end node.

Intuitively this doesn’t seem to make best use of the graph model as it means that you have to evaluate many relationships and nodes that you’re not interested in.

However, I’ve never actually tested the performance differences between the approaches so I thought I’d try it out.

I created 4 different databases which had one node with 60,000 outgoing relationships – 10,000 which we wanted to retrieve and 50,000 that were irrelevant.

I modelled the ‘relationship’ in 4 different ways…
    • Using a specific relationship type (node)-[:HAS_ADDRESS]->(address)
    • Using a generic relationship type and then filtering by end node label (node)-[:HAS]->(address:Address)
    • Using a generic relationship type and then filtering by relationship property (node)-[:HAS {type: “address”}]->(address)
    • Using a generic relationship type and then filtering by end node property (node)-[:HAS]->(address {type: “address”})
…and then measured how long it took to retrieve the ‘has address’ relationships.

The code is on github if you want to take a look.

Although it’s obviously not as precise as a JMH micro benchmark I think it’s good enough to get a feel for the difference between the approaches.

I ran a query against each database 100 times and then took the 50th, 75th and 99th percentiles (times are in ms):
Using a generic relationship type and then filtering by end node label
50%ile: 6.0    75%ile: 6.0    99%ile: 402.60999999999825
 
Using a generic relationship type and then filtering by relationship property
50%ile: 21.0   75%ile: 22.0   99%ile: 504.85999999999785
 
Using a generic relationship type and then filtering by end node label
50%ile: 4.0    75%ile: 4.0    99%ile: 145.65999999999931
 
Using a specific relationship type
50%ile: 0.0    75%ile: 1.0    99%ile: 25.749999999999872

We can drill further into why there’s a difference in the times for each of the approaches by profiling the equivalent cypher query. We’ll start with the one which uses a specific relationship name

Using a specific relationship type

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS_ADDRESS]->() return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +SimplePatternMatcher
      |
      +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+-----------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                 Other |
+----------------------+-------+--------+-----------------------------+-----------------------+
|         ColumnFilter |     1 |      0 |                             | keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                       |
| SimplePatternMatcher | 10000 |  10000 | n,   UNNAMED53,   UNNAMED35 |                       |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |          {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+-----------------------+
 
Total database accesses: 10001
Here we can see that there were 10,002 database accesses in order to get a count of our 10,000 HAS_ADDRESS relationships. We get a database access each time we load a node, relationship or property.

By contrast the other approaches have to load in a lot more data only to then filter it out:

Using a generic relationship type and then filtering by end node label

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS]->(:Address) return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+----------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                            Other |
+----------------------+-------+--------+-----------------------------+----------------------------------+
|         ColumnFilter |     1 |      0 |                             |            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                  |
|               Filter | 10000 |  10000 |                             | hasLabel(  UNNAMED45:Address(0)) |
| SimplePatternMatcher | 10000 |  60000 | n,   UNNAMED45,   UNNAMED35 |                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+----------------------------------+
 
Total database accesses: 70001


Using a generic relationship type and then filtering by relationship property

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS {type: "address"}]->() return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                                            Other |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|         ColumnFilter |     1 |      0 |                             |                            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                                  |
|               Filter | 10000 |  20000 |                             | Property(  UNNAMED35,type(0)) == {  AUTOSTRING1} |
| SimplePatternMatcher | 10000 | 120000 | n,   UNNAMED63,   UNNAMED35 |                                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
 
Total database accesses: 140001


Using a generic relationship type and then filtering by end node property

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS]->({type: "address"}) return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                                            Other |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|         ColumnFilter |     1 |      0 |                             |                            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                                  |
|               Filter | 10000 |  20000 |                             | Property(  UNNAMED45,type(0)) == {  AUTOSTRING1} |
| SimplePatternMatcher | 10000 | 120000 | n,   UNNAMED45,   UNNAMED35 |                                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
 
Total database accesses: 140001
So in summary…specific relationships #ftw!


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About the Author

Mark Needham, Developer Relations Engineer

Mark Needham Image

Mark Needham is a graph advocate and developer relations engineer at Neo4j.

As a developer relations engineer, Mark helps users embrace graph data and Neo4j, building sophisticated solutions to challenging data problems. Mark previously worked in engineering on the clustering team, helping to build the Causal Clustering feature released in Neo4j 3.1. Mark writes about his experiences of being a graphista on a popular blog at markhneedham.com. He tweets at @markhneedham.


3 Comments

Vinny says:

Hi Mark Needham,

Based on your test option 1: “Using a specific relationship type (node)-[:HAS_ADDRESS]->(address)” seems to be the best approach. Would this change if we are using parameterized queries and allow Neo4j to cache a query plan.
10k accesses – Using a specific relationship type (node)-[:HAS_ADDRESS]->(address)
70k accesses – Using a generic relationship type and then filtering by end node label (node)-[:HAS]->(address:Address)
140k accesses – Using a generic relationship type and then filtering by relationship property (node)-[:HAS {type: “address”}]->(address)
140k accesses – Using a generic relationship type and then filtering by end node property (node)-[:HAS]->(address {type: “address”})

Daniel Gabriele says:

Thanks. This is exactly what I wanted to find out 🙂

[…] we have a Group node type, we can start using specific relationships (e.g., BELONGS_TO_GROUP) since they are more performant than using generic relationships and filtering by end node labels, end node properties, or […]

Grant says:

In your benchmark, you have `Using a generic relationship type and then filtering by end node label` listed twice but never list `Using a generic relationship type and then filtering by relationship property`. Do you still have the data available to update which is which?

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