I have a small dataset loaded into Neo4J consisting of a 6 node labels with about 20 nodes for each label and there are about 10 different relationships. I was wondering if you can automatically create a picture of this data model using the data available in the database.
I would like to create something like this automatically from the data:
taken from http://neo4j.com/docs/stable/cypherdoc-movie-database.html
I know that it would be quite simple doing it manually in this example but it could come in handy looking at more complex data models.
Any suggestions?
Thank you Michael, that helped. There is also functionality in the web tool that ships with Neo4J that can do something similar although less graphically.
You click on the little bubbles in the top left corner of the interface and then there is a predefined query that extracts all lables and relations from the graph.
Related
I have 8 different datasets with the same structure. I am using Neo4j and need to query all of them at different points on the website I am developing. What would be the approaches at storing the datasets in one database?
One idea that comes to my mind is to supply for each node an additional property that would distinguish nodes of one dataset from nodes of the other ones. But that seems too repetitive and wrong for me. The other idea is just to create 8 databases and query them separately but how could I do that? Running each one in its own port seems crazy.
Any suggestions would be greatly appreciated.
If your datasets are in a tree structure, you could add a different root node to each of them that you could use for reference, similar to GraphAware TimeTree. Another option (better than a property, I think) would be to differentiate each dataset by adding a specific label to nodes from that dataset (i.e. all nodes from "dataset A" get a :DataSetA label)
I imagine that the specific structure of your dataset may yield other options. For example, if you always begin traversals of the dataset from a few set locations, you only need to be able to determine which dataset the entry points are a part of, because once entered, all traversals would be made within the same dataset <-- if that makes sense.
How to extract an entity relationship diagram from a graph database? I have all the required files that was created from my application.
You can use
call db.schema for a graph representation of the graph data model. There are a few other functions to get the properties, keys, indexes, etc like call db.indexes, call db.propertykeys etc.
The APOC procedure library has a few relevant functions that might help to get a tabular layout - or develop it yourself in Excel from the labels, property keys, etc.
You can also build a data model using the Arrows tool
Please reorient your thinking to use graph terms - the equivalent for the ER diagram would be a model built using the Arrows tool or the db.schema.
I used: CALL db.schema.visualization for visualizing the database schema. Like https://stackoverflow.com/a/45357049/7924573 already said, in graph databases this is as closest as you can to ER-diagrams. In the remote interface you can export it directly as e.g. .svg graphic
Here is an example:
(I am new to Neo4J and very excited about it)
Here is my conceptual question:
Suppose we want to represent life on earth (based on a biological taxonomy hierarchy).
However, suppose at the leaves of the taxonomy tree we want to actually identify individual organisms. For example, at the mammalia branch, the homo-sapient sub-branch we want to identify each and every one of 7 billion humans and do the same for some other branches (give an ID to every living known great Ape left in the wild and so on)
Is this type of organization done with dense nodes (in the billions) ? or is it done with extensive use of labels (do labels support nesting)?
From my point of view it's better to use multiple nodes instead of multiple labels.
But it depends on the use case and what you want to do with it.
Neo4j doesn't support nested labels or some labels hierarchy.
Here are some resources which could be interesting for you
Graph Databases in Life Sciences: Bringing Biology Back to Its Nature
Open Tree of Life and Neo4j
Anybody know of any Graph DB's that support time series data?
Ideally we're looking for one that will scale well, and ideally use Cassandra or HBase as their persistent store.
Why would you want to do that? Best practice would be to store the dependency graph (in other words, the "Model" of the time series data) in a graphdb, but the actual time series in something more suited to that. Eg a KV store or a log-specific tool like Splunk...
See the KNMI (Dutch Weather Service) example for a case study: http://vimeopro.com/neo4j/graphconnect-europe-2015/video/128351859
Cheers!
Rik
One convenient way of doing that is to build a tree structure, with a common root, years as children, months as children of each year and down to the desired granularity.
At the end you attach the events nodes as leaf to this tree, and that give you the possibility to make many types of queries, from single point in time, to ranges, and also reverse, from the event to the timestamp...
Here is an example of this concept and an implementation within Neo4j
We are working on a system where users can define their own nodes and connections, and can query them with arbitrary queries. A user can create a "branch" much like in SCM systems and later can merge back changes into the main graph.
Is it possible to create an efficient data model for that in Neo4j? What would be the best approach? Of course we don't want to duplicate all the graph data for every branch as we have several million nodes in the DB.
I have read Ian Robinson's excellent article on Time-Based Versioned Graphs and Tom Zeppenfeldt's alternative approach with Network versioning using relationnodes but unfortunately they are solving a different problem.
I Would love to know what you guys think, any thoughts appreciated.
I'm not sure what your experience level is. Any insight into that would be helpful.
It would be my guess that this system would rely heavily on tags on the nodes. maybe come up with 5-20 node types that are very broad, including the names and a few key properties. Then you could allow the users to select from those base categories and create their own spin-offs by adding tags.
Say you had your basic categories of (:Thing{Name:"",Place:""}) and (:Object{Category:"",Count:4})
Your users would have a drop-down or something with "Thing" and "Object". They'd select "Thing" for instance, and type a new label (Say "Cool"), values for "Name" and "Place", and add any custom properties (IsAwesome:True).
So now you've got a new node (:Thing:Cool{Name:"Rock",Place:"Here",IsAwesome:True}) Which allows you to query by broad categories or a users created categories. Hopefully this would keep each broad category to a proportional fraction of your overall node count.
Not sure if this is exactly what you're asking for. Good luck!
Hmm. While this isn't insane, think about the type of system you're replacing first. SQL. In SQL databases you wouldn't use branches because it's data storage. If you're trying to get data from multiple sources into one DB, I'd suggest exporting them all to CSV files and using a MERGE statement in cypher to bring them all into your DB at once.
This could manifest similar to branching by having each person run a script on their own copy of the DB when you merge that takes all the nodes and edges in their copy and puts them all into a CSV. IE
MATCH (n)-[:e]-(n2)
RETURN n,e,n2
Then comparing these CSV's as you pull them into your final DB to see what's already there from the other copies.
IMPORT CSV WITH HEADERS FROM "file:\\YourFile.CSV" AS file
MERGE (N:Node{Property1:file.Property1, Property2:file.Property2})
MERGE (N2:Node{Property1:file.Property1, Property2:file.Property2})
MERGE (N)-[E:Edge]-(N2)
This will work, as long as you're using node types that you already know about and each person isn't creating new data structures that you don't know about until the merge.