I'm new to neo4j and graph databases in general.
Given a complex Cypher query, that I don't want to store inside the application (or several applications), but keep centralized, what options are left to me?
In a SQL database I would use a stored function. Are UDF function the way to go in neo4j?
From the docs it seems to me that they're more a way to extend the database functionality by being able to access the graph internals, but I've just started studying them.
Take a look at the custom functions and procedures available in the apoc library.
https://neo4j.com/docs/labs/apoc/current/cypher-execution/cypher-based-procedures-functions/
CALL apoc.custom.asProcedure('answer','RETURN 42 as answer')
CALL custom.answer() YIELD row RETURN row.answer
Related
I have a list of IPs that I want to filter out of many queries that I have in sumo logic. Is there a way to store that list of IPs somewhere so it can be referenced, instead of copy pasting it in every query?
For example, in a perfect world it would be nice to define a list of things like:
things=foo,bar,baz
And then in another query reference it:
where mything IN things
Right now I'm just copying/pasting. I think there may be a way to do this by setting up a custom data source and putting the IPs in there, but that seems like a very round-about way of doing it, and wouldn't help to re-use parts of a query that aren't data (eg re-use statements). Also their template feature is about parameterizing a query, not re-use across many queries.
Yes. There's a notion of Lookup Tables in Sumo Logic. Consult:
https://help.sumologic.com/docs/search/lookup-tables/create-lookup-table/
for details.
It allows to store some values (either manually once, or in a scheduled way as as a result of some query) with | save operator.
And then you can refer to these values using | lookup which is conceptually similar to SQL's JOIN.
Disclaimer: I am currently employed by Sumo Logic.
how we can do data join on a common column in graphQL.
For example in SQL : Select t.name and z.address where t.id=z.id;
how is this managed by graphQL query.
How is this managed by GraphQL query?
The short answer: It's not (managed). You have to write it in your resolvers.
Your GraphQL schema is basically just a declaration of types that represent nodes, and fields that represent edges in a graph. GraphQL is the language to query this graph. At each edge in your schema, you write a function for how to get the data when the query traverses that edge. This function is called a "resolver", and you can put pretty much any code in your resolvers as long as it returns valid data. This means GraphQL is completely, absolutely database-independent. Your resolvers are responsible for talking to your database(s).
So when it comes to SQL, and joins, the concern of making the queries is entirely the responsibility of the API developer. GraphQL knows nothing about SQL or joins, so this is essentially custom logic for your application which you must write. It turns out that building SQL queries to resolve data for GraphQL queries is very difficult without running into problems like eagerly fetching too much data or the "N+1" problem.
Some tools in the open-source community have emerged to help solve this problem. Here are a couple I would recommend in the Node.js space:
DataLoader - A database-agnostic tool that batches queries and caches individual records.
Join Monster - A SQL-tailored tool for efficient data-retrieval and query batching. It examines each query and your schema and generates SQL queries dynamically.
Disclaimer: I'm a co-creator of Join Monster.
I've got my graph database, populated with nodes, relationships, properties etc. I'd like to see an overview of how the whole database is connected, each relationship to each node, properties of a node etc.
I don't mean view each individual node, but rather something like an ERD from a relational database, something like this, with the node labels. Is this possible?
You can use the metadata by running the command call db.schema().
In Neo4j v4 call db.schema() is deprecated, you can now use call db.schema.visualization()
As far as I know, there is no straight-forward way to get a nicely pictured diagram of a neo4j database structure.
There is a pre-defined query in the neo4j browser which finds all node types and their relationships. However, it traverses the complete graph and may fail due to memory errors if you have to much data.
Also, there is neoprofiler. It's a tool which claims to so what you ask. I never tried and it didn't get too many updates lately. Still worth a try: https://github.com/moxious/neoprofiler
Even though this is not a graphical representation, this query will give you an idea on what type of nodes are connected to other nodes with what type of relationship.
MATCH (n)
OPTIONAL MATCH (n)-[r]->(x)
WITH DISTINCT {l1: labels(n), r: type(r), l2: labels(x)}
AS `first degree connection`
RETURN `first degree connection`;
You could use this query to then unwind the labels to write that next cypher query dynamically (via a scripting language and using the REST API) and then paste that query back into the neo4j browser to get an example set of the data.
But this should be good enough to get an overview of your graph. Expand from here.
Can u please share any links/sample source code for generating the graph using neo4j from Oracle database tables data .
And my use case is oracle schema table names as Nodes and columns are properties. And also need to genetate graph in tree structure.
Make sure you commit the transaction after creating the nodes with tx.success(), tx.finish().
If you still don't see the nodes, please post your code and/or any exceptions.
Use JDBC to extract your oracle db data. Then use the Java API to build the corresponding nodes :
GraphDatabaseService db;
try(Transaction tx = db.beginTx()){
Node datanode = db.createNode(Labels.TABLENAME);
datanode.setProperty("column name", "column value"); //do this for each column.
tx.success();
}
Also remember to scale your transactions. I tend to use around 1500 creates per transaction and it works fine for me, but you might have to play with it a little bit.
Just do a SELECT * FROM table LIMIT 1000 OFFSET X*1000 with X being the value for how many times you've run the query before. Then keep those 1000 records stored somewhere in a collection or something so you can build your nodes with them. Repeat this until you've handled every record in your database.
Not sure what you mean with "And also need to genetate graph in tree structure.", if you mean you'd like to convert foreign keys into relationships, remember to just index the key and in stead of adding the FK as a property, create a relationship to the original node in stead. You can find it by doing an index lookup. Or you could just create your own little in-memory index with a HashMap. But since you're already storing 1000 sql records in-memory, plus you are building the transaction... you need to be a bit careful with your memory depending on your JVM settings.
You need to code this ETL process yourself. Follow the below
Write your first Neo4j example by following this article.
Understand how to model with graphs.
There are multiple ways of talking to Neo4j using Java. Choose the one that suits your needs.
I am trying to optimize requests in Gremlin on a Neo4J graph.
Here is the short version of the basic request I am using:
g.idx("myIndex")[[myId:5]].outE("HAS_PRODUCT").filter{it.shop_id:5}.inV
So I looked into indexation and got to create an index on "HAS_PRODUCT"-typed edges with key 'shop_id'.
Using the same request, I don't see a big difference.
My questions are:
Is my new index used when I query with: filter{it.shop_id:5}
If not, how can I use this new index in my request?
More generally, if idx( is the graph method to use an index, is there a pipe method for that?
Thanks!
The short answer is that Gremlin won't make use of the secondary index when using Neo4j, but please consider reading the longer answer below in relation to TinkerPop, Gremlin and its philosophy.
The longer answer is....Indices are not being used for your shop_id. When you call outE you are effectively iterating all the edges to find those with shop_id == 5. To make use of indices in Gremlin you should use a vertex query. So, rewriting your code a bit (to also use key indices) would be like:
g.V('myIndex',5).outE('HAS_PRODUCT').has('shop_id',5).inV
With Blueprints implementations that support vertex-centric indices, the use of has will utilize that index automatically. Unfortunately, Neo4j is not one of those databases yet. Blueprints implementations that do implement it, include Titan (see Vertex-centric indices) and OrientDB (as part of the yet unreleased Blueprints 2.4.0...I believe they will have a partial implementation in that release) in that case.