I am importing the following to Neo4J:
categories.csv
CategoryName1
CategoryName2
CategoryName3
...
categories_relations.csv
category_parent category_child
CategoryName3 CategoryName10
CategoryName32 CategoryName41
...
Basically, categories_relations.csv shows parent-child relationships between the categories from categories.csv.
I imported the first csv file with the following query which went well and pretty quickly:
USING PERIODIC COMMIT
LOAD CSV FROM 'file:///categories.csv' as line
CREATE (:Category {name:line[0]})
Then I imported the second csv file with:
USING PERIODIC COMMIT
LOAD CSV FROM 'file:///categories_relations.csv' as line
MATCH (a:Category),(b:Category)
WHERE a.name = line[0] AND b.name = line[1]
CREATE (a)-[r:ISPARENTOF]->(b)
I have about 2 million nodes.
I tried executing the 2nd query and it is taking quite long. Can I make the query execute more quickly?
Confirm you are matching on right property. You are setting only one property for Category node i.e. name while creating
categories. But you are matching on property id in your second
query to create the relationships between categories.
For executing the 2nd query faster you can add an index on the property (here id) which you are matching Category nodes on.
CREATE INDEX ON :Category(id)
If it still takes time, You can refer my answer to Load CSV here
Related
I've got two csv files Job (30,000 entries) and Cat (30 entries) imported into neo4j and am trying to create a relationship between them
Each Job has a cat_ID and Cat contains the category name and ID
after executing the following
LOAD CSV WITH HEADERS FROM 'file:///DimCategory.csv' AS row
MATCH (job:Job {cat_ID: row.cat_ID})
MATCH (cat:category {category: row.category})
CREATE (job)-[r:under]->(cat)
it returns (no changes, no records)
I received a prompt recommending that I index the category and so using
Create INDEX ON :Job(cat_id); I did, but I still get the same error
How do I create a relationship between the two?
I am able to get this to work on a smaller dataset
You are probably trying to match on non-existing nodes. Try
LOAD CSV WITH HEADERS FROM 'file:///DimCategory.csv' AS row
MERGE (job:Job {cat_ID: row.cat_ID})
MERGE (cat:category {category: row.category})
CREATE (job)-[r:under]->(cat)
Have a look in your logs and see if you are running out of memory.
You could try chunking the data set up into smaller pieces with Periodic Commit and see if that helps:
:auto USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM 'file:///DimCategory.csv' AS row
MATCH (job:Job {cat_ID: row.cat_ID})
MATCH (cat:category {category: row.category})
CREATE (job)-[r:under]->(cat)
I am loading simple csv data into neo4j. The data is simple as follows :-
uniqueId compound value category
ACT12_M_609 mesulfen 21 carbon
ACT12_M_609 MNAF 23 carbon
ACT12_M_609 nifluridide 20 suphate
ACT12_M_609 sulfur 23 carbon
I am loading the data from the URL using the following query -
LOAD CSV WITH HEADERS
FROM "url"
AS row
MERGE( t: Transaction { transactionId: row.uniqueId })
MERGE(c:Compound {name: row.compound})
MERGE (t)-[r:CONTAINS]->(c)
ON CREATE SET c.category= row.category
ON CREATE SET r.price =row.value
Next I do the aggregation to count total orders for a compound and create property for a node in the following way -
MATCH (c:Compound) <-[:CONTAINS]- (t:Transaction)
with c.name as name, count( distinct t.transactionId) as ord
set c.orders = ord
So far so good. I can accomplish what I want but I have the following 2 questions -
How can I create the orders property for compound node in the first step itself? .i.e. when I am loading the data I would like to perform the aggregation straight away.
For a compound node I am also setting the property for category. Theoretically, it can also be modelled as category -contains-> compound by creating Categorynode. But what advantage will I have if I do it? Because I can execute the queries and get the expected output without creating this additional node.
Thank you for your answer.
I don't think that's possible, LOAD CSV goes over one row at a time, so at row 1, it doesn't know how many more rows will follow.
I guess you could create virtual nodes and relationships, aggregate those and then use those to create the real nodes, but that would be way more complicated. Virtual Nodes/Rels
That depends on the questions/queries you want to ask.
A graph database is optimised for following relationships, so if you often do a query where the category is a criteria (e.g. MATCH (c: Category {category_id: 12})-[r]-(:Compound) ), it might be more performant to create a label for it.
If you just want to get the category in the results (e.g. RETURN compound.category), then it's fine as a property.
I have 2 csv files which I am trying to load into a Neo4j database using cypher: drivers.csv which holds every formula 1 driver and lap times.csv which stores every lap ever raced in F1.
I have managed to load in all of the nodes, although the lap times file is very large so it took quite a long time! I then tried to add relationships after, but there is so many that needs to be added that I gave up on it waiting (it was taking multiple days and still had not loaded in fully).
I’m pretty sure there is a way to load in the nodes and relationships at the same time, which would allow me to use periodic commit for the relationships which I cannot do right now. Essentially I just need to combine the 2 commands into one and after some attempts I can’t seem to work out how to do it?
// load in the lap_times.csv, changing the variable names - about half million nodes (takes 3-4 days)
PERIODIC COMMIT 25000
LOAD CSV WITH HEADERS from 'file:///lap_times.csv'
AS row
MERGE (lt: lapTimes {raceId: row.raceId, driverId: row.driverId, lap: row.lap, position: row.position, time: row.time, milliseconds: row.milliseconds})
RETURN lt;
// add a relationship between laptimes, drivers and races - takes 3-4 days
MATCH (lt:lapTimes),(d:Driver),(r:race)
WHERE lt.raceId = r.raceId AND lt.driverId = d.driverId
MERGE (d)-[rel8:LAPPING_AT]->(lt)
MERGE (r)-[rel9:TIMED_LAP]->(lt)
RETURN type(rel8), type(rel9)
Thanks in advance for any help!
You should review the documentation for indexes here:
https://neo4j.com/docs/cypher-manual/current/administration/indexes-for-search-performance/
Basically, indexes, once created, allow quick lookups of nodes of a given label, for the given property or properties. If you DON'T have an index and you do a MATCH or MERGE of a node, then for every row of that MATCH or MERGE, it has to do a label scan of all nodes of the given label and check all of their properties to find the nodes, and that becomes very expensive, especially when loading CSVs because those operations are likely happening for each row in the CSV.
For your :lapTimes nodes (though we would recommend you use singular labels in most cases), if there are none of them in your graph to start with, then a CREATE instead of a MERGE is fine. You may want a composite index on :lapTimes(raceId, driverId, lap), since that should uniquely identify the node, if you need to look it up later. Using CREATE instead of MERGE here should process much much faster.
Your second query should be MATCHing on :lapTimes nodes (label scan), and from each doing an index lookup on the :race and :driver nodes, so indexes are key here for performance.
You need indexes on: :race(raceId) and :Driver(driverId).
MATCH (lt:lapTimes)
WITH lt, lt.raceId as raceId, lt.driverId as driverId
MATCH (d:Driver), (r:race)
WHERE r.raceId = raceId AND d.driverId = driverId
MERGE (d)-[:LAPPING_AT]->(lt)
MERGE (r)-[:TIMED_LAP]->(lt)
You might consider CREATE instead of MERGE for the relationships, if you know there are no duplicate entries.
I removed your RETURN because returning the types isn't useful information.
Also, consider using consistent cases for your node labels, and that you are using the same case between the labels in your graph and the indexes you create.
Also, you would probably want to batch these changes instead of trying to process them all at once.
If you install APOC Procedures you can make use of apoc.periodic.iterate(), which can be used to batch changes, which will be faster and easier on your heap. You will still need indexes first.
CALL apoc.periodic.iterate("
MATCH (lt:lapTimes)
WITH lt, lt.raceId as raceId, lt.driverId as driverId
MATCH (d:Driver), (r:race)
WHERE r.raceId = raceId AND d.driverId = driverId
RETURN lt, d, ir",
"MERGE (d)-[:LAPPING_AT]->(lt)
MERGE (r)-[:TIMED_LAP]->(lt)", {}) YIELD batches, total, errorMessages
RETURN batches, total, errorMessages
Single CSV load
If you want to handle everything all at once in a single CSV load, you can do that, but again you will need indexes first. Here's what you'll need at a minimum:
CREATE INDEX ON :Driver(driverId);
CREATE INDEX ON :Race(raceId);
After those are created, you can use this, assuming you are starting from scratch (I fixed the case of your labels and made them singular:
USING PERIODIC COMMIT 25000
LOAD CSV WITH HEADERS from 'file:///lap_times.csv' AS row
MERGE (d:Driver {driverId:row.driverId})
MERGE (r:Race {raceId:row.raceId})
CREATE (lt:LapTime {raceId: row.raceId, driverId: row.driverId, lap: row.lap, position: row.position, time: row.time, milliseconds: row.milliseconds})
CREATE (d)-[:LAPPING_AT]->(lt)
CREATE (r)-[:TIMED_LAP]->(lt)
I am trying to achieve what is shown here:
I have 2 CSV Files, diease_mstr and Test_mstr Now in Test_mstr, I have many test to disease ID records, which means none of them are unique. The disease ID points to disease_mstr file. In disease_mstr file I have only 2 fields, ID and Disease_name (disease name is unique).
Now, I am creating 3 nodes with labels
1) Tests (only "testname" property) which will have unique tests (total 345 unique testnames)
**Properties :**
a) testname
2) Linknode (pulled entire Test_mstr file) also pulled "disease_name" for corresponding disease_ID from Disease_mstr File
**Properties**
a)tname
b)dname
c)did
3) Disease (pulled form disease_mstr) file.
**Properties**
a)did
b)diseasename
Afterwhich I run create relationships
1)MATCH (t:Tests),(n:Linknode) where t.testname = n.tname CREATE (n)-[r:TEST_2]->(t) RETURN n,r,t
2)MATCH (d:Disease), (l:Linknode) where d.did = l.did MERGE (d)-[r:FOR_DISEASE]->(l) RETURN d,r,l
To get the desired result as shown in image, I run following cypher command :
MATCH (d:Disease)-[r2:FOR_DISEASE]->(l:Linknode)-[r:TEST_2]->(t:Tests) RETURN l,r,t,r2 LIMIT 25
Can someone please help me create 2 more relationships which is marked and linked in image with BLUE and GREEN lines?.
Sample files and images can be accessed in my google folder link
Is your goal to link all diseases to tests so that for any disease you can find out which tests are relevant and for each test, which diseases it tests for?
If so, you are nearly there.
You don't need the link nodes other than to help you during linking the tests to the diseases. In your current scenario you're treating the link nodes as you would if you were creating a relational database. They won't add any value in your graph db. You can create a single relationship between diseases and tests which will do all the work.
Here's a step by step way to load your database. (It probably isn't the most efficient, but it's easy to follow and it works.)
Normalise and load your tests:
load csv with headers from "file:///test_mstr_csv.csv" as line
merge (:Test {testname:line.test_name});
Load your diseases (these looked normalised to me)
load csv with headers from "file:///disease_mstr_csv.csv" as line
create (:Disease {did:line.did, diseasename:line.disease_name});
Load your link nodes:
load csv with headers from "file:///test_mstr_csv.csv" as line
merge (:Link {testname:line.test_name, parentdiseaseid:line.parent_disease_ID});
Now you can create a direct relationship between the diseases and tests with the following query:
match(d:Disease), (l:Link) where d.did = l.parentdiseaseid
with d, l.testname as name
match(t:Test {testname:name}) create (d)<-[:TEST_FOR]-(t);
This last query will find all the link nodes for each disease and extract the test name. It then looks up the test and joins it directly to its corresponding disease.
The link nodes are redundent now, so you can delete them if you wish.
To create the 'blue lines', which I assume are meant to show where tests have diseases in common, run the query below:
match (d:Disease)<-[]-(:Test)-[]->(e:Disease) where id(d) > id(e)
merge (d)-[:BLUE_LINE]->(e);
The match clause finds all disease pairs with a common test, the where clause ensures a link is created in only one direction and the merge clause ensures only one link is created.
I am trying to load large dataset into neo4j-3 and looking for the options. I found one neo4j-import but the problem with that is it is for initial load only. I have to load 2M records around every week.
I tried loading through shell but having some performance issue, I tried following.
1) Creating constraint upfront.
2) Creating Node and relationships in separate query.
3) Heap space 8G
4) dbms.memory.pagecache 4G
Many times the import just hangs and does nothing for hours.
Edit - CSV load being executed:
USING PERIODIC COMMIT 5000
LOAD CSV WITH HEADERS
FROM "file:///my_sds_39_joe.csv"
AS row
OPTIONAL MATCH (per:Person {UID : "Person."+row.player_cardnum})
WHERE per IS NULL
MERGE (p:Person {CardNumber : row.player_cardnum})
ON CREATE SET p.Creation Date = timestamp(), p.Modification Date = timestamp() ;
EDIT
On a second look, seems like you're trying to implement some kind of conditional logic to your insert.
It looks like what you're trying to do is figure out if a :Person exists with a UID (derived from some concatenation with row.player_cardnum), and in the case where that :Person doesn't exist and the match fails, MERGE a :Person with the CardNumber given by row.player_cardnum.
If this is your goal, you're ALMOST there with your query. The problem is with your WHERE clause.
Understand that WHERE clauses are linked with a preceding MATCH, OPTIONAL MATCH, or WITH, and only affects the linked clause.
With that WHERE on that OPTIONAL MATCH, per will always be null, but more importantly, your row will still exist, and the following MERGE will ALWAYS take place for all rows in the CSV. This is probably the source of your slowdown, as it's creating new :Person nodes for all rows.
If you're trying to null out the row completely when the OPTIONAL MATCH hits on an existing :Person (so the MERGE won't happen in that case), you'll need to add a WITH clause, and make sure your WHERE clause is applied to it instead of the OPTIONAL MATCH.
Additionally, make sure that you have either unique constraints or indexes on Person.UID and Person.CardNumber. As for the UID match, I've heard that indexes are not used when there's some kind of string concatenation of the thing you're matching upon, so you may need to assemble it first and pass it in with a WITH.
Your final query would look like this:
USING PERIODIC COMMIT 5000
LOAD CSV WITH HEADERS
FROM "file:///my_sds_39_joe.csv"
AS row
// first build the UID so we can take advantage of the index
WITH row, "Person." + row.player_cardnum AS UID
OPTIONAL MATCH (per:Person {UID : UID})
// the WHERE now applies to the WITH, which will filter out and null out the row when an OPTIONAL MATCH is found
WITH row, per
WHERE per IS NULL
MERGE (p:Person {CardNumber : row.player_cardnum})
ON CREATE SET p.Creation Date = timestamp(), p.Modification Date = timestamp() ;