olso/_cypher-tips-and-tricks.adoc

## _cypher-tips-and-tricks.adoc

      
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Useful Links


Slides: Lesser known features in Cypher - Alex Price


Cypher Query Tuning - Michael


Cypher Query Tuning - Mark & Petra


## apoc.date.parse.format.cypher
call apoc.load.json("https://api.github.com/search/repositories?q=apoc") yield value
UNWIND value.items as r
return r.full_name, r.created_at, apoc.date.format(apoc.date.parse(r.created_at,'s',"yyyy-MM-dd'T'HH:mm:ss'Z'"),"s")
order by r.favorites desc

## apoc.periodic.iterate.sub-batching.cypher
CALL apoc.periodic.iterate(
"LOAD CSV WITH HEADERS FROM url AS line
WITH  apoc.coll.partition(collect(line),10000) AS batchesOfLines
UNWIND batchesOfLines as batch
RETURN batch",
"UNWIND {batch} AS user
MERGE (u:User {Email: user.Email})
SET u += apoc.map.clean(user,['Email'],null)",
{batchSize: 1, parallel: true})

## delete-node-with-any-property-in-values-from-csv.adoc

      
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From this StackOverflow Question


Hello I want to delete all nodes with the label GRAPH_OBJECT that have a property value (lets call it myprop) that is not in a list of numeric values that I have in a CSV or text file.
How do a I accomplish this with Cypher?


This should work.


// load rows from csv
LOAD CSV FROM "file://values.txt" AS row
// create a collection of the first column turned into numeric values
WITH collect(toInt(row[0])) AS blacklist
// find the nodes
MATCH (node:GRAPH_OBJECT)
// for any of the properties of the node, if it's value is in our blacklist
WHERE ANY(property in keys(node) WHERE node[property] IN blacklist)
// delete node and relationships
DETACH DELETE node;


## efficient-graph-updates-with-cypher.adoc

      
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    5 Tips & Tricks for Fast Batched Updates of Graph Structures with Neo4j and Cypher


Michael Hunger, @mesirii


When you’re writing a lot of data to the graph from your application or library, you want to be efficent.


Inefficient Solutions


These approaches are not very efficient:


hard coding values instead of using parameters


sending a single query / tx per individual update


sending many single queries within a single tx with individual updates


generating large, complex statements (hundreds of lines) and sending one of them per tx and update


sending in HUGE (millions) of updates in a single tx, will cause out-of-memory issues


Better Approach


Note


You want small enough queries, that are constant in their shape (for caching) and are using parameters.


Each query can update from a single property to a whole subgraph (100 nodes) but has to be the same in overall structure for caching.


UNWIND to the Rescue


To achieve that you just prefix your regular "single-update-query" with an UNWIND that turns a batch of data (up to 10k or 50k entries)
into individual rows, which contain the information for each of the (more or less complex) updates.


You send in a {batch} parameter (up to 10k-50k) of data (hopefully a delta) as a list of maps, which are then applied in a compact query, which is also properly compiled and cached, as it has a fixed structure.


Overall Syntax Structure


{batch: [{row1},{row2},{row3},...10k]}


UNWIND {batch} as row

// now perform updates with the data in each "row" map


Examples


Here are some examples:


Create node with properties


{batch: [{name:"Alice",age:32},{name:"Bob",age:42}]}


UNWIND {batch} as row
CREATE (n:Label)
SET n += row


MERGE node with properties


{batch: [{id:"alice@example.com",properties:{name:"Alice",age:32}},{id:"bob@example.com",properties:{name:"Bob",age:42}}]}


UNWIND {batch} as row
MERGE (n:Label {row.id})
(ON CREATE) SET n += row.properties


Node lookup and MERGE/CREATE relationship between with properties


{batch: [{from:"alice@example.com",to:"bob@example.com",properties:{since:2012}},{from:"alice@example.com",to:"charlie@example.com",properties:{since:2016}}]}


UNWIND {batch} as row
MATCH (from:Label {row.from})
MATCH (to:Label {row.to})
CREATE/MERGE (from)-[rel:KNOWS]->(to)
(ON CREATE) SET rel += row.properties


Lookup by id, or even list of ids


good for parent-child trees


Here we’re passing a single property created.
Alternatively you could pass in no properties or a map of properties to be set/updated.


{batch: [{from:123,to:[44,12,128],created:"2016-01-13"}, {from:34,to:[23,35,2983],created:"2016-01-15"},...]


UNWIND {batch} as row
MATCH (from) WHERE id(from) = row.from
MATCH (to) WHERE id(from) IN row.to // list of ids
CREATE/MERGE (from)-[rel:FOO]->(to)
SET rel.created = row.created


Faster, Better, Further: All the tricks


There are some more tricks.


You can also send in a map where the keys are node- or relationship-ids (converted to as strings) that’s more compact and faster too for the id lookup.


Update of existing nodes by id


{ batch : [{"1":334,"2":222,3:3840, ... 100k}]}


WITH {batch} as data, [k in keys({batch}) | toInt(k)] as ids
MATCH (n) WHERE id(n) IN ids

// single property value
SET n.count = data[toString(id(n))]

// or override all properties
SET n = data[toString(id(n))]

// or add all properties
SET n += data[toString(id(n))]


Update of existing relationships by id


{ batch : [{"1":334,"2":222,3:3840, ... 100k}]}


WITH {batch} as data, [k in keys({batch}) | toInt(k)] as ids
MATCH ()-[rel]->() WHERE id(rel) IN ids
SET rel.foo = data[toString(id(rel))] // single property
SET rel= data[toString(id(rel))] // all properties


Conditional Data Creation


Sometimes you want to create data dynamically based on inputs, e.g. a node with a certain label.


As cypher currently has no conditional WHEN or IF clause, and case when is just an expression, you have to use a trick I came up with many years ago.


Fortunately there is FOREACH which is meant to iterate over a list of items and execute update operations for each of them.


Fortunately a list of 0 or 1 elements can serve as a conditional of false and true, i.e. no iteration or one iteration.


General idea:


...
FOREACH (_ IN CASE WHEN predicate THEN [true] ELSE [] END |
... update operations ....
)


Note that the true value in that list could be anything, 42, "", null etc. as long as it is any single value so that we have a non-empty list.


You can achieve something similar with a RANGE(1, CASE WHEN predicate THEN 1 ELSE 0 END) which will yield an empty list when the predicate is false.
Or if you fancy filter then you can use: filter(_ IN [1] WHERE predicate).


Here is a concrete example:


LOAD CSV FROM {url} AS row
MATCH (o:Organization {name:row.org})
FOREACH (_ IN case when row.type = 'Person' then [1] else [] end|
   MERGE (p:Person {name:row.name})
   CREATE (p)-[:WORKS_FOR]->(o)
)
FOREACH (_ IN case when row.type = 'Agency' then [1] else [] end|
   MERGE (a:Agency {name:row.name})
   CREATE (a)-[:WORKS_FOR]->(o)
)


Note that identifiers created within FOREACH are not accessible from the outside, you would have to re-match the value later on, or you have to move all your update operations into the foreach.


Utilizing APOC Procedures


The APOC procedure library comes with a lot of useful procedures that can help you here, I want to highlight 3 of them:


create nodes / relationships with dynamic labels and propeties


batched transactions / iteration of updates


functions for creating and manipulating maps to be set as properties


Creating Nodes and Relationships dynamically


With apoc.create.node and apoc.create.relationship you can have dynamically computed node-labels and relationship-types as well as any map of properties.


labels is a string array


properties is just a map


UWNIND {batch} as row
CALL apoc.create.node(row.labels, row.properties) yield node
RETURN count(*)


There are also procedures in apoc.create.* for setting/updating/removing properties and labels with dynamic string keys.


UWNIND {batch} as row
MATCH (from) WHERE id(n) = row.from
MATCH (to:Label) where to.key = row.to
CALL apoc.create.relationship(from, row.type, row.properties, to) yield rel
RETURN count(*)


Batched Transactions


As mentioned at the beginning huge transactions are a problem, you can update a million records with around 2G - 4G of heap but it gets difficult with larger volumes.
My biggest volume per single transaction was about 10M nodes / relationships with 32G heap.


That’s where apoc.periodic.iterate comes in.


The idea is simple, you have two Cypher statements, the first statement provides the data to operate on and can produce a huge (many millions) stream of data (nodes, rels, scalar values).


The second statement does the actual update work, it is called for each item, but a new transaction is created only for each batch of items.


(There is a new variant of this which will go into the next version of APOC that actually does an UNWIND variant of the second statement, so it executes only one inner statement per tx).


So for example your first statement returns 5 million nodes to update, with a computed value.
The inner statement is executed once for each of those 5 M nodes.
If your batch size is 10k then that happens in batches of 10k statements per transaction.


Note


If your updates are independent of each other (think creation of nodes or updates of properties, or updates of independent subgraphs), then you can run this procedure with a parallel:true option which will use all your CPUs.


For example if you want to compute a score of many rated items and update this property in a batched fashion, this is what you would do:


call apoc.periodic.iterate('
MATCH (n:User)-[r1:LIKES]->(thing)<-[r2:RATED]-(m:User) WHERE id(n)<id(m) RETURN thing, avg( r1.rating + r2.rating ) as score
','
WITH {thing} as t SET t.score = {score}
', {batchSize:10000, parallel:true})


Creating / Updating Maps dynamically


While lists can be created and processed quite easily in Cypher with range, collect, unwind, reduce, extract, filter, size etc, maps have more limited means esp. for creation and modification.


The apoc.map.* package comes with a number of functions that make your life easier:


Creating Maps from other data:


RETURN apoc.map.fromPairs([["alice",38],["bob",42],...])
// {alice:38, bob: 42, ...}

RETURN apoc.map.fromLists(["alice","bob",...],[38,42])
// {alice:38, bob: 42, ...}

// groups nodes, relationships, maps by key, good for quick lookups by that key
RETURN apoc.map.groupBy([{name:"alice",gender:"female"},{name:"bob",gender:"male"}],"gender")
// {female:{name:"alice",gender:"female"}, male:{name:"bob",gender:"male"}}

RETURN apoc.map.groupByMulti([{name:"alice",gender:"female"},{name:"bob",gender:"male"},{name:"Jane",gender:"female"}],"gender")
// {female:[{name:"alice",gender:"female"},{name:"jane",gender:"female"}], male:[{name:"bob",gender:"male"}]}


Updating Maps


RETURN apoc.map.merge({alice: 38},{bob:42})
// {alice:38, bob: 42}

RETURN apoc.map.setKey({alice:38},"bob",42)
// {alice:38, bob: 42}

RETURN apoc.map.removeKey({alice:38, bob: 42},"alice")
// {bob: 42}

RETURN apoc.map.removeKey({alice:38, bob: 42},["alice","bob","charlie"])
// {}

// remove the given keys and values, good for data from load-csv/json/jdbc/xml
RETURN apoc.map.clean({name: "Alice", ssn:2324434, age:"n/a", location:""},["ssn"],["n/a",""])
// {name:"Alice"}


Conclusion


I used these approaches successfully for high volume update operations, and also in implementation of object graph mappers for bulk updates.


Of course you can combine these variants for more complex operations.


If you try them out and are successful, please let me know.


If you have any other tricks that helped you to achieve more write throughput with Cypher, please let me know too and I’ll update this post.


michael@neo4j.com / http://twitter.com/mesirii


Follow me on Twitter for more tips like this.


## find-null-values-load-csv.cypher
LOAD CSV WITH HEADERS FROM "file:///data2.csv" AS row
WITH ROW WHERE ANY (k in keys(row) WHERE row[k] IS NULL)
RETURN row LIMIT 100;

## graph-json-with-cypher.cypher
    OPTIONAL MATCH path = (x)<-[*..3]-() WHERE ID(x) = 65

    UNWIND nodes(path) as node
    UNWIND rels(path) as rel

    WITH collect(distinct node) as nodes,collect(distinct rel) as rels
    // todo release apoc.coll.flatten
    // WITH apoc.coll.flatten(collect(nodes(path))) as nodes, apoc.coll.flatten(collect(relationships(path))) as rels
    WITH apoc.coll.toSet([n in nodes WHERE n is not null
                | { id: id(n),label: labels(n),type:"",metadata: properties(n)  } ]) as nodes,
         apoc.coll.toSet([r in rels WHERE r is not null
                | { id: id(r),source: id(startNode(r)),relation: type(r),target: id(endNode(r)), directed: "true"  } ]) as rels

    RETURN { graph: { type:"",label: "",directed: "true",nodes: nodes,edges: rels,
             metadata:{ countNodes: size(nodes),countEdges: size(rels) } } } as graph;


## index-of.cypher
// length of the first part of a split is equivalent to index-of
RETURN length(split("European Union","pean")[0]);

START u=node:node_auto_index("fullName:*jay*")
MATCH (u:User)
WITH distinct u
RETURN {firstName : u.firstName , lastName : u.lastName, fullName : u.fullName, profilePicture : u.profilePicture, id : u.id} as user
// length of the first part of a split is equivalent to index-of
ORDER BY length(split(toLower(u.fullName,"jay")[0]);

## longest-path.cypher
// all paths
MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
RETURN p


// longest path with sorting
MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
RETURN p
order by size(p) desc
return p limit 1

// longest path by checking end-node with size() aka get-degree
MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
WHERE SIZE((shb)<-[:SHARES_HELD_BY]-())=0
RETURN p


// longest path by checking end-node with exists() which is cheaper for long chains
MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
WHERE not exists( (shb)<-[:SHARES_HELD_BY]-() )
RETURN p

## manage-relationship-status-updates-keep-10.adoc

      
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From this SO Question: Neo4j import csv / DHCP data controlling duplication


I have a csv from DHCP with _time, hostname, IP_addr


I would like to add any changed IPs as new relationships, but keep the old ip relationships with a status attribute inactive, also think I want to limt to the last 10.


I am not sure the easiest way to do this in cypher, or should I be in python for this complexity


maybe an always add (remove duplicates)/csv import


and a second query to deactivate any old ips (how do I query non current if i have time as an attribute of relationship)


and a third query to remove relationships that if more that 10 previous ips are hanging off it.


Answer


Sounds like fun. Not sure if every host-ip combination appears only once in a csv or also at later times like an "still-here" update


Import Statement


LOAD CSV FROM "url" AS row
MERGE (h:Host {name:row.hostname})
MERGE (ip:IP {name:row.IP_addr})
MERGE (h)-[:IP]->(ip) ON CREATE SET rel.created = row._time, rel.status = 1
// optional for pre-existing/previous rels
ON MATCH SET rel.status = 0
SET rel.updated = row._time;


Cleanup statement


MATCH (h:Host) WHERE size( (h)-[:IP]->() ) > 1
MATCH (h)-[rel:IP]->(:IP)
WITH h,rel ORDER BY rel.updated DESC
WITH h, collect(rel) as rels
// not necessary when the status is set above
FOREACH (r in rels[1..9] | SET r.status=0)
FOREACH (r IN rels[10..-1] | DELETE r)


When the status is set correctly in the load statement


MATCH (h:Host)-[rel:IP {status:0}]->(:IP)
WITH h,rel ORDER BY rel.updated DESC
WITH h, collect(rel) as rels
FOREACH (r IN rels[9..-1] | DELETE r)


## map-projection-pattern-comprehension.adoc

      
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    New, GraphQL inspired features in Cypher


When GraphQL was published as part of Facebooks React efforts, it made a big buzz as an straightforward means to declare what kind of projection of your domain data you need for a certain UI component.
Using a JSON-like syntax you define which properties of your entity and related entities you want to be part of the data structure you get back from the server.


Here is an example from a StackOverflow query using the model from our previous blog posts on that topic.


{
  question {
    title,
    author {
      name
    },
    tags {
      name
    },
    answers {
      text,
      author {
        name
      }
    }
  }
}


Cypher itself with its rich support for literal maps and collections and the very powerful collect aggregation function, already allows for returning complex JSON documents.


MATCH (u:User)-[:ASKED]->(q:Question)-[:TAGGED]->(t:Tag),
      (q)<-[:ANSWERS]-(a:Answer)<-[:PROVIDED]-(u2:User)

RETURN { title: q.title, author: u.name, tags: collect(t.name),
       answers: collect({text: a.text, author: u2.name})} as question


This results in a document like this, which is similar to the original StackOverflow query API result.


{
  "title": "neo4j cypher query to delete a middle node and connect all its parent node to child node",
  "author": "Soumya George",
  "tags": [
    "neo4j",
    "cypher"
  ],
  "answers": [
    {
      "text": "Some text",
      "author": "InverseFalcon"
    }
  ]
}


Some things are not as convenient as we saw in GraphQL, we thought it would be very helpful to add more syntactic sugar to the language.


Luckily my friend Andrés found some spare time to add two really neat features to Cypher in Neo4j 3.1 which we want to look into today.


Map Projections


Map Projections are very close to what you expect from a GraphQL query, you take an map or entity (node or relationship) and apply a map-like property-selector to it.


The result of that projection is a (optionally nested) map of results.


Here is the example above rewritten using a map-projection.


MATCH (u:User)-[:ASKED]->(q:Question)-[:TAGGED]->(t:Tag),
      (q)<-[:ANSWERS]-(a:Answer)<-[:PROVIDED]-(u2:User)

RETURN q{ .title, author : u.name, tags: collect(t.name),
       answers: collect( a {.text, author: u2.name})} as question


But there are some more things possible.


Within a map projection you can also add literal values or aggregations to the data that you extract from the entity.


entity { .property1, .property2, .*,  literal: value,  values: collect(numbers), variable}


Here is a full list of possible selectors:


syntax
description
example


.property

property lookup

p{.name} → {name : "John"}


.*

all properties

p{.*} → {name:"John", age:42}


variable

variable name as key, variable value as value

p{count} → {count: 1}


key : value

literal entry

p{awesome:true} → {awesome:true}


To demonstrate those options we could rewrite the statement to:


MATCH (u:User)-[:ASKED]->(q:Question)-[:TAGGED]->(t:Tag),
      (q)<-[:ANSWERS]-(a:Answer)<-[:PROVIDED]-(u2:User)

WITH q, u, collect(t.name) as tags, collect( a {.text, author: u2.name}) as answers
RETURN q{ .title, author : u{.*}, tags,  answers } as question


To pull in information from related entities, the other new feature, Pattern Comprehensions come into play.


Pattern Comprehensions


You’ve all (hopefully) used the list comprehensions in Cypher, they borrow from Haskells syntax and look like this:


[value IN list WHERE predicate(value) | expression(value)]


As a concrete example, this returns the squares of the first 5 even numbers:


RETURN [x IN range(1,10) WHERE x % 2 = 0 | x * x] -> [4, 16, 36, 64, 100]


Now, you can use any kind of collection here, also collection of maps or nodes or even paths.


Note


If you use a graph pattern as an expression, it actually yields a collection of paths.


That’s cool, because now you can use a list comprehension to do pattern matching and extract a related node without actually using MATCH and changing your cardinality.


So instead of:


MATCH (u:User)-[:POSTED]->(q:Question)
WHERE q.title CONTAINS "Neo4j"
RETURN u.name, collect(q.title) as questions


you could write:


MATCH (u:User)
RETURN u.name, [path IN (u)-[:ASKED]->(:Question)
                  WHERE (last(nodes(path))).title CONTAINS "Neo4j"
                      | (last(nodes(path))).title] as questions


Note


Btw. this statement always returns a result, potentially an empty collection, so it’s the same as if you were OPTIONAL MATCH in the previous statement.


Wow, that’s ugly. Why? Because you can’t introduce new variables, like q in such a pattern expression.
Only clauses could introduce new variables.


Until now!


With Pattern Comprehensions you actually can introduce local variables in such a pattern and use them in the WHERE filter or expression at the end.


MATCH (u:User)
RETURN u.name,
       [(u)-[:ASKED]->(q:Question) WHERE q.title CONTAINS "Neo4j" | q.title] as questions


Now let’s take a stab at our "GraphQL" query again, and see how we can rewrite it just starting from the Question node and moving all projections of attributes and patterns into the RETURN clause.


MATCH (q:Question)

RETURN q{.title,
         author : [(q)<-[:ASKED]-(u) | u.name][0],
         tags   : [(q)<-[:TAGGED]-(t) | t.name],
         answers: [(q)<-[:ANSWERS]-(a)<-[:PROVIDED]-(u2) | a{ .text, author: u2.name } ] }


As pattern comprehensions always return a collection we have to turn them into a single value as needed, e.g. with […][0] or head([…])


To combine attributes of two entites into one map you have to spell out the 2nd entities attributes.

It would be nice to get support for combining maps in the future, then we could use

answers: [(q)<-[:ANSWERS]-(a)<-[:PROVIDED]-(u2) | a{ .text } + u2{ .name} ]


If you want to test these cool new features, please grab the recently released Neo4j 3.1.0-M07 Milestone and give it a try.


We’d love to get your feedback on these and other new features like the brand-new [cypher-shell].


With a lot of thanks to Andrés for this and everyone in engineering for a really cool database,


Cheers, Michael


## match-node-expressions.cypher
// Matching dynamic objects
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WITH collect([p, m]) as pairs
UNWIND pairs as pair

// WONT WORK
MATCH (pair[1])<-[:DIRECTED]-(p:Person)
// WORKS, alias expression with variable
WITH pair[0] as p0, pair[1] as p1
MATCH (p1)<-[:DIRECTED]-(p:Person)
RETURN p.name

## meta-graph-with-virtual-entities.cypher
MATCH (a)-[r]->(b)
WITH head(labels(a)) AS l, head(labels(b)) AS l2, type(r) AS rel_type, count(*) as count
WITH collect({from:l,to:l2,type:rel_type, count:count}) as rels, apoc.coll.toSet(collect(l2)+collect(l)) as nodes
WITH apoc.map.fromPairs([name in nodes | [name, apoc.create.vNode([name],{name:name})]]) as nodes, rels
UNWIND rels as r
CALL apoc.create.vRelationship(nodes[r.from],r.type,{count:r.count},nodes[r.to]) yield rel
RETURN rel,nodes[r.from],nodes[r.to];

## pattern-comprehension.cypher
MATCH (m:Movie {title:"The Matrix"})
RETURN
m {.title, .released,
   directors: [ (m)<-[:DIRECTED]-(a) | a {.name, .born } ],
   actors: [ (m)<-[:ACTED_IN]-(a) | a {.name, .born,
       movies:[(a)-[:ACTED_IN]->(m2) | m2 { .title, .released }]
  }]} as document

## people-that-know-me-grouped-by-reverse.cypher
// group people that know me by reverse relationship ("followback")
MATCH (me:Person)<-[:KNOWS]-(o)
WHERE me.name = "me"
RETURN exists( (me)-[:KNOWS]->(o) ) as friend, collect(distinct o) as people;

## property-lookup-performance.adoc

      
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Property Lookup Performance, and what we can do about it


We often get questions like, "My query is too slow, what can I do."


If the query looks like this:


load csv from "salaries.csv" as row
match (p:Person) where p.id = row.id
set p.salary = toFloat(row.salary)


the best guess is:


You forgot to create an constraint or index on that label and property combination!


create constraint on (p:Person) assert p.id is unique;
// or
create index on :Person(id);


Currently, Neo4j, when asked to do a property lookup on a non-indexed property, has to do a full scan over all nodes with that label and compare the property with the provided value in a filter operation.
And it does that for every check, so if you have a CSV with 1M lines, then that’s 1M x full scan + filter.


Let’s look at some numbers and create an artificial dataset for that,
in our case we’re only doing a read (i.e. the lookup) to not have the write + tx operation skew the times.


We create 1M People with an id property.


UNWIND range(1,1000000) AS id
CREATE (:Person{id:id, age: id % 100});

+-------------------+
| No data returned. |
+-------------------+
Nodes created: 1000000
Properties set: 1000000
Labels added: 1000000
10723 ms


Testing


Then we try to look up 500k of those id’s …


unwind range(1,1000000,2) as id
return count(*);
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
68 ms


from the existing people:


UNWIND range(1,1000000,2) AS id
MATCH (:Person{id:id})
RETURN count(*);

... didn't finish after several minutes ...


But we have no luck, re-running this with a smaller number (100), shows us the query plan and the associated costs.


UNWIND range(1,1000000,10000) AS id
MATCH (:Person{id:id})
RETURN count(*);

+----------+
| count(*) |
+----------+
| 100      |
+----------+
1 row
72957 ms

Compiler CYPHER 3.0

Planner COST

Runtime INTERPRETED

+--------------------+----------------+-----------+-----------+----------------+-------------------+
| Operator           | Estimated Rows | Rows      | DB Hits   | Variables      | Other             |
+--------------------+----------------+-----------+-----------+----------------+-------------------+
| +ProduceResults    |           1000 |         1 |         0 | count(*)       | count(*)          |
| |                  +----------------+-----------+-----------+----------------+-------------------+
| +EagerAggregation  |           1000 |         1 |         0 | count(*)       |                   |
| |                  +----------------+-----------+-----------+----------------+-------------------+
| +Filter            |        1000000 |       100 | 100000000 | anon[44], id   | anon[44].id == id |
| |                  +----------------+-----------+-----------+----------------+-------------------+
| +Apply             |        1000000 | 100000000 |         0 | id -- anon[44] |                   |
| |\                 +----------------+-----------+-----------+----------------+-------------------+
| | +NodeByLabelScan |       10000000 | 100000000 | 100000100 | anon[44]       | :Person           |
| |                  +----------------+-----------+-----------+----------------+-------------------+
| +Unwind            |             10 |       100 |         0 | id             |                   |
| |                  +----------------+-----------+-----------+----------------+-------------------+
| +EmptyRow          |              1 |         1 |         0 |                |                   |
+--------------------+----------------+-----------+-----------+----------------+-------------------+

Total database accesses: 200000100


create constraint on (p:Person) assert p.id is unique;
// Unique constraints added: 1 -> 8617 ms

schema await
schema sample -a
unwind range(1,1000000,2) as id match (:Person{id:id}) return count(*);
unwind range(1,1000000,2) as id match (:Person{id:id}) return count(*);
/*
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
7450 ms
*/

drop constraint on (p:Person) assert p.id is unique;

create index on :Person(id);
schema await
schema sample -a
unwind range(1,1000000,2) as id match (:Person{id:id}) return count(*);
unwind range(1,1000000,2) as id match (:Person{id:id}) return count(*);
/*
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
7132 ms
*/

drop index on :Person(id);

match (p:Person)
with collect([toString(p.id),p]) as pairs
call apoc.map.fromPairs(pairs) yield value as index
unwind range(1,1000000,2) as id
with index[toString(id)] as n
return count(*);
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
4687 ms

match (p:Person)
with collect(p) as people
call apoc.map.groupBy(people,'id') yield value as index
unwind range(1,1000000,2) as id
with index[toString(id)] as n
return count(*);
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
3115 ms

with range(1,1000000,2) as ids
match (p:Person) where p.id IN ids
with collect(p) as people
call apoc.map.groupBy(people,'id') yield value as index
unwind range(1,1000000,2) as id
with index[toString(id)] as n
return count(*);
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
2344 ms

with [id IN range(1,1000000,2) | {id:id}] as rows
call apoc.map.groupBy(rows,'id') yield value as rowById
with rowById,[id IN keys(rowById) | toInt(id)] as ids
match (p:Person) where p.id IN ids
with rowById[toString(p.id)] as row
// do something with p and row
return count(*);

with [id IN range(1,1000000,2) | {id:toString(id)}] as rows
call apoc.map.groupBy(rows,'id') yield value as rowById
with rowById,keys(rowById) as ids
match (p:Person) where p.id IN ids
with rowById[p.id] as row
// do something with p and row
return count(*);
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
4746 ms


with [id IN range(1,1000000,2) | {id:toString(id)}] as rows
call apoc.map.groupBy(rows,'id') yield value as rowById
match (p:Person) where p.id IN keys(rowById)
with rowById[p.id] as row
// do something with p and row
return count(*);

profile
with range(1,1000) as ids
match (p:Person)
where p.id2 IN ids
return count(*);

profile
match (p:Person)
where p.id2 IN range(1,1000)
return count(*);


with [id IN range(1,1000000,2) | {id:toString(id)}] as rows
call apoc.map.groupBy(rows,'id') yield value as rowById
return count(*);
// 1300ms

unwind range(1,1000000,2) as id
with collect(id) as ids
match (p:Person)
where p.id IN ids
return count(*);


with range(1,1000000,2) as ids
match (p:Person)
where p.id IN ids
return count(*);
/*
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
1631 ms
*/

with range(1,1000000,2) as ids
match (p:Person)
where p.id IN ids
with collect([toString(p.id),p]) as pairs
call apoc.map.fromPairs(pairs) yield value as index
unwind range(1,1000000,2) as id
with index[toString(id)] as n
return count(*);
/*
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
3563 ms
*/

with collect([toString(p.id),p]) as pairs
call apoc.map.fromPairs(pairs) yield value as index
unwind range(1,1000000,2) as id
with index[toString(id)] as n
return count(*);

unwind range(1,1000000,2) as id
with collect(id) as ids
match (p:Person)
where p.id IN ids
return count(*);
/*
+----------+
| count(*) |
+----------+
| 500000   |
+----------+
1 row
1660 ms
*/

load csv from "salaries.csv" as row
match (p:Person) where p.id = row.id
set p.salary = toFloat(row.salary)

// rewrite to

load csv from "salaries.csv" as row
with collect(distinct row.id) as ids, collect(row) as rows
match (p:Person) where p.id IN ids
WITH collect(p) as people, rows // this aggreation is probably the only issue
UNWIND rows as row
WITH head([p in people where p.id = row.id])  as p // and perhaps this "lookup"
SET p.salary = row.salary;


// final solution
load csv from "salaries.csv" as row
with collect(row) as rows
call apoc.map.groupBy(rows,'id') yield value as rowById
match (p:Person) where p.id IN keys(rowById)
set p.salary = rowById[toString(p.id)].salary


## pure-virtual-graph-apoc.cypher
WITH ["Andres","Eve","Rik","Mark","Sophia","Praveena","Michael","Stefan","Max","Zhen"] AS names
UNWIND names as name
call apoc.create.vNode(["Person"],{name:name}) yield node
WITH names, size(names) as len, apoc.map.groupBy(collect(node),"name") as nodes
UNWIND range(1,42) as idx
CALL apoc.create.vRelationship(nodes[names[toInt(rand()*len)]],"KNOWS",{},nodes[names[toInt(rand()*len)]]) yield rel
RETURN nodes,rel;

## random-sampling.cypher
// answer to http://stackoverflow.com/questions/42691687/neo4j-slow-selection-operation-with-huge-data

MATCH (n) WITH count(*) as total
WITH [_ IN range(1,10000) | toInt(rand()*total)] as ids
MATCH (emp) WHERE id(emp) IN ids AND emp:Employee
RETURN emp LIMIT 10;

## set_map_projection.cypher
// set map projection as propertie, kudos to Adam Cowley

MERGE (n:User {screen_name:user.screen_name})
SET  n += user { .name, .location}
	call apoc.load.json("https://api.github.com/search/repositories?q=apoc") yield value
	UNWIND value.items as r
	return r.full_name, r.created_at, apoc.date.format(apoc.date.parse(r.created_at,'s',"yyyy-MM-dd'T'HH:mm:ss'Z'"),"s")
	order by r.favorites desc
	CALL apoc.periodic.iterate(
	"LOAD CSV WITH HEADERS FROM url AS line
	WITH apoc.coll.partition(collect(line),10000) AS batchesOfLines
	UNWIND batchesOfLines as batch
	RETURN batch",
	"UNWIND {batch} AS user
	MERGE (u:User {Email: user.Email})
	SET u += apoc.map.clean(user,['Email'],null)",
	{batchSize: 1, parallel: true})
	LOAD CSV WITH HEADERS FROM "file:///data2.csv" AS row
	WITH ROW WHERE ANY (k in keys(row) WHERE row[k] IS NULL)
	RETURN row LIMIT 100;
	OPTIONAL MATCH path = (x)<-[*..3]-() WHERE ID(x) = 65

	UNWIND nodes(path) as node
	UNWIND rels(path) as rel

	WITH collect(distinct node) as nodes,collect(distinct rel) as rels
	// todo release apoc.coll.flatten
	// WITH apoc.coll.flatten(collect(nodes(path))) as nodes, apoc.coll.flatten(collect(relationships(path))) as rels
	WITH apoc.coll.toSet([n in nodes WHERE n is not null
	\| { id: id(n),label: labels(n),type:"",metadata: properties(n) } ]) as nodes,
	apoc.coll.toSet([r in rels WHERE r is not null
	\| { id: id(r),source: id(startNode(r)),relation: type(r),target: id(endNode(r)), directed: "true" } ]) as rels

	RETURN { graph: { type:"",label: "",directed: "true",nodes: nodes,edges: rels,
	metadata:{ countNodes: size(nodes),countEdges: size(rels) } } } as graph;
	// length of the first part of a split is equivalent to index-of
	RETURN length(split("European Union","pean")[0]);

	START u=node:node_auto_index("fullName:jay")
	MATCH (u:User)
	WITH distinct u
	RETURN {firstName : u.firstName , lastName : u.lastName, fullName : u.fullName, profilePicture : u.profilePicture, id : u.id} as user
	// length of the first part of a split is equivalent to index-of
	ORDER BY length(split(toLower(u.fullName,"jay")[0]);
	// all paths
	MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
	RETURN p


	// longest path with sorting
	MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
	RETURN p
	order by size(p) desc
	return p limit 1

	// longest path by checking end-node with size() aka get-degree
	MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
	WHERE SIZE((shb)<-[:SHARES_HELD_BY]-())=0
	RETURN p


	// longest path by checking end-node with exists() which is cheaper for long chains
	MATCH p=(c:Organisation {duns_nbr:'216236900'})-[r:SHARES_HELD_BY*1..50]->(shb)
	WHERE not exists( (shb)<-[:SHARES_HELD_BY]-() )
	RETURN p
syntax	description	example
`.property`	property lookup	`p{.name} → {name : "John"}`
`.*`	all properties	`p{.*} → {name:"John", age:42}`
`variable`	variable name as key, variable value as value	`p{count} → {count: 1}`
`key : value`	literal entry	`p{awesome:true} → {awesome:true}`
	// Matching dynamic objects
	MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
	WITH collect([p, m]) as pairs
	UNWIND pairs as pair

	// WONT WORK
	MATCH (pair[1])<-[:DIRECTED]-(p:Person)
	// WORKS, alias expression with variable
	WITH pair[0] as p0, pair[1] as p1
	MATCH (p1)<-[:DIRECTED]-(p:Person)
	RETURN p.name
	MATCH (a)-[r]->(b)
	WITH head(labels(a)) AS l, head(labels(b)) AS l2, type(r) AS rel_type, count(*) as count
	WITH collect({from:l,to:l2,type:rel_type, count:count}) as rels, apoc.coll.toSet(collect(l2)+collect(l)) as nodes
	WITH apoc.map.fromPairs([name in nodes \| [name, apoc.create.vNode([name],{name:name})]]) as nodes, rels
	UNWIND rels as r
	CALL apoc.create.vRelationship(nodes[r.from],r.type,{count:r.count},nodes[r.to]) yield rel
	RETURN rel,nodes[r.from],nodes[r.to];
	MATCH (m:Movie {title:"The Matrix"})
	RETURN
	m {.title, .released,
	directors: [ (m)<-[:DIRECTED]-(a) \| a {.name, .born } ],
	actors: [ (m)<-[:ACTED_IN]-(a) \| a {.name, .born,
	movies:[(a)-[:ACTED_IN]->(m2) \| m2 { .title, .released }]
	}]} as document