Awesome Procedures for Neo4j 3.x

Documentation (WIP).

License

Apache License 2.0

"APOC" Name history

Apoc was the technician and driver on board of the Nebuchadnezzar in the Matrix movie. He was killed by Cypher.

APOC was also the first bundled A Package Of Components for Neo4j in 2009.

APOC also stands for "Awesome Procedures On Cypher"

Download latest release

Go to http://github.com/neo4j-contrib/neo4j-apoc-procedures/releases/latest to find the latest release and download the binary jar to place into your $NEO4J_HOME/plugins folder.

Build & install the current development branch from source

git clone http://github.com/neo4j-contrib/neo4j-apoc-procedures
cd neo4j-apoc-procedures
mvn clean install
cp target/apoc-1.0.0-SNAPSHOT.jar $NEO4J_HOME/plugins/
$NEO4J_HOME/bin/neo4j restart

If you want to run embedded or use neo4j-shell on a disk store, configure your plugins directory in conf/neo4j.conf with dbms.plugin.directory=path/to/plugins.

Calling Procedures within Cypher

Procedures can be called stand-alone with CALL procedure.name();

But you can also integrate them into your Cypher statements which makes them so much more powerful.

WITH 'https://raw.githubusercontent.com/neo4j-contrib/neo4j-apoc-procedures/master/src/test/resources/person.json' AS url

CALL apoc.load.json(url) YIELD value as person

MERGE (p:Person {name:person.name})
   ON CREATE SET p.age = person.age, p.children = size(person.children)

Included Procedures Overview

Built in Help

call apoc.help('search')

lists name, description-text and if the procedure performs writes (descriptions are WIP), search string is checked against beginning (package) or end (name) of procedure

helpful

CALL apoc.help("apoc") YIELD name, text
WITH * WHERE text IS null
RETURN name AS undocumented

Builtin Package and Procedure count

To find the procedure count with the package in Neo4j:

Cypher for getting count of procedure in a package

CALL dbms.procedures() YIELD name
RETURN head(split(name,".")) as package, count(*), collect(name) as procedures;

Manual Indexes

Index Queries

Procedures to add to and query manual indexes

Note	Please note that there are (case-sensitive) automatic schema indexes, for equality, non-equality, existence, range queries, starts with, ends-with and contains!

`apoc.index.addAllNodes('index-name',{label1:['prop1',…],…})`	add all nodes to this full text index with the given fields, additionally populates a 'search' index field with all of them in one place
`apoc.index.addNode(node,['prop1',…])`	add node to an index for each label it has
`apoc.index.addNodeByLabel(node,'Label',['prop1',…])`	add node to an index for the given label
`apoc.index.addRelationship(rel,['prop1',…])`	add relationship to an index for its type

`apoc.index.search('index-name', 'query') YIELD node, weight`	search for the first 100 nodes in the given full text index matching the given lucene query returned by relevance
`apoc.index.nodes('Label','prop:value*') YIELD node, weight`	lucene query on node index with the given label name
`apoc.index.relationships('TYPE','prop:value*') YIELD rel, weight`	lucene query on relationship index with the given type name
`apoc.index.between(node1,'TYPE',node2,'prop:value*') YIELD rel, weight`	lucene query on relationship index with the given type name bound by either or both sides (each node parameter can be null)
`apoc.index.out(node,'TYPE','prop:value*') YIELD node, weight`	lucene query on relationship index with the given type name for outgoing relationship of the given node, returns end-nodes
`apoc.index.in(node,'TYPE','prop:value*') YIELD node, weight`	lucene query on relationship index with the given type name for incoming relationship of the given node, returns start-nodes

Index Management

`CALL apoc.index.list() - YIELD type,name,config`	lists all manual indexes
`CALL apoc.index.remove('name') YIELD type,name,config`	removes manual indexes
`CALL apoc.index.forNodes('name',{config}) YIELD type,name,config`	gets or creates manual node index
`CALL apoc.index.forRelationships('name',{config}) YIELD type,name,config`	gets or creates manual relationship index

Add node to index example

match (p:Person) call apoc.index.addNode(p,["name","age"]) RETURN count(*);
// 129s for 1M People
call apoc.index.nodes('Person','name:name100*') YIELD node, weight return * limit 2

Schema Index Queries

Schema Index lookups that keep order and can apply limits

`apoc.index.orderedRange(label,key,min,max,sort-relevance,limit) yield node`	schema range scan which keeps index order and adds limit, values can be null, boundaries are inclusive
`apoc.index.orderedByText(label,key,operator,value,sort-relevance,limit) yield node`	schema string search which keeps index order and adds limit, operator is 'STARTS WITH' or 'CONTAINS'

Meta Graph

Returns a virtual graph that represents the labels and relationship-types available in your database and how they are connected.

`CALL apoc.meta.graph`	examines the full graph to create the meta-graph
`CALL apoc.meta.graphSample(sampleSize)`	examines a sample graph to create the meta-graph, default sampleSize is 100
`CALL apoc.meta.subGraph({config})`	examines a sample sub graph to create the meta-graph, default sampleSize is 100 config is: {labels:[labels],rels:[rel-types],sample:sample}
`CALL apoc.meta.data`	examines a subset of the graph to provide a tabular meta information
`CALL apoc.meta.stats yield labelCount, relTypeCount, propertyKeyCount, nodeCount, relCount, labels, relTypes, stats`	returns the information stored in the transactional database statistics
`CALL apoc.meta.type(value)`	type name of a value (`INTEGER,FLOAT,STRING,BOOLEAN,RELATIONSHIP,NODE,PATH,NULL,UNKNOWN,MAP,LIST`)
`CALL apoc.meta.isType(value,type)`	returns a row if type name matches none if not

isType example

MATCH (n:Person)
CALL apoc.meta.isType(n.age,"INTEGER")
RETURN n LIMIT 5

Schema

apoc.schema.assert({indexLabel:[indexKeys],…},{constraintLabel:[constraintKeys,…]}) yield label, key, unique, action

asserts that at the end of the operation the given indexes and unique constraints are there, each label:key pair is considered one constraint/label

Locking

`call apoc.lock.nodes([nodes])`	acquires a write lock on the given nodes
`call apoc.lock.rels([relationships])`	acquires a write lock on the given relationship
`call apoc.lock.all([nodes],[relationships])`	acquires a write lock on the given nodes and relationships

from/toJson

`CALL apoc.convert.toJson([1,2,3])`	converts value to json string
`CALL apoc.convert.toJson( {a:42,b:"foo",c:[1,2,3]})`	converts value to json map
`CALL apoc.convert.fromJsonList('[1,2,3]')`	converts json list to Cypher list
`CALL apoc.convert.fromJsonMap( '{"a":42,"b":"foo","c":[1,2,3]}')`	converts json map to Cypher map
`CALL apoc.convert.toTree([paths]) yield value`	creates a stream of nested documents representing the at least one root of these paths

Export / Import

Data is exported as cypher statements (for neo4j-shell, and partly apoc.cypher.runFile to the given file.

`apoc.export.cypherAll(file,config)`	exports whole database incl. indexes as cypher statements to the provided file
`apoc.export.cypherData(nodes,rels,file,config)`	exports given nodes and relationships incl. indexes as cypher statements to the provided file
`apoc.export.cypherQuery(query,file,config)`	exports nodes and relationships from the cypher statement incl. indexes as cypher statements to the provided file

Loading Data from RDBMS

`CALL apoc.load.jdbc('jdbc:derby:derbyDB','PERSON') YIELD row CREATE (:Person {name:row.name})`	load from relational database, either a full table or a sql statement
`CALL apoc.load.jdbc('jdbc:derby:derbyDB','SELECT * FROM PERSON WHERE AGE > 18')`	load from relational database, either a full table or a sql statement
`CALL apoc.load.driver('org.apache.derby.jdbc.EmbeddedDriver')`	register JDBC driver of source database

Loading Data from Web-APIs (JSON, XML, CSV)

`CALL apoc.load.json('http://example.com/map.json') YIELD value as person CREATE (p:Person) SET p = person`	load from JSON URL (e.g. web-api) to import JSON as stream of values if the JSON was an array or a single value if it was a map
`CALL apoc.load.xml('http://example.com/test.xml') YIELD value as doc CREATE (p:Person) SET p.name = doc.name`	load from XML URL (e.g. web-api) to import XML as single nested map with attributes and `_type`, `_text` and `_children` fields.
`CALL apoc.load.xmlSimple('http://example.com/test.xml') YIELD value as doc CREATE (p:Person) SET p.name = doc.name`	load from XML URL (e.g. web-api) to import XML as single nested map with attributes and `type`, `_text` fields and `<childtype>` collections per child-element-type.
`CALL apoc.load.csv('url',{sep:";"}) YIELD lineNo, list, map`	load CSV fom URL as stream of values config contains any of: `{skip:1,limit:5,header:false,sep:'TAB',ignore:['tmp'],arraySep:';',mapping:{years:{type:'int',arraySep:'-',array:false,name:'age',ignore:false}}`

Interacting with Elastic Search

`apoc.es.stats(host-url-Key)`	elastic search statistics
`apoc.es.get(host-or-port,index-or-null,type-or-null,id-or-null,query-or-null,payload-or-null) yield value`	perform a GET operation
`apoc.es.query(host-or-port,index-or-null,type-or-null,query-or-null,payload-or-null) yield value`	perform a SEARCH operation
`apoc.es.getRaw(host-or-port,path,payload-or-null) yield value`	perform a raw GET operation
`apoc.es.postRaw(host-or-port,path,payload-or-null) yield value`	perform a raw POST operation
`apoc.es.post(host-or-port,index-or-null,type-or-null,query-or-null,payload-or-null) yield value`	perform a POST operation
`apoc.es.put(host-or-port,index-or-null,type-or-null,query-or-null,payload-or-null) yield value`	perform a PUT operation

Interacting with MongoDB

`CALL apoc.mongodb.get(host-or-port,db-or-null,collection-or-null,query-or-null) yield value`	perform a find operation on mongodb collection
`CALL apoc.mongodb.count(host-or-port,db-or-null,collection-or-null,query-or-null) yield value`	perform a find operation on mongodb collection
`CALL apoc.mongodb.first(host-or-port,db-or-null,collection-or-null,query-or-null) yield value`	perform a first operation on mongodb collection
`CALL apoc.mongodb.find(host-or-port,db-or-null,collection-or-null,query-or-null,projection-or-null,sort-or-null) yield value`	perform a find,project,sort operation on mongodb collection
`CALL apoc.mongodb.insert(host-or-port,db-or-null,collection-or-null,list-of-maps)`	inserts the given documents into the mongodb collection
`CALL apoc.mongodb.delete(host-or-port,db-or-null,collection-or-null,list-of-maps)`	inserts the given documents into the mongodb collection
`CALL apoc.mongodb.update(host-or-port,db-or-null,collection-or-null,list-of-maps)`	inserts the given documents into the mongodb collection

Copy these jars into the plugins directory:

mvn dependency:copy-dependencies
cp target/dependency/mongodb*.jar target/dependency/bson*.jar $NEO4J_HOME/plugins/

CALL apoc.mongodb.first('mongodb://localhost:27017','test','test',{name:'testDocument'})

Streaming Data to Gephi

apoc.gephi.add(url-or-key, workspace, data)

streams provided data to Gephi

Notes

Gephi has a streaming plugin, that can provide and accept JSON-graph-data in a streaming fashion.

Make sure to install the plugin firsrt and activate it for your workspace (there is a new "Streaming"-tab besides "Layout"), right-click "Master"→"start" to start the server.

You can provide your workspace name (you might want to rename it before you start thes streaming), otherwise it defaults to workspace0

The default Gephi-URL is http://localhost:8080, resulting in http://localhost:8080/workspace0?operation=updateGraph

You can also configure it in conf/neo4j.conf via apoc.gephi.url=url or apoc.gephi.<key>.url=url

Example

match path = (:Person)-[:ACTED_IN]->(:Movie)
WITH path LIMIT 1000
with collect(path) as paths
call apoc.gephi.add(null,'workspace0', paths) yield nodes, relationships, time
return nodes, relationships, time

Creating Data

`CALL apoc.create.node(['Label'], {key:value,…})`	create node with dynamic labels
`CALL apoc.create.nodes(['Label'], [{key:value,…}])`	create multiple nodes with dynamic labels
`CALL apoc.create.addLabels( [node,id,ids,nodes], ['Label',…])`	adds the given labels to the node or nodes
`CALL apoc.create.removeLabels( [node,id,ids,nodes], ['Label',…])`	removes the given labels from the node or nodes
`CALL apoc.create.relationship(person1,'KNOWS',{key:value,…}, person2)`	create relationship with dynamic rel-type
`CALL apoc.create.uuid YIELD uuid`	creates an UUID
`CALL apoc.create.uuids(count) YIELD uuid`	creates count UUIDs

Virtual Nodes/Rels

Virtual Nodes and Relationships don’t exist in the graph, they are only returned to the UI/user for representing a graph projection. They can be visualized or processed otherwise. Please note that they have negative id’s.

`CALL apoc.create.vNode(['Label'], {key:value,…})`	returns a virtual node
`CALL apoc.create.vNodes(['Label'], [{key:value,…}])`	returns virtual nodes
`CALL apoc.create.vRelationship(nodeFrom,'KNOWS',{key:value,…}, nodeTo)`	returns a virtual relationship
`CALL apoc.create.vPattern({_labels:['LabelA'],key:value},'KNOWS',{key:value,…}, {_labels:['LabelB'],key:value})`	returns a virtual pattern
`CALL apoc.create.vPatternFull(['LabelA'],{key:value},'KNOWS',{key:value,…},['LabelB'],{key:value})`	returns a virtual pattern

Example

MATCH (a)-[r]->(b)
WITH head(labels(a)) AS l, head(labels(b)) AS l2, type(r) AS rel_type, count(*) as count
CALL apoc.create.vNode(['Meta_Node'],{name:l}) yield node as a
CALL apoc.create.vNode(['Meta_Node'],{name:l2}) yield node as b
CALL apoc.create.vRelationship(a,'META_RELATIONSHIP',{name:rel_type, count:count},b) yield rel
RETURN *;

Virtual Graph

Create a graph object (map) from information that’s passed in. It’s basic structure is: {name:"Name",properties:{properties},nodes:[nodes],relationships:[relationships]}

`apoc.graph.from(data,'name',{properties}) yield graph`	creates a virtual graph object for later processing it tries its best to extract the graph information from the data you pass in
`apoc.graph.fromData([nodes],[relationships],'name',{properties})`	creates a virtual graph object for later processing
`apoc.graph.fromPaths(path,'name',{properties})`	creates a virtual graph object for later processing
`apoc.graph.fromPaths([paths],'name',{properties})`	creates a virtual graph object for later processing
`apoc.graph.fromDB('name',{properties})`	creates a virtual graph object for later processing
`apoc.graph.fromCypher('statement',{params},'name',{properties})`	creates a virtual graph object for later processing

Warmup

(thanks @SaschaPeukert)

CALL apoc.warmup.run()

Warmup the node and relationship page-caches by loading one page at a time

Monitoring

(thanks @ikwattro)

`apoc.monitor.ids`	node and relationships-ids in total and in use
`apoc.monitor.kernel`	store information such as kernel version, start time, read-only, database-name, store-log-version etc.
`apoc.monitor.store`	store size information for the different types of stores
`apoc.monitor.tx`	number of transactions total,opened,committed,concurrent,rolled-back,last-tx-id
`apoc.monitor.locks(minWaitTime long)`	db locking information such as avertedDeadLocks, lockCount, contendedLockCount and contendedLocks etc. (enterprise)

Cypher Execution

`CALL apoc.cypher.run(fragment, params) yield value`	executes reading fragment with the given parameters
`CALL apoc.cypher.runFile(file or url) yield row, result`	runs each statement in the file, all semicolon separated - currently no schema operations
`CALL apoc.cypher.runMany('cypher;\nstatements;',{params})`	runs each semicolon separated statement and returns summary - currently no schema operations
`CALL apoc.cypher.mapParallel(fragment, params, list-to-parallelize) yield value`	executes fragment in parallel batches with the list segments being assigned to _
`CALL apoc.cypher.doIt(fragment, params) yield value`	executes writing fragment with the given parameters

TODO runFile: begin/commit/schema await/constraints/indexes

Job Management

`CALL apoc.periodic.commit(statement, params)`	repeats an batch update statement until it returns 0, this procedure is blocking
`CALL apoc.periodic.list()`	list all jobs
`CALL apoc.periodic.submit('name',statement)`	submit a one-off background statement
`CALL apoc.periodic.schedule('name',statement,repeat-time-in-seconds)`	submit a repeatedly-called background statement
`CALL apoc.periodic.countdown('name',statement,delay-in-seconds)`	submit a repeatedly-called background statement until it returns 0
`CALL apoc.periodic.rock_n_roll(statementIteration, statementAction, batchSize) YIELD batches, total`	iterate over first statement and apply action statement with given transaction batch size. Returns to numeric values holding the number of batches and the number of total processed rows. E.g.
`CALL apoc.periodic.iterate('statement returning items', 'statement per item', {batchSize:1000,parallel:true}) YIELD batches, total`	run the second statement for each item returned by the first statement. Returns number of batches and total processed rows

there are also static methods Jobs.submit, and Jobs.schedule to be used from other procedures
jobs list is checked / cleared every 10s for finished jobs

CALL apoc.periodic.rock_n_roll('match (p:Person) return id(p) as id_p', 'MATCH (p) where id(p)={id_p} SET p.lastname =p.name', 20000)

copies over the name property of each person to lastname.

Graph Refactoring

`call apoc.refactor.cloneNodes([node1,node2,…])`	clone nodes with their labels and properties
`call apoc.refactor.cloneNodesWithRelationships([node1,node2,…])`	clone nodes with their labels, properties and relationships
`call apoc.refactor.mergeNodes([node1,node2])`	merge nodes onto first in list
`call apoc.refactor.to(rel, endNode)`	redirect relationship to use new end-node
`call apoc.refactor.from(rel, startNode)`	redirect relationship to use new start-node
`call apoc.refactor.setType(rel, 'NEW-TYPE')`	change relationship-type
`call apoc.refactor.extractNode([rel1,rel2,…], [labels], 'OUT','IN')`	extract node from relationships
`call apoc.refactor.collapseNode([node1,node2],'TYPE')`	collapse node to relationship, node with one rel becomes self-relationship
`call apoc.refactor.normalizeAsBoolean(entity, propertyKey, true_values, false_values)`	normalize/convert a property to be boolean
`call apoc.refactor.categorize(node, propertyKey, type, outgoing, label)`	turn each unique propertyKey into a category node and connect to it

TODO:

merge nodes by label + property
merge relationships

Spatial

`CALL apoc.spatial.geocode('address') YIELD location, latitude, longitude, description, osmData`	look up geographic location of location from openstreetmap geocoding service
`CALL apoc.spatial.sortPathsByDistance(Collection<Path>) YIELD path, distance`	sort a given collection of paths by geographic distance based on lat/long properties on the path nodes

Helpers

Static Value Storage

`apoc.static.get(name)`	returns statically stored value from config (apoc.static.<key>) or server lifetime storage
`apoc.static.getAll(prefix)`	returns statically stored values from config (apoc.static.<prefix>) or server lifetime storage
`apoc.static.set(name, value)`	stores value under key for server livetime storage, returns previously stored or configured value

Conversion Functions

Sometimes type information gets lost, these functions help you to coerce an "Any" value to the concrete type

`apoc.convert.toString(value)`	tries it’s best to convert the value to a string
`apoc.convert.toMap(value)`	tries it’s best to convert the value to a map
`apoc.convert.toList(value)`	tries it’s best to convert the value to a list
`apoc.convert.toBoolean(value)`	tries it’s best to convert the value to a boolean
`apoc.convert.toNode(value)`	tries it’s best to convert the value to a node
`apoc.convert.toRelationship(value)`	tries it’s best to convert the value to a relationship
`apoc.convert.toSet(value)`	tries it’s best to convert the value to a set

Map Functions

`apoc.map.fromPairs([[key,value],[key2,value2],…])`	creates map from list with key-value pairs
`apoc.map.fromLists([keys],[values])`	creates map from a keys and a values list
`apoc.map.fromValues([key,value,key1,value1])`	creates map from alternating keys and values in a list
`apoc.map.setKey(map,key,value)`	returns the map with the value for this key added or replaced
`apoc.map.removeKey(map,key)`	returns the map with the key removed
`apoc.map.removeKeys(map,[keys])`	returns the map with the keys removed
`apoc.map.clean(map,[keys],[values]) yield value`	removes the keys and values (e.g. null-placeholders) contained in those lists, good for data cleaning from CSV/JSON

Collection Functions

`apoc.coll.sum([0.5,1,2.3])`	sum of all values in a list
`apoc.coll.avg([0.5,1,2.3])`	avg of all values in a list
`apoc.coll.min([0.5,1,2.3])`	minimum of all values in a list
`apoc.coll.max([0.5,1,2.3])`	maximum of all values in a list
`apoc.coll.sumLongs([1,3,3])`	sums all numeric values in a list
`apoc.coll.partition(list,batchSize)`	partitions a list into sublists of `batchSize`
`apoc.coll.zip([list1],[list2])`	all values in a list
`apoc.coll.pairs([list])`	returns `[first,second],[second,third], …
`apoc.coll.toSet([list])`	returns a unique list backed by a set
`apoc.coll.sort(coll)`	sort on Collections
`apoc.coll.sortNodes([nodes], 'name')`	sort nodes by property
`apoc.coll.contains(coll, value)`	optimized contains operation (using a HashSet) (returns single row or not)
`apoc.coll.containsAll(coll, values)`	optimized contains-all operation (using a HashSet) (returns single row or not)
`apoc.coll.containsSorted(coll, value)`	optimized contains on a sorted list operation (Collections.binarySearch) (returns single row or not)
`apoc.coll.containsAllSorted(coll, value)`	optimized contains-all on a sorted list operation (Collections.binarySearch) (returns single row or not)
`apoc.coll.union(first, second)`	creates the distinct union of the 2 lists
`apoc.coll.subtract(first, second)`	returns unique set of first list with all elements of second list removed
`apoc.coll.removeAll(first, second)`	returns first list with all elements of second list removed
`apoc.coll.intersection(first, second)`	returns the unique intersection of the two lists
`apoc.coll.disjunction(first, second)`	returns the disjunct set of the two lists
`apoc.coll.unionAll(first, second)`	creates the full union with duplicates of the two lists
`apoc.coll.split(list,value)`	splits collection on given values rows of lists, value itself will not be part of resulting lists
`apoc.coll.indexOf(coll, value)`	position of value in the list

Lookup Functions

`apoc.get.nodes(node`	id
`[ids]) yield node`	quickly returns all nodes with these id’s
`apoc.get.rels(rels`	id
`[ids]) yield rel`	quickly returns all relationships with these id’s

Phonetic Comparisons

`CALL apoc.text.phonetic(value) yield value`	Compute the US_ENGLISH phonetic soundex encoding of all words of the text value which can be a single string or a list of strings
`CALL apoc.text.phoneticDelta(text1, text2) yield phonetic1, phonetic2, delta`	Compute the US_ENGLISH soundex character difference between two given strings
`CALL apoc.text.join(['text1','text2',…], delimiter) YIELD value`	join the given strings with the given delimiter.
`CALL apoc.text.clean(text) YIELD value`	strip the given string of everything except alpha numeric characters and convert it to lower case.
`CALL apoc.text.compareCleaned(text1, text2) YIELD value`	compare the given strings stripped of everything except alpha numeric characters converted to lower case.
`CALL apoc.text.filterCleanMatches(text1, text2) YIELD value`	filter out non-matches of the given strings stripped of everything except alpha numeric characters converted to lower case.

apoc.data.domain(email_or_url) yield value

returns domain part of the value

`apoc.util.sha1([values])`	computes the sha1 of the concatenation of all string values of the list
`apoc.util.md5([values])`	computes the md5 of the concatenation of all string values of the list
`apoc.util.sleep({duration})`	sleeps for <duration> millis, transaction termination is honored

Date/time Support

(thanks @tkroman)

Conversion between formatted dates and timestamps

`apoc.date.parseDefault('2015-03-25 03:15:59','s')`	get Unix time equivalent of given date (in seconds)
`apoc.date.parse('2015/03/25 03-15-59','s', 'yyyy/MM/dd HH/mm/ss')`	same as previous, but accepts custom datetime format
`apoc.date.formatDefault(12345,'s')`	get string representation of date corresponding to given Unix time (in seconds)
`apoc.date.format(12345,'s', 'yyyy/MM/dd HH/mm/ss')`	the same as previous, but accepts custom datetime format
`apoc.date.parseDefault('2015-03-25 03:15:59','ms')`	get Unix time equivalent of given date (in milliseconds)
`apoc.date.parse('2015/03/25 03-15-59','ms','yyyy/MM/dd HH/mm/ss')`	same as previous, but accepts custom datetime format
`apoc.date.formatDefault(12345,'ms')`	get string representation of date corresponding to given time in milliseconds in UTC time zone
`apoc.date.format(12345,'ms', 'yyyy/MM/dd HH/mm/ss')`	the same as previous, but accepts custom datetime format
`apoc.date.formatTimeZone(12345,'s', 'yyyy/MM/dd HH/mm/ss', 'ABC')`	the same as previous, but accepts custom time zone

possible unit values: ms,s,m,h,d and their long forms millis,milliseconds,seconds,minutes,hours,days.
possible time zone values: Either an abbreviation such as PST, a full name such as America/Los_Angeles, or a custom ID such as GMT-8:00. Full names are recommended. You can view a list of full names in this Wikipedia page.

Reading separate datetime fields

Splits date (optionally, using given custom format) into fields returning a map from field name to its value.

apoc.date.fields('2015-03-25 03:15:59')
apoc.date.fieldsFormatted('2015-01-02 03:04:05 EET', 'yyyy-MM-dd HH:mm:ss zzz')

Bitwise operations

Provides a wrapper around the java bitwise operations.

call apoc.bitwise.op(a long, "operation", b long ) yield value as <identifier>

examples

operator	name	example	result
a & b	AND	call apoc.bitwise.op(60,"&",13)	12
a \| b	OR	call apoc.bitwise.op(60,"\|",13)	61
a ^ b	XOR	call apoc.bitwise.op(60,"&",13)	49
~a	NOT	call apoc.bitwise.op(60,"&",0)	-61
a << b	LEFT SHIFT	call apoc.bitwise.op(60,"<<",2)	240
a >> b	RIGHT SHIFT	call apoc.bitwise.op(60,">>",2)	15
a >>> b	UNSIGNED RIGHT SHIFT	call apoc.bitwise.op(60,">>>",2)	15

Path Expander

(thanks @keesvegter)

The apoc.path.expand procedure makes it possible to do variable length path traversals where you can specify the direction of the relationship per relationship type and a list of Label names which act as a "whitelist" or a "blacklist". The procedure will return a list of Paths in a variable name called "path".

call apoc.path.expand(startNode <id>|Node, relationshipFilter, labelFilter, minDepth, maxDepth ) yield path as <identifier>

expand from given nodes(s) taking the provided restrictions into account

Relationship Filter

Syntax: [<]RELATIONSHIP_TYPE1[>]|[<]RELATIONSHIP_TYPE2[>]|…

input	type	direction
`LIKES>`	`LIKES`	OUTGOING
`<FOLLOWS`	`FOLLOWS`	INCOMING
`KNOWS`	`KNOWS`	BOTH

Label Filter

Syntax: [+-/]LABEL1|LABEL2|…

input	label	result
`+Friend`	`Friend`	include label (whitelist)
`-Foe`	`Foe`	exclude label (blacklist)
`/Friend`	`Friend`	stop traversal after reaching a friend (but include him)

Parallel Node Search

Utility to find nodes in parallel (if possible). These procedures return a single list of nodes or a list of 'reduced' records with node id, labels, and the properties where the search was executed upon.

`call apoc.search.node(labelPropertyMap, searchType, search ) yield node`	A distinct set of Nodes will be returned.
`call apoc.search.nodeAll(labelPropertyMap, searchType, search ) yield node`	All the found Nodes will be returned.
`call apoc.search.nodeReduced(labelPropertyMap, searchType, search ) yield id, labels, values`	A merged set of 'minimal' Node information will be returned. One record per node (-id).
`call apoc.search.nodeAllReduced(labelPropertyMap, searchType, search ) yield id, labels, values`	All the found 'minimal' Node information will be returned. One record per label and property.

`labelPropertyMap`	`'{ label1 : "propertyOne", label2 :["propOne","propTwo"] }'`	(JSON or Map) For every Label-Property combination a search will be executed in parallel (if possible): Label1.propertyOne, label2.propOne and label2.propTwo.
`searchType`	'exact' or 'contains' or 'starts with' or 'ends with'	Case insensitive string search operators
`searchType`	"<", ">", "=", "<>", "⇐", ">=", "=~"	Operators
`search`	'Keanu'	The actual search term (string, number, etc).

example

CALL apoc.search.nodeAll('{Person: "name",Movie: ["title","tagline"]}','contains','her') YIELD node AS n RETURN n
call apoc.search.nodeReduced({Person: 'born', Movie: ['released']},'>',2000) yield id, labels, properties RETURN *

Graph Algorithms (work in progress)

Provides some graph algorithms (not very optimized yet)

`apoc.algo.dijkstra(startNode, endNode, 'KNOWS\|<WORKS_WITH\|IS_MANAGER_OF>', 'distance') YIELD path, weight`	run dijkstra with relationship property name as cost function
`apoc.algo.dijkstraWithDefaultWeight(startNode, endNode, 'KNOWS\|<WORKS_WITH\|IS_MANAGER_OF>', 'distance', 10) YIELD path, weight`	run dijkstra with relationship property name as cost function and a default weight if the property does not exist
`apoc.algo.aStar(startNode, endNode, 'KNOWS\|<WORKS_WITH\|IS_MANAGER_OF>', 'distance','lat','lon') YIELD path, weight`	run A* with relationship property name as cost function
`apoc.algo.aStar(startNode, endNode, 'KNOWS\|<WORKS_WITH\|IS_MANAGER_OF>', {weight:'dist',default:10, x:'lon',y:'lat'}) YIELD path, weight`	run A* with relationship property name as cost function
`apoc.algo.allSimplePaths(startNode, endNode, 'KNOWS\|<WORKS_WITH\|IS_MANAGER_OF>', 5) YIELD path, weight`	run allSimplePaths with relationships given and maxNodes

`apoc.algo.betweenness(['TYPE',…],nodes,BOTH) YIELD node, score`	calculate betweenness centrality for given nodes
`apoc.algo.closeness(['TYPE',…],nodes, INCOMING) YIELD node, score`	calculate closeness centrality for given nodes
`apoc.algo.cover(nodeIds) YIELD rel`	return relationships between this set of nodes

`apoc.algo.pageRank(nodes) YIELD node, score`	calculates page rank for given nodes
`apoc.algo.pageRankWithConfig(nodes,{iterations:_,types:_}) YIELD node, score`	calculates page rank for given nodes

`apoc.algo.community(times,labels,partitionKey,type,direction,weightKey,batchSize)`	simple label propagation kernel
`apoc.algo.cliques(minSize) YIELD clique`	search the graph and return all maximal cliques at least at large as the minimum size argument.
`apoc.algo.cliquesWithNode(startNode, minSize) YIELD clique`	search the graph and return all maximal cliques that are at least as large than the minimum size argument and contain this node

Example: find the weighted shortest path based on relationship property d from A to B following just :ROAD relationships

MATCH (from:Loc{name:'A'}), (to:Loc{name:'D'})
CALL apoc.algo.dijkstra(from, to, 'ROAD', 'd') yield path as path, weight as weight
RETURN path, weight
MATCH (n:Person)

Plans

move apoc.get to apoc.nodes and apoc.rels
add apoc.nodes.delete(id|ids|node|nodes)
(√) add weight/score to manual index operations, expose it, TODO add Sort.RELEVANCE sorter conditionally or unconditionally
pass in last count to rundown so you can also do batch-creates
in browser guide as apoc-help-page
(√) optimized collection functions (WIP)
Time Conversion Functions (ISO<→ts, padded long representation)
ordered, limited retrieval from index (both manual and schema index)
json to graph (mapping)
virtual graph from collection of nodes and rels, handle node-uniqueness with pk
RDF / Ontology loader
Encryption / decryption of single properties or a subset or all properties (provide decryption key as param or config)
(in progress) Graph Algorithms (Stefan, Max?)
custom expanders, e.g. with dynamic rel-type suffixes and prefixes
(√) Graph Refactorings (WIP)
(√) Job Queue (WIP) See BatchedWriter from Jake/Max
run/load shell scripts apoc.load.shell(path)
apox.save.dump() whole database, dump("statement"), dump("", "data/import/file") dump("", "URL TO PUT"), formats - binary(packstream), human readable(graphml, graphjson), compression
store arbitrary objects in properties with kryo/packstream or similar serialization
variable path length on patterns instead of single relationships. Don’t have a syntax for this to suggest, but assume you want to search for ()-[:TYPE_A]→()-[:TYPE_B]→() e.g. 2..5 times.
match (a)-[r*]→(b) where all rels in the path are this pattern ()-[:Foo]→()-[:Bar]→()
all unique pairs of a list
TopK select
apoc.schema.create(indexConfig) - {unique:[{Label:keys}], index:[{Label:keys}],existence:[{Label:keys}], }
Procedures in other languages (e.g. JS, JSR-223 scripting → apoc-unsafe project)
eval javascript
apoc.meta.validate(metagraph) validate a metagraph against the current graph and report violations
apoc.run.register(name, query[,params]), apoc.run.named(name,[params])
apoc.create.graph(nodes,rels,data-map) → {nodes:[], rels:[], data:{}} a graph data structure, e.g. for rendering, export, validation, …
auto-increment id’s (per label? → graph properties)
query neo4j databases
find relationships within a set of nodes
summary for graphs (a bit like apoc.meta.stats but for a named subgraph)
graph operations (union, intersection etc. see gradoop), but also graph summarization and FSM
path expander config for node and rel-properties, both equals with value as well as comparisons with operator → value { name: "John" weight : { > : 10, < : 100 }
represent storage-records virtually as a graph
run cypher query and return query plan as a graph
add list of values support to parallel node search, add support for HAS_LABEL (OR, AND, ALL)
run export in parallel
demonstrate how to run export in parallel just with the built in procs in periodic/cypher
give a flag to rock_n_roll that makes it run concurrently
allow to attach cypher queries to tx handler (like triggers)

Awesome Procedures for Neo4j 3.x

License

"APOC" Name history

Download latest release

Build & install the current development branch from source

Calling Procedures within Cypher

Included Procedures Overview

Built in Help

Builtin Package and Procedure count

Manual Indexes

Index Queries

Index Management

Schema Index Queries

Meta Graph

Schema

Locking

from/toJson

Export / Import

Loading Data from RDBMS

Loading Data from Web-APIs (JSON, XML, CSV)

Interacting with Elastic Search

Interacting with MongoDB

Streaming Data to Gephi

Notes

Example

Creating Data

Virtual Nodes/Rels

Virtual Graph

Warmup

Monitoring

Cypher Execution

Job Management

Graph Refactoring

Spatial

Helpers

Static Value Storage

Conversion Functions

Map Functions

Collection Functions

Lookup Functions

Phonetic Comparisons

Date/time Support

Conversion between formatted dates and timestamps

Reading separate datetime fields

Bitwise operations

Path Expander

Relationship Filter

Label Filter

Parallel Node Search

Graph Algorithms (work in progress)

Plans

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