Лекция
Сразу хочу сказать, что здесь никакой воды про графовая бд, и только нужная информация. Для того чтобы лучше понимать что такое графовая бд, neo4j, еcypher , настоятельно рекомендую прочитать все из категории Базы данных, знаний и хранилища данных. Big data, СУБД и SQL и noSQL.
Cypher - это декларативный язык запросов для neo4j , ведущей мировой графовой базы данных. Основные принципы и возможности Cypher:
Cypher сопоставляет шаблоны узлов и отношений на графике, чтобы извлечь информацию или изменить данные
. Cypher имеет концепцию идентификаторов, которые обозначают именованные, связанные элементы и параметры.
Cypher может создавать, обновлять и удалять узлы, отношения, метки и свойства.
Cypher управляет индексами и ограничениями.
Вы можете попробовать фрагменты Cypher вживую в консоли Neo4j atconsole.neo4j.org или прочитать полную документацию Cypher на docs.neo4j.org.
Если хотите моделировать живые графы, использующих Cypher, ознакомьтесь с GraphGist.
Также вы можете испольхховать вебинтерфейс для запросов
запустите службу
sudo service neo4j start
перейдите http://localhost:7474/browser/.
Вы должны увидеть интерфейс Neo4j
удалить все связанные ноды
CODE:
match (n)-[r]-() DELETE n,r
удалить все не связанные ноды (только если такие остались)
CODE:
match n DELETE n
создать ноду с Лайболом User с проперти id:1 и 2
CODE:
create (n:User {id:1})
create (n:User {id:2})
объединить ребром follow (связью ноду с проперти =1 и =2 )
CODE:
MATCH (a {id : 1}), (b {id : 2}) MERGE (a)-[r:follow]->(b) ;
создать CONSTRAINT и индексы
CODE:
CREATE CONSTRAINT ON(u : User)ASSERT u . id IS UNIQUE ;
найти всех пользователей (все ноды)
CODE:
MATCH (n:`User`) RETURN n LIMIT 25
(максимум 25 нод)
Treating every line as a collection (faster):
LOAD CSV FROM "file:///tmp/movies.csv" AS csvLine MERGE (p:Person { name: csvLine }) MERGE (m:Movie { title: csvLine }) CREATE (p)-[:PLAYED { role: csvLine }]->(m)
or a map:
LOAD CSV WITH HEADERS FROM "file:///tmp/movies.csv" AS csvLine MERGE (p:Person { name: csvLine.name}) MERGE (m:Movie { title: csvLine.title}) CREATE (p)-[:PLAYED { role: csvLine.role}]->(m) Выбрать все ноды
MATCH (n) RETURN n;
Подсчитать количество
MATCH (n:Ware {wareId:1}) RETURN "Our graph have "+count(*)+" Nodes with label Ware and wareId=1" as counter;
Создать 2 связанные ноды
CREATE (n{wareId:1})-[r:SUIT]->(m{criteriaId:1})
Связать 2 существующие ноды
MATCH (a {wareId: 1}), (b {criteriaId: 2}) MERGE (a)-[r:SUIT]->(b)
Выбрать товары, которым подходит критерий 3
MATCH (a:Ware)-->(b:Criteria {criteriaId: 3}) RETURN a;
Note: {value} denotes either literals, for ad hoc Cypher queries; or parameters, which is the best practice for applications. Neo4j properties can be strings, numbers, booleans or arrays thereof. Cypher also supports maps and collections.
RETURN *
Return the value of all identifiers.
RETURN n AS columnName
Use alias for result column name.
RETURN DISTINCT n
Return unique rows.
ORDER BY n.property
Sort the result.
ORDER BY n.property DESC
Sort the result in descending order.
SKIP {skip_number}
Skip a number of results.
LIMIT {limit_number}
Limit the number of results.
SKIP {skip_number} LIMIT {limit_number}
Skip results at the top and limit the number of results.
RETURN count(*)
The number of matching rows. See Aggregation for more.
CREATE (n {name: {value}})
Create a node with the given properties.
CREATE (n {map})
Create a node with the given properties.
CREATE (n {collectionOfMaps})
Create nodes with the given properties.
CREATE (n)-[r:KNOWS]->(m)
Create a relationship with the given type and direction; bind an identifier to it.
CREATE (n)-[:LOVES {since: {value}}]->(m)
Create a relationship with the given type, direction, and properties.
MERGE (n:Person {name: {value}}) ON CREATE SET n.created=timestamp() ON MATCH SET n.counter= coalesce(n.counter, 0) + 1, n.accessTime = timestamp()
Match pattern or create it if it does not exist. Use ON CREATE and ON MATCH for conditional updates.
MATCH (a:Person {name: {value1}}), (b:Person {name: {value2}}) MERGE (a)-[r:LOVES]->(b)
MERGE finds or creates a relationship between the nodes.
MATCH (a:Person {name: {value1}}) MERGE (a)-[r:KNOWS]->(b:Person {name: {value3}})
MERGE finds or creates subgraphs attached to the node.
CREATE INDEX ON :Person(name)
Create an index on the label Person and property name.
MATCH (n:Person) WHERE n.name = {value}
An index can be automatically used for the equality comparison. Note that for example lower(n.name) = {value} will not use an index.
MATCH (n:Person) USING INDEX n:Person(name) WHERE n.name = {value}
Index usage can be enforced, when Cypher uses a suboptimal index or more than one index should be used.
DROP INDEX ON :Person(name)
Drop the index on the label Person and property name.
Mathematical
+, -, *, /, %, ^
Comparison
=, <>, <, >, <=, >=
Boolean
AND, OR, XOR, NOT
String
+
Collection
+, IN, [x], [x .. y]
Regular Expression
=~
NULL is used to represent missing/undefined values.
NULL is not equal to NULL. Not knowing two values does not imply that they are the same value. So the expression NULL = NULL yields NULL and not TRUE. To check if an expressoin is NULL, use IS NULL.
Arithmetic expressions, comparisons and function calls (except coalesce) will return NULL if any argument is NULL.
Missing elements like a property that doesn’t exist or accessing elements that don’t exist in a collection yields NULL.
In OPTIONAL MATCH clauses, NULLs will be used for missing parts of the pattern.
CREATE (n:Person {name:{value}})
Create a node with label and property.
MERGE (n:Person {name:{value}})
Matches or creates unique node(s) with label and property.
SET n:Spouse:Parent:Employee
Add label(s) to a node.
MATCH (n:Person)
Matches nodes labeled as Person.
MATCH (n:Person) WHERE n.name = {value}
Matches nodes labeled Person with the given name.
WHERE (n:Person)
Checks existence of label on node.
labels(n)
Labels of the node.
REMOVE n:Person
Remove label from node.
length(path)
The length of the path.
nodes(path)
The nodes in the path as a collection.
relationships(path)
The relationships in the path as a collection.
MATCH path=(n)-->(m) RETURN extract(x IN nodes(path) | x.prop)
Assign a path and process its nodes.
MATCH path = (begin) -[*]-> (end) FOREACH (n IN rels(path) | SET n.marked = TRUE)
Execute a mutating operation for each relationship of a path.
length({coll})
Length of the collection.
head({coll}), last({coll}), tail({coll})
head returns the first, last the last element of the collection. Об этом говорит сайт https://intellect.icu . tail the remainder of the collection. All return null for an empty collection.
[x IN coll WHERE x.prop <> {value} | x.prop]
Combination of filter and extract in a concise notation.
extract(x IN coll | x.prop)
A collection of the value of the expression for each element in the orignal collection.
filter(x IN coll WHERE x.prop <> {value})
A filtered collection of the elements where the predicate is TRUE.
reduce(s = "", x IN coll | s + x.prop)
Evaluate expression for each element in the collection, accumulate the results.
FOREACH (value IN coll | CREATE (:Person {name:value}))
Execute a mutating operation for each element in a collection.
abs({expr})
The absolute value.
rand()
A random value. Returns a new value for each call. Also useful for selecting subset or random ordering.
round({expr})
Round to the nearest integer, ceil and floor find the next integer up or down.
sqrt({expr})
The square root.
sign({expr})
0 if zero, -1 if negative, 1 if positive.
sin({expr})
Trigonometric functions, also cos, tan, cot, asin, acos,atan, atan2, haversin.
degrees({expr}), radians({expr}), pi()
Converts radians into degrees, use radians for the reverse. pi for π.
log10({expr}), log({expr}), exp({expr}), e()
Logarithm base 10, natural logarithm, e to the power of the parameter. Value of e.
count(*)
The number of matching rows.
count(identifier)
The number of non-NULL values.
count(DISTINCT identifier)
All aggregation functions also take the DISTINCTmodifier, which removes duplicates from the values.
collect(n.property)
Collection from the values, ignores NULL.
sum(n.property)
Sum numerical values. Similar functions are avg, min,max.
percentileDisc(n.property, {percentile})
Discrete percentile. Continuous percentile ispercentileCont. The percentile argument is from 0.0 to1.0.
stdev(n.property)
Standard deviation for a sample of a population. For an entire population use stdevp.
CASE n.eyes WHEN 'blue' THEN 1 WHEN 'brown' THEN 2 ELSE 3 END
Return THEN value from the matching WHEN value. TheELSE value is optional, and substituted for NULL if missing.
CASE WHEN n.eyes = 'blue' THEN 1 WHEN n.age < 40 THEN 2 ELSE 3 END
Return THEN value from the first WHEN predicate evaluating to TRUE. Predicates are evaluated in order.
START n=node(*)
Start from all nodes.
START n=node({ids})
Start from one or more nodes specified by id.
START n=node({id1}), m=node({id2})
Multiple starting points.
START n=node:nodeIndexName(key={value})
Query the index with an exact query. Use node_auto_indexfor the automatic index.
CREATE UNIQUE (n)-[:KNOWS]->(m {property: {value}})
Match pattern or create it if it does not exist. The pattern can not include any optional parts.
Use parameters instead of literals when possible. This allows Cypher to re-use your queries instead of having to parse and build new execution plans.
Always set an upper limit for your variable length patterns. It’s easy to have a query go wild and touch all nodes in a graph by mistake.
Return only the data you need. Avoid returning whole nodes and relationships — instead, pick the data you need and return only that.
[MATCH WHERE] [OPTIONAL MATCH WHERE] [WITH [ORDER BY] [SKIP] [LIMIT]] RETURN [ORDER BY] [SKIP] [LIMIT]
MATCH (n:Person)-[:KNOWS]->(m:Person) WHERE n.name="Alice"
Node patterns can contain labels and properties.
MATCH (n)-->(m)
Any pattern can be used in MATCH.
MATCH (n {name:'Alice'})-->(m)
Patterns with node properties.
MATCH p = (n)-->(m)
Assign a path to p.
OPTIONAL MATCH (n)-[r]->(m)
Optional pattern, NULLs will be used for missing parts.
WHERE n.property <> {value}
Use a predicate to filter. Note that WHERE is always part of a MATCH, OPTIONAL MATCH, WITH or START clause. Putting it after a different clause in a query will alter what it does.
MATCH (user)-[:FRIEND]-(friend) WHERE user.name = {name} WITH user, count(friend) AS friends WHERE friends > 10 RETURN user
The WITH syntax is similar to RETURN. It separates query parts explicitly, allowing you to declare which identifiers to carry over to the next part.
MATCH (user)-[:FRIEND]-(friend) WITH user, count(friend) AS friends ORDER BY friends DESC SKIP 1 LIMIT 3 RETURN user
You can also use ORDER BY, SKIP, LIMIT with WITH.
MATCH (a)-[:KNOWS]->(b) RETURN b.name UNION MATCH (a)-[:LOVES]->(b) RETURN b.name
Returns the distinct union of all query results. Result column types and names have to match.
MATCH (a)-[:KNOWS]->(b) RETURN b.name UNION ALL MATCH (a)-[:LOVES]->(b) RETURN b.name
Returns the union of all query results, including duplicated rows.
(CREATE [UNIQUE] | MERGE)* [SET|DELETE|REMOVE|FOREACH]* [RETURN [ORDER BY] [SKIP] [LIMIT]]
[MATCH WHERE] [OPTIONAL MATCH WHERE] [WITH [ORDER BY] [SKIP] [LIMIT]] (CREATE [UNIQUE] | MERGE)* [SET|DELETE|REMOVE|FOREACH]* [RETURN [ORDER BY] [SKIP] [LIMIT]]
SET n.property = {value}, n.property2 = {value2}
Update or create a property.
SET n={map}
Set all properties. This will remove any existing properties.
SET n:Person
Adds a label Person to a node.
DELETE n, r
Delete a node and a relationship.
REMOVE n:Person
Remove a label from n.
REMOVE n.property
Remove a property.
CREATE CONSTRAINT ON (p:Person) ASSERT p.name IS UNIQUE
Create a unique constraint on the label Person and property name. If any other node with that label is updated or created with a name that already exists, the write operation will fail. This constraint will create an accompanying index.
DROP CONSTRAINT ON (p:Person) ASSERT p.name IS UNIQUE
Drop the unique constraint and index on the label Personand property name.
(n)-->(m)
A relationship from n to m exists.
(n:Person)
Matches nodes with the label Person.
(n:Person:Swedish)
Matches nodes which have both Person and Swedishlabels.
(n:Person {name: {value}})
Matches nodes with the declared properties.
(n:Person)-->(m)
Node n labeled Person has a relationship to m.
(n)--(m)
A relationship in any direction between n and m.
(m)<-[:KNOWS]-(n)
A relationship from n to m of type KNOWS exists.
(n)-[:KNOWS|LOVES]->(m)
A relationship from n to m of type KNOWS or LOVES exists.
(n)-[r]->(m)
Bind an identifier to the relationship.
(n)-[*1..5]->(m)
Variable length paths.
(n)-[*]->(m)
Any depth. See the performance tips.
(n)-[:KNOWS]->(m {property: {value}})
Match or set properties in MATCH, CREATE, CREATE UNIQUE orMERGE clauses.
shortestPath((n1:Person)-[*..6]-(n2:Person))
Find a single shortest path.
allShortestPaths((n1:Person)-->(n2:Person))
Find all shortest paths.
['a','b','c'] AS coll
Literal collections are declared in square brackets.
length({coll}) AS len, {coll} AS value
Collections can be passed in as parameters.
range({first_num},{last_num},{step}) AS coll
Range creates a collection of numbers (step is optional), other functions returning collections are: labels, nodes,relationships, rels, filter, extract.
MATCH (a)-[r:KNOWS*]->() RETURN r AS rels
Relationship identifiers of a variable length path contain a collection of relationships.
RETURN matchedNode.coll AS value, length(matchedNode.coll) AS len
Properties can be arrays/collections of strings, numbers or booleans.
coll[{idx}] AS value, coll[{start_idx}..{end_idx}] AS slice
Collection elements can be accessed with idx subscripts in square brackets. Invalid indexes return NULL. Slices can be retrieved with intervals from start_idx to end_idxeach of which can be omitted or negative. Out of range elements are ignored.
{name:'Alice', age:38, address:{city:'London', residential:true}}
Literal maps are declared in curly braces much like property maps. Nested maps and collections are supported.
MERGE (p:Person {name: {map}.name}) ON CREATE SET p={map}
Maps can be passed in as parameters and used as map or by accessing keys.
RETURN matchedNode AS map
Nodes and relationships are returned as maps of their data.
map.name, map.age, map.children
Map entries can be accessed by their keys. Invalid keys result in an error.
type(a_relationship)
String representation of the relationship type.
startNode(a_relationship)
Start node of the relationship.
endNode(a_relationship)
End node of the relationship.
id(a_relationship)
The internal id of the relationship.
n.property <> {value}
Use comparison operators.
has(n.property)
Use functions.
n.number >= 1 AND n.number <= 10
Use boolean operators to combine predicates.
n:Person
Check for node labels.
identifier IS NULL
Check if something is NULL.
NOT has(n.property) OR n.property = {value}
Either property does not exist or predicate is TRUE.
n.property = {value}
Non-existing property returns NULL, which is not equal to anything.
n.property =~ "Tob.*"
Regular expression.
(n)-[:KNOWS]->(m)
Make sure the pattern has at least one match.
NOT (n)-[:KNOWS]->(m)
Exclude matches to (n)-[:KNOWS]->(m) from the result.
n.property IN [{value1}, {value2}]
Check if an element exists in a collection.
all(x IN coll WHERE has(x.property))
Returns true if the predicate is TRUE for all elements of the collection.
any(x IN coll WHERE has(x.property))
Returns true if the predicate is TRUE for at least one element of the collection.
none(x IN coll WHERE has(x.property))
Returns TRUE if the predicate is FALSE for all elements of the collection.
single(x IN coll WHERE has(x.property))
Returns TRUE if the predicate is TRUE for exactly one element in the collection.
coalesce(n.property, {defaultValue})
The first non-NULL expression.
timestamp()
Milliseconds since midnight, January 1, 1970 UTC.
id(node_or_relationship)
The internal id of the relationship or node.
toInt({expr})
Converts the given input in an integer if possible; otherwise it returns NULL.
toFloat({expr})
Converts the given input in a floating point number if possible; otherwise it returns NULL.
str({expression})
String representation of the expression.
replace({original}, {search}, {replacement})
Replace all occurrences of search with replacement. All arguments are be expressions.
substring({original}, {begin}, {sub_length})
Get part of a string. The sub_length argument is optional.
left({original}, {sub_length}), right({original}, {sub_length})
The first part of a string. The last part of the string.
trim({original}), ltrim({original}), rtrim({original})
Trim all whitespace, or on left or right side.
upper({original}), lower({original})
UPPERCASE and lowercase.
split({original}, {delimiter})
Split a string into a collection of strings.
With Neo4j 2.0 several Cypher features in version 1.9 have been deprecated or removed.
START is optional.
MERGE will take CREATE UNIQUE's role for the unique creation of patterns. Note that they are not the same, though.
Optional relationships are handled by OPTIONAL MATCH, not question marks.
Non-existing properties return NULL, n.prop? and n.prop!have been removed.
The separator for collection functions changed form :to |.
Paths are no longer collections, use nodes(path) orrels(path).
Parentheses around nodes in patterns are no longer optional.
CREATE a={property:’value’} has been removed.
Use REMOVE to remove properties.
Parameters for index-keys and nodes in patterns are no longer allowed.
To still use the older syntax, prepend your Cypher statement with CYPHER 1.9.
А как ты думаешь, при улучшении графовая бд, будет лучше нам? Надеюсь, что теперь ты понял что такое графовая бд, neo4j, еcypher и для чего все это нужно, а если не понял, или есть замечания, то не стесняйся, пиши или спрашивай в комментариях, с удовольствием отвечу. Для того чтобы глубже понять настоятельно рекомендую изучить всю информацию из категории Базы данных, знаний и хранилища данных. Big data, СУБД и SQL и noSQL
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Базы данных, знаний и хранилища данных. Big data, СУБД и SQL и noSQL
Термины: Базы данных, знаний и хранилища данных. Big data, СУБД и SQL и noSQL