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neo4j cypher_Neo4j：Cypher –避免热切

發布時間：2023/12/3 编程问答 50 豆豆

生活随笔收集整理的這篇文章主要介紹了 neo4j cypher_Neo4j：Cypher –避免热切小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

neo4j cypher

當心渴望的管道

盡管我喜歡Cypher的LOAD CSV命令使它容易地將數據獲取到Neo4j中的方法，但它目前打破了最不驚奇的規則，因為它急切地在所有行中加載某些查詢，即使是那些使用定期提交的查詢。

這是我的同事Michael在他的第二篇博客文章中指出的，它解釋了如何成功使用LOAD CSV ：

即使遵循我之前的建議，人們遇到的最大問題是，對于超過一百萬行的大量導入，Cypher遇到了內存不足的情況。

這與提交大小無關 ，因此即使是小批量的PERIODIC COMMIT也會發生。

最近，我花了幾天時間在具有4GB RAM的Windows機器上將數據導入Neo4j，所以我比Michael建議的更早看到了這個問題。

Michael解釋了如何確定您的查詢是否遭受意外的急切評估：

如果分析該查詢，則會看到查詢計劃中有一個“急切”步驟。

那就是“拉入所有數據”的地方。

您可以通過在單詞“ PROFILE”前面加上前綴來配置查詢。您需要在Web瀏覽器的/ webadmin控制臺中或使用Neo4j shell運行查詢。

我為查詢執行了此操作，并且能夠識別得到快速評估的查詢模式，在某些情況下，我們可以解決該問題。

我們將使用Northwind數據集來演示Eager管道如何潛入我們的查詢中，但請記住，該數據集足夠小，不會引起問題。

這是文件中的行的樣子：

$ head -n 2 data/customerDb.csv OrderID,CustomerID,EmployeeID,OrderDate,RequiredDate,ShippedDate,ShipVia,Freight,ShipName,ShipAddress,ShipCity,ShipRegion,ShipPostalCode,ShipCountry,CustomerID,CustomerCompanyName,ContactName,ContactTitle,Address,City,Region,PostalCode,Country,Phone,Fax,EmployeeID,LastName,FirstName,Title,TitleOfCourtesy,BirthDate,HireDate,Address,City,Region,PostalCode,Country,HomePhone,Extension,Photo,Notes,ReportsTo,PhotoPath,OrderID,ProductID,UnitPrice,Quantity,Discount,ProductID,ProductName,SupplierID,CategoryID,QuantityPerUnit,UnitPrice,UnitsInStock,UnitsOnOrder,ReorderLevel,Discontinued,SupplierID,SupplierCompanyName,ContactName,ContactTitle,Address,City,Region,PostalCode,Country,Phone,Fax,HomePage,CategoryID,CategoryName,Description,Picture 10248,VINET,5,1996-07-04,1996-08-01,1996-07-16,3,32.38,Vins et alcools Chevalier,59 rue de l'Abbaye,Reims,,51100,France,VINET,Vins et alcools Chevalier,Paul Henriot,Accounting Manager,59 rue de l'Abbaye,Reims,,51100,France,26.47.15.10,26.47.15.11,5,Buchanan,Steven,Sales Manager,Mr.,1955-03-04,1993-10-17,14 Garrett Hill,London,,SW1 8JR,UK,(71) 555-4848,3453,\x,"Steven Buchanan graduated from St. Andrews University, Scotland, with a BSC degree in 1976. Upon joining the company as a sales representative in 1992, he spent 6 months in an orientation program at the Seattle office and then returned to his permanent post in London. He was promoted to sales manager in March 1993. Mr. Buchanan has completed the courses ""Successful Telemarketing"" and ""International Sales Management."" He is fluent in French.",2,http://accweb/emmployees/buchanan.bmp,10248,11,14,12,0,11,Queso Cabrales,5,4,1 kg pkg.,21,22,30,30,0,5,Cooperativa de Quesos 'Las Cabras',Antonio del Valle Saavedra,Export Administrator,Calle del Rosal 4,Oviedo,Asturias,33007,Spain,(98) 598 76 54,,,4,Dairy Products,Cheeses,\x

合并，合并，合并

我們要做的第一件事是為每個員工和每個訂單創建一個節點，然后在它們之間創建一個關系。

我們可以從以下查詢開始：

這樣就可以了，但是如果我們像這樣對查詢進行概要分析……

PROFILE LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row WITH row LIMIT 0 MERGE (employee:Employee {employeeId: row.EmployeeID}) MERGE (order:Order {orderId: row.OrderID}) MERGE (employee)-[:SOLD]->(order)

…我們會在第三行看到“渴望”：

==> +----------------+------+--------+----------------------------------+-----------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+----------------------------------+-----------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph(0) | 0 | 0 | employee, order, UNNAMED216 | MergePattern | ==> | Eager | 0 | 0 | | | ==> | UpdateGraph(1) | 0 | 0 | employee, employee, order, order | MergeNode; :Employee; MergeNode; :Order | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+----------------------------------+-----------------------------------------+

您會注意到，當我們分析每個查詢時，我們將刪除定期提交部分，并添加“ WITH row LIMIT 0”。這使我們能夠生成足夠的查詢計劃來標識“急切”運算符，而無需實際導入任何數據。

我們希望將該查詢分為兩個查詢，以便可以不急于處理它：

USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row WITH row LIMIT 0 MERGE (employee:Employee {employeeId: row.EmployeeID}) MERGE (order:Order {orderId: row.OrderID})==> +-------------+------+--------+----------------------------------+-----------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +-------------+------+--------+----------------------------------+-----------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | employee, employee, order, order | MergeNode; :Employee; MergeNode; :Order | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +-------------+------+--------+----------------------------------+-----------------------------------------+

現在我們已經創建了員工和訂單，我們可以將他們加入在一起：

USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MATCH (employee:Employee {employeeId: row.EmployeeID}) MATCH (order:Order {orderId: row.OrderID}) MERGE (employee)-[:SOLD]->(order)==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | employee, order, UNNAMED216 | MergePattern | ==> | Filter(0) | 0 | 0 | | Property(order,orderId) == Property(row,OrderID) | ==> | NodeByLabel(0) | 0 | 0 | order, order | :Order | ==> | Filter(1) | 0 | 0 | | Property(employee,employeeId) == Property(row,EmployeeID) | ==> | NodeByLabel(1) | 0 | 0 | employee, employee | :Employee | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+

眼中沒有渴望！

比賽，比賽，比賽，合并，合并

如果我們快進幾步，我們現在可能已經將導入腳本重構到了我們在一個查詢中創建節點并在另一個查詢中創建關系的地步。

我們的create查詢按預期工作：

USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MERGE (employee:Employee {employeeId: row.EmployeeID}) MERGE (order:Order {orderId: row.OrderID}) MERGE (product:Product {productId: row.ProductID})==> +-------------+------+--------+----------------------------------------------------+--------------------------------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +-------------+------+--------+----------------------------------------------------+--------------------------------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | employee, employee, order, order, product, product | MergeNode; :Employee; MergeNode; :Order; MergeNode; :Product | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +-------------+------+--------+----------------------------------------------------+------------------------------------------------------------

現在，我們在圖表中有了員工，產品和訂單。現在讓我們在三者之間建立關系：

USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MATCH (employee:Employee {employeeId: row.EmployeeID}) MATCH (order:Order {orderId: row.OrderID}) MATCH (product:Product {productId: row.ProductID}) MERGE (employee)-[:SOLD]->(order) MERGE (order)-[:PRODUCT]->(product)

如果我們描述，我們會發現Eager再次潛入了！

==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph(0) | 0 | 0 | order, product, UNNAMED318 | MergePattern | ==> | Eager | 0 | 0 | | | ==> | UpdateGraph(1) | 0 | 0 | employee, order, UNNAMED287 | MergePattern | ==> | Filter(0) | 0 | 0 | | Property(product,productId) == Property(row,ProductID) | ==> | NodeByLabel(0) | 0 | 0 | product, product | :Product | ==> | Filter(1) | 0 | 0 | | Property(order,orderId) == Property(row,OrderID) | ==> | NodeByLabel(1) | 0 | 0 | order, order | :Order | ==> | Filter(2) | 0 | 0 | | Property(employee,employeeId) == Property(row,EmployeeID) | ==> | NodeByLabel(2) | 0 | 0 | employee, employee | :Employee | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+

在這種情況下，Eager發生在我們第二次致電MERGE時，正如Michael在他的帖子中指出的：

問題是，在單個Cypher語句中，您必須隔離會進一步影響匹配的更改，例如，當您創建帶有標簽的節點時，該標簽突然被以后的MATCH或MERGE操作所匹配。

在這種情況下，我們可以通過使用單獨的查詢來創建關系來解決該問題：

LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MATCH (employee:Employee {employeeId: row.EmployeeID}) MATCH (order:Order {orderId: row.OrderID}) MERGE (employee)-[:SOLD]->(order)==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | employee, order, UNNAMED236 | MergePattern | ==> | Filter(0) | 0 | 0 | | Property(order,orderId) == Property(row,OrderID) | ==> | NodeByLabel(0) | 0 | 0 | order, order | :Order | ==> | Filter(1) | 0 | 0 | | Property(employee,employeeId) == Property(row,EmployeeID) | ==> | NodeByLabel(1) | 0 | 0 | employee, employee | :Employee | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+-------------------------------+-----------------------------------------------------------+USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MATCH (order:Order {orderId: row.OrderID}) MATCH (product:Product {productId: row.ProductID}) MERGE (order)-[:PRODUCT]->(product)==> +----------------+------+--------+------------------------------+--------------------------------------------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+------------------------------+--------------------------------------------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | order, product, UNNAMED229 | MergePattern | ==> | Filter(0) | 0 | 0 | | Property(product,productId) == Property(row,ProductID) | ==> | NodeByLabel(0) | 0 | 0 | product, product | :Product | ==> | Filter(1) | 0 | 0 | | Property(order,orderId) == Property(row,OrderID) | ==> | NodeByLabel(1) | 0 | 0 | order, order | :Order | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+------------------------------+--------------------------------------------------------+

合并，設置

我嘗試使LOAD CSV腳本盡可能地冪等，這樣，如果我們將更多行或更多列的數據添加到CSV中，我們可以重新運行查詢而不必重新創建所有內容。

這可以引導您進入以下創建供應商的模式：

我們要確保只有一個具有該SupplierID的Supplier，但是我們可能會逐步添加新屬性，并決定使用'SET'命令替換所有內容。如果我們分析該查詢，則“渴望”會潛伏：

==> +----------------+------+--------+--------------------+----------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +----------------+------+--------+--------------------+----------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph(0) | 0 | 0 | | PropertySet | ==> | Eager | 0 | 0 | | | ==> | UpdateGraph(1) | 0 | 0 | supplier, supplier | MergeNode; :Supplier | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +----------------+------+--------+--------------------+----------------------+

我們可以使用“ ON CREATE SET”和“ ON MATCH SET”以一些重復的代價來解決此問題：

USING PERIODIC COMMIT 1000 LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-northwind/data/customerDb.csv" AS row MERGE (supplier:Supplier {supplierId: row.SupplierID}) ON CREATE SET supplier.companyName = row.SupplierCompanyName ON MATCH SET supplier.companyName = row.SupplierCompanyName==> +-------------+------+--------+--------------------+----------------------+ ==> | Operator | Rows | DbHits | Identifiers | Other | ==> +-------------+------+--------+--------------------+----------------------+ ==> | EmptyResult | 0 | 0 | | | ==> | UpdateGraph | 0 | 0 | supplier, supplier | MergeNode; :Supplier | ==> | Slice | 0 | 0 | | { AUTOINT0} | ==> | LoadCSV | 1 | 0 | row | | ==> +-------------+------+--------+--------------------+----------------------+

使用我一直在使用的數據集，在某些情況下可以避免OutOfMemory異常，而在其他情況下，可以將運行查詢所花費的時間減少3倍。

隨著時間的流逝，我希望所有這些情況都將得到解決，但是從Neo4j 2.1.5開始，這些是我已經確定過急的模式。

如果您知道其他任何人，請告訴我，我可以將其添加到帖子中或撰寫第二部分。

翻譯自: https://www.javacodegeeks.com/2014/10/neo4j-cypher-avoiding-the-eager.html