在發布Neo4j 2.1時，我們將具有UNWIND子句，該子句使處理事物集合更加容易。

在我的有關創建鄰接矩陣的博客文章中，我們想要顯示按字母順序排列的前5個見面組中有多少人，然后檢查其他各組中有多少人。

沒有UNWIND子句，我們必須這樣做：

MATCH (g:Group) WITH g ORDER BY g.name LIMIT 5WITH COLLECT(id(g)) AS groupsMATCH (g1) WHERE id(g1) IN groups MATCH (g2) WHERE id(g2) IN groupsOPTIONAL MATCH path = (g1)<-[:MEMBER_OF]-()-[:MEMBER_OF]->(g2)RETURN g1.name, g2.name, CASE WHEN path is null THEN 0 ELSE COUNT(path) END AS overlap

在這里，我們獲得了前5個組，將其ID放入集合中，然后通過背對背具有節點ID查找的MATCH來創建組的笛卡爾積。

如果不是在“組”中傳遞節點ID，而是傳遞節點，然后在MATCH步驟中使用節點ID，那么我們將進行完整的節點掃描，隨著存儲的增長，它變得非常慢。

例如，此版本將非常慢：

MATCH (g:Group) WITH g ORDER BY g.name LIMIT 5WITH COLLECT(g) AS groupsMATCH (g1) WHERE g1 IN groups MATCH (g2) WHERE g2 IN groupsOPTIONAL MATCH path = (g1)<-[:MEMBER_OF]-()-[:MEMBER_OF]->(g2)RETURN g1.name, g2.name, CASE WHEN path is null THEN 0 ELSE COUNT(path) END AS overlap

這是原始查詢的輸出：

+-------------------------------------------------------------------------------------------------------------+ | g1.name | g2.name | overlap | +-------------------------------------------------------------------------------------------------------------+ | "Big Data Developers in London" | "Big Data / Data Science / Data Analytics Jobs" | 17 | | "Big Data Jobs in London" | "Big Data London" | 190 | | "Big Data London" | "Big Data Developers in London" | 244 | | "Cassandra London" | "Big Data / Data Science / Data Analytics Jobs" | 16 | | "Big Data Jobs in London" | "Big Data Developers in London" | 52 | | "Cassandra London" | "Cassandra London" | 0 | | "Big Data London" | "Big Data / Data Science / Data Analytics Jobs" | 36 | | "Big Data London" | "Cassandra London" | 422 | | "Big Data Jobs in London" | "Big Data Jobs in London" | 0 | | "Big Data / Data Science / Data Analytics Jobs" | "Big Data / Data Science / Data Analytics Jobs" | 0 | | "Big Data Jobs in London" | "Cassandra London" | 74 | | "Big Data Developers in London" | "Big Data London" | 244 | | "Cassandra London" | "Big Data Jobs in London" | 74 | | "Cassandra London" | "Big Data London" | 422 | | "Big Data / Data Science / Data Analytics Jobs" | "Big Data London" | 36 | | "Big Data Jobs in London" | "Big Data / Data Science / Data Analytics Jobs" | 20 | | "Big Data Developers in London" | "Big Data Jobs in London" | 52 | | "Cassandra London" | "Big Data Developers in London" | 69 | | "Big Data / Data Science / Data Analytics Jobs" | "Big Data Jobs in London" | 20 | | "Big Data Developers in London" | "Big Data Developers in London" | 0 | | "Big Data Developers in London" | "Cassandra London" | 69 | | "Big Data / Data Science / Data Analytics Jobs" | "Big Data Developers in London" | 17 | | "Big Data London" | "Big Data Jobs in London" | 190 | | "Big Data / Data Science / Data Analytics Jobs" | "Cassandra London" | 16 | | "Big Data London" | "Big Data London" | 0 | +-------------------------------------------------------------------------------------------------------------+ 25 rows

如果使用UNWIND，則不再需要傳遞節點ID，而是可以將節點收集到一個集合中，然后將其分解為笛卡爾積：

MATCH (g:Group) WITH g ORDER BY g.name LIMIT 5WITH COLLECT(g) AS groupsUNWIND groups AS g1 UNWIND groups AS g2OPTIONAL MATCH path = (g1)<-[:MEMBER_OF]-()-[:MEMBER_OF]->(g2)RETURN g1.name, g2.name, CASE WHEN path is null THEN 0 ELSE COUNT(path) END AS overlap

沒有明顯更少的代碼，但是我認為使用UNWIND可以使查詢的意圖更加清晰。

我很期待看到一旦2.1是GA，UNWIND員工就會發揮創新的作用。

翻譯自: https://www.javacodegeeks.com/2014/06/neo4j-2-1-passing-around-node-ids-vs-unwind.html

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