Oracle 如何在此查询中使用分析函数

我必须编写一个非常独特的查询，并希望在一个查询中完成所有操作，但不确定是否可以

我有一个文章列表，每个文章都有一个唯一的ID。应用程序将这个ID传递给存储过程。然后，我将检索那篇文章，以及下一篇和上一篇文章。因此，列表按日期排序，我可以得到下一个和上一个

我通过超前和滞后来实现这一点。它在一个查询中工作。但是，在某些情况下，下一篇或上一篇文章的某个字段中有一个空值。如果该字段为NULL，我基本上会得到下一篇文章，其中该字段不为NULL

还有一件事。从应用程序传递的项目已分配给它一个类别。下一篇和上一篇文章必须属于同一类别

该查询现在相当大，但它可以在子查询按日期对所有内容排序时传递文章ID的下一个和上一个。但是有了这两个新的条件，NULL因子和category因子，我不知道如何在一个查询中做到这一点

有什么想法吗？或者需要一些例子，或者我现有的查询

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感谢您的宝贵时间。

Oracle安装程序：

CREATE TABLE articles ( id, category, value, dt ) AS
  SELECT 1, 1, 1,    DATE '2017-01-01' FROM DUAL UNION ALL
  SELECT 2, 1, 2,    DATE '2017-01-02' FROM DUAL UNION ALL -- Previous row
  SELECT 3, 1, NULL, DATE '2017-01-03' FROM DUAL UNION ALL -- Ignored as value is null
  SELECT 4, 1, 1,    DATE '2017-01-04' FROM DUAL UNION ALL -- Chosen id
  SELECT 5, 2, 3,    DATE '2017-01-05' FROM DUAL UNION ALL -- Ignored as different category
  SELECT 6, 1, 5,    DATE '2017-01-06' FROM DUAL;          -- Next row

SELECT *
FROM   (
SELECT a.*,
       LAG( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS prv,
       LEAD( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS nxt
FROM   articles a
)
WHERE :your_id IN ( id, nxt, prv )
AND   ( id = :your_id OR value IS NOT NULL )
ORDER BY dt;

        ID   CATEGORY      VALUE DT                         PRV        NXT
---------- ---------- ---------- ------------------- ---------- ----------
         2          1          2 2017-01-02 00:00:00          1          4
         4          1          1 2017-01-04 00:00:00          2          6
         6          1          5 2017-01-06 00:00:00          4           

查询：

CREATE TABLE articles ( id, category, value, dt ) AS
  SELECT 1, 1, 1,    DATE '2017-01-01' FROM DUAL UNION ALL
  SELECT 2, 1, 2,    DATE '2017-01-02' FROM DUAL UNION ALL -- Previous row
  SELECT 3, 1, NULL, DATE '2017-01-03' FROM DUAL UNION ALL -- Ignored as value is null
  SELECT 4, 1, 1,    DATE '2017-01-04' FROM DUAL UNION ALL -- Chosen id
  SELECT 5, 2, 3,    DATE '2017-01-05' FROM DUAL UNION ALL -- Ignored as different category
  SELECT 6, 1, 5,    DATE '2017-01-06' FROM DUAL;          -- Next row

SELECT *
FROM   (
SELECT a.*,
       LAG( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS prv,
       LEAD( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS nxt
FROM   articles a
)
WHERE :your_id IN ( id, nxt, prv )
AND   ( id = :your_id OR value IS NOT NULL )
ORDER BY dt;

        ID   CATEGORY      VALUE DT                         PRV        NXT
---------- ---------- ---------- ------------------- ---------- ----------
         2          1          2 2017-01-02 00:00:00          1          4
         4          1          1 2017-01-04 00:00:00          2          6
         6          1          5 2017-01-06 00:00:00          4           

（

：在下面的示例输出中，您的_id

设置为4。）

输出：

CREATE TABLE articles ( id, category, value, dt ) AS
  SELECT 1, 1, 1,    DATE '2017-01-01' FROM DUAL UNION ALL
  SELECT 2, 1, 2,    DATE '2017-01-02' FROM DUAL UNION ALL -- Previous row
  SELECT 3, 1, NULL, DATE '2017-01-03' FROM DUAL UNION ALL -- Ignored as value is null
  SELECT 4, 1, 1,    DATE '2017-01-04' FROM DUAL UNION ALL -- Chosen id
  SELECT 5, 2, 3,    DATE '2017-01-05' FROM DUAL UNION ALL -- Ignored as different category
  SELECT 6, 1, 5,    DATE '2017-01-06' FROM DUAL;          -- Next row

SELECT *
FROM   (
SELECT a.*,
       LAG( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS prv,
       LEAD( CASE WHEN value IS NOT NULL THEN id END ) IGNORE NULLS OVER ( PARTITION BY category ORDER BY dt ) AS nxt
FROM   articles a
)
WHERE :your_id IN ( id, nxt, prv )
AND   ( id = :your_id OR value IS NOT NULL )
ORDER BY dt;

        ID   CATEGORY      VALUE DT                         PRV        NXT
---------- ---------- ---------- ------------------- ---------- ----------
         2          1          2 2017-01-02 00:00:00          1          4
         4          1          1 2017-01-04 00:00:00          2          6
         6          1          5 2017-01-06 00:00:00          4