Sql-server – Backup to Azure Blob Failing with Hallengren Framework

Do you know what version of each you are using? How close are you watching this in real time? I'm hoping the below quotes and links with the questions above will help spur you towards the right direction here.

When backing up to or restoring from Windows Azure storage, SQL Server acquires an infinite lease in order to lock exclusive access to the blob. When the backup or restore process is successfully completed, the lease is released. If a backup or restore fails, the backup process attempts to clean up any invalid blob.

Reference

The Lease Blob operation establishes and manages a lock on a blob for write and delete operations. The lock duration can be 15 to 60 seconds, or can be infinite. In versions prior to 2012-02-12, the lock duration is 60 seconds.

Starting in version 2012-02-12, some behaviors of the Lease Blob operation differ from previous versions. For example, in previous versions of the Lease Blob operation you could renew a lease after releasing it. Starting in version 2012-02-12, this lease request will fail, while calls using older versions of Lease Blob still succeed. See the Changes to Lease Blob introduced in version 2012-02-12 section under Remarks for a list of changes to the behavior of this operation.

Reference

Condition 1:

WITH ord as (
    SELECT ID, CustomerID, CheckInDate, CheckOutDate
        , n = ROW_NUMBER() over(partition by [CustomerID] order by [CheckInDate], [CheckOutDate])
    FROM @data d1
), first as (
    SELECT o1.ID, o1.CustomerID, o1.CheckInDate, o1.CheckOutDate, o1.n
        , m = ROW_NUMBER() over(partition by o1.[CustomerID] order by o1.[CheckInDate], o1.[CheckOutDate])
    FROM ord o1
    INNER JOIN ord o2 ON o1.CustomerID = o2.CustomerID AND o2.n+1 = o1.n AND o1.CheckInDate > o2.CheckOutDate
), groups as (
    SELECT o.ID, o.CustomerID, nx = MIN(coalesce(f.n, 1)), n = MAX(o.n)
        , p = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID)
    FROM ord o
    LEFT JOIN first f ON o.CustomerID = f.CustomerID AND o.n < f.n
    GROUP BY o.ID, o.CustomerID
), dates as (
    SELECT g.CustomerID, g.nx, CheckInDate = MIN(o.CheckInDate)
        , CheckOutDate = CASE WHEN SUM(CASE WHEN o.CheckOutDate IS NULL THEN 1 END) IS NULL THEN MAX(o.CheckOutDate) END
    FROM groups g
    INNER JOIN ord o ON g.ID = o.ID
    GROUP BY g.nx, g.CustomerID
    HAVING COUNT(g.nx) > 1
)
SELECT o.ID, o.CustomerID
    , CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
    , CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
INNER JOIN dates d on g.CustomerID = d.CustomerID AND g.nx = d.nx
ORDER BY ID

This query output the rows that must be updated:

1. ord = I first partition by CustomerID and order by CheckInDate
2. first = I join consecutive rows from previous CTE where the next CheckInDate does not overlap previous CheckInDate and CheckDate and I partition and order them
3. groups = I group by the previous partitioned number in order to know to which group a row belongs
4. dates = I join with the original data in order to get the first and last date for each groups. Groups with only 1 row are removed
5. main select output the dates for p=1 or NULL otherwise

Output:

ID  CustomerID  CheckInDate CheckOutDate
1   1           2015-03-04  NULL
3   1           NULL        NULL
4   1           NULL        NULL

Condition 2:

WITH ord as (
    SELECT ID, CustomerID, CheckInDate, CheckOutDate
        , n = ROW_NUMBER() over(partition by [CustomerID] order by [CheckInDate], [CheckOutDate])
    FROM @data d1
), first as (
    SELECT o1.ID, o1.CustomerID, o1.CheckInDate, o1.CheckOutDate, o1.n
        , m = ROW_NUMBER() over(partition by o1.[CustomerID] order by o1.[CheckInDate], o1.[CheckOutDate])
    FROM ord o1
    INNER JOIN ord o2 ON o1.CustomerID = o2.CustomerID AND o2.n+1 = o1.n AND o1.CheckInDate > o2.CheckOutDate
), groups as (
    SELECT o.ID, o.CustomerID, nx = MIN(coalesce(f.n, 1)), n = MAX(o.n)
        , p = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID)
        , last = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID DESC)
    FROM ord o
    LEFT JOIN first f ON o.CustomerID = f.CustomerID AND o.n < f.n
    GROUP BY o.ID, o.CustomerID
), dates as (
    SELECT g.CustomerID, g.nx, CheckInDate = MIN(o.CheckInDate)
        , CheckOutDate = MAX(o2.CheckOutDate)
    FROM groups g
    INNER JOIN ord o ON g.ID = o.ID
    INNER JOIN (SELECT ID, CustomerID, nx FROM groups WHERE last = 1) l ON g.CustomerID = l.CustomerID AND g.nx = l.nx
    INNER JOIN ord o2 ON l.ID = o2.ID
    GROUP BY g.nx, g.CustomerID
    HAVING COUNT(g.nx) > 1
)
SELECT o.ID, o.CustomerID
    , CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
    , CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
INNER JOIN dates d on g.CustomerID = d.CustomerID AND g.nx = d.nx
ORDER BY ID

Output:

ID  CustomerID  CheckInDate CheckOutDate
1   1           2015-03-04  2015-05-03
3   1           NULL        NULL
4   1           NULL        NULL

For Updates, replace SELECT by UPDATE:

UPDATE g SET 
    CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
    , CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM ...

Your data:

declare @data table([ID] int, [CustomerID] int, [CheckInDate] date, [CheckOutDate] date);
Insert into @data([ID], [CustomerID], [CheckInDate], [CheckOutDate])
VALUES
    (1, 1, '2015-04-02', '2015-04-05'),
    (2, 2, '2015-03-04', '2015-05-02'),
    (3, 1, '2015-04-01', NULL),
    (4, 1, '2015-03-04', '2015-05-03'),
    (5, 1, '2015-01-03', '2015-02-03')
;

It works as well with this sample:

(1, 1, '2015-04-02', '2015-04-05'),
(2, 2, '2015-03-04', '2015-05-02'),
(3, 1, '2015-04-01', NULL),
(4, 1, '2015-03-04', '2015-05-03'),
(5, 1, '2015-01-03', '2015-02-03'),
(6, 1, '2015-01-02', '2015-02-03'),
(7, 1, '2015-03-04', '2015-03-06'),
(8, 1, '2015-03-04', '2015-05-06'),
(9, 1, '2014-04-02', '2014-04-05'),
(10, 1, '2014-03-04', '2014-05-02')

If it does not work with some of your data, update Input and Output table with more relevant values.