Related
I have a table with this structure
Create Table Example (
[order] INT,
[typeID] INT
)
With this data:
order|type
1 7
2 11
3 11
4 18
5 5
6 19
7 5
8 5
9 3
10 11
11 11
12 3
I need to get the count of each type based on the order, something like:
type|count
7 1
11 **2**
18 1
5 1
19 1
5 **2**
3 1
11 **2**
3 1
Context
Lets say that this table is about houses, so I have a list houses in an order. So I have
Order 1: A red house
2: A white house
3: A white house
4: A red house
5: A blue house
6: A blue house
7: A white house
So I need to show that info condensed. I need to say:
I have 1 red house
Then I have 2 white houses
Then I have 1 red house
Then I have 2 blue houses
Then I have 1 white house
So the count is based on the order. The DENSE_RANK function would help me if I were able to reset the RANK when the partition changes.
So I have an answer, but I have to warn you it's probably going to get some raised eyebrows because of how it's done. It uses something known as a "Quirky Update". If you plan to implement this, please for the love of god read through the linked article and understand that this is an "undocumented hack" which needs to be implemented precisely to avoid unintended consequences.
If you have a tiny bit of data, I'd just do it row by agonizing row for simplicity and clarity. However if you have a lot of data and still need high performance, this might do.
Requirements
Table must have a clustered index in the order you want to progress in
Table must have no other indexes (these might cause SQL to read the data from another index which is not in the correct order, causing the quantum superposition of row order to come collapsing down).
Table must be completely locked down during the operation (tablockx)
Update must progress in serial fashion (maxdop 1)
What it does
You know how people tell you there is no implicit order to the data in a table? That's still true 99% of the time. Except we know that ultimately it HAS to be stored on disk in SOME order. And it's that order that we're exploiting here. By forcing a clustered index update and the fact that you can assign variables in the same update statement that columns are updated, you can effectively scroll through the data REALLY fast.
Let's set up the data:
if object_id('tempdb.dbo.#t') is not null drop table #t
create table #t
(
_order int primary key clustered,
_type int,
_grp int
)
insert into #t (_order, _type)
select 1,7
union all select 2,11
union all select 3,11
union all select 4,18
union all select 5,5
union all select 6,19
union all select 7,5
union all select 8,5
union all select 9,3
union all select 10,11
union all select 11,11
union all select 12,3
Here's the update statement. I'll walk through each of the components below
declare #Order int, #Type int, #Grp int
update #t with (tablockx)
set #Order = _order,
#Grp = case when _order = 1 then 1
when _type != #Type then #grp + 1
else #Grp
end,
#Type = _type,
_grp = #Grp
option (maxdop 1)
Update is performed with (tablockx). If you're working with a temp table, you know there's no contention on the table, but still it's a good habit to get into (if using this approach can even be considered a good habit to get into at all).
Set #Order = _order. This looks like a pointless statement, and it kind of is. However since _order is the primary key of the table, assigning that to a variable is what forces SQL to perform a clustered index update, which is crucial to this working
Populate an integer to represent the sequential groups you want. This is where the magic happens, and you have to think about it in terms of it scrolling through the table. When _order is 1 (the first row), just set the #Grp variable to 1. If, on any given row, the column value of _type differs from the variable value of #type, we increment the grouping variable. If the values are the same, we just stick with the #Grp we have from the previous row.
Update the #Type variable with the column _type's value. Note this HAS to come after the assignment of #Grp for it to have the correct value.
Finally, set _grp = #Grp. This is where the actual column value is updated with the results of step 3.
All this must be done with option (maxdop 1). This means the Maximum Degree of Parallelism is set to 1. In other words, SQL cannot do any task parallelization which might lead to the ordering being off.
Now it's just a matter of grouping by the _grp field. You'll have a unique _grp value for each consecutive batch of _type.
Conclusion
If this seems bananas and hacky, it is. As with all things, you need to take this with a grain of salt, and I'd recommend really playing around with the concept to fully understand it if you plan to implement it because I guarantee nobody else is going to know how to troubleshoot it if you get a call in the middle of the night that it's breaking.
This solution is using a recursive CTE and is relying on a gapless order value. If you don't have this, you can create it with ROW_NUMBER() on the fly:
DECLARE #mockup TABLE([order] INT,[type] INT);
INSERT INTO #mockup VALUES
(1,7)
,(2,11)
,(3,11)
,(4,18)
,(5,5)
,(6,19)
,(7,5)
,(8,5)
,(9,3)
,(10,11)
,(11,11)
,(12,3);
WITH recCTE AS
(
SELECT m.[order]
,m.[type]
,1 AS IncCounter
,1 AS [Rank]
FROM #mockup AS m
WHERE m.[order]=1
UNION ALL
SELECT m.[order]
,m.[type]
,CASE WHEN m.[type]=r.[type] THEN r.IncCounter+1 ELSE 1 END
,CASE WHEN m.[type]<>r.[type] THEN r.[Rank]+1 ELSE r.[Rank] END
FROM #mockup AS m
INNER JOIN recCTE AS r ON m.[order]=r.[order]+1
)
SELECT recCTE.[type]
,MAX(recCTE.[IncCounter])
,recCTE.[Rank]
FROM recCTE
GROUP BY recCTE.[type], recCTE.[Rank];
The recursion is traversing down the line increasing the counter if the type is unchanged and increasing the rank if the type is different.
The rest is a simple GROUP BY
I thought I'd post another approach I worked out, I think more along the lines of the dense_rank() work others were thinking about. The only thing this assumes is that _order is a sequential integer (i.e. no gaps).
Same data setup as before:
if object_id('tempdb.dbo.#t') is not null drop table #t
create table #t
(
_order int primary key clustered,
_type int,
_grp int
)
insert into #t (_order, _type)
select 1,7
union all select 2,11
union all select 3,11
union all select 4,18
union all select 5,5
union all select 6,19
union all select 7,5
union all select 8,5
union all select 9,3
union all select 10,11
union all select 11,11
union all select 12,3
What this approach does is row_number each _type so that regardless of where a _type exists, and how many times, the types will have a unique row_number in the order of the _order field. By subtracting that type-specific row number from the global row number (i.e. _order), you'll end up with groups. Here's the code for this one, then I'll walk through this as well.
;with tr as
(
select
-- Create an incrementing integer row_number over each _type (regardless of it's position in the sequence)
_type_rid = row_number() over (partition by _type order by _order),
-- This shows that on rows 6-8 (the transition between type 19 and 5), naively they're all assigned the same group
naive_type_rid = _order - row_number() over (partition by _type order by _order),
-- By adding a value to the type_rid which is a function of _type, those two values are distinct.
-- Originally I just added the value, but I think squaring it ensures that there can't ever be another gap of 1
true_type_rid = (_order - row_number() over (partition by _type order by _order)) + power(_type, 2),
_type,
_order
from #t
-- order by _order -- uncomment this if you want to run the inner select separately
)
select
_grp = dense_rank() over (order by max(_order)),
_type = max(_type)
from tr
group by true_type_rid
order by max(_order)
What's Going On
First things first; I didn't have to create a separate column in the src cte to return _type_rid. I did that mostly for troubleshooting and clarity. Secondly, I also didn't really have to do a second dense_rank on the final selection for the column _grp. I just did that so it matched exactly the results from my other approach.
Within each type, type_rid is unique, and increments by 1. _order also increments by one. So as long as a given type is chugging along, gapped by only 1, _order - _type_rid will be the same value. Let's look at a couple examples (This is the result of the src cte, ordered by _order):
_type_rid naive_type_rid true_type_rid _type _order
-------------------- -------------------- -------------------- ----------- -----------
1 8 17 3 9
2 10 19 3 12
1 4 29 5 5
2 5 30 5 7
3 5 30 5 8
1 0 49 7 1
1 1 122 11 2
2 1 122 11 3
3 7 128 11 10
4 7 128 11 11
1 3 327 18 4
1 5 366 19 6
First row, _order - _type_rid = 1 - 1 = 0. This assigns this row (type 7) to group 0
Second row, 2 - 1 = 1. This assigns type 11 to group 1
Third row, 3 - 2 = 1. This assigns the second sequential type 11 to group 1 also
Forth row, 4 - 1 = 3. This assigns type 18 to group 3
... and so forth.
The groups aren't sequential, but they ARE in the same order as _order which is the important part. You'll also notice I added the value of _type to that value as well. That's because when we hit some of the later rows, groups switched, but the sequence was still incremented by 1. By adding _type, we can differentiate those off-by-one values and still do it in the right order as well.
The final outer select from src orders by the max(_order) (in both my unnecessary dense_rank() _grp modification, and just the general result order).
Conclusion
This is still a little wonky, but definitely well within the bounds of "supported functionality". Given that I ran into one gotcha in there (the off-by-one thing), there might be others I haven't considered, so again, take that with a grain of salt, and do some testing.
What i have:
I have a Column
ID SerialNo
1 101
2 102
3 103
4 104
5 105
6 116
7 117
8 118
9 119
10 120
These are just the 10 dummy rows. The actual table has over 100 000 rows.
What I Want to get:
A method or formula like any sorting technique which could return me the starting and ending element of [SerialNo] Column for every sub-series. For example
Expected Result: 101-105, 115-120
The comma separation in the above result is not important, only the starting and ending elements are important.
What I have tried:
I did it by PL/SQL programming, by running a loop in which I’m getting the starting and ending elements getting stored in a TABLE.
But due to no. of rows (over 100 000) the query execution is taking around 2 minutes.
I have also searched about some sorting techniques for the SQL Server but I found nothing. Because rendering every row will take twice the time then a sorting algorithm
Assuming every sub series should contain 5 records, I got expected result using below sql. I hope this helps.
DECLARE #subSeriesRange INT=5;
CREATE TABLE #Temp(ID INT,SerialNo INT);
INSERT INTO #Temp VALUES(1,101),
(2,102),
(3,103),
(4,104),
(5,105),
(6,116),
(7,117),
(8,115),
(9,119),
(10,120);
SELECT STUFF((SELECT CONCAT(CASE ID%#subSeriesRange WHEN 1 THEN ',' ELSE '-' END,SerialNo)
FROM #Temp
WHERE ID%#subSeriesRange = 1 OR ID%#subSeriesRange=0
ORDER BY ID
FOR XML PATH('')),1,1,''
);
DROP TABLE #Temp;
Just finding the start and end of each series is quite straightforward:
declare #t table (ID int not null, SerialNo int not null)
insert into #t(ID,SerialNo) values
(1 ,101), (2 ,102), (3 ,103),
(4 ,104), (5 ,105), (6 ,116),
(7 ,117), (8 ,118), (9 ,119),
(10,120)
;With Starts as (
select t1.SerialNo,ROW_NUMBER() OVER (ORDER BY t1.SerialNo) as rn
from
#t t1
left join
#t t1_no
on t1.SerialNo = t1_no.SerialNo + 1
where t1_no.ID is null
), Ends as (
select t1.SerialNo,ROW_NUMBER() OVER (ORDER BY t1.SerialNo) as rn
from
#t t1
left join
#t t1_no
on t1.SerialNo = t1_no.SerialNo - 1
where t1_no.ID is null
)
select
s.SerialNo as StartSerial,
e.SerialNo as EndSerial
from
Starts s
inner join
Ends e
on s.rn = e.rn
The logic being that a Start is a row where there is no row that has the SerialNo one less than the current row, and an End is a row where there is no row that has the SerialNo one more than the current row.
This may still perform poorly if there is no index on the SerialNo column.
Results:
StartSerial EndSerial
----------- -----------
101 105
116 120
Which is hopefully acceptable since you didn't seem to care what the specific results look like. It's also keeping things set-based.
This is my first post - so I apologise if it's in the wrong seciton!
I'm joining two tables with a one-to-many relationship using their respective ID numbers: but I only want to return the most recent record for the joined table and I'm not entirely sure where to even start!
My original code for returning everything is shown below:
SELECT table_DATES.[date-ID], *
FROM table_CORE LEFT JOIN table_DATES ON [table_CORE].[core-ID] = table_DATES.[date-ID]
WHERE table_CORE.[core-ID] Like '*'
ORDER BY [table_CORE].[core-ID], [table_DATES].[iteration];
This returns a group of records: showing every matching ID between table_CORE and table_DATES:
table_CORE date-ID iteration
1 1 1
1 1 2
1 1 3
2 2 1
2 2 2
3 3 1
4 4 1
But I need to return only the date with the maximum value in the "iteration" field as shown below
table_CORE date-ID iteration Additional data
1 1 3 MoreInfo
2 2 2 MoreInfo
3 3 1 MoreInfo
4 4 1 MoreInfo
I really don't even know where to start - obviously it's going to be a JOIN query of some sort - but I'm not sure how to get the subquery to return only the highest iteration for each item in table 2's ID field?
Hope that makes sense - I'll reword if it comes to it!
--edit--
I'm wondering how to integrate that when I'm needing all the fields from table 1 (table_CORE in this case) and all the fields from table2 (table_DATES) joined as well?
Both tables have additional fields that will need to be merged.
I'm pretty sure I can just add the fields into the "SELECT" and "GROUP BY" clauses, but there are around 40 fields altogether (and typing all of them will be tedious!)
Try using the MAX aggregate function like this with a GROUP BY clause.
SELECT
[ID1],
[ID2],
MAX([iteration])
FROM
table_CORE
LEFT JOIN table_DATES
ON [table_CORE].[core-ID] = table_DATES.[date-ID]
WHERE
table_CORE.[core-ID] Like '*' --LIKE '%something%' ??
GROUP BY
[ID1],
[ID2]
Your example field names don't match your sample query so I'm guessing a little bit.
Just to make sure that I have everything you’re asking for right, I am going to restate some of your question and then answer it.
Your source tables look like this:
table_core:
table_dates:
And your outputs are like this:
Current:
Desired:
In order to make that happen all you need to do is use a subquery (or a CTE) as a “cross-reference” table. (I used temp tables to recreate your data example and _ in place of the - in your column names).
--Loading the example data
create table #table_core
(
core_id int not null
)
create table #table_dates
(
date_id int not null
, iteration int not null
, additional_data varchar(25) null
)
insert into #table_core values (1), (2), (3), (4)
insert into #table_dates values (1,1, 'More Info 1'),(1,2, 'More Info 2'),(1,3, 'More Info 3'),(2,1, 'More Info 4'),(2,2, 'More Info 5'),(3,1, 'More Info 6'),(4,1, 'More Info 7')
--select query needed for desired output (using a CTE)
; with iter_max as
(
select td.date_id
, max(td.iteration) as iteration_max
from #table_dates as td
group by td.date_id
)
select tc.*
, td.*
from #table_core as tc
left join iter_max as im on tc.core_id = im.date_id
inner join #table_dates as td on im.date_id = td.date_id
and im.iteration_max = td.iteration
select *
from
(
SELECT table_DATES.[date-ID], *
, row_number() over (partition by table_CORE date-ID order by iteration desc) as rn
FROM table_CORE
LEFT JOIN table_DATES
ON [table_CORE].[core-ID] = table_DATES.[date-ID]
WHERE table_CORE.[core-ID] Like '*'
) tt
where tt.rn = 1
ORDER BY [core-ID]
I'm refactoring very old code. Currently, PHP generates a separate select for every value. Say loc contains 1,2 and data contains a,b, it generates
select val from tablename where loc_id=1 and data_id=a;
select val from tablename where loc_id=1 and data_id=b;
select val from tablename where loc_id=2 and data_id=a;
select val from tablename where loc_id=2 and data_id=b;
...etc which all return either a single value or nothing. That meant I always had n(loc_id)*n(data_id) results, including nulls, which is necessary for subsequent processing. Knowing the order, this was used to generate an HTML table. Both data_id and loc_id can in theory scale up to a couple thousands (which is obviously not great in a table, but that's another concern).
+-----------+-----------+
| data_id 1 | data_id 2 |
+----------+-----------+-----------+
| loc_id 1 | - | 999.99 |
+----------+-----------+-----------+
+ loc_id 2 | 888.88 | - |
+----------+-----------+-----------+
To speed things up, I was looking at replacing this with a single query:
select val from tablename where loc_id in (1,2) and data_id in (a,b) order by loc_id asc, data_id asc;
to get a result like (below) and iterate to build my table.
Rownum VAL
------- --------
1 null
2 999.99
3 777.77
4 null
Unfortunately that approach drops the nulls from the resultset so I end up with
Rownum VAL
------- --------
1 999.99
2 777.77
Note that it is possible that neither data_id or loc_id have any match, in which case I would still need a null, null.
So I don't know which value matches which. I ways to match with the expected loc_id/data_id combination in php if I add loc_id and data_id... but that's getting messy.
Still a novice in SQL in general and that's absolutely the first time I work on PostgreSQL so hopefully that's not too obvious... As I post this I'm looking at two ways to solve this: any in array[] and joins. Will update if anything new is found.
tl;dr question
How do I do a where loc_id in (1,2) and data_id in (a,b) and keep the nulls so that I always get n(loc)*n(data) results?
You can achieve that in a single query with two steps:
Generate a matrix of all desired rows in the output.
LEFT [OUTER] JOIN to actual rows.
You get at least one row for every cell in your table.
If (loc_id, data_id) is unique, you get exactly one row.
SELECT t.val
FROM (VALUES (1), (2)) AS l(loc_id)
CROSS JOIN (VALUES ('a'), ('b')) AS d(data_id) -- generate total grid of rows
LEFT JOIN tablname t USING (loc_id, data_id) -- attach matching rows (if any)
ORDER BY l.loc_id, d.data_id;
Works for any number of columns with any number of values.
For your simple case:
SELECT t.val
FROM (
VALUES
(1, 'a'), (1, 'b')
, (2, 'a'), (2, 'b')
) AS ld (loc_id, data_id) -- total grid of rows
LEFT JOIN tablname t USING (loc_id, data_id) -- attach matching rows (if any)
ORDER BY ld.loc_id, ld.data_id;
where (loc_id in (1,2) or loc_id is null)
and (data_id in (a,b) or data_id is null)
Select the fields you use for filtering, so you know where the values came from:
select loc,data,val from tablename where loc in (1,2) and data in (a,b);
You won't get nulls this way either, but it's not a problem anymore. You know which fields are missing, and you know those are nulls.
I have a table which records values over time, similar to the following:
RecordId Time Name
========================
1 10 Running
2 18 Running
3 21 Running
4 29 Walking
5 33 Walking
6 57 Running
7 66 Running
After querying this table, I need a result similar to the following:
FromTime ToTime Name
=========================
10 29 Running
29 57 Walking
57 NULL Running
I've toyed around with some of the aggregate functions (e.g. MIN, MAX, etc.), PARTITION and CTEs, but I can't seem to hit upon the right solution. I'm hoping a SQL guru can give me a hand, or at least point me in the right direction. Is there a fairly straightforward way to query this (preferrably without a cursor?)
Finding "ToTime" By Aggregates Instead of a Join
I would like to share a really wild query that only takes 1 scan of the table with 1 logical read. By comparison, the best other answer on the page, Simon Kingston's query, takes 2 scans.
On a very large set of data (17,408 input rows, producing 8,193 result rows) it takes CPU 574 and time 2645, while Simon Kingston's query takes CPU 63,820 and time 37,108.
It's possible that with indexes the other queries on the page could perform many times better, but it is interesting to me to achieve 111x CPU improvement and 14x speed improvement just by rewriting the query.
(Please note: I mean no disrespect at all to Simon Kingston or anyone else; I am simply excited about my idea for this query panning out so well. His query is better than mine as its performance is plenty and it actually is understandable and maintainable, unlike mine.)
Here is the impossible query. It is hard to understand. It was hard to write. But it is awesome. :)
WITH Ranks AS (
SELECT
T = Dense_Rank() OVER (ORDER BY Time, Num),
N = Dense_Rank() OVER (PARTITION BY Name ORDER BY Time, Num),
*
FROM
#Data D
CROSS JOIN (
VALUES (1), (2)
) X (Num)
), Items AS (
SELECT
FromTime = Min(Time),
ToTime = Max(Time),
Name = IsNull(Min(CASE WHEN Num = 2 THEN Name END), Min(Name)),
I = IsNull(Min(CASE WHEN Num = 2 THEN T - N END), Min(T - N)),
MinNum = Min(Num)
FROM
Ranks
GROUP BY
T / 2
)
SELECT
FromTime = Min(FromTime),
ToTime = CASE WHEN MinNum = 2 THEN NULL ELSE Max(ToTime) END,
Name
FROM Items
GROUP BY
I, Name, MinNum
ORDER BY
FromTime
Note: This requires SQL 2008 or up. To make it work in SQL 2005, change the VALUES clause to SELECT 1 UNION ALL SELECT 2.
Updated Query
After thinking about this a bit, I realized that I was accomplishing two separate logical tasks at the same time, and this made the query unnecessarily complicated: 1) prune out intermediate rows that have no bearing on the final solution (rows that do not begin a new task) and 2) pull the "ToTime" value from the next row. By performing #1 before #2, the query is simpler and performs with approximately half the CPU!
So here is the simplified query that first, trims out the rows we don't care about, then gets the ToTime value using aggregates rather than a JOIN. Yes, it does have 3 windowing functions instead of 2, but ultimately because of the fewer rows (after pruning those we don't care about) it has less work to do:
WITH Ranks AS (
SELECT
Grp =
Row_Number() OVER (ORDER BY Time)
- Row_Number() OVER (PARTITION BY Name ORDER BY Time),
[Time], Name
FROM #Data D
), Ranges AS (
SELECT
Result = Row_Number() OVER (ORDER BY Min(R.[Time]), X.Num) / 2,
[Time] = Min(R.[Time]),
R.Name, X.Num
FROM
Ranks R
CROSS JOIN (VALUES (1), (2)) X (Num)
GROUP BY
R.Name, R.Grp, X.Num
)
SELECT
FromTime = Min([Time]),
ToTime = CASE WHEN Count(*) = 1 THEN NULL ELSE Max([Time]) END,
Name = IsNull(Min(CASE WHEN Num = 2 THEN Name ELSE NULL END), Min(Name))
FROM Ranges R
WHERE Result > 0
GROUP BY Result
ORDER BY FromTime;
This updated query has all the same issues as I presented in my explanation, however, they are easier to solve because I am not dealing with the extra unneeded rows. I also see that the Row_Number() / 2 value of 0 I had to exclude, and I am not sure why I didn't exclude it from the prior query, but in any case this works perfectly and is amazingly fast!
Outer Apply Tidies Things Up
Last, here is a version basically identical to Simon Kingston's query that I think is an easier to understand syntax.
SELECT
FromTime = Min(D.Time),
X.ToTime,
D.Name
FROM
#Data D
OUTER APPLY (
SELECT TOP 1 ToTime = D2.[Time]
FROM #Data D2
WHERE
D.[Time] < D2.[Time]
AND D.[Name] <> D2.[Name]
ORDER BY D2.[Time]
) X
GROUP BY
X.ToTime,
D.Name
ORDER BY
FromTime;
Here's the setup script if you want to do performance comparison on a larger data set:
CREATE TABLE #Data (
RecordId int,
[Time] int,
Name varchar(10)
);
INSERT #Data VALUES
(1, 10, 'Running'),
(2, 18, 'Running'),
(3, 21, 'Running'),
(4, 29, 'Walking'),
(5, 33, 'Walking'),
(6, 57, 'Running'),
(7, 66, 'Running'),
(8, 77, 'Running'),
(9, 81, 'Walking'),
(10, 89, 'Running'),
(11, 93, 'Walking'),
(12, 99, 'Running'),
(13, 107, 'Running'),
(14, 113, 'Walking'),
(15, 124, 'Walking'),
(16, 155, 'Walking'),
(17, 178, 'Running');
GO
insert #data select recordid + (select max(recordid) from #data), time + (select max(time) +25 from #data), name from #data
GO 10
Explanation
Here is the basic idea behind my query.
The times that represent a switch have to appear in two adjacent rows, one to end the prior activity, and one to begin the next activity. The natural solution to this is a join so that an output row can pull from its own row (for the start time) and the next changed row (for the end time).
However, my query accomplishes the need to make end times appear in two different rows by repeating the row twice, with CROSS JOIN (VALUES (1), (2)). We now have all our rows duplicated. The idea is that instead of using a JOIN to do calculation across columns, we'll use some form of aggregation to collapse each desired pair of rows into one.
The next task is to make each duplicate row split properly so that one instance goes with the prior pair and one with the next pair. This is accomplished with the T column, a ROW_NUMBER() ordered by Time, and then divided by 2 (though I changed it do a DENSE_RANK() for symmetry as in this case it returns the same value as ROW_NUMBER). For efficiency I performed the division in the next step so that the row number could be reused in another calculation (keep reading). Since row number starts at 1, and dividing by 2 implicitly converts to int, this has the effect of producing the sequence 0 1 1 2 2 3 3 4 4 ... which has the desired result: by grouping by this calculated value, since we also ordered by Num in the row number, we've now accomplished that all sets after the first one are comprised of a Num = 2 from the "prior" row, and a Num = 1 from the "next" row.
The next difficult task is figuring out a way to eliminate the rows we don't care about and somehow collapse the start time of a block into the same row as the end time of a block. What we want is a way to get each discrete set of Running or Walking to be given its own number so we can group by it. DENSE_RANK() is a natural solution, but a problem is that it pays attention to each value in the ORDER BY clause--we don't have syntax to do DENSE_RANK() OVER (PREORDER BY Time ORDER BY Name) so that the Time does not cause the RANK calculation to change except on each change in Name. After some thought I realized I could crib a bit from the logic behind Itzik Ben-Gan's grouped islands solution, and I figured out that the rank of the rows ordered by Time, subtracted from the rank of the rows partitioned by Name and ordered by Time, would yield a value that was the same for each row in the same group but different from other groups. The generic grouped islands technique is to create two calculated values that both ascend in lockstep with the rows such as 4 5 6 and 1 2 3, that when subtracted will yield the same value (in this example case 3 3 3 as the result of 4 - 1, 5 - 2, and 6 - 3). Note: I initially started with ROW_NUMBER() for my N calculation but it wasn't working. The correct answer was DENSE_RANK() though I am sorry to say I don't remember why I concluded this at the time, and I would have to dive in again to figure it out. But anyway, that is what T-N calculates: a number that can be grouped on to isolate each "island" of one status (either Running or Walking).
But this was not the end because there are some wrinkles. First of all, the "next" row in each group contains the incorrect values for Name, N, and T. We get around this by selecting, from each group, the value from the Num = 2 row when it exists (but if it doesn't, then we use the remaining value). This yields the expressions like CASE WHEN NUM = 2 THEN x END: this will properly weed out the incorrect "next" row values.
After some experimentation, I realized that it was not enough to group by T - N by itself, because both the Walking groups and the Running groups can have the same calculated value (in the case of my sample data provided up to 17, there are two T - N values of 6). But simply grouping by Name as well solves this problem. No group of either "Running" or "Walking" will have the same number of intervening values from the opposite type. That is, since the first group starts with "Running", and there are two "Walking" rows intervening before the next "Running" group, then the value for N will be 2 less than the value for T in that next "Running" group. I just realized that one way to think about this is that the T - N calculation counts the number of rows before the current row that do NOT belong to the same value "Running" or "Walking". Some thought will show that this is true: if we move on to the third "Running" group, it is only the third group by virtue of having a "Walking" group separating them, so it has a different number of intervening rows coming in before it, and due to it starting at a higher position, it is high enough so that the values cannot be duplicated.
Finally, since our final group consists of only one row (there is no end time and we need to display a NULL instead) I had to throw in a calculation that could be used to determine whether we had an end time or not. This is accomplished with the Min(Num) expression and then finally detecting that when the Min(Num) was 2 (meaning we did not have a "next" row) then display a NULL instead of the Max(ToTime) value.
I hope this explanation is of some use to people. I don't know if my "row-multiplying" technique will be generally useful and applicable to most SQL query writers in production environments because of the difficulty understanding it and and the difficulty of maintenance it will most certainly present to the next person visiting the code (the reaction is probably "What on earth is it doing!?!" followed by a quick "Time to rewrite!").
If you have made it this far then I thank you for your time and for indulging me in my little excursion into incredibly-fun-sql-puzzle-land.
See it For Yourself
A.k.a. simulating a "PREORDER BY":
One last note. To see how T - N does the job--and noting that using this part of my method may not be generally applicable to the SQL community--run the following query against the first 17 rows of the sample data:
WITH Ranks AS (
SELECT
T = Dense_Rank() OVER (ORDER BY Time),
N = Dense_Rank() OVER (PARTITION BY Name ORDER BY Time),
*
FROM
#Data D
)
SELECT
*,
T - N
FROM Ranks
ORDER BY
[Time];
This yields:
RecordId Time Name T N T - N
----------- ---- ---------- ---- ---- -----
1 10 Running 1 1 0
2 18 Running 2 2 0
3 21 Running 3 3 0
4 29 Walking 4 1 3
5 33 Walking 5 2 3
6 57 Running 6 4 2
7 66 Running 7 5 2
8 77 Running 8 6 2
9 81 Walking 9 3 6
10 89 Running 10 7 3
11 93 Walking 11 4 7
12 99 Running 12 8 4
13 107 Running 13 9 4
14 113 Walking 14 5 9
15 124 Walking 15 6 9
16 155 Walking 16 7 9
17 178 Running 17 10 7
The important part being that each group of "Walking" or "Running" has the same value for T - N that is distinct from any other group with the same name.
Performance
I don't want to belabor the point about my query being faster than other people's. However, given how striking the difference is (when there are no indexes) I wanted to show the numbers in a table format. This is a good technique when high performance of this kind of row-to-row correlation is needed.
Before each query ran, I used DBCC FREEPROCCACHE; DBCC DROPCLEANBUFFERS;. I set MAXDOP to 1 for each query to remove the time-collapsing effects of parallelism. I selected each result set into variables instead of returning them to the client so as to measure only performance and not client data transmission. All queries were given the same ORDER BY clauses. All tests used 17,408 input rows yielding 8,193 result rows.
No results are displayed for the following people/reasons:
RichardTheKiwi *Could not test--query needs updating*
ypercube *No SQL 2012 environment yet :)*
Tim S *Did not complete tests within 5 minutes*
With no index:
CPU Duration Reads Writes
----------- ----------- ----------- -----------
ErikE 344 344 99 0
Simon Kingston 68672 69582 549203 49
With index CREATE UNIQUE CLUSTERED INDEX CI_#Data ON #Data (Time);:
CPU Duration Reads Writes
----------- ----------- ----------- -----------
ErikE 328 336 99 0
Simon Kingston 70391 71291 549203 49 * basically not worse
With index CREATE UNIQUE CLUSTERED INDEX CI_#Data ON #Data (Time, Name);:
CPU Duration Reads Writes
----------- ----------- ----------- -----------
ErikE 375 414 359 0 * IO WINNER
Simon Kingston 172 189 38273 0 * CPU WINNER
So the moral of the story is:
Appropriate Indexes Are More Important Than Query Wizardry
With the appropriate index, Simon Kingston's version wins overall, especially when including query complexity/maintainability.
Heed this lesson well! 38k reads is not really that many, and Simon Kingston's version ran in half the time as mine. The speed increase of my query was entirely due to there being no index on the table, and the concomitant catastrophic cost this gave to any query needing a join (which mine didn't): a full table scan Hash Match killing its performance. With an index, his query was able to do a Nested Loop with a clustered index seek (a.k.a. a bookmark lookup) which made things really fast.
It is interesting that a clustered index on Time alone was not enough. Even though Times were unique, meaning only one Name occurred per time, it still needed Name to be part of the index in order to utilize it properly.
Adding the clustered index to the table when full of data took under 1 second! Don't neglect your indexes.
This will not work in SQL Server 2008, only in SQL Server 2012 version that has the LAG() and LEAD() analytic functions, but I'll leave it here for anyone with newer versions:
SELECT Time AS FromTime
, LEAD(Time) OVER (ORDER BY Time) AS ToTime
, Name
FROM
( SELECT Time
, LAG(Name) OVER (ORDER BY Time) AS PreviousName
, Name
FROM Data
) AS tmp
WHERE PreviousName <> Name
OR PreviousName IS NULL ;
Tested in SQL-Fiddle
With an index on (Time, Name) it will need an index scan.
Edit:
If NULL is a valid value for Name that needs to be taken as a valid entry, use the following WHERE clause:
WHERE PreviousName <> Name
OR (PreviousName IS NULL AND Name IS NOT NULL)
OR (PreviousName IS NOT NULL AND Name IS NULL) ;
I think you're essentially interested in where the 'Name' changes from one record to the next (in order of 'Time'). If you can identify where this happens you can generate your desired output.
Since you mentioned CTEs I'm going to assume you're on SQL Server 2005+ and can therefore use the ROW_NUMBER() function. You can use ROW_NUMBER() as a handy way to identify consecutive pairs of records and then to find those where the 'Name' changes.
How about this:
WITH OrderedTable AS
(
SELECT
*,
ROW_NUMBER() OVER (ORDER BY Time) AS Ordinal
FROM
[YourTable]
),
NameChange AS
(
SELECT
after.Time AS Time,
after.Name AS Name,
ROW_NUMBER() OVER (ORDER BY after.Time) AS Ordinal
FROM
OrderedTable before
RIGHT JOIN OrderedTable after ON after.Ordinal = before.Ordinal + 1
WHERE
ISNULL(before.Name, '') <> after.Name
)
SELECT
before.Time AS FromTime,
after.Time AS ToTime,
before.Name
FROM
NameChange before
LEFT JOIN NameChange after ON after.Ordinal = before.Ordinal + 1
I assume that the RecordIDs are not always sequential, hence the CTE to create a non-breaking sequential number.
SQLFiddle
;with SequentiallyNumbered as (
select *, N = row_number() over (order by RecordId)
from Data)
, Tmp as (
select A.*, RN=row_number() over (order by A.Time)
from SequentiallyNumbered A
left join SequentiallyNumbered B on B.N = A.N-1 and A.name = B.name
where B.name is null)
select A.Time FromTime, B.Time ToTime, A.Name
from Tmp A
left join Tmp B on B.RN = A.RN + 1;
The dataset I used to test
create table Data (
RecordId int,
Time int,
Name varchar(10));
insert Data values
(1 ,10 ,'Running'),
(2 ,18 ,'Running'),
(3 ,21 ,'Running'),
(4 ,29 ,'Walking'),
(5 ,33 ,'Walking'),
(6 ,57 ,'Running'),
(7 ,66 ,'Running');
Here's a CTE solution that gets the results you're seeking:
;WITH TheRecords (FirstTime,SecondTime,[Name])
AS
(
SELECT [Time],
(
SELECT MIN([Time])
FROM ActivityTable at2
WHERE at2.[Time]>at.[Time]
AND at2.[Name]<>at.[Name]
),
[Name]
FROM ActivityTable at
)
SELECT MIN(FirstTime) AS FromTime,SecondTime AS ToTime,MIN([Name]) AS [Name]
FROM TheRecords
GROUP BY SecondTime
ORDER BY FromTime,ToTime