If you include this:

```
<property name="withSummary" value="TRUE" />
```

as a property of the MagicPlan, the output file will have a header that looks like this:```
--- vhl summary ---
diff'd 8 rows in 0:00:38.011, found:
!4 row diffs
@7 column diffs
-------------------
--- row diff summary ---
1 row diffs <
3 row diffs >
------------------------
--- column diff summary ---
columns having diffs->(column3, column4, column2)
column3 has 4 diffs
column4 has 2 diffs
column2 has 1 diffs
---------------------------
--- column diffs clustered ---
columnClusters having diffs->(column3, column2.column3.column4, column3.column4)
column3 has 2 diffs
column2.column3.column4 has 1 diffs
column3.column4 has 1 diffs
---------------------------
```

Above is the output from TestCase 23, which provides functional test coverage for the results summarization feature. The input data that produced this report:

```
lhs: rhs:
column1,column2,column3,column4 column1,column2,column3,column4
---------------------------- 1, 0000, x, aaaa
2, 1111, x, aaaa ----------------------------
3, 2222, y, aaaa 3, 2222, x, aaaa
4, 0000, z, bbbb 4, 3333, x, aaaa
5, 4444, z, bbbb 5, 4444, x, aaaa
6, 5555, u, aaaa 6, 5555, x, aaaa
7, 0000, v, aaaa ----------------------------
8, 1111, x, aaaa ----------------------------
```

Note well that the primary key on both the lhs and rhs tables is column1. So DK will use column1 as the diff'ng key, to align the rows.Dissecting this report, section by section; first, there is the Very High Level (vhl) summary:

```
--- vhl summary ---
diff'd 8 rows in 0:00:38.011, found:
!4 row diffs
@7 column diffs
-------------------
```

The first line tells us how many rows were diff'd and how long it took. In this case 8 unique rows were evaluated for diffs. If a row occurs on only one side (is a ROW_DIFF), it counts as 1 row diff'd. In the case where DK is able to match the lhs row with a rhs row, that counts as 1 row diff'd, not 2. So the 8 rows that were diff'd are: the 1 row that appears only on the rhs (1), the 3 rows that appear only on the lhs (2,7,8), and the 4 rows that appear on both sides (3,4,5,6).0:00:38.011 is an ISO 8601 formatted time specification. It represents 0 hours, 0 minutes, 38 seconds, and 11 milliseconds. The next line, "!4 row diffs", starts with the ! mark, which is the symbol for ROW_DIFF in both the summary and detail sections of the report. The 4 row diffs are the rows of dashed lines in the tables above: 1 on the lhs, and 3 on the rhs. The terminology that DK uses is: "there are 4 rows missing". The final line of the vhl summary, "@7 column diffs", shows that there are a total of 7 individual column (or cell) value diffs. The @ sign is the symbol for COLUMN_DIFF in both the summary and detail sections of the report. The 7 column diffs are: row 3 column3, row 4 column2, row 4 column3, row 4 column4, row 5 column 3, row 5 column4, row 6 column3.

The next section is the row diff summary:

```
--- row diff summary ---
1 row diffs <
3 row diffs >
------------------------
```

This breaks down the row diffs according to which side they occur on. The line, "1 row diffs <", tells us that there is 1 row missing from the lhs: row 1. The next line states that there are 3 rows missing from the rhs: row 2, row 7, and row 8.Next is the column diff summary section:

```
--- column diff summary ---
columns having diffs->(column3, column4, column2)
column3 has 4 diffs
column4 has 2 diffs
column2 has 1 diffs
---------------------------
```

This is a very straightforward grouping of the COLUMN_DIFFs, grouped according to which column the diff occurs in. column3 has 4 diffs: row 3, row 4, row 5, and row 6. column4 has 2 diffs: row 4, and row 5. column2 has 1 diff: row 4.Finally, the column diffs clustered section:

```
--- column diffs clustered ---
columnClusters having diffs->(column3, column2.column3.column4, column3.column4)
column3 has 2 diffs
column2.column3.column4 has 1 diffs
column3.column4 has 1 diffs
---------------------------
```

This groups the COLUMN_DIFF columns according to which row the diffs occur in. "Cluster" is another name for "pattern of column names having diffs all in the same row". The first line tells us that there are 3 clusters, and which columns participate in each cluster. The column3 cluster has 2 diffs. That is, there are two rows where the only COLUMN_DIFFs are in column3: row 3 and row 6. The column2.column3.column4 cluster has 1 diff: row 4. Finally, the column3.column4 cluster has 1 diff: row 5. Column diff clusters are useful for spotting patterns of linked or related column diffs, which can be helpful in understanding the origin of diffs.

ReplyDeletecanada goose outletcoach outlet storemichael kors outletuggsmichael kors outletcanada goose jacketsray ban outletchristian louboutinralph lauren outletcanada goose jacketscheap nhl jerseyscoach purses on saleadidas supercolorcanada goose outletkate spadeferragamo shoescheap nike shoesnike free flyknit 4.0coach outletchristian louboutin outletthe north facearmani jeansyeezy boost 350 blackpandora jewelrylouis vuitton handbagslouboutin ukray banscanada goose outletadidas trainersyeezy boost 350moncler outletconverse shoesecco shoesZHUO20160718

Great Article Artificial Intelligence Projects

DeleteProject Center in Chennai

JavaScript Training in Chennai

JavaScript Training in Chennai

ReplyDeletetory burch outletnfl jerseysray ban eyeglassesmichael kors outlet onlinechristian louboutin outletnike blazer lowchaussures louboutinmichael kors handbagspolo outletcheap nike shoes20161124caiyan

ReplyDeletecheap uggschristian louboutin outletcheap nhl jerseysugg outlet storepandora outletugg boots canadapandora ukjordan shoesred bottomspolo ralph lauren outlet20170109

ReplyDeletenike cortez shoeslongchamp outletbirkenstock sandalscheap ray ban sunglassestory burch outlethermes beltnike outletcoach outlet onlineadidas originals zx fluxtory burch shoes2017.3.21xukaimin

ReplyDeletefred perry shirtsugg canadalongchampsuggs outletralph lauren outlet onlinecoach outlet onlinecoach factory outletadidas shoescanada goose jacketpandora charmsclb0129

This information is very useful. thank you for sharing. and I will also share information about health through the website

ReplyDeleteCara Mengatasi Diare

Obat sakit Mata Belekan Alami

Obat Sakit Dada

Penyebab sering mimisan

Cara Mengatasi Cacar Air

Cara Menghilangkan Kantung mata

Obat Telinga Berkerak dan Berair

20180710 junda

ReplyDeleteoakley sunglassesmichael kors ukgucci outletadidas trainerscoach outletugg outlettods outletmulberry handbagsray ban sunglassescoach outlet20181030 junda

ReplyDeletenike tenniskate spade outletair max 2017kate spade outletceltics jerseysmoncler outletpandora outletcoach outletgucci outletair jordan release dates20181030 junda

ReplyDeletegrizzlies jerseysfive fingers shoesgucci outletralph lauren polomichael kors handbagsmerrell shoescolumbia sportswearmizuno running shoesray ban outletsupra shoesVery Nice Blog Updation Keep Updating !!

ReplyDeletePengobatan Kelenjar Tiroid

Cara Mengobati Penyakit Ambeien

Pengobatan Penyakit Sinusitis

Pengobatan Varises dengan Bahan Alami

Obat Radang Dinding Rahim

Obat Penyakit Miom dan Kista

Cara Mengobati Penyakit Kencing Nanah

ReplyDeleteشركة تركيب جبس بورد بالرياض

معلم دهان ابواب خشب بالرياض

معلم دهانات بالرياض

شركة تركيب واجهات حجر بالرياض

شركة تركيب واجهات حجر بجدة

شركة نقل عفش من السعودية الى سوريا

ReplyDeletesupreme hoodiegiannis shoesgolden goose outletsupreme new yorkgolden gooseyeezy boost 700golden goose outletstone island salekevin durant shoesmoncler