I have a very large database I would like to split up into tables. I would like to make it so when I run a distinct, it will make a table for every distinct name. The name of the table will be the data in one of the fields.
EX:
A --------- Data 1
A --------- Data 2
B --------- Data 3
B --------- Data 4
would result in 2 tables, 1 named A and another named B. Then the entire row of data would be copied into that field.
select distinct [name] from [maintable]
-make table for each name
-select [name] from [maintable]
-copy into table name
-drop row from [maintable]
Any help would be great!
I would advise you against this.
One solution is to create indexes, so you can access the data quickly. If you have only a handful of names, though, this might not be particularly effective because the index values would have select almost all records.
Another solution is something called partitioning. The exact mechanism differs from database to database, but the underlying idea is the same. Different portions of the table (as defined by name in your case) would be stored in different places. When a query is looking only for values for a particular name, only that data gets read.
Generally, it is bad design to have multiple tables with exactly the same data columns. Here are some reasons:
Adding a column, changing a type, or adding an index has to be done times instead of one time.
It is very hard to enforce a primary key constraint on a column across the tables -- you lose the primary key.
Queries that touch more than one name become much more complicated.
Insertions and updates are more complex, because you have to first identify the right table. This often results in overuse of dynamic SQL for otherwise basic operations.
Although there may be some simplifications (security comes to mind), most databases have other mechanisms that are superior to splitting the data into separate tables.
what you want is
CREATE TABLE new_table
AS (SELECT .... //the data that you want in this table);
Related
I am trying to select a number of rows by the value of a column called ID. I know you can do this pretty easily by:
SELECT col1, col2, col3 FROM mytable WHERE id IN (1,2,3,4,5...)
However, what if there are a few million IDs I want to select and the IDs don't always have pattern (which means I can't use something like BETWEEN x AND y)? Does this select statement still work or is there better ways of doing so?
The actual application is this. Filters are specified by users, which is compared to some attributes of the records. From those filters, we create a subset of the data which is of interest to a particular user. There are about 30 million records each with roughly ~3000 attributes (which is stored in roughly 30 tables, but every table has ID as a primary key), so every time someone makes a query about their desired subset of records, we'd have to join many tables, apply those filters, and figure out what his subset looks like. In order to avoid joining many tables all the time, I thought maybe it's a better idea to join the tables once, figure out the id of the selected subset, and this way each time a new query is made, all we have to do is select the relevant columns of the rows that match the filtered ids.
This depends on the database and the interface you are using. For a few hundred or thousand values, no problem. But your question specifies millions. And that could start to get into limits on the length of the query -- either specified by the database, the tool you are using, or intermediate libraries.
If you have so many ids, I would strongly recommend that you load them into a table in the database with the id as the primary key. Then use join or exists to identify the rows in your table that match.
Often, such a list would be generated in the database anyway. In that case, you can use a subquery or CTE and just include that code in your final query.
I have the following problem.
I have a table Entries that contains 2 columns:
EntryID - unique identifier
Name - some name
I have another EntriesMapping table (many to many mapping table) that contains 2 columns :
EntryID that refers to the EntryID of the Entries table
PartID that refers to a PartID in a seprate Parts table.
I need to write a SP that will return all data from Entries table, but for each row in the Entries table I want to provide a list of all PartID's that are registered in the EntriesMapping table.
My question is how do I best approach the deisgn of the solution to this, given that the results of the SP would regularly be processed by an app so performance is quite important.
1.
Do I write a SP that will select multiple rows per entry - where if there are more than one PartID's registered for a given entry - I will return multiple rows each having the same EntryID and Name but different PartID's
OR
2.
Do I write a SP that will select 1 row per entry in the Entries table, and have a field that is a string/xml/json that contains all the different PartID's.
OR
3. There is some other solution that I am not thinking of?
Solution 1 seems to me to be the better way to go, but I will be passing lots of repeating data.
Solution 2 wont pass extra data, but the string/json/xml would need to be processed additionally, resuling in larger cpu time per item.
PS: I feel like this is quite a common problem to solve, but I was unable to find any resource that can provide common solutions or some pros/cons to different approaches.
I think you need simple JOIN:
SELECT e.EntryId, e.Name, em.PartId
FROM Entries e
JOIN EntriesMapping em ON e.EntryId = em.EntryId
This will return what you want, no need for stored procedure for that.
We have two large tables (Clients and Contacts) which undergo an ETL process every night, being inserted into a single "People" table in the data warehouse. This table is used in many places and cannot be significantly altered without a lot of work.
The source tables are populated by third party software; we used to assume that we could identify the rows that had been updated since last night by using the "UpdateDate" column in each, but more recently identified some rows that were not touched by the ETL, as the "UpdateDate" column was not behaving as we had thought; the software company do not see this as a bug, so we have to live with this fact.
As a result, we now take all source rows, transformed into a temp staging table and then Merge that into the data warehouse, using the Merge to identify any changed values. We have noticed that this process is taking too long on some days and would like to limit the number of rows that the ETL process looks at, as we believe that the reason for the hold-up is the principally the sheer volume of data that is examined and stored on the temp database. We can see no way to look purely at the source data and identify when each row last changed.
Here is a simplified pseudocode of the ETL stored procedure, although what the procedure actually does is not really relevant to the question (included just in case you disagree with me!)
CREATE #TempTable (ClientOrContact BIT NOT NULL, Id INT NOT NULL, [Some_Other_Columns])
INSERT #TempTable
SELECT 1 AS ClientOrContact, C.Id, [SomeColumns] FROM
(SELECT [SomeColumns]
FROM Source_ClientsTable C
JOIN FieldsTable F JOIN [SomeOtherTables])
PIVOT (MAX(F.FieldValue) FOR F.FieldName IN ([SomeFieldNames]));
INSERT #TempTable
SELECT 0 AS ClientOrContact, C.Id, [SomeColumns] FROM
(SELECT [SomeColumns]
FROM Source_ContactsTable C
JOIN FieldsTable F JOIN [SomeOtherTables])
PIVOT (MAX(F.FieldValue) FOR F.FieldName IN ([SomeFieldNames]));
ALTER #TempTable ADD PRIMARY KEY (ClientOrContact, Id);
MERGE Target_PeopleTable AS Tgt
USING (SELECT [SomeColumns] FROM #TempTable JOIN [SomeOtherTables]) AS Src
ON Tgt.ClientOrContact = Src.ClientOrContact AND Tgt.Id = Src.Id
WHEN MATCHED AND NOT EXISTS (SELECT Tgt.* INTERSECT SELECT Src.*)
THEN UPDATE SET ([All_NonKeyTargetColumns] = [All_NonKeySourceColumns])
WHEN NOT MATCHED BY Target THEN INSERT [All_TargetColumns] VALUES [All_SourceColumns]
OUTPUT $Action INTO #Changes;
RETURN COUNT(*) FROM #Changes;
GO
The source tables have about 1.5M rows each, but each day only a relatively small number of rows are inserted or updated (never deleted). There are about 50 columns in each table, of those, about 40 columns can have changed values each night. Most columns are VARCHAR and each table contains an independent incremental primary key column. We can add indexes to the source tables, but not alter them in any other way (They have already been indexed by a predecessor) The source tables and target table are on the same server, but different databases. Edit: The Target Table has a composite primary key on the ClientOrContact and Id columns, matching that shown on the temp table in the script above.
So, my question is this - please could you suggest any general possible strategies that might be useful to limit the number of rows we look at or copy across each night? If we only touched the rows that we needed to each night, we would be touching less than 1% of the data we do at the moment...
Before you try the following suggestion, just one thing to check is that the Target_PeopleTable has an index or primary key on the id column. It probably does but without schema information to verify I am making no assumptions and this might speed up the merge stage.
As you've identified if you could somehow limit the records in TempTable to just the changed rows then this could offer a performance win for the actual MERGE statement (depending on how expensive determining just the changed rows is).
As a general strategy I would consider some kind of checksum to try and identify the changed records only. The T-SQL Checksum function could be used to calculate a check sum across the required columns by specifying the columns as a comma separated list to that function or there are actual column types available for this such as Binary_Checksum.
Since you cannot change the source schema you would have to maintain a list of record ids and associated checksums in your target database so that you can readily compare the source checksums to the target checksums from the last run in order to identify a difference.
You can then only insert into the Temp table where there is a checksum difference between the target and source or the id does not exist in the target db.
This might just be moving the performance problem to the temp insert part but I think it's worth a try.
Have you considered triggers? I avoid them like the plague, but they really are the solution to some problems.
Put an INSERT/UPDATE [/DELETE?] trigger on your two source tables. Program it such that when rows are added or updated, the trigger will log the IDs of these rows in a (you'll have to create this) audit table, where that table would contain the ID, the type of change (update or insert – and delete, if you have to worry about those) and when the change was made. When you run ETL, join this list of “to be merged” items with the source tables. When you’re done, delete the table and it’s reset for the next run. (Use the “added on” datetime column to make sure you don’t delete rows that may have been added while you were running ETL.)
There’s lots of details behind proper use and implementation, but overall this idea should do what you need.
I have a table people with less than 100,000 records and I have taken a backup of this table using the following:
create table people_backup as select * from people
I add some new records to my people table over time, but eventually I want to merge the records from my backup table into people. Unfortunately I cannot simply DROP my table as my new records will be lost!
So I want to update the records in my people table using the records from people_backup, based on their primary key id and I have found 2 ways to do this:
MERGE the tables together
use some sort of fancy correlated update
Great! However, both of these methods use SET and make me specify what columns I want to update. Unfortunately I am lazy and the structure of people may change over time and while my CTAS statement doesn't need to be updated, my update/merge script will need changes, which feels like unnecessary work for me.
Is there a way merge entire rows without having to specify columns? I see here that not specifying columns during an INSERT will direct SQL to insert values by order, can the same methodology be applied here, is this safe?
NB: The structure of the table will not change between backups
Given that your table is small, you could simply
DELETE FROM table t
WHERE EXISTS( SELECT 1
FROM backup b
WHERE t.key = b.key );
INSERT INTO table
SELECT *
FROM backup;
That is slow and not particularly elegant (particularly if most of the data from the backup hasn't changed) but assuming the columns in the two tables match, it does allow you to not list out the columns. Personally, I'd much prefer writing out the column names (presumably those don't change all that often) so that I could do an update.
I am hopping on a project that sits on top of a Sql Server 2008 DB with what seems like an inefficient schema to me. However, I'm not an expert at anything SQL, so I am seeking for guidance.
In general, the schema has tables like this:
ID | A | B
ID is a unique identifier
A contains text, such as animal names. There's very little variety; maybe 3-4 different values in thousands of rows. This could vary with time, but still a small set.
B is one of two options, but stored as text. The set is finite.
My questions are as follows:
Should I create another table for names contained in A, with an ID and a value, and set the ID as the primary key? Or should I just put an index on that column in my table? Right now, to get a list of A's, it does "select distinct(a) from table" which seems inefficient to me.
The table has a multitude of columns for properties of A. It could be like: Color, Age, Weight, etc. I would think that this is better suited in a separate table with: ID, AnimalID, Property, Value. Each property is unique to the animal, so I'm not sure how this schema could enforce this (the current schema implies this as it's a column, so you can only have one value for each property).
Right now the DB is easily readable by a human, but its size is growing fast and I feel like the design is inefficient. There currently is not index at all anywhere. As I said I'm not a pro, but will read more on the subject. The goal is to have a fast system. Thanks for your advice!
This sounds like a database that might represent a veterinary clinic.
If the table you describe represents the various patients (animals) that come to the clinic, then having properties specific to them are probably best on the primary table. But, as you say column "A" contains a species name, it might be worthwhile to link that to a secondary table to save on the redundancy of storing those names:
For example:
Patients
--------
ID Name SpeciesID Color DOB Weight
1 Spot 1 Black/White 2008-01-01 20
Species
-------
ID Species
1 Cocker Spaniel
If your main table should be instead grouped by customer or owner, then you may want to add an Animals table and link it:
Customers
---------
ID Name
1 John Q. Sample
Animals
-------
ID CustomerID SpeciesID Name Color DOB Weight
1 1 1 Spot Black/White 2008-01-01 20
...
As for your original column B, consider converting it to a boolean (BIT) if you only need to store two states. Barring that, consider CHAR to store a fixed number of characters.
Like most things, it depends.
By having the animal names directly in the table, it makes your reporting queries more efficient by removing the need for many joins.
Going with something like 3rd normal form (having an ID/Name table for the animals) makes you database smaller, but requires more joins for reporting.
Either way, make sure to add some indexes.