Is is possible to set the seed for the identity column of a table using SubSonic?
AFAIK, you need to shell out to SQL to be able to do this in SubSonic. Every DBMS has a different syntax for changing the seed, but if you know which DBMS you're using then injecting a little SQL isn't hard. For example, for SQL Server from C#, the code would be something like this:
new InlineQuery("DBCC CHECKIDENT (\"MyTable.ColumnName\",RESEED,300)").Execute();
Related
I have a client application which querys data in Spanner..
Lets say I have a table with 10 columns and my client application can search on a combination of columns.. Lets say I've added 5 indexes to optimise searching.
According to https://cloud.google.com/spanner/docs/sql-best-practices#secondary-indexes
it says:
In this scenario, Spanner automatically uses the secondary index SingersByLastName when executing the query (as long as three days have passed since database creation; see A note about new databases). However, it's best to explicitly tell Spanner to use that index by specifying an index directive in the FROM clause:
And also https://cloud.google.com/spanner/docs/secondary-indexes#index-directive suggests
When you use SQL to query a Spanner table, Spanner automatically uses any indexes that are likely to make the query more efficient. As a result, you don't need to specify an index for SQL queries. However, for queries that are critical for your workload, Google advises you to use FORCE_INDEX directives in your SQL statements for more consistent performance.
Both links suggest YOU (The developer) should be supplying Force_Index on yours queries.. This means I now need business logic in my client to say something like:
If (object.SearchTermOne)
queryBuilder.IndexToUse = "Idx_SearchTermOne"
This feels like I'm essentially trying to do the job of the optimiser by setting the index to use.. It also means if I add an extra index I need a code change to make use of it
So what are the best practices when it comes to using Force_Index in spanner queries?
The best practice is to use the Force_Index as described in the documentation at this time.
This feels like I'm essentially trying to do the job of the optimiser by setting the index to use..
I feel the same.
https://cloud.google.com/spanner/docs/secondary-indexes#index-directive
Note: The query optimizer requires up to three days to collect the databases statistics required to select a secondary index for a SQL query. During this time, Cloud Spanner will not automatically use any indexes.
As noted in this note, even if an amount of data is added that would allow the index to function effectively, it may take up to three days for the optimizer to figure it out.
Queries during that time will probably be full scans.
If you want to prevent this other than using Force_Index, you will need to run ANALYZE DDL manually.
https://cloud.google.com/blog/products/databases/a-technical-overview-of-cloud-spanners-query-optimizer
But none of this changes the fact that we are essentially trying to do the optimizer's job...
I have the following concern regarding SAS/ACCESS facility.
Let's imagine that we have an external DB (i.e. Oracle), which we have assigned to a certain libname.
Next, we do a simple operation on one of the tables within this DB, i.e.
data db.table_new;
set db.table_old(keep=var1 var2 var3);
if var1>0 then new_var1=5;
run;
My question is the following:
Will the whole table table_old be pulled from external DB to SAS Server in order to process the data?
Will SAS/ACCESS transform the data step into DBMS operation or SQL so the whole processing will be performed outside SAS?
The documentation is unclear about it . See page 62.
Usually the rule of thumb is: if the SAS functions that are used in DATA step can be converted to native db sql functions, then SAS will let the DB server do the data processing. In your case, this seems to be the situation.
You can answer this question on any piece of code through a set of non-syntax-highlighted options that need to be simplified:
options sastrace=',,,d' sastraceloc=saslog nostsuffix;
When you run the data step, check the log. You will see information about whether SAS is able to successfully translate the code or not. If it was unsuccessful, you will see:
ACCESS ENGINE: SQL statement was not passed to the DBMS, SAS will do the processing.
If this occurs, SAS will usually send out a select * to the server and pull everything before filtering. When you see that error, try doing explicit passthrough, or redesign your query so that it can do everything on the server. It is possible to bring down the SAS server, or severely degreade performance on the Oracle server, if the table is large enough.
Some common functions you'll want to avoid using directly in the query, especially with Oracle:
datepart()
intnx()
intck()
today()
put()
input()
If I have to use any of those functions, I usually play it safe and create a macro variable of static ones beforehand (e.g. today()), filter the raw data at the lowest level first to get it into the SAS server, or use explicit SQL passthrough.
In summary, I would say it depends on your method. On the second page of Chapter 1 of the SAS/Access 9.2 document in your above link, there are two methods (among the older DBLOAD procedure) of the SAS/ACCESS facility:
LIBNAME reference - assign SAS librefs to DBMS objects such
as schemas and databases; you can then work with the table or view as you would with a SAS data set...You can use such SAS procedures as PROC SQL or DATA step programming on any libref that references DBMS data.
SQL Pass-through facility - to interact with a data source using its
native SQL syntax without leaving your SAS session. SQL statements are passed directly to the data source for processing...The DBMS optimizer can take advantage of indexes on DBMS columns to process a query more quickly and
efficiently
Hence, for the first method SAS handles processing and second method DBMS handles processing. Like most clients (Java, C#, Python script or PHP webpage) that connect to external RDMS sources, unless a direct ODBC/OLEDB or other API connection is explicitly employed and request sent, processing is handled in the frontend (i.e., calculating parameters) and the end result is updated to the backend via transactions. All SAS's libraries would live in memory (or temporary hard disk) during the appointed session and depending on the code handles data itself and passes results to external source or passes data handling entirely to another source.
Comparative Example: Microsoft Access
One good comparative example would be Microsoft Access which like SAS too provides a linked table connection and pass-through query for any ODBC-compliant RDMS including SQL Server, Oracle, MySQL, etc. It is often a misnomer to tag Access as a database when actually it is a GUI program and collection of objects, one of which is the default Windows JET/ACE engine (a .dll file) not at all restricted to Access but available to all Office programs. Notice the world default as this can be switched out to any ODBC database source.
Linked tables are essentially Access GUI objects (specifically special tabledefs) not unlike SAS's libname refs that are loaded into a JET/ACE table container with data pointing externally. One can then use a linked table like any other Access local table and use anything of the ACE SQL dialect. This special linked table (much like SAS's libname refs are established by ODBC or other connection type) points to the external source and the driver translates query command for the migration action. Therefore, an exact same Access linked table query may perform differently than same RDMS query.
Analogy
I imagine SAS behaves the same way and exists as a front-end with libname ref as local objects with pointers to the backend. All data step handling is processed locally and simply the resultset are imported or extracted by the engine. To use an analogy. A database would be the home and SAS is the garbage man, home decorator, or move-in helper. SAS (like Java's JDBC, PHP's PDO, Python's cursors, R's libraries) knocks on the door which the database answers (annoyed by so many requests). "Hey buddy, we need to take out the garbage and here are the exact items...or we need to remodel the basement and here are the exact specs...or we have new furniture to add in the truck ready for drop off...with credentials signed please carry out immediately." And like in both, pass-through methods are requests carried out on the backend engine. So SAS leaves instructions, maybe a note on the door (without exactness) for homeowner to carry out.
I have some MySQL code that looks like this:
SELECT
visitor AS team,
COUNT(*) AS rg,
SUM(vscore>hscore) AS rw,
SUM(vscore<hscore) AS rl
FROM `gamelog` WHERE status='Final'
AND date(start_et) BETWEEN %s AND %s GROUP BY visitor
I'm trying to translate this into a Django version of that query, without making multiple queries. Is this possible? I read up on how to do Sum(), and Count(), but it doesn't seem to work when I want to compare two fields like I'm doing.
Here's the best I could come up with so far, but it didn't work...
vrecord = GameLog.objects.filter(start_et__range=[start,end],visitor=i['id']
).aggregate(
Sum('vscore'>'hscore'),
Count('vscore'>'hscore'))
I also tried using 'vscore>hscore' in there, but that didn't work either. Any ideas? I need to use as few queries as possible.
Aggregation only works on single fields in the Django ORM. I looked at the code for the various aggregation functions, and noticed that the single-field restriction is hardwired. Basically, when you use, say, Sum(field), it just records that for later, then it passes it to the database-specific backend for conversion to SQL and execution. Apparently, aggregation and annotation are not standardized in SQL.
Anyway, you probably need to use a raw SQL query.
I have an old-fashioned Access database (.mdb). I have a macro-enabled Excel spreadsheet that interacts with this database via an ADODB connection.
I am wanting to perform a query on the database using a regular expression in the "like" clause; something like:
(I want to match "SM39_002xx")
"SELECT Serial from tbl1939 where Serial like RegExpMatch(""^SM+[0-9]+[_][0-9]+[a-z]?"", [Serial])"
which works perfectly within Access, but from Excel the "RegExpMatch" function isn't found and the whole thing fails.
Any help much appreciated.
User Defined Functions (UDF), which I am fairly certain is what you have in RegExpMatch, are only available within MS Access. For the most part, you should avoid creating them unless there is no other option. For example, the following query will run outside MS Access and approximates what you want:
SELECT t.Field1
FROM ATable t
WHERE t.Field1 Like "SM[0-9][0-9][_][0-9][0-9][0-9][a-z]?"
I have my own data store mechanism for store data. but I want to implement standards data manipulation and query interface for end users,so I thought QT sql is suitable for my case.
but I still cannot understand how do I involved my indexes for sql query.
let say for example,
I have table with column A(int),B(int),C(int),D(int) and column A is indexed.assume I execute query like select * from Foo where A = 10;
How do I involved my index for search the results?.
You have written your own storage system and want to manipulate it using an SQL like syntax? I don't think Qt SQL is the right tool for that job. It offers connectivity to various SQL servers and is not meant for parsing SQL statements. Qt expects to "pass through" the queries and then somehow parse the result set and transform it into a Qt friendly representation.
So if you only want to have a Qt friendly representation, I wouldn't see a reason to go the indirection with SQL.
But regarding your problem:
In SQL, indexes are usually not stated in the queries, but during the creation of the table schema. But SQL server has a possibility to "hint" indexes, is that what you are looking for?
SELECT column_list FROM table_name WITH (INDEX (index_name) [, ...]);