I have a single stored procedure which produces a number of result sets.
In short, the sp matches source records to a database of some 2 million plus accounts. The matching is performed on a number of key fields and the results are then used to obtain other details about the matched accounts before producing a number of result sets. The whole process can take minutes to hours depending upon the number of records in the source to be matched.
I am wanting to deliver a report in SSRS showing all the result sets.
However...
I am aware that SSRS will only return the first result set from the sp.
I am also aware of the two resolutions to get around this problem. These are 1) split the stored procedure up into multiple stored procs and 2) to UNION the result sets and place a filter in the datasets in SSRS.
In my case, neither of the two resolutions above are suitable. Splitting the sp is not an option due to the length of time it takes to perform the matching (eg I can't be performing matching for multiple sp). UNION is also not realistic as I have summary result sets and detail result sets containing varying datatypes and anything from a few columns up to 100 columns.
I would really like some alternative suggestions how I may convert this stored procedure that returns multiple result sets into a report on SSRS. Any ideas appreciated!
There is not enough information in your question to provide a finite answer however it is clear that waiting an hour for a report to generate would not be acceptable.
You need to create "cubes" of data which will allow your reports to run quickly. your "cubes" of data need to reflect the potential different parameters which can be passed.
I enclose the word "cubes" in quotes because you can do this formally - see SSIS and SSAS or you can just build an informal data warehouse each night using some SQL.
Related
I have a complex data model consisting of around hundred tables containing business data. Some tables are very wide, up to four hundred columns. Columns can have various data types - integers, decimals, text, dates etc. I'm looking for a way to identify relevant / important information stored in these tables.
I fully understand that business knowledge is essential to correctly process a data model. What I'm looking for are some strategies to pre-process tables and identify columns that should be taken to later stage where analysts will actually look into it. For example, I could use data profiling and statistics to find and exclude columns that don't have any data at all. Or maybe all records have the same value. This way I could potentially eliminate 30% of fields. However, I'm interested in exploring how AI and Machine Learning techniques could be used to identify important columns, hoping I could identify around 80% of relevant data. I'm aware, that relevant information will depend on the questions I want to ask. But even then, I hope I could narrow the columns to simplify the manual assesment taking place in the next stage.
Could anyone provide some guidance on how to use AI and Machine Learning to identify relevant columns in such wide tables? What strategies and techniques can be used to pre-process tables and identify columns that should be taken to the next stage?
Any help or guidance would be greatly appreciated. Thank you.
F.
The most common approach I've seen to evaluate the analytical utility of columns is the correlation method. This would tell you if there is a relationship (positive or negative) among specific column pairs. In my experience you'll be able to more easily build analysis outputs when columns are correlated - although, these analyses may not always be the most accurate.
However, before you even do that, like you indicate, you would probably need to narrow down your list of columns using much simpler methods. For example, you could surely eliminate a whole bunch of columns based on datatype and basic count statistics.
Less common analytic data types (ids, blobs, binary, etc) can probably be excluded first, followed by running simple COUNT(Distinct(ColName)), and Count(*) where ColName is null . This will help to eliminating UniqueIDs, Keys, and other similar data types. If all the rows are distinct, this would not be a good field for analysis. Same process for NULLs, if the percentage of nulls is greater than some threshold then you can eliminate those columns as well.
In order to automate it depending on your database, you could create a fairly simple stored procedure or function that loops through all the tables and columns and does a data type, count_distinct and a null percentage analysis on each field.
Once you've narrowed down list of columns, you can consider a .corr() function to run the analysis against all the remaining columns in something like a Python script.
If you wanted to keep everything in the database, Postgres also supports a corr() aggregate function, but you'll only be able to run this on 2 columns at a time, like this:
SELECT corr(column1,column2) FROM table;
so you'll need to build a procedure that evaluates multiple columns at once.
Thought about this tech challenges for some time. In general it’s AI solvable problem since there are easy features to extract such as unique values, clustering, distribution, etc.
And we want to bake this ability in https://columns.ai, obviously we haven’t gotten there yet, the first step we have done though is to collect all columns stats upon a data connection, identify columns that have similar range of unique values and generate a bunch of query templates for users to explore its dataset.
If interested, please take a look, as we keep advancing this part, it will become closer to an AI model to find relevant columns. Cheers!
I am scanning an SQLite database looking for all matches and using
OneFound:=False;
if tbl1.FieldByName('Name').AsString = 'jones' then
begin
OneFound:=True;
tbl1.Next;
end;
if OneFound then // Do something
or should I be using
if not(OneFound) then OneFound:=True;
Is it faster to just assign "True" to OneFound no matter how many times it is assigned or should I do the comparison and only change OneFuond the first time?
I know a better way would be to use FTS3, but for now I have to scan the database and the question is more on the approach to setting OneFound as many times as a match is encountered or using the compare-approach and setting it just once.
Thanks
Your question is, which is faster:
if not(OneFound) then OneFound:=True;
or
OneFound := True;
The answer is probably that the second is faster. Conditional statements involve branches which risks branch mis-prediction.
However, that line of code is trivial compared to what is around it. Running across a database one row at a time is going to be outrageously expensive. I bet that you will not be able to measure the difference between the two options because the handling of that little Boolean is simply swamped by the rest of the code. In which case choose the more readable and simpler version.
But if you care about the performance of this code you should be asking the database to do the work, as you yourself state. Write a query to perform the work.
It would be better to change your SQL statement so that the work is done in the database. If you want to know whether there is a tuple which contains the value 'jones' in the field 'name', then a quicker query would be
with tquery.create (nil) do
begin
sql.add ('select name from tbl1 where name = :p1 limit 1');
sql.params[0].asstring:= 'jones';
open;
onefound:= not isempty;
close;
free
end;
Your syntax may vary regarding the 'limit' clause but the idea is to return only one tuple from the database which matches the 'where' statement - it doesn't matter which one.
I used a parameter to avoid problems delimiting the value.
1. Search one field
If you want to search one particular field content, using an INDEX and a SELECT will be the fastest.
SELECT * FROM MYTABLE WHERE NAME='Jones';
Do not forget to create an INDEX on the column, first!
2. Fast reading
But if you want to search within a field, or within several fields, you may have to read and check the whole content. In this case, what will be slow will be calling FieldByName() for each data row: you should better use a local TField variable.
Or forget about TDataSet, and switch to direct access to SQLite3. In fact, using DB.pas and TDataSet requires a lot of data marshalling, so is slower than a direct access.
See e.g. DiSQLite3 or our DB classes, which are very fast, but a bit of higher level. Or you can use our ORM on top of those classes. Our classes are able to read more than 500,000 rows per second from a SQLite3 database, including JSON marshalling into objects fields.
3. FTS3/FTS4
But, as you guessed, the fastest would be indeed to use the FTS3/FTS4 feature of SQlite3.
You can think of FTS4/FTS4 as a "meta-index" or a "full-text index" on supplied blob of text. Just like google is able to find a word in millions of web pages: it does not use a regular database, but full-text indexing.
In short, you create a virtual FTS3/FTS4 table in your database, then you insert in this table the whole text of your main records in the FTS TEXT field, forcing the ID field to be the one of the original data row.
Then, you will query for some words on your FTS3/FTS4 table, which will give you the matching IDs, much faster than a regular scan.
Note that our ORM has dedicated TSQLRecordFTS3 / TSQLRecordFTS4 kind of classes for direct FTS process.
I have ten master tables and one Transaction table. In my transaction table (it is a memory table just like ClientDataSet) there are ten lookup fields pointing to my ten master tables.
Now i am trying to dynamically assigning key field values to all my lookup key field values (of the transaction table) from a different Server(data is coming as a soap xml). Before assigning these values i need to check whether the corresponding result value is valid in master tables or not. I am using a filter (eg status = 1 ) to check whether it is valid or not.
Currently how we are doing is, before assigning each key field value we are filtering the master tables using this filter and using the locate function to check whether it is there or not. and if located we will assign its key field value.
This will work fine if there is only few records in my master tables. Consider my master tables having fifty thousand records each (yeah, customer is having so much data), this will lead to big performance issue.
Could you please help me to handle this situation.
Thanks
Basil
The only way to know if it is slow, why, where, and what solution works best is to profile.
Don't make a priori assumptions.
That being said, minimizing round trips to the server and the amount of data transferred is often a good thing to try.
For instance, if your master tables are on the server (not 100% clear from your question), sending only 1 Query (or stored proc call) passing all the values to check at once as parameters and doing a bunch of "IF EXISTS..." and returning all the answers at once (either output params or a 1 record dataset) would be a good start.
And 50,000 records is not much, so, as I said initially, you may not even have a performance problem. Check it first!
I have an OLTP database, and am currently creating a data warehouse. There is a dimension table in the DW (DimStudents) that contains student data such as address details, email, notification settings.
In the OLTP database, this data is spread across several tables (as it is a standard OLTP database in 3rd normal form).
There are currently 10,390 records but this figure is expected to grow.
I want to use Type 2 ETL whereby if a record has changed in the OLTP database, a new record is added to the DW.
What is the best way to scan through 10,000 records in the DW and then compare the results with the results in several tables contained in the OLTP?
I'm thinking of creating a "snapshot" using a temporary table of the OLTP data and then comparing the results row by row with the data in the Dimension table in the DW.
I'm using SQL Server 2005. This doesn't seem like the most efficient way. Are there alternatives?
Introduce LastUpdated into source system (OLTP) tables. This way you have less to extract using:
WHERE LastUpdated >= some_time_here
You seem to be using SQL server, so you may also try rowversion type (8 byte db-scope-unique counter)
When importing your data into the DW, use ETL tool (SSIS, Pentaho, Talend). They all have a componenet (block, transformation) to handle SCD2 (slowly changing dimension type 2). For SSIS example see here. The transformation does exactly what you are trying to do -- all that you have to do is specify which columns to monitor and what to do when it detects the change.
It sounds like you are approaching this sort of backwards. The typical way for performing ETL (Extract, Test, Load) is:
"Extract" data from your OLTP database
Compare ("Test") your extracted data against the dimensional data to determine if there are changes or whatever other validation needs to be performed
Insert the data ("Load") in to your dimension table.
Effectively, in step #1, you'll create a physical record via a query against the multiple tables in your OLTP database, then compare that resulting record against your dimensional data to determine if a modification was made. This is the standard way of doing things. In addition, 10000 rows is pretty insignificant as far as volume goes. Any RDBMS and ETL process should be able to process through that in a matter of no more than few seconds at most. I know SQL Server has DTS, although I'm not sure if the name has changed in more recent versions. That is the perfect tool for doing something like this.
Does you OLTP database have an audit trail?
If so, then you can query the audit trail for just the records that have been touched since the last ETL.
Is it possible to provide the following type of functionality with informix client tools?
As the user types the first two characters of a name, the drop-down list is empty. At the third character, the list fills with just the names beginning with those three characters. At the fourth character, MS-Access completes the first matching name (assuming the combo's AutoExpand is on). Once enough characters are typed to identify the customer, the user tabs to the next field.
The time taken to load the combo between keystrokes is minimal. This occurs once only for each entry, unless the user backspaces through the first three characters again.
If your list still contains too many records, you can reduce them by another order of magnitude by changing the value of constant conSuburbMin from 3 to 4.
This requires a combination of two things, only one of which is partially under the control of Informix the DBMS or Informix the Client API.
First of all, you need the gadget that is accepting user input to asynchronously generate a query which matches what the user has typed, fetches some of the results from the DBMS, and shows them. Secondly, you need the DBMS to respond rapidly to such queries. Part of the issue is 'what form does the query take'. But the basic functionality is:
SELECT TitleCaseName
FROM ReferenceTable
WHERE LowerCaseName[1,3] = 'abc';
You might or might not bother with 'first rows optimization'; you might or might not bother with an ORDER BY. Your code would only select the first N rows. You might do it with some prioritization information - most frequently used names, etc.
But this is logic is basically the same for any DBMS - give or take the details such as the choice of technique for dealing with case-mapping (function call vs column) and notation for substrings vs LIKE 'abc%'.
The tricky stuff, though, is the asynchronous combination of user-input plus collecting data from the DBMS; that is best handled with multiple threads, one dealing with the user input, one dealing with the DBMS and (possibly) one dealing with the display (or that might also be the one dealing with user input). And that requires hooking into the UI API - not something that the Informix APIs do of their own accord. The UI can get at Informix (or any other DBMS) easily enough through ODBC or any other faintly similar API.