I have a big calculation that joins together about 10 tables and calculates some values from the result. I want to write a function that allows me to replace one of the tables that are joined (lets call it Table A) with a table (type) I give as an input parameter.
I have defined row and table types for table A like
create or replace TYPE t_tableA_row AS OBJECT(*All Columns of Table A*);
create or replace TYPE t_tableA_table as TABLE OF t_tableA_row;
And the same for the types of the calculation I need as an output of the function.
My functions looks like this
create or replace FUNCTION calculation_VarInput (varTableA t_tableA_table)
RETURN t_calculationResult_table AS
result_ t_calculationResult_table;
BEGIN
SELECT t_calculationResult_row (*All Columns of Calculation Result*)
BULK COLLECT INTO result_
FROM (*The calculation*)
RETURN result_;
END;
If I test this function with the normal calculation that just uses Table A(ignoring the input parameter), it works fine and takes about 3 Second. However, if I replace Table A with varTableA (the input parameter that is a table type of Table A), the calculation takes so long I have never seen it finish.
When I use table A for the calculation it looks like this
/*Inside the calculation*/
*a bunch tables being joined*
JOIN TableA A On A.Value = B.SomeOtherValue
JOIN *some other tables*
When I use varTableA its
/*Inside the calculation*/
*a bunch tables being joined*
JOIN TABLE(varTableA ) A On A.Value = B.SomeOtherValue
JOIN *some other tables*
Sorry for not posting the exact code but the calculation is huge and would really bloat this post.
Any ideas why using the table type when joining makes the calculation so much slower when compared to using the actual table?
Your function encapsulates some selection logic in a function and so hides information from the optimizer. This may lead the optimizer to make bad or inefficient decisions.
Oracle has gathered statistics for TableA so the optimizer knows how many rows it has, what columns are indexed and so on. Consequently it can figure out the best access path for the table. It has no stats for TABLE(varTableA ) so it assumes it will return 8192 (i.e. 8k) rows. This could change the execution plan if say the original TableA returned 8 rows. Or 80000. You can check this easily enough by running EXPLAIN PLAN for both versions of query.
If that is the problem add a /*+ cardinality */ to the query which accurately reflects the number of rows in the function's result set. The hint (hint, not function) tells the optimizer the number of rows it should use in its calculation.
I don't want to actually change the values in my tables permanently, I just want to know what the calculation result would be if some values were different.
Why not use a view instead? A simple view which selects from TableA and applies the required modifications in its projection. Of course I know nothing about your data and how you want to manipulate it, so this may be impractical for all sorts of reasons. But it's where I would start.
I am a SQL Server guy and just started working on Netezza, one thing pops up to me is a daily query to find out the size of a table filtered out by year: 2016,2015, 2014, ...
What I am using now is something like below and it works for me, but I wonder if there is a better way to do it:
select count(1)
from table
where extract(year from datacolumn) = 2016
extract is a built-in function, applying a function on a table with size like 10 billion+ is not imaginable in SQL Server to my knowledge.
Thank you for your advice.
The only problem i see with the query is the where clause which executes a function on the 'variable' side. That effectively disables zonemaps and thus forces netezza to scan all data pages, not only those with data from that year.
Instead write something like:
select count(1)
from table
where datecolumn between '2016-01-01' and '2016-12-31'
A more generic alternative is to create a 'date dimension table' with one row per day in your tables (and a couple of years into the future)
This is an example for Postgres: https://medium.com/#duffn/creating-a-date-dimension-table-in-postgresql-af3f8e2941ac
This enables you to write code like this:
Select count(1)
From table t join d_date d on t.datecolumn=d.date_actual
Where year_actual=2016
You may not have the generate_series() function on your system, but a 'select row_number()...' can do the same trick. A download is available here: https://www.ibm.com/developerworks/community/wikis/basic/anonymous/api/wiki/76c5f285-8577-4848-b1f3-167b8225e847/page/44d502dd-5a70-4db8-b8ee-6bbffcb32f00/attachment/6cb02340-a342-42e6-8953-aa01cbb10275/media/generate_series.tgz
A couple of further notices in 'date interval' where clauses:
Those columns are the most likely candidate for a zonemaps optimization. Add a 'organize on (datecolumn)' at the bottom of your table DDL and organize your table. That will cause netezza to move around records to pages with similar dates, and the query times will be better.
Furthermore you should ensure that the 'distribute on' clause for the table results in an even distribution across data slices of the table is big. The execution of the query will never be faster than the slowest dataslice.
I hope this helps
I was trying to get the count from a table with millions of entries. My query looks somewhat like this:
Select count(*)
from Users
where status = 'A' and office_id = '000111' and user_type = 'C'
Status can be A or C, User Type can be C or R.
Status, Office_id and User_type are Strings
The result has around 10 million rows, and its taking a lot of time. I just want the total count.
Would appreciate if anyone could tell me why its taking this much time, and workaround if any.
Do let me know in case of any more details required.
The database engine is Oracle 11g
Edit: I Added index for all three columnns. Still theres no improvement. Also tried the below query, but it always returns the total count in the table without checking the conditions.
SELECT COUNT(office_id_key)
FROM Users
WHERE EXISTS (SELECT * FROM Users WHERE status = 'A' AND office_id = '000111' AND user_type = 'C')
Why not just simply create indexes on the table on age and place this way your search will be faster then simply scanning the entire table for these values.
CREATE INDEX age_index ON Employee(age);
CREATE INDEX place_index ON Employee(place);
This should speed up the process.
AMENDED BASED ON QUERY CHANGE
CREATE INDEX status_index ON Users(status);
CREATE INDEX office_id_index ON Users(office_id);
CREATE INDEX user_type_index ON Users(user_type);
You'll want to create the following multi-column index on the Users table to improve the query:
(office_id, status, user_type)
The database can use a "covering" index with COUNT(*). Create the index with the columns in that order, due to cardinality.
After adding the indexes, I think changing where to where exists and a subquery may help as well.
Edit2: removed exists as it was returning all valid, usually the subquery has multiple joins, but I guess the case with one table returns all true. I read that count is optimized to act similar to exists when it has only one table and no where clause, so I treat the results as a table. Hopefully, this will have the same quick results.
select count(1) from
(select 1 from Employee where age = '25' and place = 'bricksgate')
Edit: When you use 'where exists' the db server doesn't load your data into memory and also takes advantage of the indexes because you will be reading values from the indexes not doing costly table lookups. You may also want to change count(*) to count(place) - that way it will limit the fields to an indexed field as well.
In your original query, your data was doing table lookups and then loading them into memory just to be counted.
count(1) works faster than count(*)
How do I optimize the following SQL query for performance?
select * from Employee where CNIC = 'some-CNIC-number'
Will using alias help making it a little faster?
I am using Microsoft SQL Server.
This is better if you tell us what RDBMS you are using, but...
1 - Don't do SELECT *. Specify which columns you need. Less data = faster query
2 - For indexing, make sure you have an index on CNIC. You also want a good clustered index on a primary key (preferably something like an ID number)
3 - You put the number in single quotes ' ' which indicates you may have it as a varchar column. If it will always be NUMERIC, it should be an int/bigint data type. This takes up less space and will be faster to retrieve and index by.
Create an index on CNIC:
CREATE INDEX ix_employee_cnic ON employee (cnic)
First thing, as I see this column will be used for storing Id card nos, then you can make your coulmn of type int rather than varchar or nvarchar as searching will faster on an integer type as compared to varchar or nvarchar.
Second, use with (no lock), like
select * from Employee with (nolock) where CNIC = 'some-CNIC-number'
This is to minimize the chances of a deadlock.
I have table with "varchar2" as primary key.
It has about 1 000 000 Transactions per day.
My app wakes up every 5 minute to generate text file by querying only new record.
It will remember last point and process only new records.
Do you have idea how to query with good performance?
I am able to add new column if necessary.
What do you think this process should do by?
plsql?
java?
Everyone here is really really close. However:
Scott Bailey's wrong about using a bitmap index if the table's under any sort of continuous DML load. That's exactly the wrong time to use a bitmap index.
Everyone else's answer about the PROCESSED CHAR(1) check in ('Y','N')column is right, but missing how to index it; you should use a function-based index like this:
CREATE INDEX MY_UNPROCESSED_ROWS_IDX ON MY_TABLE
(CASE WHEN PROCESSED_FLAG = 'N' THEN 'N' ELSE NULL END);
You'd then query it using the same expression:
SELECT * FROM MY_TABLE
WHERE (CASE WHEN PROCESSED_FLAG = 'N' THEN 'N' ELSE NULL END) = 'N';
The reason to use the function-based index is that Oracle doesn't write index entries for entirely NULL values being indexed, so the function-based index above will only contain the rows with PROCESSED_FLAG = 'N'. As you update your rows to PROCESSED_FLAG = 'Y', they'll "fall out" of the index.
Well, if you can add a new column, you could create a Processed column, which will indicate processed records, and create an index on this column for performance.
Then the query should only be for those rows that have been newly added, and not processed.
This should be easily done using sql queries.
Ah, I really hate to add another answer when the others have come so close to nailing it. But
As Ponies points out, Oracle does have a hidden column (ORA_ROWSCN - System Change Number) that can pinpoint when each row was modified. Unfortunately, the default is that it gets the information from the block instead of storing it with each row and changing that behavior will require you to rebuild a really large table. So while this answer is good for quieting the SQL Server fella, I'd not recommend it.
Astander is right there but needs a few caveats. Add a new column needs_processed CHAR(1) DEFAULT 'Y' and add a BITMAP index. For low cardinality columns ('Y'/'N') the bitmap index will be faster. Once you have the rest is pretty easy. But you've got to be careful not select the new rows, process them and mark them as processed in one step. Otherwise, rows could be inserted while you are processing that will get marked processed even though they have not been.
The easiest way would be to use pl/sql to open a cursor that selects unprocessed rows, processes them and then updates the row as processed. If you have an aversion to walking cursors, you could collect the pk's or rowids into a nested table, process them and then update using the nested table.
In MS SQL Server world where I work, we have a 'version' column of type 'timestamp' on our tables.
So, to answer #1, I would add a new column.
To answer #2, I would do it in plsql for performance.
Mark
"astander" pretty much did the work for you. You need to ALTER your table to add one more column (lets say PROCESSED)..
You can also consider creating an INDEX on the PROCESSED ( a bitmap index may be of some advantage, as the possible value can be only 'y' and 'n', but test it out ) so that when you query it will use INDEX.
Also if sure, you query only for every 5 mins, check whether you can add another column with TIMESTAMP type and partition the table with it. ( not sure, check out again ).
I would also think about writing job or some thing and write using UTL_FILE and show it front end if it can be.
If performance is really a problem and you want to create your file asynchronously, you might want to use Oracle Streams, which will actually get modification data from your redo log withou affecting performance of the main database. You may not even need a separate job, as you can configure Oracle Streams to do Asynchronous replication of the changes, through which you can trigger the file creation.
Why not create an extra table that holds two columns. The ID column and a processed flag column. Have an insert trigger on the original table place it's ID in this new table. Your logging process can than select records from this new table and mark them as processed. Finally delete the processed records from this table.
I'm pretty much in agreement with Adam's answer. But I'd want to do some serious testing compared to an alternative.
The issue I see is that you need to not only select the rows, but also do an update of those rows. While that should be pretty fast, I'd like to avoid the update. And avoid having any large transactions hanging around (see below).
The alternative would be to add CREATE_DATE date default sysdate. Index that. And then select records where create_date >= (start date/time of your previous select).
But I don't have enough data on the relative costs of setting a sysdate as default vs. setting a value of Y, updating the function based vs. date index, and doing a range select on the date vs. a specific select on a single value for the Y. You'll probably want to preserve stats or hint the query to use the index on the Y/N column, and definitely want to use a hint on a date column -- the stats on the date column will almost certainly be old.
If data are also being added to the table continuously, including during the period when your query is running, you need to watch out for transaction control. After all, you don't want to read 100,000 records that have the flag = Y, then do your update on 120,000, including the 20,000 that arrived when you query was running.
In the flag case, there are two easy ways: SET TRANSACTION before your select and commit after your update, or start by doing an update from Y to Q, then do your select for those that are Q, and then update to N. Oracle's read consistency is wonderful but needs to be handled with care.
For the date column version, if you don't mind a risk of processing a few rows more than once, just update your table that has the last processed date/time immediately before you do your select.
If there's not much information in the table, consider making it Index Organized.
What about using Materialized view logs? You have a lot of options to play with:
SQL> create table test (id_test number primary key, dummy varchar2(1000));
Table created
SQL> create materialized view log on test;
Materialized view log created
SQL> insert into test values (1, 'hello');
1 row inserted
SQL> insert into test values (2, 'bye');
1 row inserted
SQL> select * from mlog$_test;
ID_TEST SNAPTIME$$ DMLTYPE$$ OLD_NEW$$ CHANGE_VECTOR$$
---------- ----------- --------- --------- ---------------------
1 01/01/4000 I N FE
2 01/01/4000 I N FE
SQL> delete from mlog$_test where id_test in (1,2);
2 rows deleted
SQL> insert into test values (3, 'hello');
1 row inserted
SQL> insert into test values (4, 'bye');
1 row inserted
SQL> select * from mlog$_test;
ID_TEST SNAPTIME$$ DMLTYPE$$ OLD_NEW$$ CHANGE_VECTOR$$
---------- ----------- --------- --------- ---------------
3 01/01/4000 I N FE
4 01/01/4000 I N FE
I think this solution should work..
What you need to do following steps
For the first run, you will have to copy all records. In first run you need to execute following query
insert into new_table(max_rowid) as (Select max(rowid) from yourtable);
Now next time when you want to get only newly inserted values, you can do it by executing follwing command
Select * from yourtable where rowid > (select max_rowid from new_table);
Once you are done with processing above query, simply truncate new_table and insert max(rowid) from yourtable
I think this should work and would be fastest solution;