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Service manual. You can avoid unique key violations by not having unique keys on your tables. And cars won’t crash into guard rails if we take the guard rails down.[Newbie sidebar: a unique key on a table or equivalent is a column or set of columns in which any given set of values must never repeat. You can have multiple such sets of columns. One such set is designated the “primary key,” and, if you have others, they’re “alternate keys.”]It’s fine for lots of blonde females named “Alice” to live on Apple Tree Lane, and, of course, hair color + first name + street name would make a crummy unique key for a PEOPLE table. But you’d better not have three records in your PEOPLE with the same Social Security number, say. The primary key can define what the table “is” – what each record represents – and ensures you don’t have duplicates (on whatever the table “is.”) Tables without primary keys usually don’t make much sense. Unique key violations come back from database management systems, and cause Data Services jobs to bomb. “What’s in a name? That which we call a rose by any other name would smell as sweet.” As they say. Alas, alack, a rose ain’t no query. You often see dataflows like this: Query transforms in Data Services do many things. Home > Valve Corporation File extension list filtered by software name. Steam. Found 28 file extension associations related to Steam and 27 file formats developed specifically for use with Steam. Platform, operating system. Generate debug logs to resolve issues that may occur in Worry-Free Business Security (WFBS). Here’s an example of what a primary key violation looks like coming back from SQL Server: New BODS developers tend to be flummoxed by these, it seems, prone to throwing their hands up and removing the keys to “make the problem go away.” The problem doesn’t go away, folks – you’ve still got duplicate records. The key is the guard rail: when a DBMS throws a unique key violation and causes your job to bomb, it’s doing you a favor. When you add primary keys to tables and your jobs start failing, that’s a good thing. Let’s go through a couple examples and explore how to deal with all this, using “pure” Data Integrator techniques. In a separate article, we’ll explore the Match transform. In this simple PEOPLE table, we start without a primary key defined in our Query transform, which means the table created via the template table object won’t have a primary key. That’s how it works. Our opening move will be to set that SSN field right there as the primary key. You’ll notice that Mr. (C) Cinelerra | Cinelerra.ORG. Disclaimer: This site is purely for presentation and informational purposes and all the content relating to products and services are the property of our company. All content, images, and logos. Are updates available for your software? Download trial here and take the test! 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When your bad code loads bad data, errors are your friends.)We’ve declared the primary key in the query. Given how BODS works, if you leave “Drop and re- create” selected in that template table, you’ll get schema- changes- on- the- fly applied to the target, and the table will be created with a primary key. If we didn’t enable “drop and re- create,” the data would load just fine, as the table in the DBMS would remain unchanged, without a primary key. BODS doesn’t guard against primary key violations merely by virtue of setting primary key columns in Query transforms. Inside of BODS, you can have all kinds of duplicate junk, columns designated as primary keys or not. If, however, we enable “drop and re- create,” then the table will, indeed, get dropped and re- created, this time with a primary key, and then, when we try to load data in, the dbms will choke on the duplicate values in the SSN column: Despite that the job bombed, the table did get re- created: It’s indicated with a “key” symbol. Imagine that.) Our job fails, and we can’t load data. We’re making progress. To investigate various approaches to handling this, and get meaningful execution times, we’re going to need a bigger table. The following dataflow writes a table of a million rows with three columns: ROW_ID, int, in which we just number the rows using gen_row_num()RAND, in which we call rand() * 1. RAND_EXT, the same as RAND but calling rand_ext()The GEN_RANDOM_NUMBERS query: With a million rows, we’ll get lots of duplicates from rand() and some from rand_ext(), the weak and robust random number generators, respectively. Detecting Duplicates. You’ll usually (though certainly not always) want to detect duplicates to report them out, filter them out, send them to a separate stream for advanced processing, etc. So let’s focus on detection and classification first. The easiest way to detect duplicates is to declare the columns that are supposed to be unique as the primary key, in a Query transform, and then send them into a template table to let the dbms throw an error. You could, I suppose, catch that error and process it, using that as your “duplication detection technique.” But that’s brutal. Let’s actually code for the detection. For approach 1, we’ll split or multiplex the record stream, with a BODS grouped query in the one branch in which we add a column that counts by the supposed key, and then immediately join the two streams to “apply” the counts- by- key to the main stream. Because, right now, we’re only investigating various approaches to the detection of duplicates, I’m going to throw away the records after detection with Map Operation transforms set to discard everything. Essentially all the execution time will come from the upstream operations. The “group by” query looks like this: And the joining query: Downstream from the joining query, we have a CT_BY_RAND_EXT column that we can use to distinguish duplicates from non- duplicates; duplicates will, of course, have a CT_BY_RAND_EXT value greater than 1. With a million records, this takes 3. Collecting statistics for the first time, it runs in 4. Interesting, but I’m not going to investigate the why of that right now.)The second time I ran it, I moved the join rank on the “group by” side of the join up to 1. It’ll have somewhat fewer records. This shortened the execution time to… 3. Just to try to juice performance, what if we filtered- out all the group- by records except those where CT_BY_RAND_EXT > 1? That would give us a much smaller set of records in that join, and the join rank might have some teeth. Let’s do it with an outer join – if we did an inner join, we’d get only the records carrying duplicated RAND_EXT values, which isn’t quite what we want. For approach 1. A, our dataflow now looks like this: I set the join rank of the FILTER_ONLY_DUPLICATES input to 1. It ran in 2. 4 seconds, a 2. With a hundred million rows, that might start to matter. After the outer join query we have field CT_BY_RAND_EXT which holds either a null (which means “good” – no duplicates), or a number greater than one, i. You may have thought that the first STRAIGHT_SELECT_ALL query doesn’t do anything, and you’re right. Let’s drop it in approach 1. B: This accomplishes little. We’re still at 2. The Group By query is *not* pushed- down to the dbms, though you might have expected otherwise. It seems to me that a cleverer BODS might have noticed two independent reads against the database table and, thus, formed two independent SQL queries to be able to push down the grouping. But we can force the issue…Approach 2: two push- down queries – one “straight,” one grouped – with a join in BODS. If BODS refuses to make two push- down- able queries itself, we can force the issue: I’ve got the “Show optimized SQL” window up so you can see that we’re pushing- down two queries, one of which is pushing- down the grouping to the DBMS. This approach runs in 1. What might be nice is to push- down that join to the database. To do that, we could either setup two dataflows, staging the results of the group- by, and then joining them in a subsequent dataflow, or we could just use a Data Transfer transform, making sure to send the results of the group- by query back down into the same database: We regressed to 2. What if, though, we bulk- loaded that Data Transfer transform table, disabled drop- and- recreate, and cranked- up the number of records committed at once? You know – speed up that whole in- line staging exercise? With these tweaks, the dataflow takes 2. For our third approach, we could just cheat and use a SQL transform to, obviously, push the whole shebang down: selectbt. BIG_TBL bt left outer join (select rand_ext, COUNT(*) ‘ct_by_rand_ext’from big_tblgroup by rand_ext) don bt. Not bad. The dataflow degenerates into this: But this…selectbt. BIG_TBL bt left outer join (select rand_ext, COUNT(*) ‘ct_by_rand_ext’from big_tblgroup by rand_exthaving COUNT(*) > 1 ) don bt. In approach 3a, I modified the SQL query somewhat as follows: selectbt. BIG_TBL btwhere rand_ext in (select rand_extfrom big_tblgroup by rand_exthaving COUNT(*) > 1)This query alone runs in only 3 seconds, but, unfortunately, it only provides the duplicates, not the non- duplicates. To get the non- duplicates, I could form the reverse query, in a separate SQL transform: selectbt. BIG_TBL btwhere rand_ext not in (select rand_extfrom big_tblgroup by rand_exthaving COUNT(*) > 1)and then merge the results: With this, we’re at 8 seconds, which is a little slower than the approach using joins. Ugly dogs can run fast. We boosted performance by over 8. But it’s a deal with the devil: no lineage, no impact, no “View where used” on the underlying tables, bad form all around. Never like to see SQL transforms. Pushing- down HAVING Clauses Without SQL Transforms. It’s clear that the main performance improvement comes from pushing down the HAVING clause to the database. Can we do this in a Query transform? Not directly – though we have a GROUP BY tab in the BODS query editor, we don’t have any HAVING tab. But we can apply a sort of hack: use pushdown_sql() and a subquery containing HAVING. The dataflow for approach 4 looks like this: The critical part is in the PUSH_DOWN_HAVING_DUPLICATES query: BODS just takes the output of pushdown_sql() and blindly appends it to the WHERE clause of the SELECT query it pushes- down. The optimized SQL looks like this: You can see the result of the pushdown_sql() function tacked- on to the end. BODS has now pushed the join down, too. Not exactly the most elegant thing – explicit support in the Query transform for aggregated columns and HAVING clauses would be nice – but it works. This dataflow executes… not at all. It throws an error: Right. The generated SQL statement uses aliases, and it’s confused with that literal WHERE clause appendage. 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