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I recently stumbled upon a curious query on a customer’s productive Oracle database: SELECT USER FROM SYS.DUAL Two things caught my attention: • The query was executed many billions of times per month, accounting for about 0.3% of that system’s load. That’s 0.3% for something extremely silly! • I don’t think that customer would ever qualify the DUAL table as SYS.DUAL, which hints at some system functionality I found it in Oracle Enterprise Manager, but you could also find it using a query like this one: SELECT sql_id, executions, elapsed_time, ratio_to_report(elapsed_time) over() p, sql_text FROM v$sql ORDER BY p DESC; Why was this query being run so often? In Enterprise Manager, the query’s statistics overview displayed that the query originated from a function called (I don’t know yet where I could find this information in the dictionary views, manually). Naively, I had always thought that the USER pseudo column or pseudo constant is some value from the context, but like many other functions, it’s really just a function in that package. What does STANDARD.USER() do? Now, I’m not 100% sure if that source code is something that I am allowed to reproduce from a legal perspective, this being Oracle and all.
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But if you run this query here, which I am freely allowing you to: WITH s AS ( SELECT s.*, MIN(CASE WHEN upper(text) LIKE '%FUNCTION USER%' THEN line END ) OVER () s FROM all_source s WHERE owner = 'SYS' AND name = 'STANDARD' AND type = 'PACKAGE BODY' ) SELECT text FROM s WHERE line >= s AND line. Should be *way* more than 2x — Connor 馃檭 McDonald (@connor_mc_d) In this particular case, The STANDARD.USER() reference was used very often in triggers to fill in audit columns of many tables. Darwin iso vmware.
Very easy to fix. Just use sys_context('USERENV', 'CURRENT_USER') instead. Full benchmark logic SET SERVEROUTPUT ON ALTER SYSTEM FLUSH SHARED_POOL; ALTER SYSTEM FLUSH BUFFER_CACHE; CREATE TABLE results ( run NUMBER(2), stmt NUMBER(2), elapsed NUMBER ); DECLARE v_ts TIMESTAMP WITH TIME ZONE; v_repeat CONSTANT NUMBER:= 500000; v NUMBER; BEGIN -- Repeat the whole benchmark several times to -- avoid warmup penalty FOR r IN 1.5 LOOP v_ts:= SYSTIMESTAMP; FOR i IN 1. V_repeat LOOP v:= v + length(USER); END LOOP; INSERT INTO results VALUES (r, 1, SYSDATE + ((SYSTIMESTAMP - v_ts) * 86400) - SYSDATE); v_ts:= SYSTIMESTAMP; FOR i IN 1.
V_repeat LOOP SELECT v + length(USER) INTO v FROM dual; END LOOP; INSERT INTO results VALUES (r, 2, SYSDATE + ((SYSTIMESTAMP - v_ts) * 86400) - SYSDATE); v_ts:= SYSTIMESTAMP; FOR i IN 1. The question was looking at a time series of data points where some events happened.
Speaking of that, are there any jOOQ guides on how to replace JPA? Any patterns on how to work without EntityManager to manage in-memory state (EM#merge()), 1st level cache, etc. — Dmitry Gusev (@dmitrygusev) There are two important things to notice here: jOOQ is mainly used for what JPA folks call “projections” If you’re using only JPA in your application, you may have gotten used to. The term, where a projection is simply a SELECT clause in your SQL statement. Projections are useful when you know that the result of a query will only used for further data processing, but you’re not going to store any modifications to the data back into the database. There are two advantages to this: • You can project arbitrary expressions, including things that cannot be mapped to entities • You can bypass most of the entity management logic, including first and second level caches When you’re doing this, you will be using SQL – mostly because JPQL (or HQL) are very limited in scope.