APPROACH 1: Update entire object with all the 30 fields and let the rest. Abstracting the logic for dynamic query generation and null-checks. Arcv2cad 6 0 keygen cracked. ● ● ● ● ● ● What is so difficult about using SQL in applications? If you’re like me, you might have thought that there is something smelly if not outright wrong in the way applications interact with SQL databases. The ubiquitous practice of sending practically executable code – SQL statements – to the server means that we will have to construct executable code programmatically in our application. And that means that we have to be very careful to prevent security problems in that generated code. The reason we have SQL injections in the first place is that there’s no other way to interact with SQL databases than to construct code on the fly. SELECT phone, email FROM people WHERE name LIKE '%$namepart%' Whoopsie, what happens if $namepart contains a quote character? Of course, every modern database interface provides some facilities to interpolate values from our application language to the generated SQL in a safe way. This handles the vast majority of cases where you need to take care that your dynamically generated SQL doesn’t end up being something completely different you meant it to be. The SQL is generated from some kind of (textual) SQL templates, and the templating library – often integrated with the programming language’s database interface library – takes care that the generated SQL is always structurally sound. SELECT users. Username, people. Email FROM people, users WHERE users. Person = people. Id AND people. Name LIKE: namepattern AND users. Last_login >: lastdate This is what it usually looks like, with modern database interfaces. The problem of dynamic WHERE clauses But, there is a very common need for dynamic SQL that textual SQL templates do not cover very well, or at all. That is the case where we want to have a varying number of WHERE conditions in a database query depending on whether or not the listing is constrained in some specific way. For instance, the user might want to list all cities in some specific area; then, having seen there are too many to browse through, they want to restrict the search to only big cities with more than half a million people. The two queries are essentially the same, except that the latter adds a new AND condition to the WHERE clause of our generated SQL. ( -> ( select:* ) ( from:cities ) ( merge-where ( if-not ( nil? Namepattern ) [:like:name namepattern ])) ( merge-where ( if-not ( nil? Minpopulation ) [:>:population minpopulation ])) sql/format ) Dynamic SQL construction in Clojure and HoneySQL. Yet another approach to dynamic SQL generation is the ORM, or object-relational mapper. ORM is a technique which adorns native data structures with the ability to be database-backed. All changes to those objects’ state will be synced into the database, and vice versa. ORMs are superb for data updates, but outright horrible for complex queries. They shift data query logic from the database side to the application side, which makes it harder to use the database for what it excels in, and in practice results in all kinds of performance problems. However, when and how to use ORMs is a very complicated question well worthy its own blog post or several. Solving the dynamic WHERE clause problem in SQL However, there is a simpler solution – so simple that it is easy to overlook. Usually, we can trust the SQL server to behave sensibly when we shift the condition logic to the SQL side. Every database I know optimises away conditions whose truth value can be proved (such as 3. Name: db-points-near -- Return points in order of proximity to:point, along with their tag(s). Ns FROM ( SELECT id, coord, modtime, mergedto FROM location WHERE mergedto IS NULL ORDER BY coord (: point ):: point LIMIT (: limit ):: integer OFFSET (: page ):: integer *: limit ) AS loc, location_tag l, tag WHERE loc. Location AND l. Id AND ((: mindate ):: date IS NULL OR loc. Modtime >: mindate ) AND ((: maxdate ):: date IS NULL OR loc. Modtime: point ): point; Neat, right? Although I find it somewhat worrying that I have to type-annotate parameters in almost all contexts, having the full query logic in SQL is very pleasing and makes my application code more straightforward. After having the query defined thus, I only need to construct the parameter map to pass to the query. Further reading •: The need for dynamic SQL generation arises from various reasons, some good and some bad. This article, even if written for Microsoft SQL Server, has a lot of ideas and analysis that applies quite well to other SQL backends. •: Usually, you do not want to optimise before you can verify that performance problems exist (by profiling or similar techniques).
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