Detect JDBC API Misusage with JDBCLint

I’ve recently seen an advertisement for JDBCLint on the H2 User Group. JDBCLint is an Apache licensed JDBC proxy implementation that does some plausibility checks on the lifecycles of your JDBC objects. For instance, it

  • Checks if a ResultSet is closed twice
  • Checks if a ResultSet is not closed at all (in the finalizer)
  • Checks if a ResultSet yields unread columns

All of these checks can be disabled by specifying relevant properties on the proxy. And the best thing is that this proxy is so easy to integrate:

import com.maginatics.jdbclint.ConnectionProxy;
...
Connection connection =
    DriverManager.getConnection(...);
connection = ConnectionProxy.newInstance(
    connection, new Properties());
connection.close();
// reports error and optionally throws exception
connection.close();

In addition to static code analysis tools like FindBugs or Alvor, this tool can help you find very subtle memory leaks in your large legacy application.

Certainly a tool to have on your tool chain!

Deep Stack Traces Can be a Sign for Good Code Quality

The term “leaky abstractions” has been around for a while. Coining it is most often attributed to Joel Spolsky, who wrote this often-cited article about it. I’ve now stumbled upon another interpretation of a leaky abstraction, measured by the depth of a stack trace:

So, long stack traces are bad according to Geek & Poke. I’ve seen this argument before on Igor Polevoy’s blog (he’s the creator of ActiveJDBC, a Java implementation of the popular Ruby ActiveRecord query interface). Much like Joel Spolsky’s argumentation was often used to criticise ORMs, Igor’s argument was also used to compare ActiveJDBC with Hibernate. I’m citing:

One might say: so what, why do I care about the size of dependencies, depth of stack trace, etc. I think a good developer should care about these things. The thicker the framework, the more complex it is, the more memory it allocates, the more things can go wrong.

I completely agree that a framework with a certain amount of complexity tends to have longer stack traces. So if we run these axioms through our mental Prolog processors:

  • if Hibernate is a leaky abstraction, and
  • if Hibernate is complex, and
  • if complexity leads to long stack traces, then
  • leaky abstractions and long stack traces correlate

I wouldn’t go as far as claiming there’s a formal, causal connection. But a correlation seems logical.

But these things aren’t necessarily bad. In fact, long stack traces can be a good sign in terms of software quality. It can mean that the internals of a piece of software show a high amount of cohesion, a high degree of DRY-ness, which again means that there is little risk for subtle bugs deep down in your framework. Remember that a high cohesion and high DRY-ness lead to a large portion of the code being extremely relevant within the whole framework, which again means that any low-level bug will pretty much blow up the whole framework as it will lead to everything going wrong. If you do test-driven development, you’ll be rewarded by noticing immediately that your silly mistake fails 90% of your test cases.

A real-world example

Let’s use jOOQ as an example to illustrate this, as we’re already comparing Hibernate and ActiveJDBC. Some of the longest stack traces in a database access abstraction can be achieved by putting a breakpoint at the interface of that abstraction with JDBC. For instance, when fetching data from a JDBC ResultSet.

Utils.getFromResultSet(ExecuteContext, Class<T>, int) line: 1945
Utils.getFromResultSet(ExecuteContext, Field<U>, int) line: 1938
CursorImpl$CursorIterator$CursorRecordInitialiser.setValue(AbstractRecord, Field<T>, int) line: 1464
CursorImpl$CursorIterator$CursorRecordInitialiser.operate(AbstractRecord) line: 1447
CursorImpl$CursorIterator$CursorRecordInitialiser.operate(Record) line: 1
RecordDelegate<R>.operate(RecordOperation<R,E>) line: 119
CursorImpl$CursorIterator.fetchOne() line: 1413
CursorImpl$CursorIterator.next() line: 1389
CursorImpl$CursorIterator.next() line: 1
CursorImpl<R>.fetch(int) line: 202
CursorImpl<R>.fetch() line: 176
SelectQueryImpl<R>(AbstractResultQuery<R>).execute(ExecuteContext, ExecuteListener) line: 274
SelectQueryImpl<R>(AbstractQuery).execute() line: 322
T_2698Record(UpdatableRecordImpl<R>).refresh(Field<?>...) line: 438
T_2698Record(UpdatableRecordImpl<R>).refresh() line: 428
H2Test.testH2T2698InsertRecordWithDefault() line: 931

Compared to ActiveJDBC’s stack traces, that’s quite a bit more, but still less compared to Hibernate (which uses lots of reflection and instrumentation). And it involves rather cryptic inner classes with quite a bit of method overloading. How to interpret that? Let’s go through this, bottom-up (or top-down in the stack trace)

CursorRecordInitialiser

The CursorRecordInitialiser is an inner class that encapsules the initialisation of a Record by a Cursor, and it ensures that relevant parts of the ExecuteListener SPI are covered at a single place. It is the gateway to JDBC’s various ResultSet methods. It is a generic internal RecordOperation implementation that is called by…

RecordDelegate

… a RecordDelegate. While the class name is pretty meaningless, its purpose is to shield and wrap all direct record operations in a way that a central implementation of the RecordListener SPI can be achieved. This SPI can be implemented by client code to listen to active record lifecycle events. The price for keeping the implementation of this SPI DRY is a couple of elements on the stack trace, as such callbacks are the standard way to implement closures in the Java language. But keeping this logic DRY guarantees that no matter how a Record is initialised, the SPI will always be invoked. There are (almost) no forgotten corner-cases.

But we were initialising a Record in…

CursorImpl

… a CursorImpl, an implementation of a Cursor. This might appear odd, as jOOQ Cursors are used for “lazy fetching”, i.e. for fetching Records one-by-one from JDBC.

On the other hand, the SELECT query from this stack trace simply refreshes a single UpdatableRecord, jOOQ’s equivalent of an active record. Yet, still, all the lazy fetching logic is executed just as if we were fetching a large, complex data set. This is again to keep things DRY when fetching data. Of course, around 6 levels of stack trace could have been saved by simply reading the single record as we know there can be only one. But again, any subtle bug in the cursor will likely show up in some test case, even in a remote one like the test case for refreshing records.

Some may claim that all of this is wasting memory and CPU cycles. The opposite is more likely to be true. Modern JVM implementations are so good with managing and garbage-collecting short-lived objects and method calls, the slight additional complexity imposes almost no additional work to your runtime environment.

TL;DR: Long stack traces may indicate high cohesion and DRY-ness

The claim that a long stack trace is a bad thing is not necessarily correct. A long stack trace is what happens, when complex frameworks are well implemented. Complexity will inevitably lead to “leaky abstractions”. But only well-designed complexity will lead to long stack traces.

Conversely, short stack traces can mean two things:

  • Lack of complexity: The framework is simple, with few features. This matches Igor’s claim for ActiveJDBC, as he is advertising ActiveJDBC as a “simple framework”.
  • Lack of cohesion and DRY-ness: The framework is poorly written, and probably has poor test coverage and lots of bugs.

Tree data structures

As a final note, it’s worth mentioning that another case where long stack traces are inevitable is when tree structures / composite pattern structures are traversed using visitors. Anyone who has ever debugged XPath or XSLT will know how deep these traces are.

#CTMMC, the New Hashtag for Code That Made Me Cry

Our recent article about Code That Made Me Cry was really well received and had quite a few readers, both on our blog and on our syndication partners:

In each of us programmers is a little geek with a little geek humour. This is reflected by funny comics like the one about

The only valid measurement of code quality: WTF/minute

Other platforms ridiculing bad code include

But one thing missing is a hashtag (and acronym) on twitter. It should be called #CTTMC for Code That Made Me Cry.

To promote this hashtag, we have created a website, where we will list our collective programmer pain. Behold:

http://www.ctmmc.net

Silly Metrics: The Most Used Java Keywords

Tell me…

  • Haven’t you ever wondered how many times you actually “synchronized” something?
  • Didn’t you worry about not using the “do {} while ()” loop structure often enough?
  • Are you an expert in applying “volatile”?
  • Do you “catch” more often than you “try”?
  • Is your program rather “true” or rather “false?
  • And how did that “goto” make it into your source code??

Here’s a bit of a distraction among all the other, rather informative posts I’ve written recently. An utterly useless ranking of the top Java keywords in jOOQ. I mean, after all, useful metrics can already be reviewed at ohloh, or collected with FindBugs and JArchitect

Now, you can figure it out. Here’s the ranking!

Keyword      Count
public       8127
return       6801
final        6608
import       5938
static       3903
new          3110
extends      2111
int          1822
throws       1756
void         1707
if           1661
this         1464
private      1347
class        1239
case         841
else         839
package      711
boolean      506
throw        495
for          421
long         404
true         384
byte         345
interface    337
false        332
protected    293
super        265
break        200
try          149
switch       146
implements   139
catch        127
default      112
instanceof   107
char         96
short        91
abstract     54
double       43
transient    42
finally      34
float        34
enum         25
while        23
continue     12
synchronized 8
volatile     6
do           1

Curious about your own Java keyword ranking? I’ve published the script to calculate these values on GitHub, under the ASL 2.0 license. Check out the sources here:

https://github.com/lukaseder/silly-metrics

Use it, and publish your own rankings! And feel free to provide pull requests to count keywords from other languages, or to calculate entirely different silly and useless metrics.