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@DevOpsSummit: Blog Post

Software Quality Metrics for Your Continuous Delivery Pipeline | Part 3

It is safe to say that insightful logging and performance are two opposite goals

Let me ask you a question: would you say that you have implemented logging correctly for your application? Correct in the sense that it will provide you with all the insights you require to keep your business going once your users are struck by errors? And in a way that does not adversely impact your application performance? Honestly, I bet you have not. Today I will explain why you should turn off logging completely in production because of its limitations:

  • Relies on Developers
  • Lacks Context
  • Impacts Performance

Intrigued? Bear with me and I will show you how you can still establish and maintain a healthy and useful logging strategy for your deployment pipeline, from development to production, guided by metrics.

What Logging Can Do for You
Developers, including myself, often write log messages because they are lazy. Why should I set a breakpoint and fire up a debugger if it is so much more convenient to dump something to my console via a simple println()? This simple yet effective mechanism also works on headless machines where no IDE is installed, such as staging or production environments:

System.out.println("Been here, done that.");

Careful coders would use a logger to prevent random debug messages from appearing in production logs and additionally use guard statements to prevent unnecessary parameter construction:

if (logger.isDebugEnabled()) {
logger.debug("Entry number: " + i + " is " + String.valueOf(entry[i]));

Anyways, the point about logging is that that traces of log messages allow developers to better understand what their program is doing in execution. Does my program take this branch or that branch? Which statements were executed before that exception was thrown? I have done this at least a million of times, and most likely so have you:

if (condition) {
logger.debug("7: yeah!")
} else {
logger.debug("8: DAMN!!!")

Test Automation Engineers, usually developers by trade, equally use logging to better understand how the code under test complies with their test scenarios:

class AgentSpec extends spock.lang.Specification {

def "Agent.compute()"() {
def agent = AgentPool.getAgent()

def result = agent.compute(TestFixtures.getData())

logger.debug("result: "  + result);
result == expected

Logging is, undoubtedly, a helpful tool during development and I would argue that developers should use it as freely as possible if it helps them to understand and troubleshoot their code.

In production, application logging is useful for tracking certain events, such as the occurrence of a particular exception, but it usually fails to deliver what it is so often mistakenly used for: as a mechanism for analyzing application failures in production. Why?

Because approaches to achieving this goal with logging are naturally brittle: their usefulness depends heavily on developers, messages are without context, and if not designed carefully, logging may severely slow down your application.

Secretly, what you are really hoping to get from your application logs, in the one or the other form, is something like this:

A logging strategy that delivers out-of-the-box using dynaTrace: user context, all relevant data in place, zero config

The Limits of Logging
Logging Relies on Developers
Let's face it: logging is, inherently, a developer-centric mechanism. The usefulness of your application logs stands and falls with your developers. A best practice for logging in production says: "don't log too much" (see Optimal Logging @ Google testing blog). This sounds sensible, but what does this actually mean? If we recall the basic motivation behind logging in production, we could equally rephrase this as "log just enough information you need to know about a failure that enables you to take adequate actions". So, what would it take your developers to provide such actionable insights? Developers would need to correctly anticipate where in the code errors would occur in production. They would also need to collect any relevant bits of information along an execution path that bear these insights and, last but not least, present them in a meaningful way so that others can understand, too. Developers are, no doubt, a critical factor to the practicality of your application logs.

Logging Lacks Context
Logging during development is so helpful because developers and testers usually examine smaller, co-located units of code that are executed in a single thread. It is fairly easy to maintain an overview under such simulated conditions, such as a test scenario:

13:49:59 INFO - Registered user ‘foo'.
13:49:59 INFO - User ‘foo' has logged in.
13:49:59 INFO - User ‘foo' has logged out.

But how can you reliably identify an entire failing user transaction in a real-life scenario, that is, in a heavily multi-threaded environment with multiple tiers that serve piles of distributed log files? I say, hardly at all. Sure, you can go mine for certain isolated events, but you cannot easily extract causal relationships from an incoherent, distributed set of log messages:

13:49:59 INFO - User ‘foo' has logged in.
13:49:59 INFO - User ‘bar' has logged in.
13:49:60 SEVERE org.hibernate.exception.JDBCConnectionException: could not execute query
at org.hibernate.exception.SQLStateConverter.convert(

After all, the ability to identify such contexts is key to deciding why a particular user action failed.

Logging Impacts Performance
What is a thorough logging strategy worth if your users cannot use your application because it is terribly slow? In case you did not know, logging, especially during peak load times, may severely slow down your application. Let's have a quick look at some of the reasons:

Writing log messages from the application's memory to persistent storage, usually to the file system, demands substantial I/O (see Top Performance Mistakes when moving from Test to Production: Excessive Logging). Traditional logger implementations wrote files by issuing synchronous I/O requests, which put the calling thread into a wait state until the log message was fully written to disk.

In some cases, the logger itself may cause a decent bottle-neck: in the Log4j library (up to version 1.2), every single log activity results in a call to an internal template method Appender.doAppend() that is synchronized for thread-safety (see Multithreading issues - doAppend is synchronised?). The practical implication of this is that threads, which log to the same Appender, for example a FileAppender, must queue up with any other threads writing logs. Consequently, the application spends valuable time waiting in synchronization instead of doing whatever the app was actually designed to do. This will hurt performance, especially in heavily multi-thread environments like web application servers.

These performance effects can be vastly amplified when exception logging comes into play: exception data, such as error message, stack trace and any other piggy-backed exceptions ("initial cause exceptions") greatly increase the amount of data that needs to be logged. Additionally, once a system is in a faulty state, the same exceptions tend to appear over and over again, further hurting application performance. We had once monitored a 30% drawdown on CPU resources due to more than 180,000 exceptions being thrown in only 5 minutes on one of our application servers (see Performance Impact of Exceptions: Why Ops, Test and Dev need to care). If we had written these exceptions to the file system, they would have trashed I/O, filled up our disk space in no time and had considerably increased our response times.

Subsequently, it is safe to say that insightful logging and performance are two opposite goals: if you want the one, then you have to make a compromise on the other.

For more logging tips click here for the full article.

More Stories By Martin Etmajer

Martin Etmajer has 10+ years of experience as a developer and software architect, as well as in maintaining highly available and performant cluster environments. In his current role, Martin works as a Technology Strategist for Dynatrace with a focus on Continuous Delivery and DevOps. He speaks at technology conferences and meetups and publishes articles on this blog. Reach him at @metmajer

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