Let’s talk about something we’ve all experienced: a customer reports that their checkout process is “sometimes slow,” – or maybe you noticed an unexpected spike in response times. So, you dive into the logs, grep through thousands of lines, and try to match timestamps with your APM traces. Sound familiar?
At Scout, we’ve seen countless engineering teams struggle with this disconnect between their logging and APM tools. But this is like trying to solve a puzzle when half of the pieces you need are in a totally separate room. It’s time to stop this madness! That’s why we’re excited to share how integrated logging and APM can transform your debugging workflow.
The Problem with Disconnected Data
So, let’s get more specific and say you’re investigating a slowdown in your shopping cart service. You note that your APM tool is showing a spike in response times – but why? To try and figure it out, you switch to your logging platform and start digging through logs from around that same timeframe. This just raises more questions. Was it a database issue? A third-party service? An edge case in your business logic?
Without context, you’re left expending invaluable time playing detective, as you try to piece together what happened with knowledge spread across different tools. Further, this context-switching doesn’t just slow your progress down – it also makes it harder to spot patterns and connections that could have more easily led you to the root cause.
Here’s where Scout’s new integrated approach makes a difference. To illustrate, let’s take a look at two real-world scenarios where having your logs and APM data in the same context saves precious debugging time.
Scenario 1: The Long-Running Shopping Session
Imagine a customer is reporting issues during a complex checkout process. Sound intimidating? It’s absolutely not. With Scout’s contextual logging, you can:
- Track their entire session using a single identifier (like their cart ID)
- See all logs across different endpoints they touched
- View performance traces alongside relevant log entries
- Understand the full user journey, not just isolated requests
Now, instead of jumping between tools and messily trying to correlate the relevant timestamps, you have a complete picture of their experience in one place.
Scenario 2: The Mysterious Slow Trace
Let’s say your APM alerts are showing an endpoint that occasionally takes 10x longer than usual. While the trace shows the slow components, it does not elucidate exactly why they were slow. Thankfully, with Scout’s integrated log management, you can:
- Jump directly from a slow trace to all the logs recorded during that request
- See the specific conditions that triggered the slowdown
- Understand the context that led to the performance issue
- Identify patterns across similar slow requests
This kind of investigative power is particularly powerful in Ruby apps when dealing with ActiveRecord N+1 queries or Sidekiq job processing. Meanwhile, for Python developers using Django or Flask, you can track down those sneaky database connection pool issues or async worker bottlenecks.
In any case, Scout automatically instruments dozens of gems and packages, so you’ll see exactly where those milliseconds are going.
Why This Matters for Your Team
Of course, while this is quite convenient this toolkit integration isn’t just about having logs and traces living in the same UI – it’s also about having them work together intelligently. What does this mean? Every log entry in Scout knows about its request context, and every trace knows about its logs. In essence, this connection means:
- Faster problem resolution – so no more context-switching between tools
- Better pattern recognition – you can see the full story behind performance issues
- More efficient debugging workflows – you’ll get everything you need, right in one place
- Reduced mean time to resolution (MTTR) – and that means finding root causes faster
And the best part of all? Setting this up in Scout takes just 2 settings.
For Ruby apps, it’s as simple as adding gem ‘scout_apm’ and gem ‘scout_apm_logging’ to your Gemfile, then dropping in a quick config block, like so:
SCOUT_LOGS_MONITOR=true
SCOUT_LOGS_INGEST_KEY=aaaa-1111-aaaa-1111 # Provided in App Logs Page
Then, just restart your app, and you’ll see your logs and traces flowing into Scout, already connected and ready to help you debug. This means you can now find performance issues and drill down into the logs to search for the root cause:
And here’s some more good news: We include 1GB of free logging data with every account, and, furthermore, our agent automatically handles the correlation between logs and traces.
So, ready to stop guessing and start knowing exactly what’s happening in your application? Give Scout’s integrated logging and APM a try! In the world of application performance, context isn’t just helpful – it’s everything. Why settle for disconnected tools when you can have the complete picture in one place?
