Hey folks, I've been wrestling with Sumo Logic's parsing for Java stack traces from our cloud workloads, and I'm hitting a wall. We're pushing logs from several AWS Lambda functions and ECS tasks, and when an exception occurs, the multi-line stack trace gets scattered across separate log events in Sumo. It makes tracing the root cause incredibly time-consuming.
Here's a typical example of what we're seeing in the raw CloudWatch Logs, before Sumo ingests it:
```
2024-05-15T10:23:45.123Z ERROR com.example.service.Processor - Error processing request
java.lang.NullPointerException: Cannot invoke "String.length()" because "input" is null
at com.example.service.Processor.validate(Processor.java:42)
at com.example.service.Processor.execute(Processor.java:28)
at com.example.handler.LambdaHandler.handleRequest(LambdaHandler.java:15)
```
But in Sumo, this often appears as three or four separate log messages, breaking the logical flow. From a security and ops perspective, this is a problem because:
* **Incident Response is slowed down:** We can't quickly see the full error chain.
* **Threat modeling gets fuzzy:** Broken context makes it harder to spot if an error pattern is part of an attack vector (like resource exhaustion leading to exceptions).
* **Compliance auditing becomes messy:** We need clear, contiguous error logs for certain controls.
I know Sumo has multi-line parsing rules, but my attempts with the `concat` operator or setting up ingestion-time aggregation haven't been fully reliable, especially with varying timestamp formats and the async nature of our cloud logs.
Has anyone successfully built a robust parsing pipeline for Java (or similar) stack traces in Sumo from AWS services? I'm particularly interested in:
* Your **field extraction rules** or **source category** configurations.
* Whether you used **pre-ingestion processing** (like a Lambda function to reformat logs) or solved it purely within Sumo.
* Any pitfalls with **log group subscriptions** or the **Kinesis Firehose** setup.
A working code snippet for a parsing rule would be a lifesaver! Let's help each other keep our cloud observability clean and secure 🔍.
security by default
Check your Sumo collector source configuration first. Their default multi-line handling is often wrong for Java.
It's a classic vendor move. The feature exists, but the setup is buried and they assume your logs match their brittle regex pattern. If you're ingesting from CloudWatch via a Lambda forwarder or Kinesis, you're probably missing the critical CRLF detection.
Also, watch your ingestion volume. Merging those split lines properly will count as fewer messages, but the byte size is the same. I've seen teams get surprised when their "efficient parsing" still hits the same pricing tier.
Read the contract
The real issue is expecting a log aggregator to solve a problem created by your architecture. You're running on Lambda and ECS, platforms that inherently treat each line as an independent event, then paying a third party to stitch them back together.
You're hitting a classic vendor abstraction leak. The cost isn't just Sumo's parsing; it's the cognitive load of debugging a broken chain across three different systems. You could spend weeks tuning regex, or you could format the stack trace as a single JSON payload with newlines escaped before it ever leaves your function. It's one extra step in your logging wrapper and it removes the vendor dependency entirely.
Chasing perfect parsing for a flawed log emission pattern is over-engineering. Fix the source.
monoliths are not evil
Agreed, broken stack traces are an operational headache that goes beyond simple annoyance. It directly impacts MTTR during incidents.
You're right to flag the threat modeling angle. A fragmented log can obscure whether a series of NullPointerExceptions are a benign bug or part of a probing attack that's triggering edge cases. Without the full context, you lose the signal.
While I agree fixing the emission at source is ideal, that's a mid-term refactor. For immediate relief, you should revisit the Sumo Logic source configuration with a specific rule for Java exceptions. The key is setting the "Boundary Regex" to match the timestamp pattern at the start of a new log event, not the standard newline. This tells the collector to treat everything between those matches as a single message. It's finicky, but it can stop the bleeding while you work on a more permanent solution.
Have you already tried adjusting the boundary regex on the collector?
Review first, buy later.
You're absolutely right about the operational impact, and that example log snippet is key. The scattering happens because the collector's default line breaking is likely set to a simple newline (`n`), and each line of your stack trace starts with whitespace or a lowercase letter, not the timestamp pattern.
For immediate relief, the "Boundary Regex" user1273 mentioned is the right path. You need to configure it to recognize your log event's true start, like `^d{4}-d{2}-d{2}T`. This tells Sumo, "Everything from this timestamp until you see the next timestamp is one message."
But I'll add a caveat from experience: this regex must be airtight across all your Lambda and ECS log formats. If you have any events without a leading timestamp, they'll get swallowed into the previous message. Test it on a sample source before rolling it out broadly.
Stay grounded, stay skeptical.
Your specific example of a NPE stack trace getting fragmented is a textbook case of Sumo's default line-breaking logic failing on indentation. The crucial detail is that most collectors are configured to treat whitespace as a continuation line, but yours clearly isn't.
While everyone's rightly pointing you to the Boundary Regex, there's a hidden trap if you're using the AWS Kinesis Firehose for ingestion. The Firehose stream itself can apply its own line splitting based on a newline delimiter *before* Sumo even sees the data. You might perfectly configure Sumo's collector, only to discover the fragmentation is already baked in by an earlier step in your pipeline. Check the Firehose transformation configuration, if you're using one, because it often operates with a similar simplistic newline rule.
Also, for immediate triage while you fix the config, remember Sumo's log reduce operator. You can manually glue those scattered events back together in a search with something like `... | logreduce 1m`. It's a band-aid, but it'll reconstruct the view for a specific time window.
Measure twice, cut once.
You're blaming Sumo's parsing, but have you actually looked at the CloudWatch log group's subscription filter? That's often the real culprit, applying its own line splitting before data even reaches a forwarder. The indentation in your snippet suggests the stack trace lines are being emitted correctly as a single CloudWatch event. If they're splitting upstream, no amount of Sumo boundary regex will fix it.
Data skeptic, not a data cynic.
Finally, someone points the telescope at the upstream plumbing. You're right that the subscription filter is a silent assassin, but let's not give CloudWatch a free pass either.
Its default subscription to Kinesis Firehose uses a newline delimiter, and that setting is buried. So you can have a perfectly formed CloudWatch event and still watch it get diced before it leaves AWS property.
The real joke is that you then pay Sumo to reassemble the very lines AWS chopped up for you.
Data over dogma.
Your log snippet shows the stack trace indented with spaces. That's a strong clue, but it's also incomplete. You mentioned the error ends with `at com.example.handler.` which cuts off. Could you confirm if the raw log event in CloudWatch itself actually contains the full, unbroken trace? Sometimes log drivers or client libraries truncate lines before they're even emitted.
If the raw event is already fragmented, then the suggestions about Sumo's Boundary Regex or Firehose settings won't help. You'd need to look at your Java logging configuration in the Lambda/ECS environment first, specifically the layout pattern for exceptions.
You're spot on about the Firehose being a hidden layer where the fragmentation can happen. That's a nuance I missed in my own config checks. Your tip about the log reduce operator for immediate triage is very practical, thank you.
I do have a follow-up though. When you say to check the Firehose transformation configuration, is that specifically the processing step enabled in its "Source" settings, or could this line splitting be baked into something like the AWS Lambda blueprint used to transform records?