Hi everyone. I'm coming from a background of managing ERP data migrations and SaaS transitions, so I'm comfortable with structured data, but log queries feel like a different world. I've just been given access to Sumo Logic at my new role, and the interface is a bit overwhelming.
I know my end goal: I need to monitor application errors and track specific user journey events. But I'm not sure about the first steps. Should I focus on learning the query language first, or should I start by exploring the pre-built dashboards and apps?
From my experience with other platforms, I've found that jumping straight into writing queries without understanding the data source structure leads to a lot of wasted time. In Sumo Logic:
* Is there a recommended way to first map out what log sources are available?
* What are the absolute fundamental operators or commands I should learn day one?
* Any common pitfalls in structuring these queries that a beginner might not see coming?
I'd be grateful for any pointers that helped you build a solid foundation. Practical, step-by-step advice is much appreciated.
- h
Data is sacred.
You've got the right instinct: understanding the data structure is everything. Jumping straight into the query language is a mistake.
First, go to the log search page and run a wildcard search over your relevant time range: `* | count by _sourceCategory, _sourceName`. That's your data map. See what applications, environments, and log types are actually flowing in. Do this before you even look at a dashboard.
The day-one operators are `where`, `parse`, and `timeslice`. `where` filters, `parse` extracts fields from unstructured lines (use "learn" mode), and `timeslice` is for time-based aggregations. A starter chain looks like: `_sourceCategory=myapp* | parse "error=*" as error_msg | where error_msg matches "*fatal*" | count by _sourceHost, timeslice 1h`.
The major pitfall isn't syntax, it's volume. Newbies write queries that return millions of raw logs and time out. Always aggregate or limit before the final pipe. Never do `| where` on a field you haven't parsed yet. And your timestamp parsing is critical; get that wrong and your time filters are useless.
Your ERP background will help because you think in data models. Treat raw logs as your unstructured source tables, and parsing as building your dimensional model. Build from there.
Show me the benchmarks.
Your approach of mapping the data before writing queries is exactly correct. Building on the wildcard search suggestion, I'd add that you should also run `* | count by _sourceCategory` and `* | count by _sourceHost` separately to understand volume distribution, which directly impacts dashboard performance later. A high-cardinality sourceHost list can choke poorly built queries.
The fundamental operators list is good, but I'd prioritize `parse` and `lookup`. For your user journey tracking, you'll need to parse session or user IDs from logs, then use `lookup` to join against a CSV file of user metadata if you have one. This bridges your ERP structured data mindset into the log world. A pitfall beginners miss is not using the query optimizer's warnings; if you write `where error_msg = "null"` you'll get a warning suggesting `where isNull(error_msg)`, which is a crucial distinction.
For your stated goals, structure two separate starter queries. For errors: `_sourceCategory=prod/app* | parse "[*] *" as log_level, message | where log_level in ("ERROR", "FATAL")`. For user journeys: `_sourceCategory=prod/api* | parse "user_id=*," as user_id | count by user_id, _sourceName`. Start with these exact patterns, then modify the parse strings based on what you see in your actual wildcard search results.
— Harper
Oh, the point about high-cardinality choking queries is something I wouldn't have thought of. Makes total sense.
The separate `count by` queries for volume distribution is a great next step after the initial wildcard map. So it's like: map the sources, then check their weight?
For the user journey query example, what happens if the `user_id` isn't in every log line? Does the parse just skip those lines?