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Log Management Comparison: ELK vs Graylog

Production logs can help ensure application security, reveal business insights and find and understand errors, crashes, and exceptions. But as useful as logs are, they’re difficult to manage and hard to keep track of. Making matters worse is that as log data volume grows, so does the difficult task of maintaining and managing them. It’s for this reason that developers, DevOps engineers, and CTOs turn to log management tools.

Managing Centralized Data with Graylog

Central storage is vitally important in log management. Just as storing and processing logs into lumber is done in one place, a sawmill, a central repository makes it cheaper and more efficient to process event logs in one location. Moving between multiple locations to process logs can decrease performance. To continue the analogy, once boards are cut at a sawmill, a tool such as a wood jointer smoothes out the rough edges of the boards and readies them for use in making beautiful things.

Comparing a Multi-Tenant SaaS Solution vs. Single Tenant

Let me preface this article with a quick customer story. I was recently talking with the director of operations of a G2000 company and he asked in a nice, but pointed way: “All I want is a SaaS software solution to manage my applications. Why does the architecture of the software matter?”. At Sumo Logic, we couldn’t agree and disagree more.

How you can take back control over your log analytics with AI

We’ve all been there — you’re on-call, fast asleep at 3 AM when suddenly, in comes the alerts–in overdrive. Your system is notifying you of some sort of abnormal behavior, but with all the alerts and data coming through, its difficult to figure out what your system is trying to tell you. Is there potential malicious behavior? Did someone write faulty code? Is it an important issue or can it wait? Is it nothing at all?

Getting more value from your Stackdriver logs with structured data

Logs contain some of the most valuable data available to developers, DevOps practitioners, Site Reliability Engineers (SREs) and security teams, particularly when troubleshooting an incident. It’s not always easy to extract and use, though. One common challenge is that many log entries are blobs of unstructured text, making it difficult to extract the relevant information when you need it.

Garbage Collection Settings for Elasticsearch Master Nodes

Elasticsearch comes with good out-of-the-box Garbage Collection settings. So good in fact that the Definitive Guide recommends not changing them. While we agree that most use-cases wouldn’t benefit from GC tuning, especially when it turns out there simply isn’t enough heap, there are exceptions. We found that G1 GC, for example, works well on big heaps. This allows you to have less, bigger nodes, which in turn means less network traffic in a large cluster.