Accelerating Log Management with Logging as a Service
What Is Logging as a Service?
The basic goal of log management is to make log data easy to locate and understand so that users can identify how their services are performing and troubleshoot more quickly.
Logging as a Service, or LaaS, takes log management a step further by providing a solution that seamlessly scales and manages your log data via cloud-native architecture. By outsourcing the onboarding, scaling, and management to a third-party service, you’re able to focus on deriving more value from data, conserve time and resources, and prioritize other business functions.
Self-Hosted Logging Solutions
With a Logging as a Service solution, you can effectively outsource your log data management while reaping the benefits of a fully managed platform. However, some organizations prefer self-hosted solutions for a few different reasons.
By opting for a self-hosted solution, you’re essentially trading convenience for control. Users are given complete control over how they establish, administer, and manage their logging infrastructure, with the notion that they desire to oversee and manage every aspect of their solution.
Self-hosted solutions are usually either deployed on virtual machines or Kubernetes clusters. For users that are only planning to ingest a small amount of data, open source self-hosted solutions like OpenSearch offer small amounts of data that can be ingested without any initial expenses for the user.
A self-hosted solution could also be the right choice for organizations that value privacy for their data. In some cases, organizations prefer to host their data on-premises due to regulatory or compliance requirements.
Nevertheless, there are several scenarios in which these benefits are outweighed by challenges. As mentioned before, virtual machines are relatively easy to deploy and get started with, but they don’t offer the same automated scalability as Kubernetes. If logs volumes spike, VMs can become overwhelmed and could even cause log data loss.
The other option would be setting up and maintaining a self-hosted solution on Kubernetes. While this provides the scaling capabilities of Kubernetes, it also adds a layer of complexity, both in managing and maintaining it.
Some organizations may prefer a solution that simplifies and automates log management, seamlessly scales alongside growing infrastructure, and minimizes overall troubleshooting and monitoring time and costs – factors in which self-hosted solutions can fall short.
Log Management with a Service Approach
Logging as a Service solutions vary in terms of their approach to logging, but there are key benefits that are consistent among them. These solutions all aim to add simplicity at scale by utilizing data consolidation features, automation, and various other platform benefits.
Getting Started with Monitoring and Troubleshooting
To start monitoring logs, it’s essential to have a log management infrastructure that incorporates structures such as a log database, an event streaming tool, and a log analysis component.
In a self-hosted solution, the user maintains full accountability for the various tools required for these steps. It can require extensive time and technical knowledge to manually set up and configure a logging back-end, as well as troubleshoot if they malfunction. Plus, if you need any help with configuration and maintenance, you must refer to online documentation or other online resources.
With a Logging as a Service platform, the logging backend is already pre-built, enabling users to solely focus on collecting and shipping the right data. This in turn removes the technical burden of creating and managing your data pipeline, while also ensuring a high level of availability and performance for all data. If issues occur during the process, you’re able to directly reach out to the customer support team of the service provider and they’ll help troubleshoot further.
Overall, utilizing a LaaS platform’s approach to onboarding removes the complexity of data collection and scaling and enables teams to start monitoring faster.
Reliability and Scalability
As data volumes spike and dip, it’s critical that your monitoring solution is reliably performant. Most organizations cannot afford to have their troubleshooting infrastructure malfunction, especially during a production incident.
With self-hosted solutions, there is a possibility that the tool or servers hosting them may crash due to an unforeseen volume spike (depending on your logging infrastructure), and log data could consequently be lost.
While SLAs and performance vary across providers, logging as a service tools offer an assurance of consistent high reliability, regardless of the volume of data being handled. Alternatively, the SLA and performance of self-hosted tools depend on the scale and complexity of the cloud environment, as well as the skillset and engineering resources of the engineering team.
By utilizing a LaaS platform, you’re also getting the benefit of outsourced scalability. Instead of having to manually stand up servers, set up local hosting infrastructure, and spend time maintaining them, LaaS platforms have built-in capabilities that allow them to seamlessly scale alongside your telemetry data.
In summary, Logging as a Service platforms can be less prone to malfunctions, provide faster log data querying, and offer effortless scalability without manual server setup and maintenance. Embracing LaaS platforms can deliver consistent and dependable logging infrastructure so you don’t have to worry about it yourself.
Unified telemetry data analytics for observability
Correlating logs with metric and trace data is a crucial component of gaining the necessary insights when it comes to observability. Metric and trace data provide additional context and insight needed to comprehensively monitor the health and performance of your cloud applications and infrastructure.
While some self-hosted logging tools offer log, metric, and trace monitoring capabilities, they often fail to correlate between these data types, creating a siloed experience that requires you to troubleshoot across multiple environments. In effect, this leads to the prolonging of the mean time to resolution for production issues, thus being costly for teams.
Alternatively, some Logging as a Service solutions make it easy to consolidate and cross-analyze between telemetry data types through automated data correlation capabilities, visualizations, and an overall unified approach to monitoring. When data is unified within a single platform, it’s possible to correlate signals and anomalies from one data type to another, allowing for a more holistic view of the issue at hand.
For example, let’s say you are examining a metrics dashboard and notice an unusual spike in your data. With siloed systems, you would be required to investigate your metrics, understand what application or service caused the spike, and then write a query in an entirely separate tool to access the specific logs to understand what may be causing the issue in your system.
Rather than wasting time managing and investigating within multiple products, an observability platform would allow you to identify a subset of metrics and directly click into and investigate the associated logs for them, all within a single UI.
Optimize Data and Costs
Choosing the right tool for log management is highly contingent upon your organization’s business objectives and specific use cases, with cost also playing a significant role. Many opt for self-hosted tools at the start of their monitoring journey due to their perceived lower cost of entry.
However, as mentioned previously, intensive time and resources are needed to manually scale and maintain these systems to match fast-growing data volumes, which end up costing organizations in the long run. Logging as a Service solutions are automatically scaled and maintained, saving on engineering resources.
Depending on the provider, Logging as a Service platforms also seek to help users reduce data costs by providing a high-level overview of their data usage. This includes features such as cost tracking, setting data caps based on team or product usage, and offering pricing based on data ingestion. Organizations are able to utilize querying to filter out unneeded data during data migration, removing unneeded data and reducing overall storage costs.
Many platforms also offer specialized data storage options that can be used to optimize costs. You’re able to customize your data storage, allowing you to choose specific data needed for troubleshooting while the rest is safely stored within the platform in low-cost cold storage. Of course, this is also available for self-hosted toolsets – it just requires more components to set up, upgrade, and manage.
Logz.io’s approach to data optimization takes this a step further. Unlike other vendors, our Data Optimization Hub provides a centralized UI within the product where you’re able to inventory all incoming logs, metrics, and traces, and filter out low-priority data in just a few clicks. Furthermore, you can manipulate data using features like LogMetrics, enabling cost-effective data visualization without the need for indexing the data.
Logz.io for Logging as a Service
Logging as a Service solutions offer an enhanced, automated approach to log management that results in a highly performant, often faster troubleshooting solution. Logz.io offers a fully-fledged observability platform that unifies log, metric, and trace analytics. It’s built upon the Logging as a Service model, all while providing numerous cost optimization options for our users that allow for full observability at a much lower cost.
If you’re interested in seeing how our platform enhances log management, sign up for our free trial and get started today.