What’s New in Kloudfuse

We expanded the offerings within our proprietary query language, FuseQL.

We deprecated the Kloudfuse Catalog Service in a previous release. Start Using Kloudfuse Customer Scripts instead.

We enhanced the RUM Performance Overview UI.

You can now Configure Stream Isolation on Pinot Tenants and route specific telemetry streams to tagged nodes.

You can now create new folders in the Dashboards main/default page; simply click at the top right.

We made several improvements and a small number of fixes to the FuseQL functionality. Critically, we significantly enhanced the Advanced Search parsing by adding support for regular expression parse variable pattern recognition with and without start and stop anchors, and JSON pattern parsing. See FuseQL Parse Operators.

In this release, we added a session IP column, made UI improvements, and fixed a few bugs.

We made several improvements to the Help menu in this release:

We added an option to import dashboards and alerts directly from the integration page.

We also made overall improvements to the Integration UI.

We updated content on the Integrations tab, and included icons for easier identification of third-party agents, services, and so on.

We added parse and json extension support for our proprietary FuseQL log processing.

We fixed and improved several dashboards.

FuseQL, our proprietary Query Language for searching logs, now offers Advanced Search (free-form search), and fully supports the Logs Analytics screens.

We added the metric detection SLO in this release, to supplement our existing support for SLOs based on latency and availability.

For logs, metrics, events, and APM, you can now create and edit dashboard panels and alerts directly from the Analytics interfaces.

In this release, we added these two features to our RUM offering:

RUM allows you to capture and analyze data from real users as they interact with your web application, providing insights into their experience and identifying performance issues from the user’s perspective.

We encourage you to look at our introductory video on how RUM addresses the many requirements of Digital Experience Monitoring:

Read about our implementation in RUM Setup and Real User Monitoring (RUM).

Kloudfuse developed its proprietary query language, FuseQL, for a range of applications. It has flexible parameters for answering highly complex questions.

To understand the defining characteristics of FuseQL and how to use it, see FuseQL.

This release introduces two powerful new APM observability tools, Trace Heatmap and K-Lens, to assist in service-level issue detection and advanced debugging using APM Trace data.

Trace Heatmap is an interactive trace data visualization chart that helps you to visually detect deviations and outliers in the latencies reported by APM data.

K-Lens is a proprietary analysis tool that helps you to narrow down the cause of appliance performance issues; it compares thousands of attributes across span events to their baseline performance, and displays ranked results based on significance of deviation.

We encourage you to look at our introductory video about this feature:

We enhanced our SSO support by adding SAML-based authentication for providers such as Google, Okta, and many others. See Configure SSO Authentication with SAML.

Kloudfuse supports roll up and aggregation of metrics data, computed directly from the raw data stream during ingestion. For longer time intervals, our approach significantly improves query performance, and reduces chart loading times and I/O costs. Depending on the time span or step size of the query, Kloudfuse calculates results either from raw data, or from rolled up data. In the shorter time spans, we continue to use raw metrics because the calculation approach could potentially smooth out the data and miss important signals, such as outliers.

Metrics roll up is off by default, and the default aggregation is for 5 minutes. Contact us to turn on metrics roll up, and help you configure your environment.

For more information about this feature, see Metrics Roll Up.

Until this release, the Kloudfuse APM interface showed only the services that sent data within the selected time block. We now show all services, including the ones that may be stopped or paused.

To see all services, simply toggle the Show Inactive Services selector. You can see that two new services in this example, frontend-web and loadgenerator, appear in the service list. Because the services were inactive during the relevant time interval, they have empty columns.

Logs default sidebar Open facets-in-left-bar.png facets-in-left-bar.png Kloudfuse automatically extracts facets attributes from logs during ingestion. Before this release, we surfaced facets in the Logs side bar, organized under Sources.

Because the number of sources and facets can quickly become very large, the troubleshooting tasks become more difficult and the feature is not as useful as we want it to be. In this release, we re-organized the sidebar to include expandable sections as these components: Filters, Labels, and Facets.

We now organize Facets in their own section. Users can create custom groups that include the facets they want to monitor, across any number of sources.

You can group facets by source, and use the search bar to find relevant groups.

When you expand a group (here, the logs-parser), you can see some of the facets tracked in the group. We also added a search bar in each group to help you locate a facet by name.

You may have to save transactional information for compliance, legal, or other regulatory requirements. In addition to processing logs for observability and analytics, this release of Kloudfuse introduces a supplementary mechanism for archiving pre-processed logs (with identified filters, facets, and so on) into longer-term storage, and a separate mechanism to hydrate these logs to examine them for the relevant data.

The benefits of this approach extend beyond basic regulatory compliance:

You store important historical data in a cost-effective compressed format in a location that you own and control.

When uncompressed, the logs are human-readable and highly searchable because of the high level of indexing through labels and other data attributes.

You can configure the archival instructions in a manner that categorizes data consumption by internal cost center.

We currently support log archive and hydration for AWS S3.

To start hydrating previously archived logs, select Logs in the top navigation bar, and then choose Hydration.

Contact us at support@kloudfuse.com to enable this feature in your Kloudfuse cluster.

For in-depth information on this feature, see Logs Archive and Hydration.

Log labels have two main source: many are attached by the agent that delivers the logs to the Kloudfuse platform, and potentially even more are defined by users. Some labels are meaningful at the time they are defined, yet loose their relevance over time. Some are created accidentally, or as a result of not fully understanding the common use cases or the purpose of log tracking. Automatically-generated labels, from agents, are often cryptic and unnecessary.

Seeing cardinality analysis of a log helps users to remove unnecessary labels, improving data accessibility and making the collected information more actionable.

To determine log label cardinality, select the Logs tab, and then the Cardinality Analytics option in the dropdown menu.

The Logs Cardinality report shows the overall cardinality; in this case, the cardinality is 335. It further breaks out the data by Label, showing the Value Count (1h) for the preceding hour, the Value Count for the specified most recent time range (the last 5 minutes is the maximum time range and the default setting), and the Value Chart, which is a simple bar chart representation of the count of unique values over the selected time range.

To find the prevalence of a specific label value, use the filter at the top of the page to select a label, the operator, and the comparison value.

For more information, see AWS Graviton Processors documentation.

Starting with this release, we are replacing error message toasters with more specific error messages that display directly on the components that throw the error.

In this release, we added support for data collection through OpenTelemetry on Docker containers. For details, see OpenTelemetry Collector on a Docker Environment.

We made significant improvements to the performance of log search with efficient indexes; comparable searches are now an order of magnitude faster.

We implemented the necessary bug fixes.

We added default access policies for users without explicit policy assignment. See Default Policy.

We are introducing a new UI support for easier user management:

Starting with this release, KloudFuse does not support RBAC and policy configuration by editing raw configuration files.

See RBAC: Using the Admin Tab.

We made some targeted performance improvements.

We are introducing support for multiple authorization keys.