Release Notes for recent software and platform updates across all Ascent components:

• Fleet

• Flow

• Lake

• Observe

For specific information or questions relating to a given release, please contact Apica Support at [email protected].

Welcome to Apica Ascent

Apica Ascent is a powerful full-stack Telemetry Data Management and Observability platform designed to streamline and optimize your entire data life-cycle: Collect, Control, Store, and Observe.

The Ascent 2.14.2 release includes the following updates:

Bugfixes

• Fix ASM+ deadlocks after too many connections

Note

Internal backend and infrastructure optimizations have been completed to improve performance and reliability. No user-facing changes in this release.

Ascent Synthetics

Getting Started

Data Management and Pipeline Control

Observe

Bug Fixes

• Improved the “Forgot Password” experience so it no longer assumes the email address is always the same as the username, reducing confusion and login friction for users.

Ascent Synthetics

Bug Fixes

• Corrected behavior where a user deleted in Ascent could still remain configured in ASM, ensuring that user deletions are handled consistently across the system.

Flow

Bug Fixes

• Fixed an issue in the pipeline graph view where creating a new pipeline could replace existing attached pipelines, so existing connections are now preserved when adding new pipelines.

Component Versions - Ascent v2.14.3

The Ascent 2.14.1 release includes the following updates:

Enhancements

• Update the ChecksFillThroughASMAPI function to support all subsequent sync attempts from running

• Enable redirection to checks page based on license type

• Create alerts API fails on newly spun up environment

• Update icons of New Alert Destinations and New Data Sources

• Enable search filter under pending invitations to handle advanced results according to the search

• Enable ability to assign config files to fleet agents

• Other minor bugs and defects

Component Versions - Ascent v2.14.1

Fixes and Improvements

This release focuses on improving reliability and consistency in Pipeline and Check Analytics behavior. The following issues have been resolved:

Pipeline & Data Flow Fixes

• Missing Pipeline Visibility Resolved issue where a second pipeline intermittently disappeared from the Pipeline view, even though it was applied to the namespace. Both pipelines now appear correctly.

• Pipeline Preview Fails Fixed a problem where the pipeline preview in Logs & Insights failed to display results, even when valid data existed.

• Deleted Rules Still Visible Addressed a bug where deleted pipeline rules continued to appear in the UI, marked in red. These are now correctly removed from the view once deleted.

Synthetics / Check Analytics Fixes

• Incorrect Fatal Check Messages Corrected misleading error messages shown in Ascent for failed synthetic checks. The system now shows accurate messages, in line with ASM.

• Monitor Group User Assignment Bug Fixed an issue where assigning or un-assigning users to/from a monitor group appeared to have “no changes to apply,” even when actions were taken. User assignments now save and reflect correctly.

Component Versions - Ascent v2.10.8

A time-series database (TSDB) is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s).

The Apica Ascent UI is your window to your IT data, logs, metrics, events and traces - ingested from all of your data sources and converged onto a single layer. The Apica Ascent UI enables you to perform a wide range of operations - from simple uptime monitoring and error troubleshooting to capacity planning, real-time forensics, performance studies, and many more.

You can access the Apica Ascent UI by logging into your Apica Ascent instance URL using your account credentials.

The navigation bar at the right side of the UI allows you to access your:

• Dashboards

• Queries

• Alerts

• Explore - Logs, Topology, etc.

• Integrations - Forwarders, Source Extensions, Alert Destinations, Pre-created dashboards,

• Settings

The following sections in this article describe the various elements of the Apica Ascent UI and their purposes.

Dashboards

A dashboard is a collection of visualizations and queries that you've created against your log data. You could create dashboards to house visualizations and queries for specific as well as multiple data sources. Everything contained within a dashboard is updated in real-time.

The Dashboards page on the Apica Ascent UI lists all the dashboards you've created within Apica Ascent. Dashboards that you've favorited are marked with a yellow star icon and are also listed under the Dashboards dropdown menu for quick access in the navigation bar. The following images depict dashboards that you can create using Apica Ascent.

Queries

Apica Ascent enables you to write custom queries to analyze log data, display metrics and events, view and customize events and alerts, and create custom dashboards. The Queries page lists all of the queries you've created on Apica Ascent. You can mark some of them as favorites or archive the ones, not in use. Your favorite queries also appear in the drop-down menu of the Queries tab for quick access.

Alerts

Apica Ascent enables you to set alerts against events, data, or metrics of interest derived from your log data. The Alerts page on the UI lists all of the alerts you've configured on Apica Ascent. You can sort and display the list of alerts by their name, message, state, and the time they were last updated or created. Depending on your user permissions within Apica Ascent, you can click an alert to view more information or reconfigure the alert based on your need.

The following image depicts a typical Alerts page on the Apica Ascent UI.

Explore

The Explore page lists all of the log streams generated across your IT environment that are being ingested into Apica Ascent. The Explore page lists and categorizes logs based on Namespace, Application, ProcID, and when they were last updated. By default, logs are listed by the time they were ingested with the most recent applications appearing on the top. You can filter log streams by namespaces, applications, and ProcIDs. You can also filter them by custom time ranges.

You can also click into a specific application or ProcID to view logs in more detail and to search through or identify patterns within your log data.

The following image depicts a typical Explore page on the Apica Ascent UI.

Journals

The Journals page lists all the important events that have occurred in the Apica Ascent Platform. Audit Trail are listed by their Name, Message, and the time they were created. The Journals page tracks important service notifications like service restarts, license expiry, etc...

Create

The Create dropdown menu enables you to create new alerts, dashboards, queries, reports, and checks as shown in the following image.

A function-specific modal appears based on what you select from this dropdown menu.

Apica Ascent lets you connect to your MongoDB for seamless Querying of data

Adding MongoDB to Apica Ascent

The first step is to create a MongoDB data source and provide all details such as the Name of the data source, Connection String, and the Database Name of your MongoDB. Optionally you can add Replica Set Name

Querying MongoDB

The next step is to Navigate to the Query editor page and start Querying your data from your MongoDB

You can find documentation for the following AWS Data sources below

• Amazon Athena

• Amazon Cloudwatch

• Amazon Elasticsearch Service

Apica Ascent helps you to connect your RSS for faster querying and visualization of your data.

Adding RSS to Ascent

We're excited to share the latest improvements and bug fixes in Ascent 2.10.6. This release focuses on enhancing stability and user experience across all our products.

Ascent Synthetics

What's Fixed

1. Observe

• New ilert Integration Apica Ascent now natively integrates with ilert for alerting and incident management. This enables users to forward alerts from Ascent directly into ilert for on-call scheduling, escalation, and incident response.

Ascent Synthetics

New Features & Improvements

Release 2.10.5 - What's New

We're excited to share the latest improvements in version 2.10.5. This release focuses on making your monitoring and observability experience smoother with bug fixes, enhanced integrations, and better user interface improvements.

Flow

We are pleased to announce the release of Ascent v2.10.4, which brings important performance optimizations, stability improvements, and functional enhancements across the platform.

Flow

Observe

New Features & Improvements

• Notification destinations can now only be viewed by authorized users, providing improved access control and clarity.

Solving Observability Fragmentation

Before OpenTelemetry, organizations struggled with multiple proprietary monitoring agents, each producing telemetry data in incompatible formats, leading to data silos, increased complexity, and a lack of correlation between logs, metrics, and traces. These challenges made it difficult to gain a comprehensive view of system health and troubleshoot issues efficiently.

OpenTelemetry unifies telemetry collection across all environments, ensuring seamless interoperability between tools and services, allowing teams to consolidate their observability strategy while reducing operational overhead and improving incident response times.

Setting up your Amazon Athena

The first thing you’ll need to do is create an IAM user that will have permission to run queries with Amazon Athena and access the S3 buckets that contain your data.

To configure your Amazon Athena with the necessary permission, please navigate to

If you are not yet a registered user, please follow the steps below:

Signing up for Apica Ascent SaaS

To sign up for Apica Ascent SaaS, follow these steps:

1. Go to the .

Apache Druid is a real-time database to power modern analytics applications. Druid is designed to quickly ingest massive quantities of event data, and provide low-latency queries on top of the data.

Apica Ascent can connect to Druid to help you analyze your data.

Adding Druid Data Source

The first step is to add Druid Data Source to your Apica Ascent. Fill out the below fields while configuring the data source

Apica Ascent supports Amazon Elasticsearch Service as a Data Source which makes it easy for you to perform interactive log analytics, real-time application monitoring, a website search, and more. OpenSearch is an open-source, distributed search and analytics suite derived from Elasticsearch

Let's see how Amazon Elasticsearch Service works

Creating and Adding Amazon Elastic Service Data Source

The first step is to add Amazon Elasticsearch Service Data Source to your Apica Ascent. Fill out the below fields while configuring the data source

Accessing the Ascent Checks Data Source

The Ascent Checks Data Source is available to all tenants with ASM+ access. You should be able to query on all checks out of the box.

Overview

Amazon Web Services, Inc. is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. These cloud computing web services provide distributed computing processing capacity and software tools via AWS server farms.

Apica Ascent supports SQL, NoSQL, Time Series, and API data sources along with Apica Ascent's inbuilt data source to help you query data from different sources to make sense of your data. Currently supported Data Sources on Apica Ascent are shown below

Application Performance Monitoring (APM)

End-to-end distributed tracing for microservices: OpenTelemetry enables deep visibility into microservices interactions by capturing traces across service boundaries. This allows developers and operations teams to understand request flow, identify problematic dependencies, and detect failures in a distributed system. By leveraging OpenTelemetry’s context propagation, teams can follow a request from its origin to its termination, providing a clear picture of dependencies and bottlenecks. This improves troubleshooting efficiency, reduces the mean time to resolution (MTTR), and helps organizations build more resilient, scalable architectures. Additionally, OpenTelemetry supports integrations with distributed tracing backends such as Jaeger, Zipkin, and commercial solutions, ensuring flexibility in visualization and analysis.

Identifying latency bottlenecks in cloud-native environments: By collecting granular performance data, OpenTelemetry helps teams pinpoint where delays are occurring in an application. Whether it’s a slow database query, an overloaded service, or network latency, OpenTelemetry provides the data needed to optimize system responsiveness and improve user experience. With built-in support for metrics and histograms, OpenTelemetry allows teams to measure request duration, throughput, and error rates, enabling proactive performance tuning. Furthermore, OpenTelemetry facilitates real-time alerting on latency spikes, allowing DevOps teams to quickly diagnose and mitigate issues before they impact users. This level of insight is particularly beneficial for cloud-native applications where dynamic scaling and complex service interactions demand constant monitoring and optimization.

Infrastructure and Cloud Monitoring

Collecting host and container-level metrics: OpenTelemetry provides extensive support for collecting system-level and container-level metrics, including CPU, memory, disk usage, and network statistics. This enables teams to track resource consumption across distributed environments, identify performance anomalies, and optimize infrastructure utilization. By leveraging OpenTelemetry’s support for metric aggregation and real-time monitoring, organizations can ensure their applications remain resilient under varying workloads.

Monitoring Kubernetes clusters at scale: Kubernetes environments introduce unique challenges due to their dynamic and ephemeral nature. OpenTelemetry integrates seamlessly with Kubernetes to provide real-time visibility into cluster health, pod performance, and service-to-service communications. It enables DevOps teams to monitor workload scheduling efficiency, detect failing pods, and correlate application performance with underlying infrastructure issues. By centralizing observability across multiple clusters, OpenTelemetry empowers organizations to maintain high availability and reduce operational overhead in cloud-native environments.

Log Correlation with Traces and Metrics

Unified observability for root cause analysis: OpenTelemetry provides a comprehensive approach to observability by linking logs, metrics, and traces together, enabling teams to perform in-depth root cause analysis. By correlating log events with specific traces and spans, teams can identify exactly where failures occur within a distributed system, reducing the time spent diagnosing incidents and improving mean time to resolution (MTTR). This unified observability approach ensures that developers and operators have a complete understanding of system behavior, making debugging and performance optimization more efficient.

Enriching logs with trace and span context: OpenTelemetry enhances logging by automatically injecting trace and span identifiers into log messages, allowing for precise contextualization of events. This enrichment enables teams to follow an event from initiation through completion, offering clear insights into request flow and dependencies. Additionally, integrating log correlation with tracing helps detect patterns, anomalies, and dependencies that might not be immediately visible when logs are analyzed in isolation. This capability is especially beneficial in microservices architectures, where tracking down issues across multiple services can be complex without proper log-trace correlation.

Security and Compliance Observability

Capturing audit trails with OTEL logs and traces: OpenTelemetry enables organizations to create detailed audit trails by collecting logs and traces that capture user activity, API calls, and system interactions. These audit trails help organizations meet compliance requirements by providing clear, verifiable records of all system activities. By maintaining an immutable record of telemetry data, OpenTelemetry enhances accountability and security, ensuring that organizations can detect and investigate security incidents efficiently.

Detecting anomalies and unauthorized access patterns: OpenTelemetry’s advanced telemetry data collection allows security teams to analyze trends, detect anomalies, and identify unauthorized access attempts in real-time. By correlating logs, traces, and metrics, OpenTelemetry provides a holistic view of system behavior, helping teams recognize suspicious patterns, mitigate security threats, and prevent potential data breaches. This proactive security monitoring is essential for maintaining regulatory compliance and protecting sensitive data in distributed and cloud-native environments.

Business Analytics and SLO Monitoring

Defining Service Level Objectives (SLOs): Service Level Objectives (SLOs) are key performance indicators (KPIs) that define the desired reliability and performance targets for services. OpenTelemetry enables organizations to collect and analyze telemetry data that aligns with predefined SLOs, ensuring services meet business expectations. By leveraging OpenTelemetry metrics, organizations can measure service uptime, response times, and error rates, allowing teams to proactively address performance degradations before they impact end users. This approach fosters a culture of reliability engineering and helps teams adhere to Service Level Agreements (SLAs).

Analyzing user behavior and optimizing transactions: OpenTelemetry provides deep insights into user interactions and application workflows by capturing traces and metrics across distributed systems. By analyzing user journeys, organizations can identify friction points, optimize performance, and enhance user experience. OpenTelemetry allows businesses to track critical transactions, detect drop-offs, and correlate them with system behavior, ensuring continuous improvement. Additionally, businesses can leverage telemetry data to fine-tune application logic, allocate resources efficiently, and personalize user interactions based on real-time performance trends.

This Getting Started section provides a quick overview on collecting and ingesting logs into Apica using different methods:

• Collecting logs using Python

• Collecting logs using OpenTelemetry

• Collecting logs using Rsyslog

This release includes a number of fixes and improvements across the platform. Here's a breakdown of what’s been addressed, organized by product area.

Ascent Synthetics

• Improved Check Cloning Cloned checks now behave more predictably:

  • The aggregator view no longer shows the original check name.

  • Manual run messages are now accurate.

  • Deleting cloned checks works as expected.

  • Checks created via Postman no longer fail silently.

• Private Location Fixes

  • The correct private location name now displays.

  • Access group information is now visible in the Private Locations settings.

• Download Issues Resolved

  • Downloaded browser scenarios now retain their original names and extensions.

Observe

• Dashboard and Data Explorer Stability

  • Dashboards created from logs or alerts now load properly.

  • Tabs and widgets in Data Explorer no longer disappear after dashboard creation.

• UI Improvements

Flow

• Pipeline Usability

  • You can now rearrange pipeline sequences and see the updated order.

  • Creating pipelines with duplicate names is now blocked.

  • Pipeline preview works consistently on every click.

Fleet

• Agent and Configuration Management

  • Sorting by name in Fleet configurations now works across all pages.

  • You can now delete configuration files reliably.

  • The agent list filter dropdown updates dynamically based on selections.

Component Versions - Ascent v2.10.7

Discover the latest advancements and improvements of the Apica Ascent platform. This is your go-to destination for updates on platform enhancements and new features. Explore what's new to optimize your observability and data management strategies.

Synthetic Monitoring (ASM 13.26.0) - SaaS

Features

• NG Private Locations/Agents: New check-type agnostic Private Agents can be grouped into Private Locations with full self-serve functionality in the ASM UI portal. *ASM API support for full self-server ability will be added during Q3.

  Features include the creation and management of Private Location and Agent along with Private Container Repositories for Private Agent use. Private Agent install packages (.rpm and .deb) will be available with support for RHEL v8+ and Debian v10+. Private locations can be set up to use either Docker or Podman driver for check execution.

• New Browser checks will automatically accept dialogs/modals that can pop up during a test such as alert/confirmation/prompts.

• New Browser checks will attach and include control of new tabs created by the target site. I.e. the chrome WebDriver will automatically attach to new tabs that are opened during check execution of a Browser check.

• Added SAN/SNI options to SSL Cert Expiration and Fingerprint Validation for URL checks.

• Compound check is available on NG locations.

• Extended the ability to append the custom message specified in _Apica_Message collection variable to Postman check result messages in case the Postman script fails.

Bug Fixes

• Screenshots for Browser checks were not working in new tabs or windows created by the check. This is fixed as part of the above feature that include control of created tabs and windows by the target site.

• Debug scenario of Browser checks from the Edit Check page will use the same location as the check does.

• Fixed the issue where ASM UI was throwing a 500 error from Ajax while adding target value for newly created Selenium scenarios.

• Fixed the sporadic non-availability of agents in the Stockholm location issue when debugging a Selenium scenario.

Synthetic Monitoring (ASM 13H.5) - OnPrem

Features

• The display response body for failed URL calls in a ZebraTester checks the result, if available, to enable the identification of what error messages or content might be returned.

• Added support for PUT API request to add or update URL v1 checks through ASM API.

Apica Data Fabric (ADF v3.9)

Features

• Dark Mode: A new dark mode option is now available, providing a dark-themed interface for users who prefer it.

• Code Rule Preview: Users can preview and compare the data after the code rule is applied.

• : Introduced a new command-line tool in Apica Github for API management.

• Bookmark date range: Users can now bookmark specific date ranges for quick access and reference.

Bug Fixes

• Inconsistent time range when moving from ALIVE to Facet Search page: Fixed the issue where the time range was inconsistent when moving from the ALIVE to the Facet Search page.

• Orphan tab from ALIVE: Resolved the issue of orphan tabs appearing from ALIVE.

• Alert page issue showing undefined value: Corrected the problem where the Alert view page was showing undefined values.

Major Release V7.5-B (Installation Kit dated April 17, 2024)

ZebraTester 7.5-B release contains the following new features.

• Support for Color Blindness: To improve support for vision impairments and color blindness adaptation we have added new themes to the GUI configuration section.

• Ability to change request method from the ZT GUI: This gives the users the ability to change request method from the ZT GUI. Depending on the request method the Request body field will be enabled & visible or not.

• Support user agent details from a file: Provides an option in ZT personal settings GUI settings area, where user can upload a JSON file, which have all the latest User-Agents details.

• Updated Browser Agent List: All the current and latest browser agent list has been updated. • Option to Disable Page Breaks: Option to comment/disable a page break in the recorded session.

• Variables as Page Break Names: Users can use variables when setting my page-breaks names to make scripts more dynamic.

• Add OR condition for content type validation: Logical OR condition against content type validation can be tested by users.

• ZebraTester Controller Pull file (.wri): User will be able to pull files from the execagent that have been written by the feature "writetofile". For this the files need to be pulled to the controller as any other out/err/result file.

• WebSocket Extension (MS1): WebSocket implementation capabilities of Zebra Tester, allowing users to conduct more comprehensive testing of WebSocket-based applications. A detailed how guide on how to use WebSocket extension is added in the documentation folder.

In addition, Zebra Tester V7.5-B release contains the following bug fixes / improvements:

• Bug Fix for XML extractor giving 500 internal error in ZT scripts.

• .Har file conversion issue.

• Conflict when using variables as Mime Type validation.

• Zebra Tester -Auto assign Fix

Read previous Release notes,

Apica Ascent: Built on Kubernetes for Ultimate Scale & Performance

Observability isn’t just about collecting logs, metrics, and traces—it’s about ensuring real-time insights, high performance, and cost-efficiency at any scale. Traditional monitoring solutions often struggle with large-scale data ingestion, leading to performance bottlenecks, slow query times, and high storage costs.

Apica’s Ascent Platform, built on Kubernetes, solves these challenges by providing infinite scalability, AI-driven optimization, and seamless multi-cloud support. With a unified data store, OpenTelemetry-native architecture, and intelligent workload management, Apica delivers unparalleled observability performance while reducing operational complexity and costs.

More on Ascent Kubernetes Integration

InstaStore™: Kubernetes-Powered Unified Telemetry Data Lake

One of the biggest challenges in observability is data storage and retention. Traditional monitoring solutions rely on tiered storage models, leading to high costs, data fragmentation, and slow query times.

Apica’s InstaStore™ data lake, built on Kubernetes, eliminates these limitations by providing:

• Infinite scalability – Stores billions of logs, traces, and metrics without performance degradation.

• ZeroStorageTax architecture – No more storage tiering, reducing storage costs by up to 60%.

• Real-time data indexing – Instant query access to historical and real-time telemetry data.

• Multi-cloud compatibility – Supports AWS S3, Azure Blob, Ceph, MinIO, and other object storage providers.

How InstaStore™ Enhances Observability Performance

• Single source of truth – Eliminates data silos by storing logs, metrics, traces, and events in a unified repository.

• On-demand query acceleration – Uses high-speed indexing for sub-second query response times.

• Long-term retention & compliance – SOC 2, GDPR, HIPAA-compliant storage for enterprise observability data.

Result: Enterprises can store, query, and analyze observability data instantly, at a fraction of the cost of traditional solutions.

Ascent Flow (Telemetry Pipeline): Data Flow Optimization at Scale

Observability pipelines must ingest, process, and export massive volumes of telemetry data while maintaining low latency and high efficiency. Without proper optimization, unstructured data overloads monitoring systems, leading to delays, noise, and unnecessary costs.

Apica’s Telemetry Pipeline, built on Kubernetes, solves this by:

• Filtering, enriching, and transforming telemetry data in real time.

• Automatically routing observability data to the most cost-effective storage backend.

• Optimizing ingestion rates to reduce infrastructure costs and enhance performance.

• Providing a drag-and-drop interface for managing data pipelines effortlessly.

Fleet Management: Automated Deployment & Scaling of Observability Agents

The Challenge: Managing Observability Agents at Scale

In modern enterprise environments, observability data is collected from thousands of microservices, virtual machines, containers, and cloud functions. Manually deploying, configuring, and maintaining OpenTelemetry agents, Fluent-bit log collectors, and Prometheus exporters is resource-intensive and error-prone.

• Config drift leads to inconsistent telemetry data across environments.

• Manual agent updates result in security vulnerabilities and broken data pipelines.

• Lack of centralized management makes troubleshooting difficult.

Apica’s Fleet Management: Kubernetes-Powered Automation

Apica solves these challenges with Fleet Management, an automated system for managing OpenTelemetry collectors and other observability agents at enterprise scale.

• Automated Agent Deployment – Uses Kubernetes DaemonSets and StatefulSets to deploy and manage observability agents across clusters.

• Zero-Drift Configuration Management – Ensures all observability agents stay in sync with the latest configurations.

• Real-Time Health Monitoring – Continuously tracks agent status, performance, and data collection efficiency.

• Multi-Cloud & Hybrid Support – Deploys agents across AWS, Azure, GCP, on-prem environments, and edge locations.

Result: Enterprises eliminate manual observability agent management, ensuring consistent, reliable telemetry collection at scale.

We are excited to introduce the v2.10.3 release of Flow, focused on expanding metrics capabilities, enhancing pipelines, and improving system performance.

Flow

OpenTelemetry Metrics Support in Apica Telemetry Pipeline is now GA.

• OpenTelemetry Metrics in Telemetry Pipelines: Apica Flow now fully supports receiving and forwarding OpenTelemetry (OTLP)-compatible metrics within Apica telemetry pipelines.

• OTLP Metrics Endpoint: You can now ingest metrics through the /v1/metrics OTLP-compatible endpoint.

• Flexible Storage Options: Configure whether metrics are sent to internal Ascent Prometheus storage or forwarded externally to another OTLP compatible metric storage OR archive to an external object store.

Pipelines and Rules Enhancements

New Functions

• flatten(input: object): Flattens nested JSON structures into simple key-value pairs.

• unflatten(input: object): Reconstructs nested JSON objects from flattened structures, enabling full roundtrip transformations.

This release further strengthens Apica Flow’s telemetry capabilities, giving you more flexibility, deeper observability, and better control over your pipelines and metrics.

Component Versions - Ascent v2.10.3

🗓️ 12th September 2024

Features

• React-grid-layout Integration: React-grid-layout has been integrated into Data Explorer for widget flexibility and condensed dashboards.

• Legend Component: A separate component for displaying legends in Data Explorer widgets was implemented, which shows statistics for the data that is being rendered in the widget.

• Port Management via UI: Added support for enabling and disabling k8s cluster ports via the UI.

• Ping Checks: Implemented Ping Checks in Check Management.

• Port Checks: Implemented Port Checks in Check Management.

• Logs as a Data Source: Apica logs can now be integrated as a data source for Data Explorer and users can create/run queries on top of logs. This also introduces a new way to set alerts on the platform using logs.

• File Compare Graph Y-axis Scale: The Y-axis of the File Compare graph now supports two modes: PS count and percentage.

• PS Compare Anomaly Marker: Added anomaly markers for better visualization in PS Compare.

• Dashboard Data Migration: Dashboard schemas are now formatted into Data Explorer format and moved from LogiqHub to ApicaHub Github Repositories.

• Legacy Dashboard Converter: A converter was implemented to convert legacy Dashboard JSON to Data Explorer JSON format.

• Data Explorer: Editing Controls and Breakpoints: Added editing controls and breakpoints in Data Explorer.

• Scatter Chart Support: Data Explorer now supports scatter chart visualizations.

• Dark Theme: Improved dark themes for multiple screens, including Logs & Insights, Dashboards, Topology, and Pipelines.

• Dashboard Import in Data Explorer Format: Frontend changes were implemented to import dashboards in Data Explorer format.

• Check Analytics Reports Integration: Enhanced check analytics by integrating it with reporting.

• FPR Checks Consolidated Metrics: Added the ability to enrich check data at time of ingestion using a new domain-specific language (DSL).

Improvements

• Check Status Widget: Added custom configuration options for the check status widget.

• Performance Improvements: Extended efforts to improve the performance of Data Explorer for smoother usage.

• Gauge Chart Design: Modified the Gauge chart design, providing more user-configurable options and units for charts.

• New Visualizations in Data Explorer: New widget types were added, including Check Status, Stat, Size, Date/Time, and Scatter chart visualizations.

Bugs

• Invalid Log Timestamp: Fixed an issue where log timestamps were invalid.

• Tracing Volume Query Issue: Addressed an issue affecting tracing volume queries.

• File Compare Graph Display: Resolved issues with the display of the file compare graph summary.

• Data Explorer Page Crashing: Fixed errors causing the Data Explorer page to crash due to undefined values.

Apica Ascent helps you to connect to your Redshift Cluster to easily query your data and build dashboards to visualize data easily

The first step is to create a Redshift Cluster, please navigate to get started with Amazon Redshift https://docs.aws.amazon.com/redshift/latest/gsg/getting-started.html

Adding Redshift to Apica Ascent

The second step is to create and add Redshift to Apica Ascent and add fill out the below fields and save

• Name: Name the data source (e.g. Redshift)

• Host: The full URL to your instance

• Port: The port of the instance endpoint (e.g. 3306)

• User: A user of the instance

• Password: The password for the above user

• Database name: The name of the virtual database for Redshift (e.g. Redshift1)

That's it. Now navigate the Query editor page and start querying your data

Follow the steps below to create and execute a query using the Ascent Logs Data Source.

1. Go to the Queries Page

• Navigate to the queries page in your dashboard to begin

• In the Queries page, click on the "New Query" button to start a new query

2. Select "Ascent Logs" from the Left Sidebar

• On the left sidebar, click on Ascent Checks. This will display a list of all available checks that you can query.

3.Write the query

• Write the query in YAML format and execute query, for example:

Create the Elasticsearch data source

The first step is to create the data source and provide the Elasticsearch cluster URL and optionally provide the basic auth login and password.

Writing queries

In the query editor view, select the Elasticsearch data source created above. On the left column, click on the refresh icon to refresh the schemes (indexes). The schemes are expandable and show the schema details.

You can then proceed to the query editor and run the search query. The query uses JSON as passed to the Elasticsearch search API

Apica Ascent helps you to connect to Amazon RDS for MySQL data source which makes it easy for you to query MySql using its natural syntax, analyze, monitor, and create Visualization of data.

All your queried results are cached, so you don't have to wait for the same result set every time, also Apica Ascent helps you to visualize your data gathered from queries

Adding MySQL Server for Amazon RDS

The first step is to create a MySQL data source and provide all details such as the Host, Port, User, Password, and Database name of your MySQL

• Name: Name the data source

• Host: This is your MySQL server address

• Port: The port of the MySQL Server

• User: A user of the MySQL Server

• Password: The password for the above user

• Database name: The name of the database of the MySQL Server

That's it. Now navigate to the Query editor page to query and create Visualizations of your data

Syslog

1. 514 / 7514 (TLS) - RFC 5424 documentation / Read RFC 3164 Document

2. 515 / 7515 (TLS) - Syslog

3. 516 / 7516 (TLS) - Syslog Fortinet /

4. 517 - Raw RCP / catch-all for non-compliant syslog / Debug

RELP

1. 2514 / 20514 (TLS) -

Http/Https

1. 80/443 (TLS)

Logstash

1. 25224/ 25225 (TLS) -

Fluent Protocol

1. 24224/24225 (TLS) -

By utilizing the Ascent I/O Connector, we can directly send the Apache Beam metrics created , to Prometheus as Metrics.

There are two mechanisms to achieve this, namely :

Push Mechanism done via remote-write method

In the context of the push mechanism done via remote-write, Prometheus can be used to collect and store the data that is being pushed from the source system to the destination system. Prometheus has a remote-write receiver that can be configured to receive data using the remote-write protocol.

Once the data is received by the remote-write receiver, Prometheus can store the data in its database and perform real-time analysis and aggregation on the data using its powerful query language. This allows system administrators and operators to monitor the performance of various components of the system in real-time and detect any issues or anomalies.

In this way, Prometheus can replicates it data to third-party system for backup , analysis and long-term storage .

Pull Mechanism done via Push-Gateway method

In a distributed system, the pull mechanism is a common way of collecting data from various sources by querying them periodically. However, there may be cases where it's not feasible to collect data using the pull mechanism, such as when the data is only available intermittently or when it's costly to query the data source repeatedly. In such cases, the PushGateway method can be used to enable a pull mechanism via a push approach.

Prometheus offers a PushGateway component that allows applications to push metrics into it via an HTTP API. Applications can use this API to push metrics to the PushGateway instead of exposing an endpoint for Prometheus to scrape. Prometheus can then pull the data from the PushGateway, acting as if it were a normal Prometheus target.

To use the push gateway method in a pull mechanism, applications periodically push their metrics data to the Push-gateway via the HTTP API. Prometheus, in turn, periodically queries the Push-Gateway to collect the data. The Push-Gateway stores the metrics data until Prometheus scrapes it, which can be configured to occur at regular intervals.

This approach can be useful when collecting metrics from systems that are not always available or when it's not feasible to pull the data frequently. Additionally, it allows applications to expose metrics data without exposing an endpoint for Prometheus to scrape, which can be more secure.

Overall, the Push-Gateway method can be a powerful tool in enabling a pull mechanism for collecting metrics in a distributed system via Prometheus.

The "LOGIO-IO" Connector currently accepts pushing metrics to Prometheus by this method. For more info, refer to this post.

Apica Ascent lets you connect to the Microsoft SQL Server which is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence, and analytics applications in corporate IT environments.

With Apica Ascent you can easily query, monitor, and visualize the MS SQL Server data

Adding MS SQL Server to Apica Ascent

The first step is to create and add MS SQL Server to Apica Ascent and add fill out the below fields and save

• Name: Name the data source

• User: A user of the MS SQL Server which is in the form: user@server-name

• Password: The password for the above user

• Server: This is your server address without the .database-windows.net suffix

• Port: The port of the MS SQL Server

• TDS Version: TDS Version of your MS SQL Server

• Character Set: Character encoding of your MS SQL Server

• Database name: The name of the database of the MS SQL Server

Also make sure to Check out for instructions to whitelist your Apica Ascent IP address when connecting to Synapse.

That's all, now navigate to the Query Editor to query your data

Overview

Apache Beam is an open-source, unified model for defining both batch and streaming data-parallel processing pipelines. Using one of the open-source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow.

Beam is particularly useful for embarrassingly parallel data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.

Apica Ascent provides integrations to let you integrate Apica Ascent with Apache Beam. Checkout the submodules to learn more about it.

Apica Ascent can connect to your Data Bricks cluster and SQL Endpoints

Adding Data Bricks to Apica Ascent

The first step is to obtain the Host, HTTP Path, and an Access Token for your endpoint from the Data Bricks. Refer to the below link to obtain the necessary information

The next step is to add Data Source in Apica Ascent using information obtained from the above source

That's it, now navigate to the Query Editor page and start querying

Apica Ascent lets you connect to your MySQL easily and provides a rich Query editor to Query your MySQL using its natural syntax.

All your queried results are cached, so you don't have to wait for the same result set every time, also Apica Ascent helps you to visualize your data gathered from queries.

Adding MySQL Server Data Source to Apica Ascent

The first step is to create a MySQL data source and provide all details mentioned below

• Name: Name the data source

• Host: This is your server address

• Port: The port of the MySQL Server

• User: A user of the MySQL Server

• Password: The password for the above user

• Database name: The name of the database of the MySQL Server

Optionally you can use the SSL protocol for the secure transaction of information

That's it. Now navigate to the Query editor page to query your data

Apica Ascent comes with a large number of integration options for ingest and incident management. This list is growing on a weekly basis!

Ingest for Ascent

Ingest lets you connect with and securely ingest data from popular log forwarding agents, cloud services, operating systems, container applications, and on-premise infrastructure. You can secure data ingestion from your endpoints into Apica Ascent by generating a secure ingest token.

Integration Details

Apica Ascent currently integrates with over 150+ data sources via support for popular open source agents and open protocols.

Follow these links to the Ascent integration details:

Forwarder Details

Once data is ingested, Ascent allows any data to be forwarded to specific source destinations. Follow the link below for more details on available forwarders:

Apica Ascent lets you connect to your PostgreSQL easily and provides a rich Query editor to Query your PostgreSQL using its natural syntax.

All your queried results are cached, so you don't have to wait for the same result set every time, also Apica Ascent helps you to visualize your data gathered from queries.

Adding PostgreSQL to Apica Ascent

The first step is to create a PostgreSQL data source and provide all details such as the Host, Port, User, Password, and Database name of your PostgreSQL

Querying your data

The next step is to Navigate to the Query editor page and start Querying your data from your PostgreSQL schemes

The Apica Source Extension is a component designed to integrate with the Apica Synthetics and Load test platform. Its main purpose is to retrieve check results from the Apica platform and make them available for further processing or analysis within another system or tool.

This checks can also be forwarded to further downstream destinations for further processing.

Steps to create Apica ASM Source Extension

• Navigate to the Integrations page and click on the New Plugin button and select Apica option.

• Provide the Plugin Name of choice and click Next.

• Enter your Apica ASM platform credentials.

• Configure your resource requirements and click Next.

• Finally enter URL of Apica ASM Instance, Timezone, Version of Apica Source Extension Plugin and Number of workers to be used for the Apica data pull.

After entering these details, click on the Done button.

After creation of the Apica ASM source extension, you will see the check data which will have all the check details.

We’re excited to announce the release of Apica One Platform 2.12.0, delivering enhanced features, improved security, better integrations, and important fixes across all major products.

Observe

New Features & Improvements

Discover the latest advancements and improvements of the Apica Ascent platform. This is your go-to destination for updates on platform enhancements and new features. Explore what's new to optimize your observability and data management strategies.

Synthetic Monitoring (ASM 13.25.0) - SaaS

• Features

Install Rsyslog

1. For Debian/Ubuntu:

2. For RHEL/CentOS:

Verify that rsyslog is running:

Release Notes - Ascent January 2025

Overview

Introducing Apica Ascent Freemium—a FREE FOREVER version of our Intelligent Data Management Platform, now available as a convenient SaaS offering. This release democratizes intelligent observability, providing access to powerful features at no cost. Experience all the core capabilities of Ascent and take your telemetry data management to the next level.

New Features and Enhancements

Freemium Support

Introduction

The digital landscape is evolving at an unprecedented pace. Enterprises are migrating to cloud-native architectures, embracing microservices, Kubernetes, and distributed applications to stay competitive. However, this shift introduces a new set of challenges—traditional monitoring tools are struggling to keep up with the scale, complexity, and velocity of modern applications.

This is where next-generation observability platforms, like Apica’s Ascent Platform, come in. Built on Kubernetes and OpenTelemetry, Apica delivers a scalable, AI-powered, cloud-native observability solution designed to handle billions of logs, metrics, and traces in real time.

Requirements

A production deployment of Apica Ascent requires the following key components:

1. A cloud-based or k0s Kubernetes cluster to run the Apica Ascent software components. Apica Ascent OnPrem's non-cloud offering is based on .

Apica Ascent also supports external Prometheus compatible data sources e.g. Prometheus, Thanos, VictoriaMetrics. If you have any hosted such an instance in the cloud or on-premises, you can connect that in Apica Ascent as a data source. you can use your existing queries in Apica Ascent to build dashboards and create alerts.

Please see the section to know about configuring various data sources.

Apica Ascent helps you to connect your Snowflake for faster querying and visualization of your data.

Adding Snowflake to Apica Ascent

The first step is to create a Snowflake data source and provide all details mentioned below

• Name: Name the data source

Apica Ascent uses an ingest token to secure the ingestion of log data from your data sources into your Apica Ascent deployment. You can generate a secure ingest token using the Apica Ascent UI and the command-line tool, .

Obtaining an ingest token using UI

You can obtain a secure ingest token from the Account tab in the Settings page on the Apica Ascent UI.

To begin, click on the username on the navbar, click "Settings", and click on the "Account" tab if you are not brought there by default. Your secure ingest token will be displayed under the Ingest Token field. Click the Copy icon next to the token to copy it to your clipboard.

What is OpenTelemetry (OTEL)?

OpenTelemetry (OTEL) is an open-source observability framework that provides a standardized approach to collecting, processing, and exporting telemetry data—including traces, metrics, and logs—from applications and infrastructure. It is a vendor-neutral solution designed to help organizations gain deep insights into the performance, health, and behavior of their distributed systems without being locked into proprietary observability tools.

By unifying telemetry collection across different platforms, programming languages, and monitoring solutions, OpenTelemetry simplifies instrumentation, reduces integration complexities, and enhances observability capabilities for modern cloud-native applications.

Install otelcol-contrib

For DEB-based:

For RHEL-based:

Configure Collector

Edit /etc/otelcol-contrib/config.yaml and replace the content with the below

Replace the following values:

• <your_log_file_path>

  • Physical path to your log file

• <your_domain>

  • Hostname of your Apica environment (example.apica.io)

Validate and apply

When you're done with your edits, execute the below command to validate the config is valid (it should return nothing if everything is in order)

Restart OTel to apply your changes

Ascent view

Assuming everything has been done correctly, your logs will start to appear in Explore > Logs & Insight on your Ascent environment. They will show up based on the namespace and application names that you set in your config.yaml file.

Discover the latest advancements and improvements of the Apica Ascent platform. This is your go-to destination for updates on platform enhancements and new features. Explore what's new to optimize your observability and data management strategies.

Synthetic Monitoring (ASM 13.26.0) - SaaS

Features

• Browser checks will automatically accept dialogs/modals that can pop up during a test such as alert/confirmation/prompts.

• Browser checks will attach and include control of new tabs created by the target site. I.e. the chrome WebDriver will automatically attach to new tabs that are opened during check execution of a Browser check.

• Added SAN/SNI options to SSL Cert Expiration and Fingerprint Validation for URL checks.

Bugs Fixes:

• Screenshots for Browser checks were not working in new tabs or windows created by the check. This is fixed as part of the above feature that include control of created tabs and windows by the target site.

ADF v3.8

Features

• Data Explorer adds a new way to create queries, dashboards, and widgets directly from a browsable inventory of available metrics and events. With just a few clicks, a query builder is guiding the simple creation of dashboards and widgets. Please read further on this substation set of features in our product documentation:

• Code Rule is a new rule type that is introduced with this release, where user can add JavaScript code to enhance the logs. With the help of Code Block, add Code Rule to improve your pipelines. Code Rules takes in a JavaScript function that gets integrated with your pipeline. Please read further on this in the product documentation:

• Fleet 🚢 is the ultimate solution for making the collection of observability data responsive to changes in your environment using your pre-existing observability agents. With Fleet, you can collect more data when you need it and less when you don’t. And the best part? Almost all observability agents can be managed through configuration files describing how to collect, enrich, and send data. Fleet aims to simplify this process through an agent manager. The Fleet Agent Manager functions as a sidecar utility that checks for new configuration files and triggers the appropriate restart/reload functionality of the supported agent. The Agent Manager is kept intentionally simple, with the goal that it only needs to be installed once and updated infrequently. Please read further on this in the product documentation:

Improvements

• Revamped Alert API to support multiple severities (Info, Warning, Critical, Emergency) with multiple thresholds, in the same alert.

• Changed the location of Track duration in alert screens to be adjacent to the Alert condition.

• All the alert destinations (Slack, PagerDuty, Mattermost, Chatwork, Zenduty, Opsgenie, Webhook, ServiceNow, and Email) will now start receiving values that triggered that specific alert.

• Further UI changes for Alert Screens, Integrations Screen, and Distributed Tracing to align with the new design system.

Bug Fixes:

• Fixed ServiceNow alert destination API errors.

• Fixed Email settings page bug.

• Fixed User page bug because of which admin was not able to change groups of users.

• Fixed missing services in ASM+.

Others:

• Deprecated Hipchat alert destination.

IRONdb

Bugfixes

• Avoid metric index corruption by using pread(2) in jlog instead of mmap(2).

• Fix the bug where a node could crash if we closed a raw shard for delete, then tried to roll up another shard before the delete ran.

• Fix the bug where setting raw shard granularity values above 3w could cause data to get written with incorrect timestamps during rollups.

• Fix the NNTBS rollup fetch bug where we could return no value when there was valid data to return.

Improvements

• Deprecate max_ingest_age from the graphite module. Require the validation fields instead.

• Change the Prometheus module to convert nan and inf records to null.

• Add logging when when the snowth_lmdb_tool copy operation completes.

• Improve various listener error messages.

Install OpenTelemetry

1. Go to https://opentelemetry.io/docs/collector/installation/ or https://github.com/open-telemetry/opentelemetry-collector-releases/releases/ to find the package you want to install. At the point of writing this guide, 0.115.1 is the latest package so we’ll install otelcol-contrib_0.115.1_linux_amd64

2. On the machine you wish to collect metrics from, run the following 4 commands:

   1. Deb-based

      1. sudo apt-get update

      2. sudo apt-get -y install wget

      3. wget https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v0.115.1/otelcol-contrib_0.115.1_linux_amd64.deb
3. Navigate to /etc/otelcol-contrib/

4. Edit the file with your favourite file editor, for example: nano config.yaml

5. Paste the following into the config file overwriting it completely:

   2. Replace <YOUR-ASCENT-ENV>with your Ascent domain, e.g. company.apica.io

   3. Replace <YOUR-INGEST-TOKEN>with your Ascent Ingest Token, e.g. eyXXXXXXXXXXX...

6. When you’ve finished editing the config, save it and run otelcol-contrib validate --config=config.yaml

   1. If you get no error returned, the config file is valid.

7. Restart the service with sudo systemctl restart otelcol-contrib

8. Verify that the service is up and running correctly with sudo systemctl status otelcol-contrib

   1. A good result should look like this:

   3. You can also view live logs using journalctl -u otelcol-contrib -f. With the above config you would see entries every 10 seconds.

Verify metrics in the Ascent platform

1. Click on the green “+ Create” button on the top navigation bar and select Query

2. In the dropdown menu on the left hand side, select Ascent Metrics

3. In the search bar, search for system_

   1. This will present all the different system metrics that is being scraped with your Otel configuration

Create the JSON Data source

1. Navigate to Integrations > Data Sources

2. Click New Data Source

3. Select JSON

4. Create the data source

   1. Enter a name for your data source (required)

   2. Enter Basic Authentication credentials (optional)

Writing queries

1. Navigate to Queries and click New Query

2. In the drop-down on your left hand side, select your new data source

Providing HTTP Options

The following HTTP options are used for sending a query

• url - This is the URL where the RESTful API is exposed

• method - the HTTP method to use (default: get)

• headers - a dictionary of headers to send with the request

Example query:

Example query including all HTTP options:

Filtering response data: path and fields

The response data can be filtered by specifying the path and fields parameters. The path filter allows accessing attributes within the response, for e.g. if a key foo in the response contains rows of objects you want to access, specifying path foo will convert each of the objects into rows.

In the example below, we are then selecting fields volumeInfo.authors, volumeInfo.title, volumeInfo.publisher and accessInfo.webReaderLink

The resulting data from the above query is a nicely formatted table that can be searched in Apica Ascent or made available as a widget in a dashboard

Choosing the Right OTEL Components