VictoriaMetrics December 2023 Meetup: Community Update
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VictoriaMetrics Community: 2023 Highlights & Updates including stats, content & press coverage news.
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Monitoring Big Data Systems Done "The Simple Way" - Demi Ben-Ari - Codemotion...
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk, we’ll mention all of the aspects that you should take into consideration when monitoring a distributed system using tools like Web Services, Spark, Cassandra, MongoDB, AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Monitoring Big Data Systems "Done the simple way" - Demi Ben-Ari - Codemotion...
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
The Art of Container Monitoring
According to service scale, there are hundreds or thousands of running containers in your service. Should we monitor each container by microscope or monitor each microservice by magnifier? This depends which granularity can help us find and solve the problems. In this sharing, I will introduce how to use cAdvisor, Icinga2, InfluxDB and Grafana to build a self-hosted monitoring system. In addition, I also discuss with how to embrace open source and share some practical experiences.
IBM Blockchain Platform - Architectural Good Practices v1.0
How to create solutions on IBM Blockchain Platform and Hyperledger Fabric that perform well.
Citi Tech Talk: Monitoring and Performance
The objective of the engagement is for Citi to have an understanding and path forward to monitor their Confluent Platform and
- Platform Monitoring
- Maintenance and Upgrade
Provenance for Data Munging Environments
Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic.
Presented at Information Sciences Institute - August 13, 2015
Introduction to Civil Infrastructure Platform
CIP is target to establish an open source base layer of industrial grade software to enable the use and implementation of software. This slide will introduce the current status and road map in CIP
Recommended
Monitoring Big Data Systems Done "The Simple Way" - Demi Ben-Ari - Codemotion...
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk, we’ll mention all of the aspects that you should take into consideration when monitoring a distributed system using tools like Web Services, Spark, Cassandra, MongoDB, AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Monitoring Big Data Systems "Done the simple way" - Demi Ben-Ari - Codemotion...
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
The Art of Container Monitoring
According to service scale, there are hundreds or thousands of running containers in your service. Should we monitor each container by microscope or monitor each microservice by magnifier? This depends which granularity can help us find and solve the problems. In this sharing, I will introduce how to use cAdvisor, Icinga2, InfluxDB and Grafana to build a self-hosted monitoring system. In addition, I also discuss with how to embrace open source and share some practical experiences.
IBM Blockchain Platform - Architectural Good Practices v1.0
How to create solutions on IBM Blockchain Platform and Hyperledger Fabric that perform well.
Citi Tech Talk: Monitoring and Performance
The objective of the engagement is for Citi to have an understanding and path forward to monitor their Confluent Platform and
- Platform Monitoring
- Maintenance and Upgrade
Provenance for Data Munging Environments
Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic.
Presented at Information Sciences Institute - August 13, 2015
Introduction to Civil Infrastructure Platform
CIP is target to establish an open source base layer of industrial grade software to enable the use and implementation of software. This slide will introduce the current status and road map in CIP
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
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This talk was presented at the Montreal Kubernetes Meetup on 11/8/17 by Richard Li from Datawire.
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WTF is a Microservice - Rafael Schloming, Datawire
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Intro to open source telemetry linux con 2016
Abstract
As part of the team delivering Snap, an open telemetry framework, I've run through dozens of use cases where gathering disparate metrics from services can roll up into meaningful diagrams for operations engineers and developers alike. We will use Snap's plugin model to collect, process and publish these measurements into meaningful graphs using open source tools. By joining this session, you can follow along and install industry-standard open source projects, deploy them and then use Snap to collect, process and visualize these metrics.
Audience
Anyone with an operations-background (or future ahead of them) that wants to see the breadth of available open source tooling around telemetry. This proposal is designed for the hands-on user, who is comfortable running containers or virtual machines locally.
Experience Level
Intermediate
Benefits to the Ecosystem
By joining this session, you can follow along and install industry-standard open source projects, deploy them and then use Snap to collect, process and visualize these metrics. This empowers users within the Linux ecosystem to see their knowledge as powerful when visualized next to other layers of the datacenter.
CHIPS Alliance_Object Automation Inc_workshop
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Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
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The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
Defects mining in exchanges - medvedev, klimakov, yamkovi
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The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk gives an overview of the OpenTelemetry project and then outlines some production-proven architectures for improving the observability of your applications and systems.
TiDB + Mobike by Kevin Xu (@kevinsxu)
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Monitoring federation open stack infrastructure
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On the Road to Shangri-La: Scaling CD from Teams to the Enterprise
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Introducing TiDB Operator [Cologne, Germany]
This slide was delivered at the Kubernetes/Docker meetup in Cologne, Germany, hosted by Giant Swarms on how TiDB, an open source NewSQL distributed database, is deployed and managed on any Kubernetes-enabled cloud environment by applying the Operator pattern.
Network Automation Journey, A systems engineer NetOps perspective
Network devices play a crucial role; they are not just in the Data Center. It's the Wifi, VOIP, WAN and recently underlays and overlays. Network teams are essential for operations. It's about time we highlight to the configuration management community the importance of Network teams and include them in our discussions. This talk describes the personal experience of systems engineer on how to kickstart a network team into automation. Most importantly, how and where to start, challenges faced, and progress made. The network team in question uses multi-vendor network devices in a large traditional enterprise.
NetDevOps, we do not hear that term as frequent as we should. Every time we hear about automation, or configuration management, it is usually the application, if not, it is the systems that host the applications. How about the network systems and devices that interconnect and protects our services? This talk aims to describe the journey a systems engineer had as part of an automation assignment with the network management team. Building from lessons learned and challenges faced with system automation, how one can kickstart an automation project and gain small wins quickly. Where and how to start the journey? What to avoid? What to prioritise? How to overcome the lack of network skills for the automation engineer and lack of automation and Linux/Unix skills for network engineers. What challenges were faced and how to overcome them? What fights to give up? Where do I see network automation and configuration management as a systems engineer? What are the status quo and future expectations?
SDN in the Management Plane: OpenConfig and Streaming Telemetry
The networking industry has made good progress in the last few years on developing programmable interfaces and protocols for the control plane to enable a more dynamic and efficient infrastructure. Despite this progress, some parts of networking risk being left behind, most notably network management and configuration. The state-of-the-art in network management remains relegated to proprietary device interfaces (e.g., CLIs), imperative, incremental configuration, and lack of meaningful abstractions.
We propose a framework for network configuration guided by software-defined networking principles, with a focus on developing common models of network devices, and common languages to describe network structure and policies. We also propose a publish/subscribe framework for next generation network telemetry, focused on streaming structured data from network elements themselves.
Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024
This presentation covers the following topics:
What is logging?
The purpose of logging: Debugging
The purpose of logging: Security
The purpose of logging: Stats & analytics
Traditional logging
Traditional logging: Advantages
Traditional logging: Disadvantages
The solution: Large-scale logging
Large-scale logging: Core principles
Large-scale logging: Solution types
Large-scale logging: Cloud vs on-prem
Large-scale logging: Operational complexity
Large-scale logging: Security
Large-scale logging: Costs
Large-scale logging: On-prem comparison
- Elasticsearch
- Grafana Loki
- VictoriaLogs
On-prem comparison: Setup and operation
On-prem comparison: Costs
On-prem comparison: Full-text search support
On-prem comparison: How to efficiently query 100TB of logs?
On-prem comparison: Integration with CLI tools
VictoriaLogs for large-scale logging
VictoriaLogs demo instance
- Ingestion rate: 3600 messages / minute
- The number of log messages: 1.1 billion
- Uncompressed log messages’ size: 1.5TB
- Compressed log messages’ size: 23GB
- Compression ratio: 47x
- Memory usage: 150MB
VictoriaLogs CLI integration demo
- Which errors have occurred in all the apps during the last hour?
- How many errors have occurred during the last hour?
- Which apps generated the most of errors during the last hour?
- The number of per-minute errors for the last 10 minutes
- Status codes for the last hour
- Non-200 status codes for the last week
- Top client IPs for the last 4 weeks with 404 and 500 response status codes
- Per-month stats for the given IP across all the logs
Large-scale logging solution
MUST provide
excellent CLI integration
VictoriaLogs: (temporary) drawbacks
VictoriaLogs: Recap
- Easy to setup and operate
- The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki)
- Fast full-text search
- Excellent integration with traditional command-line tools for log analysis
- Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent)
- Open source and free to use!
VictoriaMetrics Q1 Meet Up '24 - Community & News Update
This slide deck provides an overview of the latest VictoriaMetrics Community stats and achievements, where VictoriaMetrics has been in the news and where to find our team.
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Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Montreal Kubernetes Meetup: Developer-first workflows (for microservices) on ...
This talk was presented at the Montreal Kubernetes Meetup on 11/8/17 by Richard Li from Datawire.
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Denver Microservices meetup presentation by Maciek Swiech & Matt Reynolds on lessons learned implementing microservices at ibotta
WTF is a Microservice - Rafael Schloming, Datawire
Rafael Schloming, Chief Architect at Datawire and AMQP spec author breaks down an understanding of microservices into People, Processes, and Technology, and when adopting microservices recommends starting with People first, rather than starting with Technology.
Intro to open source telemetry linux con 2016
Abstract
As part of the team delivering Snap, an open telemetry framework, I've run through dozens of use cases where gathering disparate metrics from services can roll up into meaningful diagrams for operations engineers and developers alike. We will use Snap's plugin model to collect, process and publish these measurements into meaningful graphs using open source tools. By joining this session, you can follow along and install industry-standard open source projects, deploy them and then use Snap to collect, process and visualize these metrics.
Audience
Anyone with an operations-background (or future ahead of them) that wants to see the breadth of available open source tooling around telemetry. This proposal is designed for the hands-on user, who is comfortable running containers or virtual machines locally.
Experience Level
Intermediate
Benefits to the Ecosystem
By joining this session, you can follow along and install industry-standard open source projects, deploy them and then use Snap to collect, process and visualize these metrics. This empowers users within the Linux ecosystem to see their knowledge as powerful when visualized next to other layers of the datacenter.
CHIPS Alliance_Object Automation Inc_workshop
Innovation by Collaboration at Semicondynamics workshop
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
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Use case of Kubernetes based NFV infrastructure used in production to run an open source evolved packet core. Presented by Facebook Connectivity and Mirantis at KubeCon + CloudNativeCon Europe 2020.
The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
Defects mining in exchanges - medvedev, klimakov, yamkovi
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TiDB + Mobike by Kevin Xu (@kevinsxu)
This presentation provides an overview of the architecture and technology of TiDB, an open-source distributed NewSQL database, and how it helps Mobike, one of the largest dockless bikeshare platform, scale its infrastructure to achieve hyper-growth.
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Monitoring GEi used in the FI-WARE and XiFi project in order to recover information of the servers, hosts and OpenStack instances
On the Road to Shangri-La: Scaling CD from Teams to the Enterprise
Gene Kim and Rob Stroud answer common questions about Continuous Delivery, including the why, how, and who of scaling from teams to the enterprise.
Introducing TiDB Operator [Cologne, Germany]
This slide was delivered at the Kubernetes/Docker meetup in Cologne, Germany, hosted by Giant Swarms on how TiDB, an open source NewSQL distributed database, is deployed and managed on any Kubernetes-enabled cloud environment by applying the Operator pattern.
Network Automation Journey, A systems engineer NetOps perspective
Network devices play a crucial role; they are not just in the Data Center. It's the Wifi, VOIP, WAN and recently underlays and overlays. Network teams are essential for operations. It's about time we highlight to the configuration management community the importance of Network teams and include them in our discussions. This talk describes the personal experience of systems engineer on how to kickstart a network team into automation. Most importantly, how and where to start, challenges faced, and progress made. The network team in question uses multi-vendor network devices in a large traditional enterprise.
NetDevOps, we do not hear that term as frequent as we should. Every time we hear about automation, or configuration management, it is usually the application, if not, it is the systems that host the applications. How about the network systems and devices that interconnect and protects our services? This talk aims to describe the journey a systems engineer had as part of an automation assignment with the network management team. Building from lessons learned and challenges faced with system automation, how one can kickstart an automation project and gain small wins quickly. Where and how to start the journey? What to avoid? What to prioritise? How to overcome the lack of network skills for the automation engineer and lack of automation and Linux/Unix skills for network engineers. What challenges were faced and how to overcome them? What fights to give up? Where do I see network automation and configuration management as a systems engineer? What are the status quo and future expectations?
SDN in the Management Plane: OpenConfig and Streaming Telemetry
The networking industry has made good progress in the last few years on developing programmable interfaces and protocols for the control plane to enable a more dynamic and efficient infrastructure. Despite this progress, some parts of networking risk being left behind, most notably network management and configuration. The state-of-the-art in network management remains relegated to proprietary device interfaces (e.g., CLIs), imperative, incremental configuration, and lack of meaningful abstractions.
We propose a framework for network configuration guided by software-defined networking principles, with a focus on developing common models of network devices, and common languages to describe network structure and policies. We also propose a publish/subscribe framework for next generation network telemetry, focused on streaming structured data from network elements themselves.
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More from VictoriaMetrics
Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024
This presentation covers the following topics:
What is logging?
The purpose of logging: Debugging
The purpose of logging: Security
The purpose of logging: Stats & analytics
Traditional logging
Traditional logging: Advantages
Traditional logging: Disadvantages
The solution: Large-scale logging
Large-scale logging: Core principles
Large-scale logging: Solution types
Large-scale logging: Cloud vs on-prem
Large-scale logging: Operational complexity
Large-scale logging: Security
Large-scale logging: Costs
Large-scale logging: On-prem comparison
- Elasticsearch
- Grafana Loki
- VictoriaLogs
On-prem comparison: Setup and operation
On-prem comparison: Costs
On-prem comparison: Full-text search support
On-prem comparison: How to efficiently query 100TB of logs?
On-prem comparison: Integration with CLI tools
VictoriaLogs for large-scale logging
VictoriaLogs demo instance
- Ingestion rate: 3600 messages / minute
- The number of log messages: 1.1 billion
- Uncompressed log messages’ size: 1.5TB
- Compressed log messages’ size: 23GB
- Compression ratio: 47x
- Memory usage: 150MB
VictoriaLogs CLI integration demo
- Which errors have occurred in all the apps during the last hour?
- How many errors have occurred during the last hour?
- Which apps generated the most of errors during the last hour?
- The number of per-minute errors for the last 10 minutes
- Status codes for the last hour
- Non-200 status codes for the last week
- Top client IPs for the last 4 weeks with 404 and 500 response status codes
- Per-month stats for the given IP across all the logs
Large-scale logging solution
MUST provide
excellent CLI integration
VictoriaLogs: (temporary) drawbacks
VictoriaLogs: Recap
- Easy to setup and operate
- The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki)
- Fast full-text search
- Excellent integration with traditional command-line tools for log analysis
- Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent)
- Open source and free to use!
VictoriaMetrics Q1 Meet Up '24 - Community & News Update
This slide deck provides an overview of the latest VictoriaMetrics Community stats and achievements, where VictoriaMetrics has been in the news and where to find our team.
VictoriaMetrics Anomaly Detection Updates: Q1 2024
Anomaly Detection launch & update
* Recap: What is anomaly detection?
* Recap: Why ML & AI for anomaly detection?
* Why VictoriaMetrics Anomaly Detection?
* What’s new: Flexible Configs
* What’s new: AutoTune
* What’s new: Docs & site updates
● Quickstart - minimalistic guide on how to set up and run `vmanomaly` (Docker, Kubernetes)
● Model types - explanations and diagrams to understand specifics of a lifecycle and find the best model for your use case
● AutoTuned model introduction - find out how to set-and-forget the model of your choice to learn from your data
● VictoriaMetrics Anomaly Detection got its own feature page
* Roadmap for 2024
● Streaming models support
● GUI: Deeper integration with anomaly detection service
● Node_exporter preset. Presets for common tasks, like “seasonal_weekly”, “testing”, “autotuned_daily”
● (Q3-Q4) Root Cause Analysis: Drill down your incidents faster and more efficient. Finishing transition from PoC to production.
What’s New in VictoriaMetrics: Q1 2024 Updates
What's new in VictoriaMetrics
* New releases
● v1.97 - long-term support (LTS) release
● v1.98 - mTLS authorization in all VictoriaMetrics components
● v1.99 - improved propagation of label filters in MetricsQL queries
● v1.100 - improvements in streaming aggregation and vmauth
* New features
** DataDog integrations
● Accept data from new versions of DataDog agent via DataDog v2 API
● Accept data from DataDog AWS lambda extensions
** OpenTelemetry
Data ingestion via Amazon Firehose: Accept OTEL metrics from Amazon Firehose
Prometheus-compatible naming
● -opentelemetry.usePrometheusNaming command-line flag
● Converts metric names and labels into Prometheus-compatible naming in the
same way as OTEL collector does
** Hetzner service discovery
● Discover scrape targets at Hetzner Cloud and Hetzner Robot
● hetzner_sd_configs
** Per-tenant and per-label downsampling (enterprise feature)
● Individual downsampling configs per distinct sets of tenants
● Individual downsampling configs per distinct sets of time series
** New Graphite query functions
● aggregateSeriesLists
● diffSeriesLists
● multiplySeriesLists
● sumSeriesLists
** mTLS support
● Every VictoriaMetrics Enterprise component verifies client TLS certificates if -mtls command-line flag is set
● Vmauth Enterprise supports mTLS-based request routing
** vmauth: improved request routing
● Host-based request routing
● Query args-based request routing
● Arbitrary header value-based request routing
** vmauth: DNS-based load balancing
● Even load balancing across IP addresses behind a single hostname
● Works great with vminsert and vmselect services
MetricsQL: new functions
● sum_eq_over_time
● sum_gt_over_time
● sum_le_over_time
● count_values_over_time - counts the number of unique sample values over time
** MetricsQL: improved label filters’ propagation
** Stream aggregation improvements
● Reduced memory usage by up to 5x
● De-duplication during data ingestion
● New aggregation functions:
○ unique_samples
○ increase_prometheus
○ total_prometheus
● Ability to drop labels before stream aggregation and de-duplication
● keep_metric_names option
** Performance improvements
● Improve registration speed for new time series by up to 10x
● Reduce memory usage when scraping targets with big number of metrics
● Optimize performance for /api/v1/labels and /api/v1/label/.../values APIs
VictoriaMetrics for the Atlas Cluster
Albert Einstein Institute:
Largest research institute in the world specializing in general relativity and beyond.
Part of the Max Planck Society https://www.mpg.de/en Founded in Potsdam (1995)
Experimental/data analysis branch in Hannover since 2002 https://www.aei.mpg.de/
Conclusions:
Hard to draw after only ∼ 90 d, but:
* VictoriaMetrics performs much better than Prometheus out of the box Queries possible which were not before
* Storage 5 GB/d vs. 41 GB/d1
Still able to put “too much” into TSDB and overload it
* Waiting for prod hardware to deploy/test clustered version
* Looks very, very good & capable!
WEDOS & VictoriaMetrics
Testimonial by: Egor Pronin and I am a DevOps in Wedos, the largest hosting provider in the Czech Republic. We own two private data centers with more than 10k servers. One of my responsibilities from the very first day in the company was monitoring.
Since ‘ancient’ times the company had its own Java-written monitoring system. It tracked only the basic server indicators: CPU, RAM, free space on file systems. The monitoring server periodically pinged targets, which served as an indicator of server availability. This simple and old system collected basic metrics and displayed alerts on the monitor in the office of administrators on duty. It operated on a server-agent model, where an agent was installed on each server to collect data and send it to the server every minute.
Besides monitoring servers and applications, the system also controlled engineering infrastructure indicators. With the overall development of infrastructure and modernization of our data centers, there arose a need to display data on temperature, humidity and other indicators in server rooms to our administrators.
This system is also used in our new data center with oil cooling. There are already much more sensors and detectors, so the monitoring system is still under development.
In addition, this system will be connected to the other engineering systems of the data center: lighting, ventilation, air conditioning, and even blinds and gates.
After familiarizing myself with the existing monitoring system and its limitations, I decided to use Zabbix, as at that time it was the most suitable tool for our purposes and tasks. However, we quickly abandoned Zabbix due to the company's transition to Kubernetes. In the new conditions, Zabbix no longer met our growing needs and did not fit into the concept of transitioning to Kubernetes.
We began looking for a Kubernetes-native solution and stopped on Prometheus. All initial setup of the new monitoring system, as well as my personal acquaintance with the internals of Kubernetes, was done on it. We added targets, wrote alert rules, and experimented with creating our own exporters. However, with time, it became clear that Prometheus could not handle the rapidly growing server park and application autoscaling, causing Out Of Memory (OOM) errors. That's when I got to know about VictoriaMetrics (VM). The transition was smooth: first, we turned off data collection in Prometheus, leaving it only as a storage, and distributed the data collection and discovery tasks to vmagents. But it was painful to see how long Prometheus took to start, sometimes up to an hour and a half. Why bother with configuration and tuning if everything is readily available in VM? Thus, we fully transitioned to monitoring using the VM ecosystem.
VictoriaMetrics December 2023 Meetup: Anomaly Detection
- - Anomaly Detection - Daria Karavaieva / Fred Navruzov
* Recap: What is anomaly detection?
* Recap: Why ML for anomaly detection?
* Why VictoriaMetrics Anomaly Detection?
* What’s new: Releases
** Release 1.6.0 (helm chart, docker img) with improved stability and integration
* What’s new: AD & RCA Handbook
* What’s coming: GUI
* What’s coming: Presets (Node Exporter alerting)
VictoriaMetrics December 2023 Meetup: Managed VictoriaMetrics Update
- - Managed VictoriaMetrics - Ivan Yatskevich
* What is Managed VictoriaMetrics?
* What’s new in Managed VictoriaMetrics?
* Alerting & Recording Rules
December 2024 Meetup: Welcome & VictoriaMetrics Updates
- What's new in VictoriaMetrics at Q4 2023 - Roman Khavronenko
* vmselect improved performance for instant queries!
* vmselect rollup cache for range queries
* vmselect instant queries can't be cached
* vmagent shard's URL templating in cluster mode
* vmagent Google PubSub and NewRelic agent
* vmcluster rerouting enhancement
* vmalert unittesting for alerting rules via vmalert-tool
* vmauth hot standby balancing mode
* vmui support functions and labels in autocomplete
What’s new in VictoriaLogs (Q3 2023)
- Improved usability for _time filters
- Support for Promtail
- Support for read-only mode
- Better observability
- Better data ingestion scalability
- Roadmap update
Q3 Meet Up '23 - Community Update
Vicky Community: Help us prepare our anniversary - by Denys Holius & Jean-Jérôme Schmidt-Soisson
- VictoriaMetrics community update
- Which communication channels to use?
- Recent user initiatives
- We're turning 5: Help us plan :-)
- Latest VictoriaMetrics News
- Where to find the VictoriaMetrics team
Managed VictoriaMetrics: Intro & Update
Managed VictoriaMetrics - by Ivan Yatskevich
- What is Managed VictoriaMetrics?
- What's new in Managed VictoriaMetrics?
- What's next: Alerting & recording rules
VM Anomaly Detection: Introduction
Anomaly Detection - by Dima Lazerka & Fedir Navruzov
- What is anomaly detection
- Why is anomaly detection even harder for metrics data?
- - Point-wise anomalies / Contextual anomalies / Collective anomalies
- Why ML for anomaly detection?
- Why VictoriaMetrics anomaly detection?
- Development plans
Q3 2023 Meet Up: What's New in VictoriaMetrics
What's new in VictoriaMetrics -by Roman Khavronenko
- IndexDB improvements: up to 5x mem reduction
- OpenTelemetry support
- vmagent updates
- vmauth updates
- vmalert updates
- vmui:
- - Active queries
- - Detect queries which match no series
- - auto-format query on-click
- - Query history
- Grafana data source plugin
- - In dashboards now
- - In k8s-stack now
- Kubernetes operator: New docs
VictoriaLogs: Open Source Log Management System - Preview
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
VictoriaMetrics: Welcome to the Virtual Meet Up March 2023
Overview of the Latest Features - Roman Khavronenko & Aliaksandr Valialkin
VictoriaMetrics 2023 Q1 Stats
- 180+ issues
- 260+ PRs
- 40 contributors
- 12 releases, from 1.86 to 1.89:
- 114 FEATURES
- 103 BUG FIXES
What's new in VictoriaMetrics at Q1 2023 - Roman Khavronenko & Aliaksandr Valialkin
* vmui Features
- Dark theme
- Explore mode
- Sticky tooltips
- Cardinality explorer
* vmalert: GCS and S3 support for config rules
* Streaming Aggregation
- Use cases & benefits
- Usage & examples
* VictoriaMetrics Remote Write Protocol
* vmauth improvements
Roadmap Review/Update (upcoming) - Roman Khavronenko
* Grafana Data Source Plugin
- Grafana datasource plugin: query trace
- OpenTelemetry ingestion protocol support
- vmalert: UI for rules management
- vmalert: hysteresis support
- vmui explore tab
Application Monitoring using Open Source: VictoriaMetrics - ClickHouse
Monitoring is the key to successful operation of any software service, but commercial solutions are complex, expensive, and slow. Let us show you how to build monitoring that is simple, cost-effective, and fast using open source stacks easily accessible to any developer.
We’ll start with the elements of monitoring systems: data ingest, query engine, visualization, and alerting. We’ll then explain and contrast two implementation approaches. The first uses VictoriaMetrics, a fast growing, high performance time series database that uses PromQL for queries. The second is based on ClickHouse, a popular real-time analytics database that speaks SQL. Fast, affordable monitoring is within reach. This webinar provides designs and working code to get you there.
VictoriaMetrics 15/12 Meet Up: Updates on Managed VictoriaMetrics
* What is Managed VictoriaMetrics?
* Features
- VictoriaMetrics Cluster and Single Version with Enterprise features
- Downsampling, Retention Filters and more features are available
- UI for ad-hoc queries and exploration
- Native integration with VictoriaMetrics or other monitoring stack
- Secured access
- Automatic version upgrades using rolling update strategy
- Support from VictoriaMetrics team
- SLA
* What's Under the Hood?
- Kubernetes and VictoriaMetrics Operator
- Karpenter for autoscalling
- Automated backups and restoration with VM BackupManager Enterprise
- Access management with VMAuth
* Roadmap
- Alternative payment providers
- Alerting and Recording rules
- UX/UI redesign
- Integrate more notification systems
- Private links in AWS
- SOC(2) compliance
- More cloud providers
- Everything that’s in VictoriaMetrics roadmap
VictoriaMetrics 2023 Roadmap
* Grafana datasource plugin
* OpenTelemetry ingestion protocol support
* Streaming aggregation
* vmalert: UI for rules management
* vmalert hysteresis support
* vmui explore tab
* VictoriaLogs
VictoriaMetrics 15/12 Meet Up: 2022 Features Highlights
2022 Features Highlights - Speaker
* MetricsQL Features
- Support for @ modifier
- keep_metric_names modifier
- Advanced label filters’ propagation
- Automatic label filters’ propagation
- Support for short numeric constants
- Distributed query tracing!
- New functions
* vmui Features
- Cardinality explorer!
- Top queries
- Significantly improved usability and stability!
* vmagent Features
- Fetch target response on behalf of vmagent
- Filter targets by url and labels
- /service-discovery page
- Relabel debugging!
- support for absolute _address_
- New service discovery mechanisms
- Multi-tenant support
- Performance improvements
* Relabeling Features
- Conditional relabeling
- Named label placeholders
- Graphite-style relabeling
* vmalert Features
- Better integration with Grafana alerts
- Reusable templates for annotations
- Debugging of alerting rules
- Improved compatibility with Prometheus
* vmctl Features
- Migrate all the data between clusters
- Data migration via Prometheus remote_read protocol
Enterprise Features
- mTLS support
- vmgateway JWT token enhancements
- Automatic restore from backups
- Automatic vmstorage discovery
- Multiple retentions
Various Enhancements
- Environment vars can be referred in command-line flags
- Performance improvements
- Deduplication and downsampling improvements
- Support for metrics push
- Support for Pushgateway data ingestion format
- VictoriaMetrics cluster: multitenancy enhancements
- vmbackup / vmrestore: Azure blob storage
- Official MacOS builds
- Official FreeBSD and OpenBSD builds
- Raspberry PI optimizations
- LTS releases
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Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024
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VictoriaMetrics Q1 Meet Up '24 - Community & News Update
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VictoriaMetrics December 2023 Meetup: Community Update
- 2. Community Update Q4 Meet Up ‘23
- 3. Some interesting stats from VictoriaMetrics for 2023
- 4. ● # of downloads: 338 million ● # of Slack users: 2,841 ● # of contributors: 219 ● # of issues 876 + 772 PRs ● 39 releases, from 1.86 to 1.95: ○ 22 of them are LTS (long term support ) ○ 262 FEATURES ○ 334 BUG FIXES
- 5. Stars Earned Over the last year
- 6. Which Companies Do Those Stargazers Belong To? Tencent - 8 ByteDance - 7 Alibaba - 5 Independent - 4 Google - 4 PingCAP - 3 IBM - 3 Shopify - 2 Alibaba Cloud - 2 Fudan University - 2 *This analysis is derived from user-provided profile company data and is intended for reference.
- 7. Count of Issues Over Time
- 8. Count of Pull requests Over the 2023
- 9. Where Are Our GitHub Participants Based?
- 10. Top 10 locations of pull requests creators China - 15 USA - 8 Ukraine - 5 Germany - 2 Viet Nam - 2 Canada - 2 Sweden - 2 India - 2 United Kingdom - 1 Netherlands - 1 *This analysis is derived from user-provided profile company data and is intended for reference.
- 11. Top 10 locations of issue creators China - 38 RF - 16 USA - 13 Germany - 2 Viet Nam - 2 Canada - 2 Sweden - 2 India - 2 United Kingdom - 1 Netherlands - 1 *This analysis is derived from user-provided profile company data and is intended for reference.
- 12. Our Top 10 belm0 - 127 n4mine - 67 faceair - 65 dxtrzhang - 55 jiangxinlingdu - 53 luzejia - 51 wf1nder - 51 kmansoft - 50 jelmd - 49 tantm3 - 46 kreedom - 20 faceair - 16 belm0 - 12 jiangxinlingdu - 8 rodrigc - 6 michal-kralik - 6 Brensted - 6 patsevanov - 6 johnseekins - 5 Howie59 - 4 Commentators Contributors
- 13. Vicky News
- 14. We’ve been publishing new content ● Performance optimization techniques in time series databases ○ Strings Interning / Function Caching / Limiting Concurrency / sync.Pool for CPU-bound operations ● Anomaly Detection for Time Series Data: An Introduction ○ Anomaly Types / Techniques & Models ● Monitoring Kubernetes costs with OpenCost and VictoriaMetrics ● VictoriaMetrics Long-Term Support (LTS): Current State ● Momentum: Announcing 268M Downloads & 320% Growth ● VictoriaMetrics Enterprise: The World’s Fastest Open Source-Based Monitoring - Try It For Free
- 15. And we’ve been in the Tech News 😎 ● The Register ● ComputerWeekly ● Datanami ● Enterprise Times ● TechRound ● Raconteur (The Times) ● Linux Magazine
- 16. Vicky Events
- 17. Where to find us in 2024 😎 ● 01 February: Meet Up @ Deutsche Bank in Berlin ○ Stay tuned for details ● 3rd & 4th of February: FOSDEM 2024 in Brussels ● 19th - 22nd of March: KubeCon Europe in Paris ● 12th to 15th of November: KubeCon North America in Salt Lake City
- 18. Follow us on social media