February 2018 Summaries

Chronograf has released several new features focused on improving dashboard usability and performance, including the ability to add annotations to graphs for context and smarter graph loading to reduce UI strain. The TICKscript editor has also been improved with testing alert handler configurations, support for multiple alert nodes per rule, and enhanced log viewing capabilities. Additionally, Chronograf now supports organizations and role-based access control, allowing teams to manage user access more easily and organize their instance around groupings that make sense for them. These updates have been made possible by contributions from the community, including Github user @jfyuen and others who helped identify issues. The team is currently working on new features such as a table graph display and redesigned hosts page.

The text discusses collecting running process counts with Telegraf, a tool for data collection and monitoring. The author uses Python and Ubuntu Linux as examples to gather information about running processes using the `ps` command and the `/proc` filesystem. They then use the `exec` input plugin in Telegraf to execute a bash script that runs the `ps` command and outputs the count of running processes with the string "python" to a file. The script is configured to run once per collection interval, and the output is sent to InfluxDB in the value format. The author provides guidance on how to use this setup for monitoring process counts, including setting up alerts based on the metric values.

Monitoring application performance on the frontend is crucial in today's fast-paced technology landscape where applications are becoming increasingly complex, and users expect instant results. To address this challenge, Margo Schaedel suggests using tools to track specific performance metrics, which can help diagnose and solve problems as they arise. A Time Series Database can serve as a valuable tool for collecting and storing performance metrics over time, enabling better analysis and decision-making. By adopting such a strategy, developers can ensure their frontend applications remain competitive and meet user expectations.

The Flux (formerly IFQL) v0.0.5 release represents a significant leap forward for the language, with over 12 new functions added to its capabilities. The new functions are designed to make it easier to chain complex operations together, using a new syntax that simplifies the process of describing these chains. This allows for more efficient and readable code, making it easier for developers to add new functions to the language without requiring extensive Go code. The release also includes improvements such as support for t-digest approximation for percentile calculations, allowing for efficient computation of percentiles while still providing exact results when needed.

The Nginx InfluxDB Module is a custom module for Kubernetes Ingress controllers that enables real-time monitoring of incoming requests using InfluxDB as the backend. The module was created to address the lack of observability features in the official Ingress controller, specifically the ability to track down incoming requests and their status codes. The module acts as a filter on each request, sending processed data to an InfluxDB backend using UDP and line protocol. It can be used with either plain usage or Telegraf as a sidecar proxy configuration. The module's API is currently being stabilized and will be released as version 1.0. The author recommends testing the module in a staging environment before deploying it in production, but notes that they are already using it in their own production environment.

InfluxDB has enhanced its time-series database by adding support for ephemeral data, a type of temporary data that requires special handling due to its short retention period and unique characteristics. This update is significant as it addresses the challenges faced by existing database solutions in managing such data, which is increasingly prevalent in IoT and container adoption. The addition of ephemeral data support aims to improve InfluxDB's ability to handle the growing amount of temporary data generated by these technologies.

Here is a neutral and interesting 1-paragraph summary of the text: InfluxData has announced updates to its InfluxDB Enterprise and InfluxDB platforms, which provide expanded support for ephemeral time series data generated by IoT sensors, containers, and Kubernetes. The company's Time Series Index (TSI) concept helps organizations handle high cardinality data streams that are often short-lived, while also improving backup functionality and enabling the storage of data for extended periods. With these updates, InfluxData aims to support the growing demand for time series databases in industries such as manufacturing, finance, energy, and telecommunications.

InfluxData has announced updates to its InfluxDB Enterprise and InfluxDB products, expanding support for high cardinality time series data streams and introducing backup functionality. According to Tim Hall, VP of Products at InfluxData, these updates will help developers better manage their time series data, providing improved performance and reliability. The new features are part of a broader effort by InfluxData to provide a more comprehensive platform for handling large volumes of time-stamped data.

Monitoring Application Performance on the Frontend in the Age of Impatience` In today's fast-paced digital age, internet users expect seamless and interactive experiences with quick load times, making it essential to monitor application performance on the frontend. As developers, we should prioritize tracking specific performance metrics to diagnose and solve problems as they arise, rather than just focusing on server-side monitoring. To achieve this, we can use various tools and techniques, such as reducing render-blocking assets, image optimization, lazy-loading, caching, and content-delivery networks, from the outset of application development. Tools like Google's Page Speed and Analytics, Yahoo's YSlow, WebPagesTest, Chrome DevTools, Firefox's Performance by Firefox, and Safari's Timeline can help us monitor browser performance, but it's also beneficial to use a Time Series Database to collect and store performance metrics over time for long-term analysis and troubleshooting. By tracking performance regularly, we can identify dips and spikes in errors, performance ratings across different browsers, and specific error patterns, ultimately improving the user experience and positively impacting the frontend performance of our web applications.

The New Stack published a podcast review of InfluxData's conference InfluxDays NYC 2018. The podcast discussed upcoming plans for the InfluxData platform, including InfluxDB and IFQL query language. It also touched on the growing interest in time series data due to sensor data and sensor monitoring. The conversation highlighted the importance of timely data analysis in various industries.

InfluxData's Co-founder and CTO Paul Dix discusses the company's future plans and presents lessons learned from the past five years. The need emerged for additional components of the platform, including Telegraf, Kapacitor, and Chronograf. A new query language called Flux f.k.a. IFQL has been introduced to support InfluxDB and other time series data applications. The article provides an overview of InfluxData's journey and its future as a platform for working with time series data.

The New Stack | Why Should I Use a Time Series Database? By Katy Farmer, published in The New Stack on February 15th, 2018. A time series database is recommended for developers due to its scalability and usability benefits. It's designed specifically for storing metrics and events, making it ideal for handling large amounts of temporal data. By choosing a purpose-built database for time series use cases, developers can improve the performance and efficiency of their applications. The key consideration is selecting a database that aligns with the specific requirements of the project, rather than relying on generic tools or products.

InfluxData secured a $35 million Series C round of funding, led by Sapphire Ventures, which received significant press coverage from top industry publications, including Tech Crunch, Startup World, SiliconANGLE, and others, highlighting its importance for developers and the broader industry. The announcement was met with notable blog posts from InfluxData's CEO and CTO, providing insights into their vision for the company and its technology.

InfluxData has secured a $35 million Series C investment led by SAP's Sapphire Ventures, with existing investors Battery Ventures, Mayfield Fund, Trinity Ventures, and Harmony Partners also participating. The funding brings the total raised to almost $60 million, highlighting investor interest in the time series data space. InfluxData was founded by co-founder and CTO Paul Dix who recognized the need for time series tools and began building the underlying open source tool kit back in 2014. With this investment, the company aims to expand its business and further develop its time series database offerings.

InfluxData, a time-series database startup, has completed a $35 million Series C round of funding led by Sapphire Ventures. Its purpose-built database, InfluxDB, is designed specifically for metrics and events, making it an attractive solution for organizations with large amounts of time-stamped data. With this latest funding round, InfluxData expects to experience significant growth in the future. The company's specialized database is built from the ground up to handle high-volume, high-velocity data streams, providing a scalable solution for businesses looking to efficiently manage their metrics and events.

The future of InfluxData platform and InfluxDB is being shaped by its evolution into a comprehensive platform for working with time series data, rather than just a database. This vision was driven by the company's early success in solving common problems faced by developers using its initial "time series API". The platform now aims to enable developers to build solutions much faster by providing tools for collecting, processing, monitoring, and visualizing time series data. A new query language called Flux has been developed to work with time series data, which is designed to be more flexible and powerful than SQL, but also easy to learn and use. The platform will unify its various components behind a common API, allowing developers to interact with the system using gRPC, REST, or GraphQL. With this goal in mind, InfluxData plans to invest heavily in delivering on its promise of making developers far more productive when working with metrics and events.

InfluxData has secured $35m in additional funding, bringing its total capital raised to over $100m. This investment, led by Sapphire Ventures, will be used to further develop the company's modern time series platform. The platform was initially developed with a focus on making developers happy and reducing the "time to awesome" for building and scaling time-series applications. Since its inception, InfluxData has become the fastest growing category in the database market, driven by the increasing adoption of IoT sensors, containerization, and serverless architectures. The company's platform is designed to handle real-time monitoring and control workloads at speed, with a focus on empowering developers to rapidly build scaled systems. With this funding, InfluxData plans to expand its sales marketing efforts, open an EMEA office, and more than double its development team in 2018.

The company InfluxData has raised $35 million in Series C funding led by Sapphire Ventures, expanding its leadership in the time series database category. The funding will be used to accelerate growth and meet increasing demand for its platform, which is already seeing rapid expansion internationally. With this investment, InfluxData aims to further build out its platform and deliver on its continued investment in open-source time series software for the broader development community. The company's platform is designed to handle time series data and empower developers to build next-generation monitoring, analytics, and IoT applications with real-time visibility and control.

The 2018 Devies Award winners were announced at DeveloperWeek, recognizing the best in developer tech, with 15 companies awarded in various categories. The awards are based on sector leadership for innovation, media coverage, and reputation within the developer community. InfluxData's InfluxDB Enterprise took home the "Best in IoT Software" award, which is a platform designed to work with time series data, including collection, storage, visualization, and monitoring and alerting.

InfluxDB has been found to outperform OpenTSDB in time series data management, surpassing it by 9x in write throughput per server, using 8x less disk space, and delivering 7x faster query response times. In contrast, OpenTSDB requires significant setup and configuration to achieve optimal performance and often relies on Apache HBase as its storage backend, which can be complex to manage. InfluxDB, on the other hand, is designed for time series workloads out-of-the-box with a custom SQL-like query language and provides simpler setup and use cases. The results of this benchmarking test suggest that InfluxDB is the clear winner for time series data ingestion, compression, and query performance.

InfluxDB outperforms Elasticsearch in time series data ingestion, on-disk compression, and query performance, delivering 3.8x greater write throughput, 9x less disk space usage, and 7.7x faster response times for tested queries. InfluxDB is a custom-built storage engine optimized for time series data, with out-of-the-box support for mathematical and statistical functions across time ranges, making it suitable for custom monitoring and metrics collection, real-time analytics, IoT, and sensor data workloads. Elasticsearch, while not a time series database per se, can be used for storing and querying time series data but requires configuration changes to optimize performance. The benchmark exercise highlights the importance of choosing a purpose-built time series database like InfluxDB for scalable time series data ingestion, compression, and query performance.

InfluxData has released a new version of its cloud-based database service, InfluxDB Cloud, with enhanced security features and faster onboarding for developers. The updated platform now supports expanded global regions, including Asia, and provides improved multi-user support, allowing multiple users to access the same data without compromising security. This release also offers enhanced visibility into workload changes and their impact on user subscriptions. Additionally, InfluxData's client Jeti Services AB has reported benefits from using this latest version of InfluxDB Cloud, citing improved performance and scalability as key advantages.

InfluxData has released a new version of InfluxDB Cloud, which offers enhanced security features, expanded global region support, and faster onboarding for developers worldwide. The latest release includes Kapacitor, a real-time streaming data processing engine, with every subscription. This update is part of the comprehensive InfluxData Platform, providing tools and services to collect, analyze, and act upon metrics and events data through powerful visualizations and notifications.

Telegraf 1.5.2 and InfluxDB 1.4.3 have been released, each addressing a small number of issues reported by the community and customers. The maintenance releases are available for download on the project's downloads page.

The latest release of InfluxDB Cloud adds enhanced security features, faster onboarding benefits, and expanded global region support, providing a secure, fully managed database-as-a-service for developers to build next-generation monitoring, analytics, and IoT applications. The new multi-user functionality allows for true separation of administrative duties, while Kapacitor's inclusion enables users to create up to 25 alert definitions with more capacity available based on user demand. The release also adds support for key regions in Asia, allowing companies to store their mission-critical metrics and events collected from their systems into different regions for isolation and failure independence. Additionally, pre-built dashboards provide customers with enhanced visibility into how changes in workloads impact their subscriptions.

I created a time-lapse video of myself installing InfluxDB and building a dashboard using the TICK Stack on Linux, achieving the entire process in under 5 minutes. The installation was done with minimal typing, relying heavily on cut-and-paste from the Downloads page. The process consisted of only nine commands, with the majority of them being executed without manual input. I challenged viewers to beat my time and encouraged them to follow me on Twitter for more discussions on Time Series, IoT, and InfluxDB.