October 2022 Summaries

InfluxDB's geo-temporal package uses S2 geometry to handle geospatial data, making it easier to calculate distances and perform other spatial operations. This package can be used to analyze time-series data with latitude and longitude values, allowing for efficient calculation of averages, standard deviations, and running means over specific regions. The package can also be used to visualize the results in a plot, such as a line chart showing the average ocean surface temperature over time. By using InfluxDB's geo-temporal package, users can take advantage of its powerful features to perform complex spatial analysis on their data.

InfluxDB is an open-source time-series database that offers a distinctive approach to handling large amounts of naturally ordered time-series data at scale. It seamlessly integrates with most programming frameworks and languages, providing well-designed query languages like InfluxQL and Flux, as well as the lightweight InfluxDB Line Protocol for fast data ingestion. Apache ECharts is an open-source JavaScript library used for rendering charts and graphs, particularly suited for mobile apps due to its low memory footprint. A basic React app can be set up with InfluxDB Cloud using the provided tutorial, which includes querying data from InfluxDB Cloud, writing Flux queries, creating a new API token, cloning a GitHub repository, and connecting to the database. The React app uses ECharts for React to plot charts and graphs, including line charts and bar charts, using sample data from an InfluxDB Cloud bucket.

InfluxDB IOx now supports native SQL queries, allowing users to leverage time series data without needing to be Flux experts. This update expands the user base for InfluxDB by providing a more accessible query language, enabling non-Flux users to interact with the platform and integrate it with third-party tools like psql and Grafana. However, SQL support is limited to querying, as writing data to InfluxDB still requires using Flux. The addition of SQL capabilities marks an exciting evolution for InfluxDB, promoting innovation, interoperability, and efficiency while meeting developers where they are by offering multiple query languages.

InfluxDB has deployed its next-generation storage engine built on InfluxDB IOx, a cloud-native, real-time, columnar database optimized for time series data. The new storage engine is a massive leap forward for the core database technology, with unlimited scale and SQL query capabilities. It represents the largest leap forward since introducing TSM storage engine in 2016, bringing metric data and event data into a single database core. InfluxDB IOx is built in Rust on top of Apache Arrow and DataFusion, providing high-performance systems software and interoperability with a wider group of developers. The new storage engine lifts cardinality limits completely, allowing users to write any kind of event data with infinite cardinality and slice-and-dice data on any dimension without sacrificing performance. It also supports SQL natively, enabling fast analytical queries across high-cardinality data.

InfluxData has announced the deployment of its next-generation time series engine, built on top of the open source project InfluxDB IOx, which delivers faster queries, unlimited time series data, and introduces SQL support for writing queries and BI tool support. The new storage engine is designed to handle high-volume data ingestion with unbounded cardinality, optimized for real-time analytics use cases, and enables developers to query data immediately after it's written, often measured in milliseconds. With the addition of SQL language support, InfluxDB now meets developers where they are, allowing them to work in yet another language they love, raising the bar for time series analytics. The new engine doubles down on scale and performance, enabling its largest customers to collect, store, and intelligently orchestrate massive time series data workloads.

InfluxDB is a leading time series database that stores and analyzes billions of devices' data every nanosecond. To get data into InfluxDB, users can use multiple formats like CSV, and translate it into line protocol using the InfluxDB CLI and client libraries. Line protocol consists of measurement name, fields, tags, and optional timestamp. The Influx CLI provides commands to manage and interact with InfluxDB, including writing data directly from annotated CSV files. The InfluxDB Client Libraries offer language-specific packages to quickly interact with the InfluxDB v2 API, allowing users to process and package data in their preferred programming language.

In a live virtual training session at InfluxDB University's InfluxDays user conference, participants will learn how to effectively use Telegraf, InfluxDB's open source data collection agent, by gaining hands-on experience configuring and running the tool. They will also discover how to work with Edge Data Replication (EDR), which allows for automatic replication of data from edge OSS instances to InfluxDB Cloud. Furthermore, participants can earn a digital badge upon completion of the training and register for free on-demand sessions when they become available. The training is part of a larger virtual user conference that includes other courses, such as Data Collection with Telegraf, Flux, InfluxDB Cloud, and more.

InfluxDB is a popular open-source time series database that can handle large amounts of data from various sources, including IoT devices. The challenge of aggregating telemetry and metrics from millions of embedded Linux devices in real-time has been addressed by combining InfluxDB with Pantavisor-powered devices using the Telegraf plugin. This integration allows for automatic onboarding of devices, streamlined device management, and centralized monitoring of device metrics. By following a step-by-step guide, users can set up their IoT fleets to stream data to InfluxDB, enabling real-time monitoring and analysis of device performance and behavior.

InfluxData compared the performance and features of InfluxDB, a purpose-built time series database, to Cassandra, a general-purpose distributed database, for common time series workloads. The benchmark tests showed that InfluxDB outperformed Cassandra in all three areas: data ingestion (5x faster), on-disk storage requirements (2.4x less), and query performance (up to 100x faster). This is because InfluxDB is designed from the ground up for time series data, providing features such as mathematical and statistical functions across time ranges, which are not available in Cassandra. The results highlight the benefits of using a purpose-built time series database like InfluxDB for real-time analytics, sensor data workloads, and other applications that require efficient handling of temporal query flexibility.

We've made significant improvements in parsing and display time for InfluxDB queries, reducing parsing/display times from 30 seconds to just 4 seconds in one test case. This change will allow users to view raw data more efficiently. Additionally, we've enhanced the capability to search for multiple measurements at the same time, allowing users to select more than one measurement and search within specific fields or tags. These changes aim to improve user experience and performance when working with InfluxDB queries, especially in large datasets or high cardinality scenarios.

Streamlit, an open source framework, enables developers to create web applications entirely in Python, simplifying the development process by utilizing preconfigured elements from the Streamlit API. This allows Python developers, machine learning engineers, data analysts, and data scientists to develop stunning applications without requiring a full software development team. Streamlit provides various features such as caching, hot reload, and GPU acceleration to speed up application development. By leveraging InfluxDB, a time series database, users can create real-time dashboards to visualize and analyze weather data. The framework provides an intuitive way to build interactive plots, including line charts, area charts, and bar charts, making it easier for developers to share their data with others.

InfluxDB Cloud offers a fast and efficient way to ingest data from IoT devices, specifically through its MQTT Native Collector. This method allows users to directly connect their message broker to InfluxDB Cloud without the need for additional software installation or extra code writing, making it ideal for applications that require real-time analysis and speed. The MQTT Native Collector is particularly useful when cloud-based infrastructure is used, as it eliminates the need for downloading data onto a server before uploading it back to InfluxDB Cloud, saving time and bandwidth. To get started, users only need to configure their message broker IP address or URL, authentication parameters, topics to subscribe to, and parsing rules, which can be set up in minutes. This method provides flexibility while being the simplest and quickest way to ingest raw data from IoT devices.

InfluxDB Cloud is a cloud-native, serverless platform that supports auto-scaling and various workloads, built with Kubernetes-based microservices. To address security concerns, InfluxData implemented a container signing solution to verify the authenticity and integrity of container images. The solution uses digital signatures created at image push time, stored in HashiCorp Vault, and made publicly available for verification by clusters consuming the images. This approach enables detection of tampering and rotation of key pairs to minimize the impact of security incidents. InfluxData's implementation leverages SigStore policy-controller and cosign tools, and is designed to be scalable, secure, and easy to manage, with a focus on minimizing additional burden for users.

InfluxData is attending AWS re:Invent 2022 in Las Vegas from November 28 to December 3. They will have a booth (#3648) where attendees can watch live demos of InfluxDB's capabilities, meet with experts and learn about the built-in developer tools that make it powerful. Additionally, they will offer one-on-one meetings, a virtual booth, and a contest to win a free conference pass for attending the event. The company's executives will also be interviewed on theCUBE and Screaming in the Cloud discussing InfluxDB enhancements such as Edge Data Replication and Native Collectors.

InfluxDB has introduced a new feature that allows users to set a custom data retention period for TSM buckets, enabling more flexibility in managing data storage and retrieval requirements. This change is particularly beneficial for developers who need to comply with specific data retention policies or have unique data management needs. The new feature is now available to PAYG and Annual Contract users, while free users are still limited to a 30-day retention period. By utilizing this updated functionality, users can optimize their disk usage, reduce storage concerns, and accelerate Time to Awesome.

InfluxDB Cloud's Native Collectors enable users to connect industry protocols to InfluxDB without hosting their own collectors, providing a cloud-only solution for IoT data ingestion and parsing. The Things Network is connected to InfluxDB using Native Collectors, allowing the ingestion of device data from LoRaWAN networks. By collecting connection credentials, defining security settings, and mapping JSON payload fields, users can successfully connect and authenticate against The Things Network's MQTT broker, parse device data into InfluxDB, and future-proof their topic subscriptions with wildcards.

Time series databases are optimized to handle typical workloads that involve measuring change over time, such as monitoring weather patterns or CPU usage. These databases store data in a way that allows for efficient querying and analysis of past performance and predicting future outcomes. They can handle both metrics and events, and provide features like automatic lifecycle management and scalability to accommodate growing datasets. Using a time series database can significantly speed up query times, especially when summarizing large periods of time, and some databases also come with built-in visualization tools. Choosing the right time series database is crucial for developers who want to focus on building applications rather than managing architecture.

InfluxDB is a leading platform for building time series applications, with significant advancements in machine learning algorithms like long short-term memory (LSTM) and Prophet, which have led to improved forecast accuracy. The Facebook Kats library is used for analyzing time series data, offering functionalities such as forecasting, detection, auto feature engineering, and utilities. In this article, the author learns how to use InfluxDB to store and access time series data with Python API and analyze it using Facebook Kats. Three popular time series algorithms, SARIMA, Holt-Winters, and Prophet, are introduced along with their performance comparison, showcasing the strengths of each model in capturing seasonality, autocorrelation, and increasing trend. The author concludes that InfluxDB is a powerful platform for building and operating time series applications, empowering developers to build transformative monitoring, analytics, and IoT applications faster and to scale.

The InfluxDB Cloud team has released a new onboarding wizard for the Arduino platform, allowing users to get started with time series and InfluxDB within minutes of logging in, eliminating the need for hours of documentation review. This simplifies the learning experience across different operating systems and environments, providing a more user-friendly way to onboard with InfluxDB using Arduino.

InfluxData has been recognized with four Comparably Workplace Culture Awards for small and midsized companies, winning awards in categories such as Happiest Employees, Best Company for Compensation, Best Company for Work-Life Balance, and Best Company for Perks and Benefits. The company's commitment to building a diverse, inclusive, and supportive community around the world has resonated with employees, who praise the positive work environment and sense of camaraderie. InfluxData's core values, including valuing each other, getting stuff done, and embracing failure, have contributed to the creation of this culture, which is reflected in awards such as being named one of America's Best Startup Employers by Forbes. The company believes that its products and solutions can only be as strong as the teams that build and support them, and that a culture of respect keeps individual team members motivated and invested in their work.

InfluxDB is a time series database platform that enables developers to use its full capabilities at the edge, while also using the same data in the cloud for different purposes. Edge Data Replication (EDR) allows developers to automatically replicate data from an open source instance of InfluxDB to an instance of InfluxDB Cloud, creating a durable and reliable data transfer between the edge and the cloud. Flux is the data scripting and query language for InfluxDB, providing powerful features such as querying time series data, setting up alerts, managing time series data life cycles, and transforming and analyzing data. With Flux, developers can execute queries server-side to increase performance, and use a variety of functions to clean, analyze, and transform data, including downsampling, aggregating, and manipulating timestamps. Edge Data Replication enables the creation of hybrid architectures by minimizing data transfer between the edge and cloud, making it more efficient.

This year’s InfluxDays will showcase the breadth and depth of the InfluxDB platform as the critical infrastructure behind today’s most impactful real-time applications. The virtual event will feature technical training, executive keynotes, live sessions for the developer community, and product announcements from InfluxData. Attendees can learn about the latest innovations in IoT, monitoring, real-time analytics, and developer toolsets, as well as gain insights into the future of time series data and InfluxDB. The event will include a range of featured sessions and educational content, including courses on Telegraf and Flux language, and will recognize developers who have made significant contributions to InfluxData’s open source products or community over the past year with its annual Community Awards.

InfluxData is excited to announce that InfluxDays 2022 will take place on November 2-3, 2022, a two-day conference dedicated to sharing knowledge and innovations in IoT, monitoring, real-time analytics, and developer toolsets. The event will feature presentations from InfluxData's executives, product managers, and engineers, showcasing the latest capabilities of InfluxDB as the smart data platform for developers building applications with time series data. Additionally, InfluxDB University will host in-person and virtual training sessions, including a free on-demand training program and instructor-led courses, while also recognizing winners of the annual Community Awards, which honor developers who have made significant contributions to InfluxData's open source products and community.

InfluxDB users may need to deep link related dashboards, which can be achieved using Flux, string interpolation, and variables. The simplest way to do this is by copying the URL of the desired dashboard and pasting it into a new dashboard's table view with Flux. However, preserving the current time range when deep linking can also be done by using the `http.pathEscape` function in conjunction with string interpolation and variables to convert start and stop times to variables in the dashboard URL link. Additionally, custom variables for orgID and dashboardID can be created to make deep linking more streamlined.

nmon is a popular monitoring tool that was originally built by IBM engineer Nigel Griffiths in the 1990s to monitor operating system performance data for AIX. Despite its original success, the tool underwent significant changes over the years, with Griffiths revisiting and revamping it multiple times. In 2018, Griffiths re-built nmon from the ground up to bring it into alignment with modern computer systems, focusing on four key areas: file format, metrics collection, data storage, and data visualization. The updated tool now uses standard JSON and InfluxDB line protocol for data exchange, collects virtually every possible system performance stat, and utilizes Grafana for real-time data visualization. With these changes, nmon has become much more powerful and useful in modern technology stacks.