June 2020 Summaries

A new maintenance release for Telegraf is now available, which includes improvements to various plugins such as Agent, Procstat Input, Ping Input, Sensors Input, x509 Cert Input, Prometheus Output, and JSON Parser. These updates address issues like incorrect tag types in configuration files, performance improvements, and fixes for specific input plugins. The release aims to enhance the overall stability and functionality of Telegraf.

The OPCUA Router is a software tool created by inray to connect industrial and commercial systems through a central platform, enabling seamless bidirectional communication and data exchange. It connects various data sources using the OPCUA protocol and stores the collected time series data in InfluxDB, a scalable and high-performing open-source time series database. The OPC Router allows customers to modernize production processes by automatically exchanging data between systems such as PLCs, sensors, and ERP systems, providing real-time monitoring and alerting capabilities. By integrating InfluxDB with the OPC Router, industrial customers can gain valuable insights from their data, optimize production scheduling, achieve cost savings, and improve business operations. The combination of the two tools enables industries to become data-driven organizations, making informed decisions based on relevant metrics.

Flux, a query language used for InfluxDB, requires a time range when querying the database. This is done using the `range()` function, which takes two parameters: `start` and `stop`, defining an absolute or relative time range. Relative time ranges are defined using durations relative to "now", while absolute time ranges use timestamps in ISO format. The `range()` function includes points that match the start time but excludes those that match the stop time.

A new release of InfluxDB 2.0 Beta is available now, featuring enhancements such as improved templates management, packaging configuration via a configuration file, and UI enhancements including the option to cancel long running queries and improved line graph rendering. The release includes regular updates as new features and issues are added, and users are encouraged to provide feedback through the InfluxDB Community Slack channel.

RingCentral built a monitoring solution that supports visibility, integrated configuration and alerting for operations efficiency, and quick DevOps cycles for its four pillars of product and functionalities using InfluxDB Enterprise, Kapacitor, and Telegraf. The company's goal was to provide a programmable way for developers and operations engineers to self-service their monitoring needs. RingCentral overcame technical requirements such as alerting and dashboarding as code, sandbox testing, and high availability with InfluxDB Enterprise, Telegraf, and Kapacitor. They also developed an in-house built Kapacitor Manager to manage the pool of Kapacitor instances and a mechanism for detecting change events. The company's monitoring solution has come a long way since migrating to InfluxDB, allowing its small-size monitoring team to keep pace with business growth.

The Telegraf Modbus input plugin allows users to collect data from Modbus devices via Modbus TCP or RTU/ASCII connections. The plugin supports digital variables such as discrete inputs, coils, holding registers, and input registers, and can be configured using a sample configuration file. InfluxDB 2.0 recently launched templates that include prepackaged configurations for common setups, including the Modbus template contributed by Ray Farias. This template includes an Island Pulse Demo Modbus Device repository for a working demo device. The plugin and template are designed to make it easy to monitor Modbus devices with InfluxDB.

This CloudFormation template allows users to collect and process time series data from various sources, including the United States Geological Survey (USGS), and write it to InfluxDB. The template creates an AWS Lambda function that runs every hour, pulls earthquake data from the USGS, and writes it to a bucket in InfluxDB. The template also defines a role with basic permission to run the Lambda, a Python library layer with necessary libraries, and an event rule to trigger the Lambda every hour. By deploying this template, users can easily create a scalable and portable environment for collecting and processing time series data.

EnerKey's IoT platform uses InfluxDB Enterprise and Microsoft Azure to analyze sensor data from various sources, enabling customers to save millions of euros by detecting energy usage fluctuations triggered by weather and geospatial data. EnerKey's CTO Martti Kontula presented on how the company powers its platform using InfluxDB and Azure, showcasing their 80+ out-of-the-box integrations with various data sources. The platform helps commercial real estate owners and managing companies understand their facilities better by collecting time series data in real-time, analyzing it, and storing raw data in InfluxDB while metadata is stored in Azure SQL. EnerKey had previously considered other options like PostgreSQL, MongoDB, and Cassandra before choosing InfluxDB Enterprise due to its high ingest and output rates and key functionalities. The company is now using InfluxDB and machine learning models for advanced analytics to further enhance their competitive advantage.

Veritas Technologies, a leader in data backup center and recovery, faced challenges with managing the cost of data storage and preventing problems from happening. To address this, they built Veritas Predictive Insights, a SaaS platform that uses Artificial intelligence (AI) and machine learning (ML) to deliver predictive support services for their appliance customers. This platform utilizes InfluxDB as its time series database to track storage consumption and reduce downtime through forecasting and predictive analytics. The solution automates the historically manual process of analyzing data from tens of data points to large-scale processing of thousands of time series and millions of data points, enabling proactive support and reducing costs and complexity. By leveraging AI and ML, Veritas can detect potential issues, offer prescriptive remediation, and mitigate problems before they occur, enhancing operational availability, resolving potential issues, reducing TCO, and optimizing storage investments.

Jorge de la Cruz, a Systems Engineer at Veeam Software, presented on "Modern vSphere Monitoring and Dashboards Using InfluxDB, Telegraf and Grafana" webinar, where he discussed the basics of monitoring, vSphere monitoring best practices, and the use of Telegraf to collect time-stamped data from various sources. He demonstrated how InfluxDB is used as a high-performance time series database and Grafana for visualizations and graphs. The webinar covered Jorge's journey in monitoring his vCenter server, including his first Grafana blog in June 2016 and his VMware vSphere Overview Dashboard being downloaded over 5,400 times. The presentation also included a live demo of the beautiful Grafana dashboards and addressed audience questions regarding customization and configuration.

InfluxDB is launching an "Inventors" program to recognize developers who build innovative solutions with or on top of its platform. The program has two types of inventors: those who pioneer the use of new products or features, and those who create novel and impactful solutions that push the boundaries of what's possible with InfluxDB. Eligible individuals will receive recognition, exclusive benefits such as an inventor t-shirt, listing in the InfluxDB Inventors Directory, and a Certificate of Invention signed by InfluxData founder Paul Dix. The program is focused on the InfluxDB Templates feature, which allows users to create and share comprehensive monitoring solutions. The first batch of inventors has already been selected, and nominations are open for the second type of inventor.

A new release of InfluxDB 2.0 Beta is available now, featuring enhanced binaries for users who want to interact with remote instances or use GitOps style workflows, improved UI performance, upgraded Flux version, and other improvements.

In Flux, two timestamp formats are available: Unix timestamp and RFC3339 timestamp. The Unix timestamp format counts time since the Unix Epoch (1970-01-01T00:00:00Z UTC) in specified units such as nanoseconds, microseconds, milliseconds, seconds, etc. To convert timestamps from Unix nanoseconds to a human readable RFC3339 timestamp, use the `time()` function, and vice versa with `uint()`. Flux does not support mathematical operations using time type values, so to calculate duration between two timestamps, it is necessary to convert them to Unix nanosecond format first, then subtract one from another, and finally apply `duration()` function. The current time can be retrieved using the `now()` function in UTC or by importing the system package and `system.time()` function for RFC3339 format.

Telegraf 1.14.4 is a new maintenance release that includes improvements to several plugins, including SQL Server Input, InfluxDB Input, Converter Processor, and InfluxDB Parser. These updates address various issues such as a null error in the SQL Server Input Plugin, a typo in the InfluxDB Input Plugin, numeric to bool conversion errors in the Converter Processor, and an issue with the Influx stream parser blocking when data is in buffer. The release is intended for maintenance purposes only and includes no major new features or functionality.

InfluxDB users can now send notifications to Discord instead of Slack by contributing a custom Flux function called `discord.send()`. This function was contributed by chobbs and allows InfluxDB users to send critical statuses to a Discord endpoint. To contribute this function, developers need to fork the Flux repository, create a new directory for their package, write the `discord.flux` file with the `send()` function, add a test for the function, compile Flux, and verify that the function has been registered locally before submitting a PR. The community is encouraged to contribute their own Flux packages, including telegraf plugins, community templates, awesome InfluxDB projects, or Third Party Flux Packages, which will continue to impress and humble them.

InfluxDB Cloud was chosen by Sudokrew, a Hawaii-based full stack development shop and software consultancy, to develop a clean energy storage device management and battery intelligence software solution for Blue Planet Energy. The solution required a time series database that could scale with increasing devices and registers, support an HTTP API, basic aggregation methods, and provide dedicated hosting. InfluxDB Cloud was selected due to its ability to scale, support APIs, and provide a hosted solution. Sudokrew deployed InfluxDB Cloud and integrated it with eGauge devices, which provided the data for the energy storage unit management system. The solution enabled Blue Planet Energy to manage sales, deploy devices, monitor energy usage, and provide a unified infrastructure to connect every user and vertical of the business. With InfluxDB Cloud, Sudokrew gained visibility into their servers, transparency, and control over how the data was handled, which led to improved customer relationships and data-driven decision-making.

InfluxDB is a time series platform that enables data ingestion and analysis, but it can be extended with serverless functions like AWS Lambda to meet specific use cases. The article demonstrates two examples of extending InfluxDB with Lambda: one using the unified API and the other using an InfluxDB client library. The first example shows how to query InfluxDB directly via the API, while the second example uses a client library to interact with InfluxDB. Both examples illustrate the ease of integrating InfluxDB with serverless functions like Lambda. The article also mentions that these initial examples demonstrate the foundation for further exploring serverless integrations with InfluxDB.

InfluxDB Cloud is now available in beta on Microsoft Azure, offering a serverless time series data platform with automatic scaling, instant-on deployment, and automatic backup. The service is based on open-source InfluxDB 2.0 and extends the availability of InfluxDB to more regions, including Azure Western Europe. This new service is designed for development and trial workloads, and users can sign up for a free account at cloud2.influxdata.com. InfluxDB Cloud on Azure differs from InfluxDB Enterprise on Azure in terms of its serverless architecture and lack of role-based access control (RBAC). The TCO (Total Cost of Ownership) of InfluxDB Cloud is estimated to be significantly lower than that of traditional time series databases, with one customer reporting a saving of half an FTE (full-time equivalent) in system administration effort. However, the service is not yet production-ready and only offers the free tier for now, with a planned general availability (GA) release date to be announced.

InfluxData has transitioned from a "we hire interns" mindset to a more formal approach, recognizing benefits such as clear processes, expectations, and measurable goals in their internship program. The company implemented a shared job description and hiring plan across teams, ensuring consistency and standardization, while also fostering collaboration among managers and peers. This intentional design allows the program to adapt to challenges like remote work during the COVID-19 pandemic, providing interns with a valuable learning experience that can contribute to full-time growth.

Flux, a data scripting language, benefits from static analysis, which enables code editors to offer autocompletion features by understanding the code without executing it. This process relies on type inference, specifically using Algorithm W, an approach that generates and solves equations related to the types of expressions within a Flux program. By assigning type variables to expressions and creating equations based on their usage, static analysis can deduce the types of all variables, helping identify errors before runtime. This method allows for efficient and accurate type determination, enhancing the development experience by providing suggestions and error messages. The complexity of type inference lies in solving numerous equations generated from even simple scripts, and Flux's implementation draws from the Hindley-Milner type system, with further insights available from extensive research in computational theory.