May 2019 Summaries

InfluxDB 2.0 Alpha has been released, featuring new visualization types including heatmaps and scatter plots, improved command-line interface arguments for session length and renewal, and updated line graph interpolation options. The release includes enhancements to the Flux library, now at version 0.31.1. This alpha build is intended for feedback on functionality, user experience, and APIs, rather than production usage or performance testing. Users are encouraged to download and explore the latest iteration, report issues or questions through the InfluxDB Github Repo or Community Site, and thank the development team for their interest and positive feedback.

InfluxDB outperformed Graphite in two tests, delivering 14x greater write throughput and using 7x less disk space when compared to Graphite's time series optimized configuration. InfluxDB also delivered 10x faster response times for tested queries, compared to the response time of cached queries from Graphite. The benchmarking exercise focused on time series use cases involving custom monitoring and metrics collection, real-time analytics, IoT and sensor data, and container or virtualization infrastructure metrics. InfluxDB is not designed for full-text search or log management use cases, where Graphite or similar full-text search engines are recommended instead. The results highlight the superiority of purpose-built time series databases like InfluxDB over general-purpose search databases like Graphite when it comes to handling large-scale time series data workloads.

The Telegraf Elasticsearch plugin allows users to monitor the performance of an Elasticsearch node. To begin, a user sets up an InfluxDB sandbox and an Elasticsearch node with Kibana, populating it with data using Kibana's Dev Tools. The user then configures the Telegraf plugin for Elasticsearch by adding its configuration to the telegraf/telegraf.conf file. With the configuration in place, the user navigates to Chronograf, a visualization tool that allows them to construct and run Flux queries to monitor the performance of the Elasticsearch node. This includes visualizing metrics such as documents inserted using the Elasticsearch tutorial, as well as accessing pre-built dashboards for additional critical metrics.

In May 2019, Forbes reported that InfluxData was one of the top companies to work for in the big data industry based on Glassdoor ratings. The ranking analyzed employee recommendations and CEO approval rates from various sources, including Computer Reseller News. InfluxData's CEO, Evan Kaplan, received a perfect score of 100% on Glassdoor, indicating high employee satisfaction with his leadership. This recognition suggests that InfluxData is an attractive employer for professionals in the data science and analytics fields.

Telegraf has released version 1.10.4, which includes several improvements to various plugins, such as Agent, CSV Parser, HTTP Output, InfluxDB v2 Output, Interrupts Input, IPMI Sensor Input, ntpq Input, and VMware vSphere Input. These updates address issues with metrics parsing, output formatting, and sensor data processing. The new release also fixes a race condition in the Wavefront Parser plugin. Users can download the latest version of Telegraf from the official downloads page.

InfluxData has released the beta version of its open-source time series database, designed to handle massive volumes of time-stamped data. The new release features a rate-limited free tier that will remain free, marking a significant shift in the company's pricing strategy. This development aims to bring big data and AI technologies beyond the hype, showcasing their practical applications in enterprise systems and architectures, particularly in the containerized and microservices world. InfluxData's time series database tools are poised to aid in the management and monitoring of these complex systems, providing a scalable solution for organizations dealing with large amounts of time-stamped data.

InfluxDB Enterprise 1.7.6 has been released, addressing various issues including a critical defect with inmem index, security vulnerability, and query regression, as well as improving credentials handling for Flux queries using the Influx Command Line Interface (CLI), and resolving Prometheus API issues.

InfluxDB Cloud 2.0 offers a fully managed and hosted time series database, dashboarding, analytics, monitoring, and serverless Flux system in the cloud, designed to be multi-tenanted with free tier usage limits for beta testing purposes. The service allows users to collect data through Telegraf agents and send it to the cloud, with limitations on query throughput and data retention. InfluxDB 2.0 Cloud will scale up with user needs and offer usage-based pricing, eliminating the need for customers to size ahead of time what instances, storage, or memory they'll need. The service is expected to be regularly updated with new features, including flow control, recursive functions, and connectors to third-party APIs. Users can provide feedback on the service through Twitter or submitting a post to HN.

InfluxData has announced the public beta of InfluxDB Cloud 2.0, a unified platform that enables deeper data insights, more flexibility, faster onboarding, and a rate-limited free tier. The new version is built on feedback from open source users and customers, aiming to improve the overall experience for developers and companies handling time series datasets. Major features include Flux language support, a single unified API, integrated visualization and dashboarding, and usage-based pricing. InfluxDB Cloud 2.0 is designed for handling massive volumes of time-stamped data produced by IoT devices, applications, networks, containers, and computers, with over 500 customers and 200,000 servers running the platform. The company invites adventurous developers to join the beta and provide feedback on shaping the future of InfluxDB Cloud.

With the public beta launch of InfluxDB Cloud 2.0, InfluxData has introduced a unified platform that enables deeper data insights, more flexibility, faster onboarding, and a rate-limited free tier for developers to join the beta and provide feedback. The new features include support for Flux, a powerful scripting language built specifically for time series data, a single unified API, integrated visualization and dashboarding, and usage-based pricing. InfluxDB Cloud 2.0 is designed to handle massive volumes of time-stamped data from IoT devices and applications, empowering developers to build transformative monitoring, analytics, and IoT applications. With over 500 customers and 200,000 servers running the open source product, InfluxData invites adventurous developers to join the beta program and shape the future of InfluxDB Cloud. The platform is available for public beta with a rate-limited free tier, making it easier for companies to get started and harvest insights from their time series datasets.

You've now seen a few of the major features of InfluxDB 2.0 including scraping metrics, running Telegraf, querying data, and writing data. You've set up a local instance of InfluxDB 2.0, created a bucket and configured the Telegraf agent to collect metrics from your machine. You've also written some sample data to the database using the UI. The new features in InfluxDB 2.0 include improved performance, easier data querying, and better support for querying and writing data. With InfluxDB 2.0, you can now scrape metrics, run Telegraf, query data, and write data with ease. The tutorial provided a step-by-step guide to setting up the local instance of InfluxDB 2.0 and exploring its features.

InfluxDB is a time series database that stores and analyzes large amounts of data. It has features such as high availability, horizontal scaling, and ease of use. The database can be queried using SQL-like commands or through a Python API. InfluxDB also supports querying data using the Flux programming language. Time series analysis is important for understanding how things change over time, which can help with decision making. A time series is a series of data points that are listed in chronological order and can display serial dependence, violating some statistical analysis assumptions. Autocorrelation measures the similarity between a given time series and its past values. It is used to uncover trends and patterns in time series data and can be used to identify seasonality. The autocorrelation function (ACF) plots one series over another to determine the degree of similarity, while the partial autocorrelation function (PACF) measures the correlation between a variable and its past values at different lags. Autocorrelation is important for time series forecasting as it can help uncover patterns in data, select the best prediction model, and evaluate the effectiveness of the model. It is used in various fields such as regression analysis, scientific applications, global positioning systems, signal processing, astrophysics, and more. The Durbin-Watson test is commonly used to detect autocorrelation in time series data. Autocorrelation can be removed using techniques such as lagged differencing and seasonal decomposition. In conclusion, autocorrelation is a crucial aspect of time series analysis that helps uncover patterns, select models, and evaluate their effectiveness, making it an essential tool for decision-making in various fields.

InfluxDB, Release Notes by Russ Savage, May 01, 2019` A new alpha release of InfluxDB 2.0 is now available, offering enhancements such as token generation from the browser and a new template for viewing local metrics during onboarding. This release also includes numerous bug fixes to various user interface components, and an updated Flux library. The update is intended for providing feedback on functionality and APIs rather than testing performance or production usage. Users are advised to back up their existing tasks before upgrading due to a breaking change that will remove them in this release.

InfluxData, the creator of open source time series database InfluxDB, has opened a second office in downtown Austin, Texas, as part of its growth strategy to expand operations worldwide. The new office is expected to nearly double the company's global workforce by the end of 2019. This move comes after another year of exceptional growth for InfluxData, with over 500 customers including major companies such as Wayfair, Mulesoft, and Cisco. The Austin office will focus on recruiting sales development representatives, technical support engineers, and database engineers to support the company's growing team and operations. With a distributed workforce already established in the U.S. and Europe, InfluxData is committed to hiring talented people from across the globe to join its team.