April 2017 Summaries

InfluxDB provides guidance on querying data in non-DEFAULT retention policies, checking field types, and the differences between querying and writing booleans. When querying data in a retention policy that is not the DEFAULT policy, it's necessary to fully qualify the measurement by specifying the database and retention policy. The SHOW FIELD KEYS query can be used to see the data type of a field starting with InfluxDB version 1.0. Additionally, boolean syntax differs for writes and queries, requiring the use of `true`, `True`, or `TRUE` to reference that value in a query.

The text discusses the integration of Prometheus and InfluxDB, highlighting their potential to work together seamlessly. Julius Volz, a creator of Prometheus, showcased a demo of Prometheus using InfluxDB as a remote storage backend, allowing for real-time data retrieval. This new feature enables Prometheus to pull metrics from remote storage backends, expanding its capabilities. The author is excited about the collaboration and plans to introduce support for pull-based methods in addition to traditional push-based methods. They also outline their vision for improving InfluxDB 2.0, including a simplified data model, a functional query language, and enhanced user experience features such as a rich query builder UI and query completion CLI.

InfluxDB has released new features in Chronograf, including template queries for data exploration, which allows users to easily create and manage complex queries. Additionally, the functions documentation has been revamped with improved syntax descriptions, practical examples, and insights into common issues. The company also offers a Tech Tips webinar that covers highlights from the past quarter, as well as various deployment options for InfluxDB and its components, including cloud and server-based installations.

InfluxData and Docker share a commitment to open source, with both companies contributing significantly to their respective projects. InfluxData uses official Docker images for its products, including the InfluxDB Cloud service, which runs on top of Docker. The company also offers an official Sandbox for experimenting with the full TICK Stack, making it easier for developers to get started with monitoring and other applications. Additionally, InfluxData has developed a program called Orbiter that makes it easy to automate scaling of containers running on Docker Swarm or underlying infrastructure using the TICK stack. This allows users to scale based on various metrics, such as queue depth or requests per second. By leveraging Docker's capabilities, InfluxData is able to deliver a better experience to its users and tighten iteration cycles on its products.

Denver DevOps Days was held with a great cloud theme venue, energetic crowd, and hard-working organizers. The conference featured excellent talks and breakout sessions. Connor Shea from GitLab presented an interesting postmortem about the recent database outage. InfluxData will be attending upcoming events such as Dockercon in Austin and DevOpsDays also in Austin.

In this article, the author explores some unexpected results that occur when working with InfluxQL functions in the SELECT statement, specifically when using the DISTINCT() function with the INTO clause and the GROUP BY time() clause. The behavior of these functions is explained by InfluxDB's internal workings, which can lead to surprising outcomes, such as missing data being written to a different measurement or timestamps not matching the original values. The author also discusses how the ELAPSED() function behaves when used with a nested function and a GROUP BY time() clause, resulting in consistent but incorrect results. These examples highlight the importance of understanding InfluxDB's functions and behavior to write effective queries.

The author of the article is Mark Herring and it was published on April 13, 2017. The article discusses a creative way to enhance one's BBQ experience by integrating technology into the process. A developer named Brian Ketelsen has successfully connected a Raspberry Pi to a fan that optimally cooks meat using Go programming language, Gobot.io, and InfluxDB for data storage. This setup enables predictive analysis via machine learning to determine optimal tenderness of meat. The article also mentions a webinar on monitoring weather stations with Raspberry Pi and InfluxDB.

In this weekly post, we recap the most interesting InfluxDB and TICK stack related issues, workarounds, how-tos and Q&A from GitHub, IRC and the InfluxData Community that might have been missed in the last week. InfluxDB assumes all numerical field values are floats unless a trailing 'i' is included on the number to write integers. For connecting an Enterprise cluster to Chronograf, one needs to include the IP of any meta node in the Meta Service Connection URL input and save changes in the CONFIGURATION tab. The setup is almost the same as for OSS InfluxDB product but includes this additional step. The TICK-stack downloads are now live on the "downloads" page, and users can get started with a free trial of InfluxDB Cloud or Enterprise featuring fully-managed clusters and intuitive UI for deploying and monitoring clusters.

Autoscaling Docker Swarm with the TICK Stack` The TICK stack, consisting of InfluxDB and its related tools Telegraf and Kapacitor, has improved the ease of use, depth of metrics, and level of control in maintaining and monitoring Docker installations. Docker's Swarm feature, introduced in version 1.12, allows for orchestration and management of a cluster of Docker hosts without requiring additional tools or frameworks. However, to effectively utilize this feature, logging and monitoring skills are necessary to understand system behavior and have visibility into each part. This is where Orbiter comes in, an open-source tool that can scale Docker Swarm clusters based on monitoring metrics using the TICK stack.

The InfluxData Sandbox is a learning tool that runs the entire suite of InfluxData products together, eliminating the need to install and configure each product separately. The sandbox makes setup transparent and simulates a production installation without management complexity. It was built using Docker and is available on GitHub for easy cloning and use. The sandbox persists data in the cloned directory, allowing for easy installation/uninstallation and inspection of data directories. With the sandbox, users can focus on understanding tasks without extra work and confusion.

InfluxDB has released an alpha build for testing its new time series indexing engine, which aims to support a large number of unique time series, with a goal of reaching 1 billion. The new engine moves the index to files on disk and uses techniques like Robin Hood Hashing for fast index lookups, allowing users to store millions of unique time series without significant memory impact. The feature addresses the issue of ephemeral time series, but does not yet solve limitations in scope and scale-out clustering. To enable the new engine, users need to download the alpha build and edit their config file with a new setting, and should only use it on testing infrastructure due to ongoing work and potential format changes.

InfluxDB's debug/vars endpoint provides diagnostic information about the instance, including key-value pairs and InfluxDB-formatted expvars that correspond to metrics such as memory usage, CPU usage, and command line arguments used to invoke the process. The output is in JSON format and can be accessed via a custom HTTP endpoint or through third-party tools like Telegraf's input plugin, which supports ingesting InfluxDB-formatted expvars from a remote HTTP endpoint. Additionally, emitting InfluxDB-formatted expvars can be done in one's own application using a minimal amount of code, allowing for centralized monitoring of multiple instances.