June 2016 Summaries

In this post, we recap the most interesting InfluxDB and TICK-stack related issues, workarounds for Kapacitor performing queries, how-tos and Q&A from GitHub, IRC, and the InfluxDB Google Group that might have been missed. Automating successive aggregations with Kapacitor is possible using Continuous Queries, but it's recommended to use Kapacitor instead due to its limitations. To write integers to the database, append an 'i' to the field value in the Line Protocol. When writing identifiers with double quotes, both escaping and quoting are necessary for correct querying. The post also provides recommendations for InfluxDB clustering, downloading the latest beta version, scheduling a consultation, and attending virtual training seminars.

InfluxDB has announced the availability of its Enterprise Beta, which offers clustering, monitoring, and data exploration capabilities. This new version allows users to deploy, manage, and monitor their InfluxDB deployments with a slick UI. The company provides persistent time-series data storage for various applications, including Cisco's renewals eCommerce application and Optinet's internal site usage data aggregation. InfluxDB is also used by Smartap for water pressure monitoring, leveraging its ease-of-use, data compression, and performance features. The company invites users to share their success stories and claim an InfluxDB hoodie as a "thank you" gift. Various events are scheduled, including meetups, virtual training sessions, and hands-on training in San Francisco.

SurgeMQ, an open-source MQTT broker, has been acquired by InfluxData, the company behind popular monitoring and analytics tools like InfluxDB, Telegraf, and Kapacitor. The acquisition aims to enhance IoT support in InfluxDB by creating a tightly integrated solution for industrial monitoring and IoT applications. With SurgeMQ now under InfluxData's umbrella, users can expect improved performance, security, and features tailored to their specific use cases. This development is expected to provide more complete solutions for IoT developers building on top of InfluxDB, ultimately enhancing the overall experience for users in these domains.

In this article, the author discusses various InfluxDB aggregation functions and TICK-stack related issues, workarounds, and best practices to improve query performance. The author explains that aggregation functions like `mean()` return epoch 0 if no specific timestamp is included in the query, and provides examples of how to include a time range in the `WHERE` clause to get accurate results. Additionally, the article offers tips on improving query performance by removing unnecessary clauses and using regular expressions in `SHOW TAG VALUES`. The author also mentions upcoming features and resources for users, including InfluxDB Enterprise Beta and virtual training seminars.

Telegraf is a metric collection daemon that can collect metrics from various inputs and write them to different outputs, making it easily extensible. It has 90+ input and output data plug-ins, including the logparser plugin for parsing logs. The logparser plugin can parse standard format logs, custom log parsing, and patterns based on the popular "grok" patterns. InfluxDB supports streaming log event data to its database with out-of-the-box patterns for popular servers like Nginx and Apache. Telegraf's logparser plugin allows users to structure any log data into queryable and searchable metrics. With the logparser plugin, users can parse real access logs and write them into InfluxDB. The plugin also supports custom patterns, enabling users to customize log parsing according to their needs. By using Telegraf with InfluxDB, developers can collect and analyze log data in a scalable and efficient manner.

This is the public beta announcement for InfluxDB Enterprise, which includes high availability and scale out clustering for InfluxDB along with management features, performance monitoring, and a new Chronograf Data Explorer interface for quickly digging into time series data. The release has features such as cluster rebalancing, role-based access control, and expanded user management capabilities, allowing administrators to give users finer-grained control over databases and within the cluster. The Enterprise Web application provides a web UI for looking at the performance of the cluster, including metrics on writes, queries, and average query latency, as well as features for data exploration and long-running query management. The beta release includes unrestricted cluster sizes and features for testing and preview, with plans to add more features in future iterations based on user feedback.

InfluxDB has announced early access to InfluxDB Enterprise, which enables clustering on infrastructure, allowing for rebalancing nodes and a user-friendly UI for deployment, management, and monitoring. The company is also hosting various events, including virtual training sessions, meetups, and conferences, offering free resources and demos. Additionally, the organization is seeking featured applications or products that use InfluxDB in production, with a "thank you" gift of an InfluxDB hoodie and stickers for those who participate.

InfluxDB is an open-source time-series database that allows users to store and query large amounts of data across various dimensions, including measurements, tags, and timestamps. To perform cross-measurement math or grouping, all data must be stored under a single measurement, as InfluxDB does not support joins. Users can use the `GROUP BY` clause with the `time()` function to group data by time intervals, but they should be aware of how InfluxDB handles time boundaries and rounding. To specify tag values in the `WHERE` clause, users must enclose them in single quotes. Additionally, when using `GROUP BY time()` queries, users may get results for multiple time intervals due to the default rounded calendar time boundaries, which can be altered by including an offset interval in the query.

InfluxDB has released InfluxDB 1.0 Beta, marking a significant milestone in its development. Early access to InfluxDB Enterprise is now available, allowing users to cluster their database on their own infrastructure and manage it with a user-friendly interface. The company also offers virtual training sessions and meetups, providing opportunities for developers to learn from experts and network with peers. Additionally, InfluxDB is seeking companies using its technology in production to feature on its website, offering a hoodie and stickers as a thank-you gift. Various events are scheduled throughout the summer, including hands-on training sessions, webinars, and meetups.

In this post, InfluxDB and TICK-stack related issues are discussed, including comparing data with Kapacitor. The issue of identical tag keys and field keys causing query confusion when trying to specify tags in the WHERE clause is addressed, and a solution using the `::tag` syntax is provided. Additionally, using Kapacitor to compare data from previous weeks to current weeks is explained, with a solution involving shifting the data to match timestamps using the `shift` function. Finally, casting functionality is discussed, with an example of how to cast floats to integers using InfluxQL's `<field_key>::<type>` syntax.

The InfluxData team has announced the immediate availability of InfluxDB 1.0 Beta, marking a significant point in the development of this time-series database and its related components, including Telegraf, Chronograf, and Kapacitor. The release is considered stable enough for production use, with many bug fixes and new features, such as exponential smoothing via Holt Winters queries and templates for Kapacitor TICKscripts. This beta marks a major step forward in the development of these projects, which have been running in production at significant scale for months. The team plans to lock down the API and make a commitment to zero breaking changes for a significant length of time, making it suitable for organizations building products on top of the InfluxData stack. Future releases will include new features such as improved rollups and aggregations, integration with query engines, and updates to indexing schemes. The team is confident that time-series data will become increasingly relevant across a broader spectrum of use cases, and InfluxDB 1.0 matters for its role in enabling real-time analytics, DevOps monitoring, and Internet of Things/sensor data.

InfluxDB has announced early access to InfluxDB Enterprise, which allows users to cluster their database on their infrastructure with the ability to rebalance nodes and manage deployments. The company also offers free virtual training sessions on June 9, covering topics such as mastering the InfluxDB CLI and fundamentals. Additionally, InfluxDB has been adopted by various companies, including Codepicnic, Certa Scale, and Digipolis, which use it for monitoring performance, storing metrics, and improving their operations. The company also invites users to claim a free InfluxDB hoodie if they have an application or product using InfluxDB in production. Various events are scheduled, including two-day hands-on training sessions and meetups.

InfluxDB has specific requirements and syntax rules when it comes to querying data, particularly when using boolean values in queries versus writing them. When querying data from a retention policy that is not the default, the measurement must be fully qualified with both the database and retention policy names. Additionally, when using boolean values in queries, they must be referenced as `true`, `True`, or `TRUE` to match the expected syntax for writes. Understanding these nuances can help users successfully query their data and avoid empty responses.