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InfluxDB 1.7 Adds Query Performance and Data Compaction Enhancements

InfluxData announced the release of InfluxDB 1.7, delivering important query performance and data compaction enhancements to the Time Series Database.

This new functionality gives developers tools to query and process time series data used for monitoring IoT, DevOps, cloud, containers, and real-time analytics data.

InfluxDB is InfluxData’s Time Series Database used as a data store for any use case involving large amounts of time-stamped data – including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics.

The open source InfluxDB 1.7 delivers major improvements around query performance and throughput when using the Time Series Index (TSI), which supports large numbers of unique time series data that is ephemeral and stored in Time Series Databases.

“Performance, data compaction and Time Series Index improvements help InfluxData maintain its leadership position in this category by delivering superior performance and meeting the advanced needs of the developers,” said Tim Hall, VP of Products. “InfluxData has been working closely with the developer community in defining the Flux language. We look forward to developers downloading Flux, using it and providing us with useful feedback to help make the language a success.”

Metrics, events, and other time-based data are being generated at an exponential rate, as there is a growing requirement for analyzing today’s complex environments. The InfluxData Platform provides a comprehensive set of tools and services to accumulate metrics and events data, analyze the data, and act on the data via powerful visualizations and notifications.

InfluxDB 1.7 is available today.

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InfluxDB 1.7 Adds Query Performance and Data Compaction Enhancements

InfluxData announced the release of InfluxDB 1.7, delivering important query performance and data compaction enhancements to the Time Series Database.

This new functionality gives developers tools to query and process time series data used for monitoring IoT, DevOps, cloud, containers, and real-time analytics data.

InfluxDB is InfluxData’s Time Series Database used as a data store for any use case involving large amounts of time-stamped data – including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics.

The open source InfluxDB 1.7 delivers major improvements around query performance and throughput when using the Time Series Index (TSI), which supports large numbers of unique time series data that is ephemeral and stored in Time Series Databases.

“Performance, data compaction and Time Series Index improvements help InfluxData maintain its leadership position in this category by delivering superior performance and meeting the advanced needs of the developers,” said Tim Hall, VP of Products. “InfluxData has been working closely with the developer community in defining the Flux language. We look forward to developers downloading Flux, using it and providing us with useful feedback to help make the language a success.”

Metrics, events, and other time-based data are being generated at an exponential rate, as there is a growing requirement for analyzing today’s complex environments. The InfluxData Platform provides a comprehensive set of tools and services to accumulate metrics and events data, analyze the data, and act on the data via powerful visualizations and notifications.

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One of the most misunderstood culprits of poor application performance is packet loss. Even minimal packet loss can cripple the throughput of a high-speed connection, making enterprise applications sluggish and frustrating for remote employee ... So, what's going wrong? And why does adding more bandwidth fail to fix the issue? ...

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Efficiency is a highly-desirable objective in business ... We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges ...

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