How to Store Vibration Sensor Data | ReductStore vs InfluxDB

In How to Store Vibration Sensor Data, we discuss the importance of efficiently storing both raw vibration data and pre-processed metrics, and the benefits of using time-series databases such as ReductStore. We explore best practices for setting up a time-series database and implementing data retention policies to effectively manage high-frequency sensor data.

We also see how to store vibration sensor values in 1-second chunks, each packaged as binary data, to optimize the storage process when dealing with high-frequency data such as vibration or acoustic measurements.

In this post, we compare ReductStore and InfluxDB in a real-world benchmark scenario, focusing on their write and read performance for high-frequency sensor data. We show how ReductStore's binary storage provides superior efficiency and scalability over InfluxDB when handling large volumes of unstructured time-series data.

The benchmark was run on an SSD drive, but results may vary depending on hardware configuration and database settings; to explore how it performs on your setup, you can run the benchmark yourself using the Reduct Vibration Example repository on GitHub.

Write Performance: ReductStore vs InfluxDB

When comparing write performance between ReductStore and InfluxDB, there are significant differences as data frequency increases.

InfluxDB Write Performance

InfluxDB struggles to maintain performance as the frequency of sensor data increases. The benchmark results show that as the frequency increases to 30,000 Hz, the time it takes to write data increases significantly. This is due to InfluxDB processing and storing the increasing number of time series data points one at a time, resulting in slower write times. In high frequency environments, this can be a challenge for applications that require real-time data storage and need to minimize write latency.

ReductStore Write Performance

On the other hand, ReductStore shows minimal impact on its write performance even as the frequency scales up. It maintains a consistent and stable write time at all frequencies tested, including the highest of 30,000 Hz. This is largely due to ReductStore's design, which stores time series data in binary format, allowing it to efficiently handle large volumes of high frequency sensor data without compromising write speed. This makes ReductStore more scalable and able to handle the massive influx of sensor data without performance degradation.

Read Performance: ReductStore vs InfluxDB

The read performance benchmark results reveal another key difference between ReductStore and InfluxDB when handling high-frequency vibration sensor data.

InfluxDB Read Performance

InfluxDB's read performance degrades as the frequency increases. This is because the higher the frequency, the larger the dataset that InfluxDB must query. As more data points accumulate in the database, querying each point becomes more time-consuming, resulting in slower read times. This can be a bottleneck for applications that require frequent and fast access to large amounts of historical vibration data, especially in time-sensitive environments such as real-time monitoring systems or predictive maintenance applications.

ReductStore Read Performance

In contrast, ReductStore maintains consistent read performance across all frequencies, demonstrating its ability to scale without compromising retrieval speed. Whether handling 1,000 Hz or 30,000 Hz vibration data, ReductStore's read times remain relatively stable due to its optimized storage format and efficient indexing. This ensures that even with high frequency vibration or acoustic data, ReductStore queries and retrievals remain fast and reliable, making it ideal for applications where fast access to high frequency sensor data is critical.

Key Factors Affecting Performance

The performance differences between ReductStore and InfluxDB can be attributed to a variety of factors, each related to how these databases handle data storage in this benchmark scenario. Below are some key factors that influence the performance of each database when managing high-frequency sensor data.

Database Architecture

InfluxDB was designed as a general-purpose time-series database for a variety of time-series applications, including logging, monitoring, and tracking changes over time. However, when tasked with storing and querying high-frequency vibration sensor readings, it struggles to maintain performance. InfluxDB's architecture is not inherently optimized for the massive amounts of data generated in high-frequency environments, resulting in slower write and read times.

ReductStore is specifically designed to handle unstructured time series data. This includes high-frequency sensor data, such as vibration measurements, which must be stored in binary chunks for efficient processing. Its lightweight architecture is designed to efficiently process and store unstructured time series data without suffering from the same scalability issues as InfluxDB. This specialization allows ReductStore to consistently deliver low-latency performance, even with high-frequency sensor data.

Data Storage Mechanism

InfluxDB uses a traditional time-series format for storing data, which works well in many cases, but becomes inefficient with high-frequency sensor data. The more data points it stores, the harder it is for the system to manage, resulting in slower read and write performance as seen in the benchmark results.

In contrast, ReductStore's storage approach allows it to handle high-frequency data with greater efficiency. By chunking data and storing it in binary format, ReductStore minimizes storage overhead and ensures fast access to data when needed. This approach optimizes both write and read operations, reducing the impact of data volume on performance.

Practical Implications for Real-World Applications

The performance results from this benchmark offer valuable insight into how ReductStore and InfluxDB perform in real-world scenarios. Each database has its strengths, depending on the nature of the data and the application requirements.

InfluxDB for Traditional Time-Series Applications

For applications dealing with low to medium frequency time series data, InfluxDB remains a strong choice. It excels at managing structured time-series data for a wide range of use cases, including

• System monitoring: InfluxDB is widely used to store and analyze IT infrastructure metrics, enabling effective tracking of server performance, network health, and more.
• Logging and event tracking: With its efficient handling of time-stamped logs and event data, InfluxDB is ideal for tracking changes over time in applications such as web services, databases, and user activity.
• IoT data: In environments where data is generated less frequently, such as temperature or environmental monitoring, InfluxDB provides excellent query and visualization capabilities, making it a popular choice for many IoT systems.

ReductStore for Time Series Unstructured Data

When it comes to handling time-series unstructured data, ReductStore is the obvious choice. Its consistent read and write performance, even at high frequencies, makes it ideal for use cases that involve massive data loads and require real-time processing. Examples of applications where ReductStore excels include

• High frequency sensor data: For industries that rely on real-time monitoring, such as Predictive Maintenance, Machine Diagnostics, and Industrial IoT, ReductStore's ability to efficiently store and retrieve data from high-frequency sensors makes it the ideal solution.

• Unstructured logs and events: In scenarios where data is generated at high frequency and lacks a predefined structure, such as audio recordings, geospatial data, or log files, ReductStore's optimized storage mechanism ensures fast access to binary data of any size, making it an ideal choice for applications that require real-time analysis of complex time-series data.

• Computer vision: ReductStore's design excels at managing time-series unstructured data, such as video frames in computer vision applications. Whether it's managing streams from surveillance cameras or processing video frames for object detection and classification tasks, ReductStore can efficiently store large amounts of time-series image data without the performance bottlenecks typically found in general-purpose databases.

Best Practices

To optimize the performance of your IoT applications using ReductStore, here are some best practices to consider:

• Set quotas to prevent storage overflows: Create a ReducStore bucket with a FIFO quota (First In, First Out) on edge devices to prevent space from running out. This ensures that older, less important data is automatically deleted as new data arrives, making disk space management more efficient.

• Enable token authentication for data security: Protect your data with token-based authentication. You can generate access tokens using the ReductStore Web Console or CLI Client. This feature ensures secure access to your stored sensor data and prevents unauthorized users from accessing sensitive information.

• Use metadata labels to filter data: Store labels with each record to provide additional context for your time series data. This allows you to filter data based on key metrics (such as high RMS or peak-to-peak values) during queries or replication processes for more targeted data management.

• Use Reducer CLI for replication and backup: Use the CLI Client to automate data replication across multiple ReductStore instances or to backup critical sensor data. This ensures that high-priority data is securely duplicated or stored for future analysis, reducing the risk of data loss.

Conclusion

The benchmark results show a clear difference between ReductStore and InfluxDB when handling vibration sensor data, especially at high frequencies.

ReductStore consistently outperforms InfluxDB in both read and write performance, maintaining stable and efficient data management across all frequencies tested. Its ability to handle unstructured time series data without significant performance degradation makes it an ideal choice for applications that require real-time data processing of vibration data, such as predictive maintenance and industrial IoT systems.

On the other hand, while InfluxDB remains a solid choice for many time-series use cases, its performance suffers as data frequency increases. For applications involving high-frequency sensor data, InfluxDB can introduce bottlenecks that can limit its effectiveness in such demanding environments.

Frequently Asked Questions (FAQs)

Can ReductStore handle other types of sensor data besides vibration data?

Yes, ReductStore is designed to efficiently manage any unstructured time series data, making it suitable for a wide range of applications. Whether you're storing audio samples, video frames, geospatial data, or log files in a time-series format, ReductStore's optimized storage mechanism ensures fast access to data without compromising performance.

Can InfluxDB be tuned for better performance on high-frequency data?

While InfluxDB can be optimized through configuration changes and additional hardware resources, its performance will still lag behind ReductStore when handling extremely high frequency data such as vibration measurements. This is due to the way data is stored and queried point-by-point in InfluxDB versus ReductStore's binary time-series storage, where it is more efficient to pack and retrieve data in binary chunks.

How does ReductStore handle data replication?

ReductStore provides built-in replication capabilities that allow you to replicate data across multiple instances or set up automated backups. You can configure replication tasks using the ReductStore CLI or Web Console, ensuring that critical data is securely duplicated.

What are the system requirements for running ReductStore?

ReductStore is designed to be lightweight and efficient, making it suitable for a wide range of hardware configurations. It can run on low-power edge devices as well as high-performance servers, depending on your application requirements. For optimal performance, we recommend running ReductStore on a system with an SSD drive or NVMe storage to ensure fast read and write speeds.

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I hope this article has been helpful. If you have any questions or feedback, don't hesitate to use the ReductStore Community Forum.