TDengine Database now supports macOS

Question

The TDengine Server and Client can now be run on x64 systems that are running macOS, Windows 10 and 11, Windows Server 2016 and 2019, CentOS 7.9 and 8, or Ubuntu 18 and 20, as well as ARM64 systems running macOS or CentOS. For the latest information on operating system support, see the documentation.

Losslessly compressed data can be completely reconstructed after decompression into the original raw data. This compression method is used in scenarios where data accuracy is important, such as compressing entire hard drives or compressing executable files. It may also be used for multimedia compression. However, lossless methods have relatively low compression ratios. Common lossless compression methods include differential coding, run-length encoding (RLE), Huffman coding, LZW compression, and arithmetic coding.
On the other hand, with lossy compression, the compressed data cannot be reconstructed into the original data – only into an approximation of that data. This compression method is used in scenarios where data accuracy is not important, such as compressing multimedia files. However, lossy compression enables high compression ratios. Common lossy compression methods include predictive codecs, fractal compression, wavelet compression, JPEG, and MPEG.
Compression methods
Commonly used compression methods for time series data are described as follows:

Metadata Storage Engine
In the metadata storage area, the previous 1.0 and 2.0 adopted a relatively simple storage mechanism, i.e. full memory storage, where data is stored in memory as hash tables, supplemented by jump table indexes, and this hash table has a Backup Storage Engine, which ensures the persistence of data. The advantage of this method is full memory and high efficiency, but the disadvantage is also obvious, when the startup, this part of the data will be loaded all into the memory, not only the memory occupation can not be precisely controlled, but also lead to a long boot time.

The one-stage compression is correspondingly compressed according to the type of data. The compression algorithms include delta-delta encoding, simple 8B method, zig-zag encoding, LZ4 and other algorithms, which we have briefly introduced above. Two-stage compression is based on one-stage compression, and then compresses with a general compression algorithm to ensure a higher compression rate.

Table of Contents
Introduction
Compression methods
TDengine implementation
Introduction
If the original data and decompressed data are exactly the same, the compression method can be considered lossless. A compression method that alters data is considered lossy.

With version 3.0.1.5, TDengine Database offers support for macOS in addition to Linux and Windows. You can now test your applications more efficiently and connect to a TDengine Server running on any operating system from your own MacBook.

KEEP 365 indicates that data is retained for 365 days, after which it is automatically deleted.
DAYS 10 indicates that each data file stores 10 days of data.
BLOCKS 4 indicates that the database has four memory blocks for data updating.
With these extensions, you can create databases that align with the storage strategy for your data. For a complete overview of database parameters, see Database.

Table of Contents
Top Data Visualization Tools
Grafana
Google Data Studio
Microsoft Power BI
Tableau
Top Data Visualization Tools
Grafana
Grafana is a popular open-source tool for data visualization. It offers customizable dashboards that you can use to monitor your data in real time. You can download premade dashboards or design your own from scratch to achieve a visual display of your key metrics. The main benefits of Grafana are as follows:

Any table or supertable belongs to a database. Before creating a table, a database must be created first.
Tables in two different databases cannot be JOIN.
Create a Supertable
An IoT system often has many types of devices, such as smart meters, transformers, TDengine Database buses, switches, etc. for power grids. In order to facilitate aggregation among multiple tables, using TDengine, it is necessary to create a STable for each type of data collection point. Taking the smart meter in Table 1 as an example, you can use the following SQL command to create a STable:

Enterprise-ready cloud solution, providing robust backup, multi-cloud replication, user privilege controls and behavior auditing, VPC peering, and IP whitelisting features. TDengine Cloud delivers the carrier-grade performance and stability that you need to support your business.
Simplified setup and management, dramatically reducing the tools needed to start, operate, and manage your time-series database at scale. As a managed service, TDengine Cloud saves you time by taking care of clustering, backup, and data retention on its own.
Easier data analytics and sharing, enabling you to gain insight from your data more conveniently than ever. You can quickly access data in TDengine Cloud with Python, Java, Go, Rust, and Node.js connectors; create dashboards and tdengine Time Series Database applications that subscribe to your topics and streams; and replicate data across your enterprise with edge-to-cloud and cloud-to-cloud synchronization.
Fast and easy data ingestion, supporting standard SQL with connectors for popular programming languages as well as an MQTT broker with which you can send data to TDengine without writing any custom code, in addition to schemaless insert protocols. With TDengine Cloud, you can choose the method for writing data into your time-series database that is most convenient for you and your business scenario.
Join the community
Register at cloud.tdengine.com today for a free account and walk through a short tutorial to quickly understand the capabilities and advantages of using TDengine to unlock the power of your time-series data.