We are thrilled to announce the release of TimechoDB v1.3.4, our enterprise-grade time series database based on Apache IoTDB， designed to meet the rigorous demands of modern data management.

This release introduces new features, optimizations, and enhanced stability, further solidifying our position as a leading choice for time series database solutions.

Version 1.3.2 introduces a host of new features, including an "explain analyze" statement for detailed SQL query analysis, a user-defined aggregate function (UDAF) framework, automatic data deletion upon reaching disk space thresholds, metadata synchronization, data point statistics for specified paths, and import/export SQL scripts. Additionally, our cluster management tool now supports rolling upgrades and plugin uploads across the entire cluster. This release also brings comprehensive enhancements to database monitoring, performance, and stability.

Key Features

Query Module: Users can now control the loading of JAR files for UDFs, PipePlugin, Trigger, and AINode through URI configuration.

System Module: Expanded UDF functionality with the addition of the pattern_match function for pattern-based time series matching.

Data Synchronization: Enhanced security with support for specifying authentication information for the receiving end at the sender side.

Ecosystem Integration: Added support for Kubernetes Operator to streamline deployment and management in Kubernetes environments.

Scripts and Tools:

• Extended the import-data/export-data scripts to support new data types, including strings, large binary objects, dates, and timestamps.

• Unified import-data/export-data scripts to support seamless data import/export for TsFile, CSV, and SQL formats.

Feature Spotlight: Pattern Matching

The newly introduced pattern_match function allows users to identify segments of a time series that resemble a given sequence, ordered by their distance values. This feature is especially useful for identifying patterns or anomalies in time series data.

Example Use Case: Linear Pattern Matching

Sample Data:

IoTDB> select s0,s1 from root.**; 
+-----------------------------+---------------+--------------+ 
|                         time|. root.db.d0.s0|. root.db.d0.s1| 
+-----------------------------+---------------+--------------+ 
|1970-01-01T08:00:00.001+08:00|           0.0|           true| 
|1970-01-01T08:00:00.002+08:00|           1.1|           true| 
|1970-01-01T08:00:00.003+08:00|           1.2|           true| 
|1970-01-01T08:00:00.004+08:00|           1.3|          false| 
Total line number = 4 

Query:

select 
     pattern_match(s0, "timePattern"="1,2,3", "valuePattern"="1.1,1.2,1.3", 
"threshold"="0.5") as match_result 
from root.**; 

Results:

+------------------------------------------------+ 
|                                    match_result| 
+------------------------------------------------+ 
|[{"distance":0.200000,"startTime":1,"endTime":3}, {"distance":0.000000,"startTime":2,"endTime":4}]| 
+------------------------------------------------+ 

This new feature empowers users to analyze and identify patterns efficiently, supporting a wide range of applications in IoT and beyond.

More Details

For more information about the V1.3.4 release, feel free to reach out to the Timecho team. We’re here to help you get the most out of TimechoDB!