Hi all,
This is the draft of the IoTDB quarter report, let me know if I missed
anything.
## Description:
The mission of Apache IoTDB is the creation and maintenance of software
related to an IoT native database with high performance for data management
and analysis.
## Project Status:
Current project status: Ongoing with high activity.
Issues for the board: no.
## Membership Data:
Apache IoTDB was founded 2020-09-16 (4 years ago)
There are currently 79 committers and 32 PMC members in this project.
The Committer-to-PMC ratio is roughly 5:2.
Community changes, past quarter:
- Hongyin Zhang was added to the PMC on 2025-11-07
- Xintao Lin was added as committer on 2025-09-09
## Project Activity:
- We released version 1.3.5 in past quarter, which fixed some bugs in 1.3.X
series.
- For 2.X series, the compatibility of the table model with standard SQL has
been further enhanced, achieving full support for all set operations,
optimizing the data query performance in memtables, and enabling the
modification of encoding and compression types for sequences in the tree
model.
- The community has three key focus areas for its future development:
1. Attempt to extend a new data type (OBJECT) designed for storing large
binary objects, enabling IoTDB to be applied to data storage scenarios
in
meteorological, audio and video fields.
2. Integrate Python-based User-Defined Functions (UDFs) to further
enhance
IoTDB’s data analytics capabilities.
3. Online Schema Change: support online modification of table names and
column names. This is a highly challenging task. To maintain the
self-parsability of TsFile, we redundantly store certain metadata in
each
data file, which means conventional methods would require rewriting
nearly
all TsFiles.
## Community Health:
Overall community health is good.
- [email protected] had a 23% decrease in traffic in the past quarter (74
emails compared to 96)
- A PhD candidate from Renmin University of China developed a project named
CompressIoTDB[1] based on IoTDB, pioneering the introduction of
Homomorphic
Compression (HC) theory into the time series domain. This work is designed
to address the core trade-off between compression and query performance in
traditional time series databases. By executing queries directly on
compressed data, it significantly improves query throughput and reduces
resource consumption, and has been published in PVLDB 2025.[2]
- Given that IoTDB’s release cycle may be relatively long, Colin Shi, a
contributor to the IoTDB community, developed a tool. Powered by AI, this
tool can search for commits related to error-causing issues, enabling the
copying of such commits to the branch of the previous official release
(ahead of IoTDB’s next official release) to generate a functional
package.[3]
[1] https://github.com/yuxin370/CompressIoTDB
[2] Tang, Yuxin, et al. "Improving Time Series Data Compression in Apache
IoTDB." Proceedings of the VLDB Endowment 18.10 (2025): 3406-3420.
[3] https://lists.apache.org/thread/q1mzqwwb2ohjyodf6h3cq5m9f3qpcsw7
Best regards,
-----------------------
Yuan Tian
