apache/iotdb

Apache IoTDB

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Summary Information

Updated 2 hours ago
Type:Server / PlatformCategory(s):Time-Series DatabasesDatabases & Storage
Added to GitGenius on June 12th, 2023
Created on November 24th, 2018
Open Issues & Pull Requests: 755 (-4)
GitHub issues: Enabled
Number of forks: 1,163
Total Stargazers: 6,405 (+0)
Total Subscribers: 110 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 26.3 hours
Mean response time: 59.5 days
90th percentile: 96.0 days
Tracked items: 274

How this project is maintained

Roughly one issue in five opened in the past year never receives a reply. 97% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 67% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 37% of issues opened in the past year have been closed. Three people close 50% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 284
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 655 days
Stale 30+ days: 275
Stale 90+ days: 262

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 2
Events in 7 days: 6

Top labels

  • Easy-Fixed (11)
  • good-first-issue (9)
  • bug (7)
  • New Feature (6)
  • Solution (4)
  • Module - Python Client (2)
  • Module - QueryProcessing (2)
  • Module - Schema (2)

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Detailed Description

Apache IoTDB is a time series database management system designed specifically for Internet of Things applications, providing data collection, storage, and analysis capabilities. Written in Java and maintained as an Apache Software Foundation project, IoTDB targets the industrial IoT field where massive datasets, high-throughput data ingestion, and complex analytical queries are common requirements. The system depends on TsFile, a columnar storage file format optimized for time series data, with the IoTDB branch of the TsFile project used for deploying snapshot versions.

The core functionality of IoTDB centers on efficient time series data management with several distinctive capabilities. The system supports flexible deployment strategies including one-click installation on cloud platforms and terminal devices, along with data synchronization tools that bridge cloud and edge environments. IoTDB achieves high compression ratios for disk storage, reducing hardware costs while maintaining performance. The database implements an efficient directory structure capable of organizing complex time series data from intelligent networked devices, supporting both device-type organization and fuzzy searching across massive and intricate data hierarchies. High-throughput read and write operations enable the system to handle connections from millions of low-power devices simultaneously, supporting both strong connection data access and high-speed operations for intelligent networking devices.

Query capabilities in IoTDB include time alignment across devices and measurements, frequency domain transformations for time series computation, and rich aggregation functions operating in the time dimension. The system provides SQL-like query language, JDBC standard API support, and import/export tools designed for ease of use. Integration with major open source ecosystems including Hadoop and Spark, as well as visualization tools like Grafana, enables seamless adoption into existing data infrastructure.

The system requires Java 17 or higher for execution, with versions 17 through 25 verified as compatible. Maven 3.6 or later is needed for compilation from source code. System resource configuration is critical, with recommendations to set maximum open files to 65535 and somaxconn to 65535 to prevent runtime errors under high load conditions. IoTDB supports deployment across Windows, macOS, and Linux platforms, with Docker containerization available through provided Dockerfiles. The project emphasizes low operational overhead while delivering the performance characteristics required for industrial IoT deployments involving high-dimensional sensor data and real-time analytics.