Background

Apache IoTDB (Internet of Things Database) is a high-performance, open-source time-series database optimized for data management and analysis in IoT scenarios. Trino (formerly PrestoSQL) is a fast distributed SQL query engine designed for running interactive analytic queries against data sources of all sizes.
Currently, while IoTDB provides strong capabilities for writing and querying time-series data, integrating it with the broader big data ecosystem for complex OLAP (Online Analytical Processing) remains a demand. A dedicated Trino connector for IoTDB will allow users to query IoTDB data using standard SQL via Trino and perform federated queries with other data sources (like Hive, MySQL, or Iceberg).

Goal

The goal of this project is to implement a trino-iotdb connector plugin based on the Trino SPI (Service Provider Interface). This connector will enable Trino to read data directly from IoTDB, supporting schema mapping, data projection, and predicate pushdown or maybe aggregate pushdown.
 

Core Tasks（Mandatory）

1. Project Scaffolding: Set up the Maven project structure for the trino-iotdb plugin and integrate the IoTDB JDBC API.

2. Metadata Implementation: Implement ConnectorMetadata to map IoTDB’s Table Mode (relational view) to Trino’s relational metadata model:

   1. 
      Map IoTDB databases to Trino Schemas.
   2. 
      Map IoTDB Tables to Trino Tables.
   3. 
      Map IoTDB Data Type to Trino Data Type.

3. Column Pruning (Projection Pushdown): Ensure the connector strictly fetches only the requested columns (measurements) from IoTDB, avoiding SELECT * overhead.

4. Predicate Pushdown: Implement optimization rules to push down SQL filters (especially time range filters and value filters) to the IoTDB engine to minimize data transfer.

5. Limit & Offset Pushdown: Map Trino’s LIMIT and OFFSET clauses to IoTDB’s native query pagination to prevent fetching excessive data during preview or pagination queries.

6. Integration Testing: Provide Docker-based integration tests to verify correctness using Trino's testing framework.

Advanced Tasks (Optional)

• Aggregation Pushdown: Implement the applyAggregation method in the connector SPI.

  • 
    Goal: Map Trino’s aggregate functions (e.g., COUNT, AVG, SUM, MIN, MAX) directly to IoTDB’s native aggregation queries.
  • 
    Benefit: Instead of fetching raw data to Trino for calculation, the connector leverages IoTDB's pre-calculated statistics or downsampling capabilities, significantly reducing network overhead and latency.

Deliverables

• 
  A fully functional trino-iotdb connector source code.(a pull request to Trino Repo)
• 
  Comprehensive integration tests covering data types and query patterns.
• 
  User documentation explaining how to configure and use the connector.

Recommended Skills

• Java: Proficiency in Java programming (Trino and IoTDB are both Java-based).

• Database Internals: Basic understanding of SQL execution, schema design, and database connectors.

• Maven: Experience with Java build systems.

• Nice to have: Familiarity with Trino SPI or IoTDB Session API.

Learning Material

• 
  Apache IoTDB: https://iotdb.apache.org/
• 
  Trino Connector Developer Guide: https://trino.io/docs/current/develop/connectors.html
• 
  Trino PG Plugin: https://github.com/trinodb/trino/tree/master/plugin/trino-postgresql/src/main/java/io/trino/plugin/postgresql
• 
  IoTDB Java JDBC API: https://iotdb.apache.org/UserGuide/latest/API/Programming-JDBC_apache.html
• 
  IoTDB Table Model Concepts: https://iotdb.apache.org/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.html
• 
  IoTDB Table Model Query Syntax: https://iotdb.apache.org/UserGuide/latest-Table/SQL-Manual/overview_apache.html

 
Difficulty: medium
Mentor: Yuan Tian (Apache IoTDB PMC Member) (jackietien@apache.org)