Introduction

The highly performant and modular runtime for statically typed, yet dynamically loaded connectors. Ingest the data from the external sources and push it further to the Iggy streams, or fetch the data from the Iggy streams and push it further to the external sources. Create your own Rust plugins by simply implementing either the Source or Sink trait and build custom pipelines for the data processing.

The docker image is available, and can be fetched via docker pull apache/iggy-connect.

Architecture

Connectors are dynamically loaded shared libraries (.so on Linux, .dylib on macOS, .dll on Windows) loaded at runtime via dlopen2. Data crossing the FFI boundary between the runtime and plugins is serialized using postcard, a compact binary format. Each plugin receives its own Tokio runtime inside the SDK, ensuring isolation.

Data Flow

The connector runtime operates in two directions - source (ingest) and sink (egress):

Key details:

• Transforms run inside the runtime process, not inside the plugins. They are applied after decoding for sources (before sending to Iggy) and after consuming from Iggy for sinks (before forwarding to the external system).
• State persistence is file-based (using MessagePack serialization) and applies to source connectors only, tracking the last polled position.
• Consumer groups handle offset tracking for sink connectors automatically, so sinks do not need to manage their own offsets.

Available Connectors

Transforms

The following built-in transforms are available:

• add_fields - add new fields to the message payload.
• delete_fields - remove existing fields from the message payload.
• filter_fields - keep or drop fields based on key and value patterns.
• update_fields - update existing fields with static or computed values such as timestamp_millis or uuid_v7.
• proto_convert - convert messages between Protocol Buffers and other formats.
• flat_buffer_convert - convert messages between FlatBuffers and other formats.
• avro_convert - convert messages between Avro and other formats.
• unwrap_envelope - promote a nested field of a JSON envelope to the top-level payload.

See the Transforms documentation for the configuration of each transform.

Supported Schemas

Messages can be decoded and encoded using the following formats: JSON, Raw bytes, Text, Protocol Buffers, FlatBuffers, and Avro. For Avro, the schema can be provided per stream via the avro_schema_json or avro_schema_path keys in the [[streams]] section.

Features

• High Performance: Utilizes Rust's performance characteristics to ensure fast data ingestion and egress.
• Low memory footprint: Designed with memory efficiency in mind, minimizing the memory footprint of the connectors.
• Modular Design: Designed with modularity in mind, allowing for easy extension and customization.
• Dynamic Loading: Supports dynamic loading of plugins, allowing integration with various data sources and sinks at runtime.
• Statically Typed: Ensures type safety and compile-time checks, reducing runtime errors.
• Easy Customization: Provides a simple interface for implementing custom connectors, making it easy to create new plugins.
• Data transformation: Supports data transformation with the help of existing functions.
• Powerful configuration: Define your sinks, sources, and transformations in the configuration file or fetch them from a remote HTTP API.
• Flexible configuration providers: Support for local file-based and HTTP-based configuration providers for centralized configuration management.

Quick Start

1. Build the project in release mode (or debug, and update the connectors paths in the config accordingly), and make sure that the plugins specified in core/connectors/runtime/example_config/connectors/ directory under path are available. The configuration must be provided in toml format. The example directory also enables connectors for ClickHouse, Delta Lake, Apache Doris, Apache Iceberg, and InfluxDB. Without their backing services (or their compiled plugins) these are reported with the Error status, but they don't block the remaining connectors. Set enabled = false in their files to skip them entirely.

2. Run docker compose up -d from /examples/rust/src/sink-data-producer which will start the Quickwit server to be used by an example sink connector. At this point, you can access the Quickwit UI at http://localhost:7280 - check this dashboard again later on, after the events index will be created.

3. Set environment variable IGGY_CONNECTORS_CONFIG_PATH=core/connectors/runtime/example_config/config.toml (adjust the path as needed) pointing to the runtime configuration file.

4. Start the Iggy server and invoke the following commands via Iggy CLI to create the example streams and topics used by the sample connectors.

   iggy --username iggy --password iggy stream create example_stream
   iggy --username iggy --password iggy topic create example_stream example_topic 1 none 1d
   iggy --username iggy --password iggy stream create qw
   iggy --username iggy --password iggy topic create qw records 1 none 1d

5. Execute cargo run --example sink-data-producer -r which will start the example data producer application, sending the messages to previously created qw stream and records topic (this will be used by the Quickwit sink connector).

6. Start the connector runtime cargo run --bin iggy-connectors -r - you should be able to browse Quickwit UI with records being constantly added to the events index. At the same time, you should see the new messages being added to the example_stream stream and example_topic topic by the Random source connector - you can use Iggy Web UI to browse the data. The messages will have applied the basic fields transformations.

Configuration

Overriding the configuration via environment variables follows the same rules as the main Iggy server.

You can provide the following envs during the runtime startup:

IGGY_CONNECTORS_ENV_PATH - path to the .env file for custom environment variables

IGGY_CONNECTORS_CONFIG_PATH - path to the connectors runtime configuration file

Any configuration section can be overridden with IGGY_CONNECTORS_ prefix, followed by the section name and the key name, e.g. IGGY_CONNECTORS_IGGY_USERNAME.

Runtime

All the connectors are implemented as Rust libraries and can be used as a part of the connector runtime. The runtime is responsible for managing the lifecycle of the connectors and providing the necessary infrastructure for the connectors to run. For more information, please refer to the runtime documentation.

Sink

Sinks are responsible for consuming the messages from the configured stream(s) and topic(s) and sending them further to the specified destination. For example, the Quickwit sink connector is responsible for sending the messages to the Quickwit indexer.

Please refer to the Sink documentation for the details about the configuration and the sample implementation.

When implementing Sink, make sure to use the sink_connector! macro to expose the FFI interface and allow the connector runtime to register the sink with the runtime. Each sink should have its own, custom configuration, which is passed along with the unique plugin ID via expected new() method.

Source

Sources are responsible for producing the messages to the configured stream(s) and topic(s). For example, the Random source connector generates random messages that are then sent to the configured stream and topic.

Please refer to the Source documentation for the details about the configuration and the sample implementation.

Building the connectors

New connector can be built simply by implementing either Sink or Source trait. Please check the sink or source documentation, as well as the existing examples under /sinks and /sources directories.

Transformations

Field transformations (depending on the supported payload formats) can be applied to the messages either before they are sent to the specified topic (e.g. when produced by the source connectors), or before consumed by the sink connectors. To add a new transformation, implement the Transform trait in a new module under the SDK's transforms directory, add a variant to the TransformType enum, and extend its from_config function - the runtime picks it up without any further changes. Each transform may have its own, custom configuration.

To find out more about the transforms, please refer to the Transforms documentation. For the stream decoders and encoders, see the SDK documentation.