- What is Apache InLong?
- Features
- When should I use InLong?
- Build InLong
- Deploy InLong
- Contribute to InLong
- Contact Us
- Documentation
- License
Apache InLong(incubating) is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data.
InLong (应龙) is a divine beast in Chinese mythology who guides river into the sea, it is regarded as a metaphor of the InLong system for reporting streams of data.
InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 40 trillion pieces of data per day) reporting services in big data scenarios. The entire platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting, and Management, so that the business only needs to provide data sources, data service quality, data landing clusters and data landing formats, that is, the data can be continuously pushed from the source to the target cluster, which greatly meets the data reporting service requirements in the business big data scenario.
For getting more information, please visit our project documentation at https://inlong.apache.org/
Apache InLong offers a variety of features:
- Ease of Use: a SaaS-based service platform, you can easily and quickly report, transfer, and distribute data by publishing and subscribing to data based on topics.
- Stability & Reliability: derived from the actual online production environment, it delivers high-performance processing capabilities for 10 trillion-level data streams and highly reliable services for 100 billion-level data streams.
- Comprehensive Features: supports various types of data access methods and can be integrated with different types of Message Queue (MQ) services, it also provides real-time data extract, transform, and load (ETL) and sorting capabilities based on rules, allows you to plug features to extend system capabilities.
- Service Integration: provides unified system monitoring and alert services, it provides fine-grained metrics to facilitate data visualization, you can view the running status of queues and topic-based data statistics in a unified data metric platform, configure the alert service based on your business requirements so that users can be alerted when errors occur.
- Scalability: adopts a pluggable architecture that allows you to plug modules into the system based on specific protocols, so you can replace components and add features based on your business requirements
InLong is based on MQ and aims to provide a one-stop, practice-tested module pluggable integration framework for massive data, based on this system, users can easily build stream-based data applications. It is suitable for environments that need to quickly build a data reporting platform, as well as an ultra-large-scale data reporting environment that InLong is very suitable for, and an environment that needs to automatically sort and land the reported data.
You can use InLong in the following ways：
- Integrate InLong, manage data streams through SDK.
- Use the InLong command-line tool to view and create data streams.
- Visualize your operations on InLong dashboard.
| Type | Name | Version | Other |
|---|---|---|---|
| Extract Node | Auto Push | None | Using SDK to send |
| File | None | CSV, Key-Value, JSON, Avro | |
| Kafka | 2.x | Canal JSON | |
| MySQL | 5.x, 8.x | Debezium JSON | |
| Load Node | Auto Consumption | None | Using MQ SDK consume messages and Parse InLongMsg |
| Hive | 2.x | TextFile, SequenceFile,OrcFile, Parquet, Avro | |
| Iceberg | 0.12.x | Parquet, Orc, Avro | |
| ClickHouse | v20+ | Canal JSON | |
| Kafka | 2.x | JSON, Canal, Avro |
More detailed instructions can be found at Quick Start section in the documentation.
Requirements:
Compile and install:
$ mvn clean install -DskipTests
(Optional) Compile using docker image:
$ docker pull maven:3.6-openjdk-8
$ docker run -v `pwd`:/inlong -w /inlong maven:3.6-openjdk-8 mvn clean install -DskipTests
after compile successfully, you could find distribution file at inlong-distribution/target.
- Agent Plugin extends a new Extract Data Node
- Sort Plugin extends a new Load Data Node
- Manger Plugin extends an Operator
- Dashboard Plugin extends new data stream page
- Report any issue on GitHub Issue
- Code pull request according to How to contribute.
- Join Apache InLong mailing lists:
Name Scope [email protected] Development-related discussions Subscribe Unsubscribe Archives - Ask questions on Apache InLong Slack
- Home page: https://inlong.apache.org/
- Issues: https://github.com/apache/incubator-inlong/issues
© Contributors Licensed under an Apache-2.0 license.