timeplus-io/proton

Fastest SQL pipeline engine for stream processing, analytics, observability and AI

What's Timeplus Proton

🚀 The fastest SQL pipeline engine in a single C++ binary, for stream processing, analytics, observability and AI. A simple, fast and efficient alternative to ksqlDB and Apache Flink, powered by ClickHouse engine.

🔥 SQL for everything : Native source/sink (Kafka, ClickHouse, MySQL, Postgres, MongoDB, S3/Iceberg, OpenSearch etc.), Streaming ingestion, Multi-stream JOINs, Incremental Materialized Views, Alerting, Tasks, UDF in Python/JS etc.

⚡ No JVM. No ZooKeeper. Zero dependencies. Just speed, control and scale.

Get started in seconds

curl https://install.timeplus.com/oss | sh

Why Timeplus Proton

• Apache Flink or ksqlDB alternative. Timeplus Proton provides powerful stream processing functionalities, such as streaming ETL, tumble/hop/session windows, watermarks, incremental materialized views maintenance, CDC and data revision processing. In contrast to pure stream processors, it also stores queryable analytical/row based materialized views within Proton itself for use in analytics dashboards and applications.

• Fast. Timeplus Proton is written in C++, with optimized performance through SIMD. For example, on an Apple MacBookPro with M2 Max, Timeplus Proton can deliver 90 million EPS, 4 millisecond end-to-end latency, and high cardinality aggregation with 1 million unique keys.

• Lightweight. Timeplus Proton is a single binary (<500MB). No JVM or any other dependencies. You can also run it with Docker, or on an AWS t2.nano instance (1 vCPU and 0.5 GiB memory).

• Powered by the fast, resource efficient ClickHouse. Timeplus Proton extends the historical data, storage, and computing functionality of ClickHouse with stream processing. Thousands of SQL functions are available in Timeplus Proton. Billions of rows are queried in milliseconds.

• Best streaming SQL engine for Kafka or Redpanda. Query the live data in Kafka or other compatible streaming data platforms, with external streams.

See our architecture doc for technical details and our FAQ for more information.

Use Cases

Timeplus Proton empowers you to build a wide range of real-time applications and data pipelines. Common use cases include:

• Real-time Analytics ETL/Pipeline: Efficiently ingest live data from sources like Kafka, perform in-pipeline transformations (filtering, enrichment, masking), and route it to downstream systems, including data warehouses like ClickHouse, other Kafka topics, or analytical stores.

• Real-time Telemetry Pipeline and Alerting: Process and route logs, metrics, and traces with in-pipeline noise reduction, real-time alerts before forwarding to Splunk, Elastic, or S3.

• Real-time Feature Pipeline for AI/ML: Compute real-time features using low-latency, high-throughput streaming SQL and materialized views with support for backfill and advanced windowing over live data.

Demo

2-minute short video👇. Check out the full video at YouTube.

https://github.com/timeplus-io/proton/assets/5076438/8ceca355-d992-4798-b861-1e0334fc4438

Deployment

A single binary:

curl https://install.timeplus.com/oss | sh

Once the proton binary is available, you can run proton server to start the server and put the config/logs/data in the current folder proton-data. Then use proton client in the other terminal to start the SQL client.

For Mac users, you can also use Homebrew to manage the install/upgrade/uninstall:

brew install timeplus-io/timeplus/proton

Docker:

docker run -d --pull always -p 8123:8123 -p 8463:8463 --name proton d.timeplus.com/timeplus-io/proton:latest

Please check Server Ports to determine which ports to expose, so that other tools can connect to Timeplus, such as DBeaver.

Docker Compose:

The Docker Compose stack demonstrates how to read/write data in Kafka/Redpanda with external streams.

Demo:

Don't want to setup by yourself? Try Timeplus Demo (https://demos.timeplus.com/)

Usage

SQL is the main interface. You can start a new terminal window with proton client to start the SQL shell.

[!NOTE] You can also integrate Timeplus Proton with Python/Java/Go SDK, REST API, or BI plugins. Please check Integrations

In the proton client, you can write SQL to create External Stream for Kafka or External Table for ClickHouse.

For example, you can read from AWS MSK and write the data to ClickHouse for the following SQL:

-- Read from AWS MSK using IAM Role
CREATE EXTERNAL STREAM aws_msk_stream (
  device string,
  temperature float
)
SETTINGS
    type='kafka',
    brokers='prefix.kafka.us-west-2.amazonaws.com:9098',
    topic='topic',
    security_protocol='SASL_SSL',
    sasl_mechanism='AWS_MSK_IAM';

-- Write to ClickHouse
CREATE EXTERNAL TABLE ch_aiven
SETTINGS type='clickhouse',
            address='abc.aivencloud.com:28851',
            user='avnadmin',
            password='..',
            secure=true,
            table='events';

-- Setup a long-running materialized view to write aggregated data to ClickHouse
CREATE MATERIALIZED VIEW mv_msk2ch INTO ch_aiven AS
SELECT window_start as timestamp, device, avg(temperature) as avg_temperature
FROM tumble(aws_msk_stream, 10s) GROUP BY window_start, device;

If you don't have immediate access to Kafka or ClickHouse, you can also run the following SQL to generate random data:

-- Create a stream with random data
CREATE RANDOM STREAM devices(
  device string default 'device'||to_string(rand()%4),
  temperature float default rand()%1000/10);

-- Run the streaming SQL
SELECT device, count(*), min(temperature), max(temperature)
FROM devices GROUP BY device;

You should see data like the following:

┌─device──┬─count()─┬─min(temperature)─┬─max(temperature)─┐
│ device0 │    2256 │                0 │             99.6 │
│ device1 │    2260 │              0.1 │             99.7 │
│ device3 │    2259 │              0.3 │             99.9 │
│ device2 │    2225 │              0.2 │             99.8 │
└─────────┴─────────┴──────────────────┴──────────────────┘

What's next

To see more examples of using Timeplus Proton, check out the examples folder.

To access more features, such as sources, sinks, dashboards, alerts, and data lineage, try Timeplus Enterprise locally.

What features are available with Timeplus Proton versus Timeplus Enterprise?

Integrations

The following drivers are available:

• https://github.com/timeplus-io/proton-java-driver JDBC and other Java clients
• https://github.com/timeplus-io/proton-go-driver
• https://github.com/timeplus-io/proton-python-driver

Integrations with other systems:

• ClickHouse https://docs.timeplus.com/proton-clickhouse-external-table
• Docker and Testcontainers https://docs.timeplus.com/tutorial-testcontainers-java
• Sling https://docs.timeplus.com/sling
• Grafana https://github.com/timeplus-io/proton-grafana-source
• Homebrew https://github.com/timeplus-io/homebrew-timeplus
• dbt https://github.com/timeplus-io/dbt-proton

Documentation

We publish full documentation for Timeplus Proton at docs.timeplus.com alongside documentation for Timeplus Enterprise.

We also have a FAQ for detailing how we chose Apache License 2.0, how Timeplus Proton is related to ClickHouse, and more.

Contributing

We welcome your contributions! If you are looking for issues to work on, try looking at the issue list.

Please see the wiki for more details, and BUILD.md to compile Timeplus Proton in different platforms.

Adding a Company Logo

If you are using Timeplus Proton and would like your company logo displayed on our Home page, please email info@timeplus.com with your request.

Need help?

Please use GitHub Discussions to share your feedbacks or questions for Timeplus Proton.

For filing bugs, suggesting improvements, or requesting new features, open GitHub Issues.

To connect with Timeplus engineers or inquire about Timeplus Enterprise, join our Timeplus Community Slack.

Licensing

Proton uses Apache License 2.0. See details in the LICENSE.