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Open-Source Databases · timeplus-io

proton

Proton is a lightweight, single-binary streaming SQL engine written in C++ that processes real-time data from Kafka, logs, and metrics with millisecond latency and high throughput. It combines stream processing capabilities with materialized views and analytics, positioning itself as an alternative to Apache Flink and ksqlDB without the overhead of a JVM.

Source: GitHub — github.com/timeplus-io/proton
2.2k
GitHub stars
112
Forks
C++
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorytimeplus-io/proton
Ownertimeplus-io
Primary languageC++
LicenseApache-2.0 — OSI-approved
Stars2.2k
Forks112
Open issues97
Latest releasev3.0.26 (2026-06-22)
Last updated2026-07-03
Sourcehttps://github.com/timeplus-io/proton

What proton is

Built on ClickHouse's columnar engine, Proton extends it with stream processing primitives: windowed aggregations, multi-stream joins, incremental materialized views, UDF support (Python/JS), and native connectors for Kafka, ClickHouse, MySQL, Postgres, MongoDB, S3/Iceberg, and OpenSearch. Single C++ binary (<500MB) with no external dependencies (no JVM, no ZooKeeper).

Quickstart

Get the proton source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/timeplus-io/proton.gitcd proton# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Real-time ETL and Data Pipeline Transformation

Ingest from Kafka or streaming sources, apply in-pipeline filtering, enrichment, and masking, then route to data warehouses (ClickHouse) or downstream Kafka topics. Avoids complex orchestration platforms for simple pipelines.

Telemetry and Observability Data Processing

Process logs, metrics, and traces with real-time noise reduction and alerting before forwarding to Splunk, Elastic, or S3. Low memory footprint enables deployment on resource-constrained observability stacks.

Feature Engineering for Real-time ML/AI

Compute low-latency features using streaming SQL and materialized views with windowing and backfill support. Enables fast feature refresh cycles for time-sensitive ML models without separate feature stores.

Implementation considerations

  • Verify connector maturity (Kafka, ClickHouse, Redpanda) for your specific versions and message schemas before production pilot.
  • Plan state management: materialized views are incremental but durability/backup strategy must be defined per deployment.
  • Test end-to-end latency and throughput on representative hardware; claimed 90M EPS/4ms latency on M2 MacBook requires validation on target infrastructure.
  • Monitor memory and CPU consumption under sustained load; single binary simplifies ops but provides no native horizontal scaling story in README.
  • Evaluate UDF performance impact (Python/JS execution cost); no benchmark data provided for stateful or complex UDF pipelines.

When to avoid it — and what to weigh

  • Mature OLTP/Transactional Workloads — Proton is optimized for analytical streaming, not row-level transactions. Not designed for systems requiring strong ACID guarantees, row-level locking, or complex transaction rollback semantics.
  • Highly Regulated Environments Without External Validation — Project created 2023-08-14 with ~2.2k stars. Requires external security audit and compliance assessment before adoption in financial services, healthcare, or other regulated sectors.
  • Existing Heavy JVM/Scala Ecosystem with Team Expertise — Switching from Flink or Kafka Streams trades deep ecosystem/library maturity for operational simplicity. Requires C++ operational knowledge for debugging or contributing to core.
  • Complex State Management and Recovery at Scale — Limited public evidence of production hardening at multi-terabyte state scale or detailed failure-recovery semantics. Unknown behavior under extreme data skew or long-running stateful operations.

License & commercial use

Licensed under Apache License 2.0 (OSI-compliant permissive license). Allows commercial use, modification, and distribution with minimal restrictions. Requires attribution and includes liability disclaimer.

Apache 2.0 permits commercial use without license fee or vendor lock-in. No closed-source proprietary extensions mentioned in provided data. Company (Timeplus) offers commercial support, cloud platform, and consulting; verify SLA/support terms independently if enterprise support required.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityLow
DEV.co fitGood
Assessment confidenceMedium
Security considerations

Apache 2.0 source code is open for review. No security audit, penetration test results, or security policy mentioned in provided data. Single C++ binary reduces attack surface vs. JVM/complex orchestration but requires secure compilation and dependency management. Network exposure (ports 8123, 8463 mentioned) requires standard hardening: firewall rules, authentication/authorization config (details Unknown), and encrypted communication validation. Kafka external stream integration requires secure credential handling; SASL/SSL options mentioned in example but credential storage/rotation practices not detailed. No mention of built-in secrets management or audit logging.

Alternatives to consider

Apache Flink

Mature, battle-tested streaming engine with broader ecosystem, stateful processing guarantees, and large community. Requires JVM and more operational complexity; higher latency than Proton's claimed milliseconds.

ksqlDB (Confluent)

Kafka-native streaming SQL with strong Kafka ecosystem integration and Confluent support. JVM-based, vendor-influenced, and typically requires Confluent Platform license for production support.

RisingWave

Open-source streaming SQL database with SQL semantics and cloud-native design. Rust-based; different architectural trade-offs (distributed by default) but less mature single-binary option than Proton.

Software development agency

Build on proton with DEV.co software developers

Evaluate Proton's fit with a proof-of-concept on your Kafka infrastructure. Get a technical architecture review and deployment assessment from Devco's streaming data experts.

Talk to DEV.co

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proton FAQ

How does Proton differ from ClickHouse?
ClickHouse is a columnar OLAP database for historical data. Proton extends ClickHouse with stream processing primitives (windowing, joins, incremental views) and Kafka connectors, enabling real-time SQL pipelines. ClickHouse can serve as a sink for Proton materialized views.
Does Proton guarantee exactly-once semantics?
Not explicitly stated in provided data. Kafka external stream integration and materialized views suggest at-least-once delivery model common to streaming engines, but exact guarantees require documentation review.
Can Proton scale horizontally across multiple nodes?
Unknown from README. Single-binary deployment is highlighted; distributed clustering architecture and scaling patterns not documented in provided excerpt. Requires vendor documentation review.
What are typical deployment sizes and resource requirements?
Single binary (<500MB) can run on AWS t2.nano (1 vCPU, 0.5 GiB RAM). Claimed throughput: 100+ GB/s, 90M events/sec on Apple M2 Max. No data on multi-node clusters, memory/CPU scaling curves, or production deployment envelope. Benchmarks require validation on your target infrastructure.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like proton. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source databases and beyond.

Ready to Build Real-time Data Pipelines?

Evaluate Proton's fit with a proof-of-concept on your Kafka infrastructure. Get a technical architecture review and deployment assessment from Devco's streaming data experts.