When should I use Faust instead of HStreamDB?

Python-native stream processing with a Kafka Streams-inspired API using async Python. Building real-time Python agents that consume and produce Kafka topics without leaving Python. Teams who want Kafka stream processing without learning a JVM-based framework

When should I use HStreamDB instead of Faust?

Persistent, ACID-compliant streaming storage merging message queue and database semantics. IoT and event sourcing workloads needing durable, replayable streams with SQL querying. Teams wanting a streaming database with long-term retention and low-latency append access

What are the main weaknesses of Faust?

Limited to Kafka as the messaging backend; not a general-purpose stream processing engine. Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads. Less active maintenance since Robinhood open-sourced it; community support has declined

What are the main weaknesses of HStreamDB?

Very early-stage project with limited community and few production deployment references. Limited tooling, connectors, and client library support across languages. Niche use case that most teams address with Kafka combined with a time-series database

Faust vs HStreamDB: Key Differences for Python Data Engineering

Stream Processing

Faust

Python Stream Processing

★ 4.5

BSD-3-Clause

pip install faust-streaming

HStreamDB

Streaming Database for IoT

★ 3.6

Apache-2.0

pip install hstreamdb

Side-by-Side Comparison

Faust

HStreamDB

Faust

HStreamDB

Best For

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

✓Persistent, ACID-compliant streaming storage merging message queue and database semantics
✓IoT and event sourcing workloads needing durable, replayable streams with SQL querying
✓Teams wanting a streaming database with long-term retention and low-latency append access

Best For

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

✓Persistent, ACID-compliant streaming storage merging message queue and database semantics
✓IoT and event sourcing workloads needing durable, replayable streams with SQL querying
✓Teams wanting a streaming database with long-term retention and low-latency append access

Weaknesses

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

•Very early-stage project with limited community and few production deployment references
•Limited tooling, connectors, and client library support across languages
•Niche use case that most teams address with Kafka combined with a time-series database

Weaknesses

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

•Very early-stage project with limited community and few production deployment references
•Limited tooling, connectors, and client library support across languages
•Niche use case that most teams address with Kafka combined with a time-series database

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install faust-streaming

pip install hstreamdb

Install

pip install faust-streaming

pip install hstreamdb

Rating

★ 4.5

★ 3.6

Rating

★ 4.5

★ 3.6

Key Features

Faust

1Python-native stream processing framework built on Kafka
2Stateful processing with RocksDB-backed tables for aggregations
3Async agents defined as `@app.agent` coroutines for each topic
4Cron-style timers and windowed aggregations built in
5Type annotations on stream records enable schema validation at runtime

HStreamDB

1Streaming database designed for IoT and real-time event data
2Persistent streams with both pub/sub and SQL query capabilities
3Subscriptions with exactly-once delivery semantics
4SQL-based stream processing with time-window aggregations
5Horizontal scaling via sharding with automatic load balancing

How Python Data Engineers Use These Tools

Faust

Faust lets Python data engineers write Kafka stream processors entirely in async Python — defining an agent as a coroutine that processes messages from a topic and produces results to another. Engineers use Faust tables for stateful aggregations (e.g., running counts or session windows) that persist across restarts via RocksDB.

HStreamDB

Python data engineers use the `hstreamdb` Python client to produce and consume events from HStreamDB streams in IoT and real-time analytics pipelines. SQL subscriptions let Python consumers receive only the records matching a filter condition, reducing the amount of data the Python processor needs to handle and filter client-side.

More Stream Processing Comparisons

Stream Processing

Apache Flink vs Apache Kafka

Stream Processing

Apache Storm vs Apache Kafka

Stream Processing

Faust vs Apache Kafka

Stream Processing

Apache Kafka vs Apache Spark Streaming

Stream Processing

Apache Kafka vs Redpanda

Stream Processing

Apache Samza vs Apache Kafka

Individual Tool Pages

View Faust details →View HStreamDB details →

Side-by-Side Comparison

Faust

HStreamDB

Faust

HStreamDB

Best For

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

✓Persistent, ACID-compliant streaming storage merging message queue and database semantics
✓IoT and event sourcing workloads needing durable, replayable streams with SQL querying
✓Teams wanting a streaming database with long-term retention and low-latency append access

Best For

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

✓Persistent, ACID-compliant streaming storage merging message queue and database semantics
✓IoT and event sourcing workloads needing durable, replayable streams with SQL querying
✓Teams wanting a streaming database with long-term retention and low-latency append access

Weaknesses

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

•Very early-stage project with limited community and few production deployment references
•Limited tooling, connectors, and client library support across languages
•Niche use case that most teams address with Kafka combined with a time-series database

Weaknesses

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

•Very early-stage project with limited community and few production deployment references
•Limited tooling, connectors, and client library support across languages
•Niche use case that most teams address with Kafka combined with a time-series database

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install faust-streaming

pip install hstreamdb

Install

pip install faust-streaming

pip install hstreamdb

Rating

★ 4.5

★ 3.6

Rating

★ 4.5

★ 3.6

Key Features

Faust

1Python-native stream processing framework built on Kafka
2Stateful processing with RocksDB-backed tables for aggregations
3Async agents defined as `@app.agent` coroutines for each topic
4Cron-style timers and windowed aggregations built in
5Type annotations on stream records enable schema validation at runtime

HStreamDB

1Streaming database designed for IoT and real-time event data
2Persistent streams with both pub/sub and SQL query capabilities
3Subscriptions with exactly-once delivery semantics
4SQL-based stream processing with time-window aggregations
5Horizontal scaling via sharding with automatic load balancing

How Python Data Engineers Use These Tools