When should I use FiloDB instead of TimescaleDB?

Distributed columnar analytics on time-series and event data using Apache Cassandra as storage. Combining fast time-range queries with Cassandra's horizontal scalability. IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

When should I use TimescaleDB instead of FiloDB?

Time-series data stored as a PostgreSQL extension with full SQL and ecosystem compatibility. Teams who want time-series capabilities without leaving the PostgreSQL tooling and ORM ecosystem. Combining time-series sensor or metrics data with relational business data in one database

What are the main weaknesses of FiloDB?

Small community with limited documentation and few active production deployments. Inactive development — the project has not seen regular releases in recent years. Requires deep expertise to deploy and tune alongside a Cassandra cluster

What are the main weaknesses of TimescaleDB?

Adds operational complexity on top of PostgreSQL; major upgrades require careful migration procedures. Compression and continuous aggregates require tuning and understanding of TimescaleDB internals. Self-managed deployment requires PostgreSQL DBA expertise plus TimescaleDB-specific knowledge

FiloDB vs TimescaleDB: Key Differences for Python Data Engineering

Databases & Data Warehouses

FiloDB

Distributed Columnar Streaming Database

★ 3.7

Apache-2.0

N/A — Scala-based, no pip install

TimescaleDB

PostgreSQL Extension for Time Series

★ 4.6

Apache-2.0 (Community)

pip install psycopg2-binary

Side-by-Side Comparison

FiloDB

TimescaleDB

FiloDB

TimescaleDB

Best For

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

✓Time-series data stored as a PostgreSQL extension with full SQL and ecosystem compatibility
✓Teams who want time-series capabilities without leaving the PostgreSQL tooling and ORM ecosystem
✓Combining time-series sensor or metrics data with relational business data in one database

Best For

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

✓Time-series data stored as a PostgreSQL extension with full SQL and ecosystem compatibility
✓Teams who want time-series capabilities without leaving the PostgreSQL tooling and ORM ecosystem
✓Combining time-series sensor or metrics data with relational business data in one database

Weaknesses

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

•Adds operational complexity on top of PostgreSQL; major upgrades require careful migration procedures
•Compression and continuous aggregates require tuning and understanding of TimescaleDB internals
•Self-managed deployment requires PostgreSQL DBA expertise plus TimescaleDB-specific knowledge

Weaknesses

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

•Adds operational complexity on top of PostgreSQL; major upgrades require careful migration procedures
•Compression and continuous aggregates require tuning and understanding of TimescaleDB internals
•Self-managed deployment requires PostgreSQL DBA expertise plus TimescaleDB-specific knowledge

License

Apache-2.0

Apache-2.0 (Community)

License

Apache-2.0

Apache-2.0 (Community)

Install

N/A — Scala-based, no pip install

pip install psycopg2-binary

Install

N/A — Scala-based, no pip install

pip install psycopg2-binary

Rating

★ 3.7

★ 4.6

Rating

★ 3.7

★ 4.6

Key Features

FiloDB

1Distributed time-series database optimized for Prometheus-style metrics
2Column-oriented storage using Apache Parquet encoding for compression
3Built on Apache Cassandra for distributed storage and replication
4Supports PromQL for querying metrics alongside native Scala/Java API
5Chunk-based storage with configurable retention and downsampling

TimescaleDB

1PostgreSQL extension that adds automatic time-series table partitioning (hypertables)
2Continuous aggregates compute and store time-bucketed metrics incrementally
3Compression reduces storage by 90%+ for older time-series data
4Full SQL compatibility — all PostgreSQL tools and drivers work unchanged
5Real-time aggregates query both compressed and uncompressed data transparently

How Python Data Engineers Use These Tools

FiloDB

Python data engineers working in observability infrastructure use FiloDB as a long-term storage backend for Prometheus metrics. Python scripts query FiloDB via its HTTP API to pull historical metric data for anomaly detection models or SLA reporting — enabling ML-driven monitoring use cases that require more data history than Prometheus retains locally.

TimescaleDB

Python data engineers use TimescaleDB as a drop-in upgrade for PostgreSQL when storing time-series data. The `psycopg2` and `asyncpg` drivers work without changes — engineers create a hypertable on the timestamp column and TimescaleDB automatically partitions data by time, enabling fast range queries on billions of rows without manual partition management.

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Individual Tool Pages

View FiloDB details →View TimescaleDB details →

Side-by-Side Comparison

FiloDB

TimescaleDB

FiloDB

TimescaleDB

Best For

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

✓Time-series data stored as a PostgreSQL extension with full SQL and ecosystem compatibility
✓Teams who want time-series capabilities without leaving the PostgreSQL tooling and ORM ecosystem
✓Combining time-series sensor or metrics data with relational business data in one database

Best For

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

✓Time-series data stored as a PostgreSQL extension with full SQL and ecosystem compatibility
✓Teams who want time-series capabilities without leaving the PostgreSQL tooling and ORM ecosystem
✓Combining time-series sensor or metrics data with relational business data in one database

Weaknesses

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

•Adds operational complexity on top of PostgreSQL; major upgrades require careful migration procedures
•Compression and continuous aggregates require tuning and understanding of TimescaleDB internals
•Self-managed deployment requires PostgreSQL DBA expertise plus TimescaleDB-specific knowledge

Weaknesses

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

•Adds operational complexity on top of PostgreSQL; major upgrades require careful migration procedures
•Compression and continuous aggregates require tuning and understanding of TimescaleDB internals
•Self-managed deployment requires PostgreSQL DBA expertise plus TimescaleDB-specific knowledge

License

Apache-2.0

Apache-2.0 (Community)

License

Apache-2.0

Apache-2.0 (Community)

Install

N/A — Scala-based, no pip install

pip install psycopg2-binary

Install

N/A — Scala-based, no pip install

pip install psycopg2-binary

Rating

★ 3.7

★ 4.6

Rating

★ 3.7

★ 4.6

Key Features

FiloDB

1Distributed time-series database optimized for Prometheus-style metrics
2Column-oriented storage using Apache Parquet encoding for compression
3Built on Apache Cassandra for distributed storage and replication
4Supports PromQL for querying metrics alongside native Scala/Java API
5Chunk-based storage with configurable retention and downsampling

TimescaleDB

1PostgreSQL extension that adds automatic time-series table partitioning (hypertables)
2Continuous aggregates compute and store time-bucketed metrics incrementally
3Compression reduces storage by 90%+ for older time-series data
4Full SQL compatibility — all PostgreSQL tools and drivers work unchanged
5Real-time aggregates query both compressed and uncompressed data transparently

How Python Data Engineers Use These Tools