How does PipelineDB compare to TimescaleDB for time-series data?

PipelineDB focuses on real-time aggregation without storing raw data, ideal for continuous queries, while TimescaleDB stores and indexes time-series data for flexible querying. Choose PipelineDB for pure aggregation workloads, but TimescaleDB if you need to query raw historical data or use newer PostgreSQL versions.

How to install PipelineDB on Ubuntu?

Installation involves building from source: install PostgreSQL dev packages and ZeroMQ dependencies, then run 'make USE_PGXS=1', 'make install', and 'make bootstrap' for initial setup, as per the README. It's more complex than installing a standard package.

Can PipelineDB handle data streams from Kafka?

PipelineDB ingests data via SQL INSERTs, so you need a separate connector or application to push Kafka events into its streams. It lacks built-in Kafka integration, requiring additional tooling for seamless ingestion.

What happens if I need to query raw data in PipelineDB?

You cannot query raw data because it's never written to disk; only aggregated results are stored. This means you lose the ability to reprocess or debug events after aggregation, which can be a limitation for some use cases.

Is PipelineDB still actively maintained?

No, after joining Confluent, PipelineDB is in maintenance mode with only critical bug fixes and no new features. For ongoing development, consider alternatives like Apache Flink or newer time-series databases.

How to chain continuous queries in PipelineDB?

Use output streams from one continuous query as input for another by defining streams and views with SQL syntax, as documented for continuous transforms. This allows building networks of SQL operations without intermediate storage.

Open-Awesome

PipelineDB

Apache-2.0C1.0.0-13

A PostgreSQL extension for high-performance time-series aggregation that stores only aggregate results, not raw data.

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2.7k stars243 forks0 contributors

What is PipelineDB?

PipelineDB is a PostgreSQL extension for high-performance time-series aggregation, designed to power realtime reporting and analytics applications. It allows users to define continuous SQL queries that perpetually aggregate streaming data, storing only the aggregate output in regular tables while raw data is never written to disk.

Target Audience

Developers and data engineers building real-time analytics applications, especially those needing efficient time-series data processing within a PostgreSQL environment.

Value Proposition

It offers extremely high-throughput, incrementally updated materialized views that never need manual refreshing, making it highly efficient for aggregation workloads by avoiding raw data storage.

Overview

High-performance time-series aggregation for PostgreSQL

Use Cases

Best For

Building real-time dashboards with live data aggregation
Processing high-volume time-series data without storing raw inputs
Creating continuously updated materialized views in PostgreSQL
Chaining SQL operations for complex streaming analytics
Reducing storage overhead in time-series analytics applications
Implementing incremental aggregation for reporting systems

Not Ideal For

Projects requiring raw time-series data storage for auditing or detailed historical analysis
Teams using PostgreSQL 12 or newer, as compatibility is limited to versions 10 and 11
Applications needing active feature development and community support beyond maintenance mode

Pros & Cons

Pros

Automatic Incremental Updates

Continuous queries update materialized views in real-time without manual REFRESH commands, as shown in the example where INSERTs immediately aggregate data into test_view.

Storage-Optimized Aggregation

Raw data is never written to disk, minimizing storage overhead for high-volume time-series workloads and focusing only on aggregated results, as emphasized in the README.

Seamless PostgreSQL Integration

It runs as a PostgreSQL extension, allowing use of standard SQL tools like psql and compatibility with existing ecosystems, demonstrated in the streaming and querying examples.

Flexible Stream Chaining

Output streams enable chaining continuous queries into networks for complex analytics pipelines, supported by features like continuous transforms as documented.

Cons

Limited Development Future

The project is in maintenance mode with no new releases beyond 1.0.0, only critical bug fixes, reducing its suitability for long-term or evolving projects.

Restricted PostgreSQL Compatibility

Only supports PostgreSQL versions 10 and 11, which are outdated, limiting adoption with newer database versions and potentially causing upgrade challenges.

Complex Build and Setup

Requires building from source with dependencies like ZeroMQ and PostgreSQL dev packages, making installation more involved compared to standard extensions, as outlined in the README.

Frequently Asked Questions

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