December 24, 2018

Continuously Query Your Time-Series Data Using PipelineDB with Derek Nelson and Usman Masood - Episode 62

1 hour 3 minutes

Summary

Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.

Preamble

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Your host is Tobias Macey and today I’m interviewing Usman Masood and Derek Nelson about PipelineDB, an open source continuous query engine for PostgreSQL

Interview

Introduction

How did you get involved in the area of data management?

Can you start by explaining what PipelineDB is and the motivation for creating it?

What are the major use cases that it enables?

What are some example applications that are uniquely well suited to the capabilities of PipelineDB?

What are the major concepts and components that users of PipelineDB should be familiar with?

Given the fact that it is a plugin for PostgreSQL, what level of compatibility exists between PipelineDB and other plugins such as Timescale and Citus?

What are some of the common patterns for populating data streams?

What are the options for scaling PipelineDB systems, both vertically and horizontally?

How much elasticity does the system support in terms of changing volumes of inbound data?

What are some of the limitations or edge cases that users should be aware of?

Given that inbound data is not persisted to disk, how do you guard against data loss?

Is it possible to archive the data in a stream, unaltered, to a separate destination table or other storage location?

Can a separate table be used as an input stream?

Since the data being processed by the continuous queries is potentially unbounded, how do you approach checkpointing or windowing the data in the continuous views?

What are some of the features that you have found to be the most useful which users might initially overlook?

What would be involved in generating an alert or notification on an aggregate output that was in some way anomalous?

What are some of the most challenging aspects of building continuous aggregates on unbounded data?

What have you found to be some of the most interesting, complex, or challenging aspects of building and maintaining PipelineDB?

What are some of the most interesting or unexpected ways that you have seen PipelineDB used?

When is PipelineDB the wrong choice?

What do you have planned for the future of PipelineDB now that you have hit the 1.0 milestone?

Contact Info

Derek

derekjn on GitHub

Usman

@usmanm on Twitter

Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

PipelineDB

Stride

PostgreSQL

Podcast Episode

AdRoll

Probabilistic Data Structures

TimescaleDB

[Podcast Episode](

Hive

Redshift

Kafka

Kinesis

ZeroMQ

Nanomsg

HyperLogLog

Bloom Filter

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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By Tobias Macey

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