README.md

pubsub_bigquery

Project status: ALPHA

A Google BigQuery sink for Google Pubsub events written in Rust. This project originated after some frustrations with writing another version of this flow using the Apache Beam SDK. Although the Apache Beam SDK is relatively straight forward, I noticed that it's either:

• Expensive (when using the Google Dataflow runner)
• Slow (when using the SparkRunner)
• In both cases quite memory intensive

Because of these reasons it seemed like a good idea to implement this flow in Rust using the Google API's directly. This application currently does not have any options for horizontal scaling

Quickstart

The application requires a few settings to be configured through a config file. The example below illustrates a simple setup:

1. Build the application using Cargo

   cargo build --release
   

2. Create a configuration file:

   #config.toml
   
   debug = false
   mode = "subscribe"
   
   [pubsub]
   project_id = "PROJECT_ID"
   topic = "projects/PROJECT_ID/topics/TOPIC"
   subscription = "projects/PROJECT_ID/subscriptions/SUBSCRIPTION"
   
   [bigquery]
   project_id = "PROJECT_ID"
   dataset = "DATASET_NAME"
   table = "TABLE_NAME"
   format = "CSV"
   delimiter = "\t" // can be any ISO-8859-1 single-byte character
   quote = ""
   auto_detect = true // auto detect schema in pubsub topic
   allow_jagged_rows = true // allow null values in last columns
   
   [limits]
   pubsub_max_messages = 500
   bigquery_time_limit = 90
   bigquery_max_messages = 1000
   

3. Run the application

   cd target/release
   ./pubsub_bigquery config.toml
   

Creating a Docker image

It's possible to create a Docker container with a size of approximately 10MB by creating a static Rust binary. You will need the rust-musl-builder for it. More information about this can be found here

1. First create a release using the following command:

   alias rust-musl-builder='docker run --rm -it -v "$(pwd)":/home/rust/src ekidd/rust-musl-builder'
   rust-musl-builder cargo build --release
   

2. Then create a Docker image using the supplied Dockerfile:

   docker build -t pubsub-bigquery:0.1 .
   

3. And lastly, run the Docker container:

   docker run pubsub-bigquery:0.1