Using Bacalhau with DuckDB
Last updated
Last updated
DuckDB is a relational table-oriented database management system that supports SQL queries for producing analytical results. It also comes with various features that are useful for data analytics.
DuckDB is suited for the following use cases:
Processing and storing tabular datasets, e.g. from CSV or Parquet files
Interactive data analysis, e.g. Joining & aggregate multiple large tables
Concurrent large changes, to multiple large tables, e.g. appending rows, adding/removing/updating columns
Large result set transfer to client
In this example tutorial, we will show how to use DuckDB with Bacalhau. The advantage of using DuckDB with Bacalhau is that you don’t need to install, and there is no need to download the datasets since the datasets are already there on IPFS or on the web.
How to run a relational database (like DUCKDB) on Bacalhau
To get started, you need to install the Bacalhau client, see more information here
You can skip this entirely and directly go to running on Bacalhau.
If you want any additional dependencies to be installed along with DuckDB, you need to build your own container.
To build your own docker container, create a Dockerfile, which contains instructions to build your DuckDB docker container.
See more information on how to containerize your script/app here
We will run docker build command to build the container;
Before running the command replace;
hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created
repo-name with the name of the container, you can name it anything you want
tag this is not required but you can use the latest tag
In our case
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
In our case
After the repo image has been pushed to Docker Hub, we can now use the container for running on Bacalhau. To submit a job, run the following Bacalhau command:
Let's look closely at the command above:
bacalhau docker run: call to bacalhau
davidgasquez/datadex:v0.2.0 : the name and the tag of the docker image we are using
/inputs/: path to input dataset
'duckdb -s "select 1"': execute DuckDB
When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.
Job status: You can check the status of the job using bacalhau list.
When it says Published or Completed, that means the job is done, and we can get the results.
Job information: You can find out more information about your job by using bacalhau describe.
Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
Each job creates 3 subfolders: the combined_results,per_shard files, and the raw directory. To view the file, run the following command:
Below is the bacalhau docker run command to to run arbitrary SQL commands over the yellow taxi trips dataset
Let's look closely at the command above:
bacalhau docker run: call to bacalhau
-i ipfs://bafybeiejgmdpwlfgo3dzfxfv3cn55qgnxmghyv7vcarqe3onmtzczohwaq \: CIDs to use on the job. Mounts them at '/inputs' in the execution.
davidgasquez/duckdb:latest: the name and the tag of the docker image we are using
/inputs: path to input dataset
duckdb -s: execute DuckDB
When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.
Job status: You can check the status of the job using bacalhau list.
Job information: You can find out more information about your job by using bacalhau describe.
Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
Each job creates 3 subfolders: the combined_results,per_shard files, and the raw directory. To view the file, run the following command:
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