For Data Engineers

Run FeatureBase with Docker

This post covers starting FeatureBase using a simple Docker compose file and ingesting a moderate amount of data using a Python script. A container is started running a standalone instance of FeatureBase and exposes port 10101 for querying and ingestion.

Ingestion is done using Python through the file. You may also ingest data using the docker-consumer example in the featurebase-examples repo.

If you would like to start an instance of FeatureBase configured for running a cluster, see the docker-cluster example.

Check Out the Repo

Clone the FeatureBase examples repo in a terminal and change into the docker-simple directory:

git clone
cd featurebase-examples/docker-simple

Create the Docker Network

Before starting the FeatureBase container(s), you will need to create a Docker network to be used by the services:

docker network create fbnet

Start the Services

Start the services using docker compose:

docker compose up

NOTE: If you have issues with docker compose, try disabling v2 by going into settings..general in Docker Desktop.

You should now have a container running:

Run the Insert Script

The script inserts "draws" of 81 different cards from Set the Game. The cards are represented with strings. For example, 3G#~ is shorthand for 3 green shaded squiggles.

Before you run the script, ensure you have the requirements installed:

pip3 install -r requirements.txt

Now run the script to insert data:

% python3


% python3
There are 1201000 existing records.
Enter the draw size (12,15,18,21,24...): 12
Enter the number of draws: 1000000
There are 1201000 total records..
There are 1301000 total records..
...a few seconds later...
There are 2101000 total records..
Generated a total of 1000000 draws.

Use the UI to Query with SQL

To check this worked, in your browser head over to and run the following query:

select count(*) from simpledocker where setcontains(draw, '3G#~');

Try other queries:

select * from simpledocker where setcontains(draw, '3G#~') and setcontains(draw, '2G○~');

Alternate Ingestion Method

To insert data using a consumer on your local machine, you may either follow the docker-consumer example, or use the Community Guide to unzip FeatureBase locally.

The community download will include the molecula-consumer-csv binary, which you can use to insert data into this example's FeatureBase instance.

When you run FeatureBase in a container, you'll have to tell your local machine to map the featurebase hostname in the local host's /etc/hosts file. Make the following entry in hosts:    featurebase

Assuming you installed FeatureBase's binaries in the ~/featurebase/ directory, you can now run the IDK consumer directly:

~/featurebase/idk/molecula-consumer-csv \
--auto-generate \
--index=allyourbase \


kord@bob featurebase $ idk/molecula-consumer-csv \
--auto-generate \
--index=simpledocker \
Molecula Consumer v3.26.0-9-g14f19300, build time 2022-12-12T20:44:21+0000
2022-12-13T14:21:33.516988Z INFO:  Serving Prometheus metrics with namespace "ingester_csv" at localhost:9093/metrics
2022-12-13T14:21:33.519780Z INFO:  start ingester 0
2022-12-13T14:21:33.520360Z INFO:  processFile: sample.csv
2022-12-13T14:21:33.520437Z INFO:  new schema: []idk.Field{idk.StringField{NameVal:"asset_tag", DestNameVal:"asset_tag", Mutex:false, Quantum:"", TTL:"", CacheConfig:(*idk.CacheConfig)(nil)}, idk.RecordTimeField{NameVal:"fan_time", DestNameVal:"fan_time", Layout:"2006-01-02", Epoch:time.Date(1, time.January, 1, 0, 0, 0, 0, time.UTC), Unit:""}, idk.StringField{NameVal:"fan_val", DestNameVal:"fan_val", Mutex:false, Quantum:"YMD", TTL:"", CacheConfig:(*idk.CacheConfig)(nil)}}
2022-12-13T14:21:33.530045Z INFO:  Listening for /debug/pprof/ and /debug/fgprof on 'localhost:6062'
2022-12-13T14:21:33.564520Z INFO:  translating batch of 1 took: 42.707875ms
2022-12-13T14:21:33.564819Z INFO:  making fragments for batch of 1 took 303.708µs

2022-12-13T14:21:33.975317Z INFO:  importing fragments took 305.542µs
2022-12-13T14:21:33.975485Z INFO:  records processed 0-> (10)
2022-12-13T14:21:33.975493Z INFO:  metrics: import=454.011583ms

Tear It Down

To remove the deployment run the following:

docker-compose down 

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