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Walden is a small data lake meant for solitary use. Read more about it on our website.

Component diagram: Superset, Trino, Hive Metastore, MinIO


There are a few things you need before you create your Walden deployment:

  • You need a Linux environment from which to run the code. The code was tested against Ubuntu 20.04 LTS as well as Arch Linux.
  • You need a Kubernetes Cluster. If you don't know what that is, check out K3s.
  • Your Kubernetes cluster needs to have at least 4 nodes (regular PCs are fine), with at least 6GB of RAM. We run this on 4 machines, each with 16 GB RAM. It works.
  • You need to install Terraform, a tool for deploying things. This is used to manage the deployment of Walden into your cluster.

Running Walden

Deploy Walden

git clone
cd walden/tf

# requires Terraform and kubectl access to cluster:
tf apply

You should see a whole bunch of text resulting from the deploy command. As long as no obvious errors show up, that's expected.

To check the health of your cluster run:

kubectl get pods -n walden

(If you're using k3s locally, preface this command like so: k3s kubectl get pods -n walden)

A healthy deployment looks like this:

NAME                               READY   STATUS    RESTARTS   AGE
devserver-65d668b5c6-s62m7         1/1     Running   0          49s
metastore-8696bf6b46-455qx         1/1     Running   0          49s
superset-worker-69cd7c966-pwsp8    1/1     Running   0          49s
superset-postgres-0                1/1     Running   0          48s
metastore-postgres-0               1/1     Running   0          48s
superset-66fb584c7d-wc8c4          2/2     Running   0          48s
trino-coordinator-8c6bc455-w2qdw   1/1     Running   0          48s
trino-worker-86d9484f75-7j8cw      1/1     Running   0          47s
superset-redis-0                   1/1     Running   0          47s
minio-0                            1/1     Running   0          47s
minio-1                            1/1     Running   0          36s
minio-2                            1/1     Running   0          27s
minio-3                            1/1     Running   0          21s

By default, Walden expects your cluster to have at least four machines/nodes. If you don't have four nodes, you may see MinIO pods that are stuck in Pending. You can get things unstuck by manually editing the minio StatefulSet and removing the affinity block, allowing multiple MinIO pods to deploy on the same machine:

# Edit MinIO StatefulSet, remove affinity block
$ kubectl edit statefulset -n walden minio

If something else has gone wrong, kubectl logs -n walden [name of pod] should help most of the time. If you need to do more debugging because something is failing but are new to Kubernetes, about now would be a good time to go through a tutorial.

Use devserver to access Trino CLI

Assuming the deployment succeeded, you can ssh into the devserver pod like so:

$ kubectl exec -it -n walden deployment/devserver -- /bin/bash

The following steps are performed from within the devserver pod.

Create a MinIO bucket

Now that you are logged in to the devserver, you are ready to interact with your glorious data pond!

Let's start by creating a bucket in MinIO, the object store service that's included in Walden. We will store data in these buckets:

devserver# mc alias set walden-minio/ http://minio:9000 $MINIO_ACCESS_KEY_ID $MINIO_ACCESS_KEY_SECRET
Added `walden-minio` successfully.

devserver# mc mb walden-minio/direct
Bucket created successfully: `walden-minio/direct`

Note -- walden-minio is an alias to the MinIO deployment created automatically when we start the devserver. We have created a bucket called "direct".

Connect Trino to MinIO directly

First, run the following command from the devserver shell. This starts a trino-cli session with the direct schema against the hive data storage provided by Walden.

devserver# trino direct

We will use this schema to talk directly to the MinIO storage. The direct schema does not actually exist in the Hive metastore yet, so we need to create it:

trino:direct> CREATE SCHEMA IF NOT EXISTS direct WITH (location='s3a://direct/');

If you run SHOW SCHEMAS you should see:

trino:direct> SHOW SCHEMAS;
(3 rows)

Now we can create a table and store some data:

trino:direct> CREATE TABLE IF NOT EXISTS dim_foo(key VARCHAR, val BIGINT);

trino:direct> INSERT INTO dim_foo VALUES ('this', 1), ('is', 2), ('a', 3), ('test', 4);
INSERT: 4 rows

Assuming everything is working, you should be able to query the stored values:

trino:direct> SELECT key, val FROM dim_foo;
 key  | val
 this |   1
 is   |   2
 a    |   3
 test |   4
(4 rows)

Now we can press Ctrl+D to exit the Trino console session, and look at the files created in the underlying MinIO bucket we created earlier:

trino:direct> ^D

devserver# mc ls -r walden-minio/direct
[2022-03-11 06:22:24 UTC]     0B STANDARD dim_foo/
[2022-03-11 06:21:42 UTC]   356B STANDARD 20220311_062141_00005_26e8n_9d96d247-6da3-49f9-a537-b0bc897879b9

We can clean up our test data by deleting the table and then the schema:

devserver# trino direct

trino:direct> DROP TABLE dim_foo;
trino:direct> DROP SCHEMA direct;
trino:direct> ^D

devserver# mc ls walden-minio/direct

Connect Trino to MinIO via Alluxio (optional)

Walden comes optionally packaged with Alluxio, which provides several benefits for processing data across a variety of backend storage systems. Alluxio can be used to support other storage types that are not natively supported by Trino, such as external NFS servers, while also providing a caching layer in front of that underlying storage. However, if you are not using these additional features, you can also leave Alluxio turned off (default), and instead use Trino's own built-in Hive caching.

To enable Alluxio, create a custom terraform.tfvars file with alluxio_enabled = true before deploying Walden into your cluster.

For testing Alluxio, we will use a separate bucket named alluxio that we will create in the included MinIO instance.

In the devserver pod, we create a new alluxio bucket using the MinIO CLI. Then we start a new Trino session and create an alluxio schema that points to the alluxio service:

$ kubectl exec -it -n walden deployment/devserver -- /bin/bash

devserver# mc mb walden-minio/alluxio
Bucket created successfully: `walden-minio/alluxio`

devserver# trino alluxio

trino:alluxio> CREATE SCHEMA IF NOT EXISTS alluxio WITH (location='alluxio://alluxio:19998/');

Again we can check SHOW SCHEMAS:

trino:alluxio> SHOW SCHEMAS;
(3 rows)

We create a table and store some data:

trino:alluxio> CREATE TABLE IF NOT EXISTS dim_bar(key VARCHAR, val BIGINT);

trino:alluxio> INSERT INTO dim_bar VALUES ('this', 4), ('is', 5), ('another', 6), ('test', 7);
INSERT: 4 rows

And finally we can fetch the data back:

trino:alluxio> SELECT key, val FROM dim_bar;
   key   | val
 this    |   4
 is      |   5
 another |   6
 test    |   7
(4 rows)

Now we can press Ctrl+D to exit the Trino session, then use the MinIO CLI to check the contents of the alluxio bucket:

devserver # mc ls -r walden-minio/alluxio
[2022-03-11 07:17:16 UTC]     0B STANDARD dim_bar/
[2022-03-11 07:23:53 UTC]   380B STANDARD dim_bar/20220311_102351_00139_giawv_ecfd5036-44d3-47da-9f87-6e02e04b8c5b

The data can be cleaned up via trino:

devserver# trino alluxio

trino:alluxio> DROP TABLE dim_bar;
trino:alluxio> DROP SCHEMA alluxio;
trino:alluxio> ^D

devserver# mc ls walden-minio/alluxio

Connect Trino to NFS via Alluxio

In the previous section we connected Alluxio to the MinIO instance, and then used Trino to interact with Alluxio. This does have some benefit in terms of caching, but the real value in using Alluxio comes when we are integrating with external services.

In this example, we will connect Walden to an NFS server, using Alluxio to broker the NFS volume and serve it to Trino.

Walden exposes two configuration options for specifying an external NFS mount that can be applied by creating your own terraform.tfvars file:

alluxio_nfs_server = "<ip/hostname>"
alluxio_nfs_path = "</nfs/server/directory>"

When those options are both provided, the worker pods will all automatically mount the NFS volume to /mnt/nfs, making it available for access and caching in Alluxio, and therefore it in Trino.

We can use the Alluxio CLI to mount the NFS volume to /nfs in the virtual Alluxio filesystem:

$ kubectl exec -it -n walden statefulset/alluxio-leader -c leader -- /bin/bash

alluxio$ ./bin/alluxio fs mount --shared /nfs /mnt/nfs
Mounted /mnt/nfs at /nfs

We then view the list of volumes and verify that files we expect are mounted into alluxio:

alluxio$ ./bin/alluxio fs mount
/mnt/nfs       on  /nfs  (local, capacity=123TB, used=12TB(10%), not read-only, shared, properties={})
s3://alluxio/  on  /     (s3, capacity=-1B, used=-1B, not read-only, not shared, properties={})

alluxio$ ./bin/alluxio fs ls /nfs
drwxrwxrwx nobody users 0           PERSISTED 02-13-2022 21:02:18:000  DIR /nfs/example1
-rw-rw-rw- nobody users 21520507993 PERSISTED 02-08-2022 20:02:39:000   0% /nfs/example2.zst

We can now query the mounted NFS volume from Trino, which has come preconfigured to access Alluxio.

In this example we have a dataset of Wikipedia articles that have been converted to ORC format.

The converted .orc files are in /mnt/nfs/wiki directory on the NFS volume. We use CREATE TABLE with external_location pointing to the existing location within alluxio:

$ kubectl exec -it -n walden deployment/devserver -- /bin/bash

devserver # trino alluxio

trino:alluxio> CREATE TABLE IF NOT EXISTS wiki (
  title VARCHAR,
  text VARCHAR,
  external_location = 'alluxio://alluxio:19998/nfs/wiki/orc/',
  format = 'ORC');

Now the ORC data on our NFS volume can be queried by Trino:

trino:alluxio> SELECT title,links FROM wiki WHERE cardinality(links) > 0 LIMIT 3;
                        title                        |                       links                      >
 Helen Hughes (economist)                            | [Prague, Sydney, Centre for Independent Studies, >
 Category:Canyons and gorges of Georgia (U.S. state) | [Wikipedia:Category, canyon, U.S. state, Georgia >
 Transnational Corporation of Nigeria                | [Energy, Electric power, Hospitality, Lagos, Lago>

For this example we've added an NFS mount into Alluxio, and then defined Trino tables to query structured data located on that NFS mount.

Using Alluxio for this allows us to support filesystems like NFS, while also providing its own caching for the benefit of query performance on repeated lookups.

Explore data with Superset

Superset provides a convienient UI for exploring the data that you've stored in Trino. Walden includes an instance of Superset that's preconfigured to connect to Trino.

Superset has been configured with a walden user and a randomly generated password.

Get the password for logging into Superset:

$ kubectl get secret -n walden superset-admin -o 'jsonpath={.data.pass}' | base64 -d && echo

Set up a port-forward to access Superset on port 8088:

$ kubectl port-forward -n walden deployment/superset 8088

Go to and log in with user=walden and the password you got earlier.

Two Trino databases should have been automatically added to Superset by Walden:

  • walden-trino has Trino data, including what we added to a test schema in earlier steps. The data itself is being stored to Minio in Hive columnar table format, with the Metastore acting as the index.
  • walden-trino-system has various Trino System statistics like node connectivity that may also be interesting to explore. In addition to these autoconfigured, you should be able to add other external databases to Superset as well via Data > Databases in the top menu.

We can start exploring by going to SQL Lab > SQL Editor in the top menu. In the SQL Editor view, select the walden-trino database, then the test schema that we created earlier. The values we added to this schema earlier should also be visible via Superset.

Screenshot of Superset UI showing test data

View the Trino Dashboard

Trino also provides a dashboard UI showing basic activity. Set up a port-forward to access the dashboard on port 8080:

$ kubectl port-forward -n walden deployment/trino-coordinator 8080:80

Go to and log in with any value for the username (it doesn't matter).

Screenshot of Trino Dashboard


That's it, this is an easy way to get a small data lake working. This is meant to be a fully functional starting point that can be expanded and customized to fit your needs. Everything here is provided as-is, so your mileage may vary. Please report any bugs or issues and we will try to get to them.

Cloud Provider Installation

Walden can be used either on-premise or in hosted Kubernetes environments. Here are some example steps for setting up Walden in various cloud providers.


This tutorial assumes you already have an AWS account set up. Instructions should be run from either Mac or Linux machines. Also keep in mind this is likely to cost a few dollars in AWS costs to try out. We've been able to keep costs below $5 USD when running a minimal cluster for a short amount of time.

Configure AWS EKS Admin

To manage the EKS cluster programmatically from your local machine, you will need to create a new AWS IAM user and grant it appropriate permissions.

The easiest way to do so reproducibly involves using a "CloudShell" session. To bring one up, search for "CloudShell" in your AWS console. Note that you should be logged in with your AWS root account when running these operations.

  1. Create IAM user

    Run the following in your AWS cloud shell session

    aws iam create-user --user-name eksadmin

    Note: you can skip this step if you already have an IAM user you would like to use. Simply replace eksadmin with your user name where necessary.

  2. Create IAM policies

    To be able to spin up an EKS cluster via eksctl we will need to define two new policies, with which we will associate our EKS admin user. The policy definitions are available in policy documents stored in this repositories. To access them, clone the repository first (from your AWS cloud shell session):

    git clone
    cd walden

    We will preserve the account identifier for future use:

    export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query "Account" --output text)
    echo $AWS_ACCOUNT_ID

    We can now render the policies, i.e. by substituting the AWS_ACCOUNT_ID we just captured:

    cat aws/eks-all-access.json | envsubst > eks-all-access-rendered.json
    cat aws/iam-limited-access.json | envsubst > iam-limited-access-rendered.json

    Then we will create our policies:

    aws iam create-policy --policy-name EKSAllAccess --policy-document file://eks-all-access-rendered.json
    aws iam create-policy --policy-name IAMLimitedAccess --policy-document file://iam-limited-access-rendered.json
  3. Create IAM EKS Admin group

    First we create our group and add our EKS admin user to it:

    aws iam create-group --group-name EKSAdmins
    aws iam add-user-to-group --group-name EKSAdmins --user-name eksadmin

    We are then ready to attach permissions to the group:

    aws iam attach-group-policy --group-name EKSAdmins --policy-arn arn:aws:iam::$AWS_ACCOUNT_ID:policy/EKSAllAccess
    aws iam attach-group-policy --group-name EKSAdmins --policy-arn arn:aws:iam::$AWS_ACCOUNT_ID:policy/IAMLimitedAccess
    aws iam attach-group-policy --group-name EKSAdmins --policy-arn arn:aws:iam::aws:policy/AmazonEC2FullAccess
    aws iam attach-group-policy --group-name EKSAdmins --policy-arn arn:aws:iam::aws:policy/AWSCloudFormationFullAccess

    You are now ready to interact with the cluster using your eksadmin user. The rest of the interactions will occurr via your regular machine, rather than a cloud shell instance.

Deploy EKS cluster

The following set of operations are meant to run on your home machine (Mac or Linux only).

To provision this cluster you will need to have the following software installed:

  1. Set up your AWS CLI authentication

    Edit ~/.aws/config and add your AWS access key ID and secret (your access key ID starts with AKIA):

    aws_access_key_id=[YOUR ACCESS KEY ID GOES HERE]
    aws_secret_access_key=[YOUR ACCESS SECRET GOES HERE]

    Then run:

    export AWS_PROFILE=walden
    aws sts get-caller-identity

    You should see something like:

        "UserId": "AIDA**********",
        "Account": "1234567890",
        "Arn": "arn:aws:iam::1234567890:user/[someusername]"
  2. Create minimal EKS cluster using eksctl

    To create a minimal cluster, run:

    eksctl create cluster --name=eks-walden --nodes=4 --node-type=r5.large --spot

    This command will create an EKS cluster, and one default node group with 4 nodes in it. This is purely a test cluster -- eksctl being a very powerful tool that allows you to customize your cluster whichever way you see fit.

    The command will take about 30 minutes to run, while AWS provisions requisite resources.

    Once the cluster creation has succeeded, run:

    kubectl get nodes

Deploy Walden

You now have a working EKS cluster, on which you can deploy Walden just as you would on an on-premise cluster. Follow these instructions to deploy it.

Clean up

First, get the name of your nodegroup:

eksctl get nodegroup --cluster eks-walden

Then, delete the nodegroup:

eksctl delete nodegroup [NODEGROUP NAME GOES HERE] --cluster eks-walden

You can now delete your cluster:

eksctl delete cluster eks-walden

Finally, you should clean up your EBS volumes. You can do so by visiting the Volumes section in your AWS console.

NOTE: please take care when cleaning your EBS volumes. You may lose data you care about. Make sure you understand what volumes you're deleting.


This tutorial assumes you have a working Azure account, with default quota settings. You will likely need to activate pay-as-you-go billing to be able to provision the AKS cluster described here.

Create AKS cluster

First, create a dedicated resource group, this example uses the centralus region:

az group create --name WaldenResourceGroup -l centralus

You are now ready to create your cluster:

az aks create -g WaldenResourceGroup -n WaldenAKS --node-count 5 --node-vm-size Standard_B2ms

To connect to the cluster:

az aks get-credentials --resource-group WaldenResourceGroup --name WaldenAKS

Deploy Walden

You now have a working AKS cluster, on which you can deploy Walden just as you would on an on-premise cluster. Follow these instructions to deploy it.

Clean up

First, delete the cluster:

az aks delete --resource-group WaldenResourceGroup --name walden

You can now delete the resource group:

az group delete --resource-group WaldenResourceGroup

Advanced topics

Adding external data sources via Trino

External databases can be added to Walden by connecting them to Trino as a separate "Catalog". The new Trino Catalog can then be added to Superset.

This strategy allows using both Trino and Superset to interact with the external data. However, some data types (such as GIS geometry columns) may not work well with Trino. In those cases you can instead connect Superset to the external database directly as described in the next section below.

  1. Create a separate Kubernetes ConfigMap named trino-catalog-extra in the walden namespace. This ConfigMap should contain one or more .properties files for each Trino connector that you want.
  2. If Walden is already deployed, restart the trino-* pods manually for the change to take effect.
    $ kubectl delete pod -n walden trino-coordinator-xxxx-yyyy trino-worker-xxxx-yyyy
  3. Verify that the external data source is accessible by logging in to the devserver pod as described above and checking for a data source named custom.
    $ kubectl exec -it -n walden deployment/devserver -- /bin/bash
    # trino-cli --server trino --catalog custom
    trino> SHOW SCHEMAS;
    trino> DESCRIBE <schemaname>.<tablename>;

Now we should be able to add the new Trino catalog to Superset:

  1. Open the Superset UI and log in as described above.
    $ kubectl get secret -n walden superset-admin -o 'jsonpath={.data.pass}' | base64 -d && echo
    $ kubectl port-forward -n walden deployment/superset 8088
  2. Go to Data > Databases via the top menu and click the + Database on the upper right to add a new Database.
  3. Select the Trino database type from the pull down menu.
  4. Set the SQLAlchemy URI to trino://trino/custom.
  5. (OPTIONAL) Switch to the Advanced tab and enable the following:
    • SQL Lab:
      • Expose database in SQL Lab, followed by...
      • Allow Multi Schema Metadata Fetch (optional: don't enable if the DB is very large)
      • Enable query cost estimation
      • Allow this database to be explored
    • Performance:
      • Asynchronous query execution
  6. Click Connect to create the new Database entry.

The new Database entry can be reconfigured again later if needed.

Now you can switch to SQL Lab > SQL Editor and preview the new Database, confirming that it looks as expected.

Check the Trino and Superset docs for any additional information on configuring particular database types.

Screenshot of Superset UI showing external PostGIS data via Trino

Adding external data sources (and other CLI configuration) to Superset

Instead of connecting an external database via Trino, the external database may instead be connected to Superset directly. This means the data will only be accessible via the Superset UI, and will not be accessible via Trino.

Follow the above steps for logging into the Superset UI and adding a new Database entry, except this time you should pick the type of database that you are adding, instead of Trino. The steps are otherwise similar. If your datatype isn't listed, you may need to build a custom walden-superset Docker image that installs the required python module(s).

Check the Superset docs for any additional information on configuring particular database types.

If you wish to provide the additional datasources declaratively via a YAML file, you can do so with something like the following custom ConfigMap. The special script allows running your own custom CLI commands on Superset startup. The superset-custom ConfigMap will take effect after restarting the superset and superset-worker pods:

apiVersion: v1
kind: ConfigMap
  name: superset-custom
  namespace: walden
data: |
    superset import_datasources -p /app/pythonpath/superset_datasources_custom.yaml
  superset_datasources_custom.yaml: |
    - name: my_database
      ...config here...

Adding/overriding configuration

The provided provides a reasonable base configuration for integrating with Walden, using Postgres as the metastore and Redis as the cache.

You may want to customize this configuration, for example to configure a custom authentication provider. To do this, create your own ConfigMap named superset-custom which contains your own, and/or any other files that should be included in the same directory as The content of your custom will be concatenated to the end of the default Walden, and any additional files you provide will be copied into the same directory.

Here is a minimal example of configuring custom additions to, which will take effect after restarting the superset and superset-worker pods. This can be combined with the above example of running custom superset CLI commands on pod startup:

apiVersion: v1
kind: ConfigMap
  name: superset-custom
  namespace: walden
data: |
    print("hello world! this is a custom config") |
    # for example, this could have your custom SupersetSecurityManager implementation
    # see docs:

Similarly, if you need to provide some credentials for your config, they can be specified in a separate Secret that's also named superset-custom:

apiVersion: v1
kind: Secret
  name: superset-custom
  namespace: walden
  oauth_secrets.json: |
    { ... secret keys here ... }

Building images using Kaniko

Cheat sheet for building images from within an existing cluster. This can also be done locally via the Docker CLI or similar.

kubectl create secret -n walden docker-registry regcred --docker-server= --docker-username=[your-docker-username] --docker-password=[your-docker-password]
kubectl apply -f kube-build/templates/kaniko-devserver.yaml

After building/pushing new release images, update the tags for the affected image_* defaults in tf/

Deploying with custom images

Walden can be deployed with custom images from your registry/organization.

  1. Create your own terraform.tfvars file with custom overrides for the image_* values listed under tf/
  2. Build and push images: Run docker/*/ and docker/*/
  3. Deploy using custom images: Run tf apply under the tf/ directory

Deploying more MinIO nodes

MinIO must be deployed with at least four nodes, which is the default number used by Walden. If you'd like to deploy more MinIO nodes, create a terraform.tfvars file with a custom override of the minio_replicas setting, then apply using tf apply under the tf/ directory.

Deploying MinIO on alternate architectures

The MinIO images are multi-arch and so can be configured to run on nodes with non-amd64 architectures. In our case, we have a mixed-architecture cluster where several arm64 Raspberry Pis provide local storage, making them a convenient place for running the MinIO pods. To deploy with MinIO nodes on a different architecture, edit the minio_arch setting in your terraform.tfvars file. Note that we do not support custom architectures for the walden-* images themselves, as the underlying software doesn't deal with it well.


A small data lake meant for solitary use







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