DataLens

DataLens is a modern business intelligence and data visualization system. It was developed and extensively used as a primary BI tool in Yandex and is also available as a part of Yandex Cloud platform. See also our roadmap, releases notes and community in telegram.

Getting started

Installing Docker

DataLens requires Docker to be installed. Follow these instructions depending on the platform you use:

• macOS
• Linux
• Windows

* Note about docker compose

• New docker compose plugin available as docker-compose-v2 package on Ubuntu 20.04/22.04/24.04 from base APT repository

• Minimal supported version of legacy docker-compose utility as separate package is 1.29.0. It includes in base APT repository as docker-compose package only on Ubuntu 22.04

Running containers

Use the following command to start DataLens containers:

git clone https://github.com/datalens-tech/datalens && cd datalens

HC=1 docker compose up

# or with an external metadata database
METADATA_POSTGRES_DSN_LIST="postgres://{user}:{password}@{host}:{port}/{database}" HC=1 docker compose up

This command will launch all containers required to run DataLens and UI will be available on http://localhost:8080

If you want to use a different port (e.g. 8081), you can set it using the UI_PORT env variable:

UI_PORT=8081 docker compose up

  Highcharts is a proprietary commercial product. If you enable highcharts in your DataLens instance (with `HC=1`` variable), you should comply with Highcharts license (https://github.com/highcharts/highcharts/blob/master/license.txt).

  When Highcharts is disabled in DataLens, we use D3.js instead. However, currently only few visualization types are compatible with D3.js. We are actively working on adding D3 support to additional visualizations and are going to completely replace Highcharts with D3 in DataLens.

YANDEX_MAP_ENABLED=1 YANDEX_MAP_TOKEN=XXXXXXXXX docker compose up

How to update

Just pull the new docker-compose.yml and restart.

docker compose down
git pull
docker compose up

All your user settings will be stored in the metadata folder.

Parts of the project

DataLens consists of the three main parts:

• UI is a SPA application with corresponding Node.js part. It provides user interface, proxies requests from users to backend services and also applies some light data postprocessing for charts.
• Backend is a set of Python applications and libraries. It is responsible for connecting to data sources, generating queries for them and post-processing the data (including formula calculations). The result of this work is an abstract dataset that can be used in UI for charts data request.
• UnitedStorage (US) is a Node.js service that uses PostgreSQL to store metadata and configuration of all DataLens objects.

What's already available

We are releasing DataLens with first minimal set of available connectors (clickhouse, clickhouse over ytsaurus and postgresql) as well as other core functionality such as data processing engine and user interface. However, to kick off this project in a reasonable timeframe we have chosen to drop some of the features out of the first release: this version does not contain middleware and components for user sessions, object ACLs and multitenancy (although code contains entry-points for such extensions). We are planning to add missing features based on our understanding of community priorities and your feedback.

Cloud Providers

Below is a list of cloud providers offering DataLens as a service:

1. Yandex Cloud platform
2. DoubleCloud platform

Authentication (beta)

DataLens supports authentication via Zitadel identity platform.

Use the following command to initialize Zitadel (you need to do this only once):

bash init.sh

Notice the updated .env file after initialization: it contains Zitadel access keys. Keep that file safe and do not share it's contents.

After initialization you can start DataLens containers using special version of docker compose file:

HC=1 docker compose -f docker-compose.zitadel.yml up

After that you can login to DataLens on http://localhost:8080 using the default user credentials:

You can use the same credentials to configure Zitadel and add new users using Zitadel control panel at http://localhost:8085/. Don't forget to login there at least once to change the default password.

By default in DataLens with authentication enabled, all users have a datalens.viewer role. This allows them to use all collections and workbooks in read-only mode. They are not allowed to create or modify any objects with this role. To be able to create or edit objects, they need to have a datalens.editor or datalens.admin role. To grant these roles, open Zitadel at http://localhost:8085/ui/console/grants, then find the user to whom you want to grant a new role and click on the user and select the new role.

DataLens supports the following roles:

• datalens.viewer: allows viewing all collections and workbooks, but does not allow creating or editing any object.
• datalens.editor: includes the datalens.viewer role and allows creating, editing and deleting any object.
• datalens.admin: currently equal to datalens.editor. In the future releases, users with this role will be able to manage system-wide settings and perform administrative functions.

FAQ

Where does DataLens store it's metadata?

We use the metadata folder to store PostgreSQL data. If you want to start over, you can delete this folder: it will be recreated with demo objects on the next start of the datalens-us container.

I use the METADATA_POSTGRES_DSN_LIST param for external metadata database and the app doesn't start. What could be the reason?

We use some PostgresSQL extensions for the metadata database and the application checks them at startup and tries to install them if they haven't been already installed. Check your database user's rights for installing extensions by trying to install them manually:

CREATE EXTENSION IF NOT EXISTS pg_trgm;
CREATE EXTENSION IF NOT EXISTS btree_gin;
CREATE EXTENSION IF NOT EXISTS btree_gist;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

If this attempt is unsuccessful, try to install dependencies by database admin and add param METADATA_SKIP_INSTALL_DB_EXTENSIONS=1 on startup, this parameter allows the app to skip installing extensions.

If you're using managed database, it's also possible that extensions for your database cluster are controlled by external system and could be changed only using it's UI or API. In such case, consult with documentation for managed database service which you're using. Don't forget to add METADATA_SKIP_INSTALL_DB_EXTENSIONS=1 after installing extensions this way.

My PostgresSQL cluster has multiple hosts, how can I specify them in METADATA_POSTGRES_DSN_LIST param?

You can write all cluster hosts separated by commas:

METADATA_POSTGRES_DSN_LIST="postgres://{user}:{password}@{host_1}:{port}/{database},postgres://{user}:{password}@{host_2}:{port}/{database},postgres://{user}:{password}@{host_3}:{port}/{database}" ...

How can I specify custom certificate for connecting to metadata database?

You can add additional certificates to the database in ./certs/root.crt, they will be used to connect to the database from the datalens-us container.

If datalens-us container does not start even though you provided correct certificates, try to change METADATA_POSTGRES_DSN_LIST like this: METADATA_POSTGRES_DSN_LIST="postgres://{user}:{password}@{host}:{port}/{database}?sslmode=verify-full&sslrootcert=/certs/root.crt"

Why do i see two compose files: docker-compose.yml & docker-compose-dev.yml?

docker-compose-dev.yml is a special compose file that is needed only for development purposes. When you run DataLens in production mode, you always need to use docker-compose.yml. The docker-compose up command uses it by default.

What are the minimum system requirements?

• datalens-ui - 512 MB RAM

• datalens-data-api - 1 GB RAM

• datalens-control-api - 512 MB RAM

• datalens-us - 512 MB RAM

• datalens-pg-compeng - 1 GB RAM

• datalens-pg-us - 512 MB RAM

Summary:

• RAM - 4 GB

• CPU - 2 CORES

This is minimal basic system requirements for OpenSource DataLens installation. Аctual consumption of VM resources depends on the complexity of requests to connections, connections types, the number of users and processing speed at the source level