The data directory

The data directory is the unique location on the DSS server where DSS stores all its configuration and data files.

Notably, you will find here:

  • Startup scripts to start and stop DSS
  • Settings and definitions of your datasets, recipes, projects, …
  • The actual data of your machine learning models
  • Some of the data for your datasets (those stored in local connections)
  • Logs
  • Temporary files
  • Caches

The data directory is the directory which you set during the installation of DSS on your server (the -d option).

If you did not install DSS yourself, you can find the path to the data directory by going to Administration > Maintenance > System info (you need to be a DSS administrator for this).

Main folders of the data directory

The data directory is made of the following folders:

R.lib

This folder contains the R libraries installed by calling install.packages() from a R notebook or recipe, as well as the R libraries that DSS installs when running install-R-integration.

Note that we recommend using code environments rather than calling install.packages() manually. For more information, see Code environments.

analysis-data

This folder contains the data for the models trained in the Lab part of DSS.

The data is organized by project, then by visual analysis. The data for a visual analysis is removed when the analysis is removed in DSS.

Depending on the ML engine used, this folder can contain the train and test splits data, which can become big (in splits folders). It is possible to remove these CSV files, but you will lose some of the ability to compare exactly models since they won’t be based on the same splits.

bin

This folder contains various programs and scripts to manage DSS.

  • dss: main start/stop script
  • dssadmin: for offline administration tasks
  • dsscli: for various administration tasks
  • env-default.sh, env-hadoop.sh, env-spark.sh, dku, fek, jek, python: internal usage, not for use by end users
  • env-site.sh for advanced environment customization
  • pip: for management of the builtin Python environment. For more information, see Code environments.

caches

This folder contains various precomputed information. It is safe to remove elements in this folder, but some operations in DSS will to recompute them (displaying explore samples and charts)

code-envs

Note

This folder is called acode-envs on the automation node

This folder contains the definitions of all code environments, as well as the actual packages

config

This is the most important folder, where all user configuration and data is stored:

  • All projects, datasets, recipes, notebooks, webapps, ….
  • Users and security settings
  • Connections
  • API keys

This folder contains several Git repositories

databases

This folder contains several internal databases used for operation of DSS:

  • Usage statistics
  • Jobs and scenarios histories
  • Metrics and checks histories
  • Users watches and stars
  • Users notifications

Some of the information in these databases can be accessed from DSS itself using the “Internal stats” and “Metrics” virtual datasets

data-catalog

This folder contains the indices and staging data for the DSS data catalog.

exports

This folder contains download files for exports made by users.

It is safe to remove old folders in this folder - exports will not be available anymore for download by users

install-support

Internal support files

jobs

This folder contains the job logs and support files for all flow build jobs in DSS, both running and previous.

It is safe to remove folders of jobs that are not currently running. Logs of these jobs will not be available anymore, but the existence of the job will still be registered in the DSS UI.

jupyter-run

This folder contains internal runtime support file for the Juypter notebook.

The “current working directory” of all notebooks is initialized within this folder. If a user’s notebook code writes files to the current working directory, these files will appear in jupyter-run.

lib

This folder contains administrator-installed global custom libraries (Python and R), as well as JDBC drivers.

local

This folder contains administrator-installed files for serving in web applications

managed_datasets

This is the location of the “filesystem_managed” connection which is installed by default in DSS. It contains datasets data written in this connection.

managed_folders

This is the location of the “filesystem_folders” connection which is installed by default in DSS. It contains folders data written in this connection.

notebook_results

This folder contains the query results for SQL / Hive / Impala notebooks

plugins

This folder contains the plugins (both installed in DSS, and developed directly in DSS)

privtmp

This folder contains security-sensitive temporary files that should not be modified.

pyenv

This folder contains the builtin Python environment of DSS

run

This folder contains all core log files of DSS. See Diagnosing and debugging issues for more information.

saved_models

This folder contains the data for the models trained in the Flow.

The data is organized by project, then by model id.

timelines

This folder contains databases storing the “timelines” information associated to each kind of DSS object.

tmp

This folder contains temporary files. See below for more information

uploads

This folder contains the files that have been uploaded to DSS to use as datasets.

Managing temporary files

Several locations in DSS contain files that can be considered as temporary and that can be cleaned under certain conditions:

  • caches
  • uploads
  • exports