Under the hood

Turning code into a Datagrok script

Briefly, you only need to do the following:

• Create a new script file in the Datagrok script editor
• Write the code in any supported language.
• Add header: the set of annotation comments specifying the script language, tags, input and output variables.

That's all.

Runtime environment

For each language, Datagrok uses a predefined header and creates an environment with the set of pre-installed packages.

Python

import os
import io
import json
import pandas as pd
import requests
from datetime import datetime, timedelta

R

require(httr)
require(repr)
require(data.table)
require(R.utils)

Octave

Octave scripts run in a separate container on server. This container has following Octave packages pre-installed:

• symbolic
• optim

Always provide pkg load %PACKAGE_NAME% in your scripts, since script runs are fully independent.

Julia

using HTTP
using DataFrames
using CSV
using Compat
using MbedTLS
using Dates
using JSON

JavaScript

import * as grok from 'datagrok-api/grok';
import * as ui from 'datagrok-api/ui';
import * as DG from 'datagrok-api/dg';
import * as OCL from 'openchemlib/full.js';
import * as rxjs from 'rxjs';
import * as rxjs.operators from 'rxjs/operators';
import * as $ from 'cash-dom';
import * as dayjs from 'dayjs';
import * as wu from 'wu';
import * as exceljs from 'ExcelJS';
import * as html2canvas from 'html2canvas';

Environment isolation

You can specify an environment for the script to use, including the language version and a set of libraries.

Datagrok uses Conda as the environment management system. A Conda environment:

• isolates each script run,
• resolves dependencies,
• and takes time to be created before a script run.

Later, the script uses the pre-created environment with no delay for resolving dependencies.

In Datagrok, Conda environment configuration can be specified either right inside the script, or as part of a package.