bump deps; intro tutorial
Ziyang Hu
2 years ago
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3df9ce6e-3722-4334-abf9-703d62c27eab",
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"metadata": {},
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"source": [
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"# The Cozo database tutorial"
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]
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},
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{
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"cell_type": "markdown",
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"id": "63c695dc-6e85-42bb-b39b-1f0fd14ed5eb",
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"metadata": {},
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"source": [
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"This tutorial will teach you the basics of using the Cozo database with the query language CozoScript.\n",
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"There are no database-specific prerequisites, \n",
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"though it would be helpful if you already know some other databases, \n",
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"especially SQL databases."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c2bfcfe4-c06c-4b02-a040-6fd840b77820",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb1fefcf-81b5-4ccf-8179-631fdb011bae",
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"metadata": {},
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"source": [
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"The best way to learn from this tutorial is to run the queries as they are introduced.\n",
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"For this, you need to install the Cozo database on your local machine."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7f26f0f5-a3ff-4145-a9d5-84c65e368afa",
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"metadata": {},
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"source": [
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"Cozo is distributed as a single gzipped/zipped binary. \n",
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"Go to https://github.com/cozodb/cozo/releases and download the latest release binary for your operating system to a local directory.\n",
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"\n",
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"If your operating system is Linux/Mac, \n",
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"open a terminal command prompt, \n",
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"`cd` into the directory of your download,\n",
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"and run"
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]
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},
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{
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"cell_type": "raw",
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"id": "72a21b8e-d66a-48d3-b684-e9554ea382cc",
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"metadata": {},
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"source": [
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"gunzip cozoserver-*.gz\n",
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"mv cozoserver-* cozoserver\n",
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"chmod +x cozoserver"
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]
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},
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{
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"cell_type": "markdown",
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"id": "536fb265-e25f-469d-843e-935b272c7298",
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"metadata": {},
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"source": [
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"If you are on Windows instead, open a PowerShell and run"
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]
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},
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{
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"cell_type": "raw",
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"id": "a11f0388-dd7b-49a2-825f-0dde834ceb6b",
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"metadata": {},
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"source": [
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"Expand-Archive -Path .\\cozoserver-*.zip -DestinationPath ."
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]
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},
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{
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"cell_type": "markdown",
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"id": "12da9726-3c18-4011-98ea-e2aae105ee08",
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"metadata": {},
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"source": [
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"To run the server, you need to specify a directory to store persistent data on your file system. \n",
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"In the following we will use a directory called `tutorial-data` in the same directory as the binary executable.\n",
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"In the terminal, run"
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]
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},
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{
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"cell_type": "raw",
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"id": "b4f837e4-6f26-4c57-a7a9-fb5765e4bc92",
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"metadata": {},
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"source": [
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"./cozoserver ./tutorial-data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41820cf5-a800-4c81-8a17-227e9692aae2",
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"metadata": {},
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"source": [
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"The same command should work in powershell as well.\n",
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"\n",
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"If you see something like `Database web API running at ...` displayed in your terminal, \n",
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"then the server is successfully started. \n",
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"Keep the server running when you are following the tutorial.\n",
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"When you are done, `CTRL-C` in the terminal will stop the server.\n",
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"You can restart the server again by running the command again.\n",
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"\n",
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"More options when starting the server is available. Run"
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]
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},
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{
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"cell_type": "raw",
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"id": "e7bd8545-8d42-43a4-ae3b-e946fc5ff19b",
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"metadata": {},
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"source": [
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"./cozoserver -h"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f192cf0b-a30d-46ad-85a1-d81f7590b78b",
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"metadata": {},
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"source": [
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"for more details."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2ec06d2e-1187-48c8-8777-f84b4c71c741",
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"metadata": {},
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"source": [
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"## A place to run queries\n",
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"\n",
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"Cozo exposes an HTTP API, so theoretically you can follow along using tools like `curl`. \n",
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"If you are interested, consult the manual for the request format the API expects.\n",
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"For better user experience, though, we suggest following one of the following two subsections instead."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4e8b790-7c0f-4538-b20e-2c3fb4065dc8",
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"metadata": {},
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"source": [
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"### Option 1: the JupyterLab notebook\n",
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"\n",
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"This option provides the best user experience but also requires you to install quite a lot of things, \n",
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"though you may already have them installed on your computer if you use the python data science stack.\n",
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"First you will need python installed. \n",
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"Then install JupyterLab by following the instruction at https://jupyter.org/install.\n",
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"Next, run the following to install a Jupyter extension to help querying Cozo:"
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]
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},
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{
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"cell_type": "raw",
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"id": "6ffa6d95-101b-4229-900a-ce9b14685439",
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"metadata": {},
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"source": [
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"pip install pycozo pandas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c07f306-9abc-4dd2-b78c-5b46a823982e",
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"metadata": {},
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"source": [
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"While you are at it, go to the source of this tutorial at https://github.com/cozodb/cozo/blob/main/tutorial/tutorial.ipynb, \n",
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"right-click on `Raw` and save the tutorial document to your disk.\n",
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"\n",
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"Then run jupyter lab, open a the saved tutorial document, and follow along."
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]
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},
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{
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"cell_type": "markdown",
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"id": "5154e084-d997-4f22-97fb-ce5808fb1da3",
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"metadata": {},
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"source": [
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"We need to enable the extension in the notebook. Run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2e0d11d7-64d7-4577-a28d-7541ee8875fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext pycozo.ipyext_direct"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3f0d80d-9854-4a21-9121-3220e7bcc73b",
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"metadata": {},
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"source": [
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"Then the \"hello world\" query:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f3dfb8a1-35f0-4dc2-b8d7-e81fa3d45b75",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<p style=\"font-size: 75%\">Completed in 0ms</p>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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"#T_9e17d_row0_col0, #T_9e17d_row0_col1, #T_9e17d_row0_col2 {\n",
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" color: black;\n",
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"}\n",
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"</style>\n",
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"<table id=\"T_9e17d\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th class=\"blank level0\" > </th>\n",
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" <th id=\"T_9e17d_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
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" <th id=\"T_9e17d_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
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" <th id=\"T_9e17d_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th id=\"T_9e17d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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" <td id=\"T_9e17d_row0_col0\" class=\"data row0 col0\" >hello</td>\n",
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" <td id=\"T_9e17d_row0_col1\" class=\"data row0 col1\" >world</td>\n",
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" <td id=\"T_9e17d_row0_col2\" class=\"data row0 col2\" >Cozo!</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n"
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],
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"text/plain": [
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"<pandas.io.formats.style.Styler at 0x117904640>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"?[] <- [['hello', 'world', 'Cozo!']]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7e6dda2a-393a-4d87-8ba1-41c5d0b229d5",
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"metadata": {},
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"source": [
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"If you get the same words back formatted in a table, congratulations! \n",
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"You can skip to the next section where we starts learning CozoScript proper.\n",
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"If you want to know more about what the `pycozo` extension did, read the manual."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac86f14f-72c7-4206-9e6b-6daf9e77b67a",
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"metadata": {},
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"source": [
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"### Option 2: the JavaScript console in your browser"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8ddc55d2-604b-4218-af45-8c62d79db201",
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"metadata": {},
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"source": [
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"If you have never used Python before, the first option may be overwhelming. \n",
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"Or you just want to try Cozo out first to decide quickly if it has anything interesting for you.\n",
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"Whatever your reason for not wanting to install the whole python toolchain, we have you covered."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1147466d-d925-4c32-8b6c-28bdabc656fb",
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"metadata": {},
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"source": [
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"Your local machine at least have a modern web browser, like a recent version of Firefox, Chrome, or Edge, right?\n",
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"Good. \n",
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"\n",
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"Use your browser to navigate to http://127.0.0.1:9070 (the address shown in your terminal when you run `cozoserver`).\n",
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"You should be greeted be a page saying that the server is running.\n",
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"Now open the developer tools of your browser by right clicking the page and select \"Inspect\" from the menu \n",
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"(if you cannot find it, you may need to fiddle with your browser settings to enable the developer tools).\n",
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"Switch to the \"Console\" tab of the developer tools if it is not already open. \n",
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"\n",
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"If you see some messages where \n",
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"the \"Cozo Makeshift Javascript Console\" welcomes you, you are ready. Run the \"hello world\" query by typing the following into the console and press enter:"
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]
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},
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{
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"cell_type": "raw",
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"id": "fb8fee0c-4069-45c8-bb2a-9aa96037a6ed",
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"metadata": {},
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"source": [
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"await run(`?[] <- [['hello', 'world', 'Cozo!']]`)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "81fe58e2-3c52-4859-828d-533b25a62468",
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"metadata": {},
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"source": [
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"If you see the three words echoed back in a table, you are successful. When following the tutorial, you have to wrap all queries within the backticks `` in the above command to run them in the JavaScript console."
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]
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},
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{
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"cell_type": "markdown",
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"id": "545ed77c-eeb9-4008-972e-0bdc1d6b7478",
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"metadata": {},
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"source": [
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"## Rules and relations"
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]
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},
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{
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"cell_type": "markdown",
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"id": "61d8ce38-d2e0-4b5f-a9f7-64e9a65e0fe0",
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"metadata": {},
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"source": [
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"Cozo is a relational database. As you might have guessed, the \"hello world\" query"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "30b8148b-4daa-4de0-a844-d8254a07d990",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<p style=\"font-size: 75%\">Completed in 0ms</p>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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"#T_3b07f_row0_col0, #T_3b07f_row0_col1, #T_3b07f_row0_col2 {\n",
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" color: black;\n",
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"}\n",
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"</style>\n",
|
||||||
|
"<table id=\"T_3b07f\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th class=\"blank level0\" > </th>\n",
|
||||||
|
" <th id=\"T_3b07f_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
|
||||||
|
" <th id=\"T_3b07f_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
|
||||||
|
" <th id=\"T_3b07f_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_3b07f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
||||||
|
" <td id=\"T_3b07f_row0_col0\" class=\"data row0 col0\" >hello</td>\n",
|
||||||
|
" <td id=\"T_3b07f_row0_col1\" class=\"data row0 col1\" >world</td>\n",
|
||||||
|
" <td id=\"T_3b07f_row0_col2\" class=\"data row0 col2\" >Cozo!</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x117905840>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"?[] <- [['hello', 'world', 'Cozo!']]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "a7de1353-7970-41ee-843e-cbe6287ded8d",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"simply passes an ad hoc relation, here represented by a list of lists, and ask the database to return the relation to you.\n",
|
||||||
|
"You can pass more rows, or a different number of columns, to corroborate further your guess:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"id": "27995b4b-35b3-427f-af32-c3945d177463",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<p style=\"font-size: 75%\">Completed in 0ms</p>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<IPython.core.display.HTML object>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<style type=\"text/css\">\n",
|
||||||
|
"#T_88c45_row0_col0, #T_88c45_row0_col1, #T_88c45_row0_col2 {\n",
|
||||||
|
" color: #307fc1;\n",
|
||||||
|
"}\n",
|
||||||
|
"#T_88c45_row1_col0, #T_88c45_row1_col1, #T_88c45_row1_col2 {\n",
|
||||||
|
" color: black;\n",
|
||||||
|
"}\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table id=\"T_88c45\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th class=\"blank level0\" > </th>\n",
|
||||||
|
" <th id=\"T_88c45_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
|
||||||
|
" <th id=\"T_88c45_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
|
||||||
|
" <th id=\"T_88c45_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_88c45_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
||||||
|
" <td id=\"T_88c45_row0_col0\" class=\"data row0 col0\" >1</td>\n",
|
||||||
|
" <td id=\"T_88c45_row0_col1\" class=\"data row0 col1\" >2</td>\n",
|
||||||
|
" <td id=\"T_88c45_row0_col2\" class=\"data row0 col2\" >3</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_88c45_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
||||||
|
" <td id=\"T_88c45_row1_col0\" class=\"data row1 col0\" >a</td>\n",
|
||||||
|
" <td id=\"T_88c45_row1_col1\" class=\"data row1 col1\" >b</td>\n",
|
||||||
|
" <td id=\"T_88c45_row1_col2\" class=\"data row1 col2\" >c</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x117906d40>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"?[] <- [[1, 2, 3], ['a', 'b', 'c']]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 15,
|
||||||
|
"id": "54901c40-ff7e-4c26-bfed-0d38aa7a80dc",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<p style=\"font-size: 75%\">Completed in 0ms</p>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<IPython.core.display.HTML object>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<style type=\"text/css\">\n",
|
||||||
|
"#T_ef1e5_row0_col0, #T_ef1e5_row0_col1, #T_ef1e5_row0_col3, #T_ef1e5_row1_col0, #T_ef1e5_row1_col1, #T_ef1e5_row1_col2, #T_ef1e5_row1_col3, #T_ef1e5_row1_col4 {\n",
|
||||||
|
" color: #307fc1;\n",
|
||||||
|
"}\n",
|
||||||
|
"#T_ef1e5_row0_col2 {\n",
|
||||||
|
" color: #bf5b3d;\n",
|
||||||
|
"}\n",
|
||||||
|
"#T_ef1e5_row0_col4, #T_ef1e5_row2_col0, #T_ef1e5_row2_col1, #T_ef1e5_row2_col2, #T_ef1e5_row2_col3, #T_ef1e5_row2_col4 {\n",
|
||||||
|
" color: black;\n",
|
||||||
|
"}\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table id=\"T_ef1e5\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th class=\"blank level0\" > </th>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
||||||
|
" <td id=\"T_ef1e5_row0_col0\" class=\"data row0 col0\" >True</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row0_col1\" class=\"data row0 col1\" >False</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row0_col2\" class=\"data row0 col2\" >None</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row0_col3\" class=\"data row0 col3\" >-0.014000</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row0_col4\" class=\"data row0 col4\" >A string with double quotes</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
||||||
|
" <td id=\"T_ef1e5_row1_col0\" class=\"data row1 col0\" >1.500000</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row1_col1\" class=\"data row1 col1\" >2.500000</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row1_col2\" class=\"data row1 col2\" >3</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row1_col3\" class=\"data row1 col3\" >4</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row1_col4\" class=\"data row1 col4\" >5.500000</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_ef1e5_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
|
||||||
|
" <td id=\"T_ef1e5_row2_col0\" class=\"data row2 col0\" >aA</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row2_col1\" class=\"data row2 col1\" >bB</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row2_col2\" class=\"data row2 col2\" >cC</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row2_col3\" class=\"data row2 col3\" >dD</td>\n",
|
||||||
|
" <td id=\"T_ef1e5_row2_col4\" class=\"data row2 col4\" >eE</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x117907c10>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"?[] <- [[1.5, 2.5, 3, 4, 5.5], \n",
|
||||||
|
" ['aA', 'bB', 'cC', 'dD', 'eE'], \n",
|
||||||
|
" [true, false, null, -1.4e-2, \"A string with double quotes\"]]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "615e35d7-f5c0-4d71-ac62-6934c658a715",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"The last example shows how to enter literals for numbers, strings, booleans and `null`.\n",
|
||||||
|
"The literal representations are similar to those in JavaScript. \n",
|
||||||
|
"In particular, strings in double quotes are guaranteed to be interpreted in exactly the same way as in JSON."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "1d76ab84-36a3-4d1d-9da2-a891de52b011",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"You may be surprised by the order of the returned rows in the last example: the returned order is not the same as the input order.\n",
|
||||||
|
"This is because in Cozo relations are stored (either in memory or on disk) as trees, and trees are always sorted.\n",
|
||||||
|
"Another consequence of trees is that you can have no duplicate rows:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 14,
|
||||||
|
"id": "28ab3f6e-0e32-486d-83fb-4fd5581e20dc",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<p style=\"font-size: 75%\">Completed in 0ms</p>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<IPython.core.display.HTML object>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<style type=\"text/css\">\n",
|
||||||
|
"#T_470e0_row0_col0, #T_470e0_row1_col0 {\n",
|
||||||
|
" color: #307fc1;\n",
|
||||||
|
"}\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table id=\"T_470e0\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th class=\"blank level0\" > </th>\n",
|
||||||
|
" <th id=\"T_470e0_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_470e0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
||||||
|
" <td id=\"T_470e0_row0_col0\" class=\"data row0 col0\" >1</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th id=\"T_470e0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
||||||
|
" <td id=\"T_470e0_row1_col0\" class=\"data row1 col0\" >2</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x117904310>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 14,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"?[] <- [[1], [2], [1], [2], [1]]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5a5d9022-c9b2-48c9-b447-ae69a2fc3a21",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"We say that relations in Cozo follow _set semantics_ where de-duplication is automatic. \n",
|
||||||
|
"By constrast, SQL usually follows _bag semantics_ (some databases does this by secretly having a unique internal key for every row, in Cozo you must do this explicitly if you need to simulate duplicate rows).\n",
|
||||||
|
"Why does Cozo break tradition and go with set semantics?\n",
|
||||||
|
"Set semantics is much more convenient when you have recursions between relations involved,\n",
|
||||||
|
"and Cozo is designed to deal with very complicated recursions."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "a55e4ab1-e0f1-4126-9252-db2b16718106",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.6"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue