Blog

In this tutorial, we will explore how to extract color palettes from images captured via a webcam using Node.js, GridDB, and OpenAI. By leveraging Node.js for server-side scripting, GridDB for efficient data storage, and OpenAI for advanced image processing, we will create a seamless pipeline to capture images, analyze them, and generate dynamic color palettes. This guide will walk you through setting up your environment, capturing images from your webcam, and using AI to extract and store color data effectively. Prerequisites Before we dive in, ensure the following software is installed on your machine: Node.js GridDB OpenAI API access Browser with a webcam access Running The Project Clone the source code from this GitHub repository. git clone https://github.com/griddbnet/Blogs.git –branch color-extraction This project also needs to install Node.js and GridDB for this project to run. If the software requirements are installed, change the directory to the apps project directory and then install all the dependencies: cd color-detection-openai cd apps npm install Create a .env file and copy all environment variables from the .env.example file. We need an OpenAI key for this project, please look in the “Getting Started” section to get started. OPENAI_API_KEY=sk-proj-secret VITE_APP_URL=http://localhost:3000 You can change the VITE_APP_URL to your needs and then run the project by running this command: npm run start:build Go to the browser and enter the URL set on VITE_APP_URL, which in this case is http://localhost:3000. Make sure to enable the webcam in your browser, then click the Capture button to take a photo using the web camera. Setting Up the Environment 1. Installing Node.js This project will run on the Node.js platform. You need to install it from here. For this project, we will use the nvm package manager and Node.js v16.20.2 LTS version. # installs nvm (Node Version Manager) curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash # download and install Node.js nvm install 16 # verifies the right Node.js version is in the environment node -v # should print `v16.20.2` # verifies the right NPM version is in the environment npm -v # should print `8.19.4“ To connect Node.js and GridDB database, we need the gridb-node-api npm package which is a Node.js binding developed using GridDB C Client and Node addon API. 2. Setting Up GridDB We will use the GridDB database to save recipes and it’s nutrition analysis. Please look at the guide for detailed installation. We will use Ubuntu 20.04 LTS here. Run GridDB and check if the service is running. Use this command: sudo systemctl status gridstore If not running try to run the database with this command: sudo systemctl start gridstore 3. Get The OpenAI Key To get the OpenAI key, create a project first and then create a key. The important thing is you should save the OpenAI key on the .env file and ensure not to include it in version control by adding it to the .gitignore. OPENAI_API_KEY=sk-proj-secret Another crucial factor is to select models that are accessible for the project. For this project, we will utilize gpt-4o models for image recognition and extracting colors from the image. The AI model’s response is non-deterministic, which means sometimes the response is not exactly what we want. By default this project uses the gpt-4o-mini model, in case the response is not quite right, you can change it to a more powerful model, such as the gpt-4o model. Capturing Images with MediaStream To capture images, we can use MediaStream API. It is an API related to WebRTC which provides support for streaming audio and video data. Before capturing an image from the web camera, we first need to initialize the web camera: const initializeWebcam = () => { navigator.mediaDevices.getUserMedia({ video: true }) .then(stream => { videoRef.current.srcObject = stream }) .catch(error => { console.error(‘getUserMedia error:’, error) }) } And then to capture the image from the video, we can use the drawImage() function: const captureImage = () => { const context = canvasRef.current.getContext(‘2d’) context.drawImage(videoRef.current, 0, 0, canvasRef.current.width, canvasRef.current.height) const base64Image = canvasRef.current.toDataURL(‘image/jpeg’) processImage(base64Image) } The drawImage() function will capture the current frame from the video stream and render it onto the canvas. This allows for further image data manipulation, processing, or conversion. In the provided code, the drawn image on the canvas is converted to a base64-encoded string using the toDataURL() function, which is then sent to a server for processing. Processing Images with OpenAI The image processing on the server is quite simple. The web app will send a base64-encoded image to the /process-image route. app.post(‘/process-image’, async (req, res) => { const { image } = req.body if (!image) { return res.status(400).json({ error: ‘No image provided’ }) } // eslint-disable-next-line no-undef const result = await getColorAnalysis(image) res.json(result.choices[0]) }) Then to get the color analysis from the image, we will use the gpt-4o-mini model from OpenAI. The getColorAnalysis() function will take the base64-encoded image and then process it. async function getColorAnalysis(base64Image) { const response = await openai.chat.completions.create({ model: “gpt-4o-mini-2024-07-18”, messages: [{ role: “system”, content: systemPrompt }, { role: “user”, content: [{ type: “image_url”, image_url: { url: base64Image } }, { type: “text”, text: userPrompt } ] } ], temperature: 0.51, max_tokens: 3000, top_p: 1, frequency_penalty: 0, presence_penalty: 0, }); return response; } OpenAI’s model response is determined by the prompt given. For a color analysis, use the specific prompt: const userPrompt = “Extract the seven most prominent colors from the provided image. Use color clustering techniques to identify and present these colors in Hex values. Answer with the raw array values ONLY. DO NOT FORMAT IT.”; We can get a better result by adding a system prompt to the OpenAI model. This system prompt behaves like a command for the OpenAI model to behave for a specific persona, which is a professional color analyst. const systemPrompt = `You are an AI specialized in colorimetry, the science and technology of color detection and measurement. You possess deep knowledge of the principles of color science, including color spaces, color matching functions, and the use of devices such as spectrophotometers and colorimeters. You provide accurate and detailed analyses of color properties, offer solutions for color consistency issues, and assist in applications ranging from imaging and printing to manufacturing and display technologies. Use your expertise to answer questions, solve problems, and provide color detection and measurement guidance.`; The prompt can also specify the model format response. In this project, we want the array of colors from the image colors analysis. The OpenAI model response should be in the form: [‘#2A2C9B’, ‘#F08A7D’, ‘#8E5DB2’, ‘#E8A1A3’, ‘#4D3B9E’, ‘#7F3C8F’, ‘#B57AB3’] Where each item in the array is a color in the hex format. Storing Data in GridDB We utilize the GridDB database for data storage. Here are the main data fields along with their descriptions: Column Name Type Description id INTEGER Unique identifier for each row. picture BLOB Base64 image encoding. colors STRING List of colors in Hex format. The saveData() function is a wrapper for the insert() function in the libs\griddb.cjs file. It is responsible for saving data into the database. Only two main fields are saved in the database. export async function saveData({ image, genColors }) { const id = generateRandomID() const picture = Buffer(image) const colors = String(genColors) const packetInfo = [parseInt(id), picture, colors] const saveStatus = await GridDB.insert(packetInfo, collectionDb) return saveStatus } The save data function will be executed on the server route /process-image after the color analysis of the image. Every time a user captures an image, it will be automatically sent to the server and the resulting data will be saved to the database. app.post(‘/process-image’, async (req, res) => { const { image } = req.body if (!image) { return res.status(400).json({ error: ‘No image provided’ }) } // eslint-disable-next-line no-undef const result = await getColorAnalysis(image) const colorsArray = result.choices[0].message.content // save data to the database const saveStatus = await saveData(image, colorsArray) console.log(saveStatus) res.json(result.choices[0]) }) Building User Interfaces The UI comprises two primary user interfaces: image capture and color palettes. React.js is utilized in this project to improve component management. Image Capture The image capture user interface is simply an HTML5 video view. This is the snippet code that shows the main HTML tags used: // WebcamContainer.js const WebcamContainer = ({ onColorsExtracted }) => { const captureImage = () => { const context = canvasRef.current.getContext(‘2d’) context.drawImage(videoRef.current, 0, 0, canvasRef.current.width, canvasRef.current.height) const base64Image = canvasRef.current.toDataURL(‘image/jpeg’) processImage(base64Image) } // code processing here return ( Capture Switch Camera ) } export default WebcamContainer When you click the Capture button the captureImage() function will capture the image on a specific video frame and send it for further processing. The full source code for the image capture user interface is in the WebcamContainer.jsx file. Color Palettes The color palette UI can be created using a series of dynamically colored svg rectangles. // eslint-disable-next-line react/prop-types const ColorRectangles = ({ colors }) => { return ( {colors.map((color, index) => ( ))} ) } export default ColorRectangles For example, if the colors array data is: [‘#4B8B3B’, ‘#C4B600’, ‘#7D7D7D’, ‘#E3D4A0’, ‘#2E2E2E’, ‘#F6F1D3’, ‘#A6A6A6’] Then the colors will be rendered on the web as the screenshot below: Server Routes The are four server routes to handle the client request. POST /process-image Process an image for color analysis. GET /colors The /colors route will retrieve all data from the database. GET /colors/:id Retrieve stored color data based on the ID. The data response for the picture field is a Buffer type so to process it in the browser, we need to change it into a readable format first. /** * Extracting the buffer data * Assume the result data name is jsonData */ const bufferData = jsonData[0][1].data; // Converting buffer data to Uint8Array object const uint8Array = new Uint8Array(bufferData); // Converting Uint8Array to UTF-8 string const utf8String = new TextDecoder(‘utf-8’).decode(uint8Array); console.log(utf8String); GET /delete/:id Delete specific data in the database by its ID. For example, to delete data with id 8900: http://localhost:3000/delete/8900 Tools like Postman can be used to test APIs. SQL Data Test To check the data in the database, we can use CLI commands. In this project, we use Ubuntu 20.04 LTS. Login to the GridDB user: sudo su gsadm and then type this command to enter the GridDB shell: gs_sh In this shell, we can list all containers and run any SQL queries. gs[public]> showcontainer gs[public]> select * from ColorPalettes; 1 results (38ms) gs[public]> delete from ColorPalettes where

With the release of a completely free GridDB Cloud, we wanted to pair its free service with Grafana Cloud, another free Cloud-based service which can get you up and running in seconds. For this article, we will walk through the steps of how to display time-series data from your GridDB Cloud shared instance to Grafana Cloud. If you are unfamiliar with GridDB Cloud, you can read our quick start guide here: GridDB Cloud Quick Start Guide — that article will teach you how to sign up, how to begin using GridDB Cloud and more of the basics: who, what, when, where, why. If you are also unfamiliar with Grafana, you can read about its capabilities and strength from their docs: https://grafana.com/docs/grafana/latest/introduction/. If you are curious as to how Grafana can enhance your GridDB experience, I will point you to a previous article we have written here: (Creating Heatmaps of Geometry Data using Grafana & GridDB)[https://griddb.net/en/blog/creating-heatmaps-grafana/]. Essentially, Grafana’s advanced visualization tools allow for some creative ways of analyzing and peering into your data. This article’s goal is simple: showcase how to use the cloud offerings from both vendors to display some GridDB Data in Grafana Cloud. We will go through this process step-by-step and explain any idiosyncrasies along the way. Let’s get started! Implementation First, here’s a link to the Grafana dashboard that we will be using for this article: https://imru.grafana.net/public-dashboards/8a9f9f8ed9d34582aecca867a50c9613. Source code can be found here: $ git clone https://github.com/griddbnet/Blogs.git –branch 4_grafana_cloud Prereqs To follow along, you will need to have access to a free account of both GridDB Cloud and Grafana Cloud. Technical Overview To query our GridDB Cloud data from Grafana, we will be sending HTTP Requests directly from Grafana to our GridDB Cloud. And indeed, any sort of interactions we want to make with our free GridDB Cloud instance will be done via Web API interface; this topic is covered in the quick start linked above, as well as in this article: GridDB WebAPI. You can also of course check out the official docs: GridDB_Web_API_Reference. The specifics of how to form our query and how to create our allowlist to get around GridDB’s firewall will be the subject of our next few sections. Adding Grafana’s IP Addresses to GridDB’s Allowlist In order for our Grafana Cloud instance to send HTTP Requests which are accepted as “safe” by our GridDB Cloud, we need to be able to add all potential IP Addresses to our GridDB Cloud instance. Browsing through the Grafana documentation, we see that they have these lists readily available for these exact scenarios: https://grafana.com/docs/grafana-cloud/account-management/allow-list/. The list we need is called ‘Hosted Grafana’, and if you take a quick peek, you’ll see the list is easily over 100 lines, so then how do we efficiently add all of these to our GridDB Cloud management portal? Well, luckily we have already encountered this scenario in our previous article: Create a Serverless IoT Hub using GridDB Cloud and Microsoft Azure. To solve the issue, we wrote a simple bash script which will take the .txt file as the input and add each ip address to the allowlist of GridDB Cloud’s online portal. Source code and instructions found in that blog in the “#whitelist” section. Here’s the script: #!/bin/bash file=$1 #EXAMPLE #runCurl() { #curl ‘https://cloud57.griddb.com/mfcloud57/dbaas/web-api/contracts/m01wc1a/access-list’ -X POST -H ‘User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:133.0) Gecko/20100101 Firefox/133.0’ -H ‘Accept: application/json, text/plain, */*’ -H ‘Accept-Language: en-US,en;q=0.5’ -H ‘Accept-Encoding: gzip, deflate, br, zstd’ -H ‘Content-Type: application/json;charset=utf-8’ -H ‘Access-Control-Allow-Origin: *’ -H ‘Authorization: Bearer eyJ0eXAiOiJBY2Nlc3MiLCJIUzI1NiJ9.eyJzdWIiOiJkMTg4NjlhZC1mYjUxLTQwMWMtOWQ0Yy03YzI3MGNkZTBmZDkiLCJleHAiOjE3MzEwMTEyMTMsInJvbGUiOiJBZG1pbiIsInN5c3RlbVR5cGUiOjF9.B1MsV9-Nu8m8mJbsp6dKABjJDBjQDdc9aRLffTlTcVM’ -H ‘Origin: https://cloud5197.griddb.com’ -H ‘Connection: keep-alive’ -H ‘Referer: https://cloud5197.griddb.com/mfcloud5197/portal/’ -H ‘Sec-Fetch-Dest: empty’ -H ‘Sec-Fetch-Mode: cors’ -H ‘Sec-Fetch-Site: same-origin’ -H ‘Priority: u=0’ -H ‘Pragma: no-cache’ -H ‘Cache-Control: no-cache’ –data-raw $1 #} runCurl() { $1 } while IFS= read -r line; do for ip in ${line//,/ }; do echo “Whitelisting IP Address: $ip” runCurl $ip done done < "$file" Querying GridDB Cloud from Grafana Cloud Out of the box, Grafana does have a method of sending HTTP Requests to your services, but from what I could tell, they're geared towards specific services (ie. Prometheus) and are limited to HTTP GET Requests. In our case, all of our requests to GridDB Cloud require POST requests, so we needed to find a solution for this: enter the Infinity plugin. Once installed, we will be able to add it as a datasource. Using Infinity as a Data Source From within your Grafana Cloud instance, select Connections --> Data Sources. If you installed the Infinity data source properly, it should show up in this section as an option — please select it. From here, we can add all of our pertinent GridDB Cloud information to forge our connection — we will need to add our basic authentication, and allow our portal’s hostname in the allowed hosts list. Once added, lastly click on the health check section and add your Web API + “/checkConnection” (ie. https://cloud5197.griddb.com:443/griddb/v2/gs_clustermfcloud5197/dbs/ZV8YUlQ8/checkConnection) as a simple sanity check AND health check. Hit Save & Test. We should be able to query our GridDB Cloud Database now! Ingesting Usable Time Series Data Before we query our data, let’s first ensure that we have working data in our GridDB Cloud. If you are following along and have just now made a new account, you can follow our quick start guide to ingest an IoT sample data that can be found on Kaggle. Here is a direct link to the section in the guide: https://griddb.net/en/blog/griddb-cloud-quick-start-guide/#ingest. Here, we are ingesting a csv file and calling the container device1. Forming Our HTTP Requests Now that we can communicate between services, let’s get the data we want. From the Grafana Cloud menu, select Dashboards and then select “new” in the top right corner and then finally Add visualization. From here, select Infinity and you will now have a blank graph and a place to put your query. And now for some options: Type: JSON Parser: Backend Source: URL Format: Data Frame Method: POST Note: Here is a screenshot of the entire query we will form in the ensuing lines of text (screenshot will be displayed again at the end once you can better understand what all of the words mean) The parser and format being what they are allows for us to properly name and label the data being received from GridDB Cloud because of the unusual way in which responds to the requestor with data. Instead of sending back the rows of data in JSON format (which, to be fair, if you’ve got a thousand rows, it’s a lot of unnecessary bloat), GridDB Cloud sends back the information as a JSON file, but the actual rows of data are in array form, with the schema being listed under another JSON key name (columns). As for the URL, you can take a look at the links above about how to form your Web API Request, but here are two we will be using: SQL: https://cloud5197.griddb.com/griddb/v2/gs_clustermfcloud5197/dbs/ZV8YUlQ8/sql/dml/query API: https://cloud5197.griddb.com:443/griddb/v2/gs_clustermfcloud5197/dbs/ZV8YUlQ8/containers/device1/rows In each case, we will have different selectors for our returned data, as well as different body payloads that we will be sending off as a request. First, let’s take a look at the SQL Query. Making a Simple SQL-Select Query First, set your URL to match the query above. The format is as follows: https://[cloud-portal-name].griddb.com/griddb/[clustername]/dbs/[database-name]/sql/dml/query. And then we form our SQL Query within the body of the request. To properly parse this, we set the format as data frame, and then under parsing options & Results fields, we type in “results” in the JSONata/rows selector box and then click add columns. To start, we simply want a couple of columns from our dataset, so we can se the columns like so: Selector: 0 #array index as: ts format-as: Time And then we select the column we want to track in our graph, let’s take a look at temperature Selector: 7 as: temperature format-as: Number Lastly, because it’s a POST request, we must send off something within the body of our request, but in this case, it’s going to be a SQL query. Right under the Method dropdown menu, there’s a button that says Headers, Body, Request Params. Click this. Fro that sub menu, set the Body Type to Raw and set the Body Content Type to JSON. And in the large text box you can add your actual body — or in our case, our SQL query: [ {“stmt” : “select * from device1 LIMIT 1000”} ] Note: I highly recommend using a limit on your query, otherwise Grafana may malfunction trying to show all of your data points. In the graph above, click “zoom in on data” if necessary and your data will be displayed! Cool! More SQL Queries (Group By Range, Aggregations) With the advent of GridDB 5.7, you can make some more complex SQL Queries as well and are not limited to SELECT statements. For example, we can use the SQL Group By Range which allows for us to perform a aggregation operations over a given time span. For example, this is what our query looks like: select ts,temp from device1 WHERE ts BETWEEN TIMESTAMP(‘2020-07-12T01:00:25.984Z’) AND TIMESTAMP(‘2020-07-12T01:22:29.050Z’) GROUP BY RANGE (ts) EVERY (1, SECOND) FILL (LINEAR). So we can simply plug this in to our Infinity plugin and see the results (Hint: you may need to change your column selectors and refresh the graph). You can also do other SQL aggregation queries, really there is no limit; you can read more about that in the GridDB SQL docs: https://griddb.org/docs-en/manuals/GridDB_SQL_Reference.html Using the Group By Range feature is excellent for creating dense graphs even if you don’t have a dense enough dataset! API Query We can also skip SQL and just the API to make queries using simple JSON options in our body request. The URL will be built out as follows: https://[cloud-portal-name].griddb.com/griddb/[clustername]/dbs/[database-name]/containers/[container-name]/rows So enter in your URL and, set the Body Content Type to JSON, and the Body content to some combination of the following: { “offset” : 0, “limit” : 100, “condition” : “temp >= 30”, “sort” : “temp desc” } As you can see, you set the condition and sort options for your dataset. The column options remain the same, except the results of the data is now called ‘rows’, so change that option in the Parsing Options & Result Fields section; you can keep the same column selectors as those stay the same. Conclusion Once you are done querying and adding in the data you like, you can of course save your dashboard for future use. And that’s all! If you have followed along, you have now been able to pair GridDB Cloud with Grafana Cloud to display your

GridDB v5.7.0 has released and we would like to go over some of the new features. You can download the new release directly from GitHub and from the Downloads page. In this article, we specifically want to take a closer look at two of the new features: WebAPI changes and SQL Working Memory limit. GridDB Web API Changes in v5.7.0 The GridDB WebAPI allows for you to interact with your GridDB server via HTTP Requests. The major changes in this release are expanded capability of the SQL functionality, as well as a new way to install the package itself. Packaged within this new release are SQL commands which were previously unavailable, including DDL, DML, and DCL commands. On the github page, you will also find .deb and .rpm files for installation, which will also add installing the web api as a service. The following sections’ information can be found on the project’s github page: https://github.com/griddb/webapi/blob/master/GridDB_Web_API_Reference.md Installation Changes As explained above, you can now install the GridDB Web API as a package, meaning it will auto-populate all necessary directories for you and also create a symlink so that you can launch the service using systemctl, similar to how the GridDB server operates. To do so, grab the .deb file from https://github.com/griddb/webapi/releases/tag/5.7.0 and run: $ sudo dpkg -i https://github.com/griddb/webapi/releases/download/5.7.0/griddb-ce-webapi_5.7.0_amd64.deb Once installed, you can make the changes you deem necessary in the following directory: /var/lib/gridstore/webapi/conf. And then to start: $ sudo systemctl start griddb-webapi.service SQL DDL (Data Definition Language) To me, the standout feature for the Web API is the inclusion of SQL DDL Commands. With this, we can now create tables using the Web API with the familar SQL syntax and are no longer required to use the TQL API for creating our tables. And though the previous methods worked just fine before, the benefits of this addition are two-fold: 1, SQL is a widely-known query language and therefore easier to work with, and 2, TQL commands did not allow for manipulating or creating partitioned tables/containers. Now let’s take a look at creating a table using the new SQL DDL Command. The URL path is as follows: /:cluster/dbs/:database/sql/ddl. Here’s a working example: curl –location ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/sql/ddl’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ {“stmt” : “CREATE TABLE IF NOT EXISTS pyIntPart2 (date TIMESTAMP NOT NULL PRIMARY KEY, value STRING) WITH (expiration_type=’\”PARTITION’\”,expiration_time=10,expiration_time_unit=’\”DAY’\”) PARTITION BY RANGE (date) EVERY (5, DAY);”}, {“stmt” : “ALTER TABLE pyIntPart2 ADD temp STRING”} ]’ Here we are making two statements, one to create a partitioned table with expiry rules (you can read about that here: https://griddb.net/en/blog/griddb-partitioning-and-expiry/, and another to alter that same table and add a new column. Again, this was not possible before because of the nature of partitioned tabled. SQL DML (Data Manipulation Language) With DML commands, we can run SELECT, INSERT, UPDATE, etc commands on our containers. This functionality was partly there prior to the v5.7.0 release, though it was a lot more limited and is no longer recommended to be used. To conduct a DML request, the path is similar to the one above: /:cluster/dbs/:database/sql/dml/query. With this, you can run a SELECT statement to run a lookup of some data from your container. For example curl –location ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/sql/dml/query’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ {“stmt” : “select AVG(total_points_per_game) from Top_NBA_Playoff_Scorers”} ] ‘ Again, if we wanted to query data from a partitioned table using the TQL method, the command will fail: curl –location ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/tql’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ {“name” : “pyIntPart2”, “stmt” : “select *”, “columns” : null} ]’ DML Update We can also update rows by using the same rowkey or we can add new ones by using a fresh key. For example: curl –location ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/sql/dml/update’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ {“stmt” : “INSERT INTO pyIntPart2(date, value, temp) VALUES (NOW(), ‘\”blog_test’\”, ‘\”cold’\”)”} ] ‘ Again, the nice thing about using SQL here instead of the old TQL is that we can use the special GridDB Time Series SQL commands such as NOW(). You can read more about those commands in the docs: https://docs.griddb.net/sqlreference/sql-commands-supported/#time-functions. If we tried to use the NOW() command with the TQL version of an insert, it fails: curl –location –request PUT ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/containers/pyIntPart2/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ [NOW(), “failure”,”hot”] ]’ This command will fail. SQL DCL (Data Control Language) The SQL Data Control Language is mostly concerned with rights/permissions for your database. This one isn’t as exciting but it can be useful if managing many databases or users. For eexample, if you create a new database and a new user, you can grant that user access to that DB with the Web API. curl –location ‘http://192.168.50.206:8082/griddb/v2/myCluster/dbs/public/sql/dcl’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: ••••••’ \ –data ‘[ {“stmt” : “REVOKE all on testing1 from israel”}, {“stmt” : “GRANT all on testing1 to israel”} ]’ And you can verify this by using the GridDB CLI tool gs[public]> showuser israel Name : israel Type : General User GrantedDB: public testing1

One of GridDB’s main draws are its inherent strengths at managing an IoT system, with its unique key-container data model and memory-first data architecture. With that in mind, we wanted to showcase how you could build out an IoT system completely on the cloud, using Azure’s IoT Hub for the IoT devices and event handling, and GridDB Cloud for the data storage. The easiest way to manage this is to send data from IoT Hub device via HTTP Requests through the use of Azure Functions. For this article, we want to use the VS Code extensions for the Azure IoT Hub to create all of the resources we will need to create, test, and manage our virtual devices with our IoT Hub. We will also be utilizing the Azure CLI Tools to get a list of possible IP Addresses to be whitelisted in the GridDB Cloud. And then finally, we will create some Azure Functions (with VS Code), deploy them onto your Azure subscription, and then pair it with your Iot Hub. Again, the goal is for the device to emit data, trigger an event, and then send the data payload out to GridDB Cloud, seamlessly. Implementation We will now go through how to make this project work. Prerequisites To follow along, you will need an account with Microsoft Azure with credits (unfortunately we can’t get this up and running for free) and a free trial to GridDB Cloud. The cheapest available IoT Hub is estimated at $10/month. Though not required, this article will reference completing many of the actions through VS Code and through the Azure CLI tool. Creating Azure Resources Now let’s go through and create our Azure resources. We will need to create the following: Azure IoT Hub Virtual Device(s) Azure Functions Creating the Azure IoT Hub This is our main hub; it will be a one-stop shop for our event triggers and our virtual devices. To create a new hub, you can either create it in the Azure Web Portal (straightforward) or through the VS Code extension (potentially easier). To build with VS Code, first install the proper extension: https://marketplace.visualstudio.com/items?itemName=vsciot-vscode.azure-iot-toolkit. Next, open up the Command Palette (F1 key) and select Azure IoT Hub: Create IoT Hub and fill out all of the necessary information, including region, subscription, and choose a globally unique name (the hub needs to be unique because it gets is own public facing DNS name). Adding Virtual Devices to the Hub Now we want to add virtual IoT devices, so once again open up the Command Palette and select Azure IoT Hub: Create Device and give it a device name. You can add as many devices as you’d like, but for now we’ll keep it at one device. We will dicuss how to send data from this device to the Hub later. Creating Azure Functions for Monitor Events Lastly we would like to utilize Azure Functions to monitor our device to detect changes; once an event is detected, we want to be able to control what occurs next, in our case, an HTTP Request to be made. To do so, we will need one more VS Code Extension: Azure Functions And now, once again, open up your Command Palette and select Azure Functions: Create Function App in Azure… to create a function. At this stage, we are naming it, creating a local instance of it, and choosing a runtime, of which we chose the latest version of node.js available. For more information on how these event monitoring system works within Azure, here is some of the documentation: https://learn.microsoft.com/en-us/azure/iot-hub/iot-hub-event-grid Sending our Data Payloads to GridDB Cloud With our resources in place, the next step is to set up the event monitoring. We want for our hub to make HTTP Requests of our data payloads whenever it detects that one of its devices emits data. To do so, we will use the Azure Function that we created in the previous step and event monitoring. Azure Function: Source Code for when Event is Triggered We have already created the source code necessary for this step, so please clone the repo as indicated above. The code is very simple: it takes the sample code built by Azure for eventGridTriggers and simply adds a component to make HTTP Requests whenever the trigger is fired. Here is what the source code looks like: const { app } = require(‘@azure/functions’); const axios = require(‘axios’); require(‘dotenv’).config() app.eventGrid(‘eventGridTrigger1’, { handler: (event, context) => { context.log(‘Event grid function processed event:’, event); const container = ‘azureTest’ const auth = { username: process.env.CLOUD_USERNAME, password: process.env.CLOUD_PASSWORD } const headers = { ‘Content-Type’: ‘application/json’ } //HTTP Request to create our container called azureTest const dataCreation = { “container_name”: container, “container_type”: “COLLECTION”, “rowkey”: false, “columns”: [ { “name”: “test”, “type”: “STRING” } ] } const configCreation = { method: ‘POST’, maxBodyLength: Infinity, url: process.env.CLOUD_URL + “/containers”, headers, data: dataCreation, auth } axios.request(configCreation) .then((response) => { context.log(response.statusText); context.log(JSON.stringify(response.data)); }) .catch((error) => { context.log(error); context.error(error) }); //HTTP Request to send data to our container const data = JSON.stringify([ [“GRID EVENT TRIGGERED”] ]); let config = { method: ‘PUT’, maxBodyLength: Infinity, url: process.env.CLOUD_URL + “/containers/” + container + “/rows”, headers, data, auth }; axios.request(config) .then((response) => { context.log(response.statusText); context.log(JSON.stringify(response.data)); }) .catch((error) => { context.log(error); context.error(error) }); } }); We are using the GridDB Cloud Web API to build our HTTP Request to send the payload to be saved. With everything in place, we can deploy our function to the Azure Cloud. You will need to copy the .env-example file and rename it to .env and fill out the values yourself. For the CLOUD URL variable, please just copy the GridDB WebAPI URL from your GridDB Portal as is. Deploy Azure Function to Azure Cloud So now, once again, make sure you have the source code provided by this blog in the repo pointed out above and make sure that your VS Code current folder/project is inside of a directory that has the source code. Next, in the Command Palette, select Azure Functions: Deploy to Function App and select the Function you created above. This will create a zip of your current working directory (meaning all of the source code you downloaded from this article’s repo) and deploy it directly to your Azure Account. And now we want to tie this source code with our IoT Hub and our test virtual device. Tying Azure Function to IoT Hub For this last step, we would like for our hub to utilize the Azure Function created in the previous step. For this step, we will use the Azure Web Portal. Open up the portal and find your IoT Hub Resource. Within that page navigate to: Events -> Azure Function. We will be creating a new event, so give it a name and system topic. For the “filter for event types” box, keep it selected only to Device Telemetry. And lastly, click Configure an Endpoint. In the side panel, most likely everything will self-populate, but if not, choose the azure function app and functions we made previous (ie. function is called: eventGridTrigger1). NOTE: For this to work, your account will need the registry Microsoft.EventGrid in the subscription page enabled. Receiving Data from IoT Hub Lastly, even if we were to trigger an event of some payload to our hub from our device, the HTTP Request would fail because the GridDB Cloud requires all incoming IP Addresses to be whitelisted. The issue now arises that we are using a lightweight Azure Function to handle our events, not a full blown server/VM with one static IP Address. Luckily, there is a way to retrieve all possible IP Addresses used by our Azure Function as shown in these docs: https://learn.microsoft.com/en-us/azure/azure-functions/ip-addresses. You will need to install the Azure CLI and login to your proper account/subscription. Once there, you can run the following command: $ az functionapp show –resource-group <INSERT RESOURCE NAME> –name <INSERT AZURE FUNCTION NAME> –query possibleOutboundIpAddresses –output tsv > outbound.txt This will output a list of about 40 possible IP Addresses and save it into a text file called outbound.txt. As long as we add all of these to the GridDB Cloud Whitelist, we will be able to successfully save all of our events as they occur. Whitelist IP Addresses Obviously, while hand-writing each of these IP addresses is technically feasible, it’d be tedious and horrifying. To add each of these with a simple do while loop in bash, we first need to grab the endpoint with all authorization headers. To do so, open up your GridDB Cloud Web Portal, open up the dev console, navigate to the network tab, filter for XHR requests, clear everything out, and add one of the 40 IP addresses on your list. When you hit submit, you will see a 201 POST Request, now do the following: right click -> Copy Value -> Copy as cURL. You will now have the cURL Command saved in your clipboard. Open up the whitelistIp.sh script and enter in the endpoint unique to you inside of the runCurl script sans the IP Address at the end. Here is what the entire script looks like: #!/bin/bash file=$1 #EXAMPLE #runCurl() { # curl ‘https://cloud57.griddb.com/mfcloud57/dbaas/web-api/contracts/m01wc1a/access-list’ -X POST -H ‘User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:133.0) Gecko/20100101 Firefox/133.0’ -H ‘Accept: application/json, text/plain, */*’ -H ‘Accept-Language: en-US,en;q=0.5’ -H ‘Accept-Encoding: gzip, deflate, br, zstd’ -H ‘Content-Type: application/json;charset=utf-8’ -H ‘Access-Control-Allow-Origin: *’ -H ‘Authorization: Bearer eyJ0eXAiOiJBY2Nlc3MiLCJIUzI1NiJ9.eyJzdWIiOiJkMTg4NjlhZC1mYjUxLTQwMWMtOWQ0Yy03YzI3MGNkZTBmZDkiLCJleHAiOjE3MzEwMTEyMTMsInJvbGUiOiJBZG1pbiIsInN5c3RlbVR5cGUiOjF9.B1MsV9-Nu8m8mJbsp6dKABjJDBjQDdc9aRLffTlTcVM’ -H ‘Origin: https://cloud5197.griddb.com’ -H ‘Connection: keep-alive’ -H ‘Referer: https://cloud5197.griddb.com/mfcloud5197/portal/’ -H ‘Sec-Fetch-Dest: empty’ -H ‘Sec-Fetch-Mode: cors’ -H ‘Sec-Fetch-Site: same-origin’ -H ‘Priority: u=0’ -H ‘Pragma: no-cache’ -H ‘Cache-Control: no-cache’ –data-raw $1 #} runCurl() { <paste VALUE HERE> $1 } while IFS= read -r line; do for ip in ${line//,/ }; do echo “Whitelisting IP Address: $ip” runCurl $ip done done < “$file”</paste> The script will take the outputs from the Azure CLI command to make a string of IP Addresses, seperate out the value by the comma, and then run the cURL command for each individual address. This script expects the file name as a CLI argument when running ie: $ ./whitelistIp.sh outbound.txt. Sending Data from Device to Cloud Now that we’ve got everything set up, the last thing we will do is send data from our virtual device to the cloud. We then expect to see our test string being published into our GridDB Cloud instance. In the source code, we are simply saving a string which contains the characters “GRID EVENT TRIGGERED” to a new container called azureTest as a test to make sure our infrastructure works as expected. From your VS Code window, in the explorer tab, find the Azure IoT Hub Resource panel and find your IoT Hub Manager and its devices. Right click the device you want to use and select Send D2C Messages to IoT Hub (D2C = Device to Cloud). Once your message is sent, you should have a new container in your GridDB Cloud instance called ‘azureTest’ and it should have one row of data inside of it with a value of ‘GRID EVENT TRIGGERED’ — cool! Conclusion And that does it! You can now expand your Cloud IoT Infrastructure as much as you’d like. Make new data containers with real schemas tied to real devices and sync them up and save all data into GridDB Cloud for a purely server-less

GridDB Cloud v2.0 has officially been released, has a new free tier, and is officially available worldwide. In this quick start guide, you will learn how to insert IoT data into the GridDB Cloud, learn how to test the viability of your connection, and learn the basic CRUD commands (Create, Read, Update, Delete). Contents 0 Preparation 1 Whitelisting Your IP Address 2 Adding GridDB Users and Granting DB Access 3 Checking your GridDB Connection 4 Creating your First Time Series & Collection Containers 5 CRUD with GridDB Cloud (Create, Read, Update, Delete) 6 Ingesting Sample IoT Data 7 Running Data Analysis Preparation Before we jump into how to use the GridDB Cloud, let’s go over some basic preparation. Sign Up for GridDB Cloud Free Plan If you would like to sign up for a GridDB Cloud Free instance, you can do so in the following link: https://form.ict-toshiba.jp/download_form_griddb_cloud_freeplan_e. You can watch a video on how easy it is to sign up here: Clone GitHub Repository To follow along and run these sample code snippets, please clone the repository here: $ git clone https://github.com/griddbnet/Blogs.git –branch cloud-quick-start-worldwide Source Code Overview We have prepared some basic HTTP Requests that can help jump-start your journey with GridDB Cloud. These requests are shared via three different programming interfaces: CLI scripts (aka bash, in the bash/ dir), node.js, and python. For example, the first command will be to make sure the connection between your machine and the cloud can be made; to do so, we will run an HTTP Requests to a specific endpoint using just bash (with cURL), and then with node.js/python scripts. Set Up For Running Sample Code To ensure your HTTP Requests have the proper connection details, copy the env.example file (from the GitHub Repo shared in the section above) and rename it to .env. You must fill in your personal variables (ie. your GridDB Cloud Web API endpoint as well as your user/pass combo encoded into base64). To get proper creds, you will need to gather the username/password combination into base 64separated by a colon; for example: admin:admin becomes YWRtaW46YWRtaW4=. You can encode those values by using the following website: https://www.base64encode.org/ The WEBAPI Url can found on the main page of your GridDB Dashboard. Here is an example of a .env file. export GRIDDB_WEBAPI_URL=”https://cloud51e32re97.griddb.com:443/griddb/v2/gs_clustermfcloud5314927/dbs/ZV8YUerterlQ8″ export USER_PASS=”TTAxZ2FYMFrewwerZrRy1pc3JrerehZWw6avdfcvxXNyYWVs” Now run $ source .env to load these values into your environment so that you can run these scripts from your CLI. First Steps with GridDB Cloud Your GridDB Cloud instance can be communicated with via HTTP Requests; every action needed to interact with GridDB will require formulating and issuing an HTTP Request with different URLs, parameters, methods, and payload bodies. 1. Whitelisting Your IP Address If you haven’t already, please whitelist your public IP address in the network settings of your GridDB Cloud Management dashboard. You can find your own IP Address by using a simple Google Search: “What is my IP Address?” Or you can go here: https://whatismyipaddress.com/ Go to the network tab and add in your IP address. Note: CIDR Ranges are compatible, so please feel free to add your own if you know it. 2. Adding GridDB Users and Granting DB Access Next, we should create a new GridDB User. From the side panel, click the icon which says GridDB User. From this page, click CREATE DATABASE USER. This user’s name and password will be attached to all of our HTTP Requests as a Basic Authorization Header when using the Web API. Once you create the new user, you will also need to grant access to your database. Click on the user from the table of users in GridDB Users page and from this page, grant access to your database (either READ or ALL). Now we can move on to making actual HTTP requests. 3. Checking your GridDB Connection Let’s start with a sanity check and make sure that we can reach out to the GridDB Cloud instance. With your .env file made and ready to go, you can make sure you get the variables loaded into your environment using the following command: $ source .env. And now run the bash script: If you see a status code of 200, that’s good! That means your connection was succesful. If you are met with a status code of 403 Forbidden, that likely means you have not whitelisted your machine’s IP Address in the network tab. Likewise, a returned code of 401 Unauthorized likely means your credentials are incorrect — please make sure you use user:pass encoded into base64! Check Connection URL Endpoint The Web API uses a base url which we will use and expand upon to build out our requests. The base url looks like this: https://cloud<number>.griddb.com/griddb/v2/<clusterName>/dbs/<database name> To check that our connection exists, we can append the following to our base url /checkConnection. Because we are not sending any data back to the server, we will use the GET HTTP method. Lastly, we need to include basic authentication in our HTTP Request’s headers. For this, we will need to include our username and password encoded into base64. With all that said, here is the final result https://cloud51ergege97.griddb.com/griddb/v2/gs_clustermfcloud51fgerge97/dbs/B2vderewDSJy/checkConnection We can now use this URL with any number of programming languages to communicate with our database. And as a note, the examples in this article will contain the URLs and credentials hardcoded into the example, but the source code (linked above) uses environment variables. cURL Request To check our connection with cURL, you can use the following command (check_connection.sh) curl -i –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/checkConnection’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ Because it’s a GET request, it’s rather simple and we only needed to add in the authorization header. You should be able to run this and get an HTTP Response of 200. Python Request Here is that same request written in Python # check_connection.py import requests url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/checkConnection” payload = {} headers = { ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“GET”, url, headers=headers, data=payload) print(response.status_code) node.js Request //checkConnection.js const request = require(‘request’); const options = { ‘method’: ‘GET’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/checkConnection’, ‘headers’: { ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ } }; request(options, function (error, response) { if (error) throw new Error(error); console.log(“Response Status Code: “, response.statusCode); }); 4. Creating your First Time Series & Collection Containers With our connection firmly established, we can create our first containers — both Collection and Time Series — of which are similar to relational tables. You can read more about that here: GridDB Data Model. The URL suffix looks like this: /containers. This request can sometimes require a multitude of data and can have a big range, therefore this request will require an HTTP method of POST. The body of the request requires container name, container type, whether a rowkey exists (bool), and the schema. Let’s first take a look at the structure outside of the context of an HTTP Request and then we will send it inside of a Request body. We will also need to include in our Request’s headers that we are sending a data payload of type JSON like so: ‘Content-Type: application/json’ Time Series Container First, let’s create a Time Series container — we can see here that we select the container type as TIME_SERIES and the first column is of type timestamp. There is also a rowkey section, but this is optional as in a time series container, the rowkey is always the timestamp by default. { “container_name”: “device1”, “container_type”: “TIME_SERIES”, “rowkey”: true, “columns”: [ { “name”: “ts”, “type”: “TIMESTAMP” }, { “name”: “co”, “type”: “DOUBLE” }, { “name”: “humidity”, “type”: “DOUBLE” }, { “name”: “light”, “type”: “BOOL” }, { “name”: “lpg”, “type”: “DOUBLE” }, { “name”: “motion”, “type”: “BOOL” }, { “name”: “smoke”, “type”: “DOUBLE” }, { “name”: “temp”, “type”: “DOUBLE” } ] } Now we simply attach this to the body when we make our Request and we should create our new container. If successful, you should get a status code of 201 (Created). cURL #create_container.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘{ “container_name”: “device1”, “container_type”: “TIME_SERIES”, “rowkey”: true, “columns”: [ { “name”: “ts”, “type”: “TIMESTAMP” }, { “name”: “co”, “type”: “DOUBLE” }, { “name”: “humidity”, “type”: “DOUBLE” }, { “name”: “light”, “type”: “BOOL” }, { “name”: “lpg”, “type”: “DOUBLE” }, { “name”: “motion”, “type”: “BOOL” }, { “name”: “smoke”, “type”: “DOUBLE” }, { “name”: “temp”, “type”: “DOUBLE” } ] }’ Python #create_container.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers” payload = json.dumps({ “container_name”: “device1”, “container_type”: “TIME_SERIES”, “rowkey”: True, “columns”: [ { “name”: “ts”, “type”: “TIMESTAMP” }, { “name”: “co”, “type”: “DOUBLE” }, { “name”: “humidity”, “type”: “DOUBLE” }, { “name”: “light”, “type”: “BOOL” }, { “name”: “lpg”, “type”: “DOUBLE” }, { “name”: “motion”, “type”: “BOOL” }, { “name”: “smoke”, “type”: “DOUBLE” }, { “name”: “temp”, “type”: “DOUBLE” } ] }) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.status_code) node.js //createContainer.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify({ “container_name”: “device1”, “container_type”: “TIME_SERIES”, “rowkey”: true, “columns”: [ { “name”: “ts”, “type”: “TIMESTAMP” }, { “name”: “co”, “type”: “DOUBLE” }, { “name”: “humidity”, “type”: “DOUBLE” }, { “name”: “light”, “type”: “BOOL” }, { “name”: “lpg”, “type”: “DOUBLE” }, { “name”: “motion”, “type”: “BOOL” }, { “name”: “smoke”, “type”: “DOUBLE” }, { “name”: “temp”, “type”: “DOUBLE” } ] }) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(“Response Status Code: “, response.statusCode); }); Collection Container Now let’s create a collection container. These containers don’t require a time series column (but they are allowed) and also don’t require rowkey to be set to true. Here are some examples: cURL #create_collection.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘{ “container_name”: “deviceMaster”, “container_type”: “COLLECTION”, “rowkey”: true, “columns”: [ { “name”: “equipment”, “type”: “STRING” }, { “name”: “equipmentID”, “type”: “STRING” }, { “name”: “location”, “type”: “STRING” }, { “name”: “serialNumber”, “type”: “STRING” }, { “name”: “lastInspection”, “type”: “TIMESTAMP” }, { “name”: “information”, “type”: “STRING” } ] }’ Python #create_collection.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers” payload = json.dumps({ “container_name”: “deviceMaster”, “container_type”: “COLLECTION”, “rowkey”: True, “columns”: [ { “name”: “equipment”, “type”: “STRING” }, { “name”: “equipmentID”, “type”: “STRING” }, { “name”: “location”, “type”: “STRING” }, { “name”: “serialNumber”, “type”: “STRING” }, { “name”: “lastInspection”, “type”: “TIMESTAMP” }, { “name”: “information”, “type”: “STRING” } ] }) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.status_code) node.js //createCollection.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify({ “container_name”: “deviceMaster”, “container_type”: “COLLECTION”, “rowkey”: true, “columns”: [ { “name”: “equipment”, “type”: “STRING” }, { “name”: “equipmentID”, “type”: “STRING” }, { “name”: “location”, “type”: “STRING” }, { “name”: “serialNumber”, “type”: “STRING” }, { “name”: “lastInspection”, “type”: “TIMESTAMP” }, { “name”: “information”, “type”: “STRING” } ] }) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.statusCode); }); 5. CRUD with GridDB Cloud (Create, Read, Update, Delete) 5. CRUD with GridDB Cloud (Create, Read, Update, Delete) Next, let’s go over the commands to Create, Read, Update, and Delete. Adding Rows of Data (Create) We have already created some containers before, but to add to that, we will be creating rows of data to add to our container. We can add rows of data directly inside of our containers. The URL suffix: /containers/:container/rows To PUT a row of data into our container, we will need to use the HTTP Method PUT. Similar to before, we will need to specify that our content is JSON and we will include the row data in our Request body. You can add multiple rows at once, you just need to make sure that your payload is formed to accomdate extra rows and that you don’t have a trailing comma on the last row. Let’s add rows to our device1 container. [ [“2024-01-09T10:00:01.234Z”, 0.003551, 50.0, false, 0.00754352, false, 0.0232432, 21.6], [“2024-01-09T11:00:01.234Z”, 0.303551, 60.0, false, 0.00754352, true, 0.1232432, 25.3], [“2024-01-09T12:00:01.234Z”, 0.603411, 70.0, true, 0.00754352, true, 0.4232432, 41.5] ] You of course also need to be sure that your row’s schema matches your container’s. If it doesn’t, you will be met with an error message and a status code of 400 (Bad Request). cURL https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers #add_rows.sh curl –location –request PUT ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ [“2024-01-09T10:00:01.234Z”, 0.003551, 50.0, false, 0.00754352, false, 0.0232432, 21.6], [“2024-01-09T11:00:01.234Z”, 0.303551, 60.0, false, 0.00754352, true, 0.1232432, 25.3], [“2024-01-09T12:00:01.234Z”, 0.603411, 70.0, true, 0.00754352, true, 0.4232432, 41.5] ]’ Python #add_rows.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows” payload = json.dumps([ [ “2024-01-09T10:00:01.234Z”, 0.003551, 50, False, 0.00754352, False, 0.0232432, 21.6 ], [ “2024-01-09T11:00:01.234Z”, 0.303551, 60, False, 0.00754352, True, 0.1232432, 25.3 ], [ “2024-01-09T12:00:01.234Z”, 0.603411, 70, True, 0.00754352, True, 0.4232432, 41.5 ] ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“PUT”, url, headers=headers, data=payload) print(response.text) node.js //addRows.js var request = require(‘request’); var options = { ‘method’: ‘PUT’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ [ “2024-01-09T10:00:01.234Z”, 0.003551, 50, false, 0.00754352, false, 0.0232432, 21.6 ], [ “2024-01-09T11:00:01.234Z”, 0.303551, 60, false, 0.00754352, true, 0.1232432, 25.3 ], [ “2024-01-09T12:00:01.234Z”, 0.603411, 70, true, 0.00754352, true, 0.4232432, 41.5 ] ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); Querying Container (Read) After writing to our containers, we will want to read from our containers. The URL suffix is exactly the same as before: /:cluster/dbs/:database/containers/:container/rows except now we will be using the POST method request. The data expected by the server in these requests are how we expect our row data returned to us — for example, we can choose a row limit, an offset, any conditions, and a sort method. Here is what that body looks like: { “offset” : 0, “limit” : 100, “condition” : “temp >= 30”, “sort” : “temp desc” } The one caveat with making this Request is that because it is a POST request, you will need to send something in the body of the request. Any of the parameters above will do, but including the limit is likely the easiest option to include and has the added benefit of reducing server strain. If successful, you should get a server response with a status code of 200 (OK) and a body with the data requested. #query_container.sh { “columns”: [ { “name”: “ts”, “type”: “TIMESTAMP”, “timePrecision”: “MILLISECOND” }, { “name”: “co”, “type”: “DOUBLE” }, { “name”: “humidity”, “type”: “DOUBLE” }, { “name”: “light”, “type”: “BOOL” }, { “name”: “lpg”, “type”: “DOUBLE” }, { “name”: “motion”, “type”: “BOOL” }, { “name”: “smoke”, “type”: “DOUBLE” }, { “name”: “temp”, “type”: “DOUBLE” } ], “rows”: [ [ “2024-01-09T12:00:01.234Z”, 0.603411, 70.0, true, 0.00754352, true, 0.4232432, 41.5 ] ], “offset”: 0, “limit”: 100, “total”: 1 } cURL curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘{ “offset” : 0, “limit” : 100, “condition” : “temp >= 30”, “sort” : “temp desc” }’ Python #query_container.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows” payload = json.dumps({ “offset”: 0, “limit”: 100, “condition”: “temp >= 30”, “sort”: “temp desc” }) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) nodejs //queryContainer.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify({ “offset”: 0, “limit”: 100, “condition”: “temp >= 30”, “sort”: “temp desc” }) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); Updating a Row (Update) To cover updates, adding rows of data can be considered updating, but we can also directly update a row (if the container has rowkeys). The way it works is if you push a row of data to your container which has rowkey set as true, and send up a row of data with a rowkey that already exists in your container, it will update the row with whatever new information is pushed along. Let’s push data to our deviceMaster collection container once to add data, and then again to update the row. Let’s craft our row of data [ [“device1”, “01”, “CA”, “23412”, “2023-12-15T10:45:00.032Z”, “working”] ] Now let’s form our HTTP Requests cURL #update_collection.sh curl -i –location –request PUT ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ [“device1”, “01”, “CA”, “23412”, “2023-12-15T10:45:00.032Z”, “working”] ]’ Python #update_collection.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows” payload = json.dumps([ [ “device1”, “01”, “CA”, “23412”, “2023-12-15T10:45:00.032Z”, “working” ] ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“PUT”, url, headers=headers, data=payload) print(response.text) node.js //updateCollection.js var request = require(‘request’); var options = { ‘method’: ‘PUT’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ [ “device1”, “01”, “CA”, “23412”, “2023-12-15T10:45:00.032Z”, “working” ] ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); Updating a Row And now with that data in there, if you change any of the values outside of the first one (the rowkey, the device name), it will update that device’s metadata will keeping the row inside of your container. curl -i –location –request PUT ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ [“device1”, “01”, “NY”, “23412”, “2023-12-20T10:45:00.032Z”, “working”] ]’ Here we are changing the location and the time of last inspection. If you look at your dashboard, the values will be have been updated. Deleting a Row (Delete) We can delete a row simply by using the appropriate HTTP Method (Delete) and then sending in a valid rowkey and container to our server. Let’s delete our deviceMaster’s lone row. The body of the request looks like this. You can add multiple rowkeys inside here to delete multiple rows at once. [ “device1” ] cURL #delete_row.sh curl -v –location –request DELETE ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ “device1” ]’ Python #delete_row.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows” payload = json.dumps([ “device1” ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“DELETE”, url, headers=headers, data=payload) print(response.status_code) node.js //deleteRow.js var request = require(‘request’); var options = { ‘method’: ‘DELETE’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/deviceMaster/rows’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ “device1” ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.statusCode); }); Deleting a Row from Time Series Container You can also delete the row of a time series container. As stated before, the time stamp will always be the rowkey in a time series container, so here we just add our time stamp and those rows will be deleted. #delete_container.sh curl –location –request DELETE ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers/device1/rows’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ “2024-01-09T10:00:01.234Z”, “2024-01-09T12:00:01.234Z” ]’ TQL Next, let’s try running a TQL Query. If you’re unfamiliar, TQL is GridDB’s special query language. Let’s run a simple query. First, this is what the URL looks like base url + /tql The body of the request will need the container name followed by your query statement. We can also query multiple containers at once: [ {“name” : “deviceMaster”, “stmt” : “select * limit 100”, “columns” : null}, {“name” : “device1”, “stmt” : “select * where temp>=24”, “columns” : [“temp”, “co”]}, ] And this is the response of the above query: [ { “columns”: [ { “name”: “equipment”, “type”: “STRING” }, { “name”: “equipmentID”, “type”: “STRING” }, { “name”: “location”, “type”: “STRING” }, { “name”: “serialNumber”, “type”: “STRING” }, { “name”: “lastInspection”, “type”: “TIMESTAMP”, “timePrecision”: “MILLISECOND” }, { “name”: “information”, “type”: “STRING” } ], “results”: [ [ “device1”, “01”, “NY”, “23412”, “2023-12-20T10:45:00.032Z”, “working” ] ], “offset”: 0, “limit”: 100, “total”: 1, “responseSizeByte”: 31 }, { “columns”: [ { “name”: “temp”, “type”: “DOUBLE” }, { “name”: “co”, “type”: “DOUBLE” } ], “results”: [ [ 25.3, 0.303551 ], [ 41.5, 0.603411 ] ], “offset”: 0, “limit”: 1000000, “total”: 2, “responseSizeByte”: 32 } ] cURL #tql.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/tql’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘ [ {“name” : “deviceMaster”, “stmt” : “select * limit 100”, “columns” : null}, {“name” : “device1”, “stmt” : “select * where temp>=24”, “columns” : [“temp”, “co”]} ] ‘ Python #tql.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/tql” payload = json.dumps([ { “name”: “deviceMaster”, “stmt”: “select * limit 100”, “columns”: None }, { “name”: “device1”, “stmt”: “select * where temp>=24”, “columns”: [ “temp”, “co” ] } ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) node.js //tql.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/tql’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ { “name”: “deviceMaster”, “stmt”: “select * limit 100”, “columns”: null }, { “name”: “device1”, “stmt”: “select * where temp>=24”, “columns”: [ “temp”, “co” ] } ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); SQL On top of TQL, GridDB and the GridDB Cloud also have SQL functionality. Though the SQL functionality for the GridDB Cloud is limited to reading results (SELECT) and updating some rows (UPDATE). SQL SELECT base url + /sql [ {“type” : “sql-select”, “stmt” : “SELECT * FROM deviceMaster”}, {“type” : “sql-select”, “stmt” : “SELECT temp, co FROM device1 WHERE temp>=24″} ] It is very similar to TQL but we call on the container name from within the query itself, just like “normal” SQL statements. Here is the response body: [ { “columns”: [ { “name”: “equipment”, “type”: “STRING” }, { “name”: “equipmentID”, “type”: “STRING” }, { “name”: “location”, “type”: “STRING” }, { “name”: “serialNumber”, “type”: “STRING” }, { “name”: “lastInspection”, “type”: “TIMESTAMP”, “timePrecision”: “MILLISECOND” }, { “name”: “information”, “type”: “STRING” } ], “results”: [ [ “device1”, “01”, “CA”, “23412”, “2023-12-15T10:45:00.032Z”, “working” ] ], “responseSizeByte”: 47 }, { “columns”: [ { “name”: “temp”, “type”: “DOUBLE” }, { “name”: “co”, “type”: “DOUBLE” } ], “results”: [ [ 25.3, 0.303551 ], [ 41.5, 0.603411 ] ], “responseSizeByte”: 32 } ] cURL #sql_select.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ {“type” : “sql-select”, “stmt” : “SELECT * FROM deviceMaster”}, {“type” : “sql-select”, “stmt” : “SELECT temp, co FROM device1 WHERE temp>=24”} ] ‘ Python #sql_select.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql” payload = json.dumps([ { “type”: “sql-select”, “stmt”: “SELECT * FROM deviceMaster” }, { “type”: “sql-select”, “stmt”: “SELECT temp, co FROM device1 WHERE temp>=24” } ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) node.js //sqlSelect.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ { “type”: “sql-select”, “stmt”: “SELECT * FROM deviceMaster” }, { “type”: “sql-select”, “stmt”: “SELECT temp, co FROM device1 WHERE temp>=24” } ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); SQL SELECT GROUP BY RANGE Because we are using SQL Select, you can use the GridDB’s Group By Range as well. You can learn more about that here: Exploring GridDB’s Group By Range Functionality. We will make our query and group by hours: [ {“type” : “sql-select”, “stmt” : “SELECT temp, co FROM device1 WHERE ts > TO_TIMESTAMP_MS(1594515625984) AND ts < TO_TIMESTAMP_MS(1595040779336) GROUP BY RANGE (ts) EVERY (1, HOUR)”} ] cURL #sql_select_groupby.sh curl -i –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ {“type” : “sql-select”, “stmt” : “SELECT temp, co FROM device1 WHERE ts > TO_TIMESTAMP_MS(1594515625984) AND ts < TO_TIMESTAMP_MS(1595040779336) GROUP BY RANGE (ts) EVERY (1, HOUR)”} ] ‘ Python # sql_select_groupby.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql” payload = json.dumps([ { “type”: “sql-select”, “stmt”: “SELECT temp, co FROM device1 WHERE ts > TO_TIMESTAMP_MS(1594515625984) AND ts < TO_TIMESTAMP_MS(1595040779336) GROUP BY RANGE (ts) EVERY (1, HOUR)” } ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0′ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) SQL Insert The base URL is the same as SELECT, but you need to append ‘update’ base url + /:cluster/dbs/:database/sql/update [ {“stmt” : “insert into deviceMaster(equipment, equipmentID, location, serialNumber, lastInspection, information) values(‘device2′, ’02’, ‘MA’, ‘34412’, TIMESTAMP(‘2023-12-21T10:45:00.032Z’), ‘working’)”} ] cURL #sql_insert.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ {“stmt” : “insert into deviceMaster(equipment, equipmentID, location, serialNumber, lastInspection, information) values(‘\”device2’\”, ‘\”02’\”, ‘\”MA’\”, ‘\”34412’\”, TIMESTAMP(‘\”2023-12-21T10:45:00.032Z’\”), ‘\”working’\”)”} ]’ Python #sql_insert.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update” payload = json.dumps([ { “stmt”: “insert into deviceMaster(equipment, equipmentID, location, serialNumber, lastInspection, information) values(‘device2′, ’02’, ‘MA’, ‘34412’, TIMESTAMP(‘2023-12-21T10:45:00.032Z’), ‘working’)” } ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) node.js //sqlInsert.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ { “stmt”: “insert into deviceMaster(equipment, equipmentID, location, serialNumber, lastInspection, information) values(‘device2′, ’02’, ‘MA’, ‘34412’, TIMESTAMP(‘2023-12-21T10:45:00.032Z’), ‘working’)” } ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); SQL Update The base URL is the same as above, but you need to append ‘update’ base url + /sql/update [ {“stmt” : “update deviceMaster set location = ‘LA’ where equipmentID = ’01′”} ] This command allows you to Update, similar to the NoSQL method described above. We can both update existing rows, or update containers to add new rows. cURL #sql_update.sh curl -i -X POST –location ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ {“stmt” : “update deviceMaster set location = ‘\”LA’\” where equipmentID = ‘\”01’\””} ]’ Python #sql_update.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update” payload = json.dumps([ { “stmt”: “update deviceMaster set location = ‘LA’ where equipmentID = ’01′” } ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“POST”, url, headers=headers, data=payload) print(response.text) node.js //sqlUpdate.js var request = require(‘request’); var options = { ‘method’: ‘POST’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/sql/update’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ { “stmt”: “update deviceMaster set location = ‘LA’ where equipmentID = ’01′” } ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.body); }); Dropping Containers We can also drop containers. base url + /containers The request’s body can contain one or multiple container names of which will be dropped once we make our request. [ “deviceMaster” ] If successful, you will receive a status code of 204 (No Content) cURL #delete_container.sh curl -i –location –request DELETE ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’ \ –header ‘Content-Type: application/json’ \ –header ‘Authorization: Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ \ –data ‘[ “deviceMaster” ]’ Python #delete_container.py import requests import json url = “https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers” payload = json.dumps([ “deviceMaster” ]) headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, ‘User-Agent’: ‘PostmanRuntime/7.29.0’ } response = requests.request(“DELETE”, url, headers=headers, data=payload) print(response.status_code) node.js //deleteContainer.js var request = require(‘request’); var options = { ‘method’: ‘DELETE’, ‘url’: ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/containers’, ‘headers’: { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’ }, body: JSON.stringify([ “deviceMaster” ]) }; request(options, function (error, response) { if (error) throw new Error(error); console.log(response.statusCode); }); 6. Ingesting Sample IoT Data Next, let’s take a look at ingesting CSV data. We will ingest IoT data from Kaggle. You can download the raw file from their website here: https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k. Here is the python script you can use to ingest the data into our device2 container. We have already created our device1 container, but we will use the same schema to create a new container called device2 #data_ingest.py import pandas as pd import numpy as np import json import requests from datetime import datetime as dt, timezone headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, “User-Agent”:”PostmanRuntime/7.29.0″ } base_url = ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/’ data_obj = { “container_name”: “device1”, “container_type”: “TIME_SERIES”, “rowkey”: True, “columns”: [] } input_variables = [ “ts”,”co”,”humidity”,”light”,”lpg”,”motion”,”smoke”,”temp” ] data_types = [ “TIMESTAMP”, “DOUBLE”, “DOUBLE”, “BOOL”, “DOUBLE”, “BOOL”, “DOUBLE”,”DOUBLE” ] for variable, data_type in zip(input_variables, data_types): column = { “name”: variable, “type”: data_type } data_obj[“columns”].append(column) #Create Container url = base_url + ‘containers’ r = requests.post(url, json = data_obj, headers = headers) All of this is pretty straightforward. Next, let’s actually ingest the data. To do so, we will use the pandas python library to read the csv file which we will include inside of our body’s request to be ingested. The pandas library also allows us to easily target and transform specific columns in our csv data to make it possible to be ingested. And one last note, because the csv file is over eight megabytes, we will break up our data into chunks and send them to our Cloud that way. Download data from here: https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k Here is the rest of the code: iot_data = pd.read_csv(‘iot_telemetry_data.csv’) #2023-12-15T10:25:00.253Z iot_data[‘ts’] = pd.to_datetime(iot_data[‘ts’], unit=’s’).dt.strftime(“%Y-%m-%dT%I:%M:%S.%fZ”) print(iot_data[“ts”]) iot_data = iot_data.drop(‘device’, axis=1) #print(iot_data.dtypes) iot_subsets = np.array_split(iot_data, 20) #Ingest Data url = base_url + ‘containers/device1/rows’ for subset in iot_subsets: #Convert the data in the dataframe to the JSON format iot_subsets_json = subset.to_json(orient=’values’) request_body_subset = iot_subsets_json r = requests.put(url, data=request_body_subset, headers=headers) print(‘~~~~~~~~~~~~~~~~~~~~~~~~~~~~~’) print(‘_______________’,r.text,’___________’) print(‘~~~~~~~~~~~~~~~~~~~~~~~~~~~~~’) if r.status_code > 299: print(r.status_code) break else: print(‘Success for chunk’) As you run the script, it should be printing our successful messages for each chunk uploaded, complete with how many rows got successfully updated. Once done, you can check out your device1 with an HTTP Request query or through the portal. 7. Running Data Analysis Lastly, let’s do some simple python analysis. We will query our iot data and then use that data to do some simple analysis and charting of our data. #data_analysis.py import pandas as pd import numpy as np import requests import plotly.express as px from IPython.display import Image # ts object # co float64 # humidity float64 # light bool # lpg float64 # motion bool # smoke float64 # temp float64 headers = { ‘Content-Type’: ‘application/json’, ‘Authorization’: ‘Basic TTAxMU1sd0MxYS1pc3ewrwqJhZWw6aXNyYWVs’, “User-Agent”:”PostmanRuntime/7.29.0″ } base_url = ‘https://cloud5197422.griddb.com/griddb/v2/gs_clustermfcloud5197422/dbs/B2vdfewfwDSJy/’ sql_query1 = (f”””SELECT * from device1 WHERE co < 0.0019050147565559603 “””) url = base_url + ‘sql’ request_body = ‘[{“type”:”sql-select”, “stmt”:”‘+sql_query1+'”}]’ data_req1 = requests.post(url, data=request_body, headers=headers) myJson = data_req1.json() dataset = pd.DataFrame(myJson[0][“results”],columns=[myJson[0][“columns”][0][“name”],myJson[0][“columns”][1][“name”],myJson[0][“columns”][2][“name”], myJson[0][“columns”][3][“name”],myJson[0][“columns”][4][“name”],myJson[0][“columns”][5][“name”],myJson[0][“columns”][6][“name”],myJson[0][“columns”][7][“name”]]) print(dataset) lowest_col = dataset.sort_values(‘co’, ascending=False).head(20000) scatter_plot = px.scatter(lowest_col, x=’ts’, y=’co’, size=’co’, color=’co’, color_continuous_scale=’plasma’, hover_name=’co’) # Customize the plot scatter_plot.update_layout( title=’Data Analysis’, xaxis_title=’CO2 Emissions’, yaxis_title=’Time’ ) scatter_plot.update_layout(template=’plotly_dark’) # Show the plot scatter_plot.show() Conclusion And with that, we have shown you how to interact with your GridDB Cloud and do all sorts of database-related