{"id":46729,"date":"2022-10-20T00:00:00","date_gmt":"2022-10-20T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/using-griddb-to-analyze-factors-affecting-used-car-sales\/"},"modified":"2025-11-13T12:56:18","modified_gmt":"2025-11-13T20:56:18","slug":"using-griddb-to-analyze-factors-affecting-used-car-sales","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/using-griddb-to-analyze-factors-affecting-used-car-sales\/","title":{"rendered":"Using GridDB to Analyze Factors Affecting Used Car Sales"},"content":{"rendered":"

The automobile industry<\/a>, which spans many adjacent industries, is a pillar of the global economy, a key contributor to macroeconomic growth and stability, and technological advancement in the first and third world nations.<\/p>\n

The global auto industry is booming, and to bring about new changes, they have turned to produce new electric vehicles. However, this article will not discuss new inventions or automobiles; instead, we will examine the market for used fossil fuel-based cars and which factors are responsible for causing an impact on this market.<\/p>\n

GridDB will be used to analyze the factors that influence used car sales because it is a highly scalable and optimized in-memory No SQL database that allows parallel processing for increased performance and efficiency, particularly for time-series databases. GridDB’s node js client will be used, which allows us to connect GridDB to node js and import or export data in real-time. In addition, we will use the DanfoJS library to work with data frames for data analysis to create interesting visualizations and data findings.<\/p>\n

The dataset, which is in csv format, was obtained from Kaggle<\/a>. We will see what the data represents later in the Data Analysis section.<\/p>\n

Full Source code found here<\/a><\/p>\n

Exporting Dataset into GridDB<\/h2>\n

To begin, we must initialize the GridDB node modules griddb node, danfojs-node, and csv-parser. Griddb node starts the node so we can work on GridDB, Danfojs-node is initialized as a variable, df, which is used in data analysis, and csv-parser will upload our dataset into GridDB by reading the csv file, which is as follows:<\/p>\n

\n
var griddb = require('griddb_node');\n\nconst dfd = require(\"danfojs-node\")\nconst csv = require('csv-parser');\nconst fs = require('fs');\nfs.createReadStream('.\/Dataset\/UserCarData.csv')\n  .pipe(csv())\n  .on('data', (row) => {\n    lst.push(row);\n    console.log(lst);\n  })<\/code><\/pre>\n<\/div>\n
Following variable initialization, we will generate the GridDB container to create the database schema. In the container, we have to define the data types of columns in our dataset. We can then use the container later to access the stored data, completing our data insertion into the GridDB.<\/p>\n
\n
const conInfo = new griddb.ContainerInfo({\n    'name': \"usedcaranalysis\",\n    'columnInfoList': [\n      [\"name\", griddb.Type.STRING],\n      [\"Sales_ID\", griddb.Type.INTEGER],\n        [\"name\", griddb.Type.STRING],\n        [\"year\", griddb.Type.INTEGER],\n        [\"selling_price\", griddb.Type.INTEGER],\n        [\"km_driven\", griddb.Type.INTEGER],\n        [\"Region\", griddb.Type.STRING],\n        [\"State or Province\", griddb.Type.STRING],\n        [\"City\", griddb.Type.STRING],\n        [\"fuel\", griddb.Type.STRING],\n        [\"seller_type\", griddb.Type.STRING],\n        [\"transmission\", griddb.Type.STRING],\n        [\"owner\", griddb.Type.STRING],\n        [\"mileage\", griddb.Type.DOUBLE],\n        [\"engine\", griddb.Type.INTEGER],\n        [\"max_power\", griddb.Type.DOUBLE],\n        [\"torque\", griddb.Type.STRING],\n        [\"seats\", griddb.Type.INTEGER],\n        [\"sold\", griddb.Type.STRING]\n    ],\n    'type': griddb.ContainerType.COLLECTION, 'rowKey': true\n});\n\n\/\/ Inserting Data into GridDB\n\n    for(let i=0;i<lst.length;i++){\n\n    store.putContainer(conInfo, false)\n        .then(cont => {\n            container = cont;\n            return container.createIndex({ 'columnName': 'name', 'indexType': griddb.IndexType.DEFAULT });\n        })\n        .then(() => {\n            idx++;\n            container.setAutoCommit(false);\n            return container.put([String(idx), lst[i]['Sales_ID'],lst[i][\"name\"],lst[i][\"year\"],lst[i][\"selling_price\"],lst[i][\"km_driven\"],lst[i][\"Region\"],lst[i][\"State or Province\"],lst[i][\"City\"],lst[i][\"fuel\"],lst[i][\"seller_type\"],lst[i][\"transmission\"],lst[i][\"owner\"],lst[i][\"mileage\"],lst[i][\"engine\"],lst[i][\"max_power\"],lst[i][\"torque\"],lst[i][\"seats\"],lst[i][\"sold\"]]);\n        })\n        .then(() => {\n            return container.commit();\n        })      \n        .catch(err => {\n            if (err.constructor.name == \"GSException\") {\n                for (var i = 0; i < err.getErrorStackSize(); i++) {\n                    console.log(\"[\", i, \"]\");\n                    console.log(err.getErrorCode(i));\n                    console.log(err.getMessage(i));\n                }\n            } else {\n                console.log(err);\n            }\n        });\n    }<\/code><\/pre>\n<\/div>\n
Importing Dataset from GridDB<\/h2>\n
Since we’ve already saved all of our data in the container, all we have to do now is retrieve it using TQL, GridDB’s SQL-like query language. So, to begin, we will construct a container for the retrieved data, named obtained_data. The next step is to extract the rows in the column order, named query, and save them in a data frame, named df, for data visualization and analysis, completing our data import.<\/p>\n
\n
# Get the containers\nobtained_data = gridstore.get_container(\"usedcaranalysis\")\n    \n# Fetch all rows - language_tag_container\nquery = obtained_data.query(\"select *\")\n\n# Creating Data Frame variable\nlet df = await dfd.readCSV(\".\/out.csv\")<\/code><\/pre>\n<\/div>\n
Data Analysis<\/h2>\n
To begin our data analysis, it is always best to start with the most basic steps possible. As a result, we will look at our dataset’s columns and what they represent, as well as the total number of rows and columns:<\/p>\n