{"id":46727,"date":"2022-09-02T00:00:00","date_gmt":"2022-09-02T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/using-griddb-to-analyze-gdp-of-different-countries\/"},"modified":"2025-11-13T12:56:17","modified_gmt":"2025-11-13T20:56:17","slug":"using-griddb-to-analyze-gdp-of-different-countries","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/using-griddb-to-analyze-gdp-of-different-countries\/","title":{"rendered":"Using GridDB to Analyze GDP of Different Countries"},"content":{"rendered":"

GDP<\/a>, which stands for “Gross Domestic Product,” is the total monetary value of all final goods and services produced (and sold on the market) within a country over a given period (typically one year). It is used as a metric for any country’s economic growth.<\/p>\n

We will use GridDB to analyze the economic status of various countries. GridDB is a highly scalable and optimized in-memory No SQL database that allows parallel processing for higher performance and efficiency, especially for time-series databases. We will be using GridDB’s node js client, which allows us to connect GridDB to node js and import or export data in real-time. Furthermore, we will use the DanfoJS library to work with data frames for data analysis.<\/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 and dataset 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\/GDP by Country 1999-2022.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': \"gdpanalysis\",\n    'columnInfoList': [\n      [\"name\", griddb.Type.STRING],\n      [\"Country\", griddb.Type.STRING],\n        [\"1999\", griddb.Type.DOUBLE],\n        [\"2000\", griddb.Type.DOUBLE],\n        [\"2001\", griddb.Type.DOUBLE],\n        [\"2002\", griddb.Type.DOUBLE],\n        [\"2003\", griddb.Type.DOUBLE],\n        [\"2004\", griddb.Type.DOUBLE],\n        [\"2005\", griddb.Type.DOUBLE],\n        [\"2006\", griddb.Type.DOUBLE],\n        [\"2007\", griddb.Type.DOUBLE],\n        [\"2008\", griddb.Type.DOUBLE],\n        [\"2009\", griddb.Type.DOUBLE],\n        [\"2010\", griddb.Type.DOUBLE],\n        [\"2011\", griddb.Type.DOUBLE],\n        [\"2012\", griddb.Type.DOUBLE],\n        [\"2013\", griddb.Type.DOUBLE],\n        [\"2014\", griddb.Type.DOUBLE],\n        [\"2015\", griddb.Type.DOUBLE],\n        [\"2016\", griddb.Type.DOUBLE],\n        [\"2017\", griddb.Type.DOUBLE],\n        [\"2018\", griddb.Type.DOUBLE],\n        [\"2019\", griddb.Type.DOUBLE],\n        [\"2020\", griddb.Type.DOUBLE],\n        [\"2021\", griddb.Type.DOUBLE]\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]['Country'],lst[i][\"1999\"],lst[i][\"2000\"],lst[i][\"2001\"],lst[i][\"2002\"],lst[i][\"2003\"],lst[i][\"2004\"],lst[i][\"2005\"],lst[i][\"2006\"],lst[i][\"2007\"],lst[i][\"2008\"],lst[i][\"2009\"],lst[i][\"2010\"],lst[i][\"2011\"],lst[i][\"2012\"],lst[i][\"2013\"],lst[i][\"2014\"],lst[i][\"2015\"],lst[i][\"2016\"],lst[i][\"2017\"],lst[i][\"2018\"],lst[i][\"2019\"],lst[i][\"2020\"],lst[i][\"2021\"]]);\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(\"gdpanalysis\")\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, we will examine the columns of our dataset and what they represent, as well as the total number of rows and columns in our dataset:<\/p>\n