Abstract
This article describes a TSQL JSON parser and provides the source. It is also designed to illustrate a number of string manipulation techniques and also eliminate the issues while dealing with the JSON document containing special symbols like (“/” , ”-”....) in T-SQL. With it you can do things like this to extract the data from a JSON file or document which contains noise and complexities.
Summary For Implementation
The code for the JSON Parser will run in SQL Server 2005, and even in SQL Server 2000 (note: some modifications are necessary).
First the function stores all strings in the temporary table, even the name of the elements, since they are 'escapes' in a different way, and may contain, unescaped, brackets, Special Characters which denote objects or lists. These are replaced in the json string by tokens which represent the strings.
After this fetch all the json keywords and values for further processing by using the regular expressions, various string functions and a list of SQL queries and variables to store the values for a particular object.
And at the last function will return a whole table which contains rows and columns with no noise in the values as the other tables in the particular database.
Figure 1:- Json Input
Figure 2:- Function Output
Background
TSQL isn’t really designed for doing complex string parsing which contains special characters and particularly where strings represent nested data structures such as XML, JSON, or XHTML.
You can do it but it is not a pretty sight; but If you ever want to do it anyway ?
(note You can now do this rather more easily using SQL Server 2016’s built-in JSON support.)
But If the SQL Server version is older or not compatible with the built-in JSON support then you can use this customized function to get the desired output by parsing any type of json document.
There is so much stuff behind that all happens to you. For example, it could be that DBA doesn’t allow a CLR, or you lack the necessary skills with procedural scripting. Sometimes, there isn’t any application, or you want to run code unobtrusively across databases or servers.
The Traditional way for dealing with data like this is to let a separate business layer parse a JSON ‘document’ into some meaningful structure(Like Tree) and then update the database by making a series of calls and lots of sql procedures. This is pretty, but can get more complicated and headache if you need to ensure that the updates to the database are wrapped into one transaction so that if anything goes wrong or any issues occur, then the whole transaction can be rolled back. This is why a TSQL approach has advantages.
Adjacency list tables have the same structure whatever the data in them. This means that you can define a single Table-Valued Type and pass data structures around between stored procedures.
Converting the data to Hierarchical table form will be different for each application, but is easy with a TSQL. You can, alternatively, convert the hierarchical table into JSON and interrogate that with SQL.
JSON format
JSON is one of the most popular lightweight markup languages, and is probably the best choice for transfer of object data from a web page.
JSON is designed to be as lightweight as possible and so it has only two structures. The first, delimited by curly brackets, is a collection of Key/value pairs, separated by commas. The key is followed by a colon.
The first snag for TSQL is that the curly or square brackets are not ‘escaped’ within a string, so that there is no way of partitioning a JSON ‘document’ simply. It is difficult to differentiate a bracket used as the delimiter of an array or structure, and one that is within a string.
The second complication is that, unlike YAML, the datatypes of values can’t be explicitly declared. You have to pass them out from applying the rules from the JSON Specification.
Implementation
The JSON outputter is a great deal simpler, since one can be sure of the input, but essentially it does the reverse process, working from the root of the json document to the leaves. The only complication is working out the indent of the formatted output string.
In the implementation, you’ll see a fairly heavy use of PATINDEX.This uses a RegEx. However, it is all we have, and can be pressed into service by chopping the string it is searching (if only it had an optional third parameter like CHARINDEX that specified the index of the start position of the search!). The STUFF function is also important for this sort of string-manipulation work.
CREATE FUNCTION [Platform].[parseJSON] (@JSON NVARCHAR(MAX))
RETURNS @hierarchy TABLE (
Element_ID INT IDENTITY(1, 1) NOT NULL /* internal surrogate primary key gives the order of parsing and the list order */
,SequenceNo [int] NULL /* the place in the sequence for the element */
,Parent_ID INT NULL /* if the element has a parent then it is in this column. The document is the ultimate parent, so you can get the structure from recursing from the document */
,[Object_ID] INT NULL /* each list or object has an object id. This ties all elements to a parent. Lists are treated as objects here */
,[Name] NVARCHAR(2000) NULL /* the Name of the object */
,StringValue NVARCHAR(MAX) NOT NULL /*the string representation of the value of the element. */
,ValueType VARCHAR(10) NOT NULL /* the declared type of the value represented as a string in StringValue*/
)
AS
BEGIN
DECLARE @FirstObject INT --the index of the first open bracket found in the JSON string
,@OpenDelimiter INT --the index of the next open bracket found in the JSON string
,@NextOpenDelimiter INT --the index of subsequent open bracket found in the JSON string
,@NextCloseDelimiter INT --the index of subsequent close bracket found in the JSON string
,@Type NVARCHAR(10) --whether it denotes an object or an array
,@NextCloseDelimiterChar CHAR(1) --either a '}' or a ']'
,@Contents NVARCHAR(MAX) --the unparsed contents of the bracketed expression
,@Start INT --index of the start of the token that you are parsing
,@end INT --index of the end of the token that you are parsing
,@param INT --the parameter at the end of the next Object/Array token
,@EndOfName INT --the index of the start of the parameter at end of Object/Array token
,@token NVARCHAR(200) --either a string or object
,@value NVARCHAR(MAX) -- the value as a string
,@SequenceNo INT -- the sequence number within a list
,@Name NVARCHAR(200) --the Name as a string
,@Parent_ID INT --the next parent ID to allocate
,@lenJSON INT --the current length of the JSON String
,@characters NCHAR(36) --used to convert hex to decimal
,@result BIGINT --the value of the hex symbol being parsed
,@index SMALLINT --used for parsing the hex value
,@Escape INT --the index of the next escape character
/* in this temporary table we keep all strings, even the Names of the elements, since they are 'escaped' in a different way, and may contain, unescaped, brackets denoting objects or lists. These are replaced in the JSON string by tokens representing the string */
DECLARE @Strings TABLE (
String_ID INT IDENTITY(1, 1)
,StringValue NVARCHAR(MAX)
)
IF ISNULL(@JSON, '') = ''
RETURN
SELECT @characters = '0123456789abcdefghijklmnopqrstuvwxyz' --initialise the characters to convert hex to ascii
,@SequenceNo = 0 --set the sequence no. to something sensible.
,@Parent_ID = 0;
/* firstly we process all strings. This is done because [{} and ] aren't escaped in strings, which complicates an iterative parse. */
WHILE 1 = 1 --forever until there is nothing more to do
BEGIN
SELECT @start = PATINDEX('%[^a-zA-Z]["]%', @json collate SQL_Latin1_General_CP850_Bin);--next delimited string
IF @start = 0
BREAK --no more so drop through the WHILE loop
IF SUBSTRING(@json, @start + 1, 1) = '"'
BEGIN --Delimited Name
SET @start = @Start + 1;
SET @end = PATINDEX('%[^\]["]%', RIGHT(@json, LEN(@json + '|') - @start) collate SQL_Latin1_General_CP850_Bin);
END
IF @end = 0 --either the end or no end delimiter to last string
BEGIN -- check if ending with a double slash...
SET @end = PATINDEX('%[\][\]["]%', RIGHT(@json, LEN(@json + '|') - @start) collate SQL_Latin1_General_CP850_Bin);
IF @end = 0 --we really have reached the end
BEGIN
BREAK --assume all tokens found
END
END
SELECT @token = SUBSTRING(@json, @start + 1, @end - 1)
--now put in the escaped control characters
SELECT @token = REPLACE(@token, FromString, ToString)
FROM (
SELECT '\b'
,CHAR(08)
UNION ALL
SELECT '\f'
,CHAR(12)
UNION ALL
SELECT '\n'
,CHAR(10)
UNION ALL
SELECT '\r'
,CHAR(13)
UNION ALL
SELECT '\t'
,CHAR(09)
UNION ALL
SELECT '\"'
,'"'
UNION ALL
SELECT '\/'
,'/'
) substitutions(FromString, ToString)
SELECT @token = Replace(@token, '\\', '\')
SELECT @result = 0
,@escape = 1
--Begin to take out any hex escape codes
WHILE @escape > 0
BEGIN
SELECT @index = 0 --find the next hex escape sequence
,@escape = PATINDEX('%\x[0-9a-f][0-9a-f][0-9a-f][0-9a-f]%', @token collate SQL_Latin1_General_CP850_Bin)
IF @escape > 0 --if there is one
BEGIN
WHILE @index < 4 --there are always four digits to a \x sequence
BEGIN
SELECT --determine its value
@result = @result + POWER(16, @index) * (CHARINDEX(SUBSTRING(@token, @escape + 2 + 3 - @index, 1), @characters) - 1)
,@index = @index + 1;
END
-- and replace the hex sequence by its unicode value
SELECT @token = STUFF(@token, @escape, 6, NCHAR(@result))
END
END
--now store the string away
INSERT INTO @Strings (StringValue)
SELECT @token
-- and replace the string with a token
SELECT @JSON = STUFF(@json, @start, @end + 1, '@string' + CONVERT(NCHAR(5), @@identity))
END
-- all strings are now removed. Now we find the first leaf.
WHILE 1 = 1 --forever until there is nothing more to do
BEGIN
SELECT @Parent_ID = @Parent_ID + 1
--find the first object or list by looking for the open bracket
SELECT @FirstObject = PATINDEX('%[{[[]%', @json collate SQL_Latin1_General_CP850_Bin) --object or array
IF @FirstObject = 0
BREAK
IF (SUBSTRING(@json, @FirstObject, 1) = '{')
SELECT @NextCloseDelimiterChar = '}'
,@type = 'object'
ELSE
SELECT @NextCloseDelimiterChar = ']'
,@type = 'array'
SELECT @OpenDelimiter = @firstObject
WHILE 1 = 1 --find the innermost object or list...
BEGIN
SELECT @lenJSON = LEN(@JSON + '|') - 1
--find the matching close-delimiter proceeding after the open-delimiter
SELECT @NextCloseDelimiter = CHARINDEX(@NextCloseDelimiterChar, @json, @OpenDelimiter + 1)
--is there an intervening open-delimiter of either type
SELECT @NextOpenDelimiter = PATINDEX('%[{[[]%', RIGHT(@json, @lenJSON - @OpenDelimiter) collate SQL_Latin1_General_CP850_Bin) --object
IF @NextOpenDelimiter = 0
BREAK
SELECT @NextOpenDelimiter = @NextOpenDelimiter + @OpenDelimiter
IF @NextCloseDelimiter < @NextOpenDelimiter
BREAK
IF SUBSTRING(@json, @NextOpenDelimiter, 1) = '{'
SELECT @NextCloseDelimiterChar = '}'
,@type = 'object'
ELSE
SELECT @NextCloseDelimiterChar = ']'
,@type = 'array'
SELECT @OpenDelimiter = @NextOpenDelimiter
END
---and parse out the list or Name/value pairs
SELECT @contents = SUBSTRING(@json, @OpenDelimiter + 1, @NextCloseDelimiter - @OpenDelimiter - 1)
SELECT @JSON = STUFF(@json, @OpenDelimiter, @NextCloseDelimiter - @OpenDelimiter + 1, '@' + @type + CONVERT(NCHAR(5), @Parent_ID))
WHILE (PATINDEX('%[A-Za-z0-9@+.e]%', @contents collate SQL_Latin1_General_CP850_Bin)) <> 0
BEGIN
IF @Type = 'object' --it will be a 0-n list containing a string followed by a string, number,boolean, or null
BEGIN
SELECT @SequenceNo = 0
,@end = CHARINDEX(':', ' ' + @contents) --if there is anything, it will be a string-based Name.
SELECT @start = PATINDEX('%[^A-Za-z@][@]%', ' ' + @contents collate SQL_Latin1_General_CP850_Bin) --AAAAAAAA
SELECT @token = RTrim(Substring(' ' + @contents, @start + 1, @End - @Start - 1))
,@endofName = PATINDEX('%[0-9]%', @token collate SQL_Latin1_General_CP850_Bin)
,@param = RIGHT(@token, LEN(@token) - @endofName + 1)
SELECT @token = LEFT(@token, @endofName - 1)
,@Contents = RIGHT(' ' + @contents, LEN(' ' + @contents + '|') - @end - 1)
SELECT @Name = StringValue
FROM @strings
WHERE string_id = @param --fetch the Name
END
ELSE
SELECT @Name = NULL
,@SequenceNo = @SequenceNo + 1
SELECT @end = CHARINDEX(',', @contents) -- a string-token, object-token, list-token, number,boolean, or null
IF @end = 0
--HR Engineering notation bugfix start
IF ISNUMERIC(@contents) = 1
SELECT @end = LEN(@contents) + 1
ELSE
--HR Engineering notation bugfix end
SELECT @end = PATINDEX('%[A-Za-z0-9@+.e][^A-Za-z0-9@+.e]%', @contents + ' ' collate SQL_Latin1_General_CP850_Bin) + 1
SELECT @start = PATINDEX('%[^A-Za-z0-9@+.e][-A-Za-z0-9@+.e]%', ' ' + @contents collate SQL_Latin1_General_CP850_Bin)
--select @start,@end, LEN(@contents+'|'), @contents
SELECT @Value = RTRIM(SUBSTRING(@contents, @start, @End - @Start))
,@Contents = RIGHT(@contents + ' ', LEN(@contents + '|') - @end)
IF SUBSTRING(@value, 1, 7) = '@object'
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,[Object_ID]
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,SUBSTRING(@value, 8, 5)
,SUBSTRING(@value, 8, 5)
,'object'
ELSE IF SUBSTRING(@value, 1, 6) = '@array'
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,[Object_ID]
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,SUBSTRING(@value, 7, 5)
,SUBSTRING(@value, 7, 5)
,'array'
ELSE IF SUBSTRING(@value, 1, 7) = '@string'
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,StringValue
,'string'
FROM @strings
WHERE string_id = SUBSTRING(@value, 8, 5)
ELSE IF @value IN ('true', 'false')
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,@value
,'boolean'
ELSE IF @value = 'null'
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,@value
,'null'
ELSE IF PATINDEX('%[^0-9-]%', @value collate SQL_Latin1_General_CP850_Bin) > 0
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,@value
,'real'
ELSE
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,ValueType
)
SELECT @Name
,@SequenceNo
,@Parent_ID
,@value
,'int'
IF @Contents = ' '
SELECT @SequenceNo = 0
END
END
INSERT INTO @hierarchy (
[Name]
,SequenceNo
,Parent_ID
,StringValue
,[Object_ID]
,ValueType
)
SELECT '-'
,1
,NULL
,''
,@Parent_ID - 1
,@type
RETURN
END
Code Snippet 1:- ParseJson Function
Closure
The so-called ‘impedance-mismatch’ between applications and databases is an illusion. if the developer has understood the data correctly then there is less complexity while processing it.
But has been trickier with other formats such as JSON. By using techniques like this, it should be possible to liberate the application or website from having to do the mapping from the object model to the relational, and spraying the database with ad-hoc T-SQL that uses the fact/dimension tables or updateable views. If the database can be provided with the JSON, or the Table-Valued parameter, then there is a better chance of maintaining full transactional integrity for the more complex updates.
The database developer already has the tools to do the work with XML, but why not the simpler, and more practical JSON? I hope these routines get you started with experimenting with all this for your requirements.
Scenario: If someone say you Hey, can you transfer one of MySQL data to another MySQL data and we think about SSIS or other Thing if yes then these article made for you to reduce your effort and save your time Introduction: In the dynamic landscape of database management, the need to seamlessly access and integrate data from multiple sources has become paramount. Whether it's consolidating information from disparate servers or synchronizing databases for backup and redundancy, MySQL offers a robust solution through its querying capabilities. In this guide, we delve into the art of fetching data from one MySQL server to another using SQL queries. This method, often overlooked in favor of complex data transfer mechanisms, provides a streamlined approach to data migration, enabling developers and database administrators to efficiently manage their resources. Through a combination of MySQL's versatile querying language and the innovative use of the FEDERATED storage engine, we'll explore how to establish connections between servers, replicate table structures, and effortlessly transfer data across the network. From setting up the environment to executing queries and troubleshooting common challenges, this tutorial equips you with the knowledge and tools to navigate the intricacies of cross-server data retrieval with ease. As we know We gonna use FEDERATED feature of MySQL workbench so first we need to check that our workbench support FEDERATED engine or not? Simply open workbench and run below code show engines; It shows all engines and check our system support FEDERATED OR NOT If your system also not support don't worry we gonna enable it Open your folder where you save MySQL serve file In my case it in my C drive C>ProgramData>MySQL>MySQL Server 8.0>my.ini open it in notepad++ or preferable software Insert FEDERATED key word in script like below Now need to restart MySQL Press Window+R button and paste services.msc press ok> find MySQL and restart it Now go to workbence and run show engines; code Now your FEDERATED engine get supported It show like below Now our system Support FEDERATED engine This same process need to apply on destination side because both server (from source to destination server) need to support FEDERATED engine Now we make sure to we have permission of access source server for that we need to make user and and give permission of database and tables Below code demonstrate to make user and give permission to user CREATE USER 'hmysql'@'192.168.1.173' IDENTIFIED BY 'Hardik...'; GRANT ALL PRIVILEGES ON *.* TO 'hmysql'@'192.168.1.173' WITH GRANT OPTION; FLUSH PRIVILEGES; Now make connection of that user(we make above on source side) on destination server(our system) Click on plus(+) icon as shown in image and fill all detail Below image is for detail of user connection After filling details our user added like below image Go to user(hardikmysql) and find from which table we want to take data using MySQL query Here i am taking 'actor' table from 'sakila' database which look like below Now we need to run FEDERATED query on our system(destination server) with url string Our MySQL query like below CREATE TABLE `actor` ( `actor_id` smallint unsigned NOT NULL AUTO_INCREMENT, `first_name` varchar(45) NOT NULL, `last_name` varchar(45) NOT NULL, `last_update` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, PRIMARY KEY (`actor_id`), KEY `idx_actor_last_name` (`last_name`) ) ENGINE=FEDERATED default charset=utf8mb4 CONNECTION='mysql://hmysql:[email protected]:3306/sakila/actor'; Here main part is below ENGINE=FEDERATED default charset=utf8mb4 CONNECTION='mysql://hmysql:[email protected]:3306/sakila/actor'; Here 'mysql' is mandatory for connection string you can not use other word. 'hmysql' is user name 'Hardik...' is password for user '192.168.1.173' is server adderess '3306' is port number 'sakila' is database name 'actor' is table name Now run above table code and you get data in our system(destination server)
Setting up replication in SQL Server can be a powerful way to ensure data consistency and availability across multiple servers. In this step-by-step guide, we'll walk through the process of configuring replication on SQL Servers. Step 1: Understand Replication Types Before diving into configuration, it's crucial to understand the types of replication available in SQL Server. Snapshot Replication: Takes a snapshot of the data at a specific point in time. Transactional Replication: Replicates changes in real-time as they occur. Merge Replication: Allows bidirectional data synchronization between servers. Choose the replication type that aligns with your specific needs and database architecture. Step 2: Prepare Your Environment Ensure that your SQL Server environment is ready for replication. This involves verifying that you have the necessary permissions and establishing proper connectivity between the SQL Server instances. Remember that replication involves three key components: Publisher, Distributor, and Subscribers. The Distributor can be on the same server as the Publisher or a separate server. Step 3: Configure Distributor If a Distributor isn't already set up, proceed to configure one. This involves specifying the server that will act as the Distributor and setting up distribution databases. Use either SQL Server Management Studio (SSMS) or T-SQL scripts for this configuration. Step 4: Enable Replication on the Publisher 1. Open SSMS and connect to the Publisher. 2. Right-click on the target database and choose "Tasks" > "Replication" > "Configure Distribution." 3. Follow the wizard, specifying the Distributor configured in Step 3. Step 5: Choose Articles Define the articles by selecting the tables, views, or stored procedures you want to replicate. This step allows you to fine-tune your replication by specifying data filters, choosing columns to replicate, and configuring additional options based on your specific requirements. Step 6: Configure Subscribers 1. Connect to the Subscribers in SSMS. 2. Right-click on the Replication folder and choose "Configure Distribution." 3. Follow the wizard, specifying the Distributor and configuring additional settings based on your chosen replication type. Step 7: Configure Subscription With the Distributor and Subscribers configured, it's time to set up subscriptions. 1. In SSMS, navigate to the Replication folder on the Publisher. 2. Right-click on the Local Publications and choose "New Subscriptions." 3. Follow the wizard to configure the subscription, specifying the Subscribers and defining any additional settings. Step 8: Monitor and Maintain Regular monitoring and maintenance are essential for a healthy replication environment. - Use the Replication Monitor in SSMS to view the status of publications, subscriptions, and any potential errors. - Implement routine maintenance tasks such as backing up and restoring the replication databases. Conclusion Configuring replication in SQL Server involves a series of well-defined steps. By understanding your replication needs, preparing your environment, and carefully configuring each component, you can establish a robust and reliable replication setup. Regular monitoring and maintenance ensure the ongoing efficiency and performance of your replication environment.
In the ever-evolving landscape of business intelligence (BI), the need for seamless interaction with data is paramount. Imagine a world where you could effortlessly pose natural language questions to your datasets and receive insightful answers in return. Welcome to the future of BI, where the power of conversational interfaces meets the robust capabilities of Domo. This blog post serves as your comprehensive guide to implementing a BI ChatBot within the Domo platform, a revolutionary step towards making data exploration and analysis more intuitive and accessible than ever before. Gone are the days of wrestling with complex queries or navigating through intricate dashboards. With the BI ChatBot in Domo, users can now simply articulate their questions in plain language and navigate through datasets with unprecedented ease. Join us on this journey as we break down the process into manageable steps, allowing you to harness the full potential of BI ChatBot integration within the Domo ecosystem. Whether you're a seasoned data analyst or a business professional seeking data-driven insights, this guide will empower you to unlock the true value of your data through natural language interactions. Get ready to elevate your BI experience and transform the way you interact with your datasets. Let's dive into the future of business intelligence with the implementation of a BI ChatBot in Domo. Prerequisites: ChatGPT API Key: Prepare for the integration of natural language to SQL conversion by obtaining a ChatGPT API Key. This key will empower your system to seamlessly translate user queries in natural language into SQL commands. DOMO Access: Ensure that you have the necessary access rights to create a new application within the Domo platform. This step is crucial for configuring and deploying the BI ChatBot effectively within your Domo environment. 1: Integrate the HTML Easy Bricks App. Begin the process by incorporating the HTML Easy Bricks App into your project. Navigate to the AppStore and add the HTML Easy Bricks to your collection. Save it to your dashboard for easy access. Upon opening the App for the first time, it will have a default appearance. To enhance its visual appeal and functionality, customize it by incorporating the HTML and CSS code. This transformation will result in the refined look illustrated below. Image 1: DOMO HTML Easy Brick UI 2: Map/Connect the Dataset to the Card. In this phase, establish a connection between the dataset and the card where users will pose their inquiries. Refer to the image below, where the "Key" dataset is linked to "dataset0." Extend this mapping to accommodate up to three datasets. If your project involves more datasets, consider using the DDX-TEN-DATASETS App instead of HTML Easy Bricks for a more scalable solution. This ensures seamless integration and accessibility for users interacting with various datasets within your Domo environment. Image 2: Attach Dataset With Card 3: Execute the Query on the Dataset for Results. In this phase, you'll implement the code to execute a query on the dataset, fetching the desired results. Before this, initiate a call to the ChatGPT API to dynamically generate an SQL query based on the user's natural language question. It's essential to note that the below code is designed to only accept valid column names in the query, adhering strictly to MySQL syntax. To facilitate accurate query generation from ChatGPT, create a prompt that includes the dataset schema and provides clear guidance for obtaining precise SQL queries. Here is a call to the ChatGPT API to get SQL Query. VAR GPTKEY = 'key' VAR Prompt = 'Write effective prompt' $.ajax({ url: 'https://api.openai.com/v1/chat/completions', headers: { 'Authorization': 'Bearer ' + GPTKEY, 'Content-Type': 'application/json' }, method: 'POST', data: JSON.stringify({ model: 'gpt-3.5-turbo', messages: Prompt, max_tokens: 100, temperature: 0.5, top_p: 1.0, frequency_penalty: 0.0, presence_penalty: 0.0 }), success: function (response) { //Write code to store the Query into the variable } }); Refer to the code snippet below for executing the query on Domo and retrieving the results. var domo = window.domo; var datasets = window.datasets; domo.post('/sql/v1/'+ 'dataset0', SQLQuery, {contentType: 'text/plain'}).then(function(data) { //Write your Java or JQuery code to print data. }); The above code will accept the SQL queries generated by ChatGPT. It's important to highlight that, in the code, there is a hardcoded specification that every query will be applied to the dataset mapped as 'dataset0'. It's advisable to customize this part based on user selection. The code is designed to accept datasets with names such as 'dataset0', 'dataset1', and so forth. Ensure that any modifications align with the chosen dataset for optimal functionality, you can also use the domo.get method to get data for more information visit here. The outcome will be presented in JSON format, offering flexibility for further processing. You can seamlessly transfer this data to a table format and display or print it as needed. Conclusion Incorporating a BI ChatBot in Domo revolutionizes data interaction, seamlessly translating natural language queries into actionable insights. The guide's step-by-step approach simplifies integration, offering both analysts and business professionals an intuitive and accessible data exploration experience. As datasets effortlessly respond to user inquiries, this transformative synergy between ChatGPT and Domo reshapes how we extract value from data, heralding a future of conversational and insightful business intelligence. Dive into this dynamic integration to propel your decision-making processes into a new era of efficiency and accessibility.
Project Lead in Magnusminds
Hardik is working as Project Lead of MSBI in INDIA. Hardik started his career working on SQL Server and MSBI. Hardik is having 5+ years of experience. In the starting of his career he was working on SQL Server, SSIS and SSRS. Hardik likes to explore technical things on SQL Server.