Blog Post

Azure
1 MIN READ

Auto-Pause delay modifications not working

FelixR13's avatar
FelixR13
Copper Contributor
Mar 05, 2024

I have an Azure SQL server hosting my database. For our current project this database should always be accessible and never be slept. The users should be able to log in to our app using their credentials, which is checked in the db, without having the server timeout error because the database server was asleep.

 

So I've tried modifying the auto-pause delay, using -1 as a parameters, then 0 has suggested by official microsoft documentation and even tried it at a value of 800 minutes. But no matter what values I give to the delay it always seem to pause itself after 15 to 60 minutes of inactivity. 

We have a General Purpose Serverless Gen5 2vCores pricing tier. 
Can anyone help me fix the auto-pause delay so that it doesn't pause or suggest a different approach to my problem.

Thanks in advance.

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Build a self-serve analytics platform by letting your customers ask questions about data in plain English with OpenAI and Azure Data Explorer.

","body":"

Introduction

\n

Data is the new oil. Simply having access to data is not enough. To make informed decisions, you must be able to visualize and analyze your data in a meaningful way.

\n

In this article I will show you how you can build a self-serve analytics platform by letting your customers ask questions about data in plain English.

\n


Azure Data Explorer (aka Kusto)

\n

Azure Data Explorer is a powerful analytics platform designed to handle large volumes of data with low latency.

\n

It is a cloud-based service in Azure that enables users to perform real-time data analysis on large datasets, making it ideal for businesses that need to process vast quantities of data quickly.
One of the key benefits of Kusto is its scalability. The platform is capable of handling petabytes of data with ease, making it ideal for businesses of all sizes. This means that companies can easily scale up or down their data processing capabilities as their needs change, without having to worry about investing in additional hardware or infrastructure.
Kusto is also highly versatile, supporting a wide range of data sources, including structured, semi-structured, and unstructured data. This makes it an ideal choice for businesses that need to process a diverse range of data types, such as log files, IoT data, and social media feeds.
One of the most popular use cases for Kusto is in conjunction with Power BI, Microsoft's business intelligence tool.

\n

By integrating Kusto with Power BI, businesses can build powerful analytics solutions that enable them to gain insights into their data quickly and easily. This integration enables users to create interactive dashboards and reports that provide a real-time view of their data, helping them make more informed decisions.

\n


Building a Self-serve analytics platforms

\n

The need for self-serve data analytics is becoming increasingly important, customers want to be able to access and analyze their own data, without having to rely on engineering or data analysts.

\n

This is where the ability to query a database in plain English becomes essential.

With the help of GPT 3.5, you can translate natural language into queries. By using this paradigm you can now offer your customers the ability to ask questions about their data in plain English.

\n

This means that customers can ask questions like

\n

\"What were my sales last month?\" or \"Which products are selling the most?\" and your system will automatically translate these questions into queries and query the database.

\n

 

\n

Using Azure OpenAI to let your customers query your data in English

\n

OpenAI can be used to query databases in two ways, by translating plain English customer queries into query language or by using embeddings to convert the customer's question into a numerical representation that can be matched with the stored data, the best approach to use depends on the type of data that is being analyzed.

\n\n

This is because embeddings can capture the semantic meaning of text data, even if the wording or phrasing of a question is not precise. For more information on how to do this you can read my previous article – Build a chatbot to query your documentation using Langchain and Azure OpenAI

\n\n

In this article we will use the first approach to “translate” the customer’s data into a query language, in our case KQL (Kusto query language) and we will query an analytical DB containing sales data.

\n

 

\n

The Flow

\n

\n

 

\n

Loading the data into Azure Data Explorer

\n\n

Here is the link on how to create a free cluster for this demo

\n\n

\n

 

\n\n

\n

 

\n

 

\n

\n

 

\n\n

\n

 

\n

\n

 

\n\n

\n

 

\n\n

\n

 

\n\n

\n

 

\n\n

 

\n

 

\n
AAD_TENANT_ID = \"<your aad tenant id>\"\nKUSTO_CLUSTER =  \"https://<your azure data explorer name>.westeurope.kusto.windows.net\"\nKUSTO_DATABASE = \"<your kusto database name>\"\nKUSTO_TABLE = \"sales\"\nKUSTO_MANAGED_IDENTITY_APP_ID = \"<your aad app registration id>\"\nKUSTO_MANAGED_IDENTITY_SECRET = \"<your kusto managed identity secret>\"
\n

 

\n

 

\n

Create an Azure OpenAI deployment

\n\n

\n

 

\n

 

\n\n

 

\n

 

\n
OPENAI_DEPLOYMENT_ENDPOINT = \"https://<your openai>.openai.azure.com/\"\nOPENAI_API_KEY = \"<your openai api key>\"\nOPENAI_DEPLOYMENT_NAME = \"<your gpt35 deployment name>\"\nOPENAI_DEPLOYMENT_VERSION = \"2023-03-15-preview\"\nOPENAI_MODEL_NAME=\"gpt-35-turbo\"
\n

 

\n

 

\n

The code

\n\n

 

\n

 

\n
from azure.kusto.data import KustoClient, KustoConnectionStringBuilder\nfrom azure.kusto.data.exceptions import KustoServiceError\nfrom azure.kusto.data.helpers import dataframe_from_result_table\nfrom dotenv import load_dotenv\nimport utils\nimport pandas as pd\nimport os\nimport openai\nimport json \n\nload_dotenv()\nOPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\") \nOPENAI_DEPLOYMENT_ENDPOINT = os.getenv(\"OPENAI_DEPLOYMENT_ENDPOINT\")\nOPENAI_DEPLOYMENT_NAME = os.getenv(\"OPENAI_DEPLOYMENT_NAME\")\nOPENAI_MODEL_NAME = os.getenv(\"OPENAI_MODEL_NAME\")\nOPENAI_DEPLOYMENT_VERSION = os.getenv(\"OPENAI_DEPLOYMENT_VERSION\")\n# Configure OpenAI API\nopenai.api_type = \"azure\"\nopenai.api_version = OPENAI_DEPLOYMENT_VERSION\nopenai.api_base = OPENAI_DEPLOYMENT_ENDPOINT\nopenai.api_key = OPENAI_API_KEY\n\nAAD_TENANT_ID = os.getenv(\"AAD_TENANT_ID\")\nKUSTO_CLUSTER = os.getenv(\"KUSTO_CLUSTER\")\nKUSTO_DATABASE = os.getenv(\"KUSTO_DATABASE\")\nKUSTO_TABLE = os.getenv(\"KUSTO_TABLE\")\nKUSTO_MANAGED_IDENTITY_APP_ID = os.getenv(\"KUSTO_MANAGED_IDENTITY_APP_ID\")\nKUSTO_MANAGED_IDENTITY_SECRET = os.getenv(\"KUSTO_MANAGED_IDENTITY_SECRET\")
\n

 

\n

 

\n\n

 

\n

 

\n
cluster = KUSTO_CLUSTER\nkcsb = KustoConnectionStringBuilder.with_aad_application_key_authentication(cluster, KUSTO_MANAGED_IDENTITY_APP_ID, KUSTO_MANAGED_IDENTITY_SECRET,  AAD_TENANT_ID)\nclient = KustoClient(kcsb)
\n

 

\n

 

\n\n

 

\n

 

\n
query = \"sales | take 10\"\n\nresponse = client.execute(\"retail\", query)\nfor row in response.primary_results[0]:\n    print(\"Item:{}\".format(row[\"item_description\"]))
\n

 

\n

 

\n

\n

 

\n\n

 

\n

 

\n
kusto_query_template_prefix = \"\"\"\n<|im_start|>system\nI have an Azure Data Explorer (Kusto) table containing the following columns: \nyear, month, supplier, item_code, item_description,item_type,retail_sales,retail_transfers,warehouse_sales\n\nWrite an KQL query based on the user input below. Answer in a concise manner. Answer only with the KQL query where the table name is T, no extra text.\n\nuser input: \n\"\"\"\ntemplate_sufix = \"<|im_end|>\\n<|im_start|>assistant\"
\n

 

\n

 

\n\n

 

\n

 

\n
def call_openai(template_prefix, text):\n    prompt = template_prefix + text + template_sufix\n    response = openai.Completion.create(\n        engine=utils.OPENAI_DEPLOYMENT_NAME,\n        prompt=prompt,\n        temperature=0,\n        max_tokens=4096,\n        top_p=0.95,\n        frequency_penalty=0,\n        presence_penalty=0,\n        stop=[\"<|im_end|>\"])\n    response = response['choices'][0]['text']\n    response = utils.remove_chars(\"\\n\", response)\n    response=utils.start_after_string(\"Answer:\", response)\n    response=utils.remove_tail_tags(\"<|im_end|>\", response)\n    return response\n\ndef call_openai_kql_response(text):\n    response = call_openai(kusto_query_template_prefix, text)\n    query = response.replace(\"T\", \"sales\")\n    query = query.replace(\"```\", \"\")\n    response = client.execute(\"retail\", query)\n    df = dataframe_from_result_table(response.primary_results[0])\n    return df
\n

 

\n

 

\n\n

 

\n

 

\n
df = call_openai_kql_response(\"I would like to get the total retail sales for 2020 by months\")\nprint(df)
\n

 

\n

 

\n

\n\n

\n

 

\n\n

 

\n

 

\n
df = call_openai_kql_response(\"I would like to get the top 10 suppliers by total retail sales\")\nprint(df)
\n

 

\n

 

\n\n

\n

 

\n\n

 

\n

 

\n
df = call_openai_kql_response(\"I would like to get the total retail sales by year and month for the top 10 suppliers\")\nprint(df)
\n

 

\n

 

\n

 

\n

\n

What’s next

\n

This is just sample code.

\n

In order to create a highly scalable and efficient system to let your customers query your data in English, you must build a robust infrastructure.

\n

Some of the things you need to consider are:

\n\n

The source code for this article can be found in this repo:

\n

azure-data-and-ai-examples/openai/ask your Analytical DB questions in english.ipynb at master · denisa-ms/azure-data-and-ai-examples (github.com)

\n

 

\n

I hope you found this article enjoyable and easy to follow and it will help you get into the right direction in order to build a self-serve analytics application.

\n

Thanks

\n

Denise

","body@stringLength":"23222","rawBody":"

Introduction

\n

Data is the new oil. Simply having access to data is not enough. To make informed decisions, you must be able to visualize and analyze your data in a meaningful way.

\n

In this article I will show you how you can build a self-serve analytics platform by letting your customers ask questions about data in plain English.

\n


Azure Data Explorer (aka Kusto)

\n

Azure Data Explorer is a powerful analytics platform designed to handle large volumes of data with low latency.

\n

It is a cloud-based service in Azure that enables users to perform real-time data analysis on large datasets, making it ideal for businesses that need to process vast quantities of data quickly.
One of the key benefits of Kusto is its scalability. The platform is capable of handling petabytes of data with ease, making it ideal for businesses of all sizes. This means that companies can easily scale up or down their data processing capabilities as their needs change, without having to worry about investing in additional hardware or infrastructure.
Kusto is also highly versatile, supporting a wide range of data sources, including structured, semi-structured, and unstructured data. This makes it an ideal choice for businesses that need to process a diverse range of data types, such as log files, IoT data, and social media feeds.
One of the most popular use cases for Kusto is in conjunction with Power BI, Microsoft's business intelligence tool.

\n

By integrating Kusto with Power BI, businesses can build powerful analytics solutions that enable them to gain insights into their data quickly and easily. This integration enables users to create interactive dashboards and reports that provide a real-time view of their data, helping them make more informed decisions.

\n


Building a Self-serve analytics platforms

\n

The need for self-serve data analytics is becoming increasingly important, customers want to be able to access and analyze their own data, without having to rely on engineering or data analysts.

\n

This is where the ability to query a database in plain English becomes essential.

With the help of GPT 3.5, you can translate natural language into queries. By using this paradigm you can now offer your customers the ability to ask questions about their data in plain English.

\n

This means that customers can ask questions like

\n

\"What were my sales last month?\" or \"Which products are selling the most?\" and your system will automatically translate these questions into queries and query the database.

\n

 

\n

Using Azure OpenAI to let your customers query your data in English

\n

OpenAI can be used to query databases in two ways, by translating plain English customer queries into query language or by using embeddings to convert the customer's question into a numerical representation that can be matched with the stored data, the best approach to use depends on the type of data that is being analyzed.

\n\n

This is because embeddings can capture the semantic meaning of text data, even if the wording or phrasing of a question is not precise. For more information on how to do this you can read my previous article – Build a chatbot to query your documentation using Langchain and Azure OpenAI

\n\n

In this article we will use the first approach to “translate” the customer’s data into a query language, in our case KQL (Kusto query language) and we will query an analytical DB containing sales data.

\n

 

\n

The Flow

\n

\n

 

\n

Loading the data into Azure Data Explorer

\n\n

Here is the link on how to create a free cluster for this demo

\n\n

\n

 

\n\n

\n

 

\n

 

\n

\n

 

\n\n

\n

 

\n

\n

 

\n\n

\n

 

\n\n

\n

 

\n\n

\n

 

\n\n

 

\n

 

\nAAD_TENANT_ID = \"<your aad tenant id>\"\nKUSTO_CLUSTER = \"https://<your azure data explorer name>.westeurope.kusto.windows.net\"\nKUSTO_DATABASE = \"<your kusto database name>\"\nKUSTO_TABLE = \"sales\"\nKUSTO_MANAGED_IDENTITY_APP_ID = \"<your aad app registration id>\"\nKUSTO_MANAGED_IDENTITY_SECRET = \"<your kusto managed identity secret>\"\n

 

\n

 

\n

Create an Azure OpenAI deployment

\n\n

\n

 

\n

 

\n\n

 

\n

 

\nOPENAI_DEPLOYMENT_ENDPOINT = \"https://<your openai>.openai.azure.com/\"\nOPENAI_API_KEY = \"<your openai api key>\"\nOPENAI_DEPLOYMENT_NAME = \"<your gpt35 deployment name>\"\nOPENAI_DEPLOYMENT_VERSION = \"2023-03-15-preview\"\nOPENAI_MODEL_NAME=\"gpt-35-turbo\"\n

 

\n

 

\n

The code

\n\n

 

\n

 

\nfrom azure.kusto.data import KustoClient, KustoConnectionStringBuilder\nfrom azure.kusto.data.exceptions import KustoServiceError\nfrom azure.kusto.data.helpers import dataframe_from_result_table\nfrom dotenv import load_dotenv\nimport utils\nimport pandas as pd\nimport os\nimport openai\nimport json \n\nload_dotenv()\nOPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\") \nOPENAI_DEPLOYMENT_ENDPOINT = os.getenv(\"OPENAI_DEPLOYMENT_ENDPOINT\")\nOPENAI_DEPLOYMENT_NAME = os.getenv(\"OPENAI_DEPLOYMENT_NAME\")\nOPENAI_MODEL_NAME = os.getenv(\"OPENAI_MODEL_NAME\")\nOPENAI_DEPLOYMENT_VERSION = os.getenv(\"OPENAI_DEPLOYMENT_VERSION\")\n# Configure OpenAI API\nopenai.api_type = \"azure\"\nopenai.api_version = OPENAI_DEPLOYMENT_VERSION\nopenai.api_base = OPENAI_DEPLOYMENT_ENDPOINT\nopenai.api_key = OPENAI_API_KEY\n\nAAD_TENANT_ID = os.getenv(\"AAD_TENANT_ID\")\nKUSTO_CLUSTER = os.getenv(\"KUSTO_CLUSTER\")\nKUSTO_DATABASE = os.getenv(\"KUSTO_DATABASE\")\nKUSTO_TABLE = os.getenv(\"KUSTO_TABLE\")\nKUSTO_MANAGED_IDENTITY_APP_ID = os.getenv(\"KUSTO_MANAGED_IDENTITY_APP_ID\")\nKUSTO_MANAGED_IDENTITY_SECRET = os.getenv(\"KUSTO_MANAGED_IDENTITY_SECRET\")\n

 

\n

 

\n\n

 

\n

 

\ncluster = KUSTO_CLUSTER\nkcsb = KustoConnectionStringBuilder.with_aad_application_key_authentication(cluster, KUSTO_MANAGED_IDENTITY_APP_ID, KUSTO_MANAGED_IDENTITY_SECRET, AAD_TENANT_ID)\nclient = KustoClient(kcsb)\n

 

\n

 

\n\n

 

\n

 

\nquery = \"sales | take 10\"\n\nresponse = client.execute(\"retail\", query)\nfor row in response.primary_results[0]:\n print(\"Item:{}\".format(row[\"item_description\"]))\n

 

\n

 

\n

\n

 

\n\n

 

\n

 

\nkusto_query_template_prefix = \"\"\"\n<|im_start|>system\nI have an Azure Data Explorer (Kusto) table containing the following columns: \nyear, month, supplier, item_code, item_description,item_type,retail_sales,retail_transfers,warehouse_sales\n\nWrite an KQL query based on the user input below. Answer in a concise manner. Answer only with the KQL query where the table name is T, no extra text.\n\nuser input: \n\"\"\"\ntemplate_sufix = \"<|im_end|>\\n<|im_start|>assistant\"\n

 

\n

 

\n\n

 

\n

 

\ndef call_openai(template_prefix, text):\n prompt = template_prefix + text + template_sufix\n response = openai.Completion.create(\n engine=utils.OPENAI_DEPLOYMENT_NAME,\n prompt=prompt,\n temperature=0,\n max_tokens=4096,\n top_p=0.95,\n frequency_penalty=0,\n presence_penalty=0,\n stop=[\"<|im_end|>\"])\n response = response['choices'][0]['text']\n response = utils.remove_chars(\"\\n\", response)\n response=utils.start_after_string(\"Answer:\", response)\n response=utils.remove_tail_tags(\"<|im_end|>\", response)\n return response\n\ndef call_openai_kql_response(text):\n response = call_openai(kusto_query_template_prefix, text)\n query = response.replace(\"T\", \"sales\")\n query = query.replace(\"```\", \"\")\n response = client.execute(\"retail\", query)\n df = dataframe_from_result_table(response.primary_results[0])\n return df\n

 

\n

 

\n\n

 

\n

 

\ndf = call_openai_kql_response(\"I would like to get the total retail sales for 2020 by months\")\nprint(df)\n

 

\n

 

\n

\n\n

\n

 

\n\n

 

\n

 

\ndf = call_openai_kql_response(\"I would like to get the top 10 suppliers by total retail sales\")\nprint(df)\n

 

\n

 

\n\n

\n

 

\n\n

 

\n

 

\ndf = call_openai_kql_response(\"I would like to get the total retail sales by year and month for the top 10 suppliers\")\nprint(df)\n

 

\n

 

\n

 

\n

\n

What’s next

\n

This is just sample code.

\n

In order to create a highly scalable and efficient system to let your customers query your data in English, you must build a robust infrastructure.

\n

Some of the things you need to consider are:

\n\n

The source code for this article can be found in this repo:

\n

azure-data-and-ai-examples/openai/ask your Analytical DB questions in english.ipynb at master · denisa-ms/azure-data-and-ai-examples (github.com)

\n

 

\n

I hope you found this article enjoyable and easy to follow and it will help you get into the right direction in order to build a self-serve analytics application.

\n

Thanks

\n

Denise

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Building a self-serve analytics platform with Azure Data Explorer and OpenAI","moderationData":{"__ref":"ModerationData:moderation_data:4358264"},"body":"

Thank you for this article Denise_Schlesingergood ideas. 
I was wondering, how you would integrate this with Power BI for the visualization as you suggested?
How would you allow the user to ask a random question and as a result visualize it in Power BI?
1 result might be visualized in a pie chart, the other in a bar chart and another one in a table.
Any idea how this could work? 
I know about Q&A visual in Power BI as well as Copilot.
But in your use case above,  I am wondering what is your idea on bringing the output of the question into a visualisation in Power BI (embedded)?

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Great article and many thanks for sharing it. I've just installed it in my environment and start to play around with it.

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