Optimize Azure SQL Upsert scenarios

Introduction

Customers often need to move a dataset from a source system into a new destination, inserting rows that doesn’t exist in a target table and update with new values those where a given key already exists. This scenario is usually referred as “upsert”, and can be very time consuming if executed row by row on 10s or 100s of thousands of records. With the technique described in this article, we’ve been able to optimize Azure SQL upsert of a dataset of 2M rows against a target table with 30M rows from 20 hours to 20 minutes.

It’s always important to remember that Azure SQL Database provides high availability out of the box, as clearly described in this article. Different service tiers are providing this capability through different underlying implementations but, as a consequence of that, generally speaking in Azure SQL Database things like minimal logging, simple or bulk logged recovery modes are just not available and every operation on persistent tables is fully logged.

Well known batching techniques can be leveraged to minimize the impact of fully logged database operations in traditional workloads.

For other scenarios like bulk loading or bulk insert this can significantly impact performance if compared to on premises systems where minimal logging is available. Limits in log generation rate for both general purpose and mission critical service tiers are documented here and cannot be crossed.

Optimize Azure SQL upsert scenarios

This example demonstrates how to optimize a specific scenario where customers need to regularly update large datasets into Azure SQL Database, and then execute upsert activities that will either modify existing records if they already exists (by key) in a target table, or insert them if they don’t.

Generally speaking, there 2 major approaches to achieve this:

1. Iterate on the dataset on the application tier, and for every row invoke a stored proc that will execute an INSERT/UPDATE operation depending on the existence of record with a certain key. This approach can work well if the amount of records to upsert is relatively small, otherwise roundtrips and log writes will significantly impact performance.
2. Leverage bulk insert techniques, like using SqlBulkCopy class in ADO.NET, to upload the entire dataset to Azure SQL Database, and then execute all the INSERT/UPDATE (or MERGE) operation within a single batch, to mininize roundtrips and log writes and maximize throughput. This approach can reduce overall execution times from hours to minutes/seconds, even if the incoming dataset is made of millions of records.

#azure sql #data-loading #developers #optimization #performance #sql

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Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

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How to set up Azure Data Sync between Azure SQL databases and on-premises SQL Server

In this article, you learn how to set up Azure Data Sync services. In addition, you will also learn how to create and set up a data sync group between Azure SQL database and on-premises SQL Server.

In this article, you will see:

• Overview of Azure SQL Data Sync feature
• Discuss key components
• Comparison between Azure SQL Data sync with the other Azure Data option
• Setup Azure SQL Data Sync
• More…

Azure Data Sync

Azure Data Sync —a synchronization service set up on an Azure SQL Database. This service synchronizes the data across multiple SQL databases. You can set up bi-directional data synchronization where data ingest and egest process happens between the SQL databases—It can be between Azure SQL database and on-premises and/or within the cloud Azure SQL database. At this moment, the only limitation is that it will not support Azure SQL Managed Instance.

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Creating and Cataloging SQL pools in Azure SQL Server

This article will walk you through creating a new SQL pool within an existing Azure SQL Server as well as catalog the same using the Azure Purview service.

Introduction

Data is generated by transactional systems and typically stored in relational data repositories. This data is generally used by live applications and for operational reporting. As this data volume grows, this data is often required by other analytical repositories and data warehouses where it can be used for referential purposes and adding more context to other data from across the organization. Transactional systems (also known as Online Transaction Processing (OLTP) systems) usually need a relational database engine, while analytical systems (also known as Online Analytical Processing (OLAP) systems) usually need analytical data processing engines. On Azure cloud, it is usually known that for OLTP requirements, SQL Server or Azure SQL Database can be employed, and for analytical data processing needs, Azure Synapse and other similar services can be employed. SQL Pools in Azure Synapse host the data on an SQL Server environment that can process the data in a massively parallel processing model, and the address of this environment is generally the name of the Azure Synapse workspace environment. At times, when one has already an Azure SQL Server in production or in use, the need is to have these SQL Pools on an existing Azure SQL Server instance, so data in these SQL pools can be processed per the requirements on an OLAP system as well as the data can be co-located with data generated by OLTP systems. This can be done by creating SQL Pools within the Azure SQL Server instance itself. In this article, we will learn to create a new SQL Pool within an existing Azure SQL Server followed by cataloging the same using the Azure Purview service.

Pre-requisite

As we intend to create a new SQL Pool in an existing Azure SQL Server instance, we need to have an instance of Azure SQL in place. Navigate to Azure Portal, search for Azure SQL and create a new instance of it. We can create an instance with the most basic configuration for demonstration purposes. Once the instance is created, we can navigate to the dashboard page of the instance and it would look as shown below.

As we are going to catalog the data in the dedicated SQL Pool hosted on Azure SQL instance, we also need to create an instance of Azure Purview. We would be using the Azure Purview studio from the dashboard of this instance, tonregister this SQL Pool as the source and catalog the instance.

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Optimize Azure SQL Upsert scenarios

Introduction

Customers often need to move a dataset from a source system into a new destination, inserting rows that doesn’t exist in a target table and update with new values those where a given key already exists. This scenario is usually referred as “upsert”, and can be very time consuming if executed row by row on 10s or 100s of thousands of records. With the technique described in this article, we’ve been able to optimize Azure SQL upsert of a dataset of 2M rows against a target table with 30M rows from 20 hours to 20 minutes.

It’s always important to remember that Azure SQL Database provides high availability out of the box, as clearly described in this article. Different service tiers are providing this capability through different underlying implementations but, as a consequence of that, generally speaking in Azure SQL Database things like minimal logging, simple or bulk logged recovery modes are just not available and every operation on persistent tables is fully logged.

Well known batching techniques can be leveraged to minimize the impact of fully logged database operations in traditional workloads.

For other scenarios like bulk loading or bulk insert this can significantly impact performance if compared to on premises systems where minimal logging is available. Limits in log generation rate for both general purpose and mission critical service tiers are documented here and cannot be crossed.

Optimize Azure SQL upsert scenarios

This example demonstrates how to optimize a specific scenario where customers need to regularly update large datasets into Azure SQL Database, and then execute upsert activities that will either modify existing records if they already exists (by key) in a target table, or insert them if they don’t.

Generally speaking, there 2 major approaches to achieve this:

1. Iterate on the dataset on the application tier, and for every row invoke a stored proc that will execute an INSERT/UPDATE operation depending on the existence of record with a certain key. This approach can work well if the amount of records to upsert is relatively small, otherwise roundtrips and log writes will significantly impact performance.
2. Leverage bulk insert techniques, like using SqlBulkCopy class in ADO.NET, to upload the entire dataset to Azure SQL Database, and then execute all the INSERT/UPDATE (or MERGE) operation within a single batch, to mininize roundtrips and log writes and maximize throughput. This approach can reduce overall execution times from hours to minutes/seconds, even if the incoming dataset is made of millions of records.

#azure sql #data-loading #developers #optimization #performance #sql

Sourcing data from Azure SQL Database in Azure Machine Learning

In this article, we will show how to source data from Azure SQL Database to use in a Machine Learning workflow.

Introduction

Azure offers a variety of data repositories for operational as well as analytical purposes. One of the most popular and highly adopted database services is Azure SQL Database, which is typically used to host transactional data in Online Transaction Processing (OLTP) systems. A typical data pipeline involves ingesting data into different types of data repositories. Data from different repositories may be optionally enriched or standardized using approaches like Master Data Management (MDM). Data is generally moved using Extract Transform Load (ETL) or Extract Load Transform (ELT) mechanisms. Once the data is in a proper state, it may be stored in a data warehouse in a structured format or in a data lake which is a mix of structured, semi-structured, and unstructured formats. SQL Database is one of those versatile data repositories that can store different types of data, which makes it an ideal candidate for being used as a data warehouse or data mart too. Once data is in operational and analytical repositories, this data is used for various types of analytics, prediction, forecasting, and other types of data intelligence.

Machine learning is one of the most popular means of extracting intelligence out of data. Azure offers Azure ML service which is one of the mainstream services for authoring machine learning workflows. Like other data processing systems, Azure Machine Learning service requires and supports sourcing data from different types of data repositories including Azure SQL Database. Sourcing data is usually the first step while authoring Azure Machine Learning workflows. Let’s go ahead and see how you can source data from SQL Database to use in an Azure Machine Learning workflow.

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