Measure query performance

Query performance in Azure involves several factors like resource allocation, data indexing, query design, and optimization techniques. It extends to understanding the ways in which data is stored, cached, indexed, and retrieved in Azure services and how data engineers can utilize these services better.

Azure SQL provides numerous metrics to monitor and identify the key aspects that are impacting the query performance. Some of these metrics include CPU utilization, data I/O, log write percentage, and DTU (Database Transaction Unit) consumption.

A query may perform slower due to high memory or CPU utilization, large data retrieval, complex joins, or lack of indexes. Identifying these issues and resolving them is fundamental in optimizing the overall system performance.

Techniques to Measure Query Performance

Azure SQL provides a handful of methods and tools to measure query performance.

Query Store

Query Store in Azure SQL captures a history of queries, plans, and runtime statistics, and it retains these statistics for your review. It separates data by time windows, making it possible to see database usage patterns and understand when query plan changes affect performance.

Performance Insights

Azure SQL’s Performance Insights is an easy-to-use tool that provides an overview of database performance from various angles, like CPU, I/O, wait stats, and Active sessions.

Dynamic Management Views (DMVs)

Azure provides DMVs (Dynamic Management Views), which return server state information that can be used to monitor the health of a server instance, diagnose problems, and tune performance.

For example, by using the sys.dm_exec_requests DMV, we can identify which queries are currently running, how much CPU they are consuming, and how long they have been running.

SELECT session_id, start_time, status, command, cpu_time, total_elapsed_time
FROM sys.dm_exec_requests
WHERE session_id > 50

Query Optimization Techniques

Several strategies can be employed to optimize query performance in Azure SQL.

Index Usage

Indexes can dramatically improve query performance. Azure SQL recommends creating indexes based on the queries you frequently run.

Query Design

Limiting the number of rows returned by the queries, avoiding the use of wildcard characters at the beginning of a LIKE pattern, and breaking a large query into smaller ones are some ways to improve query performance.

Using Query Hints

Query hints can be used to override the default behavior of the SQL Server query optimizer during query execution.

Partitioning

Partitioning divides a table into smaller, more manageable pieces, and can improve query performance, and make maintenance tasks more efficient.

In conclusion, query performance measurement is a crucial skill for an Azure Data Engineer. Using various tools provided by Azure, such as Query Store, Performance Insights, and DMVs, alongside implementing optimization techniques, can result in a significantly improved data solutions. It’s essential for professional data engineers to understand how to appropriately measure and optimize the performance of queries to make the most out of the Azure SQL resources.

Practice Test

True or False: Azure SQL includes features and capabilities that help you measure query performance.

• True
• False

Answer: True.

Explanation: Azure SQL Database and Managed Instance help you understand performance characteristics of your workload and how to adjust settings to improve it.

Which of the following can be used to measure query performance in Azure?

• a) Query Store
• b) Performance Insight
• c) Query Profiler
• d) All of the above

Answer: d) All of the above.

Explanation: All the options are tools provided by Azure to measure and optimize the query performance.

True or False: Query performance can be evaluated without monitoring the latency and throughput of your workload.

• True
• False

Answer: False.

Explanation: Evaluating query performance involves monitoring latency (how quickly the data is returned) and throughput (amount of data processed over time).

When measuring query performance, it is necessary to _______?

• a) Use an old version of database for comparison
• b) Implement sorting of data
• c) Keep track of each query and its response time
• d) Randomly select queries to measure

Answer: c) Keep track of each query and its response time.

Explanation: In order to evaluate performance, it is essential to monitor each query and its response time, this helps to identify most time-consuming queries.

True or False: You should ignore queries that frequently time out or fail to execute while measuring query performance.

• True
• False

Answer: False.

Explanation: These queries could be an indication of potential performance problems and should not be ignored.

Performance monitoring should ideally be done during?

• a) Off-peak hours
• b) Peak hours
• c) Non-business hours
• d) Business hours

Answer: b) Peak hours.

Explanation: Monitoring during peak hours can provide most accurate representation of normal load and performance.

Which tool can you use to see the top resource-consuming queries in Azure?

• a) Query Performance Insight
• b) SQL data sync
• c) Query editor
• d) SQL pool

Answer: a) Query Performance Insight.

Explanation: Query Performance Insight provides deeper insight into your databases resource (DTU) consumption and helps find the top resource-consuming queries.

True or False: The Query Store feature in Azure SQL automatically captures a history of queries, plans, and runtime statistics.

• True
• False

Answer: True.

Explanation: Query Store collects detailed performance data for queries and allows you to see a history of their executions.

Which of the following can be used to identify slow executing queries in Azure Database for PostgreSQL?

• a) Query Store
• b) Query Profiler
• c) Workload Query Insights
• d) Query Performance Insight

Answer: c) Workload Query Insights.

Explanation: Workload Query Insights on Azure Database for PostgreSQL help to identify slow executing queries.

True or False: Query Performance Insight can only show statistics for a limited time period back, currently set to one week.

• True
• False

Answer: False.

Explanation: Query Performance Insight shows statistics for up to last 14 days for single databases and elastic pools.

When improving query performance, it is best practice to?

• a) Increase database size
• b) Decrease the number of joins
• c) Both a) and b)
• d) None of the above

Answer: b) Decrease the number of joins

Explanation: Reducing the number of joins can help to improve performance, as each join creates additional computational load.

True or False: For a more thorough query performance analysis, you can use Query Store in combination with Performance Insights.

• True
• False

Answer: True.

Explanation: Query Store provides detailed information about queries, while Performance Insights gives an overall view of database performance, together they create comprehensive performance analysis tool.

For Azure Synapse Analytics, which feature allows exploration of query execution details and any encountered errors?

• a) Data Lake Store
• b) SQL Data Warehouse
• c) Azure Monitor
• d) Query Insight

Answer: d) Query Insight.

Explanation: Query Insight in Azure Synapse Analytics provides deeper level detail for each query execution including any encountered errors.

The _____ feature in Azure SQL Database and SQL Managed Instance helps understand performance patterns related to your workload.

• a) Query Store
• b) Power BI
• c) Data Factory
• d) Azure Monitor

Answer: a) Query Store.

Explanation: The Query Store feature automatically captures a history of queries, plans, and runtime statistics, and retains these for your review.

True or False: Increasing the count of read replicas can improve the read performance of your database.

• True
• False

Answer: True.

Explanation: Replicas allow for load balancing of read requests, which can significantly improve read performance.

Interview Questions

What is the purpose of Azure Metrics in the context of measuring query performance?

Azure Metrics provides numerical values at regular intervals of time to help monitor query performance. They display average, minimum, and maximum values, as well as the total and count for such data.

What is Azure Monitor Logs and how is it used in monitoring query performance?

Azure Monitor Logs is a feature in Azure that collects and organizes log and performance data. It enables analysis across multiple sources, to gain insight into the performance and operational health of your Azure resources, applications, and services.

How can you monitor query performance of Azure SQL Database and SQL Managed Instance?

Query performance of Azure SQL Database and SQL Managed Instance can be monitored using Query Store, Performance Insights, and Query Performance Insight features.

What does the Query Performance Insight tool do in Azure SQL Database and SQL Managed Instances?

The Query Performance Insight tool in Azure SQL Database and SQL Managed Instances provides insight into your database’s top resource consuming queries. It also provides a history of the Query Store retention policy.

How does Azure Synapse Analytics allow evaluation of query performance?

Azure Synapse Analytics provides a view named QUERY_REQUEST_STEP_DMV that shows detailed steps of the executed query. This allows evaluation of query performance by understanding the processing workload and bottlenecks, if any.

What Azure service would you use to create custom telemetry data to monitor query performance?

The Azure Application Insights service can be used to create custom telemetry data for monitoring query performance.

How can we get real-time telemetry for Azure cosmos DB?

Azure Cosmos DB provides real-time telemetry through Azure Monitor and Cosmos DB diagnostic logs.

What are Extended Events and how do they help measure query performance in Azure SQL Database?

Extended Events is a lightweight performance monitoring system that can be used to collect data for query performance troubleshooting. They offer a method to collect as much or as little data as necessary for diagnosing performance issues without incurring heavy performance costs.

What is dynamic management view in Azure Synapse Analytics?

Dynamic Management View (DMV) in Azure Synapse Analytics is a feature that provides insights on the health of the data warehouse and aids in diagnosing performance problems.

What are performance tiers in Azure Cosmos DB and how do they affect query performance?

Performance tiers in Azure Cosmos DB are levels of throughput assigned to a container or database and measured in Request Units. Higher performance tiers generally provide better and more consistent performance.

What is Query Store in Azure SQL Database?

Query Store in Azure SQL Database is a feature that provides insight into the performance of query execution over different periods of time. It’s used for monitoring and identifying problematic queries, comparing query performance differences, and more.

How can you implement monitoring on Data Lake Analytics for query performance?

Monitoring on Data Lake Analytics for query performance can be implemented using Azure Monitor, which can collect and analyze log data, create alarms, and visualise real-time metrics.

What is the purpose of Performance Recommendations in Azure SQL Database Query Performance Insight?

The purpose of Performance Recommendations in Azure SQL Database Query Performance Insight is to provide tailored recommendations for creating or dropping indexes to improve database performance.

Does Azure Monitor cost anything and how does it help in measuring query performance?

Yes, Azure Monitor does cost based on the volume of data ingested and retained. It helps in measuring query performance by providing metrics and logs for most Azure services, allowing you to deeply explore performance-related data, spot patterns, identify trends, and set alerts.

What is the purpose of Azure SQL Database’s automatic tuning?

Automatic tuning in Azure SQL Database is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executions and detect performance anomalies. It provides peak performance and stable workloads through continuous performance tuning utilizing AI.