Query Tuning Assistant Download ((HOT))

The Query Store feature introduced in SQL Server 2016 continuously monitors the performance of your queries. It caches query plans and gathers information that you can use to troubleshoot slow running queries. You can enable Query Store on any database on an instance of SQL Server 2016 or higher, including databases that have been migrated and continue to operate with an older compatibility level. The Query Tuning Assistant works alongside the Query Store. It uses the query performance statistics gathered when a database was migrated with its original compatibility level and compares them to the metrics gathered after the compatibility level is moved. By analyzing the differences, QTA finds queries that are regressing.

Starting with SQL Server 2014 (12.x), and with every new version, all query optimizer changes are gated to the latest database compatibility level, so execution plans aren't changed right at point of upgrade but rather when a user changes the COMPATIBILITY_LEVEL database option to the latest available. For more information on query optimizer changes introduced in SQL Server 2014 (12.x), see Cardinality Estimator. For more information about compatibility levels and how they can affect upgrades, see Compatibility Levels and Database Engine Upgrades.

Query Tuning Assistant Download

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This gating capability provided by the database compatibility level, in combination with Query Store gives you a great level of control over the query performance in the upgrade process if the upgrade follows the recommended workflow seen below. For more information on the recommended workflow for upgrading the compatibility level, see Change the Database Compatibility Mode and Use the Query Store.

See below how QTA only changes the last steps of the recommended workflow for upgrading the compatibility level using Query Store seen above. Instead of having the option to choose between the currently inefficient execution plan and the last known good execution plan, QTA presents tuning options that are specific for the selected regressed queries, to create a new improved state with tuned execution plans.

QTA targets known possible patterns of query regressions due to changes in Cardinality Estimator (CE) versions. For example, when upgrading a database from SQL Server 2012 (11.x) and database compatibility level 110, to SQL Server 2017 (14.x) and database compatibility level 140, some queries may regress because they were designed specifically to work with the CE version that existed in SQL Server 2012 (11.x) (CE 70). This doesn't mean that reverting from CE 140 to CE 70 is the only option. If only a specific change in the newer version is introducing the regression, then it is possible to hint that query to use just the relevant part of the previous CE version that was working better for the specific query, while still using all other improvements of newer CE versions. And also allow other queries in the workload that haven't regressed to benefit from newer CE improvements.

As a last resort, if the narrow scoped hints aren't yielding good enough results for the eligible query patterns, then full use of CE 70 is also considered, by using the query hint USE HINT ('FORCE_LEGACY_CARDINALITY_ESTIMATION') to generate an execution plan.

QTA is a session-based feature that stores session state in the msqta schema of the user database where a session is created for the first time. Multiple tuning sessions can be created on a single database over time, but only one active session can exist for any given database.

Verification shows the deployment status of previously selected queries for this session. The list in this page differs from the previous page by changing the Can Deploy column to Can Rollback. This column can be True or False depending on whether the deployed query optimization can be rolled back and its plan guide removed.

If at a later date there is a need to roll back on a proposed optimization, then select the relevant query and select Rollback. That query plan guide is removed and the list updated to remove the rolled back query. Note in the picture below that query 8 was removed.

This article will cover managing a SQL Server database upgrade using new features in SQL Server Management Studio 18 including the query tuning assistant wizard, database upgrade feature, query store and more

SQL Server 2016 introduced Query Store that helps to identify performance issue with the workload. We can do analysis of the execution plan change for any particular query and if required, we can revert to the previous execution plan to get a better performance. The Query store can always be helpful for any database level changes, SQL Server restart, database upgrade, index changes etc.

You can see here, first we captured the baseline before changing the compatibility mode to the recent version as SQL Server 2019 (150). We changed the compatibility level and viewed the built-in Query Store reports later, forcing the last known good plan if there is any change in the query performance.

In SQL Server 2017, we come across Understanding automatic tuning in SQL Server 2017. The Automatic tuning feature allows SQL Server to compare the execution plan before and after the change and then forced the plan automatically.

In the next screen, we can configure the Query Store so performance data can be captured. It gives the current state of the query store recommendation along with the recommendations. We can either use the recommendation or specify custom settings.

Step 1 take the user query

Step 2 convert the query into one or more SQL statements to be performed on a read only duplicate of the actual sales database for security.

Step 3 Run those SQL statements on the data and record the resulting data.

Step 4 Pass the returned SQL results to the model as part of the prompt and include the original query and ask the model to provide output.

I fine-tuned the gpt-3.5-turbo-1106 model with some example prompts. The job finished, and I can see the new model in the /models endpoint. If I try to create an assistant using /assistants with the custom model, I get an error.

This section provides system configuration tips and best practices to help ensure optimal performance of the Security Console in an enterprise-scale deployment. However, smaller environments may still benefit from some of these recommendations, particularly the sections on tuning the database and disaster recovery.

Now, I tried that with GPT and it seems to work fine, but it is restricted to the amount of documenst and seems not too configurable due to the limiations set. Now, the other options are using the assistant API or fine-tuning a model.

Fine-tuning seems to be very expensive and for this case probably too complicated and unneccassary. Now another solution would be using the assistant api and combining it with a vector database, but I am unsure where to start there.

Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice operated systems do not work. Current speech recognition systems are trained primarily with data from fluent speakers and as a consequence do not generalize well to speech with dysfluencies such as sound or word repetitions, sound prolongations, or audible blocks. The focus of this work is on quantitative analysis of a consumer speech recognition system on individuals who stutter and production-oriented approaches for improving performance for common voice assistant tasks (i.e., "what is the weather?"). At baseline, this system introduces a significant number of insertion and substitution errors resulting in intended speech Word Error Rates (isWER) that are 13.64% worse (absolute) for individuals with fluency disorders. We show that by simply tuning the decoding parameters in an existing hybrid speech recognition system one can improve isWER by 24% (relative) for individuals with fluency disorders. Tuning these parameters translates to 3.6% better domain recognition and 1.7% better intent recognition relative to the default setup for the 18 study participants across all stuttering severities.

Join this free online course to learn how to analyze query performance issues in SAP HANA and optimize queries yourself, including investigating out-of-memory (OOM) issues deriving from queries and finding solutions to improve execution time.

"I like this course because I learned a lot of new tips and tricks. I also would like to see a successor course that dives deep into performance analysis and tuning of calculation views. In the business intelligence and analytics area these calculation views are heavily used because they provide a lot of benefits." Read the original post

SukHyun An is an SAP HANA database product senior specialist with the SQL Performance Consulting Team.

SukHyun designs, develops, and publishes technical information such as the SAP HANA Performance Guide for Developers, SAP Notes, and training courses on SAP HANA SQL performance. Together with other SAP colleagues, he also solves query performance issues for customers.

Follow SukHyun on LinkedIn

Jin Yeon Lee is an SAP HANA database product expert with the SQL Performance Consulting Team in SAP HANA Development. He leads the team that transfers SAP HANA SQL performance tuning knowledge to customers.

As well as the topic of SQL performance, Jin Yeon is also in charge of the team in APJ, helping SAP HANA customers in SAP HANA Development.

In the example above, a security analyst wants to find windows security logs with failed login events. The model knows which index and sourcetype to use, and knows it has to filter by EventCode=4625. It also provides a step by step explanation of the predicted SPL query, and suggests a list of related Splunk Documentation contents.

Last year, we used the Text-to-Text Transfer Transformer (T5), a publicly available pretrained model introduced by Google in February 2020. It is a standard encoder-decoder Transformer trained on the C4 dataset, a 750GB collection of English texts from the public Common Crawl web scrape. We fine-tuned a 60M parameter model version called codet5-small on about 2k training examples of English to SPL translation. Such fine-tuning can be done on a single V100 GPU for a couple of dollars. We decided to refresh our codet5-small model by mixing different training objectives (e.g. writing an SPL query from an English description, generating a multi-step English description of an SPL query, etc.) and augmenting our training set with synthetically generated data and user generated data from Splunk employees. It resulted in a training set that was 300 times larger than last year. 006ab0faaa