classifier queries

create workload classifier (transact-sql) - sql server | microsoft docs

create workload classifier (transact-sql) - sql server | microsoft docs

Creates a classifier object for use in workload management. The classifier assigns incoming requests to a workload group based on the parameters specified in the classifier statement definition. Classifiers are evaluated with every request submitted. If a request is not matched to a classifier, it is assigned to the default workload group. The default workload group is the smallrc resource class.

The workload classifier takes the place of sp_addrolemember resource class assignment. After workload classifiers are created, execute sp_droprolemember to remove any redundant resource class mappings.

WORKLOAD_GROUP = 'name' When the conditions are met by the classifier rules, name maps the request to a workload group. name is a sysname. It can be up to 128 characters long and must be a valid workload group name at the time of classifier creation.

MEMBERNAME = 'security_account' The security account used to classified against. Security_account is a sysname, with no default. Security_account can be a database user, database role, Azure Active Directory login, or Azure Active Directory group.

Use the user_name() function, when connected to the system, to verify the membername that the classification process will use to classify the request. Verifying the membername with the user_name() function can be helpful troubleshooting AAD or service principle classification issues. If user_name() returns "dbo" you can use "dbo" as the membername to classify the requests. Keep in mind all members of the "dbo" role will be classified. Additional classification parameters such as WLM_LABEL or WLM_CONTEXT can also be used to specifically classify requests from multiple AAD accounts mapping to the "dbo" role.

WLM_LABEL Specifies the label value that a request can be classified against. Label is an optional parameter of type nvarchar(255). Use the OPTION (LABEL) in the request to match the classifier configuration.

WLM_CONTEXT Specifies the session context value that a request can be classified against. context is an optional parameter of type nvarchar(255). Use the sys.sp_set_session_context with the variable name equal to wlm_context prior to submitting a request to set the session context.

START_TIME and END_TIME Specifies the start_time and end_time that a request can be classified against. Both start_time and end_time are of the HH:MM format in UTC time zone. Start_time and end_time must be specified together.

If importance is not specified, the importance setting of the workload group is used. The default workload group importance is normal. Importance influences the order which requests are scheduled, thus giving first access to resources and locks.

A request can match against multiple classifiers. There is a weighting for the classifier parameters. The higher weighted matching classifier is used to assign a workload group and importance. The weighting goes as follows:

The user userloginA is configured for both classifiers. If userloginA runs a query with a label equal to salesreport between 6PM and 7AM UTC, the request will be classified to the wgDashboards workload group with HIGH importance. The expectation may be to classify the request to wgUserQueries with LOW importance for off-hours reporting, but the weighting of WLM_LABEL is higher than START_TIME/END_TIME. The weighting of classifierA is 80 (64 for user, plus 16 for WLM_LABEL). The weighting of classifierB is 68 (64 for user, 4 for START_TIME/END_TIME). In this case, you can add WLM_LABEL to classifierB.

quickstart: create a workload classifier - t-sql - azure synapse analytics | microsoft docs

quickstart: create a workload classifier - t-sql - azure synapse analytics | microsoft docs

In this quickstart, you'll quickly create a workload classifier with high importance for the CEO of your organization. This workload classifier will allow CEO queries to take precedence over other queries with lower importance in the queue.

This quickstart assumes you have already provisioned a dedicated SQL pool in Azure Synapse Analytics and that you have CONTROL DATABASE permissions. If you need to create one, use Create and Connect - portal to create a dedicated SQL pool called mySampleDataWarehouse.

workload classification for dedicated sql pool - azure synapse analytics | microsoft docs

workload classification for dedicated sql pool - azure synapse analytics | microsoft docs

While there are many ways to classify data warehousing workloads, the simplest and most common classification is load and query. You load data with insert, update, and delete statements. You query the data using selects. A data warehousing solution will often have a workload policy for load activity, such as assigning a higher resource class with more resources. A different workload policy could apply to queries, such as lower importance compared to load activities.

You can also subclassify your load and query workloads. Subclassification gives you more control of your workloads. For example, query workloads can consist of cube refreshes, dashboard queries or ad-hoc queries. You can classify each of these query workloads with different resource classes or importance settings. Load can also benefit from subclassification. Large transformations can be assigned to larger resource classes. Higher importance can be used to ensure key sales data is loader before weather data or a social data feed.

Classification for dedicated SQL pool is achieved today by assigning users to a role that has a corresponding resource class assigned to it using sp_addrolemember. The ability to characterize requests beyond a login to a resource class is limited with this capability. A richer method for classification is now available with the CREATE WORKLOAD CLASSIFIER syntax. With this syntax, dedicated SQL pool users can assign importance and how much system resources are assigned to a request via the workload_group parameter.

If a user is a member of multiple roles with different resource classes assigned or matched in multiple classifiers, the user is given the highest resource class assignment. This behavior is consistent with existing resource class assignment behavior.

Workload classification has system workload classifiers. The system classifiers map existing resource class role memberships to resource class resource allocations with normal importance. System classifiers can't be dropped. To view system classifiers, you can run the below query:

System classifiers created on your behalf provide an easy path to migrate to workload classification. Using resource class role mappings with classification precedence, can lead to misclassification as you start to create new classifiers with importance.

To simplify troubleshooting misclassification, we recommended you remove resource class role mappings as you create workload classifiers. The code below returns existing resource class role memberships. Run sp_droprolemember for each member name returned from the corresponding resource class.

manage and monitor workload importance in dedicated sql pool - azure synapse analytics | microsoft docs

manage and monitor workload importance in dedicated sql pool - azure synapse analytics | microsoft docs

Monitor importance using the new importance column in the sys.dm_pdw_exec_requests dynamic management view. The below monitoring query shows submit time and start time for queries. Review the submit time and start time along with importance to see how importance influenced scheduling.

The catalog view, sys.workload_management_workload_classifier_details, contains information on the parameters used in creation of the classifier. The below query shows that ExecReportsClassifier was created on the membername parameter for values with ExecutiveReports:

To simplify troubleshooting misclassification, we recommended you remove resource class role mappings as you create workload classifiers. The code below returns existing resource class role memberships. Run sp_droprolemember for each membername returned from the corresponding resource class. Below is an example of checking for existence before dropping a workload classifier:

gremlin vertex classification queries in neptune ml - amazon neptune

gremlin vertex classification queries in neptune ml - amazon neptune

The model is trained on one property of the vertices. The set of unique values of this property are referred to as a set of node classes, or simply, classes.

The node class or categorical property value of a vertex's property can be inferred from the node classification model. This is useful where this property is not already attached to the vertex.

In order to fetch one or more classes from a node classification model, you need to use the with() step with the predicate Neptune#ml.classification to configure the properties() step. The output format is similar to what you would expect if those were vertex properties.

The properties() step contains the key on which the model was trained, and has .with("Neptune#ml.classification") to indicate that this is a vertex classification ML inference query.

Multiple property keys are not currently supported in a properties().with("Neptune#ml.classification") step. For example, the following query results in an exception:

If both the inference endpoint and the corresponding IAM role have been saved in your DB cluster parameter group, a vertex-classification query can be as simple as this:

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create & test classifier user-defined function - resource governor - sql server | microsoft docs

create & test classifier user-defined function - resource governor - sql server | microsoft docs

A table (TblClassificationTimeTable) is created in master to hold start and end times that can be evaluated against a login time. This must be created in master because Resource Governor uses schema binding for classifier functions.

Create the classifier function that uses time functions and values that can be evaluated against the times in the lookup table. For information about using Lookup Tables in a classifier function, see "Best practices for using Lookup Tables in a classifier function" in this topic.

Do not use a lookup table unless it is absolutely necessary. If you need to use a lookup table, it can be hard-coded into the function itself; however, this needs to be balanced with the complexity and dynamic changes of the classifier function.

If you are updating the table contents, make sure to use a snapshot isolation level transaction in the classifier function to prevent Writer blocking Readers. Note that using the NOLOCK hint should also mitigate this.

We highly recommend following these best practices. If there are issues that prevent you from following the best practices, we recommend that you contact Microsoft Support so that you can proactively prevent any future problems.

Resource Governor Enable Resource Governor Resource Governor Resource Pool Resource Governor Workload Group Configure Resource Governor Using a Template View Resource Governor Properties ALTER RESOURCE GOVERNOR (Transact-SQL) CREATE RESOURCE POOL (Transact-SQL) CREATE WORKLOAD GROUP (Transact-SQL) CREATE FUNCTION (Transact-SQL) ALTER RESOURCE GOVERNOR (Transact-SQL)

precomputing search features for fast and accurate query classification - microsoft research

precomputing search features for fast and accurate query classification - microsoft research

Query intent classication is crucial for web search and advertising. It is known to be challenging because web queries contain less than three words on average, and so provide little signal to base classication decisions on. At the same time, the vocabulary used in search queries is vast: thus, classiers based on word-occurrence have to deal with a very sparse feature space, and often require large amounts of training data. Prior efforts to address the issue of feature sparseness augmented the feature space using features computed from the results obtained by issuing the query to be classied against a web search engine. However, these approaches induce high latency, making them unacceptable in practice. In this paper, we propose a new class of features that realizes the benet of search-based features without high latency. These leverage cooccurrence between the query keywords and tags applied to documents in search results, resulting in a signicant boost to web query classication accuracy. By pre-computing the tag incidence for a suitably chosen set of keyword-combinations, we are able to generate the features online with low latency and memory requirements. We evaluate the accuracy of our approach using a large corpus of real web queries in the context of commercial search.

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