🗃️ About Aggregates
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📄️ Monitoring Aggregate Usage
The Aggregates page displays all aggregate instances that have been defined for your deployed models, as well as your aggregate build history. You can view what aggregates have been created, when they were last built, and how often they have been queried.
📄️ Defining Aggregates Yourself
You can define your own aggregates for use cases that fall outside of those covered by system-defined aggregates.
📄️ Changing the Schema for Aggregate Tables
AtScale creates its aggregate tables in your data warehouse in a schema that a system administrator creates before installing AtScale. It is possible to change the schema after AtScale is installed.
📄️ Dimensionally modified aggregates
Some of the queries processed by AtScale request calculated measures as well as calculation groups. The runtime performance of such queries can be significantly improved by enabling and configuring Dimensionally Modified Aggregates (DMA). This allows you to:
📄️ Disabling the Creation of System-Defined Aggregates for a Dataset
For some fact datasets, you might want to disable the creation of system-defined aggregates entirely.
📄️ Priming the System with Demand-Defined Aggregates
The AtScale engine can generate demand-defined aggregates before analysts start querying your data, which may improve the performance of the initial queries they issue. Priming is done by enabling Training Mode on the Settings page, then running queries representative of the queries that analysts will issue.
📄️ Excluding Specific Attributes from Aggregates
You can exclude specific attributes from being used as grouping keys in aggregations. These settings can be applied to both level attributes and secondary dimensional attributes.
📄️ Exporting and Importing System-Defined Aggregate Definitions
AtScale supports migrating system-defined aggregate definitions for models and catalogs from one system to another. Aggregate definitions may be exported to a file and then imported into a separate AtScale system where the same model has been published.
📄️ Handling NULL Values to Prevent Incomplete Aggregate Tables and Unexpected Query Results
When NULL key values are present in the relationship key column of either a fact table or dimension table it can result in incomplete aggregate tables and unexpected query results.
📄️ Working With Aggregate Partitions
You can add system-defined aggregate partitions for models by modifying their underlying SML.
📄️ Dimensionally modified aggregates
Some of the queries processed by AtScale request calculated measures as well as calculation groups. The runtime performance of such queries can be significantly improved by enabling and configuring Dimensionally Modified Aggregates (DMA). This allows you to:
📄️ Setting Properties to Allow Incremental Rebuilds of Aggregates
You can enable incremental rebuilds of aggregates for your models.
📄️ Rebuilding Aggregates Manually
You can manually perform an initial build or a rebuild all aggregates for a deployed model.
📄️ Rebuilding Aggregates Using the REST API
You can use the aggregates-batch endpoint of the AtScale engine REST API to trigger full or incremental builds for all aggregates of a deployed model. When running an incremental build, you can optionally specify grace period overrides for specific datasets in the model.
📄️ Scheduling Aggregate Builds
You can configure aggregates to be built according to a schedule using the aggregates-creation/scheduler endpoint of the AtScale API. This enables you to schedule builds to run at convenient times, rather than immediately after catalog deployment, to save on resources and costs.
📄️ Performing Full Rebuilds of Incremental Aggregates
Incremental aggregates process only new windows of fact data rows, rather than processing all of the data. In some cases, you may want to reprocess all of the data to ensure that aggregates are accurate. For example, if the data of a dimension dataset has changed significantly or if you have older data that missed a processing window.
📄️ Aggregate Maintenance
AtScale features a built-in job for maintaining aggregates. The aggregate maintenance job performs tasks like health checks on all active aggregate instances and cleanup of orphaned or unreliable aggregates. This job runs periodically to improve the performance of AtScale.
📄️ Invalidating Aggregates for a Deployed Model
You can invalidate all aggregates for a deployed model. When an aggregate is invalidated, its status is changed to Inactive, so that it is cleared from the system by aggregate maintenance. This provides a way to remove unwanted aggregates; for example, before deploying a model from your development environment to production, you may want to clear aggregates created during testing.
📄️ Aggregate Data Security
You can configure AtScale so that queries can simultaneously access data across multiple physical locations. By default, AtScale may create copies or aggregations of data across data warehouses to improve query performance.
📄️ Inbound SQL Session Parameters
AtScale supports four SQL session parameters that you can use in an SQL session.
📄️ Inbound SQL Hints to Control the Use and Generation of Aggregate Tables
There are two SQL hints that you can insert into SQL statements to control both whether the AtScale engine will use an aggregate to satisfy a query, and whether the AtScale engine will generate an aggregate that is based on a query.