Query Offload Pipelines for Elastic Database Workloads
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Solution Overview
Problem
Existing database systems face challenges in efficiently handling compute-intensive workloads due to unpredictable query patterns, resource wastage, and inefficient offloading of subqueries, leading to suboptimal performance and cost inefficiencies.
Innovation Solution
A compute-offloading framework that dynamically provisions resources, supports heterogeneous hardware, and enables fine-grained offloading with pipeline execution, semi-stateful execution, and skew-tolerant hybrid execution to optimize query execution across CPU, GPU, FPGA, and ASICs, allowing for elastic scaling and cost-effective query acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the database cluster is oversized to handle query spikes, then query performance during spikes is improved, but resource waste increases during normal operation
Solution Approach 1:
The patent implements dynamic cluster provisioning where the number of database nodes is automatically adjusted based on query workload patterns. The system transitions from static cluster sizing to dynamic sizing using machine learning predictions of query patterns, allowing the cluster to expand during spikes and contract during normal operation, thereby resolving the contradiction between handling spikes and avoiding resource waste
Solution Approach 2:
The system performs preliminary actions by predicting future query patterns using machine learning models trained on historical data. This allows the cluster to be provisioned in advance for expected workload patterns, optimizing resource allocation before spikes occur while avoiding over-provisioning for unlikely scenarios
2Loss of energy
If the database cluster is undersized to avoid resource waste, then cost efficiency is improved, but query execution speed decreases during spikes
Solution Approach 1:
The system dynamically adjusts cluster size based on predicted workload, transitioning from static undersized clusters to dynamically scaled clusters that match actual demand, ensuring adequate resources are available when needed without permanently maintaining oversized infrastructure
Solution Approach 2:
By using machine learning to predict query patterns in advance, the system can provision additional resources before spikes occur, ensuring query execution speed is maintained during high-demand periods while avoiding the permanent cost burden of oversized clusters
3Quantity of substance
If new nodes are added to the cluster during runtime, then cluster size increases to handle load, but the nodes are only picked up by future queries not current executing queries
Solution Approach 1:
The patent segments query execution into distinct phases: current executing queries continue on existing nodes while new nodes are provisioned and assigned to future queries. This segmentation allows independent scaling of active workloads without interrupting current processing, resolving the contradiction between increasing cluster size and maintaining productivity
Solution Approach 2:
The system performs preliminary actions by provisioning new nodes before they are needed for current queries, using predictions about future workload patterns to pre-allocate resources that will be assigned to subsequent queries, avoiding the limitation of nodes only being available for future queries
4Productivity
If subqueries are extracted and submitted as standalone queries to remote nodes, then offloading is enabled, but result materialization and conversion overheads increase
Solution Approach 1:
The patent merges the execution of subqueries with the main query by executing them as a unified query plan across distributed nodes rather than extracting and submitting as separate standalone queries. This integration eliminates intermediate result materialization and conversion overheads while maintaining the offloading capability, resolving the contradiction between productivity and energy loss
5Device complexity
If the database cluster uses homogeneous machine types, then hardware provisioning is simplified, but adaptability to different compute patterns is reduced
Solution Approach 1:
The patent segments the database cluster into logical groups of nodes with different hardware capabilities (e.g., storage nodes, compute nodes, GPU nodes) while maintaining a manageable provisioning framework. This segmentation enables the system to adapt to different compute patterns by assigning appropriate node types to specific workloads without overwhelming complexity in hardware provisioning
Data Source
AI summary
A database system compiles an execution plan to generate an offload-enabled plan for execution by a compute offload runtime. Compiling the execution plan comprises dividing the offload-enabled plan into one or more pipelines. Each pipeline comprises a pipeline template and a resource binding. Each pipeline template comprises one or more logical tasks, each comprising code for processing one or more data items. The database system executes the offload-enabled plan using a set of compute nodes in the database system or the compute offload server using the compute offload runtime, comprising, for each given logical task of each given pipeline, executing one or more microtasks, each being an instantiation of the given logical task processing a particular data item based on the resource binding of the given pipeline.