Storage-Node Compute Offloading for Query Spike Handling
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Solution Overview
Problem
Existing database systems face challenges in efficiently handling compute-intensive workloads due to unpredictable query spikes, 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, skew-tolerant hybrid execution, and dynamic fallback to optimize query execution across CPU, GPU, FPGA, and ASIC platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the database cluster is statically provisioned to handle query spikes, then the cluster can handle peak workloads, but resources are wasted during low-demand periods
Solution Approach 1:
The patent implements dynamic cluster provisioning where the number of active nodes is adjusted based on current workload demands. The system automatically adds nodes during query spikes and removes nodes during low-demand periods, transitioning from static to dynamic cluster configuration. This resolves the contradiction by enabling the cluster to handle peak workloads without maintaining oversized resources during normal operation.
Solution Approach 2:
The system changes the parameter of cluster size dynamically based on workload conditions. By monitoring query patterns and resource utilization, the system adjusts the number of active nodes in the cluster, transforming the cluster from a fixed configuration to a variable one that adapts to demand. This parameter change enables efficient resource allocation that balances reliability during spikes with cost efficiency during low-demand periods.
2Loss of energy
If dynamically adjusting the cluster size is implemented, then resource efficiency improves, but new nodes are only picked up by next queries not currently executing queries
Solution Approach 1:
The system performs preliminary actions by pre-provisioning a pool of standby nodes that can be quickly activated. When a query spike is detected, nodes are already prepared and can be immediately assigned to ongoing queries rather than waiting for the next query cycle. This preliminary preparation eliminates the delay associated with node provisioning during query execution.
Solution Approach 2:
The patent introduces an intermediary node management layer that coordinates between the query processor and available compute nodes. This intermediary system maintains a pool of ready nodes and facilitates their rapid assignment to ongoing queries, enabling dynamic cluster adjustment without interrupting current query execution. The intermediary layer abstracts the complexity of node provisioning from the query processing pipeline.
3Ease of manufacture
If the database cluster uses homogenous machine types, then provisioning is simplified, but flexibility to handle diverse compute needs is limited
Solution Approach 1:
The patent segments the cluster into different node types with specialized hardware capabilities (e.g., CPU-only nodes, GPU-accelerated nodes, storage-optimized nodes). Each segment is designed for specific workloads, allowing the system to provision the appropriate node type for specific queries. This segmentation maintains provisioning simplicity through standardized node definitions while enabling hardware flexibility to match diverse compute requirements.
Solution Approach 2:
The system implements a universal node provisioning framework that can deploy multiple hardware types within the same cluster. The framework provides unified management for heterogeneous nodes while maintaining the ability to select appropriate node types for different workloads. This multi-functionality approach preserves the simplicity of centralized provisioning while achieving the flexibility to handle diverse compute needs through a single unified system.
4Productivity
If subqueries are extracted and submitted as standalone queries to remote nodes, then offloading is implemented, but result materialization and conversion overheads are imposed
Solution Approach 1:
The patent implements nested execution where subqueries are executed as nested operations within the parent query's execution plan rather than as separate standalone queries. The result sets are passed directly between nested operations through shared memory or efficient data channels, eliminating the need for full materialization and conversion overheads. This nesting approach maintains offloading capability while significantly reducing the energy and resource costs associated with inter-query data transfer.
Solution Approach 2:
The system introduces an intermediary execution engine that manages the execution of offloaded subqueries and their integration with parent queries. This intermediary layer handles the complex coordination of distributed execution, result passing, and data type conversion efficiently, reducing overheads compared to traditional standalone query submission. The intermediary acts as a bridge that enables seamless integration between remote subquery results and the main query workflow.
Data Source
AI summary
A database system compiles an execution plan to generate a compute-offload plan for execution by a compute-offload runtime. The compute-offload plan specifies a set of tasks to be offloaded and metadata specifying resource binding parameter values, associating a set of data items stored in one or more storage nodes with the set of tasks. Executing the compute-offload plan comprises sending, using a first communication path, the set of tasks and the resource binding parameter values from the database system to a compute-offload cluster; transferring, using a second communication path, the set of data items from the storage nodes to offload execution nodes based on the resource binding parameter values; and executing the set of one or more tasks on the offload execution nodes to process the set of data items.