Dynamic Data Source Selection for Multi-Tenant Streaming

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Solution Overview

Problem

Traditional data streaming services face inefficiencies and performance degradation due to the need to process and store metrics for all tenants, including unsubscribed ones, leading to unnecessary resource utilization and system performance issues.

Innovation Solution

Implementing a dynamic selection of data sources within a data streaming service that allows for real-time identification and streaming of data only from subscribed tenants, using identifiers to specify the host of a multi-tenant database and the tenant, enabling incremental scaling of system resources based on subscription status.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If a data streaming service processes data from all tenants in a multi-tenant database, then complete data coverage is achieved, but system resources are wasted on unsubscribed tenants

Engineering Contradiction:

Improvedata processing volumeVSAvoidsystem resource utilization

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The patent extracts and processes only the necessary subset of tenant data that is relevant to subscribed users. By implementing dynamic data source selection, the system identifies and processes only data from tenants who have active subscriptions, excluding data from unsubscribed tenants. This extraction principle directly reduces unnecessary data processing volume while maintaining energy efficiency.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system dynamically adjusts data processing based on subscription status. The data streaming service continuously monitors which tenants have active subscriptions and adapts its data source selection in real-time. This dynamic approach allows the system to optimize resource utilization by processing different data subsets depending on current subscription states, rather than statically processing all tenant data.

Inventive Principle:

Principle #15Dynamics

2Reliability

If a data streaming service streams data for all tenants, then comprehensive metrics are generated, but system performance degrades due to unnecessary processing

Engineering Contradiction:

Improvedata completenessVSAvoidsystem performance

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent applies local quality by providing different data processing treatments to different tenants based on their subscription status. Subscribed tenants receive full data processing and metric generation, while unsubscribed tenants are excluded from processing. This localized approach ensures that data completeness is maintained for relevant tenants while improving overall system performance by eliminating unnecessary processing for irrelevant tenants.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs partial action by processing only the portion of tenant data that is necessary for subscribed users. Instead of processing all tenant data excessively, the system identifies and processes only the relevant subset. This partial processing approach maintains sufficient data completeness for service subscribers while significantly improving system productivity by avoiding excessive processing of unsubscribed tenant data.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If the data streaming service dynamically selects data sources, then resource allocation is optimized, but system complexity increases

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoiddata source selection mechanism

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The data streaming service implements self-service by automatically monitoring subscription status and dynamically selecting appropriate data sources without manual intervention. The system autonomously identifies which tenants have active subscriptions and adjusts its data processing accordingly. This self-service capability optimizes resource allocation efficiency while managing system complexity through automation rather than manual configuration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system employs feedback mechanisms to continuously monitor subscription status changes and adjust data source selection in response. When subscription status changes occur, the system receives feedback and dynamically reconfigures its data processing to include or exclude the appropriate tenants. This feedback-driven approach optimizes resource allocation while managing complexity through automated response to system state changes rather than requiring complex pre-planning.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10936596B2Dynamic selection of data sources for streaming dynamic data

Publication Date: 2021.03.02 SALESFORCE INC

AI summary

Dynamic selection of data sources for streaming dynamic data is described. A data streaming service receives a dynamic selection of a data source after the data streaming service begins executing, the dynamic selection of the data source including an identifier of a host of a multi-tenant database and an identifier of a tenant of multiple tenants storing data in the multi-tenant database. The data streaming service streams dynamic data from the data source to a data destination.