Distributed Time-Series Database Scalable Architecture
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Solution Overview
Problem
As the scale and complexity of distributed database systems increase, managing and querying large volumes of time-series data becomes increasingly challenging due to scalability issues, latency, and the need for efficient storage and processing of data across multiple tiers.
Innovation Solution
A scalable architecture for a distributed time-series database is implemented, featuring independent scaling of stages such as ingestion, storage, and query processing, along with the use of hierarchical clustering and tiered storage (hot and cold tiers) to optimize data retrieval and analysis, and the introduction of continuous functions to simplify mathematical operations on time-series segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the scale and scope of database systems are increased to handle larger volumes of time-series data, then the data storage capacity and processing power are improved, but the complexity of provisioning, administering, and managing system resources increases significantly
Solution Approach 1:
The database system is divided into multiple independent nodes that can operate autonomously. Each node handles a portion of the data storage and processing tasks, allowing the system to scale horizontally without proportionally increasing management complexity. The segmentation enables independent provisioning and administration of individual nodes while maintaining overall system functionality.
2Productivity
If the size and scale of data sets are increased to improve analytical capabilities, then the depth of analysis and processing power are improved, but the costs to search, analyze, and manage the data sets increase
Solution Approach 1:
The system implements hierarchical data organization where frequently accessed data is stored in high-performance storage tiers with optimized access patterns, while less frequently accessed data is placed in lower-cost storage. This local quality differentiation allows efficient analysis of relevant data subsets without incurring the full cost of managing and accessing the entire data set, reducing overall data management expenses while maintaining analytical capability.
Data Source
AI summary
Methods, systems, and computer-readable media for a scalable architecture for a distributed time-series database are disclosed. Using a fleet of ingestion routers, time-series data generated by a plurality of client devices is stored into a plurality of durable partitions. The time-series data comprises a plurality of time series, and an amount of the ingestion routers is determined based at least in part on an ingestion rate of the time-series data. Using a fleet of stream processors, the time-series data from the durable partitions is stored into a plurality of storage tiers including a first storage tier and a second storage tier. A retention period for the first storage tier differs from a retention period for the second storage tier. An amount of the stream processors is determined based at least in part on the time-series data in the durable partitions.