Worker Node Record Partitioning for Distributed Data Query
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing large sets of diverse data from various sources, including structured, semi-structured, and unstructured data, due to limited scope and unidirectional processing flows that restrict access to external data systems.
Innovation Solution
A data intake and query system with a search process master and query coordinators, combined with a scalable network of distributed nodes, enables comprehensive search and analytics across diverse data systems, extending processing capabilities to external data sources like MySQL, PostgreSQL, NoSQL data stores, cloud storage, and Hadoop, and facilitating big data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a centralized data system with unidirectional processing flow, then data security and system simplicity are maintained, but the ability to search and analyze external data sources is restricted
Solution Approach 1:
The system is segmented into multiple independent worker nodes that can independently access and process data from external data sources. Each worker node operates autonomously to execute search and analytics tasks on distributed data, enabling the system to handle diverse external data sources without requiring a completely centralized architecture.
Solution Approach 2:
The system transitions from a single-dimensional centralized processing model to a multi-dimensional distributed architecture where worker nodes can access data across multiple external data sources simultaneously. This dimensional expansion allows the system to search and analyze data from various external sources while maintaining operational independence at each node.
2Adaptability or versatility
If all generated data is stored as raw data for later analysis, then data flexibility and analysis completeness are improved, but storage costs and data processing complexity increase
Solution Approach 1:
The system extracts and processes only the specific data subsets that are relevant to current search and analytics tasks from the stored raw data. By using distributed worker nodes to query external data sources directly, the system retrieves only the necessary data portions rather than moving or processing all stored raw data, thereby reducing storage requirements while maintaining analysis flexibility.
Solution Approach 2:
Instead of processing or moving all stored raw data, the system performs partial actions by querying and retrieving only the specific data subsets needed for current analytics tasks. This partial processing approach reduces the effective data volume handled while preserving the ability to analyze all generated data when needed.
3Productivity
If data is pre-processed based on anticipated analysis needs, then retrieval efficiency is improved, but data flexibility and potential insights are reduced
Solution Approach 1:
The system dynamically adapts its data processing approach based on actual search and analytics needs rather than relying on static pre-processing. Distributed worker nodes can dynamically query external data sources in real-time according to specific task requirements, allowing the system to optimize retrieval efficiency for each particular analysis task while maintaining flexibility to access any data subset.
Solution Approach 2:
Distributed worker nodes serve as intermediaries between the centralized system and external data sources. These intermediaries handle the complexity of accessing and retrieving data from external sources, allowing the central system to maintain simplicity while achieving efficient data retrieval. The worker nodes can perform targeted queries on external data sources based on actual analysis needs rather than pre-processing all data.
Data Source
AI summary
Systems and methods are described for partitioning and reducing records at ingest of a worker node. The worker node receives chunks of data from one or more indexers of a data intake and query system based on the execution of a query by the data intake and query system. The worker node assigns records to different record groups based on the content of the records. The system also assigns the record to a partition of a group of partitions. Record data of the records in a particular partition is combined. The system processes the partitions based on the query.