Distributed Query Execution for Petabyte-Scale Data
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Solution Overview
Problem
Existing database management systems face inefficiencies when handling large data sets, particularly with queries involving trillions of records, as they often require massive server clusters and struggle with sophisticated operations like JOINs, leading to performance issues and increased hardware costs.
Innovation Solution
A system that indexes files using a two-tiered index comprising an index dictionary and a Bloom filter, allowing for efficient time-based file searching and dynamic script generation for distributed query execution across a cluster of computing devices, optimizing queries to reduce the need for extensive hardware investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing RDBMS solutions are used to query massive data sets, then proper indexing can be implemented, but the system breaks down when attempting to execute queries involving trillions of records comprising petabytes of data
Solution Approach 1:
The patent segments the massive data set into distributed partitions across multiple nodes in a cloud environment. Each partition can be independently queried and processed, allowing the system to handle trillions of records by dividing the workload rather than requiring a single massive database system.
Solution Approach 2:
The patent introduces an intermediary layer (the distributed query processing system) between the user query and the raw data storage. This intermediary handles query optimization, distribution, and aggregation, enabling complex queries on petabyte-scale data without directly burdening the underlying storage infrastructure.
2Productivity
If massive clusters of servers are deployed to query petabytes of data, then more computing power is available, but the hardware investment becomes proportional and costly
Solution Approach 1:
The patent creates a universal distributed query processing system that can handle multiple types of queries (SELECT, JOIN, aggregation, regex searches) across diverse data formats using a unified architecture. This multi-functional system eliminates the need for separate specialized hardware for different query types, reducing overall hardware requirements.
Solution Approach 2:
The patent changes the operational parameters of the system by implementing intelligent query optimization, selective data retrieval, and efficient aggregation strategies. These parameter changes allow the system to achieve high productivity by processing only necessary data portions rather than scanning entire petabyte datasets, reducing the computational resources needed.
3Ease of operation
If traditional indexing methods are used, then single-record queries can be efficient, but sophisticated search techniques like regex search and JOIN operations fail to perform well on large data sets
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing data in multiple formats (including inverted indexes for text search, spatial indexes for location data, and metadata indexes) during data ingestion. This preliminary indexing enables sophisticated search techniques like regex and JOIN operations to execute efficiently without scanning entire datasets during query time.
Solution Approach 2:
The patent adds another dimension to traditional indexing by implementing multi-dimensional indexes that can simultaneously support different types of queries. For example, a single indexing structure can handle both exact match queries and regex pattern matching, or combine temporal, spatial, and categorical dimensions, allowing sophisticated searches to execute quickly without sacrificing capability.
Data Source
AI summary
Computer-implemented methods and systems are disclosed for receiving and indexing a plurality of files for later querying, for dynamically generating scripts to be executed during a query of a data store, and for horizontally distributing a query and aggregating results of the distributed query.