StreamSphere
Unlike conventional Stream solutions dedicated solely to data collection, StreamSphere integrates with ShardSphere to enable high-performance processing and data refinement.
What is StreamSphere?
- Massive real-time streaming data generation : Numerous IoT devices and internet users continuously generate massive amounts of real-time data.
- Necessity of high-performance data processing : High-performance data processing is essential to perform real-time ETL (Extraction, Transformation, Loading).
- Versatile usage : It can be used for data extraction, text search filtering, and alert filtering by summarizing and aggregating dynamic data to enable hourly and feature-based analysis.
StreamSphere
- Operational Integration
- Consolidation of fragmented tools : Traditional product suites are often built separately (e.g., Kafka + Time Series DB + RDB, etc.).
- All-in-One Solution : StreamSphere is a single unified product that allows simultaneous access and processing of both Static Data and Stream Data based on business needs.
- Easy Accessibility
- Standard SQL Support : Utilizes Standard SQL.
- Intuitive DDL : Provides easy-to-understand Stream DDL.
- Continuous Aggregation Query : Offers continuous aggregation queries.
- Probabilistic Algorithm Functions : Provides probabilistic algorithm functions such as Top-k, HyperLogLog, and t-digest.
Easy Compatibility : Ensures smooth compatibility using RESTful APIs.
StreamSphere Streaming SQL
- Standard SQL Support : Utilizes Standard SQL.
- Continuous Aggregation Query : Offers continuous aggregation queries.
- Probabilistic Algorithm Functions : Provides probabilistic algorithm functions such as Top-k, HyperLogLog, and t-digest.
StreamSphere Test
Meteorological Data Test Case
- Internal Test Case : Internal testing was conducted by replacing RDB with StreamSphere to verify real-time query results.
- Performance degradation in traditional queries : Conventional queries suffer performance drops and high resource (Memory Temp) usage proportional to table data volume.
- Resource efficiency : CISPHERE Continuous Query maintains performance and low resource usage even as data volume increases.
- Efficiency through intermediate result reuse : Traditional RDBMS queries are inefficient as they cannot reuse intermediate results across multiple queries and must re-execute every query. In contrast, CISPHERE Continuous Query connects multiple Streams to reuse intermediate results, significantly increasing operational efficiency.
- Enhanced query readability :Traditional queries construct intermediate results using complex VIEWs, making queries lengthy and difficult to comprehend. CISPHERE Continuous Query builds intermediate results into Streams to write the final Stream, making each Stream intuitive, easy to read, and simple to understand.
