High-performance real-time data processing solution based on Tinybird
When working with large-scale real-time data, latency issues often stem from poorly architected data pipelines or under-optimized queries.Tinybird significantly reduces latency by..:
- ClickHouse Optimization Engine: Utilizes columnar storage and a vectorized execution engine that is more than 100 times faster than traditional databases
- Physical view acceleration: utilization
CREATE MATERIALIZED VIEWPre-calculated aggregation results to reduce response time from seconds to milliseconds - Data pipeline optimization: Splitting complex queries into multiple nodes via .pipe files for incremental computation
Specific operational steps:
- Create materialized views:
CREATE MATERIALIZED VIEW user_actions_mv TO processed_data AS SELECT user_id, count() FROM events GROUP BY user_id - Automatically clean up old data using TTL policies to maintain optimal table size
- Monitor query performance and identify slow queries through Observability UI
In a typical application scenario, real-time click analysis for e-commerce is reduced from the original 3 seconds delay to 50 milliseconds, while supporting 2000+ QPS concurrent queries.
This answer comes from the articleTinybird: a platform for rapidly building real-time data analytics APIsThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of high latency in large-scale real-time data processing?
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