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ClickhouseProblem & solution
How Sony LIV uses ClickHouse Cloud to deliver live streaming analytics at billion-row scale
Summary
Sony LIV’s legacy analytics environment combined batch pipelines, Elasticsearch, BigQuery, and other telemetry stores, creating slow, costly, fragmented analysis as streaming activity reached billions of events per day. During live sports, the platform needed sub-second queries, high ingestion throughput, cost-efficient long-term storage, and horizontal scaling for sudden traffic spikes. Sony LIV consolidated these workloads on ClickHouse Cloud: client, streaming, CDN, and advertising systems send events through Amazon Kinesis and ClickPipes into a ClickHouse cluster serving dashboards, product analytics, QoE, and SRE workflows. The source reports that queries previously taking tens of seconds to minutes now run in sub-second to low-single-digit seconds, improving operational responsiveness, engineering productivity, and the handling of major live-event traffic.
Context
Sony LIV’s analytics stack was fragmented across batch pipelines, Elasticsearch, BigQuery, and multiple telemetry stores. As telemetry volumes reached billions of events per day and live sports caused sharp traffic spikes, the team faced slow queries, rising storage and query costs, and difficulty combining playback, CDN, application, clickstream, and advertising data quickly enough for operational decisions.
Approach / What changed
Sony LIV consolidated its analytics workloads on ClickHouse Cloud. Client SDKs, streaming services, CDN infrastructure, and ad systems send events to Amazon Kinesis; ClickPipes provides real-time processing into a ClickHouse analytics cluster. The cluster serves as a shared data foundation for operational dashboards, product analytics, playback QoE, observability, and SRE workflows, using ClickHouse’s columnar storage, compression, and distributed architecture.
Takeaways
Sony LIV’s ClickHouse architecture receives telemetry through Amazon Kinesis and ClickPipes, then provides a single queryable platform for QoE, CDN, product, advertising, dashboard, and SRE use cases.
Queries over high-volume telemetry that previously took tens of seconds to minutes now complete in sub-second to low-single-digit seconds, according to the source.
The platform was selected for large-scale aggregation and time-series performance, high compression ratios, near-real-time analytics, and horizontal scaling for unpredictable live-sports traffic.
Source and freshness
- Source
- clickhouse.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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