Solution · 02
リアルタイムストリーミング
瞬時に取り込み、処理し、実行します。
高頻度なデータストリームをリアルタイムに処理し、シグナルを即時の行動に変えます。
<1 ms
End-to-end ingest to query
1 M+
Events per second per node
0
Buffering / staging layers
In-flight
Pattern detection
Why this matters
ひとつの継続的なシステム内で、データの流れから意思決定まで
ストリーミングシステムはしばしば取り込み、処理、分析のレイヤーを別々に必要とします。MonkDB はそれらをひとつのエンジンに統合します。
ストリーミング、分析、実行を統合します
What you get
What MonkDB makes possible for リアルタイムストリーミング
0101 / 04
Continuous ingestion of events, logs, and signals
Kafka, MQTT, CDC, S3, OPC UA, native, no broker required.
0202 / 04
Instant querying and aggregation on incoming streams
Streaming SQL with sub-millisecond write-to-query latency.
0303 / 04
Real-time detection of patterns and anomalies
Vector and rule-based detection on live event streams.
0404 / 04
Immediate triggering of actions
Decisions trigger workflows directly inside the engine.
How it works
Three steps, one continuous loop
INGEST
1Continuous ingestion at line rate
Streams, events, logs, and transactions land directly in the query engine.
PROCESS
2Detect patterns in flight
Aggregations, anomaly checks, and joins run on the live stream and historical state.
ACT
3Trigger the next action
Decisions land in the systems that operate the business, not on a dashboard.
Latency dropped from minutes to single-digit milliseconds. We retired Kafka + Flink + a cache layer. Operators see live state, not a delayed projection.
Outcome in numbers
- 8×Faster time-to-decision
- 3Stack tiers collapsed
- <1 msPipeline latency
Other solutions