Structured Streaming
E705278
Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Apache Spark Structured Streaming | 1 |
| Structured Streaming canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7984803 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Structured Streaming Context triple: [Apache Spark, component, Structured Streaming]
-
A.
Apache Flink
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
-
B.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
-
C.
KSQL
KSQL is the ICAO airport code for San Carlos Airport, a general aviation facility serving the San Francisco Bay Area in California.
-
D.
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Structured Streaming Target entity description: Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
-
A.
Apache Flink
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
-
B.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
C.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
-
D.
KSQL
KSQL is the ICAO airport code for San Carlos Airport, a general aviation facility serving the San Francisco Bay Area in California.
-
E.
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Apache Spark component
ⓘ
stream processing engine ⓘ |
| APIStyle |
declarative
ⓘ
unified batch and streaming API ⓘ |
| designedFor | exactly-once processing with idempotent sinks ⓘ |
| developedBy | Apache Software Foundation ⓘ |
| documentationURL | https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html ⓘ |
| exposesAPI |
DataFrame API
ⓘ
Dataset API ⓘ Spark SQL API ⓘ
linked to:
Spark SQL engine
|
| integratesWith |
Spark MLlib
ⓘ
Spark SQL ⓘ
linked to:
Spark SQL engine
Spark Structured APIs ⓘ
linked to:
Spark Datasets
|
| introducedIn |
Apache Spark 2.0
ⓘ
linked to:
Apache Spark
|
| partOf | Apache Spark ⓘ |
| provides |
backpressure handling
ⓘ
end-to-end event-time processing ⓘ exactly-once semantics (under certain conditions) ⓘ fault tolerance ⓘ stateful stream processing ⓘ watermarking for late data ⓘ windowed aggregations ⓘ |
| replaced | DStreams for many use cases ⓘ |
| stores |
offsets
ⓘ
state in state store ⓘ streaming query progress metadata ⓘ |
| supports |
checkpointing
ⓘ
continuous processing ⓘ event-time windows ⓘ micro-batch processing ⓘ near real-time data processing ⓘ session windows ⓘ sliding windows ⓘ stream processing ⓘ |
| supportsMode |
append output mode
ⓘ
complete output mode ⓘ update output mode ⓘ |
| supportsSink |
Kafka sink
ⓘ
console sink ⓘ file sink ⓘ foreach sink ⓘ memory sink ⓘ |
| supportsSource |
Kafka
ⓘ
file source ⓘ rate source ⓘ socket source ⓘ |
| uses |
Catalyst optimizer
ⓘ
linked to:
Catalyst query optimizer
Spark SQL engine ⓘ Tungsten execution engine ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Structured Streaming Description of subject: Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Structured Streaming