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259:- A stream can be windowed to create finite sets of tuples. For example, a window of size 5 minutes would contain all the tuples in a given 5 minute period. Window definitions can allow complex selections of messages, based on tuple field values. Once a finite batch of tuples is created, analytics such as count, average, max, etc., can be applied.
153:(rows). StreamSQL adds the ability to manipulate streams, which are infinite sequences of tuples that are not all available at the same time. Because streams are infinite, operations over streams must be
243:- A stream can be joined with a relation to produce a new stream. Each tuple on the stream is joined with the current value of the relation based on a predicate to produce 0 or more tuples.
265:- A pair of streams can also be windowed and then joined together. Tuples within the join windows will combine to create resulting tuples if they fulfill the predicate.
285:, a team of 30 professors and students on project Aurora worked collaboratively from 2001 through 2003 to develop the core principles behind StreamSQL.
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StreamSQL extends the type system of SQL to support streams in addition to tables. Several new operations are introduced to manipulate streams.
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of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be
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statement can be issued against a stream to calculate functions (using the target list) or filter out unwanted tuples (using a
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order. Merging is more deterministic, combining streams according to a sort key.
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292:. Borealis is a distributed multi-processor version of Aurora.
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The StreamSQL language is typically used in the context of a
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Other streaming and continuous variants of SQL include
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273:StreamSQL is derived from academic research into
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237:clause). The result will be a new stream.
288:The Aurora project was superseded by the
145:(also known as tables), which are finite
118:Learn how and when to remove this message
137:with the ability to process real-time
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172:analytics and real-time compliance (
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257:Windowing and Aggregation
279:complex event processing
275:Event Stream Processing
227:Selecting from a stream
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108:October 2018
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193:Kafka KSQL
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143:relations
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