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author | Burak Yavuz <brkyvz@gmail.com> | 2016-04-01 13:19:24 -0700 |
---|---|---|
committer | Michael Armbrust <michael@databricks.com> | 2016-04-01 13:19:24 -0700 |
commit | 1b829ce13990b40fd8d7c9efcc2ae55c4dbc861c (patch) | |
tree | 3dd5b6bfd14b9eafde58bed77dc89ae43712a599 /sql/core | |
parent | 1e886159849e3918445d3fdc3c4cef86c6c1a236 (diff) | |
download | spark-1b829ce13990b40fd8d7c9efcc2ae55c4dbc861c.tar.gz spark-1b829ce13990b40fd8d7c9efcc2ae55c4dbc861c.tar.bz2 spark-1b829ce13990b40fd8d7c9efcc2ae55c4dbc861c.zip |
[SPARK-14160] Time Windowing functions for Datasets
## What changes were proposed in this pull request?
This PR adds the function `window` as a column expression.
`window` can be used to bucket rows into time windows given a time column. With this expression, performing time series analysis on batch data, as well as streaming data should become much more simpler.
### Usage
Assume the following schema:
`sensor_id, measurement, timestamp`
To average 5 minute data every 1 minute (window length of 5 minutes, slide duration of 1 minute), we will use:
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:00-09:05:00
09:01:00-09:06:00
09:02:00-09:07:00 ...
```
Intervals will start at every `slideDuration` starting at the unix epoch (1970-01-01 00:00:00 UTC).
To start intervals at a different point of time, e.g. 30 seconds after a minute, the `startTime` parameter can be used.
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”, "30 second"), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:30-09:05:30
09:01:30-09:06:30
09:02:30-09:07:30 ...
```
Support for Python will be made in a follow up PR after this.
## How was this patch tested?
This patch has some basic unit tests for the `TimeWindow` expression testing that the parameters pass validation, and it also has some unit/integration tests testing the correctness of the windowing and usability in complex operations (multi-column grouping, multi-column projections, joins).
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes #12008 from brkyvz/df-time-window.
Diffstat (limited to 'sql/core')
-rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/functions.scala | 137 | ||||
-rw-r--r-- | sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala | 242 |
2 files changed, 379 insertions, 0 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 7ce15e3f35..74906050ac 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -2550,6 +2550,143 @@ object functions { ToUTCTimestamp(ts.expr, Literal(tz)) } + /** + * Bucketize rows into one or more time windows given a timestamp specifying column. Window + * starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window + * [12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in + * the order of months are not supported. The following example takes the average stock price for + * a one minute window every 10 seconds starting 5 seconds after the hour: + * + * {{{ + * val df = ... // schema => timestamp: TimestampType, stockId: StringType, price: DoubleType + * df.groupBy(window($"time", "1 minute", "10 seconds", "5 seconds"), $"stockId") + * .agg(mean("price")) + * }}} + * + * The windows will look like: + * + * {{{ + * 09:00:05-09:01:05 + * 09:00:15-09:01:15 + * 09:00:25-09:01:25 ... + * }}} + * + * For a continuous query, you may use the function `current_timestamp` to generate windows on + * processing time. + * + * @param timeColumn The column or the expression to use as the timestamp for windowing by time. + * The time can be as TimestampType or LongType, however when using LongType, + * the time must be given in seconds. + * @param windowDuration A string specifying the width of the window, e.g. `10 minutes`, + * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for + * valid duration identifiers. + * @param slideDuration A string specifying the sliding interval of the window, e.g. `1 minute`. + * A new window will be generated every `slideDuration`. Must be less than + * or equal to the `windowDuration`. Check + * [[org.apache.spark.unsafe.types.CalendarInterval]] for valid duration + * identifiers. + * @param startTime The offset with respect to 1970-01-01 00:00:00 UTC with which to start + * window intervals. For example, in order to have hourly tumbling windows that + * start 15 minutes past the hour, e.g. 12:15-13:15, 13:15-14:15... provide + * `startTime` as `15 minutes`. + * + * @group datetime_funcs + * @since 2.0.0 + */ + @Experimental + def window( + timeColumn: Column, + windowDuration: String, + slideDuration: String, + startTime: String): Column = { + withExpr { + TimeWindow(timeColumn.expr, windowDuration, slideDuration, startTime) + }.as("window") + } + + + /** + * Bucketize rows into one or more time windows given a timestamp specifying column. Window + * starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window + * [12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in + * the order of months are not supported. The windows start beginning at 1970-01-01 00:00:00 UTC. + * The following example takes the average stock price for a one minute window every 10 seconds: + * + * {{{ + * val df = ... // schema => timestamp: TimestampType, stockId: StringType, price: DoubleType + * df.groupBy(window($"time", "1 minute", "10 seconds"), $"stockId") + * .agg(mean("price")) + * }}} + * + * The windows will look like: + * + * {{{ + * 09:00:00-09:01:00 + * 09:00:10-09:01:10 + * 09:00:20-09:01:20 ... + * }}} + * + * For a continuous query, you may use the function `current_timestamp` to generate windows on + * processing time. + * + * @param timeColumn The column or the expression to use as the timestamp for windowing by time. + * The time can be as TimestampType or LongType, however when using LongType, + * the time must be given in seconds. + * @param windowDuration A string specifying the width of the window, e.g. `10 minutes`, + * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for + * valid duration identifiers. + * @param slideDuration A string specifying the sliding interval of the window, e.g. `1 minute`. + * A new window will be generated every `slideDuration`. Must be less than + * or equal to the `windowDuration`. Check + * [[org.apache.spark.unsafe.types.CalendarInterval]] for valid duration. + * + * @group datetime_funcs + * @since 2.0.0 + */ + @Experimental + def window(timeColumn: Column, windowDuration: String, slideDuration: String): Column = { + window(timeColumn, windowDuration, slideDuration, "0 second") + } + + /** + * Generates tumbling time windows given a timestamp specifying column. Window + * starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window + * [12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in + * the order of months are not supported. The windows start beginning at 1970-01-01 00:00:00 UTC. + * The following example takes the average stock price for a one minute tumbling window: + * + * {{{ + * val df = ... // schema => timestamp: TimestampType, stockId: StringType, price: DoubleType + * df.groupBy(window($"time", "1 minute"), $"stockId") + * .agg(mean("price")) + * }}} + * + * The windows will look like: + * + * {{{ + * 09:00:00-09:01:00 + * 09:01:00-09:02:00 + * 09:02:00-09:03:00 ... + * }}} + * + * For a continuous query, you may use the function `current_timestamp` to generate windows on + * processing time. + * + * @param timeColumn The column or the expression to use as the timestamp for windowing by time. + * The time can be as TimestampType or LongType, however when using LongType, + * the time must be given in seconds. + * @param windowDuration A string specifying the width of the window, e.g. `10 minutes`, + * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for + * valid duration identifiers. + * + * @group datetime_funcs + * @since 2.0.0 + */ + @Experimental + def window(timeColumn: Column, windowDuration: String): Column = { + window(timeColumn, windowDuration, windowDuration, "0 second") + } + ////////////////////////////////////////////////////////////////////////////////////////////// // Collection functions ////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala new file mode 100644 index 0000000000..e8103a31d5 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala @@ -0,0 +1,242 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.util.TimeZone + +import org.scalatest.BeforeAndAfterEach + +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.test.SharedSQLContext +import org.apache.spark.sql.types.StringType + +class DataFrameTimeWindowingSuite extends QueryTest with SharedSQLContext with BeforeAndAfterEach { + + import testImplicits._ + + override def beforeEach(): Unit = { + super.beforeEach() + TimeZone.setDefault(TimeZone.getTimeZone("UTC")) + } + + override def afterEach(): Unit = { + super.beforeEach() + TimeZone.setDefault(null) + } + + test("tumbling window groupBy statement") { + val df = Seq( + ("2016-03-27 19:39:34", 1, "a"), + ("2016-03-27 19:39:56", 2, "a"), + ("2016-03-27 19:39:27", 4, "b")).toDF("time", "value", "id") + checkAnswer( + df.groupBy(window($"time", "10 seconds")) + .agg(count("*").as("counts")) + .orderBy($"window.start".asc) + .select("counts"), + Seq(Row(1), Row(1), Row(1)) + ) + } + + test("tumbling window groupBy statement with startTime") { + val df = Seq( + ("2016-03-27 19:39:34", 1, "a"), + ("2016-03-27 19:39:56", 2, "a"), + ("2016-03-27 19:39:27", 4, "b")).toDF("time", "value", "id") + + checkAnswer( + df.groupBy(window($"time", "10 seconds", "10 seconds", "5 seconds"), $"id") + .agg(count("*").as("counts")) + .orderBy($"window.start".asc) + .select("counts"), + Seq(Row(1), Row(1), Row(1))) + } + + test("tumbling window with multi-column projection") { + val df = Seq( + ("2016-03-27 19:39:34", 1, "a"), + ("2016-03-27 19:39:56", 2, "a"), + ("2016-03-27 19:39:27", 4, "b")).toDF("time", "value", "id") + + checkAnswer( + df.select(window($"time", "10 seconds"), $"value") + .orderBy($"window.start".asc) + .select($"window.start".cast("string"), $"window.end".cast("string"), $"value"), + Seq( + Row("2016-03-27 19:39:20", "2016-03-27 19:39:30", 4), + Row("2016-03-27 19:39:30", "2016-03-27 19:39:40", 1), + Row("2016-03-27 19:39:50", "2016-03-27 19:40:00", 2) + ) + ) + } + + test("sliding window grouping") { + val df = Seq( + ("2016-03-27 19:39:34", 1, "a"), + ("2016-03-27 19:39:56", 2, "a"), + ("2016-03-27 19:39:27", 4, "b")).toDF("time", "value", "id") + + checkAnswer( + df.groupBy(window($"time", "10 seconds", "3 seconds", "0 second")) + .agg(count("*").as("counts")) + .orderBy($"window.start".asc) + .select($"window.start".cast("string"), $"window.end".cast("string"), $"counts"), + // 2016-03-27 19:39:27 UTC -> 4 bins + // 2016-03-27 19:39:34 UTC -> 3 bins + // 2016-03-27 19:39:56 UTC -> 3 bins + Seq( + Row("2016-03-27 19:39:18", "2016-03-27 19:39:28", 1), + Row("2016-03-27 19:39:21", "2016-03-27 19:39:31", 1), + Row("2016-03-27 19:39:24", "2016-03-27 19:39:34", 1), + Row("2016-03-27 19:39:27", "2016-03-27 19:39:37", 2), + Row("2016-03-27 19:39:30", "2016-03-27 19:39:40", 1), + Row("2016-03-27 19:39:33", "2016-03-27 19:39:43", 1), + Row("2016-03-27 19:39:48", "2016-03-27 19:39:58", 1), + Row("2016-03-27 19:39:51", "2016-03-27 19:40:01", 1), + Row("2016-03-27 19:39:54", "2016-03-27 19:40:04", 1)) + ) + } + + test("sliding window projection") { + val df = Seq( + ("2016-03-27 19:39:34", 1, "a"), + ("2016-03-27 19:39:56", 2, "a"), + ("2016-03-27 19:39:27", 4, "b")).toDF("time", "value", "id") + + checkAnswer( + df.select(window($"time", "10 seconds", "3 seconds", "0 second"), $"value") + .orderBy($"window.start".asc, $"value".desc).select("value"), + // 2016-03-27 19:39:27 UTC -> 4 bins + // 2016-03-27 19:39:34 UTC -> 3 bins + // 2016-03-27 19:39:56 UTC -> 3 bins + Seq(Row(4), Row(4), Row(4), Row(4), Row(1), Row(1), Row(1), Row(2), Row(2), Row(2)) + ) + } + + test("windowing combined with explode expression") { + val df = Seq( + ("2016-03-27 19:39:34", 1, Seq("a", "b")), + ("2016-03-27 19:39:56", 2, Seq("a", "c", "d"))).toDF("time", "value", "ids") + + checkAnswer( + df.select(window($"time", "10 seconds"), $"value", explode($"ids")) + .orderBy($"window.start".asc).select("value"), + // first window exploded to two rows for "a", and "b", second window exploded to 3 rows + Seq(Row(1), Row(1), Row(2), Row(2), Row(2)) + ) + } + + test("null timestamps") { + val df = Seq( + ("2016-03-27 09:00:05", 1), + ("2016-03-27 09:00:32", 2), + (null, 3), + (null, 4)).toDF("time", "value") + + checkDataset( + df.select(window($"time", "10 seconds"), $"value") + .orderBy($"window.start".asc) + .select("value") + .as[Int], + 1, 2) // null columns are dropped + } + + test("time window joins") { + val df = Seq( + ("2016-03-27 09:00:05", 1), + ("2016-03-27 09:00:32", 2), + (null, 3), + (null, 4)).toDF("time", "value") + + val df2 = Seq( + ("2016-03-27 09:00:02", 3), + ("2016-03-27 09:00:35", 6)).toDF("time", "othervalue") + + checkAnswer( + df.select(window($"time", "10 seconds"), $"value").join( + df2.select(window($"time", "10 seconds"), $"othervalue"), Seq("window")) + .groupBy("window") + .agg((sum("value") + sum("othervalue")).as("total")) + .orderBy($"window.start".asc).select("total"), + Seq(Row(4), Row(8))) + } + + test("negative timestamps") { + val df4 = Seq( + ("1970-01-01 00:00:02", 1), + ("1970-01-01 00:00:12", 2)).toDF("time", "value") + checkAnswer( + df4.select(window($"time", "10 seconds", "10 seconds", "5 seconds"), $"value") + .orderBy($"window.start".asc) + .select($"window.start".cast(StringType), $"window.end".cast(StringType), $"value"), + Seq( + Row("1969-12-31 23:59:55", "1970-01-01 00:00:05", 1), + Row("1970-01-01 00:00:05", "1970-01-01 00:00:15", 2)) + ) + } + + test("multiple time windows in a single operator throws nice exception") { + val df = Seq( + ("2016-03-27 09:00:02", 3), + ("2016-03-27 09:00:35", 6)).toDF("time", "value") + val e = intercept[AnalysisException] { + df.select(window($"time", "10 second"), window($"time", "15 second")).collect() + } + assert(e.getMessage.contains( + "Multiple time window expressions would result in a cartesian product")) + } + + test("aliased windows") { + val df = Seq( + ("2016-03-27 19:39:34", 1, Seq("a", "b")), + ("2016-03-27 19:39:56", 2, Seq("a", "c", "d"))).toDF("time", "value", "ids") + + checkAnswer( + df.select(window($"time", "10 seconds").as("time_window"), $"value") + .orderBy($"time_window.start".asc) + .select("value"), + Seq(Row(1), Row(2)) + ) + } + + test("millisecond precision sliding windows") { + val df = Seq( + ("2016-03-27 09:00:00.41", 3), + ("2016-03-27 09:00:00.62", 6), + ("2016-03-27 09:00:00.715", 8)).toDF("time", "value") + checkAnswer( + df.groupBy(window($"time", "200 milliseconds", "40 milliseconds", "0 milliseconds")) + .agg(count("*").as("counts")) + .orderBy($"window.start".asc) + .select($"window.start".cast(StringType), $"window.end".cast(StringType), $"counts"), + Seq( + Row("2016-03-27 09:00:00.24", "2016-03-27 09:00:00.44", 1), + Row("2016-03-27 09:00:00.28", "2016-03-27 09:00:00.48", 1), + Row("2016-03-27 09:00:00.32", "2016-03-27 09:00:00.52", 1), + Row("2016-03-27 09:00:00.36", "2016-03-27 09:00:00.56", 1), + Row("2016-03-27 09:00:00.4", "2016-03-27 09:00:00.6", 1), + Row("2016-03-27 09:00:00.44", "2016-03-27 09:00:00.64", 1), + Row("2016-03-27 09:00:00.48", "2016-03-27 09:00:00.68", 1), + Row("2016-03-27 09:00:00.52", "2016-03-27 09:00:00.72", 2), + Row("2016-03-27 09:00:00.56", "2016-03-27 09:00:00.76", 2), + Row("2016-03-27 09:00:00.6", "2016-03-27 09:00:00.8", 2), + Row("2016-03-27 09:00:00.64", "2016-03-27 09:00:00.84", 1), + Row("2016-03-27 09:00:00.68", "2016-03-27 09:00:00.88", 1)) + ) + } +} |