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https://issues.apache.org/jira/browse/SPARK-22014?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Simon Schiff closed SPARK-22014.
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> Sample windows in Spark SQL
> ---------------------------
>
> Key: SPARK-22014
> URL: https://issues.apache.org/jira/browse/SPARK-22014
> Project: Spark
> Issue Type: Wish
> Components: DStreams, SQL
> Affects Versions: 2.2.0
> Reporter: Simon Schiff
> Priority: Minor
>
> Hello,
> I am using spark to process measurement data. It is possible to create sample
> windows in Spark Streaming, where the duration of the window is smaller than
> the slide. But when I try to do the same with Spark SQL (The measurement data
> has a time stamp column) then I got an analysis exception:
> {code}
> Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot
> resolve 'timewindow(timestamp, 60000000, 180000000, 0)' due to data type
> mismatch: The slide duration (180000000) must be less than or equal to the
> windowDuration (60000000)
> {code}
> Here is a example:
> {code:java}
> import java.sql.Timestamp;
> import java.text.SimpleDateFormat;
> import java.util.ArrayList;
> import java.util.Date;
> import java.util.List;
> import org.apache.spark.api.java.function.Function;
> import org.apache.spark.sql.Dataset;
> import org.apache.spark.sql.Encoders;
> import org.apache.spark.sql.Row;
> import org.apache.spark.sql.RowFactory;
> import org.apache.spark.sql.SparkSession;
> import org.apache.spark.sql.functions;
> import org.apache.spark.sql.types.DataTypes;
> import org.apache.spark.sql.types.StructField;
> import org.apache.spark.sql.types.StructType;
> public class App {
> public static Timestamp createTimestamp(String in) throws Exception {
> SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd
> hh:mm:ss");
> Date parsedDate = dateFormat.parse(in);
> return new Timestamp(parsedDate.getTime());
> }
>
> public static void main(String[] args) {
> SparkSession spark = SparkSession.builder().appName("Window
> Sampling Example").getOrCreate();
>
> List<String> sensorData = new ArrayList<String>();
> sensorData.add("2017-08-04 00:00:00, 22.75");
> sensorData.add("2017-08-04 00:01:00, 23.82");
> sensorData.add("2017-08-04 00:02:00, 24.15");
> sensorData.add("2017-08-04 00:03:00, 23.16");
> sensorData.add("2017-08-04 00:04:00, 22.62");
> sensorData.add("2017-08-04 00:05:00, 22.89");
> sensorData.add("2017-08-04 00:06:00, 23.21");
> sensorData.add("2017-08-04 00:07:00, 24.59");
> sensorData.add("2017-08-04 00:08:00, 24.44");
>
> Dataset<String> in = spark.createDataset(sensorData,
> Encoders.STRING());
>
> StructType sensorSchema = DataTypes.createStructType(new
> StructField[] {
> DataTypes.createStructField("timestamp",
> DataTypes.TimestampType, false),
> DataTypes.createStructField("value",
> DataTypes.DoubleType, false),
> });
>
> Dataset<Row> data =
> spark.createDataFrame(in.toJavaRDD().map(new Function<String, Row>() {
> public Row call(String line) throws Exception {
> return
> RowFactory.create(createTimestamp(line.split(",")[0]),
> Double.parseDouble(line.split(",")[1]));
> }
> }), sensorSchema);
>
> data.groupBy(functions.window(data.col("timestamp"), "1
> minutes", "3 minutes")).avg("value").orderBy("window").show(false);
> }
> }
> {code}
> I think there should be no difference (duration and slide) in a "Spark
> Streaming window" and a "Spark SQL window" function.
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