Dataset row

Dataset row DEFAULT

Best Java code snippets using org.apache.spark.sql.Dataset.columns(Showing top 20 results out of 315)

  • Common ways to obtain Dataset

    private void myMethod () {

    Datasetd=
    • Codota IconSparkSession sparkSession;JavaRDD javaRDD;StructType structType;sparkSession.createDataFrame(javaRDD, structType)
    • Codota IconSparkSession sparkSession;String str;sparkSession.read().text(str)
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    }

    + " ORDER BY col1+col2, col4"); List<Row> rows = dataset.collectAsList(); ResultSet rs = new SparkResultSet(rows, dataset.columns()); assertTrue(rs.next()); assertEquals("a", rs.getString(3));
    sqlContext.sql(query); List<Row> rows = dataset.collectAsList(); ResultSet rs = new SparkResultSet(rows, dataset.columns()); assertTrue(rs.next()); assertEquals("bb",rs.getString(1));
    private String getRefFieldName(Map<String, Dataset<Row>> dependencies) { return refFieldName == null ? dependencies.get(refStepName).columns()[0] : refFieldName; }
    sqlContext.sql(query); List<Row> rows = dataset.collectAsList(); ResultSet rs = new SparkResultSet(rows, dataset.columns()); dataset = sqlContext.sql(query); rows = dataset.collectAsList(); rs = new SparkResultSet(rows, dataset.columns()); assertTrue(rs.next()); assertEquals("b",rs.getString(1));
    + tableName + " ORDER BY `CF1.A`,`CF2.C`"); List<Row> rows = dataset.collectAsList(); ResultSet rs = new SparkResultSet(rows, dataset.columns()); + tableName + " ORDER BY COL2"); rows = dataset.collectAsList(); rs = new SparkResultSet(rows, dataset.columns());
    private String getFieldName(Map<String, Dataset<Row>> dependencies) { return fieldName == null ? dependencies.get(getStepName(dependencies)).columns()[0] : fieldName; }
    publicstatic ResultSet executeQuery(Connection conn, QueryBuilder queryBuilder, String url, Configuration config) throws SQLException { SQLContext sqlContext = getSparkSession().sqlContext(); boolean forceRowKeyOrder = conn.unwrap(PhoenixConnection.class).getQueryServices().getProps() .getBoolean(QueryServices.FORCE_ROW_KEY_ORDER_ATTRIB, false); String prevOrderBy = queryBuilder.getOrderByClause(); if (forceRowKeyOrder && (queryBuilder.getOrderByClause()==null || queryBuilder.getOrderByClause().isEmpty())) { queryBuilder.setOrderByClause(Joiner.on(", ").join(queryBuilder.getRequiredColumns())); } Dataset phoenixDataSet = getSparkSession().read().format("phoenix") .option(DataSourceOptions.TABLE_KEY, queryBuilder.getFullTableName()) .option(PhoenixDataSource.ZOOKEEPER_URL, url).load(); phoenixDataSet.createOrReplaceTempView(queryBuilder.getFullTableName()); Dataset<Row> dataset = sqlContext.sql(queryBuilder.build()); SparkPlan plan = dataset.queryExecution().executedPlan(); List<Row> rows = dataset.collectAsList(); queryBuilder.setOrderByClause(prevOrderBy); ResultSet rs = new SparkResultSet(rows, dataset.columns()); return rs; } }
    @Override public Dataset<Row> derive(Map<String, Dataset<Row>> dependencies) throws Exception { dependencyCheck(dependencies); Dataset<Row> sourceStep = dependencies.get(stepName); if (useIncludeFields){ if (!Arrays.asList(sourceStep.columns()).containsAll(includeFields)){ thrownew RuntimeException("Columns specified in " + INCLUDE_FIELDS + " are not found in input dependency schema \n" + "Available columns: " + Arrays.toString(sourceStep.columns())); } String firstCol = includeFields.get(0); includeFields.remove(0); return sourceStep.select(firstCol, includeFields.toArray(new String[0])); } else { if (!Arrays.asList(sourceStep.columns()).containsAll(excludeFields)){ thrownew RuntimeException("Columns specified in " + EXCLUDE_FIELDS + " are not found in input dependency schema \n" + "Available columns: " + Arrays.toString(sourceStep.columns())); } return sourceStep.drop(JavaConverters.collectionAsScalaIterableConverter(excludeFields).asScala().toSeq()); } }
    publicstatic Dataset<Row> union(final Dataset<Row> ds1, final Dataset<Row> ds2) { Set<String> ds1Cols = Sets.newHashSet(ds1.columns()); Set<String> ds2Cols = Sets.newHashSet(ds2.columns()); final Set<String> total = Sets.newHashSet(ds1Cols); total.addAll(ds2Cols); return ds1.select(expr(ds1Cols, total)).union(ds2.select(expr(ds2Cols, total))); }
    publicstatic DataRowsFacade zeromeanUnitVariance(DataRowsFacade frame, List<String> skipColumns) { List<String> columnsList = DataFrames.toList(frame.get().columns()); columnsList.removeAll(skipColumns); String[] columnNames = DataFrames.toArray(columnsList); List<Row> stdDevMean = stdDevMeanColumns(frame, columnNames); for (int i = 0; i < columnNames.length; i++) { String columnName = columnNames[i]; double std = ((Number) stdDevMean.get(0).get(i)).doubleValue(); double mean = ((Number) stdDevMean.get(1).get(i)).doubleValue(); if (std == 0.0) std = 1; frame = dataRows(frame.get().withColumn(columnName, frame.get().col(columnName).minus(mean).divide(std))); } return frame; }
    publicstatic DataRowsFacade zeromeanUnitVariance(DataRowsFacade frame, List<String> skipColumns) { List<String> columnsList = DataFrames.toList(frame.get().columns()); columnsList.removeAll(skipColumns); String[] columnNames = DataFrames.toArray(columnsList); List<Row> stdDevMean = stdDevMeanColumns(frame, columnNames); for (int i = 0; i < columnNames.length; i++) { String columnName = columnNames[i]; double std = ((Number) stdDevMean.get(0).get(i)).doubleValue(); double mean = ((Number) stdDevMean.get(1).get(i)).doubleValue(); if (std == 0.0) std = 1; frame = dataRows(frame.get().withColumn(columnName, frame.get().col(columnName).minus(mean).divide(std))); } return frame; }
    publicstatic DataRowsFacade normalize(DataRowsFacade dataFrame, double min, double max, List<String> skipColumns) { List<String> columnsList = DataFrames.toList(dataFrame.get().columns()); columnsList.removeAll(skipColumns); String[] columnNames = DataFrames.toArray(columnsList); List<Row> minMax = minMaxColumns(dataFrame, columnNames); for (int i = 0; i < columnNames.length; i++) { String columnName = columnNames[i]; double dMin = ((Number) minMax.get(0).get(i)).doubleValue(); double dMax = ((Number) minMax.get(1).get(i)).doubleValue(); double maxSubMin = (dMax - dMin); if (maxSubMin == 0) maxSubMin = 1; Column newCol = dataFrame.get().col(columnName).minus(dMin).divide(maxSubMin).multiply(max - min).plus(min); dataFrame = dataRows(dataFrame.get().withColumn(columnName, newCol)); } return dataFrame; }
    publicstatic DataRowsFacade normalize(DataRowsFacade dataFrame, double min, double max, List<String> skipColumns) { List<String> columnsList = DataFrames.toList(dataFrame.get().columns()); columnsList.removeAll(skipColumns); String[] columnNames = DataFrames.toArray(columnsList); List<Row> minMax = minMaxColumns(dataFrame, columnNames); for (int i = 0; i < columnNames.length; i++) { String columnName = columnNames[i]; double dMin = ((Number) minMax.get(0).get(i)).doubleValue(); double dMax = ((Number) minMax.get(1).get(i)).doubleValue(); double maxSubMin = (dMax - dMin); if (maxSubMin == 0) maxSubMin = 1; Column newCol = dataFrame.get().col(columnName).minus(dMin).divide(maxSubMin).multiply(max - min).plus(min); dataFrame = dataRows(dataFrame.get().withColumn(columnName, newCol)); } return dataFrame; }
    @Test publicvoid fitAndTransform() { KMeans kmeans = new KMeans().setK(k).setSeed(1); KMeansModel model = kmeans.fit(dataset); Vector[] centers = model.clusterCenters(); assertEquals(k, centers.length); Dataset<Row> transformed = model.transform(dataset); List<String> columns = Arrays.asList(transformed.columns()); List<String> expectedColumns = Arrays.asList("features", "prediction"); for (String column : expectedColumns) { assertTrue(columns.contains(column)); } } }
    @Test publicvoid fitAndTransform() { KMeans kmeans = new KMeans().setK(k).setSeed(1); KMeansModel model = kmeans.fit(dataset); Vector[] centers = model.clusterCenters(); assertEquals(k, centers.length); Dataset<Row> transformed = model.transform(dataset); List<String> columns = Arrays.asList(transformed.columns()); List<String> expectedColumns = Arrays.asList("features", "prediction"); for (String column : expectedColumns) { assertTrue(columns.contains(column)); } } }
    @Test publicvoid fitAndTransform() { KMeans kmeans = new KMeans().setK(k).setSeed(1); KMeansModel model = kmeans.fit(dataset); Vector[] centers = model.clusterCenters(); assertEquals(k, centers.length); Dataset<Row> transformed = model.transform(dataset); List<String> columns = Arrays.asList(transformed.columns()); List<String> expectedColumns = Arrays.asList("features", "prediction"); for (String column : expectedColumns) { assertTrue(columns.contains(column)); } } }
    @Test publicvoid verifyLibSVMDF() { Dataset<Row> dataset = spark.read().format("libsvm").option("vectorType", "dense") .load(path); Assert.assertEquals("label", dataset.columns()[0]); Assert.assertEquals("features", dataset.columns()[1]); Row r = dataset.first(); Assert.assertEquals(1.0, r.getDouble(0), 1e-15); DenseVector v = r.getAs(1); Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v); } }
    @Test publicvoid verifyLibSVMDF() { Dataset<Row> dataset = spark.read().format("libsvm").option("vectorType", "dense") .load(path); Assert.assertEquals("label", dataset.columns()[0]); Assert.assertEquals("features", dataset.columns()[1]); Row r = dataset.first(); Assert.assertEquals(1.0, r.getDouble(0), 1e-15); DenseVector v = r.getAs(1); Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v); } }
    @Test publicvoid verifyLibSVMDF() { Dataset<Row> dataset = spark.read().format("libsvm").option("vectorType", "dense") .load(path); Assert.assertEquals("label", dataset.columns()[0]); Assert.assertEquals("features", dataset.columns()[1]); Row r = dataset.first(); Assert.assertEquals(1.0, r.getDouble(0), 1e-15); DenseVector v = r.getAs(1); Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v); } }
    privatevoid start() { SparkSession spark = SparkSession.builder() .appName("CSV to Dataset") .master("local") .getOrCreate(); String filename = "data/tuple-data-file.csv"; Dataset<Row> df = spark.read().format("csv") .option("inferSchema", "true") .option("header", "false") .load(filename); df.show(); int count = df.columns().length; for (int i = 0; i < count; i++) { String oldColName = "_c" + i; String newColName = "C" + i; df = df.withColumn(newColName, df.col(oldColName)).drop(oldColName); } df.show(); } }
    Sours: https://www.tabnine.com/code/java/methods/org.apache.spark.sql.Dataset/columns

    Best Java code snippets using org.apache.spark.sql.Dataset.show(Showing top 20 results out of 315)

    • Common ways to obtain Dataset

      private void myMethod () {

      Datasetd=
      • Codota IconSparkSession sparkSession;JavaRDD javaRDD;StructType structType;sparkSession.createDataFrame(javaRDD, structType)
      • Codota IconSparkSession sparkSession;String str;sparkSession.read().text(str)
      • Smart code suggestions by Tabnine

      }

      @Ignore publicvoid testShow() { Dataset<Row> df = spark.table("testData"); df.show(); df.show(1000); }
      @Ignore publicvoid testShow() { Dataset<Row> df = spark.table("testData"); df.show(); df.show(1000); }
      @Ignore publicvoid testShow() { Dataset<Row> df = spark.table("testData"); df.show(); df.show(1000); }
      publicvoid show(int numLines) { this.dataframe.show(numLines); }
      publicvoid show(){ this.dataframe.show(); }
      publicvoid show() { this.dataframe.show(); }
      publicvoid show(int numLines){ this.dataframe.show(numLines); }
      @VisibleForTesting publicvoid show() { this.dataset.show(); }
      privatevoid printData() { if (config.hasPath(PRINT_DATA_LIMIT_PROPERTY)) { int limit = config.getInt(PRINT_DATA_LIMIT_PROPERTY); data.limit(limit).show(); } else { data.show(); } }
      Dataset<Row> sqlResult = spark.sql(query); sqlResult.show(); sqlResult.write().parquet(output + "/parquetFormat"); sqlResult.rdd().saveAsTextFile(output + "/textFormat");
      privatevoid start() { SparkSession spark = SparkSession.builder().appName( "Complex JSON array to Dataset").master("local").getOrCreate(); String filename = "data/array-complex.json"; long start = System.currentTimeMillis(); Dataset<Row> df = spark.read().json(filename); long stop = System.currentTimeMillis(); System.out.println("Processing took " + (stop - start) + " ms"); df.show(); df.printSchema(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder().appName("JSON map to Dataset") .master("local").getOrCreate(); String filename = "data/map.json"; long start = System.currentTimeMillis(); Dataset<Row> df = spark.read().json(filename); long stop = System.currentTimeMillis(); System.out.println("Processing took " + (stop - start) + " ms"); df.show(); df.printSchema(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder() .appName("Array to Dataset<String>") .master("local") .getOrCreate(); String[] l = new String[] { "a", "b", "c", "d" }; List<String> data = Arrays.asList(l); Dataset<String> df = spark.createDataset(data, Encoders.STRING()); df.show(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder() .appName("Array to Dataframe (Dataset<Row>)") .master("local") .getOrCreate(); String[] l = new String[] { "a", "b", "c", "d" }; List<String> data = Arrays.asList(l); Dataset<String> ds = spark.createDataset(data, Encoders.STRING()); Dataset<Row> df = ds.toDF(); df.show(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder() .appName("Dataset from Text File") .master("local[*]") .getOrCreate(); String filename = "data/simple-data-file.txt"; Dataset<Row> df = spark.read().text(filename); df.show(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder() .appName("Dataset from Text File") .master("local[*]") .getOrCreate(); String filename = "data/simple-data-file.txt"; Dataset<Row> df = spark.read().text(filename); df.show(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder().appName("CSV to Dataset") .master("local").getOrCreate(); String filename = "data/csv-quoted.txt"; Dataset<Row> df = spark.read().option("inferSchema", "true").option( "header", "true").csv(filename); df.show(); df.printSchema(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder().appName("CSV to Dataset") .master("local").getOrCreate(); String filename = "data/csv-q.txt"; Dataset<Row> df = spark.read().option("inferSchema", "true").option( "header", "true").csv(filename); df.show(); df.printSchema(); } }
      privatevoid start() { SparkSession spark = SparkSession.builder().appName("For Each Book").master( "local").getOrCreate(); String filename = "data/books.csv"; Dataset<Row> df = spark.read().format("csv").option("inferSchema", "true") .option("header", "true") .load(filename); df.show(); df.foreach(new BookPrinter()); } }
      privatevoid start() { SparkSession spark = SparkSession.builder().appName("For Each Claim") .master("local").getOrCreate(); String filename = "data/claims.csv"; Dataset<Row> claimsDf = spark.read().format("csv").option("inferSchema", "true").option("header", "true") .load(filename); claimsDf.show(); claimsDf.foreach(new ClaimPrepAndProcess()); } }
      Sours: https://www.tabnine.com/code/java/methods/org.apache.spark.sql.Dataset/show
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      Columns and Rows in Datasets

      A dataset stores data in a file storage system, where data is organized by strings (dimensions) and numbers (measures). To make it easier to visualize, you can think of a dataset as a table, where the fields are columns and the values are rows.

      The dataset has six columns and eight rows.
      • A column represents a category of information, such as an opportunity source or account name. Each column has a name, a data type, and other properties.
      • A row represents an instance of data in the dataset. Rows can contain transactional data, such as individual invoices, or they can contain summary data, such as weekly invoice totals. What’s important is that rows in a dataset should contain the same level of granularity, such as all invoice transactions or all weekly totals, rather than mixed levels.
      Sours: https://help.salesforce.com/apex/HTViewHelpDoc?id=sf.bi_integrate_dataset_columns_and_rows.htm&language=en_US
      Excel VBA - Final Row and Final Column of DataSet
      Modifier and TypeMethod and Description

      Aggregates on the entire Dataset without groups.

      Aggregates on the entire Dataset without groups.

      (Scala-specific) Aggregates on the entire Dataset without groups.

      (Java-specific) Aggregates on the entire Dataset without groups.

      (Scala-specific) Aggregates on the entire Dataset without groups.

      Returns a new Dataset with an alias set.

      (Scala-specific) Returns a new Dataset with an alias set.

      Selects column based on the column name and returns it as a .

      :: Experimental :: Returns a new Dataset where each record has been mapped on to the specified type.

      Returns a new Dataset with an alias set.

      (Scala-specific) Returns a new Dataset with an alias set.

      Persist this Dataset with the default storage level ().

      Eagerly checkpoint a Dataset and return the new Dataset.

      Returns a checkpointed version of this Dataset.

       

      Returns a new Dataset that has exactly partitions, when the fewer partitions are requested.

      Selects column based on the column name and returns it as a .

      Returns an array that contains all rows in this Dataset.

      Returns a Java list that contains all rows in this Dataset.

      Selects column based on the column name specified as a regex and returns it as .

      Returns all column names as an array.

      Returns the number of rows in the Dataset.

      Creates a global temporary view using the given name.

      Creates or replaces a global temporary view using the given name.

      Creates a local temporary view using the given name.

      Creates a local temporary view using the given name.

      Explicit cartesian join with another .

      Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.

      Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max.

      Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max.

      Returns a new Dataset that contains only the unique rows from this Dataset.

      Returns a new Dataset with a column dropped.

      Returns a new Dataset with columns dropped.

      Returns a new Dataset with columns dropped.

      Returns a new Dataset with a column dropped.

      Returns a new Dataset that contains only the unique rows from this Dataset.

      (Scala-specific) Returns a new Dataset with duplicate rows removed, considering only the subset of columns.

      Returns a new Dataset with duplicate rows removed, considering only the subset of columns.

      Returns a new with duplicate rows removed, considering only the subset of columns.

      Returns a new with duplicate rows removed, considering only the subset of columns.

      Returns all column names and their data types as an array.

      Returns a new Dataset containing rows in this Dataset but not in another Dataset.

      Prints the physical plan to the console for debugging purposes.

      Prints the plans (logical and physical) to the console for debugging purposes.

      Filters rows using the given condition.

      :: Experimental :: (Java-specific) Returns a new Dataset that only contains elements where returns .

      :: Experimental :: (Scala-specific) Returns a new Dataset that only contains elements where returns .

      Filters rows using the given SQL expression.

      Returns the first row.

      :: Experimental :: (Java-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.

      :: Experimental :: (Scala-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.

      (Java-specific) Runs on each element of this Dataset.

      Applies a function to all rows.

      (Java-specific) Runs on each partition of this Dataset.

      Applies a function to each partition of this Dataset.

      Groups the Dataset using the specified columns, so we can run aggregation on them.

      Groups the Dataset using the specified columns, so we can run aggregation on them.

      Groups the Dataset using the specified columns, so that we can run aggregation on them.

      Groups the Dataset using the specified columns, so that we can run aggregation on them.

      :: Experimental :: (Scala-specific) Returns a where the data is grouped by the given key .

      :: Experimental :: (Java-specific) Returns a where the data is grouped by the given key .

      Returns the first row.

      Returns the first rows.

      Specifies some hint on the current Dataset.

      Specifies some hint on the current Dataset.

      Returns a best-effort snapshot of the files that compose this Dataset.

      Returns a new Dataset containing rows only in both this Dataset and another Dataset.

      Returns true if the and methods can be run locally (without any Spark executors).

      Returns true if this Dataset contains one or more sources that continuously return data as it arrives.

      Returns the content of the Dataset as a of s.

      Join with another .

      Inner join with another , using the given join expression.

      Join with another , using the given join expression.

      Inner equi-join with another using the given columns.

      Equi-join with another using the given columns.

      Inner equi-join with another using the given column.

      :: Experimental :: Using inner equi-join to join this Dataset returning a for each pair where evaluates to true.

      :: Experimental :: Joins this Dataset returning a for each pair where evaluates to true.

      Returns a new Dataset by taking the first rows.

      Eagerly locally checkpoints a Dataset and return the new Dataset.

      Locally checkpoints a Dataset and return the new Dataset.

      :: Experimental :: (Scala-specific) Returns a new Dataset that contains the result of applying to each element.

      :: Experimental :: (Java-specific) Returns a new Dataset that contains the result of applying to each element.

      :: Experimental :: (Scala-specific) Returns a new Dataset that contains the result of applying to each partition.

      :: Experimental :: (Java-specific) Returns a new Dataset that contains the result of applying to each partition.

      Returns a for working with missing data.

       

      Returns a new Dataset sorted by the given expressions.

      Returns a new Dataset sorted by the given expressions.

      Returns a new Dataset sorted by the given expressions.

      Returns a new Dataset sorted by the given expressions.

      Persist this Dataset with the default storage level ().

      Persist this Dataset with the given storage level.

      Prints the schema to the console in a nice tree format.

       

      Randomly splits this Dataset with the provided weights.

      Randomly splits this Dataset with the provided weights.

      Returns a Java list that contains randomly split Dataset with the provided weights.

      Represents the content of the Dataset as an of .

      :: Experimental :: (Scala-specific) Reduces the elements of this Dataset using the specified binary function.

      :: Experimental :: (Java-specific) Reduces the elements of this Dataset using the specified binary function.

      Returns a new Dataset partitioned by the given partitioning expressions, using as number of partitions.

      Returns a new Dataset that has exactly partitions.

      Returns a new Dataset partitioned by the given partitioning expressions into .

      Returns a new Dataset partitioned by the given partitioning expressions into .

      Returns a new Dataset partitioned by the given partitioning expressions, using as number of partitions.

      Returns a new Dataset partitioned by the given partitioning expressions, using as number of partitions.

      Returns a new Dataset partitioned by the given partitioning expressions into .

      Returns a new Dataset partitioned by the given partitioning expressions into .

      Returns a new Dataset partitioned by the given partitioning expressions, using as number of partitions.

      Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.

      Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.

      Returns a new by sampling a fraction of rows, using a random seed.

      Returns a new by sampling a fraction of rows, using a user-supplied seed.

      Returns a new by sampling a fraction of rows (without replacement), using a random seed.

      Returns a new by sampling a fraction of rows (without replacement), using a user-supplied seed.

      Returns the schema of this Dataset.

      Selects a set of column based expressions.

      Selects a set of column based expressions.

      Selects a set of columns.

      Selects a set of columns.

      :: Experimental :: Returns a new Dataset by computing the given expression for each element.

      :: Experimental :: Returns a new Dataset by computing the given expressions for each element.

      :: Experimental :: Returns a new Dataset by computing the given expressions for each element.

      :: Experimental :: Returns a new Dataset by computing the given expressions for each element.

      :: Experimental :: Returns a new Dataset by computing the given expressions for each element.

      Selects a set of SQL expressions.

      Selects a set of SQL expressions.

      Displays the top 20 rows of Dataset in a tabular form.

      Displays the top 20 rows of Dataset in a tabular form.

      Displays the Dataset in a tabular form.

      Displays the Dataset in a tabular form.

      Displays the Dataset in a tabular form.

      Displays the Dataset in a tabular form.

      Returns a new Dataset sorted by the given expressions.

      Returns a new Dataset sorted by the given expressions.

      Returns a new Dataset sorted by the specified column, all in ascending order.

      Returns a new Dataset sorted by the specified column, all in ascending order.

      Returns a new Dataset with each partition sorted by the given expressions.

      Returns a new Dataset with each partition sorted by the given expressions.

      Returns a new Dataset with each partition sorted by the given expressions.

      Returns a new Dataset with each partition sorted by the given expressions.

        

      Returns a for working statistic functions support.

      Get the Dataset's current storage level, or StorageLevel.NONE if not persisted.

      Computes specified statistics for numeric and string columns.

      Computes specified statistics for numeric and string columns.

      Returns the first rows in the Dataset.

      Returns the first rows in the Dataset as a list.

      Converts this strongly typed collection of data to generic Dataframe.

      Converts this strongly typed collection of data to generic with columns renamed.

      Converts this strongly typed collection of data to generic with columns renamed.

      Returns the content of the Dataset as a of s.

      Returns the content of the Dataset as a Dataset of JSON strings.

      Returns an iterator that contains all rows in this Dataset.

       

      Concise syntax for chaining custom transformations.

      Returns a new Dataset containing union of rows in this Dataset and another Dataset.

      Returns a new Dataset containing union of rows in this Dataset and another Dataset.

      Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.

      Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.

      Filters rows using the given condition.

      Filters rows using the given SQL expression.

      Returns a new Dataset by adding a column or replacing the existing column that has the same name.

      Returns a new Dataset with a column renamed.

      Defines an event time watermark for this .

      Interface for saving the content of the non-streaming Dataset out into external storage.

      Interface for saving the content of the streaming Dataset out into external storage.

      Sours: https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/sql/Dataset.html

      Row dataset

      DataTable.Rows Property

      Gets the collection of rows that belong to this table.

      Property Value

      DataRowCollection

      A DataRowCollection that contains DataRow objects.

      Attributes

      BrowsableAttributeDataSysDescriptionAttribute

      Examples

      The following shows two examples of returning and setting rows. The first example uses the Rows property and prints the value of each column for every row. The second example uses the DataTable object's NewRow method to create a new DataRow object with the schema of the DataTable. After setting the row values, the row is added to the DataRowCollection through the method.

      Remarks

      To create a new DataRow, you must use the NewRow method to return a new object. Such an object is automatically configured according to the schema defined for the DataTable through its collection of DataColumn objects. After creating a new row and setting the values for each column in the row, add the row to the DataRowCollection using the method.

      Each DataRow in the collection represents a row of data in the table. To commit a change to the value of a column in the row, you must invoke the AcceptChanges method.

      Applies to

      See also

      Sours: https://docs.microsoft.com/en-us/dotnet/api/system.data.datatable.rows
      Excel VBA - Final Row and Final Column of DataSet

      The Problem:

      Salesforce has made Einstein Analytics available to many more customers by including some level of Einstein Analytics based on the newest partner program. However, Salesforce doesn’t provide an easy way to calculate the org’s total or remaining Einstein Analytics dataset rows.

      This creates a problem for ISVs. At some point, you’ll run out of rows and you won’t be able to proceed unless you make a purchase from Salesforce — and if you’re in the middle of a project with a tight timeline, delays like this can cause a project to stall out.

      I’ve put together a solution that does two things:

      1. Helps you calculate your total dataset rows (depending on the various licenses you have, this number can widely vary)
      2. Determine how many rows you have remaining in your org

      To simplify the process a bit, I’ve added the entirety of the code to a repo here: https://github.com/drewcoparker/sf-analytics-api-service/blob/master/AnalyticsAPIService.cls

      Solution: An Apex Approach

      While Salesforce does not provide a direct way to query your total Einstein Analytics dataset rows, you can still come about this number by using a combination of two techniques:

      1. Get the total amount of dataset rows you’ve used so far: Use Saleforce’s Analytics REST API to fetch all your datasets, loop through each, and sum their rows used.
      2. Get your org’s allotted amount of dataset rows: Perform a SOQL query for all the pertinent Einstein Analytics licenses your org has (there could be a variety) and run a tally of rows each may give you (this will require hard-coding values gleaned from documentation, more on that later).

      Before you can interact with the Analytics REST API, you’ll first need to add your org’s base url to the allowed sites in Remote Site Settings, i.e., `https://app-flow-3189-dev-ed.lightning.force.com/`

      Interact with the Analytics REST API

      Salesforce provides excellent documentation for the Analytics REST API on the Developer Guide page. To access the API, you’ll need some apex code to initiate a GET request. Below is a method that demonstrates how to request data from the Analytics API.

      Later, I’ll introduce another method that will use the one above to fetch Einstein Analytics datasets.

      Analytics REST API: Fetch your org’s total dataset rows

      You may have several Einstein Analytics projects within your org, each with zero or more associated datasets. Each of those datasets may be using zero or more rows. The Analytics API is your tool of choice for accessing your total rows, but there’s a small problem: you can’t fetch them in one call. Here’s how you’ll need to do it:

      1. First fetch all your org’s dataset Ids (each will have info on its row usage).
      2. Then fetch the total rows used for each dataset.

      The method below demonstrates how to structure the first part of this multi-part fetch request, getting all of the dataset Ids.

      Now, you can create a final method that will use the one above to loop through each dataset and make additional requests for their totalRows. Here’s an example of how to do precisely that:

      The above two methods constitute the first part of this blog’s solution: getting the total amount of dataset rows you’ve used so far. Now, let’s examine how to compute your org’s total allotted amount.

      Einstein Analytics Limits: Dataset Row Storage Allocations

      Salesforce states that “Your org’s total row storage limit is a combination of your org’s active licenses.” We can create a method that uses SOQL to do just that. We’ll use the chart in the Dataset Row Storage Allocations section of Einstein Analytics documentation to describe the allocation rules programmatically.

      It is important to note that these limits are set by Salesforce and will likely be adjusted in the future. To ensure this code works appropriately, please reference the documentation and make updates where necessary.

      Let’s begin by determining which EA license level you’re using, Einstein Analytics Plus or Einstein Analytics Growth.

      Einstein Analytics Plus gives you an allotment of 10 billion of dataset rows while Einstein Analytics Growth yields 100 million. Observe that we query the PermissionSetLicense sObject for licenses that contain the word “Einstein” in the label and initialize a variable to account for their allotment.

      From there, we’ll need to further our query to find any related licences that can grant additional allotments towards the base 10 billion or 100 million. The documentation is very specific with how additional allotments are tallied. It states, “If your org has an Einstein Analytics Plus license and adds the Event Monitoring license, your total row limit becomes 10.05 billion. (10 billion plus 50 million).”

      Let’s take a look at how that can be reflected as an addendum to our code above:

      The full method representing the two snippets in this section can be found here. Once run, it will return to you precisely how many rows you can use in your org. While having this information on hand will be good enough for some ISVs, you can take this a step further and create components to showcase this information like in our mock up below.

      To simplify accessibility, all code referenced in this post can be accessed in full on GitHub here.


      Curious how your app can leverage Einstein Analytics? CodeScience enables businesses to do more on Salesforce than they thought possible. Our mission is to help every partner thrive on the AppExchange. Get in touch today!

      Related Posts

      Sours: https://www.codescience.com/blog/2020/how-to-calculate-your-total-and-remaining-einstein-analytics-dataset-rows/

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