Working with DataFrames in Snowpark Scala¶

Specifying Columns and Expressions¶

When calling these transformation methods, you might need to specify columns or expressions that use columns. For example, when calling the select method, you need to specify the columns that should be selected.

To refer to a column, create a Column object by calling the col function in the com.snowflake.snowpark.functions object.

// Import the col function from the functions object.
import com.snowflake.snowpark.functions._

val dfProductInfo = session.table("sample_product_data").select(col("id"), col("name"))
dfProductInfo.show()

When specifying a filter, projection, join condition, etc., you can use Column objects in an expression. For example:

• You can use Column objects with the filter method to specify a filter condition:

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  // Specify the equivalent of "WHERE id = 20"
  // in an SQL SELECT statement.
  df.filter(col("id") === 20)
  

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  // Specify the equivalent of "WHERE a + b < 10"
  // in an SQL SELECT statement.
  df.filter((col("a") + col("b")) < 10)
  

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• You can use Column objects with the select method to define an alias:

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  // Specify the equivalent of "SELECT b * 10 AS c"
  // in an SQL SELECT statement.
  df.select((col("b") * 10) as "c")
  

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• You can use Column objects with the join method to define a join condition:

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  // Specify the equivalent of "X JOIN Y on X.a_in_X = Y.b_in_Y"
  // in an SQL SELECT statement.
  dfX.join(dfY, col("a_in_X") === col("b_in_Y"))
  

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// Create a DataFrame that joins two other DataFrames (dfLhs and dfRhs).
// Use the DataFrame.col method to refer to the columns used in the join.
val dfJoined = dfLhs.join(dfRhs, dfLhs.col("key") === dfRhs.col("key")).select(dfLhs.col("value").as("L"), dfRhs.col("value").as("R"))

// Create a DataFrame that joins two other DataFrames (dfLhs and dfRhs).
// Use the DataFrame.apply method to refer to the columns used in the join.
// Note that dfLhs("key") is shorthand for dfLhs.apply("key").
val dfJoined = dfLhs.join(dfRhs, dfLhs("key") === dfRhs("key")).select(dfLhs("value").as("L"), dfRhs("value").as("R"))

Using Shorthand For a Column Object¶

As an alternative to using the col function, you can refer to a column in one of these ways:

• Use a dollar sign in front of the quoted column name ($"column_name").

• Use an apostrophe (a single quote) in front of the unquoted column name ('column_name).

To do this, import the names from the implicits object after you create a Session object:

val session = Session.builder.configFile("/path/to/properties").create

// Import this after you create the session.
import session.implicits._

// Use the $ (dollar sign) shorthand.
val df = session.table("T").filter($"id" === 10).filter(($"a" + $"b") < 10)

// Use ' (apostrophe) shorthand.
val df = session.table("T").filter('id === 10).filter(('a + 'b) < 10).select('b * 10)

Using Double Quotes Around Object Identifiers (Table Names, Column Names, etc.)¶

The names of databases, schemas, tables, and stages that you specify must conform to the Snowflake identifier requirements. When you specify a name, Snowflake considers the name to be in upper case. For example, the following calls are equivalent:

// The following calls are equivalent:
df.select(col("id123"))
df.select(col("ID123"))

If the name does not conform to the identifier requirements, you must use double quotes (") around the name. Use a backslash (\) to escape the double quote character within a Scala string literal. For example, the following table name does not start with a letter or an underscore, so you must use double quotes around the name:

val df = session.table("\"10tablename\"")

Note that when specifying the name of a column, you don’t need to use double quotes around the name. The Snowpark library automatically encloses the column name in double quotes for you if the name does not comply with the identifier requirements:.

// The following calls are equivalent:
df.select(col("3rdID"))
df.select(col("\"3rdID\""))

// The following calls are equivalent:
df.select(col("id with space"))
df.select(col("\"id with space\""))

If you have already added double quotes around a column name, the library does not insert additional double quotes around the name.

In some cases, the column name might contain double quote characters:

describe table quoted;
+------------------------+ ...
| name                   | ...
|------------------------+ ...
| name_with_"air"_quotes | ...
| "column_name_quoted"   | ...
+------------------------+ ...

As explained in Identifier requirements, for each double quote character within a double-quoted identifier, you must use two double quote characters (e.g. "name_with_""air""_quotes" and """column_name_quoted"""):

val dfTable = session.table("quoted")
dfTable.select("\"name_with_\"\"air\"\"_quotes\"").show()
dfTable.select("\"\"\"column_name_quoted\"\"\"").show()

Keep in mind that when an identifier is enclosed in double quotes (whether you explicitly added the quotes or the library added the quotes for you), Snowflake treats the identifier as case-sensitive:

// The following calls are NOT equivalent!
// The Snowpark library adds double quotes around the column name,
// which makes Snowflake treat the column name as case-sensitive.
df.select(col("id with space"))
df.select(col("ID WITH SPACE"))

Using Literals as Column Objects¶

To use a literal in a method that passes in a Column object, create a Column object for the literal by passing the literal to the lit function in the com.snowflake.snowpark.functions object. For example:

// Import for the lit and col functions.
import com.snowflake.snowpark.functions._

// Show the first 10 rows in which num_items is greater than 5.
// Use `lit(5)` to create a Column object for the literal 5.
df.filter(col("num_items").gt(lit(5))).show()

If the literal is a floating point or double value in Scala (e.g. 0.05 is treated as a Double by default), the Snowpark library generates SQL that implicitly casts the value to the corresponding Snowpark data type (e.g. 0.05::DOUBLE). This can produce an approximate value that differs from the exact number specified.

For example, the following code displays no matching rows, even though the filter (that matches values greater than or equal to 0.05) should match the rows in the DataFrame:

// Create a DataFrame that contains the value 0.05.
val df = session.sql("select 0.05 :: Numeric(5, 2) as a")

// Applying this filter results in no matching rows in the DataFrame.
df.filter(col("a") <= lit(0.06) - lit(0.01)).show()

The problem is that lit(0.06) and lit(0.01) produce approximate values for 0.06 and 0.01, not the exact values.

To avoid this problem, you can use one of the following approaches:

• Option 1: Cast the literal to the Snowpark type that you want to use. For example, to use a NUMBER with a precision of 5 and a scale of 2:

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  df.filter(col("a") <= lit(0.06).cast(new DecimalType(5, 2)) - lit(0.01).cast(new DecimalType(5, 2))).show()
  

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• Option 2: Cast the value to the type that you want to use before passing the value to the lit function. For example, if you want to use the BigDecimal type:

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  df.filter(col("a") <= lit(BigDecimal(0.06)) - lit(BigDecimal(0.01))).show()
  

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Casting a Column Object to a Specific Type¶

To cast a Column object to a specific type, call the Column.cast method, and pass in a type object from the com.snowflake.snowpark.types package. For example, to cast a literal as a NUMBER with a precision of 5 and a scale of 2:

// Import for the lit function.
import com.snowflake.snowpark.functions._
// Import for the DecimalType class..
import com.snowflake.snowpark.types._

val decimalValue = lit(0.05).cast(new DecimalType(5,2))

Chaining Method Calls¶

Because each method that transforms a DataFrame object returns a new DataFrame object that has the transformation applied, you can chain method calls to produce a new DataFrame that is transformed in additional ways.

The following example returns a DataFrame that is configured to:

• Query the sample_product_data table.

• Return the row with id = 1.

• Select the name and serial_number columns.

val dfProductInfo = session.table("sample_product_data").filter(col("id") === 1).select(col("name"), col("serial_number"))
dfProductInfo.show()

In this example:

• session.table("sample_product_data") returns a DataFrame for the sample_product_data table.

  Although the DataFrame does not yet contain the data from the table, the object does contain the definitions of the columns in the table.

• filter(col("id") === 1) returns a DataFrame for the sample_product_data table that is set up to return the row with id = 1.

  Note again that the DataFrame does not yet contain the matching row from the table. The matching row is not retrieved until you call an action method.

• select(col("name"), col("serial_number")) returns a DataFrame that contains the name and serial_number columns for the row in the sample_product_data table that has id = 1.

When you chain method calls, keep in mind that the order of calls is important. Each method call returns a DataFrame that has been transformed. Make sure that subsequent calls work with the transformed DataFrame.

For example, in the code below, the select method returns a DataFrame that just contains two columns: name and serial_number. The filter method call on this DataFrame fails because it uses the id column, which is not in the transformed DataFrame.

// This fails with the error "invalid identifier 'ID'."
val dfProductInfo = session.table("sample_product_data").select(col("name"), col("serial_number")).filter(col("id") === 1)

In contrast, the following code executes successfully because the filter() method is called on a DataFrame that contains all of the columns in the sample_product_data table (including the id column):

// This succeeds because the DataFrame returned by the table() method
// includes the "id" column.
val dfProductInfo = session.table("sample_product_data").filter(col("id") === 1).select(col("name"), col("serial_number"))
dfProductInfo.show()

Keep in mind that you might need to make the select and filter method calls in a different order than you would use the equivalent keywords (SELECT and WHERE) in a SQL statement.

Limiting the Number of Rows in a DataFrame¶

To limit the number of rows in a DataFrame, you can use the DataFrame.limit transformation method.

The Snowpark API also provides action methods for retrieving and printing out a limited number of rows:

• the DataFrame.first action method (to execute the query and return the first n rows)

• the DataFrame.show action method (to execute the query and print the first n rows)

These methods effectively add a LIMIT clause to the SQL statement that is executed.

As explained in the usage notes for LIMIT, the results are non-deterministic unless you specify a sort order (ORDER BY) in conjunction with LIMIT.

To keep the ORDER BY clause with the LIMIT clause (e.g. so that ORDER BY is not in a separate subquery), you must call the method that limits results on the DataFrame returned by the sort method.

For example, if you are chaining method calls:

// Limit the number of rows to 5, sorted by parent_id.
var dfSubset = df.sort(col("parent_id")).limit(5);

// Return the first 5 rows, sorted by parent_id.
var arrayOfRows = df.sort(col("parent_id")).first(5)

// Print the first 5 rows, sorted by parent_id.
df.sort(col("parent_id")).show(5)

Retrieving Column Definitions¶

To retrieve the definition of the columns in the dataset for the DataFrame, call the schema method. This method returns a StructType object that contains an Array of StructField objects. Each StructField object contains the definition of a column.

// Get the StructType object that describes the columns in the
// underlying rowset.
val tableSchema = session.table("sample_product_data").schema
println("Schema for sample_product_data: " + tableSchema);

In the returned StructType object, the column names are always normalized. Unquoted identifiers are returned in uppercase, and quoted identifiers are returned in the exact case in which they were defined.

The following example creates a DataFrame containing the columns named ID and 3rd. For the column name 3rd, the Snowpark library automatically encloses the name in double quotes ("3rd") because the name does not comply with the requirements for an identifier.

The example calls the schema method and then calls the names method on the returned StructType object to get an ArraySeq of column names. The names are normalized in the StructType returned by the schema method.

// Create a DataFrame containing the "id" and "3rd" columns.
val dfSelectedColumns = session.table("sample_product_data").select(col("id"), col("3rd"))
// Print out the names of the columns in the schema. This prints out:
//   ArraySeq(ID, "3rd")
println(dfSelectedColumns.schema.names.toSeq)

Joining DataFrames¶

To join DataFrame objects, call the DataFrame.join method.

The following sections explain how to use DataFrames to perform a join:

• Setting up the Sample Data for the Joins

• Specifying the Columns for the Join

• Performing a Natural Join

• Specifying the Type of Join

• Joining Multiple Tables

• Performing a Self-Join

create or replace table sample_a (
  id_a integer,
  name_a varchar,
  value integer
);
insert into sample_a (id_a, name_a, value) values
  (10, 'A1', 5),
  (40, 'A2', 10),
  (80, 'A3', 15),
  (90, 'A4', 20)
;
create or replace table sample_b (
  id_b integer,
  name_b varchar,
  id_a integer,
  value integer
);
insert into sample_b (id_b, name_b, id_a, value) values
  (4000, 'B1', 40, 10),
  (4001, 'B2', 10, 5),
  (9000, 'B3', 80, 15),
  (9099, 'B4', null, 200)
;
create or replace table sample_c (
  id_c integer,
  name_c varchar,
  id_a integer,
  id_b integer
);
insert into sample_c (id_c, name_c, id_a, id_b) values
  (1012, 'C1', 10, null),
  (1040, 'C2', 40, 4000),
  (1041, 'C3', 40, 4001)
;

// Create a DataFrame that joins the DataFrames for the tables
// "sample_a" and "sample_b" on the column named "id_a".
val dfLhs = session.table("sample_a")
val dfRhs = session.table("sample_b")
val dfJoined = dfLhs.join(dfRhs, dfLhs.col("id_a") === dfRhs.col("id_a"))
dfJoined.show()

----------------------------------------------------------------------
|"ID_A"  |"NAME_A"  |"VALUE"  |"ID_B"  |"NAME_B"  |"ID_A"  |"VALUE"  |
----------------------------------------------------------------------
|10      |A1        |5        |4001    |B2        |10      |5        |
|40      |A2        |10       |4000    |B1        |40      |10       |
|80      |A3        |15       |9000    |B3        |80      |15       |
----------------------------------------------------------------------

val dfLhs = session.table("sample_a")
val dfRhs = session.table("sample_b")
val dfJoined = dfLhs.join(dfRhs, dfLhs.col("id_a") === dfRhs.col("id_a"))
val dfSelected = dfJoined.select(dfLhs.col("value").as("LeftValue"), dfRhs.col("value").as("RightValue"))
dfSelected.show()

------------------------------
|"LEFTVALUE"  |"RIGHTVALUE"  |
------------------------------
|5            |5             |
|10           |10            |
|15           |15            |
------------------------------

--------------------------------------------------------------------------------------------------
|"l_ZSz7_ID_A"  |"NAME_A"  |"l_ZSz7_VALUE"  |"ID_B"  |"NAME_B"  |"r_heec_ID_A"  |"r_heec_VALUE"  |
--------------------------------------------------------------------------------------------------
|10             |A1        |5               |4001    |B2        |10             |5               |
|40             |A2        |10              |4000    |B1        |40             |10              |
|80             |A3        |15              |9000    |B3        |80             |15              |
--------------------------------------------------------------------------------------------------

val dfLhs = session.table("sample_a")
val dfRhs = session.table("sample_b")
val dfJoined = dfLhs.naturalJoin(dfRhs)
dfJoined.show()

---------------------------------------------------
|"ID_A"  |"VALUE"  |"NAME_A"  |"ID_B"  |"NAME_B"  |
---------------------------------------------------
|10      |5        |A1        |4001    |B2        |
|40      |10       |A2        |4000    |B1        |
|80      |15       |A3        |9000    |B3        |
---------------------------------------------------

// Create a DataFrame that performs a left outer join on
// "sample_a" and "sample_b" on the column named "id_a".
val dfLhs = session.table("sample_a")
val dfRhs = session.table("sample_b")
val dfLeftOuterJoin = dfLhs.join(dfRhs, dfLhs.col("id_a") === dfRhs.col("id_a"), "left")
dfLeftOuterJoin.show()

----------------------------------------------------------------------
|"ID_A"  |"NAME_A"  |"VALUE"  |"ID_B"  |"NAME_B"  |"ID_A"  |"VALUE"  |
----------------------------------------------------------------------
|40      |A2        |10       |4000    |B1        |40      |10       |
|10      |A1        |5        |4001    |B2        |10      |5        |
|80      |A3        |15       |9000    |B3        |80      |15       |
|90      |A4        |20       |NULL    |NULL      |NULL    |NULL     |
----------------------------------------------------------------------

val dfFirst = session.table("sample_a")
val dfSecond  = session.table("sample_b")
val dfThird = session.table("sample_c")
val dfJoinThreeTables = dfFirst.join(dfSecond, dfFirst.col("id_a") === dfSecond.col("id_a")).join(dfThird, dfFirst.col("id_a") === dfThird.col("id_a"))
dfJoinThreeTables.show()

------------------------------------------------------------------------------------------------------------
|"ID_A"  |"NAME_A"  |"VALUE"  |"ID_B"  |"NAME_B"  |"ID_A"  |"VALUE"  |"ID_C"  |"NAME_C"  |"ID_A"  |"ID_B"  |
------------------------------------------------------------------------------------------------------------
|10      |A1        |5        |4001    |B2        |10      |5        |1012    |C1        |10      |NULL    |
|40      |A2        |10       |4000    |B1        |40      |10       |1040    |C2        |40      |4000    |
|40      |A2        |10       |4000    |B1        |40      |10       |1041    |C3        |40      |4001    |
------------------------------------------------------------------------------------------------------------

// This fails because columns named "id" and "parent_id"
// are in the left and right DataFrames in the join.
val df = session.table("sample_product_data");
val dfJoined = df.join(df, col("id") === col("parent_id"))

// This fails because columns named "id" and "parent_id"
// are in the left and right DataFrames in the join.
val df = session.table("sample_product_data");
val dfJoined = df.join(df, df("id") === df("parent_id"))

Exception in thread "main" com.snowflake.snowpark.SnowparkClientException:
  Joining a DataFrame to itself can lead to incorrect results due to ambiguity of column references.
  Instead, join this DataFrame to a clone() of itself.

// Create a DataFrame object for the "sample_product_data" table for the left-hand side of the join.
val dfLhs = session.table("sample_product_data")
// Clone the DataFrame object to use as the right-hand side of the join.
val dfRhs = dfLhs.clone()

// Create a DataFrame that joins the two DataFrames
// for the "sample_product_data" table on the
// "id" and "parent_id" columns.
val dfJoined = dfLhs.join(dfRhs, dfLhs.col("id") === dfRhs.col("parent_id"))
dfJoined.show()

// Create a DataFrame that performs a self-join on a DataFrame
// using the column named "key".
val df = session.table("sample_product_data");
val dfJoined = df.join(df, Seq("key"))

Performing an Action Synchronously¶

To perform an action synchronously, call one of the following action methods:

To execute the query and return the number of results, call the count method:

// Create a DataFrame for the "sample_product_data" table.
val dfProducts = session.table("sample_product_data")

// Send the query to the server for execution and
// print the count of rows in the table.
println("Rows returned: " + dfProducts.count())

You can also call action methods to:

• Execute a query against a table and return the results.

• Execute a query and print the results to the console.

Note: If you are calling the schema method to get the definitions of the columns in the DataFrame, you do not need to call an action method.

Performing an Action Asynchronously¶

To perform an action asynchronously, call the async method to return an “async actor” object (e.g. DataFrameAsyncActor), and call an asynchronous action method in that object.

These action methods of an async actor object return a TypedAsyncJob object, which you can use to check the status of the asynchronous action and retrieve the results of the action.

The next sections explain how to perform actions asynchronously and check the results.

• Understanding the Basic Flow of Asynchronous Actions

• Specifying the Maximum Number of Seconds to Wait

• Accessing an Asynchronous Query by ID

// Create a DataFrame with the "id" and "name" columns from the "sample_product_data" table.
// This does not execute the query.
val df = session.table("sample_product_data").select(col("id"), col("name"))

// Execute the query asynchronously.
// This call does not block.
val asyncJob = df.async.collect()
// Check if the query has completed execution.
println(s"Is query ${asyncJob.getQueryId()} done? ${asyncJob.isDone()}")
// Get an Array of Rows containing the results, and print the results.
// Note that getResult is a blocking call.
val results = asyncJob.getResult()
results.foreach(println)

// Create a DataFrame for the "sample_product_data" table.
val dfProducts = session.table("sample_product_data")

// Execute the query asynchronously.
// This call does not block.
val asyncJob = df.async.count()
// Check if the query has completed execution.
println(s"Is query ${asyncJob.getQueryId()} done? ${asyncJob.isDone()}")
// Print the count of rows in the table.
// Note that getResult is a blocking call.
println("Rows returned: " + asyncJob.getResult())

// Wait a maximum of 10 seconds for the query to complete before retrieving the results.
val results = asyncJob.getResult(10)

val asyncJob = session.createAsyncJob(myQueryId)
// Check if the query has completed execution.
println(s"Is query ${asyncJob.getQueryId()} done? ${asyncJob.isDone()}")
// If you need to retrieve the results, call getRows to return an Array of Rows containing the results.
// Note that getRows is a blocking call.
val rows = asyncJob.getRows()
rows.foreach(println)

Returning All Rows¶

To return all rows at once, call the DataFrame.collect method. This method returns an Array of Row objects. To retrieve the values from the row, call the getType method (e.g. getString, getInt, etc.).

For example:

import com.snowflake.snowpark.functions_

val rows = session.table("sample_product_data").select(col("name"), col("category_id")).sort(col("name")).collect()
for (row <- rows) {
  println(s"Name: ${row.getString(0)}; Category ID: ${row.getInt(1)}")
}

Returning an Iterator for the Rows¶

If you want to use an Iterator to iterate over the Row objects in the results, call DataFrame.toLocalIterator. If the amount of data in the results is large, the method loads the rows by chunk to avoid loading all rows into memory at once.

For example:

import com.snowflake.snowpark.functions_

while (rowIterator.hasNext) {
  val row = rowIterator.next()
  println(s"Name: ${row.getString(0)}; Category ID: ${row.getInt(1)}")
}

Returning the First n Rows¶

To return the first n rows, call the DataFrame.first method, passing in the number of rows to return.

As explained in Limiting the Number of Rows in a DataFrame, the results are non-deterministic. If you want the results to be deterministic, call this method on a sorted DataFrame (df.sort().first()).

For example:

import com.snowflake.snowpark.functions_

val df = session.table("sample_product_data")
val rows = df.sort(col("name")).first(5)
rows.foreach(println)

Updating Rows in a Table¶

For the update method, pass in a Map that associates the columns to update and the corresponding values to assign to those columns. update returns an UpdateResult object, which contains the number of rows that were updated. (See UpdateResult.)

For example, to replace the values in the column named count with the value 1:

val updatableDf = session.table("sample_product_data")
val updateResult = updatableDf.update(Map("count" -> lit(1)))
println(s"Number of rows updated: ${updateResult.rowsUpdated}")

The example above uses the name of the column to identify the column. You can also use a column expression:

val updateResult = updatableDf.update(Map(col("count") -> lit(1)))

If the update should be made only when a condition is met, you can specify that condition as an argument. For example, to replace the values in the column named count for rows in which the category_id column has the value 20:

val updateResult = updatableDf.update(Map(col("count") -> lit(1)), col("category_id") === 20)

If you need to base the condition on a join with a different DataFrame object, you can pass that DataFrame in as an argument and use that DataFrame in the condition. For example, to replace the values in the column named count for rows in which the category_id column matches the category_id in the DataFrame dfParts:

val updatableDf = session.table("sample_product_data")
val dfParts = session.table("parts")
val updateResult = updatableDf.update(Map(col("count") -> lit(1)), updatableDf("category_id") === dfParts("category_id"), dfParts)

Deleting Rows in a Table¶

For the delete method, you can specify a condition that identifies the rows to delete, and you can base that condition on a join with another DataFrame. delete returns a DeleteResult object, which contains the number of rows that were deleted. (See DeleteResult.)

For example, to delete the rows that have the value 1 in the category_id column:

val updatableDf = session.table("sample_product_data")
val deleteResult = updatableDf.delete(updatableDf("category_id") === 1)
println(s"Number of rows deleted: ${deleteResult.rowsDeleted}")

If the condition refers to columns in a different DataFrame, pass that DataFrame in as the second argument. For example, to delete the rows in which the category_id column matches the category_id in the DataFrame dfParts, pass in dfParts as the second argument:

val updatableDf = session.table("sample_product_data")
val deleteResult = updatableDf.delete(updatableDf("category_id") === dfParts("category_id"), dfParts)
println(s"Number of rows deleted: ${deleteResult.rowsDeleted}")

Merging Rows into a Table¶

To insert, update, and deletes rows in one table based on values in a second table or a subquery (the equivalent of the MERGE command in SQL), do the following:

1. In the Updatable object for the table where you want the data merged in, call the merge method, passing in the DataFrame object for the other table and the column expression for the join condition.

   This returns a MergeBuilder object that you can use to specify the actions to take (e.g. insert, update, or delete) on the rows that match and the rows that don’t match. (See MergeBuilder.)

2. Using the MergeBuilder object:

   • To specify the update or deletion that should be performed on matching rows, call the whenMatched method.

     If you need to specify an additional condition whe rows should be updated or deleted, you can pass in a column expression for that condition.

     This method returns a MatchedClauseBuilder object that you can use to specify the action to perform. (See MatchedClauseBuilder.)

     Call the update or delete method in the MatchedClauseBuilder object to specify the update or delete action that should be performed on matching rows. These methods return a MergeBuilder object that you can use to specify additional clauses.

   • To specify the insert that should be performed when rows do not match, call the whenNotMatched method.

     If you need to specify an additional condition when rows should be inserted, you can pass in a column expression for that condition.

     This method returns a NotMatchedClauseBuilder object that you can use to specify the action to perform. (See NotMatchedClauseBuilder.)

     Call the insert method in the NotMatchedClauseBuilder object to specify the insert action that should be performed when rows do not match. These methods return a MergeBuilder object that you can use to specify additional clauses.

3. When you are done specifying the inserts, updates, and deletions that should be performed, call the collect method of the MergeBuilder object to perform the specified inserts, updates, and deletions on the table.

   collect returns a MergeResult object, which contains the number of rows that were inserted, updated, and deleted. (See MergeResult.)

The following example inserts a row with the id and value columns from the source table into the target table if the target table does not contain a row with a matching ID:

val mergeResult = target.merge(source, target("id") === source("id"))
                      .whenNotMatched.insert(Seq(source("id"), source("value")))
                      .collect()

The following example updates a row in the target table with the value of the value column from the row in the source table that has the same ID:

val mergeResult = target.merge(source, target("id") === source("id"))
                      .whenMatched.update(Map("value" -> source("value")))
                      .collect()

Uploading and Downloading Files in a Stage¶

To upload and download files in a stage, use the FileOperation object:

• Uploading Files to a Stage

• Downloading Files from a Stage

Using Input Streams to Upload and Download Data in a Stage¶

To use input streams to upload data to a file on a stage and download data from a file on a stage, use the uploadStream and downloadStream methods of the FileOperation object:

• Using an Input Stream to Upload Data to a File on a Stage

• Using an Input Stream to Download Data from a File on a Stage

import java.io.InputStream
...
val compressData = true
val pathToFileOnStage = "@myStage/path/file"
session.file.uploadStream(pathToFileOnStage, new ByteArrayInputStream(fileContent.getBytes()), compressData)