Using the Spark Connector¶

Specifying the Data Source Class Name¶

To use Snowflake as a data source in Spark, use the .format option to provide the Snowflake connector class name that defines the data source.

net.snowflake.spark.snowflake

To ensure a compile-time check of the class name, Snowflake highly recommends defining a variable for the class name. For example:

val SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"

Also, for convenience, the Utils class provides the variable, which can be imported as follows:

import net.snowflake.spark.snowflake.Utils.SNOWFLAKE_SOURCE_NAME

Enabling/Disabling Pushdown in a Session¶

Version 2.1.0 (and higher) of the connector supports query pushdown, which can significantly improve performance by pushing query processing to Snowflake when Snowflake is the Spark data source.

By default, pushdown is enabled.

To disable pushdown within a Spark session for a given DataFrame:

1. After instantiating a SparkSession object, call the SnowflakeConnectorUtils.disablePushdownSession static method, passing in the SparkSession object. For example:

   SnowflakeConnectorUtils.disablePushdownSession(spark)
   

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   Where spark is your SparkSession object.

2. Create a DataFrame with the autopushdown option set to off. For example:

   val df = sparkSession.read.format(SNOWFLAKE_SOURCE_NAME)
     .options(sfOptions)
     .option("query", query)
     .option("autopushdown", "off")
     .load()
   

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   Note that you can also set the autopushdown option in a Map that you pass to the options method (e.g. in sfOptions in the example above).

To enable pushdown again after disabling it, call the SnowflakeConnectorUtils.enablePushdownSession static method (passing in the SparkSession object), and create a DataFrame with autopushdown enabled.

Moving Data from Snowflake to Spark¶

To read data from Snowflake into a Spark DataFrame:

1. Use the read() method of the SqlContext object to construct a DataFrameReader.

2. Specify SNOWFLAKE_SOURCE_NAME using the format() method. For the definition, see Specifying the Data Source Class Name (in this topic).

3. Specify the connector options using either the option() or options() method. For more information, see Setting Configuration Options for the Connector (in this topic).

4. Specify one of the following options for the table data to be read:

   • dbtable: The name of the table to be read. All columns and records are retrieved (i.e. it is equivalent to SELECT * FROM db_table).

   • query: The exact query (SELECT statement) to run.

Moving Data from Spark to Snowflake¶

The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:

1. Use the write() method of the DataFrame to construct a DataFrameWriter.

2. Specify SNOWFLAKE_SOURCE_NAME using the format() method. For the definition, see Specifying the Data Source Class Name (in this topic).

3. Specify the connector options using either the option() or options() method. For more information, see Setting Configuration Options for the Connector (in this topic).

4. Use the dbtable option to specify the table to which data is written.

5. Use the mode() method to specify the save mode for the content.

   For more information, see SaveMode (Spark documentation).

Exporting JSON from Spark to Snowflake¶

Spark DataFrames can contain JSON objects, serialized as strings. The following code provides an example of converting a regular DataFrame to a DataFrame containing JSON data:

val rdd = myDataFrame.toJSON
val schema = new StructType(Array(StructField("JSON", StringType)))
val jsonDataFrame = sqlContext.createDataFrame(
            rdd.map(s => Row(s)), schema)

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Note that the resulting jsonDataFrame contains a single column of type StringType. As a result, when this DataFrame is exported to Snowflake with the common SaveMode.Overwrite mode, a new table in Snowflake is created with a single column of type VARCHAR.

To load jsonDataFrame into a VARIANT column:

1. Create a Snowflake table (connecting to Snowflake in Java using the Snowflake JDBC Driver). For explanations of the connection parameters used in the example, see JDBC Driver connection parameter reference.

   import java.sql.Connection;
   import java.sql.DriverManager;
   import java.sql.ResultSet;
   import java.sql.ResultSetMetaData;
   import java.sql.SQLException;
   import java.sql.Statement;
   import java.util.Properties;
   public class SnowflakeJDBCExample {
     public static void main(String[] args) throws Exception {
       String jdbcUrl = "jdbc:snowflake://myorganization-myaccount.snowflakecomputing.com/";
   
       Properties properties = new Properties();
       properties.put("user", "peter");
       properties.put("password", "test");
       properties.put("account", "myorganization-myaccount");
       properties.put("warehouse", "mywh");
       properties.put("db", "mydb");
       properties.put("schema", "public");
   
       // get connection
       System.out.println("Create JDBC connection");
       Connection connection = DriverManager.getConnection(jdbcUrl, properties);
       System.out.println("Done creating JDBC connection\n");
       // create statement
       System.out.println("Create JDBC statement");
       Statement statement = connection.createStatement();
       System.out.println("Done creating JDBC statement\n");
       // create a table
       System.out.println("Create my_variant_table table");
       statement.executeUpdate("create or replace table my_variant_table(json VARIANT)");
       statement.close();
       System.out.println("Done creating demo table\n");
   
       connection.close();
       System.out.println("Close connection\n");
     }
   }
   

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2. Instead of using SaveMode.Overwrite, use SaveMode.Append, to reuse the existing table. When the string value representing JSON is loaded into Snowflake, because the target column is of type VARIANT, it is parsed as JSON. For example:

   df.write
       .format(SNOWFLAKE_SOURCE_NAME)
       .options(sfOptions)
       .option("dbtable", "my_variant_table")
       .mode(SaveMode.Append)
       .save()
   

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Executing DDL/DML SQL Statements¶

Use the runQuery() method of the Utils object to execute DDL/DML SQL statements, in addition to queries, for example:

var sfOptions = Map(
    "sfURL" -> "<account_identifier>.snowflakecomputing.com",
    "sfUser" -> "<user_name>",
    "sfPassword" -> "<password>",
    "sfDatabase" -> "<database>",
    "sfSchema" -> "<schema>",
    "sfWarehouse" -> "<warehouse>"
    )
Utils.runQuery(sfOptions, "CREATE TABLE MY_TABLE(A INTEGER)")

where sfOptions is the parameters map used to read/write DataFrames.

The runQuery method returns only TRUE or FALSE. It is intended for statements that do not return a result set, for example DDL statements like CREATE TABLE and DML statements like INSERT, UPDATE, and DELETE. It is not useful for statements that return a result set, such as SELECT or SHOW.

Working with Timestamps and Time Zones¶

Spark provides only one type of timestamp, equivalent to the Scala/Java Timestamp type. It is almost identical in behavior to the TIMESTAMP_LTZ (local time zone) data type in Snowflake. As such, when transferring data between Spark and Snowflake, Snowflake recommends using the following approaches to preserve time correctly, relative to time zones:

• Use only the TIMESTAMP_LTZ data type in Snowflake.

  Note

  The default timestamp data type mapping is TIMESTAMP_NTZ (no time zone), so you must explicitly set the TIMESTAMP_TYPE_MAPPING parameter to use TIMESTAMP_LTZ.

• Set the Spark time zone to UTC and use this time zone in Snowflake (i.e. don’t set the sfTimezone option for the connector, and don’t explicitly set a time zone in Snowflake). In this scenario, TIMESTAMP_LTZ and TIMESTAMP_NTZ are effectively equivalent.

  To set the time zone, add the following line to your Spark code:

  java.util.TimeZone.setDefault(java.util.TimeZone.getTimeZone("UTC"))
  

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If you don’t implement either of these approaches, undesired time modifications might occur. For example, consider the following scenario:

• The time zone in Spark is set to America/New_York.

• The time zone in Snowflake is set to Europe/Warsaw, which can happen by either:

  • Setting sfTimezone to Europe/Warsaw for the connector.

  • Setting sfTimezone to snowflake for the connector and setting the TIMEZONE session parameter in Snowflake to Europe/Warsaw.

• Both TIMESTAMP_NTZ and TIMESTAMP_LTZ are in use in Snowflake.

In this scenario:

1. If a value representing 12:00:00 in a TIMESTAMP_NTZ column in Snowflake is sent to Spark, this value doesn’t carry any time zone information. Spark treats the value as 12:00:00 in New York.

2. If Spark sends this value 12:00:00 (in New York) back to Snowflake to be loaded into a TIMESTAMP_LTZ column, it is automatically converted and loaded as 18:00:00 (for the Warsaw time zone).

3. If this value is then converted to TIMESTAMP_NTZ in Snowflake, the user sees 18:00:00, which is different from the original value, 12:00:00.

To summarize, Snowflake recommends strictly following at least one of these rules:

• Use the same time zone, ideally UTC, for both Spark and Snowflake.

• Use only the TIMESTAMP_LTZ data type for transferring data between Spark and Snowflake.

Sample Scala Program¶

The following Scala program provides a full use case for the Snowflake Connector for Spark. Before using the code, replace the following strings with the appropriate values, as described in Setting Configuration Options for the Connector (in this topic):

• <account_identifier>: Your account identifier.

• <user_name> , <password>: Login credentials for the Snowflake user.

• <database> , <schema> , <warehouse>: Defaults for the Snowflake session.

The sample Scala program uses basic authentication (i.e. username and password). If you wish to authenticate with OAuth, see Using External OAuth (in this topic).

import org.apache.spark.sql._

//
// Configure your Snowflake environment
//
var sfOptions = Map(
    "sfURL" -> "<account_identifier>.snowflakecomputing.com",
    "sfUser" -> "<user_name>",
    "sfPassword" -> "<password>",
    "sfDatabase" -> "<database>",
    "sfSchema" -> "<schema>",
    "sfWarehouse" -> "<warehouse>"
)

//
// Create a DataFrame from a Snowflake table
//
val df: DataFrame = sqlContext.read
    .format(SNOWFLAKE_SOURCE_NAME)
    .options(sfOptions)
    .option("dbtable", "t1")
    .load()

//
// DataFrames can also be populated via a SQL query
//
val df: DataFrame = sqlContext.read
    .format(SNOWFLAKE_SOURCE_NAME)
    .options(sfOptions)
    .option("query", "select c1, count(*) from t1 group by c1")
    .load()

//
// Join, augment, aggregate, etc. the data in Spark and then use the
// Data Source API to write the data back to a table in Snowflake
//
df.write
    .format(SNOWFLAKE_SOURCE_NAME)
    .options(sfOptions)
    .option("dbtable", "t2")
    .mode(SaveMode.Overwrite)
    .save()

Configuring the pyspark Script¶

The pyspark script must be configured similarly to the spark-shell script, using the --packages or --jars options. For example:

bin/pyspark --packages net.snowflake:snowflake-jdbc:3.13.22,net.snowflake:spark-snowflake_2.12:2.11.0-spark_3.3

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Don’t forget to include the Snowflake Spark Connector and JDBC Connector .jar files in your CLASSPATH environment variable.

For more information about configuring the spark-shell script, see Step 4: Configure the Local Spark Cluster or Amazon EMR-hosted Spark Environment.

Enabling/Disabling Pushdown in a Session¶

Version 2.1.0 (and higher) of the connector supports query pushdown, which can significantly improve performance by pushing query processing to Snowflake when Snowflake is the Spark data source.

By default, pushdown is enabled.

To disable pushdown within a Spark session for a given DataFrame:

1. After instantiating a SparkSession object, call the SnowflakeConnectorUtils.disablePushdownSession static method, passing in the SparkSession object. For example:

   sc._jvm.net.snowflake.spark.snowflake.SnowflakeConnectorUtils.disablePushdownSession(sc._jvm.org.apache.spark.sql.SparkSession.builder().getOrCreate())
   

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2. Create a DataFrame with the autopushdown option set to off. For example:

   df = spark.read.format(SNOWFLAKE_SOURCE_NAME) \
     .options(**sfOptions) \
     .option("query",  query) \
     .option("autopushdown", "off") \
     .load()
   

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   Note that you can also set the autopushdown option in a Dictionary that you pass to the options method (e.g. in sfOptions in the example above).

To enable pushdown again after disabling it, call the SnowflakeConnectorUtils.enablePushdownSession static method (passing in the SparkSession object), and create a DataFrame with autopushdown enabled.

Sample Python Script¶

Once the pyspark script has been configured, you can perform SQL queries and other operations. Here’s an example Python script that performs a simple SQL query. This script illustrates basic connector usage. Most of the Scala examples in this document can be adapted with minimal effort/changes for use with Python.

The sample Python script uses basic authentication (i.e. username and password). If you wish to authenticate with OAuth, see Using External OAuth (in this topic).

from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark import SparkConf, SparkContext

sc = SparkContext("local", "Simple App")
spark = SQLContext(sc)
spark_conf = SparkConf().setMaster('local').setAppName('<APP_NAME>')

# You might need to set these
sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "<AWS_KEY>")
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", "<AWS_SECRET>")

# Set options below
sfOptions = {
  "sfURL" : "<account_identifier>.snowflakecomputing.com",
  "sfUser" : "<user_name>",
  "sfPassword" : "<password>",
  "sfDatabase" : "<database>",
  "sfSchema" : "<schema>",
  "sfWarehouse" : "<warehouse>"
}

SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"

df = spark.read.format(SNOWFLAKE_SOURCE_NAME) \
  .options(**sfOptions) \
  .option("query",  "select 1 as my_num union all select 2 as my_num") \
  .load()

df.show()

From Spark SQL to Snowflake¶

Spark Data Type	Snowflake Data Type
ArrayType	VARIANT
BinaryType	Not supported
BooleanType	BOOLEAN
ByteType	INTEGER. Snowflake does not support the BYTE type.
DateType	DATE
DecimalType	DECIMAL
DoubleType	DOUBLE
FloatType	FLOAT
IntegerType	INTEGER
LongType	INTEGER
MapType	VARIANT
ShortType	INTEGER
StringType	If length is specified, VARCHAR(N); otherwise, VARCHAR
StructType	VARIANT
TimestampType	TIMESTAMP

From Snowflake to Spark SQL¶

Snowflake Data Type	Spark Data Type
ARRAY	StringType
BIGINT	DecimalType(38, 0)
BINARY	Not supported
BLOB	Not supported
BOOLEAN	BooleanType
CHAR	StringType
CLOB	StringType
DATE	DateType
DECIMAL	DecimalType
DOUBLE	DoubleType
FLOAT	DoubleType
INTEGER	DecimalType(38, 0)
OBJECT	StringType
TIMESTAMP	TimestampType
TIME	StringType (Spark Connector Version 2.4.14 or later)
VARIANT	StringType

Required Connection Options¶

The following options are required for connecting to Snowflake:

sfUrl

Specifies the hostname for your account in the following format:

account_identifier.snowflakecomputing.com

account_identifier is your account identifier.

sfUser

Login name for the Snowflake user.

You must also use one of the following options to authenticate:

• sfPassword

  Password for the Snowflake user.

• pem_private_key

  Private key (in PEM format) for key pair authentication. For instructions, see Key-pair authentication and key-pair rotation.

• sfAuthenticator

  Specifies using External OAuth to authenticate to Snowflake. Set the value to oauth.

  Using External OAuth requires setting the sfToken parameter.

sfToken

(Required if using External OAuth) Set the value to your External OAuth access token.

This connection parameter requires setting the sfAuthenticator parameter value to oauth.

Default is none.

Required Context Options¶

The following options are required for setting the database and schema context for the session:

sfDatabase

The database to use for the session after connecting.

sfSchema

The schema to use for the session after connecting.

Additional Context Options¶

The options listed in this section are not required.

sfAccount

Account identifier (e.g. myorganization-myaccount). This option is no longer required because the account identifier is specified in sfUrl. It is documented here only for backward compatibility.

sfWarehouse

The default virtual warehouse to use for the session after connecting.

sfRole

The default security role to use for the session after connecting.

Proxy Options¶

The options listed in this section are not required.

use_proxy

Specifies whether the connector should use a proxy:

• true specifies that the connector should use a proxy.

• false specifies that the connector should not use a proxy.

The default value is false.

proxy_host

(Required if use_proxy is true) Specifies the hostname of the proxy server to use.

proxy_port

(Required if use_proxy is true) Specifies the port number of the proxy server to use.

proxy_protocol

Specifies the protocol used to connect to the proxy server. You can specify one of the following values:

• http

• https

The default value is http.

This is only supported for Snowflake on AWS.

This option was added in version 2.11.1 of the Spark Connector.

proxy_user

Specifies the user name for authenticating to the proxy server. Set this if the proxy server requires authentication.

This is only supported for Snowflake on AWS.

proxy_password

Specifies the password of proxy_user for authenticating to the proxy server. Set this if the proxy server requires authentication.

This is only supported for Snowflake on AWS.

non_proxy_hosts

Specifies the list of hosts that the connector should connect to directly, bypassing the proxy server.

Separate the hostnames with a URL-escaped pipe symbol (%7C). You can also use an asterisk (*) as a wildcard

This is only supported for Snowflake on AWS.

Additional Options¶

The options listed in this section are not required.

sfTimezone

The time zone to be used by Snowflake when working with Spark. Note that the parameter only sets the time zone in Snowflake; the Spark environment remains unmodified. The supported values are:

• spark: Use the time zone from Spark (default).

• snowflake: Use the current time zone for Snowflake.

• sf_default: Use the default time zone for the Snowflake user who is connecting.

• time_zone: Use a specific time zone (e.g. America/New_York), if valid.

  For more information about the impact of setting this option, see Working with Timestamps and Time Zones (in this topic).

sfCompress

If set to on (default), the data passed between Snowflake and Spark is compressed.

s3MaxFileSize

The size of the file used when moving data from Snowflake to Spark. The default is 10MB.

preactions

A semicolon-separated list of SQL commands that are executed before data is transferred between Spark and Snowflake.

If a SQL command contains %s, the %s is replaced with the table name referenced for the operation.

postactions

A semicolon-separated list of SQL commands that are executed after data is transferred between Spark and Snowflake.

If a SQL command contains %s, it is replaced with the table name referenced for the operation.

truncate_columns

If set to on (default), a COPY command automatically truncates text strings that exceed the target column length. If set to off, the command produces an error if a loaded string exceeds the target column length.

truncate_table

This parameter controls whether Snowflake retains the schema of a Snowflake target table when overwriting that table.

By default, when a target table in Snowflake is overwritten, the schema of that target table is also overwritten; the new schema is based on the schema of the source table (the Spark dataframe).

However, sometimes the schema of the source is not ideal. For example, a user might want a Snowflake target table to be able to store FLOAT values in the future even though the data type of the initial source column is INTEGER. In that case, the Snowflake table’s schema should not be overwritten; the Snowflake table should merely be truncated and then reused with its current schema.

The possible values of this parameter are:

• on

• off

If this parameter is on, the original schema of the target table is kept. If this parameter is off, then the old schema of the table is ignored, and a new schema is generated based on the schema of the source.

This parameter is optional.

The default value of this parameter is off (i.e. by default the original table schema is overwritten).

For details about mapping Spark data types to Snowflake data types (and vice versa), see: Data Type Mappings (in this topic).

continue_on_error

This variable controls whether the COPY command aborts if the user enters invalid data (for example, invalid JSON format for a variant data type column).

The possible values are:

• on

• off

The value on means continue even if an error occurs. The value off means abort if an error is hit.

This parameter is optional.

The default value of this parameter is off.

Turning this option on is not recommended. If any errors are reported while COPYing into Snowflake with the Spark connector, then this is likely to result in missing data.

Note

If rows are rejected or missing, and those rows are not clearly faulty in the input source, please report it to Snowflake.

usestagingtable

This parameter controls whether data loading uses a staging table.

A staging table is a normal table (with a temporary name) that is created by the connector; if the data loading operation is successful, the original target table is dropped and the staging table is renamed to the original target table’s name. If the data loading operation fails, the staging table is dropped and the target table is left with the data that it had immediately prior to the operation. Thus the staging table allows the original target table data to be retained if the operation fails. For safety, Snowflake strongly recommends using a staging table in most circumstances.

In order for the connector to create a staging table, the user executing the COPY via the Spark connector must have sufficient privileges to create a table. Direct loading (i.e. loading without using a staging table) is useful if the user does not have permission to create a table.

The possible values of this parameter are:

• on

• off

If the parameter is on, a staging table is used. If this parameter is off, then the data is loaded directly into the target table.

This parameter is optional.

The default value of this parameter is on (i.e. use a staging table).

autopushdown

This parameter controls whether automatic query pushdown is enabled.

If pushdown is enabled, then when a query is run on Spark, if part of the query can be “pushed down” to the Snowflake server, it is pushed down. This improves performance of some queries.

This parameter is optional.

The default value is on if the connector is plugged into a compatible version of Spark. Otherwise, the default value is off.

If the connector is plugged into a different version of Spark than the connector is intended for (e.g. if version 3.2 of the connector is plugged into version 3.3 of Spark), then auto-pushdown is disabled even if this parameter is set to on.

purge

If this is set to on, then the connector deletes temporary files created when transferring from Spark to Snowflake via external data transfer. If this parameter is set to off, then those files are not automatically deleted by the connector.

Purging works only for transfers from Spark to Snowflake, not for transfers from Snowflake to Spark.

The possible values are

• on

• off

The default value is off.

columnmap

This parameter is useful when writing data from Spark to Snowflake and the column names in the Snowflake table do not match the column names in the Spark table. You can create a map that indicates which Spark source column corresponds to each Snowflake destination column.

The parameter is a single string literal, in the form of:

"Map(col_2 -> col_b, col_3 -> col_a)"

For example, consider the following scenario:

• A Dataframe named df in Spark has three columns:

  col_1 , col_2 , col_3

• A table named tb in Snowflake has two columns:

  col_a , col_b

• You wish to copy the following values:

  • From df.col_2 to tb.col_b.

  • From df.col_3 to tb.col_a.

The value of the columnmap parameter would be:

Map(col_2 -> col_b, col_3 -> col_a)

You can generate this value by executing the following Scala code:

Map("col_2"->"col_b","col_3"->"col_a").toString()

The default value of this parameter is null. In other words, by default, column names in the source and destination tables should match.

This parameter is used only when writing from Spark to Snowflake; it does not apply when writing from Snowflake to Spark.

keep_column_case

When writing a table from Spark to Snowflake, the Spark connector defaults to shifting the letters in column names to uppercase, unless the column names are in double quotes.

When writing a table from Snowflake to Spark, the Spark connector defaults to adding double quotes around any column name that contains any characters except uppercase letters, underscores, and digits.

If you set keep_column_case to on, then the Spark connector will not make these changes.

The possible values are:

• on

• off

The default value is off.

column_mapping

The connector must map columns from the Spark data frame to the Snowflake table. This can be done based on column names (regardless of order), or based on column order (i.e. the first column in the data frame is mapped to the first column in the table, regardless of column name).

By default, the mapping is done based on order. You can override that by setting this parameter to name, which tells the connector to map columns based on column names. (The name mapping is case-insensitive.)

The possible values of this parameter are:

• order

• name

The default value is order.

column_mismatch_behavior

This parameter applies only when the column_mapping parameter is set to name.

If the column names in the Spark data frame and the Snowflake table do not match, then:

• If column_mismatch_behavior is error, then the Spark Connector reports an error.

• If column_mismatch_behavior is ignore, then the Spark Connector ignores the error.

  • The driver discards any column in the Spark data frame that does not have a corresponding column in the Snowflake table.

  • The driver inserts NULLs into any column in the Snowflake table that does not have a corresponding column in the Spark data frame.

Potential errors include:

• The Spark data frame could contain columns that are identical except for case (uppercase/lowercase). Because column name mapping is case-insensitive, it is not possible to determine the correct mapping from the data frame to the table.

• The Snowflake table could contain columns that are identical except for case (uppercase/lowercase). Because column name mapping is case-insensitive, it is not possible to determine the correct mapping from the data frame to the table.

• The Spark data frame and the Snowflake table might have no column names in common. In theory, the Spark Connector could insert NULLs into every column of every row, but this is usually pointless, so the connector throws an error even if the column_mismatch_behavior is set to ignore.

The possible values of this parameter are:

• error

• ignore

The default value is error.

time_output_format

This parameter allows the user to specify the format for TIME data returned.

The possible values of this parameter are the possible values for time formats specified at Time Formats.

This parameter affects only output, not input.

timestamp_ntz_output_format, . timestamp_ltz_output_format, . timestamp_tz_output_format

These options specify the output format for timestamp values. The default values of these options are:

Configuration Option	Default Value
timestamp_ntz_output_format	"YYYY-MM-DD HH24:MI:SS.FF3"
timestamp_ltz_output_format	"TZHTZM YYYY-MM-DD HH24:MI:SS.FF3"
timestamp_tz_output_format	"TZHTZM YYYY-MM-DD HH24:MI:SS.FF3"

If these options are set to "sf_current", the connector uses the formats specified for the session.

partition_size_in_mb

This parameter is used when the query result set is very large and needs to be split into multiple DataFrame partitions. This parameter specifies the recommended uncompressed size for each DataFrame partition. To reduce the number of partitions, make this size larger.

This size is used as a recommended size; the actual size of partitions could be smaller or larger.

This option applies only when the use_copy_unload parameter is FALSE.

This parameter is optional.

The default value is 100 (MB).

use_copy_unload

If this is FALSE, Snowflake uses the Arrow data format when SELECTing data. If this is set to TRUE, then Snowflake reverts to the old behavior of using the COPY UNLOAD command to transmit selected data.

This parameter is optional.

The default value is FALSE.

treat_decimal_as_long

If TRUE, configures the Spark Connector to return Long values (rather than BigDecimal values) for queries that return the type Decimal(precision, 0).

The default value is FALSE.

This option was added in version 2.11.1 of the Spark Connector.

s3_stage_vpce_dns_name

Specifies the DNS name of your VPC Endpoint for access to internal stages.

This option was added in version 2.11.1 of the Spark Connector.

support_share_connection

If FALSE, configures the Spark Connector to create a new JDBC connection for each job or action that uses the same Spark Connector options to access Snowflake.

The default value is TRUE, which means that the different jobs and actions share the same JDBC connection if they use the same Spark Connector options to access Snowflake.

If you need to enable or disable this setting programmatically, use the following global static functions:

• SparkConnectorContext.disableSharedConnection()

• SparkConnectorContext.enableSharingJDBCConnection()

Note

In the following special cases, the Spark Connector does not use a shared JDBC connection:

• If preactions or postactions are set, and those preactions or postactions are not CREATE TABLE, DROP TABLE, or MERGE INTO, the Spark Connector does not use the shared connection.

• Utility functions in Utils such as Utils.runQuery() and Utils.getJDBCConnection() do not use the shared connection.

This option was added in version 2.11.2 of the Spark Connector.

force_skip_pre_post_action_check_for_shared_session

If TRUE, configures the Spark Connector to disable the validation of preactions and postactions for session sharing.

The default value is FALSE.

Important

Before setting this option, make sure that the queries in preactions and postactions don’t affect the session settings. Otherwise, you may encounter issues with results.

This option was added in version 2.11.3 of the Spark Connector.

Using Key Pair Authentication & Key Pair Rotation¶

The Spark connector supports key pair authentication and key rotation.

1. To start, complete the initial configuration for key pair authentication as shown in Key-pair authentication and key-pair rotation.

2. Send an unencrypted copy of the private key using the pem_private_key connection option.

In the Python example, note that the pem_private_key file, rsa_key.p8, is:

• Being read directly from a password-protected file, using the environment variable PRIVATE_KEY_PASSPHRASE.

• Using the expression pkb in the sfOptions string.

To connect, you can save the Python example to a file (i.e. <file.py>) and then execute the following command:

spark-submit --packages net.snowflake:snowflake-jdbc:3.13.22,net.snowflake:spark-snowflake_2.12:2.11.0-spark_3.3 <file.py>

Python

from pyspark.sql import SQLContext
from pyspark import SparkConf, SparkContext
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization
import re
import os

with open("<path>/rsa_key.p8", "rb") as key_file:
  p_key = serialization.load_pem_private_key(
    key_file.read(),
    password = os.environ['PRIVATE_KEY_PASSPHRASE'].encode(),
    backend = default_backend()
    )

pkb = p_key.private_bytes(
  encoding = serialization.Encoding.PEM,
  format = serialization.PrivateFormat.PKCS8,
  encryption_algorithm = serialization.NoEncryption()
  )

pkb = pkb.decode("UTF-8")
pkb = re.sub("-*(BEGIN|END) PRIVATE KEY-*\n","",pkb).replace("\n","")

sc = SparkContext("local", "Simple App")
spark = SQLContext(sc)
spark_conf = SparkConf().setMaster('local').setAppName('Simple App')

sfOptions = {
  "sfURL" : "<account_identifier>.snowflakecomputing.com",
  "sfUser" : "<user_name>",
  "pem_private_key" : pkb,
  "sfDatabase" : "<database>",
  "sfSchema" : "schema",
  "sfWarehouse" : "<warehouse>"
}

SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"

df = spark.read.format(SNOWFLAKE_SOURCE_NAME) \
    .options(**sfOptions) \
    .option("query", "COLORS") \
    .load()

df.show()

Using External OAuth¶

Starting with Spark Connector version 2.7.0, you can use External OAuth to authenticate to Snowflake using either the sample Scala program or the sample Python script.

Before using External OAuth and the Spark Connector to authenticate to Snowflake, configure an External OAuth security integration for one of the supported External OAuth authorization servers or an External OAuth custom client.

In the Scala and Python examples, note the replacement of the sfPassword parameter with the sfAuthenticator and sfToken parameters.

Scala:

// spark connector version

val SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"
import net.snowflake.spark.snowflake2.Utils.SNOWFLAKE_SOURCE_NAME
import org.apache.spark.sql.DataFrame

var sfOptions = Map(
    "sfURL" -> "<account_identifier>.snowflakecomputing.com",
    "sfUser" -> "<username>",
    "sfAuthenticator" -> "oauth",
    "sfToken" -> "<external_oauth_access_token>",
    "sfDatabase" -> "<database>",
    "sfSchema" -> "<schema>",
    "sfWarehouse" -> "<warehouse>"
)

//
// Create a DataFrame from a Snowflake table
//
val df: DataFrame = sqlContext.read
    .format(SNOWFLAKE_SOURCE_NAME)
    .options(sfOptions)
    .option("dbtable", "region")
    .load()

//
// Join, augment, aggregate, etc. the data in Spark and then use the
// Data Source API to write the data back to a table in Snowflake
//
df.write
    .format(SNOWFLAKE_SOURCE_NAME)
    .options(sfOptions)
    .option("dbtable", "t2")
    .mode(SaveMode.Overwrite)
    .save()

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Python:

from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import *

sc = SparkContext("local", "Simple App")
spark = SQLContext(sc)
spark_conf = SparkConf().setMaster('local').setAppName('<APP_NAME>')

# You might need to set these
sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "<AWS_KEY>")
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", "<AWS_SECRET>")

# Set options below
sfOptions = {
  "sfURL" : "<account_identifier>.snowflakecomputing.com",
  "sfUser" : "<user_name>",
  "sfAuthenticator" : "oauth",
  "sfToken" : "<external_oauth_access_token>",
  "sfDatabase" : "<database>",
  "sfSchema" : "<schema>",
  "sfWarehouse" : "<warehouse>"
}

SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"

df = spark.read.format(SNOWFLAKE_SOURCE_NAME) \
  .options(**sfOptions) \
  .option("query",  "select 1 as my_num union all select 2 as my_num") \
  .load()

df.show()

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AWS Options for External Data Transfer¶

These options are used to specify the Amazon S3 location where temporary data is stored and provide authentication details for accessing the location. They are required only if you are doing an external data transfer. External data transfers are required if either of the following is true:

• You are using version 2.1.x or lower of the Spark Connector (which does not support internal transfers), or

• Your transfer is likely to take 36 hours or more (internal transfers use temporary credentials that expire after 36 hours).

tempDir

The S3 location where intermediate data is stored (e.g. s3n://xy12345-bucket/spark-snowflake-tmp/).

If tempDir is specified, you must also specify either:

• awsAccessKey , awsSecretKey . or

• temporary_aws_access_key_id , temporary_aws_secret_access_key, temporary_aws_session_token

awsAccessKey , awsSecretKey

These are standard AWS credentials that allow access to the location specified in tempDir. Note that both of these options must be set together.

If they are set, they can be retrieved from the existing SparkContext object.

If you specify these variables, you must also specify tempDir.

These credentials should also be set for the Hadoop cluster.

temporary_aws_access_key_id , temporary_aws_secret_access_key, temporary_aws_session_token

These are temporary AWS credentials that allow access to the location specified in tempDir. Note that all three of these options must be set together.

Also, if these options are set, they take precedence over the awsAccessKey and awsSecretKey options.

If you specify temporary_aws_access_key_id , temporary_aws_secret_access_key, and temporary_aws_session_token , you must also specify tempDir. Otherwise, these parameters are ignored.

check_bucket_configuration

If set to on (default), the connector checks if the bucket used for data transfer has a lifecycle policy configured (see Preparing an AWS External S3 Bucket for more information). If there is no lifecycle policy present, a warning is logged.

Disabling this option (by setting to off) skips this check. This can be useful if a user can access the bucket data operations, but not the bucket lifecycle policies. Disabling the option can also speed up query execution times slightly.

For details, see Authenticating S3 for Data Exchange (in this topic).

Azure Options for External Data Transfer¶

This section describes the parameters that apply to Azure Blob storage when doing external data transfers. External data transfers are required if either of the following is true:

• You are using version 2.1.x or lower of the Spark Connector (which does not support internal transfers), or

• Your transfer is likely to take 36 hours or more (internal transfers use temporary credentials that expire after 36 hours).

When using an external transfer with Azure Blob storage, you specify the location of the Azure container and the SAS (shared-access signature) for that container using the parameters described below.

tempDir

The Azure Blob storage container where intermediate data is stored. This is in the form of a URL, for example:

wasb://<azure_container>@<azure_account>.<azure_endpoint>/

temporary_azure_sas_token

Specify the SAS token for Azure Blob storage.

For details, see Authenticating Azure for Data Exchange (in this topic).

Using Permanent AWS Credentials¶

This is the standard AWS authentication method. It requires a pair of awsAccessKey and awsSecretKey values.

For example:

sc.hadoopConfiguration.set("fs.s3n.awsAccessKeyId", "<access_key>")
sc.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey", "<secret_key>")

// Then, configure your Snowflake environment
//
var sfOptions = Map(
    "sfURL" -> "<account_identifier>.snowflakecomputing.com",
    "sfUser" -> "<user_name>",
    "sfPassword" -> "<password>",
    "sfDatabase" -> "<database>",
    "sfSchema" -> "<schema>",
    "sfWarehouse" -> "<warehouse>",
    "awsAccessKey" -> sc.hadoopConfiguration.get("fs.s3n.awsAccessKeyId"),
    "awsSecretKey" -> sc.hadoopConfiguration.get("fs.s3n.awsSecretAccessKey"),
    "tempdir" -> "s3n://<temp-bucket-name>"
)

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For details about the options supported by sfOptions, see AWS Options for External Data Transfer (in this topic).

Using Temporary AWS Credentials¶

This method uses the temporary_aws_access_key_id, temporary_aws_secret_access_key, and temporary_aws_session_token configuration options for the connector.

This method allows additional security by providing Snowflake with only temporary access to the S3 bucket/directory used for data exchange.

The following Scala code sample provides an example of authenticating using temporary credentials:

import com.amazonaws.services.securitytoken.AWSSecurityTokenServiceClient
import com.amazonaws.services.securitytoken.model.GetSessionTokenRequest

import net.snowflake.spark.snowflake.Parameters

// ...

val sts_client = new AWSSecurityTokenServiceClient()
val session_token_request = new GetSessionTokenRequest()

// Set the token duration to 2 hours.

session_token_request.setDurationSeconds(7200)
val session_token_result = sts_client.getSessionToken(session_token_request)
val session_creds = session_token_result.getCredentials()

// Create a new set of Snowflake connector options, based on the existing
// sfOptions definition, with additional temporary credential options that override
// the credential options in sfOptions.
// Note that constants from Parameters are used to guarantee correct
// key names, but literal values, such as temporary_aws_access_key_id are, of course,
// also allowed.

var sfOptions2 = collection.mutable.Map[String, String]() ++= sfOptions
sfOptions2 += (Parameters.PARAM_TEMP_KEY_ID -> session_creds.getAccessKeyId())
sfOptions2 += (Parameters.PARAM_TEMP_KEY_SECRET -> session_creds.getSecretAccessKey())
sfOptions2 += (Parameters.PARAM_TEMP_SESSION_TOKEN -> session_creds.getSessionToken())

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sfOptions2 can now be used with the options() DataFrame method.

Using Azure Credentials¶

This is the standard Azure Blob storage authentication method. It requires a pair of values: tempDir (a URL) and temporary_azure_sas_token values.

For example:

sc.hadoopConfiguration.set("fs.azure", "org.apache.hadoop.fs.azure.NativeAzureFileSystem")
sc.hadoopConfiguration.set("fs.AbstractFileSystem.wasb.impl", "org.apache.hadoop.fs.azure.Wasb")
sc.hadoopConfiguration.set("fs.azure.sas.<container>.<account>.<azure_endpoint>", <azure_sas>)

// Then, configure your Snowflake environment
//
val sfOptions = Map(
  "sfURL" -> "<account_identifier>.snowflakecomputing.com",
  "sfUser" -> "<user_name>",
  "sfPassword" -> "<password>",
  "sfDatabase" -> "<database_name>",
  "sfSchema" -> "<schema_name>",
  "sfWarehouse" -> "<warehouse_name>",
  "sfCompress" -> "on",
  "sfSSL" -> "on",
  "tempdir" -> "wasb://<azure_container>@<azure_account>.<Azure_endpoint>/",
  "temporary_azure_sas_token" -> "<azure_sas>"
)

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For details about the options supported by sfOptions, see Azure Options for External Data Transfer (in this topic).