doc.go

/*
Package gosnowflake is a pure Go Snowflake driver for the database/sql package.

Clients can use the database/sql package directly. For example:

	import (
		"database/sql"

		_ "github.com/snowflakedb/gosnowflake"

		"log"
	)

	func main() {
		db, err := sql.Open("snowflake", "user:password@my_organization-my_account/mydb")
		if err != nil {
			log.Fatal(err)
		}
		defer db.Close()
		...
	}

# Connection String

Use the Open() function to create a database handle with connection parameters:

	db, err := sql.Open("snowflake", "<connection string>")

The Go Snowflake Driver supports the following connection syntaxes (or data source name (DSN) formats):

  - username[:password]@<account_identifier>/dbname/schemaname[?param1=value&...&paramN=valueN]
  - username[:password]@<account_identifier>/dbname[?param1=value&...&paramN=valueN]
  - username[:password]@hostname:port/dbname/schemaname?account=<account_identifier>[&param1=value&...&paramN=valueN]

where all parameters must be escaped or use Config and DSN to construct a DSN string.

For information about account identifiers, see the Snowflake documentation
(https://docs.snowflake.com/en/user-guide/admin-account-identifier.html).

The following example opens a database handle with the Snowflake account
named "my_account" under the organization named "my_organization",
where the username is "jsmith", password is "mypassword", database is "mydb",
schema is "testschema", and warehouse is "mywh":

	db, err := sql.Open("snowflake", "jsmith:mypassword@my_organization-my_account/mydb/testschema?warehouse=mywh")

# Connection Parameters

The connection string (DSN) can contain both connection parameters (described below) and session parameters
(https://docs.snowflake.com/en/sql-reference/parameters.html).

The following connection parameters are supported:

  - account <string>: Specifies your Snowflake account, where "<string>" is the account
    identifier assigned to your account by Snowflake.
    For information about account identifiers, see the Snowflake documentation
    (https://docs.snowflake.com/en/user-guide/admin-account-identifier.html).

    If you are using a global URL, then append the connection group and ".global"
    (e.g. "<account_identifier>-<connection_group>.global"). The account identifier and the
    connection group are separated by a dash ("-"), as shown above.

    This parameter is optional if your account identifier is specified after the "@" character
    in the connection string.

  - region <string>: DEPRECATED. You may specify a region, such as
    "eu-central-1", with this parameter. However, since this parameter
    is deprecated, it is best to specify the region as part of the
    account parameter. For details, see the description of the account
    parameter.

  - database: Specifies the database to use by default in the client session
    (can be changed after login).

  - schema: Specifies the database schema to use by default in the client
    session (can be changed after login).

  - warehouse: Specifies the virtual warehouse to use by default for queries,
    loading, etc. in the client session (can be changed after login).

  - role: Specifies the role to use by default for accessing Snowflake
    objects in the client session (can be changed after login).

  - passcode: Specifies the passcode provided by Duo when using multi-factor authentication (MFA) for login.

  - passcodeInPassword: false by default. Set to true if the MFA passcode is embedded
    in the login password. Appends the MFA passcode to the end of the password.

  - loginTimeout: Specifies the timeout, in seconds, for login. The default
    is 60 seconds. The login request gives up after the timeout length if the
    HTTP response is success.

  - requestTimeout: Specifies the timeout, in seconds, for a query to complete.
    0 (zero) specifies that the driver should wait indefinitely. The default is 0 seconds.
    The query request gives up after the timeout length if the HTTP response is success.

  - authenticator: Specifies the authenticator to use for authenticating user credentials:

  - To use the internal Snowflake authenticator, specify snowflake (Default). If you want to cache your MFA logins, use AuthTypeUsernamePasswordMFA authenticator.

  - To authenticate through Okta, specify https://<okta_account_name>.okta.com (URL prefix for Okta).

  - To authenticate using your IDP via a browser, specify externalbrowser.

  - To authenticate via OAuth, specify oauth and provide an OAuth Access Token (see the token parameter below).

  - application: Identifies your application to Snowflake Support.

  - insecureMode: false by default. Set to true to bypass the Online
    Certificate Status Protocol (OCSP) certificate revocation check.
    IMPORTANT: Change the default value for testing or emergency situations only.

  - token: a token that can be used to authenticate. Should be used in conjunction with the "oauth" authenticator.

  - client_session_keep_alive: Set to true have a heartbeat in the background every hour to keep the connection alive
    such that the connection session will never expire. Care should be taken in using this option as it opens up
    the access forever as long as the process is alive.

  - ocspFailOpen: true by default. Set to false to make OCSP check fail closed mode.

  - validateDefaultParameters: true by default. Set to false to disable checks on existence and privileges check for
    Database, Schema, Warehouse and Role when setting up the connection

  - tracing: Specifies the logging level to be used. Set to error by default.
    Valid values are trace, debug, info, print, warning, error, fatal, panic.

  - disableQueryContextCache: disables parsing of query context returned from server and resending it to server as well.
    Default value is false.

  - clientConfigFile: specifies the location of the client configuration json file.
    In this file you can configure Easy Logging feature.

  - disableSamlURLCheck: disables the SAML URL check. Default value is false.

All other parameters are interpreted as session parameters (https://docs.snowflake.com/en/sql-reference/parameters.html).
For example, the TIMESTAMP_OUTPUT_FORMAT session parameter can be set by adding:

	...&TIMESTAMP_OUTPUT_FORMAT=MM-DD-YYYY...

A complete connection string looks similar to the following:

		my_user_name:my_password@ac123456/my_database/my_schema?my_warehouse=inventory_warehouse&role=my_user_role&DATE_OUTPUT_FORMAT=YYYY-MM-DD
	                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	                                                                      connection                     connection           session
	                                                                      parameter                      parameter            parameter

Session-level parameters can also be set by using the SQL command "ALTER SESSION"
(https://docs.snowflake.com/en/sql-reference/sql/alter-session.html).

Alternatively, use OpenWithConfig() function to create a database handle with the specified Config.

# Connection Config
You can also connect to your warehouse using the connection config. The dbSql library states that when you want to take advantage of driver-specific connection features that aren’t
available in a connection string. Each driver supports its own set of connection properties, often providing ways to customize the connection request specific to the DBMS
For example:

	c := &gosnowflake.Config{
		~your credentials go here~
	}
	connector := gosnowflake.NewConnector(gosnowflake.SnowflakeDriver{}, *c)
	db := sql.OpenDB(connector)

If you are using this method, you dont need to pass a driver name to specify the driver type in which
you are looking to connect. Since the driver name is not needed, you can optionally bypass driver registration
on startup. To do this, set `GOSNOWFLAKE_SKIP_REGISTERATION` in your environment. This is useful you wish to
register multiple verions of the driver.

Note: GOSNOWFLAKE_SKIP_REGISTERATION should not be used if sql.Open() is used as the method
to connect to the server, as sql.Open will require registration so it can map the driver name
to the driver type, which in this case is "snowflake" and SnowflakeDriver{}.

You can load the connnection configuration with .toml file format.
With two environment variables SNOWFLAKE_HOME(connections.toml file directory) SNOWFLAKE_DEFAULT_CONNECTION_NAME(DSN name),
the driver will search the config file and load the connection. You can find how to use this connection way at ./cmd/tomlfileconnection
or Snowflake doc: https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/connecting/specify-credentials

# Proxy

The Go Snowflake Driver honors the environment variables HTTP_PROXY, HTTPS_PROXY and NO_PROXY for the forward proxy setting.

NO_PROXY specifies which hostname endings should be allowed to bypass the proxy server, e.g. no_proxy=.amazonaws.com means that Amazon S3 access does not need to go through the proxy.

NO_PROXY does not support wildcards. Each value specified should be one of the following:

  - The end of a hostname (or a complete hostname), for example: ".amazonaws.com" or "xy12345.snowflakecomputing.com".

  - An IP address, for example "192.196.1.15".

If more than one value is specified, values should be separated by commas, for example:

	no_proxy=localhost,.my_company.com,xy12345.snowflakecomputing.com,192.168.1.15,192.168.1.16

# Logging

By default, the driver's builtin logger is exposing logrus's FieldLogger and default at INFO level.
Users can use SetLogger in driver.go to set a customized logger for gosnowflake package.

In order to enable debug logging for the driver, user could use SetLogLevel("debug") in SFLogger interface
as shown in demo code at cmd/logger.go. To redirect the logs SFlogger.SetOutput method could do the work.

# Query tag

A custom query tag can be set in the context. Each query run with this context
will include the custom query tag as metadata that will appear in the Query Tag
column in the Query History log. For example:

	queryTag := "my custom query tag"
	ctxWithQueryTag := WithQueryTag(ctx, queryTag)
	rows, err := db.QueryContext(ctxWithQueryTag, query)

# Query request ID

A specific query request ID can be set in the context and will be passed through
in place of the default randomized request ID. For example:

	requestID := ParseUUID("6ba7b812-9dad-11d1-80b4-00c04fd430c8")
	ctxWithID := WithRequestID(ctx, requestID)
	rows, err := db.QueryContext(ctxWithID, query)

# Last query ID

If you need query ID for your query you have to use raw connection.

For queries:
```

	err := conn.Raw(func(x any) error {
		stmt, err := x.(driver.ConnPrepareContext).PrepareContext(ctx, "SELECT 1")
		rows, err := stmt.(driver.StmtQueryContext).QueryContext(ctx, nil)
		rows.(SnowflakeRows).GetQueryID()
		stmt.(SnowflakeStmt).GetQueryID()
		return nil
	}

```

For execs:
```

	err := conn.Raw(func(x any) error {
		stmt, err := x.(driver.ConnPrepareContext).PrepareContext(ctx, "INSERT INTO TestStatementQueryIdForExecs VALUES (1)")
		result, err := stmt.(driver.StmtExecContext).ExecContext(ctx, nil)
		result.(SnowflakeResult).GetQueryID()
		stmt.(SnowflakeStmt).GetQueryID()
		return nil
	}

```

# Fetch Results by Query ID

The result of your query can be retrieved by setting the query ID in the WithFetchResultByID context.
```

	// Get the query ID using raw connection as mentioned above:
	err := conn.Raw(func(x any) error {
		rows1, err = x.(driver.QueryerContext).QueryContext(ctx, "SELECT 1", nil)
		queryID = rows1.(sf.SnowflakeRows).GetQueryID()
		return nil
	}

	// Update the Context object to specify the query ID
	fetchResultByIDCtx = sf.WithFetchResultByID(ctx, queryID)

	// Execute an empty string query
	rows2, err := db.QueryContext(fetchResultByIDCtx, "")

	// Retrieve the results as usual
	for rows2.Next()  {
		err = rows2.Scan(...)
		...
	}

```

# Canceling Query by CtrlC

From 0.5.0, a signal handling responsibility has moved to the applications. If you want to cancel a
query/command by Ctrl+C, add a os.Interrupt trap in context to execute methods that can take the context parameter
(e.g. QueryContext, ExecContext).

	// handle interrupt signal
	ctx, cancel := context.WithCancel(context.Background())
	c := make(chan os.Signal, 1)
	signal.Notify(c, os.Interrupt)
	defer func() {
		signal.Stop(c)
	}()
	go func() {
		select {
		case <-c:
			cancel()
		case <-ctx.Done():
		}
	}()
	... (connection)
	// execute a query
	rows, err := db.QueryContext(ctx, query)
	... (Ctrl+C to cancel the query)

See cmd/selectmany.go for the full example.

# Supported Data Types

The Go Snowflake Driver now supports the Arrow data format for data transfers
between Snowflake and the Golang client. The Arrow data format avoids extra
conversions between binary and textual representations of the data. The Arrow
data format can improve performance and reduce memory consumption in clients.

Snowflake continues to support the JSON data format.

The data format is controlled by the session-level parameter
GO_QUERY_RESULT_FORMAT. To use JSON format, execute:

	ALTER SESSION SET GO_QUERY_RESULT_FORMAT = 'JSON';

The valid values for the parameter are:

  - ARROW (default)
  - JSON

If the user attempts to set the parameter to an invalid value, an error is
returned.

The parameter name and the parameter value are case-insensitive.

This parameter can be set only at the session level.

Usage notes:

  - The Arrow data format reduces rounding errors in floating point numbers. You might see slightly
    different values for floating point numbers when using Arrow format than when using JSON format.
    In order to take advantage of the increased precision, you must pass in the context.Context object
    provided by the WithHigherPrecision function when querying.

  - Traditionally, the rows.Scan() method returned a string when a variable of types interface was passed
    in. Turning on the flag ENABLE_HIGHER_PRECISION via WithHigherPrecision will return the natural,
    expected data type as well.

  - For some numeric data types, the driver can retrieve larger values when using the Arrow format than
    when using the JSON format. For example, using Arrow format allows the full range of SQL NUMERIC(38,0)
    values to be retrieved, while using JSON format allows only values in the range supported by the
    Golang int64 data type.

    Users should ensure that Golang variables are declared using the appropriate data type for the full
    range of values contained in the column. For an example, see below.

When using the Arrow format, the driver supports more Golang data types and
more ways to convert SQL values to those Golang data types. The table below
lists the supported Snowflake SQL data types and the corresponding Golang
data types. The columns are:

 1. The SQL data type.

 2. The default Golang data type that is returned when you use snowflakeRows.Scan() to read data from
    Arrow data format via an interface{}.

 3. The possible Golang data types that can be returned when you use snowflakeRows.Scan() to read data
    from Arrow data format directly.

 4. The default Golang data type that is returned when you use snowflakeRows.Scan() to read data from
    JSON data format via an interface{}. (All returned values are strings.)

 5. The standard Golang data type that is returned when you use snowflakeRows.Scan() to read data from
    JSON data format directly.

    Go Data Types for Scan()
    ===================================================================================================================
    |                    ARROW                    |                    JSON
    ===================================================================================================================
    SQL Data Type          | Default Go Data Type   | Supported Go Data  | Default Go Data Type   | Supported Go Data
    | for Scan() interface{} | Types for Scan()   | for Scan() interface{} | Types for Scan()
    ===================================================================================================================
    BOOLEAN              | bool                                        | string                 | bool
    -------------------------------------------------------------------------------------------------------------------
    VARCHAR              | string                                      | string
    -------------------------------------------------------------------------------------------------------------------
    DOUBLE               | float32, float64                  [1] , [2] | string                 | float32, float64
    -------------------------------------------------------------------------------------------------------------------
    INTEGER that         | int, int8, int16, int32, int64              | string                 | int, int8, int16,
    fits in int64        |                                   [1] , [2] |                        | int32, int64
    -------------------------------------------------------------------------------------------------------------------
    INTEGER that doesn't | int, int8, int16, int32, int64,  *big.Int   | string                 | error
    fit in int64         |                       [1] , [2] , [3] , [4] |
    -------------------------------------------------------------------------------------------------------------------
    NUMBER(P, S)         | float32, float64,  *big.Float               | string                 | float32, float64
    where S > 0          |                       [1] , [2] , [3] , [5] |
    -------------------------------------------------------------------------------------------------------------------
    DATE                 | time.Time                                   | string                 | time.Time
    -------------------------------------------------------------------------------------------------------------------
    TIME                 | time.Time                                   | string                 | time.Time
    -------------------------------------------------------------------------------------------------------------------
    TIMESTAMP_LTZ        | time.Time                                   | string                 | time.Time
    -------------------------------------------------------------------------------------------------------------------
    TIMESTAMP_NTZ        | time.Time                                   | string                 | time.Time
    -------------------------------------------------------------------------------------------------------------------
    TIMESTAMP_TZ         | time.Time                                   | string                 | time.Time
    -------------------------------------------------------------------------------------------------------------------
    BINARY               | []byte                                      | string                 | []byte
    -------------------------------------------------------------------------------------------------------------------
    ARRAY [6]            | string / array                              | string / array
    -------------------------------------------------------------------------------------------------------------------
    OBJECT [6]           | string / struct                             | string / struct
    -------------------------------------------------------------------------------------------------------------------
    VARIANT              | string                                      | string
    -------------------------------------------------------------------------------------------------------------------
    MAP                  | map                                         | map

    [1] Converting from a higher precision data type to a lower precision data type via the snowflakeRows.Scan()
    method can lose low bits (lose precision), lose high bits (completely change the value), or result in error.

    [2] Attempting to convert from a higher precision data type to a lower precision data type via interface{}
    causes an error.

    [3] Higher precision data types like *big.Int and *big.Float can be accessed by querying with a context
    returned by WithHigherPrecision().

    [4] You cannot directly Scan() into the alternative data types via snowflakeRows.Scan(), but can convert to
    those data types by using .Int64()/.String()/.Uint64() methods. For an example, see below.

    [5] You cannot directly Scan() into the alternative data types via snowflakeRows.Scan(), but can convert to
    those data types by using .Float32()/.String()/.Float64() methods. For an example, see below.

    [6] Arrays and objects can be either semistructured or structured, see more info in section below.

Note: SQL NULL values are converted to Golang nil values, and vice-versa.

# Semistructured and structured types

Snowflake supports two flavours of "structured data" - semistructured and structured.
Semistructured types are variants, objects and arrays without schema.
When data is fetched, it's represented as strings and the client is responsible for its interpretation.
Example table definition:

	CREATE TABLE semistructured (v VARIANT, o OBJECT, a ARRAY)

The data not have any corresponding schema, so values in table may be slightly different.

Semistuctured variants, objects and arrays are always represented as strings for scanning:

	rows, err := db.Query("SELECT {'a': 'b'}::OBJECT")
	// handle error
	defer rows.Close()
	rows.Next()
	var v string
	err := rows.Scan(&v)

When inserting, a marker indicating correct type must be used, for example:

	db.Exec("CREATE TABLE test_object_binding (obj OBJECT)")
	db.Exec("INSERT INTO test_object_binding SELECT (?)", DataTypeObject, "{'s': 'some string'}")

Structured types differentiate from semistructured types by having specific schema.
In all rows of the table, values must conform to this schema.
Example table definition:

	CREATE TABLE structured (o OBJECT(s VARCHAR, i INTEGER), a ARRAY(INTEGER), m MAP(VARCHAR, BOOLEAN))

To retrieve structured objects, follow these steps:

1. Create a struct implementing sql.Scanner interface, example:

a)

	type simpleObject struct {
		s string
		i int32
	}

	func (so *simpleObject) Scan(val any) error {
		st := val.(StructuredObject)
		var err error
		if so.s, err = st.GetString("s"); err != nil {
			return err
		}
		if so.i, err = st.GetInt32("i"); err != nil {
			return err
		}
		return nil
	}

b)

	type simpleObject struct {
		S string `sf:"otherName"`
		I int32 `sf:"i,ignore"`
	}

	func (so *simpleObject) Scan(val any) error {
		st := val.(StructuredObject)
		return st.ScanTo(so)
	}

Automatic scan goes through all fields in a struct and read object fields.
Struct fields have to be public.
Embedded structs have to be pointers.
Matching name is built using struct field name with first letter lowercase.
Additionally, `sf` tag can be added:
- first value is always a name of a field in an SQL object
- additionally `ignore` parameter can be passed to omit this field

2. Use WithStructuredTypesEnabled context while querying data.
3. Use it in regular scan:

	var res simpleObject
	err := rows.Scan(&res)

See StructuredObject for all available operations including null support, embedding nested structs, etc.

Retrieving array of simple types works exactly the same like normal values - using Scan function.

You can use WithMapValuesNullable and WithArrayValuesNullable contexts to handle null values in, respectively, maps
and arrays of simple types in the database. In that case, sql null types will be used:

	ctx := WithArrayValuesNullable(WithStructuredTypesEnabled(context.Background))
	...
	var res []sql.NullBool
	err := rows.Scan(&res)

If you want to scan array of structs, you have to use a helper function ScanArrayOfScanners:

	var res []*simpleObject
	err := rows.Scan(ScanArrayOfScanners(&res))

Retrieving structured maps is very similar to retrieving arrays:

	var res map[string]*simpleObject
	err := rows.Scan(ScanMapOfScanners(&res))

To bind structured objects use:

1. Create a type which implements a StructuredObjectWriter interface, example:

a)

	type simpleObject struct {
		s string
		i int32
	}

	func (so *simpleObject) Write(sowc StructuredObjectWriterContext) error {
		if err := sowc.WriteString("s", so.s); err != nil {
			return err
		}
		if err := sowc.WriteInt32("i", so.i); err != nil {
			return err
		}
		return nil
	}

b)

	type simpleObject struct {
		S string `sf:"otherName"`
		I int32 `sf:"i,ignore"`
	}

	func (so *simpleObject) Write(sowc StructuredObjectWriterContext) error {
		return sowc.WriteAll(so)
	}

2. Use an instance as regular bind.
3. If you need to bind nil value, use special syntax:

	db.Exec('INSERT INTO some_table VALUES ?', sf.DataTypeNilObject, reflect.TypeOf(simpleObject{})

Binding structured arrays are like any other parameter.
The only difference is - if you want to insert empty array (not nil but empty), you have to use:

	db.Exec('INSERT INTO some_table VALUES ?', sf.DataTypeEmptyArray, reflect.TypeOf(simpleObject{}))

# Using higher precision numbers

The following example shows how to retrieve very large values using the math/big
package. This example retrieves a large INTEGER value to an interface and then
extracts a big.Int value from that interface. If the value fits into an int64,
then the code also copies the value to a variable of type int64. Note that a
context that enables higher precision must be passed in with the query.

	import "context"
	import "math/big"

	...

	var my_interface interface{}
	var my_big_int_pointer *big.Int
	var my_int64 int64
	var rows snowflakeRows

	...
	rows = db.QueryContext(WithHigherPrecision(context.Background), <query>)
	rows.Scan(&my_interface)
	my_big_int_pointer, ok = my_interface.(*big.Int)
	if my_big_int_pointer.IsInt64() {
	    my_int64 = my_big_int_pointer.Int64()
	}

If the variable named "rows" is known to contain a big.Int, then you can use the following instead of scanning into an interface
and then converting to a big.Int:

	rows.Scan(&my_big_int_pointer)

If the variable named "rows" contains a big.Int, then each of the following fails:

	rows.Scan(&my_int64)

	my_int64, _ = my_interface.(int64)

Similar code and rules also apply to big.Float values.

If you are not sure what data type will be returned, you can use code similar to the following to check the data type
of the returned value:

	// Create variables into which you can scan the returned values.
	var i64 int64
	var bigIntPtr *big.Int

	for rows.Next() {
	    // Get the data type info.
	    column_types, err := rows.ColumnTypes()
	    if err != nil {
	        log.Fatalf("ERROR: ColumnTypes() failed. err: %v", err)
	    }
	    // The data type of the zeroeth column in the row.
	    column_type := column_types[0].ScanType()
	    // Choose the appropriate variable based on the data type.
	    switch column_type {
	        case reflect.TypeOf(i64):
	            err = rows.Scan(&i64)
	            fmt.Println("INFO: retrieved int64 value:")
	            fmt.Println(i64)
	        case reflect.TypeOf(bigIntPtr):
	            err = rows.Scan(&bigIntPtr)
	            fmt.Println("INFO: retrieved bigIntPtr value:")
	            fmt.Println(bigIntPtr)
	    }
	}

# Arrow batches

You can retrieve data in a columnar format similar to the format a server returns, without transposing them to rows.
When working with the arrow columnar format in go driver, ArrowBatch structs are used. These are structs
mostly corresponding to data chunks received from the backend. They allow for access to specific arrow.Record structs.

An ArrowBatch can exist in a state where the underlying data has not yet been loaded. The data is downloaded and
translated only on demand. Translation options are retrieved from a context.Context interface, which is either
passed from query context or set by the user using WithContext(ctx) method.

In order to access them you must use `WithArrowBatches` context, similar to the following:

	    var rows driver.Rows
		err = conn.Raw(func(x interface{}) error {
			rows, err = x.(driver.QueryerContext).QueryContext(ctx, query, nil)
			return err
		})

		...

		batches, err := rows.(sf.SnowflakeRows).GetArrowBatches()

		... // use Arrow records

This returns []*ArrowBatch.

ArrowBatch functions:

GetRowCount():
Returns the number of rows in the ArrowBatch. Note that this returns 0 if the data has not yet been loaded,
irrespective of it’s actual size.

WithContext(ctx context.Context):
Sets the context of the ArrowBatch to the one provided. Note that the context will not retroactively apply to data
that has already been downloaded. For example:

	records1, _ := batch.Fetch()
	records2, _ := batch.WithContext(ctx).Fetch()

will produce the same result in records1 and records2, irrespective of the newly provided ctx. Context worth noting are:
-WithArrowBatchesTimestampOption
-WithHigherPrecision
-WithArrowBatchesUtf8Validation
described in more detail later.

Fetch():
Returns the underlying records as *[]arrow.Record. When this function is called, the ArrowBatch checks whether
the underlying data has already been loaded, and downloads it if not.

Limitations:

 1. For some queries Snowflake may decide to return data in JSON format (examples: `SHOW PARAMETERS` or `ls @stage`). You cannot use JSON with Arrow batches context.
 2. Snowflake handles timestamps in a range which is broader than available space in Arrow timestamp type. Because of that special treatment should be used (see below).
 3. When using numbers, Snowflake chooses the smallest type that covers all values in a batch. So even when your column is NUMBER(38, 0), if all values are 8bits, array.Int8 is used.

How to handle timestamps in Arrow batches:

Snowflake returns timestamps natively (from backend to driver) in multiple formats.
The Arrow timestamp is an 8-byte data type, which is insufficient to handle the larger date and time ranges used by Snowflake.
Also, Snowflake supports 0-9 (nanosecond) digit precision for seconds, while Arrow supports only 3 (millisecond), 6 (microsecond), an 9 (nanosecond) precision.
Consequently, Snowflake uses a custom timestamp format in Arrow, which differs on timestamp type and precision.

If you want to use timestamps in Arrow batches, you have two options:

 1. The Go driver can reduce timestamp struct into simple Arrow Timestamp, if you set `WithArrowBatchesTimestampOption` to nanosecond, microsecond, millisecond or second.
    For nanosecond, some timestamp values might not fit into Arrow timestamp. E.g after year 2262 or before 1677.
 2. You can use native Snowflake values. In that case you will receive complex structs as described above. To transform Snowflake values into the Golang time.Time struct you can use `ArrowSnowflakeTimestampToTime`.
    To enable this feature, you must use `WithArrowBatchesTimestampOption` context with value set to`UseOriginalTimestamp`.

How to handle invalid UTF-8 characters in Arrow batches:

Snowflake previously allowed users to upload data with invalid UTF-8 characters. Consequently, Arrow records containing string columns in Snowflake could include these invalid UTF-8 characters.
However, according to the Arrow specifications (https://arrow.apache.org/docs/cpp/api/datatype.html
and https://github.com/apache/arrow/blob/a03d957b5b8d0425f9d5b6c98b6ee1efa56a1248/go/arrow/datatype.go#L73-L74),
Arrow string columns should only contain UTF-8 characters.

To address this issue and prevent potential downstream disruptions, the context WithArrowBatchesUtf8Validation, is introduced.
When enabled, this feature iterates through all values in string columns, identifying and replacing any invalid characters with `�`.
This ensures that Arrow records conform to the UTF-8 standards, preventing validation failures in downstream services like the Rust Arrow library that impose strict validation checks.

How to handle higher precision in Arrow batches:

To preserve BigDecimal values within Arrow batches, use WithHigherPrecision.
This offers two main benefits: it helps avoid precision loss and defers the conversion to upstream services.
Alternatively, without this setting, all non-zero scale numbers will be converted to float64, potentially resulting in loss of precision.
Zero-scale numbers (DECIMAL256, DECIMAL128) will be converted to int64, which could lead to overflow.

# Binding Parameters

Binding allows a SQL statement to use a value that is stored in a Golang variable.

Without binding, a SQL statement specifies values by specifying literals inside the statement.
For example, the following statement uses the literal value “42“ in an UPDATE statement:

	_, err = db.Exec("UPDATE table1 SET integer_column = 42 WHERE ID = 1000")

With binding, you can execute a SQL statement that uses a value that is inside a variable. For example:

	var my_integer_variable int = 42
	_, err = db.Exec("UPDATE table1 SET integer_column = ? WHERE ID = 1000", my_integer_variable)

The “?“ inside the “VALUES“ clause specifies that the SQL statement uses the value from a variable.

Binding data that involves time zones can require special handling. For details, see the section
titled "Timestamps with Time Zones".

Version 1.6.23 (and later) of the driver takes advantage of sql.Null types which enables the proper handling of null parameters inside function calls, i.e.:

	rows, err := db.Query("SELECT * FROM TABLE(SOMEFUNCTION(?))", sql.NullBool{})

The timestamp nullability had to be achieved by wrapping the sql.NullTime type as the Snowflake provides several date and time types
which are mapped to single Go time.Time type:

	rows, err := db.Query("SELECT * FROM TABLE(SOMEFUNCTION(?))", sf.TypedNullTime{sql.NullTime{}, sf.TimestampLTZType})

# Binding Parameters to Array Variables

Version 1.3.9 (and later) of the Go Snowflake Driver supports the ability to bind an array variable to a parameter in a SQL
INSERT statement. You can use this technique to insert multiple rows in a single batch.

As an example, the following code inserts rows into a table that contains integer, float, boolean, and string columns. The example
binds arrays to the parameters in the INSERT statement.

	// Create a table containing an integer, float, boolean, and string column.
	_, err = db.Exec("create or replace table my_table(c1 int, c2 float, c3 boolean, c4 string)")
	...
	// Define the arrays containing the data to insert.
	intArray := []int{1, 2, 3}
	fltArray := []float64{0.1, 2.34, 5.678}
	boolArray := []bool{true, false, true}
	strArray := []string{"test1", "test2", "test3"}
	...
	// Insert the data from the arrays and wrap in an Array() function into the table.
	_, err = db.Exec("insert into my_table values (?, ?, ?, ?)", Array(&intArray), Array(&fltArray), Array(&boolArray), Array(&strArray))

If the array contains SQL NULL values, use slice []interface{}, which allows Golang nil values.
This feature is available in version 1.6.12 (and later) of the driver. For example,

	 	// Define the arrays containing the data to insert.
	 	strArray := make([]interface{}, 3)
		strArray[0] = "test1"
		strArray[1] = "test2"
		strArray[2] = nil // This line is optional as nil is the default value.
		...
		// Create a table and insert the data from the array as shown above.
		_, err = db.Exec("create or replace table my_table(c1 string)")
		_, err = db.Exec("insert into my_table values (?)", Array(&strArray))
		...
		// Use sql.NullString to fetch the string column that contains NULL values.
		var s sql.NullString
		rows, _ := db.Query("select * from my_table")
		for rows.Next() {
			err := rows.Scan(&s)
			if err != nil {
				log.Fatalf("Failed to scan. err: %v", err)
			}
			if s.Valid {
				fmt.Println("Retrieved value:", s.String)
			} else {
				fmt.Println("Retrieved value: NULL")
			}
		}

For slices []interface{} containing time.Time values, a binding parameter flag is required for the preceding array variable in the Array() function.
This feature is available in version 1.6.13 (and later) of the driver. For example,

	_, err = db.Exec("create or replace table my_table(c1 timestamp_ntz, c2 timestamp_ltz)")
	_, err = db.Exec("insert into my_table values (?,?)", Array(&ntzArray, sf.TimestampNTZType), Array(&ltzArray, sf.TimestampLTZType))

Note: For alternative ways to load data into the Snowflake database (including bulk loading using the COPY command), see
Loading Data into Snowflake (https://docs.snowflake.com/en/user-guide-data-load.html).

# Batch Inserts and Binding Parameters

When you use array binding to insert a large number of values, the driver can
improve performance by streaming the data (without creating files on the local
machine) to a temporary stage for ingestion. The driver automatically does this
when the number of values exceeds a threshold (no changes are needed to user code).

In order for the driver to send the data to a temporary stage, the user must have the following privilege on the schema:

	CREATE STAGE

If the user does not have this privilege, the driver falls back to sending the data with the query to the Snowflake database.

In addition, the current database and schema for the session must be set. If these are not set,
the CREATE TEMPORARY STAGE command executed by the driver can fail with the following error:

	CREATE TEMPORARY STAGE SYSTEM$BIND file_format=(type=csv field_optionally_enclosed_by='"')
	Cannot perform CREATE STAGE. This session does not have a current schema. Call 'USE SCHEMA', or use a qualified name.

For alternative ways to load data into the Snowflake database (including bulk loading using the COPY command),
see Loading Data into Snowflake (https://docs.snowflake.com/en/user-guide-data-load.html).

# Binding a Parameter to a Time Type

Go's database/sql package supports the ability to bind a parameter in a SQL statement to a time.Time variable.
However, when the client binds data to send to the server, the driver cannot determine the correct Snowflake date/timestamp data
type to associate with the binding parameter. For example:

	dbt.mustExec("CREATE OR REPLACE TABLE tztest (id int, ntz, timestamp_ntz, ltz timestamp_ltz)")
	// ...
	stmt, err :=dbt.db.Prepare("INSERT INTO tztest(id,ntz,ltz) VALUES(1, ?, ?)")
	// ...
	tmValue time.Now()
	// ... Is tmValue a TIMESTAMP_NTZ or TIMESTAMP_LTZ?
	_, err = stmt.Exec(tmValue, tmValue)

To resolve this issue, a binding parameter flag is introduced that associates
any subsequent time.Time type to the DATE, TIME, TIMESTAMP_LTZ, TIMESTAMP_NTZ
or BINARY data type. The above example could be rewritten as follows:

	import (
		sf "github.com/snowflakedb/gosnowflake"
	)
	dbt.mustExec("CREATE OR REPLACE TABLE tztest (id int, ntz, timestamp_ntz, ltz timestamp_ltz)")
	// ...
	stmt, err :=dbt.db.Prepare("INSERT INTO tztest(id,ntz,ltz) VALUES(1, ?, ?)")
	// ...
	tmValue time.Now()
	// ...
	_, err = stmt.Exec(sf.DataTypeTimestampNtz, tmValue, sf.DataTypeTimestampLtz, tmValue)

# Timestamps with Time Zones

The driver fetches TIMESTAMP_TZ (timestamp with time zone) data using the
offset-based Location types, which represent a collection of time offsets in
use in a geographical area, such as CET (Central European Time) or UTC
(Coordinated Universal Time). The offset-based Location data is generated and
cached when a Go Snowflake Driver application starts, and if the given offset
is not in the cache, it is generated dynamically.

Currently, Snowflake does not support the name-based Location types (e.g. "America/Los_Angeles").

For more information about Location types, see the Go documentation for https://golang.org/pkg/time/#Location.

# Binary Data

Internally, this feature leverages the []byte data type. As a result, BINARY
data cannot be bound without the binding parameter flag. In the following
example, sf is an alias for the gosnowflake package:

	var b = []byte{0x01, 0x02, 0x03}
	_, err = stmt.Exec(sf.DataTypeBinary, b)

# Maximum Number of Result Set Chunk Downloader

The driver directly downloads a result set from the cloud storage if the size is large. It is
required to shift workloads from the Snowflake database to the clients for scale. The download takes place by goroutine
named "Chunk Downloader" asynchronously so that the driver can fetch the next result set while the application can
consume the current result set.

The application may change the number of result set chunk downloader if required. Note this does not help reduce
memory footprint by itself. Consider Custom JSON Decoder.

	import (
		sf "github.com/snowflakedb/gosnowflake"
	)
	sf.MaxChunkDownloadWorkers = 2

Custom JSON Decoder for Parsing Result Set (Experimental)

The application may have the driver use a custom JSON decoder that incrementally parses the result set as follows.

	import (
		sf "github.com/snowflakedb/gosnowflake"
	)
	sf.CustomJSONDecoderEnabled = true
	...

This option will reduce the memory footprint to half or even quarter, but it can significantly degrade the
performance depending on the environment. The test cases running on Travis Ubuntu box show five times less memory
footprint while four times slower. Be cautious when using the option.

# JWT authentication

The Go Snowflake Driver supports JWT (JSON Web Token) authentication.

To enable this feature, construct the DSN with fields "authenticator=SNOWFLAKE_JWT&privateKey=<your_private_key>",
or using a Config structure specifying:

	config := &Config{
		...
		Authenticator: AuthTypeJwt,
		PrivateKey:   "<your_private_key_struct in *rsa.PrivateKey type>",
	}

The <your_private_key> should be a base64 URL encoded PKCS8 rsa private key string. One way to encode a byte slice to URL
base 64 URL format is through the base64.URLEncoding.EncodeToString() function.

On the server side, you can alter the public key with the SQL command:

	ALTER USER <your_user_name> SET RSA_PUBLIC_KEY='<your_public_key>';

The <your_public_key> should be a base64 Standard encoded PKI public key string. One way to encode a byte slice to base
64 Standard format is through the base64.StdEncoding.EncodeToString() function.

To generate the valid key pair, you can execute the following commands in the shell:

		# generate 2048-bit pkcs8 encoded RSA private key
		openssl genpkey -algorithm RSA \
	    	-pkeyopt rsa_keygen_bits:2048 \
	    	-pkeyopt rsa_keygen_pubexp:65537 | \
	  		openssl pkcs8 -topk8 -outform der > rsa-2048-private-key.p8

		# extract 2048-bit PKI encoded RSA public key from the private key
		openssl pkey -pubout -inform der -outform der \
	    	-in rsa-2048-private-key.p8 \
	    	-out rsa-2048-public-key.spki

Note: As of February 2020, Golang's official library does not support passcode-encrypted PKCS8 private key.
For security purposes, Snowflake highly recommends that you store the passcode-encrypted private key on the disk and
decrypt the key in your application using a library you trust.

JWT tokens are recreated on each retry and they are valid (`exp` claim) for `jwtTimeout` seconds.
Each retry timeout is configured by `jwtClientTimeout`.
Retries are limited by total time of `loginTimeout`.

# External browser authentication

The driver allows to authenticate using the external browser.

When a connection is created, the driver will open the browser window and ask the user to sign in.

To enable this feature, construct the DSN with field "authenticator=EXTERNALBROWSER" or using a Config structure with
following Authenticator specified:

	config := &Config{
		...
		Authenticator: AuthTypeExternalBrowser,
	}

The external browser authentication implements timeout mechanism. This prevents the driver from hanging interminably when
browser window was closed, or not responding.

Timeout defaults to 120s and can be changed through setting DSN field "externalBrowserTimeout=240" (time in seconds)
or using a Config structure with following ExternalBrowserTimeout specified:

	config := &Config{
		ExternalBrowserTimeout: 240 * time.Second, // Requires time.Duration
	}

# Executing Multiple Statements in One Call

This feature is available in version 1.3.8 or later of the driver.

By default, Snowflake returns an error for queries issued with multiple statements.
This restriction helps protect against SQL Injection attacks (https://en.wikipedia.org/wiki/SQL_injection).

The multi-statement feature allows users skip this restriction and execute multiple SQL statements through a
single Golang function call. However, this opens up the possibility for SQL injection, so it should be used carefully.
The risk can be reduced by specifying the exact number of statements to be executed, which makes it more difficult to
inject a statement by appending it. More details are below.

The Go Snowflake Driver provides two functions that can execute multiple SQL statements in a single call:

  - db.QueryContext(): This function is used to execute queries, such as SELECT statements, that return a result set.
  - db.ExecContext(): This function is used to execute statements that don't return a result set (i.e. most DML and DDL statements).

To compose a multi-statement query, simply create a string that contains all the queries, separated by semicolons,
in the order in which the statements should be executed.

To protect against SQL Injection attacks while using the multi-statement feature, pass a Context that specifies
the number of statements in the string. For example:

	import (
		"context"
		"database/sql"
	)

	var multi_statement_query = "SELECT c1 FROM t1; SELECT c2 FROM t2"
	var number_of_statements = 2
	blank_context = context.Background()
	multi_statement_context, _ := WithMultiStatement(blank_context, number_of_statements)
	rows, err := db.QueryContext(multi_statement_context, multi_statement_query)

When multiple queries are executed by a single call to QueryContext(), multiple result sets are returned. After
you process the first result set, get the next result set (for the next SQL statement) by calling NextResultSet().

The following pseudo-code shows how to process multiple result sets:

	Execute the statement and get the result set(s):

		rows, err := db.QueryContext(ctx, multiStmtQuery)

	Retrieve the rows in the first query's result set:

		while rows.Next() {
			err = rows.Scan(&variable_1)
			if err != nil {
				t.Errorf("failed to scan: %#v", err)
			}
			...
		}

	Retrieve the remaining result sets and the rows in them:

		while rows.NextResultSet()  {

			while rows.Next() {
				...
			}

		}

The function db.ExecContext() returns a single result, which is the sum of the number of rows changed by each
individual statement. For example, if your multi-statement query executed two UPDATE statements, each of which
updated 10 rows, then the result returned would be 20. Individual row counts for individual statements are not
available.

The following code shows how to retrieve the result of a multi-statement query executed through db.ExecContext():

	Execute the SQL statements:

	    res, err := db.ExecContext(ctx, multiStmtQuery)

	Get the summed result and store it in the variable named count:

	    count, err := res.RowsAffected()

Note: Because a multi-statement ExecContext() returns a single value, you cannot detect offsetting errors.
For example, suppose you expected the return value to be 20 because you expected each UPDATE statement to
update 10 rows. If one UPDATE statement updated 15 rows and the other UPDATE statement updated only 5
rows, the total would still be 20. You would see no indication that the UPDATES had not functioned as
expected.

The ExecContext() function does not return an error if passed a query (e.g. a SELECT statement). However, it
still returns only a single value, not a result set, so using it to execute queries (or a mix of queries and non-query
statements) is impractical.

The QueryContext() function does not return an error if passed non-query statements (e.g. DML). The function
returns a result set for each statement, whether or not the statement is a query. For each non-query statement, the
result set contains a single row that contains a single column; the value is the number of rows changed by the
statement.

If you want to execute a mix of query and non-query statements (e.g. a mix of SELECT and DML statements) in a
multi-statement query, use QueryContext(). You can retrieve the result sets for the queries,
and you can retrieve or ignore the row counts for the non-query statements.

Note: PUT statements are not supported for multi-statement queries.

If a SQL statement passed to ExecQuery() or QueryContext() fails to compile or execute, that statement is
aborted, and subsequent statements are not executed. Any statements prior to the aborted statement are unaffected.

For example, if the statements below are run as one multi-statement query, the multi-statement query fails on the
third statement, and an exception is thrown.

	CREATE OR REPLACE TABLE test(n int);
	INSERT INTO TEST VALUES (1), (2);
	INSERT INTO TEST VALUES ('not_an_integer');  -- execution fails here
	INSERT INTO TEST VALUES (3);

If you then query the contents of the table named "test", the values 1 and 2 would be present.

When using the QueryContext() and ExecContext() functions, golang code can check for errors the usual way. For
example:

	rows, err := db.QueryContext(ctx, multiStmtQuery)
	if err != nil {
		Fatalf("failed to query multiple statements: %v", err)
	}

Preparing statements and using bind variables are also not supported for multi-statement queries.

# Asynchronous Queries

The Go Snowflake Driver supports asynchronous execution of SQL statements.
Asynchronous execution allows you to start executing a statement and then
retrieve the result later without being blocked while waiting. While waiting
for the result of a SQL statement, you can perform other tasks, including
executing other SQL statements.

Most of the steps to execute an asynchronous query are the same as the
steps to execute a synchronous query. However, there is an additional step,
which is that you must call the WithAsyncMode() function to update
your Context object to specify that asynchronous mode is enabled.

In the code below, the call to "WithAsyncMode()" is specific
to asynchronous mode. The rest of the code is compatible with both
asynchronous mode and synchronous mode.

	...

	// Update your Context object to specify asynchronous mode:
	ctx := WithAsyncMode(context.Background())

	// Execute your query as usual by calling:
	rows, _ := db.QueryContext(ctx, query_string)

	// Retrieve the results as usual by calling:
	for rows.Next()  {
		err := rows.Scan(...)
		...
	}

The function db.QueryContext() returns an object of type snowflakeRows
regardless of whether the query is synchronous or asynchronous. However:

  - If the query is synchronous, then db.QueryContext() does not return until
    the query has finished and the result set has been loaded into the
    snowflakeRows object.
  - If the query is asynchronous, then db.QueryContext() returns a
    potentially incomplete snowflakeRows object that is filled in later
    in the background.

The call to the Next() function of snowflakeRows is always synchronous (i.e. blocking).
If the query has not yet completed and the snowflakeRows object (named "rows" in this
example) has not been filled in yet, then rows.Next() waits until the result set has been filled in.

More generally, calls to any Golang SQL API function implemented in snowflakeRows or
snowflakeResult are blocking calls, and wait if results are not yet available.
(Examples of other synchronous calls include: snowflakeRows.Err(), snowflakeRows.Columns(),
snowflakeRows.columnTypes(), snowflakeRows.Scan(), and snowflakeResult.RowsAffected().)

Because the example code above executes only one query and no other activity, there is
no significant difference in behavior between asynchronous and synchronous behavior.
The differences become significant if, for example, you want to perform some other
activity after the query starts and before it completes. The example code below starts
a query, which run in the background, and then retrieves the results later.

This example uses small SELECT statements that do not retrieve enough data to require
asynchronous handling. However, the technique works for larger data sets, and for
situations where the programmer might want to do other work after starting the queries
and before retrieving the results. For a more elaborative example please see cmd/async/async.go

		package gosnowflake

		import  (
			"context"
			"database/sql"
			"database/sql/driver"
			"fmt"
			"log"
			"os"
			sf "github.com/snowflakedb/gosnowflake"
	    )

		...

		func DemonstrateAsyncMode(db *sql.DB) {
			// Enable asynchronous mode
			ctx := sf.WithAsyncMode(context.Background())

			// Run the query with asynchronous context
			rows, err := db.QueryContext(ctx, "select 1")
			if err != nil {
				// handle error
			}

			// do something as the workflow continues whereas the query is computing in the background
			...

			// Get the data when you are ready to handle it
			var val int
			err = rows.Scan(&val)
			if err != nil {
				// handle error
			}

			...
		}

# Support For PUT and GET

The Go Snowflake Driver supports the PUT and GET commands.

The PUT command copies a file from a local computer (the computer where the
Golang client is running) to a stage on the cloud platform. The GET command
copies data files from a stage on the cloud platform to a local computer.

See the following for information on the syntax and supported parameters:

  - PUT: https://docs.snowflake.com/en/sql-reference/sql/put.html
  - GET: https://docs.snowflake.com/en/sql-reference/sql/get.html

Using PUT:

The following example shows how to run a PUT command by passing a string to the
db.Query() function:

	db.Query("PUT file://<local_file> <stage_identifier> <optional_parameters>")

"<local_file>" should include the file path as well as the name. Snowflake recommends
using an absolute path rather than a relative path. For example:

	db.Query("PUT file:///tmp/my_data_file @~ auto_compress=false overwrite=false")

Different client platforms (e.g. linux, Windows) have different path name
conventions. Ensure that you specify path names appropriately. This is
particularly important on Windows, which uses the backslash character as
both an escape character and as a separator in path names.

To send information from a stream (rather than a file) use code similar to the code below.
(The ReplaceAll() function is needed on Windows to handle backslashes in the path to the file.)

	fileStream, _ := os.Open(fname)
	defer func() {
		if fileStream != nil {
			fileStream.Close()
		}
	} ()

	sql := "put 'file://%v' @%%%v auto_compress=true parallel=30"
	sqlText := fmt.Sprintf(sql,
		strings.ReplaceAll(fname, "\\", "\\\\"),
		tableName)
	dbt.mustExecContext(WithFileStream(context.Background(), fileStream),
		sqlText)

Note: PUT statements are not supported for multi-statement queries.

Using GET:

The following example shows how to run a GET command by passing a string to the
db.Query() function:

	db.Query("GET <internal_stage_identifier> file://<local_file> <optional_parameters>")

"<local_file>" should include the file path as well as the name. Snowflake recommends using
an absolute path rather than a relative path. For example:

	db.Query("GET @~ file:///tmp/my_data_file auto_compress=false overwrite=false")

To download a file into an in-memory stream (rather than a file) use code similar to the code below.

	var streamBuf bytes.Buffer
	ctx := WithFileTransferOptions(context.Background(), &SnowflakeFileTransferOptions{GetFileToStream: true})
	ctx = WithFileGetStream(ctx, &streamBuf)

	sql := "get @~/data1.txt.gz file:///tmp/testData"
	dbt.mustExecContext(ctx, sql)
	// streamBuf is now filled with the stream. Use bytes.NewReader(streamBuf.Bytes()) to read uncompressed stream or
	// use gzip.NewReader(&streamBuf) for to read compressed stream.

Note: GET statements are not supported for multi-statement queries.

Specifying temporary directory for encryption and compression:

Putting and getting requires compression and/or encryption, which is done in the OS temporary directory.
If you cannot use default temporary directory for your OS or you want to specify it yourself, you can use "tmpDirPath" DSN parameter.
Remember, to encode slashes.
Example:

	u:p@a.r.c.snowflakecomputing.com/db/s?account=a.r.c&tmpDirPath=%2Fother%2Ftmp

Using custom configuration for PUT/GET:

If you want to override some default configuration options, you can use `WithFileTransferOptions` context.
There are multiple config parameters including progress bars or compression.
*/
package gosnowflake