Consuming results¶

Returning results inline¶

The most common way of consuming results is by passing a complete callback to connection.execute(). When the statement has finished executing and the result is ready to be consumed, the complete callback is invoked with the result rows returned inline:

connection.execute({
  sqlText: 'SELECT * FROM sometable',
  complete: function(err, stmt, rows) {
    if (err) {
      console.error('Failed to execute statement due to the following error: ' + err.message);
    } else {
      console.log('Number of rows produced: ' + rows.length);
    }
  }
});

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Streaming results¶

You can also consume a result as a stream of rows by setting the streamResult connection parameter to true in connection.execute when calling the statement.streamRows() method. Enabling this parameter causes the method to return a Node.js Readable stream, which you can use to consume rows as they are received.

Important

For any result set that might exceed Node’s default memory, Snowflake highly recommends that you set streamResult to true when streaming results. With the default value (false), the connector stores all of the rows in an array before streaming the results. With smaller result sets, this factor is not normally an issue. However, with larger result sets, storing all the results in memory can contribute to an OOM error.

Recent versions of the Snowflake Node.js driver (1.6.23 and later) implement backpressure functionality to ensure that, when consuming results, data is not pushed to the stream faster than data is read from the stream.

For example, the following code fragment consumes the results using the Readable event:

var connection = snowflake.createConnection({
    account: process.env.SFACCOUNT,
    username: process.env.SFUSER,
    // ...
    streamResult: true
});

// [..rest of the code..]

connection.execute({
  sqlText: "select L_COMMENT from SNOWFLAKE_SAMPLE_DATA.TPCH_SF100.LINEITEM limit 100000;",
  streamResult: true,
  complete: function (err, stmt)
  {
    var stream = stmt.streamRows();
    // Read data from the stream when it is available
    stream.on('readable', function (row)
    {
      while ((row = this.read()) !== null)
      {
        console.log(row);
      }
    }).on('end', function ()
    {
      console.log('done');
    }).on('error', function (err)
    {
      console.log(err);
    });
  }
});

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Batch processing results¶

By default, the statement.streamRows() method produces a stream that includes every row in the result. However, if you only want to consume a subset of the result, or if you want to consume result rows in batches, you can call streamRows() with start and end arguments. When these additional options are specified, only rows in the requested range are streamed:

connection.execute({
  sqlText: 'SELECT * FROM sometable',
  streamResult: true, // prevent rows from being returned inline in the complete callback
  complete: function(err, stmt, rows) {
    // no rows returned inline because streamResult was set to true
    console.log('rows: ' + rows); // 'rows: undefined'

    // only consume at most the last 5 rows in the result
    rows = [];
    stmt.streamRows({
      start: Math.max(0, stmt.getNumRows() - 5),
      end: stmt.getNumRows() - 1,
    })
    .on('error', function(err) {
      console.error('Unable to consume requested rows');
    })
    .on('data', function(row) {
      rows.push(row);
    })
    .on('end', function() {
      console.log('Number of rows consumed: ' + rows.length);
    });
  }
})

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Data type casting¶

When result rows are produced, the driver automatically maps SQL data types to their corresponding JavaScript equivalents. For example, values of type TIMESTAMP and DATE are returned as JavaScript Date objects.

For the full mapping of JavaScript to SQL data types, refer to the table below:

SQL Data Type

JavaScript Data Type

Notes

VARCHAR, CHAR, CHARACTER, STRING, TEXT

String

INT, INTEGER, BIGINT, SMALLINT

Number

This is the default mapping. Use the session parameter JS_TREAT_INTEGER_AS_BIGINT to map to JavaScript Bigint.

NUMBER(precision, scale), DECIMAL(p, s), NUMERIC(p, s) where scale = 0

Number

This is the default mapping. Use the session parameter JS_TREAT_INTEGER_AS_BIGINT to map to JavaScript Bigint.

NUMBER(precision, scale), DECIMAL(p, s), NUMERIC(p, s) where scale > 0

Number

FLOAT, FLOAT4, FLOAT8, DOUBLE, DOUBLE PRECISION, REAL

Number

TIMESTAMP, TIMESTAMP_LTZ, TIMESTAMP_NTZ, TIMESTAMP_TZ

Date

TIMESTAMP_NTZ values are returned in UTC.

DATE

Date

TIME

String

The TIME data type in SQL has no equivalent in JavaScript, so it is mapped to a JavaScript string.

BOOLEAN

Boolean

VARIANT, ARRAY, OBJECT

JSON

SQL Data Type	JavaScript Data Type	Notes
VARCHAR, CHAR, CHARACTER, STRING, TEXT	String
INT, INTEGER, BIGINT, SMALLINT	Number	This is the default mapping. Use the session parameter JS_TREAT_INTEGER_AS_BIGINT to map to JavaScript Bigint.
NUMBER(precision, scale), DECIMAL(p, s), NUMERIC(p, s) where `scale` = 0	Number	This is the default mapping. Use the session parameter JS_TREAT_INTEGER_AS_BIGINT to map to JavaScript Bigint.
NUMBER(precision, scale), DECIMAL(p, s), NUMERIC(p, s) where `scale` > 0	Number
FLOAT, FLOAT4, FLOAT8, DOUBLE, DOUBLE PRECISION, REAL	Number
TIMESTAMP, TIMESTAMP_LTZ, TIMESTAMP_NTZ, TIMESTAMP_TZ	Date	TIMESTAMP_NTZ values are returned in UTC.
DATE	Date
TIME	String	The TIME data type in SQL has no equivalent in JavaScript, so it is mapped to a JavaScript string.
BOOLEAN	Boolean
VARIANT, ARRAY, OBJECT	JSON

Fetching integer data types as Bigint¶

By default, Snowflake INTEGER columns (including BIGINT, NUMBER(p, 0), etc.) are converted to JavaScript’s Number data type. However, the largest legal Snowflake integer values are larger than the largest legal JavaScript Number values. To convert Snowflake INTEGER columns to JavaScript Bigint, which can store larger values than JavaScript Number, set the session parameter JS_TREAT_INTEGER_AS_BIGINT.

You can use the following methods to set these parameter values:

Use the ALTER SESSION statement, as shown below:

connection.execute( {
                    sqlText: 'ALTER SESSION SET JS_TREAT_INTEGER_AS_BIGINT = TRUE',
                    complete: function ...
                    }
                  );

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Specify the parameter in the connection configuration information:

var connection = snowflake.createConnection(
      {
      username: 'fakeusername',
      password: 'fakepassword',
      account: 'fakeaccountidentifier',
      jsTreatIntegerAsBigInt: true
      }
    );

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Fetching data types as strings¶

When calling connection.execute(), you can use the fetchAsString option to return the following data types as strings: Boolean, Number, Date, Buffer, and JSON.

You can use this option, for example, to return:

Formatted versions of values of type DATE and TIMESTAMP (or its variants).
String versions of numerical SQL types that can’t be converted to JavaScript numbers without loss in precision.

The following example uses fetchAsString to convert a high-precision Number value to a string.:

connection.execute({
  sqlText: 'SELECT 1.123456789123456789123456789 as "c1"',
  fetchAsString: ['Number'],
  complete: function(err, stmt, rows) {
    if (err) {
      console.error('Failed to execute statement due to the following error: ' + err.message);
    } else {
      console.log('c1: ' + rows[0].c1); // c1: 1.123456789123456789123456789
    }
  }
});

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Parsing XML data¶

Beginning with version 1.7.0 of the driver, you can use the following fast-xml-parser library configuration options to customize how the driver processes XML document attributes when querying columns with XML content. For more information about these supported options and how they affect XML data parsing, see xmlParserConfig options.

By default, the Node.js driver ignores XML element attributes when returning XML data from a query. For example, in the following XML content, the <piece> element includes an id attribute:

<exhibit name="Art Show">
  <piece id="000001">
    <name>Mona Lisa</name>
    <artist>Leonardo da Vinci</artist>
    <year>1503</year>
  </piece>
  <piece id="000002">
    <name>The Starry Night</name>
    <artist>Vincent van Gogh</artist>
    <year>1889</year>
  </piece>
</exhibit>

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By default, when the Node.js driver returns the result set, it ignores the id attribute and returns the following output. Notice the attribute names and values are not included.

{
  exhibit: {
    piece: [
      {
        "name": "Mona Lisa",
        "artist": "Leonardo da Vinci",
        "year": "1503",
      },
      {
        "name": "The Starry Night",
        "artist": "Vincent van Gogh",
        "year": "1889",
      }
    ]
  }
}

To set the fast-xml-parser options, create an xmlParserConfig element similar to the following example:

const snowflake = require('snowflake-sdk');
snowflake.configure({
  xmlParserConfig: {
    /*  Parameters that you can override
    *   ignoreAttributes - default true,
    *   attributeNamePrefix - default '@_',
    *   attributesGroupName - default unset,
    *   alwaysCreateTextNode - default false
    */
    ignoreAttributes: false, attributesGroupName: '@', attributeNamePrefix: ''
  }
});

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With these settings, the driver parses the XML data and produces the following:

{
  exhibit: {
    piece: [
      {
        "name": "Mona Lisa",
        "artist": "Leonardo da Vinci",
        "year": "1503",
        '@': { id: '000001' }
      },
      {
        "name": "The Starry Night",
        "artist": "Vincent van Gogh",
        "year": "1889",
        '@': { id: '000002' }
      }
    ],
    '@': { name: 'Art Show' }
  }

Returning result sets that contain duplicate column names¶

In version 1.8.0, the Snowflake Node.js Driver introduced a new rowMode configuration option that lets you specify how you want the driver to return result sets that contain duplicate column names.

Prior to version 1.8.0, the Snowflake Node.js Driver always returned the result set from a SELECT command as a JavaScript object. In cases where the result set contained duplicate column names and values, some elements could be omitted due to the way JavaScript objects handle duplicate names.

The rowMode option lets you specify how result set data is returned to avoid potential loss of information, including as:

array
object (default)
object_with_renamed_duplicated_columns

To illustrate, assume you submit the following query:

select *
from (select 'a' as key, 1 as foo, 3 as name) as table1
join (select 'a' as key, 2 as foo, 3 as name2) as table2 on table1.key = table2.key
join (select 'a' as key, 3 as foo) as table3 on table1.key = table3.key

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Based on the value of rowMode, the driver returns the result sets as follows:

object (or unset)
```
{KEY: 'a', FOO: 3, NAME: 3, NAME2: 3};
```
array
```
['a', 1, 3, 'a', 2, 3, 'a', 3];
```

object_with_renamed_duplicated_columns

{KEY: 'a', FOO: 1, NAME: 3, KEY_2: 'a', FOO_2: 2, NAME2: 3, KEY_3: 'a', FOO_3: 3};

You can set the rowMode parameter in the connection or statement configuration level, as shown below. If set in both places, the statement level value takes precedence.

Configuration level

snowflake.createConnection({
  account: account,
  username: username,
  ...
  rowMode: 'array'})

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Statement level

connection.execute({
  sqlText: sql,
  rowMode: 'array',
  ...
  )}

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Customizing how result sets process JSON and XML data¶

The Snowflake Node.js driver provides the following default parsers for processing JSON and XML data in result sets:

JSON: Returns the result from a new Function object.
XML: fast-xml-parser.

The default fast-xml-parser module supports a subset of features, as described in Parsing XML data.

If you prefer to use a custom parser, you can use the following examples to configure them:

Use the eval JSON parser, which the driver used prior to version 1.6.21:

const snowflake = require('snowflake-sdk');
snowflake.configure({
  jsonColumnVariantParser: rawColumnValue => JSON.parse(rawColumnValue)
})

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Use the fast-xml-parser parser, with the ability to customize all of its options (https://github.com/NaturalIntelligence/fast-xml-parser/blob/master/docs/v4/2.XMLparseOptions.md):

const snowflake = require('snowflake-sdk');
snowflake.configure({
  xmlColumnVariantParser: rawColumnValue => new (require("fast-xml-parser")).XMLParser().parse(rawColumnValue)
})

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Configure custom parsers for both in the same declaration:

const snowflake = require('snowflake-sdk');
snowflake.configure({
  jsonColumnVariantParser: rawColumnValue => JSON.parse(rawColumnValue),
  xmlColumnVariantParser: rawColumnValue => new (require("fast-xml-parser")).XMLParser().parse(rawColumnValue)
})

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