Class RedshiftDataSpec
- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<RedshiftDataSpec.Builder,
RedshiftDataSpec>
Describes the data specification of an Amazon Redshift DataSource
.
- See Also:
-
Nested Class Summary
-
Method Summary
Modifier and TypeMethodDescriptionstatic RedshiftDataSpec.Builder
builder()
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.final RedshiftDatabase
Describes theDatabaseName
andClusterIdentifier
for an Amazon RedshiftDataSource
.final String
A JSON string that represents the splitting and rearrangement processing to be applied to aDataSource
.final String
A JSON string that represents the schema for an Amazon RedshiftDataSource
.final String
Describes the schema location for an Amazon RedshiftDataSource
.final boolean
final boolean
equalsBySdkFields
(Object obj) Indicates whether some other object is "equal to" this one by SDK fields.final <T> Optional
<T> getValueForField
(String fieldName, Class<T> clazz) final int
hashCode()
final String
Describes an Amazon S3 location to store the result set of theSelectSqlQuery
query.final String
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon RedshiftDataSource
.static Class
<? extends RedshiftDataSpec.Builder> Take this object and create a builder that contains all of the current property values of this object.final String
toString()
Returns a string representation of this object.Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
-
Method Details
-
databaseInformation
Describes the
DatabaseName
andClusterIdentifier
for an Amazon RedshiftDataSource
.- Returns:
- Describes the
DatabaseName
andClusterIdentifier
for an Amazon RedshiftDataSource
.
-
selectSqlQuery
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift
DataSource
.- Returns:
- Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift
DataSource
.
-
databaseCredentials
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
- Returns:
- Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
-
s3StagingLocation
Describes an Amazon S3 location to store the result set of the
SelectSqlQuery
query.- Returns:
- Describes an Amazon S3 location to store the result set of the
SelectSqlQuery
query.
-
dataRearrangement
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource
. If theDataRearrangement
parameter is not provided, all of the input data is used to create theDatasource
.There are multiple parameters that control what data is used to create a datasource:
-
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource. -
percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource. -
complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation:
{"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training:
{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
-
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
- Returns:
- A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource
. If theDataRearrangement
parameter is not provided, all of the input data is used to create theDatasource
.There are multiple parameters that control what data is used to create a datasource:
-
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource. -
percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource. -
complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation:
{"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training:
{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
-
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
-
-
-
dataSchema
A JSON string that represents the schema for an Amazon Redshift
DataSource
. TheDataSchema
defines the structure of the observation data in the data file(s) referenced in theDataSource
.A
DataSchema
is not required if you specify aDataSchemaUri
.Define your
DataSchema
as a series of key-value pairs.attributes
andexcludedVariableNames
have an array of key-value pairs for their value. Use the following format to define yourDataSchema
.{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
- Returns:
- A JSON string that represents the schema for an Amazon Redshift
DataSource
. TheDataSchema
defines the structure of the observation data in the data file(s) referenced in theDataSource
.A
DataSchema
is not required if you specify aDataSchemaUri
.Define your
DataSchema
as a series of key-value pairs.attributes
andexcludedVariableNames
have an array of key-value pairs for their value. Use the following format to define yourDataSchema
.{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
-
dataSchemaUri
Describes the schema location for an Amazon Redshift
DataSource
.- Returns:
- Describes the schema location for an Amazon Redshift
DataSource
.
-
toBuilder
Description copied from interface:ToCopyableBuilder
Take this object and create a builder that contains all of the current property values of this object.- Specified by:
toBuilder
in interfaceToCopyableBuilder<RedshiftDataSpec.Builder,
RedshiftDataSpec> - Returns:
- a builder for type T
-
builder
-
serializableBuilderClass
-
hashCode
public final int hashCode() -
equals
-
equalsBySdkFields
Description copied from interface:SdkPojo
Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in anSdkPojo
class, and is generated based on a service model.If an
SdkPojo
class does not have any inherited fields,equalsBySdkFields
andequals
are essentially the same.- Specified by:
equalsBySdkFields
in interfaceSdkPojo
- Parameters:
obj
- the object to be compared with- Returns:
- true if the other object equals to this object by sdk fields, false otherwise.
-
toString
Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value. -
getValueForField
-
sdkFields
-