Use ODBC to save and load R objects in SQL Server Machine Learning Services

Article
03/03/2023

Applies to: SQL Server 2016 (13.x) and later versions

Learn how to use the RevoScaleR package to store serialized R objects in a table and then load the object from the table as needed with SQL Server Machine Learning Services. This can be used when training and saving a model, and then use it later for scoring or analysis.

RevoScaleR package

The RevoScaleR package includes serialization and deserialization functions that can R objects compactly to SQL Server and then read the objects from the table. In general, each function call uses a simple key value store, in which the key is the name of the object, and the value associated with the key is the varbinary R object to be moved in or out of a table.

To save R objects to SQL Server directly from an R environment, you must:

established a connection to SQL Server using the RxOdbcData data source.
Call the new functions over the ODBC connection
Optionally, you can specify that the object not be serialized. Then, choose a new compression algorithm to use instead of the default compression algorithm.

By default, any object that you call from R to move to SQL Server is serialized and compressed. Conversely, when you load an object from a SQL Server table to use in your R code, the object is deserialized and decompressed.

List of new functions

rxWriteObject writes an R object into SQL Server using the ODBC data source.
rxReadObject reads an R object from a SQL Server database, using an ODBC data source
rxDeleteObject deletes an R object from the SQL Server database specified in the ODBC data source. If there are multiple objects identified by the key/version combination, all are deleted.
rxListKeys lists as key-value pairs all the available objects. This helps you determine the names and versions of the R objects.

For detailed help on the syntax of each function, use R help. Details are also available in the ScaleR reference.

How to store R objects in SQL Server using ODBC

This procedure demonstrates how you can use the new functions to create a model and save it to SQL Server.

Set up the connection string for the SQL Server.

conStr <- 'Driver={SQL Server};Server=localhost;Database=storedb;Trusted_Connection=true'

Create an rxOdbcData data source object in R using the connection string.
```
ds <- RxOdbcData(table="robjects", connectionString=conStr)
```

Delete the table if it already exists, and you don't want to track old versions of the objects.

if(rxSqlServerTableExists(ds@table, ds@connectionString)) {
    rxSqlServerDropTable(ds@table, ds@connectionString)
    }

Define a table that can be used to store binary objects.

ddl <- paste(" CREATE TABLE [", ds@table, "] 
   (","  [id] varchar(200) NOT NULL,
    "," [value] varbinary(max),
    "," CONSTRAINT unique_id UNIQUE (id))", 
    sep = "")

Open the ODBC connection to create the table, and when the DDL statement has completed, close the connection.
```
 rxOpen(ds, "w") 
 rxExecuteSQLDDL(ds, ddl) 
 rxClose(ds)
```

Generate the R objects that you want to store.

infertLogit <- rxLogit(case ~ age + parity + education + spontaneous + induced, 
  data = infert)

Use the RxOdbcData object created earlier to save the model to the database.
```
rxWriteObject(ds, "logit.model", infertLogit)
```

How to read R objects from SQL Server using ODBC

This procedure demonstrates how you can use the new functions to load a model from SQL Server.

Set up the connection string for the SQL Server.

conStr2 <- 'Driver={SQL Server};Server=localhost;Database=storedb;Trusted_Connection=true'

Create an rxOdbcData data source object in R, using the connection string.
```
ds <- RxOdbcData(table="robjects", connectionString=conStr2)
```
Read the model from the table by specifying its R object name.
```
 infertLogit2 <- rxReadObject(ds, "logit.model")
```

Next steps

What is SQL Server Machine Learning Services?

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