tiny changes to exposed example, adding exposed query to compare with df

Author: Jolanrensen

examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/exposed/main.kt

@@ -1,12 +1,13 @@
 package org.jetbrains.kotlinx.dataframe.examples.exposed
 
 import org.jetbrains.exposed.v1.core.Column
-import org.jetbrains.exposed.v1.core.StdOutSqlLogger
+import org.jetbrains.exposed.v1.core.SortOrder
+import org.jetbrains.exposed.v1.core.count
 import org.jetbrains.exposed.v1.jdbc.Database
 import org.jetbrains.exposed.v1.jdbc.SchemaUtils
-import org.jetbrains.exposed.v1.jdbc.addLogger
 import org.jetbrains.exposed.v1.jdbc.batchInsert
 import org.jetbrains.exposed.v1.jdbc.deleteAll
+import org.jetbrains.exposed.v1.jdbc.select
 import org.jetbrains.exposed.v1.jdbc.selectAll
 import org.jetbrains.exposed.v1.jdbc.transactions.transaction
 import org.jetbrains.kotlinx.dataframe.api.asSequence
@@ -29,25 +30,41 @@ fun main() {
 
     // let's read the database!
     val df = transaction(db) {
-        addLogger(StdOutSqlLogger)
+        // addLogger(StdOutSqlLogger) // enable if you want to see verbose logs
 
         // tables in Exposed need to be defined, see tables.kt
         SchemaUtils.create(Customers, Artists, Albums)
 
+        println()
+
+        // In Exposed, we can write queries like this.
+        // Here, we count per country how many customers there are and print the results:
+        Customers
+            .select(Customers.country, Customers.customerId.count())
+            .groupBy(Customers.country)
+            .orderBy(Customers.customerId.count() to SortOrder.DESC)
+            .forEach {
+                println("${it[Customers.country]}: ${it[Customers.customerId.count()]} customers")
+            }
+
+        println()
+
         // Perform the specific query you want to read into the DataFrame.
         // Note: DataFrames are in-memory structures, so don't make it too large if you don't have the RAM ;)
         val query = Customers.selectAll() // .where { Customers.company.isNotNull() }
 
+        println()
+
         // read and convert the query to a typed DataFrame
         // see compatibilityLayer.kt for how we created convertToDataFrame<>()
-        // and see tables.kt for how we created CustomersDf!
-        query.convertToDataFrame<CustomersDf>()
+        // and see tables.kt for how we created DfCustomers!
+        query.convertToDataFrame<DfCustomers>()
     }
 
     println(df.size())
 
     // now we have a DataFrame, we can perform DataFrame operations,
-    // like seeing how often a country is represented
+    // like doing the same operation as we did in Exposed above
     df.groupBy { country }.count()
         .sortByDesc { "count"<Int>() }
         .print(columnTypes = true, borders = true)
@@ -60,15 +77,18 @@ fun main() {
 
     // writing a DataFrame back into an SQL database with Exposed can also be done easily!
     transaction(db) {
-        addLogger(StdOutSqlLogger)
+        // addLogger(StdOutSqlLogger) // enable if you want to see verbose logs
 
         // first delete the original contents
         Customers.deleteAll()
 
+        println()
+
         // batch-insert our dataframe back into the SQL database as a sequence of rows
         Customers.batchInsert(df.asSequence()) { dfRow ->
             // we simply go over each value in the row and put it in the right place in the Exposed statement
             for (column in Customers.columns) {
+                @Suppress("UNCHECKED_CAST")
                 this[column as Column<Any?>] = dfRow[column.name]
             }
         }

examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/exposed/tables.kt

@@ -6,7 +6,6 @@ import org.jetbrains.kotlinx.dataframe.annotations.ColumnName
 import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
 import org.jetbrains.kotlinx.dataframe.api.generateDataClasses
 import org.jetbrains.kotlinx.dataframe.api.print
-import org.jetbrains.kotlinx.dataframe.codeGen.NameNormalizer
 
 object Albums : Table() {
     val albumId: Column<Int> = integer("AlbumId").autoIncrement()
@@ -60,15 +59,15 @@ fun main() {
     // we use a NameNormalizer to let DataFrame generate the same accessors as in the Table
     // while keeping the correct column names
     schema.generateDataClasses(
-        name = "CustomersDf",
+        name = "DfCustomers",
         nameNormalizer = nameNormalizer,
     ).print()
 }
 
 // created by Customers.toDataFrameSchema()
 // The same can be done for the other tables
 @DataSchema
-data class CustomersDf(
+data class DfCustomers(
     @ColumnName("Address")
     val address: String?,
     @ColumnName("City")