chore: slim down tableutils

cloud_gcp/src/test/scala/ai/chronon/integrations/cloud_gcp/BigQueryCatalogTest.scala

@@ -66,8 +66,6 @@ class BigQueryCatalogTest extends AnyFlatSpec with MockitoSugar {
     val nativeTable = "data.sample_native"
     val table = tableUtils.loadTable(nativeTable)
     table.show
-    val partitioned = tableUtils.isPartitioned(nativeTable)
-    println(partitioned)
     // val database = tableUtils.createDatabase("test_database")
     val allParts = tableUtils.allPartitions(nativeTable)
     println(allParts)
@@ -80,8 +78,6 @@ class BigQueryCatalogTest extends AnyFlatSpec with MockitoSugar {
     println(bs)
     val table = tableUtils.loadTable(externalTable)
     table.show
-    val partitioned = tableUtils.isPartitioned(externalTable)
-    println(partitioned)
     // val database = tableUtils.createDatabase("test_database")
     val allParts = tableUtils.allPartitions(externalTable)
     println(allParts)

spark/src/main/scala/ai/chronon/spark/Extensions.scala

@@ -162,10 +162,6 @@ object Extensions {
                                                    sortByCols = sortByCols)
     }
 
-    def saveUnPartitioned(tableName: String, tableProperties: Map[String, String] = null): Unit = {
-      TableUtils(df.sparkSession).insertUnPartitioned(df, tableName, tableProperties)
-    }
-
     def prefixColumnNames(prefix: String, columns: Seq[String]): DataFrame = {
       columns.foldLeft(df) { (renamedDf, key) =>
         renamedDf.withColumnRenamed(key, s"${prefix}_$key")

spark/src/main/scala/ai/chronon/spark/GroupByUpload.scala

@@ -258,7 +258,7 @@ object GroupByUpload {
     kvDf
       .union(metaDf)
       .withColumn("ds", lit(endDs))
-      .saveUnPartitioned(groupByConf.metaData.uploadTable, groupByConf.metaData.tableProps)
+      .save(groupByConf.metaData.uploadTable, groupByConf.metaData.tableProps, partitionColumns = List.empty)
 
     val kvDfReloaded = tableUtils
       .loadTable(groupByConf.metaData.uploadTable)

spark/src/main/scala/ai/chronon/spark/StagingQuery.scala

@@ -51,7 +51,7 @@ class StagingQuery(stagingQueryConf: api.StagingQuery, endPartition: String, tab
     }
     // the input table is not partitioned, usually for data testing or for kaggle demos
     if (stagingQueryConf.startPartition == null) {
-      tableUtils.sql(stagingQueryConf.query).saveUnPartitioned(outputTable)
+      tableUtils.sql(stagingQueryConf.query).save(outputTable, partitionColumns = List.empty)
     } else {
       val overrideStart = overrideStartPartition.getOrElse(stagingQueryConf.startPartition)
       val unfilledRanges =

spark/src/main/scala/ai/chronon/spark/TableUtils.scala

@@ -30,7 +30,7 @@ import ai.chronon.spark.TableUtils.{
   TableCreationStatus
 }
 import ai.chronon.spark.format.CreationUtils.alterTablePropertiesSql
-import ai.chronon.spark.format.{DefaultFormatProvider, Format, FormatProvider}
+import ai.chronon.spark.format.{DefaultFormatProvider, FormatProvider}
 import org.apache.hadoop.hive.metastore.api.AlreadyExistsException
 import org.apache.spark.sql.catalyst.analysis.TableAlreadyExistsException
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, Project}
@@ -111,7 +111,6 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
   private val aggregationParallelism: Int = sparkSession.conf.get("spark.chronon.group_by.parallelism", "1000").toInt
 
   sparkSession.sparkContext.setLogLevel("ERROR")
-  // converts String-s like "a=b/c=d" to Map("a" -> "b", "c" -> "d")
 
   def preAggRepartition(df: DataFrame): DataFrame =
     if (df.rdd.getNumPartitions < aggregationParallelism) {
@@ -122,7 +121,7 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
 
   def tableReachable(tableName: String): Boolean = {
     try {
-      tableReadFormat(tableName).isDefined
+      tableFormatProvider.readFormat(tableName).isDefined
     } catch {
       case ex: Exception =>
         logger.info(s"""Couldn't reach $tableName. Error: ${ex.getMessage.red}
@@ -137,12 +136,6 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
     sparkSession.read.load(DataPointer.from(tableName, sparkSession))
   }
 
-  def isPartitioned(tableName: String): Boolean = {
-    // TODO: use proper way to detect if a table is partitioned or not
-    val schema = getSchemaFromTable(tableName)
-    schema.fieldNames.contains(partitionColumn)
-  }
-
   // Needs provider
   def createDatabase(database: String): Boolean = {
     try {
@@ -159,17 +152,16 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
     }
   }
 
-  def tableReadFormat(tableName: String): Option[Format] = tableFormatProvider.readFormat(tableName)
-
-  // Needs provider
   // return all specified partition columns in a table in format of Map[partitionName, PartitionValue]
   def allPartitions(tableName: String, partitionColumnsFilter: Seq[String] = Seq.empty): Seq[Map[String, String]] = {
 
     if (!tableReachable(tableName)) return Seq.empty[Map[String, String]]
 
-    val format = tableReadFormat(tableName).getOrElse(
-      throw new IllegalStateException(
-        s"Could not determine read format of table ${tableName}. It is no longer reachable."))
+    val format = tableFormatProvider
+      .readFormat(tableName)
+      .getOrElse(
+        throw new IllegalStateException(
+          s"Could not determine read format of table ${tableName}. It is no longer reachable."))
     val partitionSeq = format.partitions(tableName)(sparkSession)
 
     if (partitionColumnsFilter.isEmpty) {
@@ -189,7 +181,8 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
                  subPartitionsFilter: Map[String, String] = Map.empty,
                  partitionColumnName: String = partitionColumn): Seq[String] = {
 
-    tableReadFormat(tableName)
+    tableFormatProvider
+      .readFormat(tableName)
       .map((format) => {
         val partitions = format.primaryPartitions(tableName, partitionColumnName, subPartitionsFilter)(sparkSession)
 
@@ -385,23 +378,6 @@ class TableUtils(@transient val sparkSession: SparkSession) extends Serializable
     }
   }
 
-  // Needs provider
-  def insertUnPartitioned(df: DataFrame,
-                          tableName: String,
-                          tableProperties: Map[String, String] = null,
-                          saveMode: SaveMode = SaveMode.Overwrite,
-                          fileFormat: String = "PARQUET"): Unit = {
-
-    val creationStatus = createTable(df, tableName, Seq.empty[String], tableProperties, fileFormat)
-
-    creationStatus match {
-      case TableUtils.TableCreatedWithoutInitialData | TableUtils.TableAlreadyExists =>
-        repartitionAndWrite(df, tableName, saveMode, None, partitionColumns = Seq.empty)
-      case TableUtils.TableCreatedWithInitialData =>
-    }
-
-  }
-
   def columnSizeEstimator(dataType: DataType): Long = {
     dataType match {
       // TODO: improve upon this very basic estimate approach

spark/src/main/scala/ai/chronon/spark/stats/CompareJob.scala

@@ -79,13 +79,13 @@ class CompareJob(
     logger.info("Saving comparison output..")
     logger.info(
       s"Comparison schema ${compareDf.schema.fields.map(sb => (sb.name, sb.dataType)).toMap.mkString("\n - ")}")
-    compareDf.saveUnPartitioned(comparisonTableName, tableProps)
+    compareDf.save(comparisonTableName, tableProps, partitionColumns = List.empty)
 
     // Save the metrics table
     logger.info("Saving metrics output..")
     val metricsDf = metricsTimedKvRdd.toFlatDf
     logger.info(s"Metrics schema ${metricsDf.schema.fields.map(sb => (sb.name, sb.dataType)).toMap.mkString("\n - ")}")
-    metricsDf.saveUnPartitioned(metricsTableName, tableProps)
+    metricsDf.save(metricsTableName, tableProps, partitionColumns = List.empty)
 
     logger.info("Printing basic comparison results..")
     logger.info("(Note: This is just an estimation and not a detailed analysis of results)")

spark/src/main/scala/ai/chronon/spark/utils/PartitionRunner.scala

@@ -119,7 +119,7 @@ class PartitionRunner[T](verb: String,
       if (outputDf.columns.contains(tu.partitionColumn)) {
         outputDf.save(outputTable)
       } else {
-        outputDf.saveUnPartitioned(outputTable)
+        outputDf.save(outputTable, partitionColumns = List.empty)
       }
       println(s"""
            |Finished computing range ${i + 1}/$n

spark/src/test/scala/ai/chronon/spark/test/TableUtilsFormatTest.scala

@@ -147,7 +147,7 @@ class TableUtilsFormatTest extends AnyFlatSpec {
   it should "return empty read format if table doesn't exist" in {
     val dbName = s"db_${System.currentTimeMillis()}"
     val tableName = s"$dbName.test_table_nonexistent_$format"
-    assertTrue(tableUtils.tableReadFormat(tableName).isEmpty)
+    assertTrue(tableUtils.tableFormatProvider.readFormat(tableName).isEmpty)
     assertFalse(tableUtils.tableReachable(tableName))
   }
 }
@@ -188,7 +188,7 @@ object TableUtilsFormatTest {
     tableUtils.insertPartitions(df2, tableName, autoExpand = true)
 
     // check that we wrote out a table in the right format
-    val readTableFormat = tableUtils.tableReadFormat(tableName).get.toString
+    val readTableFormat = tableUtils.tableFormatProvider.readFormat(tableName).get.toString
     assertTrue(s"Mismatch in table format: $readTableFormat; expected: $format", readTableFormat.toLowerCase == format)
 
     // check we have all the partitions written