Bootstrapping

The bootstrapping function provided by Hudi converts historical tables into Hudi tables without any change by generating Hoodie management files based on historical Parquet tables.

The following shows an example of converting a Hive table in the hdfs://hacluster/user/hive/warehouse/pq1 directory on HDFS to a Hudi table and save it in the hdfs://hacluster/tmp/hudi_bootstrap_test directory.

• Bootstrapping and write operations cannot be executed concurrently. Bootstrapping is used only to creat new Hudi tables rather to execute existing Hudi tables.
• Bootstrapping supports only the write operations on COW tables.

spark-shell

import collection.JavaConverters._
import org.apache.hadoop.fs.FileSystem
import org.apache.hudi.bootstrap.SparkParquetBootstrapDataProvider
import org.apache.hudi.client.bootstrap.selector.FullRecordBootstrapModeSelector
import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions, HoodieDataSourceHelpers}
import org.apache.hudi.common.fs.FSUtils
import org.apache.hudi.common.table.timeline.HoodieTimeline
import org.apache.hudi.config.{HoodieBootstrapConfig, HoodieCompactionConfig, HoodieWriteConfig}
import org.apache.hudi.keygen.SimpleKeyGenerator
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.functions.{col, lit}
import org.apache.spark.sql.{SaveMode, SparkSession}
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
import java.time._
import java.util.Collections

val timestamp = Instant.now.toEpochMilli
val jsc = JavaSparkContext.fromSparkContext(spark.sparkContext)
val numRecords: Int = 100
val srcPath = "hdfs://hacluster/user/hive/warehouse/pq1"
val basePath = "hdfs://hacluster/tmp/hudi_bootstrap_test"

// Hudi configuration information
val commonOpts: Map[String, String] = Map(
    HoodieWriteConfig.INSERT_PARALLELISM -> "4",
    HoodieWriteConfig.UPSERT_PARALLELISM -> "4",
    HoodieWriteConfig.DELETE_PARALLELISM -> "4",
    HoodieWriteConfig.BULKINSERT_PARALLELISM -> "4",
    HoodieWriteConfig.FINALIZE_WRITE_PARALLELISM -> "4",
    HoodieBootstrapConfig.BOOTSTRAP_PARALLELISM -> "4",
    DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "col1",
    DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "partition",
    DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "timestamp",
    HoodieWriteConfig.TABLE_NAME -> "hoodie_test"
  )
// Bootstrapping
val bootstrapDF = spark.emptyDataFrame
bootstrapDF.write.
  format("hudi").
  options(commonOpts).
  option(DataSourceWriteOptions.OPERATION_OPT_KEY, DataSourceWriteOptions.BOOTSTRAP_OPERATION_OPT_VAL).
  option(DataSourceWriteOptions.TABLE_TYPE_OPT_KEY, DataSourceWriteOptions.COW_TABLE_TYPE_OPT_VAL).
  option(HoodieBootstrapConfig.BOOTSTRAP_BASE_PATH_PROP, srcPath).
  option(HoodieBootstrapConfig.BOOTSTRAP_KEYGEN_CLASS, classOf[SimpleKeyGenerator].getName).
  mode(SaveMode.Overwrite).
  save(basePath)

// Query data after bootstrapping.
var hoodieROViewDF1 = spark.read.format("hudi").load(basePath + "/*")
hoodieROViewDF1.show