Hudi maintains metadata such as commit timeline and indexes to manage a dataset. The commit timelines helps to understand the actions happening on a dataset as well as the current state of a dataset. Indexes are used by Hudi to maintain a record key to file id mapping to efficiently locate a record. At the moment, Hudi supports writing only parquet columnar formats. To be able to start using Hudi for your existing dataset, you will need to migrate your existing dataset into a Hudi managed dataset. There are a couple of ways to achieve this.

Approaches

Use Hudi for new partitions alone

Hudi can be used to manage an existing dataset without affecting/altering the historical data already present in the dataset. Hudi has been implemented to be compatible with such a mixed dataset with a caveat that either the complete Hive partition is Hudi managed or not. Thus the lowest granularity at which Hudi manages a dataset is a Hive partition. Start using the datasource API or the WriteClient to write to the dataset and make sure you start writing to a new partition or convert your last N partitions into Hudi instead of the entire table. Note, since the historical partitions are not managed by HUDI, none of the primitives provided by HUDI work on the data in those partitions. More concretely, one cannot perform upserts or incremental pull on such older partitions not managed by the HUDI dataset. Take this approach if your dataset is an append only type of dataset and you do not expect to perform any updates to existing (or non Hudi managed) partitions.

Convert existing dataset to Hudi

Import your existing dataset into a Hudi managed dataset. Since all the data is Hudi managed, none of the limitations of Approach 1 apply here. Updates spanning any partitions can be applied to this dataset and Hudi will efficiently make the update available to queries. Note that not only do you get to use all Hudi primitives on this dataset, there are other additional advantages of doing this. Hudi automatically manages file sizes of a Hudi managed dataset . You can define the desired file size when converting this dataset and Hudi will ensure it writes out files adhering to the config. It will also ensure that smaller files later get corrected by routing some new inserts into small files rather than writing new small ones thus maintaining the health of your cluster.

There are a few options when choosing this approach.

Option 1

Use the HDFSParquetImporter tool. As the name suggests, this only works if your existing dataset is in parquet file format. This tool essentially starts a Spark Job to read the existing parquet dataset and converts it into a HUDI managed dataset by re-writing all the data.

Option 2

For huge datasets, this could be as simple as :

for partition in [list of partitions in source dataset] {
        val inputDF = spark.read.format("any_input_format").load("partition_path")
        inputDF.write.format("org.apache.hudi").option()....save("basePath")
}

Option 3

Write your own custom logic of how to load an existing dataset into a Hudi managed one. Please read about the RDD API here.

Using the HDFSParquetImporter Tool. Once hudi has been built via `mvn clean install -DskipTests`, the shell can be
fired by via `cd hudi-cli && ./hudi-cli.sh`.
hudi->hdfsparquetimport
        --upsert false
        --srcPath /user/parquet/dataset/basepath
        --targetPath
        /user/hoodie/dataset/basepath
        --tableName hoodie_table
        --tableType COPY_ON_WRITE
        --rowKeyField _row_key
        --partitionPathField partitionStr
        --parallelism 1500
        --schemaFilePath /user/table/schema
        --format parquet
        --sparkMemory 6g
        --retry 2