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Version: Current

Encryption

Since Hudi 0.11.0, Spark 3.2 support has been added and accompanying that, Parquet 1.12 has been included, which brings encryption feature to Hudi. In this section, we will show a guide on how to enable encryption in Hudi tables.

Encrypt Copy-on-Write tables

First, make sure Hudi Spark 3.2 bundle jar is used.

Then, set the following Parquet configurations to make data written to Hudi COW tables encrypted.

// Activate Parquet encryption, driven by Hadoop properties
jsc.hadoopConfiguration().set("parquet.crypto.factory.class", "org.apache.parquet.crypto.keytools.PropertiesDrivenCryptoFactory")
// Explicit master keys (base64 encoded) - required only for mock InMemoryKMS
jsc.hadoopConfiguration().set("parquet.encryption.kms.client.class" , "org.apache.parquet.crypto.keytools.mocks.InMemoryKMS")
jsc.hadoopConfiguration().set("parquet.encryption.key.list", "k1:AAECAwQFBgcICQoLDA0ODw==, k2:AAECAAECAAECAAECAAECAA==")
// Write encrypted dataframe files.
// Column "rider" will be protected with master key "key2".
// Parquet file footers will be protected with master key "key1"
jsc.hadoopConfiguration().set("parquet.encryption.footer.key", "k1")
jsc.hadoopConfiguration().set("parquet.encryption.column.keys", "k2:rider")

spark.read().format("org.apache.hudi").load("path").show();

Here is an example.

JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
jsc.hadoopConfiguration().set("parquet.crypto.factory.class", "org.apache.parquet.crypto.keytools.PropertiesDrivenCryptoFactory");
jsc.hadoopConfiguration().set("parquet.encryption.kms.client.class" , "org.apache.parquet.crypto.keytools.mocks.InMemoryKMS");
jsc.hadoopConfiguration().set("parquet.encryption.footer.key", "k1");
jsc.hadoopConfiguration().set("parquet.encryption.column.keys", "k2:rider");
jsc.hadoopConfiguration().set("parquet.encryption.key.list", "k1:AAECAwQFBgcICQoLDA0ODw==, k2:AAECAAECAAECAAECAAECAA==");

QuickstartUtils.DataGenerator dataGen = new QuickstartUtils.DataGenerator();
List<String> inserts = convertToStringList(dataGen.generateInserts(3));
Dataset<Row> inputDF1 = spark.read().json(jsc.parallelize(inserts, 1));
inputDF1.write().format("org.apache.hudi")
.option("hoodie.table.name", "encryption_table")
.option("hoodie.upsert.shuffle.parallelism","2")
.option("hoodie.insert.shuffle.parallelism","2")
.option("hoodie.delete.shuffle.parallelism","2")
.option("hoodie.bulkinsert.shuffle.parallelism","2")
.mode(SaveMode.Overwrite)
.save("path");

spark.read().format("org.apache.hudi").load("path").select("rider").show();

Reading the table works if configured correctly

+---------+
|rider |
+---------+
|rider-213|
|rider-213|
|rider-213|
+---------+

Read more from Spark docs and Parquet docs.

Note

This feature is currently only available for COW tables due to only Parquet base files present there.