Delta makes it easy to update certain disk partitions with the replaceWhere option.

Selectively applying updates to certain partitions isn’t always possible (sometimes the entire lake needs the update), but can result in significant speed gains.

Let’s start with a simple example and then explore situations where the replaceWhere update pattern is applicable.

Simple example

Suppose we have the following five rows of data in a CSV file:

first_name,last_name,country Ernesto,Guevara,Argentina Vladimir,Putin,Russia Maria,Sharapova,Russia Bruce,Lee,China Jack,Ma,China

Let’s create a Delta lake from the CSV file:

val df = spark .read .option("header", "true") .option("charset", "UTF8") .csv(path) .withColumn("continent", lit(null).cast(StringType)) val deltaPath = new java.io.File("./tmp/country_partitioned_lake/").getCanonicalPath df .repartition(col("country")) .write .partitionBy("country") .format("delta") .mode("overwrite") .save(deltaPath)

We’re appending a blank continent column to the DataFrame before writing it out as a Delta table, so we won’t have any schema mismatch issues.

Let’s define a custom DataFrame transformation that’ll append a continent column to a DataFrame:

def withContinent()(df: DataFrame): DataFrame = { df.withColumn( "continent", when(col("country") === "Russia", "Europe") .when(col("country") === "China", "Asia") .when(col("country") === "Argentina", "South America") ) }

Suppose the business would like us to populate the continent column, but only for the China partition. We can use replaceWhere to only update the China partition.

spark.read.format("delta").load(deltaPath) .where(col("country") === "China") .transform(withContinent()) .write .format("delta") .option("replaceWhere", "country = 'China'") .mode("overwrite") .save(deltaPath)

Let’s view the contents of the Delta lake:

spark .read .format("delta") .load(deltaPath) .show(false) +----------+---------+---------+---------+ |first_name|last_name|country |continent| +----------+---------+---------+---------+ |Ernesto |Guevara |Argentina|null | |Bruce |Lee |China |Asia | |Jack |Ma |China |Asia | |Vladimir |Putin |Russia |null | |Maria |Sharapova|Russia |null | +----------+---------+---------+---------+

Let’s view the transaction log and confirm that only the China partition was updated. Here are the contents of the _delta_log/00000000000000000001.json file:

{ "add":{ "path":"country=China/part-00000-3abbdd5f-1f0f-48bd-8618-5992823d1a37.c000.snappy.parquet", "partitionValues":{ "country":"China" }, "size":854, "modificationTime":1571835829000, "dataChange":true } } { "remove":{ "path":"country=China/part-00059-dfa81c0d-2a5e-443c-9e15-1e5c40834d68.c000.snappy.parquet", "deletionTimestamp":1571835830680, "dataChange":true } }

Practical use case

replaceWhere is particularly useful when you have to run a computationally expensive algorithm, but only on certain partitions.

Suppose you have a personLikesSalsa() algorithm that is super complex and cannot be run on the entire dataset for performance reasons.

If your dataset is partitioned by country, you can specify to only run the personLikesSalsa() algorithm on the most relevant partitions (e.g. Puerto Rico, Colombia, and Cuba).

It might not be ideal to only run an algorithm on a certain partition of your data, but it might be a reality you’re forced to face.

Summary