itachi

itachi brings useful functions from modern database management systems to Apache Spark :)

For example, you can import the Postgres extensions and write Spark code that looks just like Postgres.

The functions are implemented as native Spark functions, so they're performant.

In general, only those functions that difficult for the Apache Spark Community to maintain in the master branch will be added to this library.

Installation

Fetch the JAR file from Maven.

`scala libraryDependencies += "com.github.yaooqinn" %% "itachi" % "0.1.0" `

Here's the Maven link where the JAR files are stored.

itachi requires Spark 3+.

Config your spark applications with spark.sql.extensions, e.g. spark.sql.extensions=org.apache.spark.sql.extra.PostgreSQLExtensions

• org.apache.spark.sql.extra.PostgreSQLExtensions
• org.apache.spark.sql.extra.TeradataExtensions

Simple example

Suppose you have the following data table and would like to join the two arrays, with the familiar array_cat function from Postgres.:

+------+------+
|  arr1|  arr2|
+------+------+
|[1, 2]|    []|
|[1, 2]|[1, 3]|
+------+------+

Concatenate the two arrays::

spark
  .sql("select array_cat(arr1, arr2) as both_arrays from some_data")
  .show()

+------------+
| both_arrays|
+------------+
|      [1, 2]|
|[1, 2, 1, 3]|
+------------+

itachi lets you write Spark SQL code that looks just like Postgres SQL!

Spark SQL Compliance

This is a Spark SQL extension supplying add-on or aliased functions to the Apache Spark SQL builtin standard functions.

The functions in this library take precedence over the native Spark functions in the even of a name conflict.

Contributing

More popular modern dbms system function can be added with your help