This library makes it easier to write Spark code:
- provides access to the Spark SQL functions that aren't exposed in the Scala API (e.g.
regexp_extract_all) - additional helpful function with performant underlying implementation
Fetch the JAR from Maven:
libraryDependencies += "com.github.mrpowers" %% "bebe" % "0.1.0"bebe depends on Spark internals, so you need to be careful to select the right version.
| Spark | Scala | bebe |
|---|---|---|
| 3.1.1 | 2.12 | 0.1.0 |
| 3.0.1 | 2.12 | 0.0.2 |
You can find all the published bebe versions here.
The Spark maintainers intentionally exclude some SQL functions from the Scala/PySpark APIs.
For example, regexp_extract_all is not part of the Scala API.
See this email thread for a detailed discussion on why the Spark core developers want to limit the size of the Scala API, even for functions that are in the SQL API.
Some of these excluded functions are quite useful for Scala/PySpark developers and that's why they're exposed via the bebe library.
bebe also goes a step farther to provide access to additional functions that are commonly used (e.g. beginning of day). All functions are implemented as catalyst expressions, so they're performant and can be optimized by Spark.
Import the functions like this:
import org.apache.spark.sql.BebeFunctions._Here are the useful functions / missing functions that this lib provides access to:
- beginningOfDay
- beginningOfMonth
- bebe_approx_percentile
- bebe_cardinality
- bebe_cot
- bebe_count_if
- bebe_character_length
- bebe_chr
- bebe_e
- bebe_if_null
- bebe_inline
- bebe_is_not_null
- bebe_left
- bebe_like
- bebe_make_date
- bebe_make_timestamp
- bebe_nvl2
- bebe_octet_length
- bebe_stack
- bebe_parse_url
- bebe_percentile
- bebe_regexp_extract_all
- bebe_right
- bebe_sentences
- bebe_space
- bebe_substr
- bebe_uuid
- bebe_weekday
Keep reading for examples on how to use these functions.
Suppose you have the following DataFrame:
+-------------------+
| some_time|
+-------------------+
|2020-01-15 08:01:32|
|2020-01-20 23:03:22|
| null|
+-------------------+
Calculate the beginning of the day:
df.withColumn("actual", beginningOfDay(col("some_time")))+-------------------+-------------------+
| some_time| actual|
+-------------------+-------------------+
|2020-01-15 08:01:32|2020-01-15 00:00:00|
|2020-01-20 23:03:22|2020-01-20 00:00:00|
| null| null|
+-------------------+-------------------+
Suppose you have the following DataFrame:
+----------+
| some_date|
+----------+
|2020-01-15|
|2020-01-20|
| null|
+----------+
Calculate the beginning of the month:
val resDF = df.withColumn("actual", beginningOfMonth(col("some_date")))+----------+----------+
| some_date| actual|
+----------+----------+
|2020-01-15|2020-01-01|
|2020-01-20|2020-01-01|
| null| null|
+----------+----------+
Here's how to create a DataFrame with the numbers from 1 to 1000 and then compute the 25th and 99th percentiles:
val df = (1 to 1000).toDF("col")
val resDF = df.select(bebe_approx_percentile(col("col"), array(lit(0.25), lit(0.99))))
resDF.show()+------------------------------------------------+
|percentile_approx(col, array(0.25, 0.99), 10000)|
+------------------------------------------------+
| [250, 990]|
+------------------------------------------------+
The function returns an array of results that correspond to the inputted percentiled.
Suppose you have the following DataFrame:
+------------+
|some_strings|
+------------+
| [23, 44]|
| []|
| null|
+------------+
Calculate the number of elements in the some_strings array, returning -1 if the column is null:
val resDF = df.withColumn("actual", bebe_cardinality(col("some_strings")))+------------+------+
|some_strings|actual|
+------------+------+
| [23, 44]| 2|
| []| 0|
| null| -1|
+------------+------+
Suppose you have the following DataFrame:
+-----------+
|some_degree|
+-----------+
| 60|
| 100|
| null|
+-----------+
Calculate the cotangent of these values:
df.withColumn("actual", bebe_cot(col("some_degree")))+-----------+------------------+
|some_degree| actual|
+-----------+------------------+
| 60| 3.124605622242308|
| 100|-1.702956919426469|
| null| null|
+-----------+------------------+
Suppose you have the following DataFrame:
+--------+
|some_int|
+--------+
| 4|
| 3|
| 10|
+--------+
Calculate the count of all the integers that are less than five:
val resDF = df.agg(bebe_count_if(col("some_int") < 5).as("lt_five_count"))
resDF.show()+-------------+
|lt_five_count|
+-------------+
| 2|
+-------------+
Suppose you have the following DataFrame:
+-----------+
|some_string|
+-----------+
| Spark SQL |
| |
| null|
+-----------+
Calculate the number of characters in each string (the second row is the empty string).
val resDF = df.withColumn("actual", bebe_character_length(col("some_string")))
resDF.show()+-----------+------+
|some_string|actual|
+-----------+------+
| Spark SQL | 10|
| | 0|
| null| null|
+-----------+------+
Suppose you have the following DataFrame:
+--------+
|some_int|
+--------+
| 118|
| 65|
| null|
+--------+
Calculate the character associated with the ordinal position of an integer.
df.withColumn("actual", bebe_chr(col("some_int")))+--------+------+
|some_int|actual|
+--------+------+
| 118| v|
| 65| A|
| null| null|
+--------+------+
Suppose you have the following DataFrame:
+--------+
|some_int|
+--------+
| 118|
| null|
+--------+
Append a column with Euler's number (e).
df.withColumn("actual", bebe_e())+--------+-----------------+
|some_int| actual|
+--------+-----------------+
| 118|2.718281828459045|
| null|2.718281828459045|
+--------+-----------------+
Suppose you have the following DataFrame:
+-----+-----+
| col1| col2|
+-----+-----+
| null|expr2|
|expr1| null|
|expr1|expr2|
+-----+-----+
Add a column that returns col2 if col1 isn't null:
df.withColumn("actual", bebe_if_null(col("col1"), col("col2")))+-----+-----+------+
| col1| col2|actual|
+-----+-----+------+
| null|expr2| expr2|
|expr1| null| expr1|
|expr1|expr2| expr1|
+-----+-----+------+
Suppose you have the following DataFrame, with an interpretations column that is an array of structs:
+---------------------+----------------+
|animal_interpretation| interpretations|
+---------------------+----------------+
| {true, true}| [{true, true}]|
| {false, false}|[{false, false}]|
| {false, true}| [{false, true}]|
+---------------------+----------------+
Explode the interpretations column into a table.
val res = actualDF.select(bebe_inline(col("interpretations")))
res.show()+---------------+---------+
|is_large_animal|is_mammal|
+---------------+---------+
| true| true|
| false| false|
| false| true|
+---------------+---------+
Suppose you have the following DataFrame:
+-----------+
|some_string|
+-----------+
| null|
| hi|
+-----------+
Append a column that returns true if some_string is not null.
df.withColumn("actual", bebe_is_not_null(col("some_string")))+-----------+------+
|some_string|actual|
+-----------+------+
| null| false|
| hi| true|
+-----------+------+
Suppose you have the following DataFrame:
+----------------------+
|some_string |
+----------------------+
|this 23 has 44 numbers|
|no numbers |
|null |
+----------------------+
Append a column with the leftmost two characters in some_string.
df.withColumn("actual", bebe_left(col("some_string"), lit(2)))+----------------------+------+
|some_string |actual|
+----------------------+------+
|this 23 has 44 numbers|th |
|no numbers |no |
|null |null |
+----------------------+------+
Suppose you have the following DataFrame:
+------------------+-----------+
| some_string|like_regexp|
+------------------+-----------+
| hi!| hi_|
|hello there person| hello%|
| whatever| hello%|
| null| null|
+------------------+-----------+
Append a column that returns true when some_string matches the like_regexp (with a like pattern).
df.withColumn("actual", bebe_like(col("some_string"), col("like_regexp")))+------------------+-----------+------+
| some_string|like_regexp|actual|
+------------------+-----------+------+
| hi!| hi_| true|
|hello there person| hello%| true|
| whatever| hello%| false|
| null| null| null|
+------------------+-----------+------+
Suppose you have the following DataFrame:
+----+-----+----+
|year|month| day|
+----+-----+----+
|2020| 1| 1|
|2021| 3| 5|
|null| null|null|
+----+-----+----+
Calculate the date using the year, month, and day columns.
df.withColumn("actual", bebe_like(col("some_string"), col("like_regexp")))+----+-----+----+----------+
|year|month| day| actual|
+----+-----+----+----------+
|2020| 1| 1|2020-01-01|
|2021| 3| 5|2021-03-05|
|null| null|null| null|
+----+-----+----+----------+
Suppose you have the following DataFrame:
+----+-----+----+-----+-------+-------+
|year|month| day|hours|minutes|seconds|
+----+-----+----+-----+-------+-------+
|2020| 1| 1| 5| 3| 45|
|2021| 3| 5| 11| 1| 13|
|null| null|null| null| null| null|
+----+-----+----+-----+-------+-------+
Calculate the timestamp using the year, month, day, hours, minutes, and seconds columns.
df.withColumn(
"actual",
bebe_make_timestamp(
col("year"),
col("month"),
col("day"),
col("hours"),
col("minutes"),
col("seconds")
)
)+----+-----+----+-----+-------+-------+-------------------+
|year|month| day|hours|minutes|seconds| actual|
+----+-----+----+-----+-------+-------+-------------------+
|2020| 1| 1| 5| 3| 45|2020-01-01 05:03:45|
|2021| 3| 5| 11| 1| 13|2021-03-05 11:01:13|
|null| null|null| null| null| null| null|
+----+-----+----+-----+-------+-------+-------------------+
Suppose you have the following DataFrame:
+-----------+
|some_string|
+-----------+
| €|
| Spark SQL|
| null|
+-----------+
Calculate the octet length of some_string.
df.withColumn("actual", bebe_octet_length(col("some_string")))+-----------+------+
|some_string|actual|
+-----------+------+
| €| 3|
| Spark SQL| 9|
| null| null|
+-----------+------+
Suppose you have the following DataFrame:
+------------------------------------+---------------+
|some_string |part_to_extract|
+------------------------------------+---------------+
|http://spark.apache.org/path?query=1|HOST |
|http://spark.apache.org/path?query=1|QUERY |
|null |null |
+------------------------------------+---------------+
Calculate the different parts of the URL:
df.withColumn("actual", bebe_parse_url(col("some_string"), col("part_to_extract")))+------------------------------------+---------------+----------------+
|some_string |part_to_extract|actual |
+------------------------------------+---------------+----------------+
|http://spark.apache.org/path?query=1|HOST |spark.apache.org|
|http://spark.apache.org/path?query=1|QUERY |query=1 |
|null |null |null |
+------------------------------------+---------------+----------------+
Suppose you have the following DataFrame:
+--------+
|some_int|
+--------+
| 0|
| 10|
+--------+
Calculate the 50th percentile:
df.agg(bebe_percentile(col("some_int"), lit(0.5)).as("50_percentile"))+-------------+
|50_percentile|
+-------------+
| 5.0|
+-------------+
Suppose you'd like to extract all the numbers from the some_string column in the following DataFrame:
+----------------------+
|some_string |
+----------------------+
|this 23 has 44 numbers|
|no numbers |
|null |
+----------------------+
Use bebe_regexp_extract_all to create an ArrayType column with all the numbers from some_string:
import org.apache.spark.sql.BebeFunctions._
df
.withColumn("actual", bebe_regexp_extract_all(col("some_string"), lit("(\\d+)"), lit(1)))
.show(false)+----------------------+--------+
|some_string |actual |
+----------------------+--------+
|this 23 has 44 numbers|[23, 44]|
|no numbers |[] |
|null |null |
+----------------------+--------+
Suppose you have the following DataFrame:
+----------------------+
|some_string |
+----------------------+
|this 23 has 44 numbers|
|no dice |
|null |
+----------------------+
Grab the rightmost 2 characters from the some_string column.
df.withColumn("actual", bebe_right(col("some_string"), lit(2)))+----------------------+------+
|some_string |actual|
+----------------------+------+
|this 23 has 44 numbers|rs |
|no dice |ce |
|null |null |
+----------------------+------+
Suppose you have the following DataFrame:
+-----------------------+
|some_string |
+-----------------------+
|Hi there! Good morning.|
|you are funny |
|null |
+-----------------------+
Split up some_string into arrays of words in the sentences.
df.withColumn("actual", bebe_sentences(col("some_string")))+-----------------------+------------------------------+
|some_string |actual |
+-----------------------+------------------------------+
|Hi there! Good morning.|[[Hi, there], [Good, morning]]|
|you are funny |[[you, are, funny]] |
|null |null |
+-----------------------+------------------------------+
Suppose you have the following DataFrame:
+----+-----+------+
|str1| str2|spaces|
+----+-----+------+
|some|thing| 2|
|like|pizza| 3|
|null| null| null|
+----+-----+------+
Concatenate str1 and str2 with the number of spaces specified in the spaces column.
df.withColumn("actual", concat(col("str1"), bebe_space(col("spaces")), col("str2")))+----+-----+------+------------+
|str1| str2|spaces| actual|
+----+-----+------+------------+
|some|thing| 2| some thing|
|like|pizza| 3|like pizza|
|null| null| null| null|
+----+-----+------+------------+
Suppose you have the following DataFrame:
+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
| 1| 2| 3| 4|
| 6| 7| 8| 9|
+----+----+----+----+
Stack the rows into columns.
df.select(bebe_stack(lit(2), col("col1"), col("col2"), col("col3"), col("col4")))+----+----+
|col0|col1|
+----+----+
| 1| 2|
| 3| 4|
| 6| 7|
| 8| 9|
+----+----+
Suppose you have the following DataFrame:
+-----------+----+
|some_string| pos|
+-----------+----+
| brasil| 3|
| peru| -2|
| null|null|
+-----------+----+
Get the tail elements starting at pos elements into the string. Start counting from the right if pos is negative.
df.withColumn("actual", bebe_substr(col("some_string"), col("pos")))+-----------+----+------+
|some_string| pos|actual|
+-----------+----+------+
| brasil| 3| asil|
| peru| -2| ru|
| null|null| null|
+-----------+----+------+
Append a uuid column to a DataFrame.
df.withColumn("actual", bebe_uuid()).show()+------------------------------------+
|actual |
+------------------------------------+
|9334166c-b138-4c9a-aa58-58e4830c5ddb|
|ad3c95df-5a0e-4a58-8ee0-c7d56e13681d|
+------------------------------------+
Suppose you have the following DataFrame:
+----------+
|some_date |
+----------+
|2021-03-15|
|2021-03-17|
|2021-03-21|
|null |
+----------+
Calculate the integer corresponding with the day of the week.
df.withColumn("actual", bebe_weekday(col("some_date")))+----------+------+
|some_date |actual|
+----------+------+
|2021-03-15|0 |
|2021-03-17|2 |
|2021-03-21|6 |
|null |null |
+----------+------+
Monday is considered the first day of the week (2021-03-15 was a Monday).
The BebeFunctions are prefixed with bebe in case any of these functions are added to org.apache.spark.sql.fucntions in the future. Seems unlikely.
Everyone is welcome to contribute.
Best way to get involved is to open an issue/PR or start a discussion.
We're particularly interested in contributions to add functionality that's commonly needed by the Spark programming community, but missing from the Spark API. All functions should be implemented as performant Catalyst expressions. A Stackoverflow question with a lot of views is good evidence that functionality is widely used by the Spark community.
-
Version bump commit and create GitHub tag
-
<TODO: add this> Publish documentation with
sbt ghpagesPushSite -
Publish JAR
Run sbt to open the SBT console.
Run > ; + publishSigned; sonatypeBundleRelease to create the JAR files and release them to Maven. These commands are made available by the sbt-sonatype plugin.
When the release command is run, you'll be prompted to enter your GPG passphrase.
The Sonatype credentials should be stored in the ~/.sbt/sonatype_credentials file in this format:
realm=Sonatype Nexus Repository Manager
host=oss.sonatype.org
user=$USERNAME
password=$PASSWORD