Geni (/gɜni/ or "gurney" without the r) is a Clojure dataframe library that runs on Apache Spark. The name means "fire" in Javanese.

Overview

Geni provides an idiomatic Spark interface for Clojure without the hassle of Java or Scala interop. Geni uses Clojure's -> threading macro as the main way to compose Spark's Dataset and Column operations in place of the usual method chaining in Scala. It also provides a greater degree of dynamism by allowing args of mixed types such as columns, strings and keywords in a single function invocation. See the docs section on Geni semantics for more details.

Resources

Docs	Cookbook
A Simple Performance Benchmark Code of Conduct Contributing Guide Creating Spark Schemas Examples Design Goals Geni Semantics Manual Dataset Creation Optional XGBoost Support Pandas, NumPy and Other Idioms Using Dataproc Using Kubernetes Where's The Spark Session Why? Working with SQL Maps Collecting Data from Spark Datasets	Getting Started with Clojure, Geni and Spark Reading and Writing Datasets Selecting Rows and Columns Grouping and Aggregating Combining Datasets with Joins and Unions String Operations Cleaning up Messy Data Timestamps and Dates Window Functions Reading from and Writing to SQL Databases Avoiding Repeated Computations with Caching Basic ML Pipelines Customer Segmentation with NMF

Basic Examples

All examples below use the Statlib California housing prices data available for free on Kaggle.

Spark SQL API for data wrangling:

(require '[zero-one.geni.core :as g])

(def dataframe (g/read-parquet! "test/resources/housing.parquet"))

(g/count dataframe)
=> 5000

(g/print-schema dataframe)
; root
;  |-- longitude: double (nullable = true)
;  |-- latitude: double (nullable = true)
;  |-- housing_median_age: double (nullable = true)
;  |-- total_rooms: double (nullable = true)
;  |-- total_bedrooms: double (nullable = true)
;  |-- population: double (nullable = true)
;  |-- households: double (nullable = true)
;  |-- median_income: double (nullable = true)
;  |-- median_house_value: double (nullable = true)
;  |-- ocean_proximity: string (nullable = true)

(-> dataframe (g/limit 5) g/show)
; +---------+--------+------------------+-----------+--------------+----------+----------+-------------+------------------+---------------+
; |longitude|latitude|housing_median_age|total_rooms|total_bedrooms|population|households|median_income|median_house_value|ocean_proximity|
; +---------+--------+------------------+-----------+--------------+----------+----------+-------------+------------------+---------------+
; |-122.23  |37.88   |41.0              |880.0      |129.0         |322.0     |126.0     |8.3252       |452600.0          |NEAR BAY       |
; |-122.22  |37.86   |21.0              |7099.0     |1106.0        |2401.0    |1138.0    |8.3014       |358500.0          |NEAR BAY       |
; |-122.24  |37.85   |52.0              |1467.0     |190.0         |496.0     |177.0     |7.2574       |352100.0          |NEAR BAY       |
; |-122.25  |37.85   |52.0              |1274.0     |235.0         |558.0     |219.0     |5.6431       |341300.0          |NEAR BAY       |
; |-122.25  |37.85   |52.0              |1627.0     |280.0         |565.0     |259.0     |3.8462       |342200.0          |NEAR BAY       |
; +---------+--------+------------------+-----------+--------------+----------+----------+-------------+------------------+---------------+

(-> dataframe (g/describe :housing_median_age :total_rooms :population) g/show)
; +-------+------------------+------------------+-----------------+
; |summary|housing_median_age|total_rooms       |population       |
; +-------+------------------+------------------+-----------------+
; |count  |5000              |5000              |5000             |
; |mean   |30.9842           |2393.2132         |1334.9684        |
; |stddev |12.969656616832669|1812.4457510408017|954.0206427949117|
; |min    |1.0               |1000.0            |100.0            |
; |max    |9.0               |999.0             |999.0            |
; +-------+------------------+------------------+-----------------+

(-> dataframe
    (g/group-by :ocean_proximity)
    (g/agg {:count        (g/count "*")
            :mean-rooms   (g/mean :total_rooms)
            :distinct-lat (g/count-distinct (g/int :latitude))})
    (g/order-by (g/desc :count))
    g/show)
; +---------------+-----+------------------+------------+
; |ocean_proximity|count|mean-rooms        |distinct-lat|
; +---------------+-----+------------------+------------+
; |INLAND         |1823 |2358.181020296215 |10          |
; |<1H OCEAN      |1783 |2467.5361749859785|7           |
; |NEAR BAY       |1287 |2368.72027972028  |2           |
; |NEAR OCEAN     |107  |2046.1869158878505|2           |
; +---------------+-----+------------------+------------+

(-> dataframe
    (g/select {:ocean :ocean_proximity
               :house (g/struct {:rooms (g/struct :total_rooms :total_bedrooms)
                                 :age   :housing_median_age})
               :coord (g/struct {:lat :latitude :long :longitude})})
    (g/limit 3)
    g/collect)
=> ({:ocean "NEAR BAY",
     :house {:rooms {:total_rooms 880.0, :total_bedrooms 129.0}, 
             :age 41.0},
     :coord {:lat 37.88, :long -122.23}}
    {:ocean "NEAR BAY",
     :house {:rooms {:total_rooms 7099.0, :total_bedrooms 1106.0}, 
             :age 21.0},
     :coord {:lat 37.86, :long -122.22}}
    {:ocean "NEAR BAY",
     :house {:rooms {:total_rooms 1467.0, :total_bedrooms 190.0}, 
             :age 52.0},
     :coord {:lat 37.85, :long -122.24}})

Spark ML example translated from Spark's programming guide:

(require '[zero-one.geni.core :as g])
(require '[zero-one.geni.ml :as ml])

(def training-set
  (g/table->dataset
    [[0 "a b c d e spark"  1.0]
     [1 "b d"              0.0]
     [2 "spark f g h"      1.0]
     [3 "hadoop mapreduce" 0.0]]
    [:id :text :label]))

(def pipeline
  (ml/pipeline
    (ml/tokenizer {:input-col :text
                   :output-col :words})
    (ml/hashing-tf {:num-features 1000
                    :input-col :words
                    :output-col :features})
    (ml/logistic-regression {:max-iter 10
                             :reg-param 0.001})))

(def model (ml/fit training-set pipeline))

(def test-set
  (g/table->dataset
    [[4 "spark i j k"]
     [5 "l m n"]
     [6 "spark hadoop spark"]
     [7 "apache hadoop"]]
    [:id :text]))

(-> test-set
    (ml/transform model)
    (g/select :id :text :probability :prediction)
    g/show)
;; +---+------------------+----------------------------------------+----------+
;; |id |text              |probability                             |prediction|
;; +---+------------------+----------------------------------------+----------+
;; |4  |spark i j k       |[0.1596407738787411,0.8403592261212589] |1.0       |
;; |5  |l m n             |[0.8378325685476612,0.16216743145233883]|0.0       |
;; |6  |spark hadoop spark|[0.0692663313297627,0.9307336686702373] |1.0       |
;; |7  |apache hadoop     |[0.9821575333444208,0.01784246665557917]|0.0       |
;; +---+------------------+----------------------------------------+----------+

More detailed examples can be found here.

Quick Start

Install Geni

Install the geni script to /usr/local/bin with:

wget https://raw.githubusercontent.com/zero-one-group/geni/develop/scripts/geni
chmod a+x geni
sudo mv geni /usr/local/bin/

The command geni downloads the latest Geni uberjar and places it in ~/.geni/geni-repl-uberjar.jar, and runs it with java -jar.

Uberjar

Download the latest Geni REPL uberjar from the release page. Run the uberjar as follows:

java -jar <uberjar-name>

The uberjar app prints the default SparkSession instance, starts an nREPL server with an .nrepl-port file for easy text-editor connection and steps into a Clojure REPL(-y).

Leiningen Template

Use Leiningen to create a template of a Geni project:

lein new geni <project-name>

cd into the project directory and do lein run. The templated app runs a Spark ML example, and then steps into a Clojure REPL-y with an .nrepl-port file.

Screencast Demos

Install	Uberjar	Leiningen

Installation

Add the following to your project.clj dependency:

You would also need to add Spark as provided dependencies. For instance, have the following key-value pair for the :profiles map:

:provided
{:dependencies [;; Spark
                [org.apache.spark/spark-avro_2.12 "3.1.1"]
                [org.apache.spark/spark-core_2.12 "3.1.1"]
                [org.apache.spark/spark-hive_2.12 "3.1.1"]
                [org.apache.spark/spark-mllib_2.12 "3.1.1"]
                [org.apache.spark/spark-sql_2.12 "3.1.1"]
                [org.apache.spark/spark-streaming_2.12 "3.1.1"]
                [com.github.fommil.netlib/all "1.1.2" :extension "pom"]
                ; Arrow
                [org.apache.arrow/arrow-memory-netty "2.0.0"]
                [org.apache.arrow/arrow-memory-core "2.0.0"]
                [org.apache.arrow/arrow-vector "2.0.0"
                :exclusions [commons-codec com.fasterxml.jackson.core/jackson-databind]]
                ;; Databases
                [mysql/mysql-connector-java "8.0.23"]
                [org.postgresql/postgresql "42.2.19"]
                [org.xerial/sqlite-jdbc "3.34.0"]
                ;; Optional: Spark XGBoost
                [ml.dmlc/xgboost4j-spark_2.12 "1.2.0"]
                [ml.dmlc/xgboost4j_2.12 "1.2.0"]]}

You may also need to install libatlas3-base and libopenblas-base to use a native BLAS, and install libgomp1 to train XGBoost4J models. When the optional dependencies are not present, the vars to the corresponding functions (such as ml/xgboost-classifier) will be left unbound.

License

Copyright 2020 Zero One Group.

Geni is licensed under Apache License v2.0, see LICENSE.

Mentions

Some parts of the project have been taken from or inspired by:

• finagle-clojure for Scala interop functions.
• LispCast for exponential backoff.
• Reddit users /u/borkdude and /u/czan for with-dynamic-import.
• StackOverflow user whocaresanyway's answer for arg-count.
• Julia Evans' Pandas Cookbook for its syllabus.
• Reddit user /u/joinr for helping with unit-testing the REPL.
• Sparkling, sparkplug and Gabriel Borges for helping with the RDD function serialisation.
• Chris Nuernberger and Tomasz Sulej for helping with tech.ml.dataset and tablecloth.
• Ubuntu, Django and Conjure for their codes of conduct.
• FZF for their issue template.