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

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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
  1. Getting Started with Clojure, Geni and Spark
  2. Reading and Writing Datasets
  3. Selecting Rows and Columns
  4. Grouping and Aggregating
  5. Combining Datasets with Joins and Unions
  6. String Operations
  7. Cleaning up Messy Data
  8. Timestamps and Dates
  9. Window Functions
  10. Reading from and Writing to SQL Databases
  11. Avoiding Repeated Computations with Caching
  12. Basic ML Pipelines
  13. Customer Segmentation with NMF

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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:

Clojars Project

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 "3.0.1"]
                [org.apache.spark/spark-core_2.12 "3.0.1"]
                [org.apache.spark/spark-hive_2.12 "3.0.1"]
                [org.apache.spark/spark-mllib_2.12 "3.0.1"]
                [org.apache.spark/spark-sql_2.12 "3.0.1"]
                [org.apache.spark/spark-streaming_2.12 "3.0.1"]
                [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 "42.2.18"]
                [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:


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