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https://github.com/zero-one-group/geni
A Clojure dataframe library that runs on Spark
https://github.com/zero-one-group/geni
big-data clojure clojure-library clojure-repl data-engineering data-science dataframe distributed-computing high-performance-computing machine-learning parallel-computing spark
Last synced: 1 day ago
JSON representation
A Clojure dataframe library that runs on Spark
- Host: GitHub
- URL: https://github.com/zero-one-group/geni
- Owner: zero-one-group
- License: apache-2.0
- Created: 2020-04-18T10:46:14.000Z (over 4 years ago)
- Default Branch: develop
- Last Pushed: 2023-11-28T17:22:38.000Z (about 1 year ago)
- Last Synced: 2024-11-29T08:58:35.693Z (13 days ago)
- Topics: big-data, clojure, clojure-library, clojure-repl, data-engineering, data-science, dataframe, distributed-computing, high-performance-computing, machine-learning, parallel-computing, spark
- Language: Clojure
- Homepage:
- Size: 1.86 MB
- Stars: 286
- Watchers: 13
- Forks: 28
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-articles - Geni - Clojure Dataframes
README
Geni (*/gɜni/* or "gurney" without the r) is a [Clojure](https://clojure.org/) dataframe library that runs on [Apache Spark](https://spark.apache.org/). The name means "fire" in Javanese.
[](https://github.com/zero-one-group/geni/actions)
[](https://codecov.io/gh/zero-one-group/geni)
[](http://clojars.org/zero.one/geni)
[](LICENSE)
## 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](docs/semantics.md) 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
[](https://cljdoc.org/d/zero.one/geni/CURRENT)
[](https://clojurians.slack.com/messages/geni/)
[](https://clojurians.zulipchat.com/#narrow/stream/256615-geni)
## Basic Examples
All examples below use the Statlib California housing prices data available for free on [Kaggle](https://www.kaggle.com/camnugent/california-housing-prices).
Spark SQL API for data wrangling:
```clojure
(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](https://spark.apache.org/docs/latest/ml-pipeline.html):
```clojure
(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](examples/README.md).
## Quick Start
### Install Geni
Install the `geni` script to `/usr/local/bin` with:
```bash
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](https://github.com/zero-one-group/geni/releases) page. Run the uberjar as follows:
```bash
java -jar
```
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](http://leiningen.org/) to create a [template](https://github.com/zero-one-group/geni-template) of a Geni project:
```bash
lein new geni
```
`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:
[](http://clojars.org/zero.one/geni)
You would also need to add Spark as provided dependencies. For instance, have the following key-value pair for the `:profiles` map:
```clojure
:provided
{:dependencies [;; Spark
[org.apache.spark/spark-avro_2.12 "3.3.3"]
[org.apache.spark/spark-core_2.12 "3.3.3"]
[org.apache.spark/spark-hive_2.12 "3.3.3"]
[org.apache.spark/spark-mllib_2.12 "3.3.3"]
[org.apache.spark/spark-sql_2.12 "3.3.3"]
[org.apache.spark/spark-streaming_2.12 "3.3.3"]
; Arrow
[org.apache.arrow/arrow-memory-netty "4.0.0"]
[org.apache.arrow/arrow-memory-core "4.0.0"]
[org.apache.arrow/arrow-vector "4.0.0"
:exclusions [commons-codec com.fasterxml.jackson.core/jackson-databind]]
;; Databases
[mysql/mysql-connector-java "8.0.25"]
[org.postgresql/postgresql "42.2.20"]
[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](LICENSE).
## Mentions
Some parts of the project have been taken from or inspired by:
* [finagle-clojure](https://github.com/finagle/finagle-clojure) for Scala interop functions.
* [LispCast](https://lispcast.com/) for [exponential backoff](https://lispcast.com/exponential-backoff/).
* Reddit users [/u/borkdude](https://old.reddit.com/user/borkdude) and [/u/czan](https://old.reddit.com/user/czan) for [with-dynamic-import](src/zero_one/geni/utils.clj).
* StackOverflow user [whocaresanyway's answer](https://stackoverflow.com/questions/1696693/clojure-how-to-find-out-the-arity-of-function-at-runtime) for `arg-count`.
* [Julia Evans'](https://jvns.ca/) [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) for its syllabus.
* Reddit user [/u/joinr](https://old.reddit.com/user/joinr) for helping with [unit-testing the REPL](test/zero_one/geni/main_test.clj).
* [Sparkling](https://github.com/gorillalabs/sparkling), [sparkplug](https://github.com/amperity/sparkplug) and [Gabriel Borges](https://github.com/borgesgabriel) for helping with the RDD function serialisation.
* [Chris Nuernberger](https://github.com/cnuernber) and [Tomasz Sulej](https://github.com/tsulej) for helping with [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) and [tablecloth](https://github.com/scicloj/tablecloth).
* [Ubuntu](https://ubuntu.com/community/code-of-conduct), [Django](https://www.djangoproject.com/conduct/) and [Conjure](https://github.com/Olical/conjure/blob/master/.github/CODE_OF_CONDUCT.md) for their codes of conduct.
* [FZF](https://github.com/junegunn/fzf) for their issue template.