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sarplus (preview)

pronounced sUrplus as it's simply better if not best!

Features

Scalable PySpark based implementation
Fast C++ based predictions
Reduced memory consumption: similarity matrix cached in-memory once per worker, shared accross python executors
Easy setup using Spark Packages

Benchmarks

# Users	# Items	# Ratings	Runtime	Environment	Dataset
2.5mio	35k	100mio	1.3h	Databricks, 8 workers, Azure Standard DS3 v2 (4 core machines)

Top-K Recommendation Optimization

There are a couple of key optimizations:

map item ids (e.g. strings) to a continuous set of indexes to optmize storage and simplify access
convert similarity matrix to exactly the representation the C++ component needs, thus enabling simple shared, memory mapping of the cache file and avoid parsing. This requires a customer formatter, written in Scala
shared read-only memory mapping allows us to re-use the same memory from multiple python executors on the same worker node
partition the input test users and past seen items by users, allowing for scale out
perform as much of the work as possible in PySpark (way simpler)
top-k computation ** reverse the join by summing reverse joining the users past seen items with any related items ** make sure to always just keep top-k items in-memory ** use standard join using binary search between users past seen items and the related items

Usage

import pandas as pd
from pysarplus import SARPlus

# spark dataframe with user/item/rating/optional timestamp tuples
train_df = spark.createDataFrame(
      pd.DataFrame({
        'user_id': [1, 1, 2, 3, 3],
        'item_id': [1, 2, 1, 1, 3],
        'rating':  [1, 1, 1, 1, 1],
    }))
   
# spark dataframe with user/item tuples
test_df = spark.createDataFrame(
      pd.DataFrame({
        'user_id': [1, 3],
        'item_id': [1, 3],
        'rating':  [1, 1],
    }))
    
model = SARPlus(spark, col_user='user_id', col_item='item_id', col_rating='rating', col_timestamp='timestamp')
model.fit(train_df, similarity_type='jaccard')


model.recommend_k_items(test_df, 'sarplus_cache', top_k=3).show()

# For databricks
# model.recommend_k_items(test_df, 'dbfs:/mnt/sarpluscache', top_k=3).show()

Jupyter Notebook

Insert this cell prior to the code above.

import os

SUBMIT_ARGS = "--packages eisber:sarplus:0.2.2 pyspark-shell"
os.environ["PYSPARK_SUBMIT_ARGS"] = SUBMIT_ARGS

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.appName("sample")
    .master("local[*]")
    .config("memory", "1G")
    .config("spark.sql.shuffle.partitions", "1")
    .config("spark.sql.crossJoin.enabled", True)
    .config("spark.ui.enabled", False)
    .getOrCreate()
)

PySpark Shell

pip install pysarplus
pyspark --packages eisber:sarplus:0.2.2 --conf spark.sql.crossJoin.enabled=true

Databricks

One must set the crossJoin property to enable calculation of the similarity matrix (Clusters / < Cluster > / Configuration / Spark Config)

spark.sql.crossJoin.enabled true

Navigate to your workspace
Create library
Under 'Source' select 'Maven Coordinate'
Enter 'eisber:sarplus:0.2.4'
Hit 'Create Library'
Attach to your cluster
Create 2nd library
Under 'Source' select 'Upload Python Egg or PyPI'
Enter 'pysarplus'
Hit 'Create Library'

This will install C++, Python and Scala code on your cluster.

You'll also have to mount shared storage

Create Azure Storage Blob
Create storage account (e.g. )
Create container (e.g. sarpluscache)
Navigate to User / User Settings
Generate new token: enter 'sarplus'
Use databricks shell (installation here)
databricks configure --token 4.1. Host: e.g. https://westus.azuredatabricks.net
databricks secrets create-scope --scope all --initial-manage-principal users
databricks secrets put --scope all --key sarpluscache 6.1. enter Azure Storage Blob key of Azure Storage created before
Run mount code

dbutils.fs.mount(
  source = "wasbs://sarpluscache@<accountname>.blob.core.windows.net",
  mount_point = "/mnt/sarpluscache",
  extra_configs = {"fs.azure.account.key.<accountname>.blob.core.windows.net":dbutils.secrets.get(scope = "all", key = "sarpluscache")})

Packaging

For databricks to properly install a C++ extension, one must take a detour through pypi. Use twine to upload the package to pypi.

cd python

python setup.py sdist

twine upload dist/pysarplus-*.tar.gz

On Spark one can install all 3 components (C++, Python, Scala) in one pass by creating a Spark Package. Documentation is rather sparse. Steps to install

Package and publish the pip package (see above)
Package the Spark package, which includes the Scala formatter and references the pip package (see below)
Upload the zipped Scala package to Spark Package through a browser. sbt spPublish has a few issues so it always fails for me. Don't use spPublishLocal as the packages are not created properly (names don't match up, issue) and furthermore fail to install if published to Spark-Packages.org.

cd scala
sbt spPublish

Testing

To test the python UDF + C++ backend

cd python 
python setup.py install && pytest -s tests/

To test the Scala formatter

cd scala
sbt test

(use ~test and it will automatically check for changes in source files, but not build.sbt)

6.5 KiB Исходник Ответственный История