DataFrame & ML PipeLine 林煒清/WayneLin slide at : https://goo.gl/txohc2

關於講者 林煒清/Wayne [email protected] 軟體工程師 相關經驗：Hadoop , Spark 興趣 : 任何能夠讓電腦更聰明的東西

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Agenda DataFrame What Why API WordCount hand-on demo 1 ML PipeLine What Why API hand-on demo 2

What is DataFrame ● A distributed tabular data structure on semi-structured data names , types , properties relational query, declarative transformations partition1 DataFrame Name | Age | Sex Name | Age | Sex Name | Age | Sex Name | Age | Sex Name | Age | Sex Name | Age | Sex partition2 RDD ● DataFrame = RDD + Schema + Domain-Specific Language

Why use DataFrame ● similar as single-node tools (Python’s pandas, R’s dplyr) ● same capacity as SQL. & syntax consistency. ● more build-in functions (good news for python user) ● Spark automatically define and optimize RDD computaion for you

Why use DataFrame « future of Spark »

API Overview ● SQLContext ,HiveContext ● DataFrame ● Column ● sql.functions ● sql.types ● Row ● DataFrameNaFunctions ● DataFrameStatFunctions (1.4) ● GroupedData ● Window (1.4) ● DataFrameReader (1.4) ● DataFrameWriter (1.4)

share DataFrames via Metastore Look up tables Read table into DataFrame Save DataFrame to metastore in-memory DataFrame persistent DataFrame hiveContext.tables() hiveContext.table(“name”) df.saveAsTable(“name”) df.registerTempTable(“name”)

DataFrame , Pandas , RDD DataFrame <--> Pandas spark_df = context.createDataFrame(pandas_df) pandas_df = spark_df.toPandas() DataFrame <--> RDD people_df = people_rdd.toDF() people_rdd = people_df.rdd()

Operators Select Where GroupBy UDF (User defined Function) Join Union In When Over Between Like ...

Pandas DataFrame style df[df['age']>18][['name','age']] Projection & Filter SQL SELECT name, age FROM table WHERE age > 18 Spark DataFrame style df.filter(df['age']>18).select('name','age')

Aggregation Pandas / Spark DataFrame style from pyspark.sql.functions import avg,max df.groupBy('country').agg(avg('age'),max('age')) SQL SELECT name, AVG(age), MAX(age) FROM table GROUP BY name

from pyspark.sql.functions import udf from pyspark.sql.types import StringType,ArrayType split = udf(lambda x: x.split(), ArrayType(StringType())) df.select(split(df['sentence'])) User Defined Function (UDF) 指定輸出SQL類別 SparkSQL UDF your python function

+--------------+ | sentence| +--------------+ |spark spark fast fast fast| +--------------+ Word Count by DataFrame df = cxt.createDataFrame([('spark spark fast fast fast',)],['sentence']) from pyspark.sql.functions import udf from pyspark.sql.types import StringType,ArrayType from pyspark.sql.functions import explode +-----+-----+ | word|count| +-----+-----+ |spark| 2| | fast| 3| +-----+-----+ .groupBy('word').count()\ .show() groupBy +-----------------------------+ | split(sentence)| +-----------------------------+ |ArrayBuffer(spark, spark, ...| +-----------------------------+ split_udf = udf(lambda x: x.split(), ArrayType(StringType())) UDF split split_udf(df['sentence']) +-----+ | word| +-----+ |spark| |spark| | fast| | fast| | fast| +-----+ explode df.withColumn('word',explode(split_udf(df['sentence'])))\

還有感謝 TW Spark Group 眾多高手大大們幫忙架構系統環境 感謝 http://161.202.33.19:8000/

ML: ● high-level pipeline abstraction ● based on Spark Dataframe ● ML = scikit-learn pipeline + pandas dataframe What is ML MLlib: ● low-level implementation ● based on RDD

Spark ML Typical Machine Learning workflow(training phase) pre-processing feature extraction training tunning evaluation Pipeline

● Pipeline: clear and unified interface for complex machine learning workflow ○ same tool(dataframe) for data wrangling and machine learning . ○ easy inspection on any intermediate features. Why ML Learning Algorithm Feature Model tunning ● Model Tuning

Terms Pipeline is an Estimator and chains Transformers and Estimators together. Transformer can transform DataFrame. Estimator can be fitted on DataFrame. Transformer transform Estimator fit Pipeline fit fit

PipeLine Transformer Estimator DF1 Pipeline fitting DF2 Transformer rule: for each component, fit then transform DF3 Transform Transformer Estimator Transformer

Pipeline API from pyspark.ml.feature import * from pyspark.ml.classification import * from pyspark.ml import * stage1 = OneHotEncoder(inputCol="category",outputCol="code") stage2 = VectorAssembler(inputCol=["code","count"], outputCol="features") stage3 = LogisticRegression(featuresCol="features",labelCol="spam") pipeline = Pipeline(stages=[stage1,stage2,stage3]) model = pipeline.fit(training_df) prediction_df = model.transform(testing_df) Estimator Transformer OneHotEncoder VectorAssembler LogisticRegression "category" "count"

Q&A

All Databrick’s talks and blogs about Dataframe and ML pipeline till 2015/9/20 Spark Programming Guide http://0x0fff.com/spark-dataframes-are-faster-arent-they/ http://blog.madhukaraphatak.com/anatomy-of-spark-dataframe-api/ https://medium.com/@chris_bour/6-differences-between-pandas-and-spark- dataframes-1380cec394d2 https://software.intel.com/en-us/blogs/2015/05/01/restudy-schemardd- in-sparksql http://www.infoobjects.com/journey-of-schema-in-big-data-world/ https://issues.apache.org/jira/browse/SPARK-3530 Reference

Scala Q&A RDD SparkS QL DataF rame MLlib Stream ing SQL ML AL S Kafka

Q&A

Why use DataFrame(Cont.) RDD data.map(lambda x: (x[0], [int(x[1]), 1])) .reduceByKey(lambda x, y: [x[0] + y[0], x[1] + y[1]]) .map(lambda x: [x[0], x[1][0] / x[1][1]]) DataFrame data.groupBy("country").avg("age")

Indexing df.loc[[4,3,2],’A’] Mutation df[‘col’] = 3 grouped data indexing df.groupBy(‘key’)[‘A’,’B’] difference from Pandas

DataFrame Read/Write df = context.read .format("json") .options("samplingRatio","0.1") .load("/path/data.json") df.write .format("parquet") .mode("append") .partitionBy("year") .save("/path/data.json")

Pipeline fitting(cont.) fit on a DataFrame Pipeline Transformer Estimator Model Transformer Model training data data prediction