Introducing DataFrames in Spark for Large Scale Data Science
127 likes41,475 views
The document discusses the evolution of data processing from MapReduce to Apache Spark, highlighting the benefits of using DataFrames for large-scale data science. It outlines the features of DataFrames, including structured data handling, performance optimizations, and integration with machine learning pipelines. The presentation includes practical demonstrations and emphasizes the design philosophy aimed at making big data programming simpler and more efficient.
![Machine Learning Integration
27
tokenizer = Tokenizer(inputCol="text", outputCol="words”)
hashingTF = HashingTF(inputCol="words", outputCol="features”)
lr = LogisticRegression(maxIter=10, regParam=0.01)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
df = context.load("/path/to/data")
model = pipeline.fit(df)](https://image.slidesharecdn.com/2015-02-17dataframes-150218163844-conversion-gate01/85/Introducing-DataFrames-in-Spark-for-Large-Scale-Data-Science-27-320.jpg)
Introducing DataFrames in Spark for Large Scale Data Science
- 1. DataFrames for Large-scale Data Science Reynold Xin @rxin Feb 17, 2015 (Spark User Meetup)
- 2. 2 Year of the lamb, goat, sheep, and ram …?
- 3. A slide from 2013 … 3
- 4. From MapReduce to Spark public static class WordCountMapClass extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer itr = new StringTokenizer(line); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); output.collect(word, one); } } } public static class WorkdCountReduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } } val file = spark.textFile("hdfs://...") val counts = file.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.saveAsTextFile("hdfs://...")
- 5. Spark’s Growth 5 Google Trends for “Apache Spark”
- 6. Beyond Hadoop Users 6 Early adopters Data Scientists Statisticians R users … PyData Users Understands MapReduce & functional APIs
- 7. RDD API • Most data is structured (JSON, CSV, Avro, Parquet, Hive …) – Programming RDDs inevitably ends up with a lot of tuples (_1, _2, …) • Functional transformations (e.g. map/reduce) are not as intuitive 7
- 8. 8
- 9. DataFrames in Spark • Distributed collection of data grouped into named columns (i.e. RDD with schema) • Domain-specific functions designed for common tasks – Metadata – Sampling – Project, filter, aggregation, join, … – UDFs • Available in Python, Scala, Java, and R (via SparkR) 9
- 10. 10 0 2 4 6 8 10 RDD Scala RDD Python Spark Scala DF Spark Python DF Runtime performance of aggregating 10 million int pairs (secs)
- 11. Agenda • Introduction • Learn by demo • Design & internals – API design – Plan optimization – Integration with data sources 11
- 12. Learn by Demo (in a Databricks Cloud Notebook) • Creation • Project • Filter • Aggregations • Join • SQL • UDFs • Pandas 12 For the purpose of distributing the slides online, I’m attaching screenshots of the notebooks.
- 13. 13
- 14. 14
- 15. 15
- 16. 16
- 17. 17
- 18. 18
- 19. 19
- 20. 20
- 21. 21
- 22. 22
- 23. 23
- 24. 24
- 25. 25
- 26. 26
- 27. Machine Learning Integration 27 tokenizer = Tokenizer(inputCol="text", outputCol="words”) hashingTF = HashingTF(inputCol="words", outputCol="features”) lr = LogisticRegression(maxIter=10, regParam=0.01) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) df = context.load("/path/to/data") model = pipeline.fit(df)
- 28. Design Philosophy Simple tasks easy - DSL for common operations - Infer schema automatically (CSV, Parquet, JSON, …) - MLlib pipeline integration Performance - Catalyst optimizer - Code generation Complex tasks possible - RDD API - Full expression library Interoperability - Various data sources and formats - Pandas, R, Hive … 28
- 29. DataFrame Internals • Represented internally as a “logical plan” • Execution is lazy, allowing it to be optimized by Catalyst 29
- 30. Plan Optimization & Execution 30 SQL AST DataFrame Unresolved Logical Plan Logical Plan Op;mized Logical Plan Physical Plans Physical Plans RDDs Selected Physical Plan Analysis Logical Op;miza;on Physical Planning Cost Model Physical Plans Code Genera;on Catalog DataFrames and SQL share the same optimization/execution pipeline
- 31. 31
- 32. 32 joined = users.join(events, users.id == events.uid) filtered = joined.filter(events.date >= ”2015-01-01”) logical plan filter join scan (users) scan (events) physical plan join scan (users) filter scan (events) this join is expensive à
- 33. Data Sources supported by DataFrames 33 { JSON } built-in external JDBC and more …
- 34. More Than Naïve Scans • Data Sources API can automatically prune columns and push filters to the source – Parquet: skip irrelevant columns and blocks of data; turn string comparison into integer comparisons for dictionary encoded data – JDBC: Rewrite queries to push predicates down 34
- 35. 35 joined = users.join(events, users.id == events.uid) filtered = joined.filter(events.date > ”2015-01-01”) logical plan filter join scan (users) scan (events) optimized plan join scan (users) filter scan (events) optimized plan with intelligent data sources join scan (users) filter scan (events)
- 36. DataFrames in Spark • APIs in Python, Java, Scala, and R (via SparkR) • For new users: make it easier to program Big Data • For existing users: make Spark programs simpler & easier to understand, while improving performance • Experimental API in Spark 1.3 (early March) 36
- 37. Our Vision 37
- 39. More Information Blog post introducing DataFrames: http://tinyurl.com/spark-dataframes Build from source: http://github.com/apache/spark (branch-1.3)