Introducing DataFrames in Spark for Large Scale Data Science
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View video of this presentation here: https://www.youtube.com/watch?v=vxeLcoELaP4 Introducing DataFrames in Spark for Large-scale Data Science
![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)
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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
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- 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.
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- 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
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- 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)