The Nitty Gritty of Advanced Analytics Using Apache Spark in Python
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Apache Spark is the next big data processing tool for Data Scientist. As seen on the recent StackOverflow analysis, it's the hottest big data technology on their site! In this talk, I'll use the PySpark interface to leverage the speed and performance of Apache Spark. I'll focus on the end to end workflow for getting data into a distributed platform, and leverage Spark to process the data for advanced analytics. I'll discuss the popular Spark APIs used for data preparation, SQL analysis, and ML algorithms. I'll explain the performance differences between Scala and Python, and how Spark has bridged the gap in performance. I'll focus on PySpark as the interface to the platform, and walk through a demo to showcase the APIs. Talk Overview: Spark's Architecture. What's out now and what's in Spark 2.0Spark APIs: Most common APIs used by Spark Common misconceptions and proper techniques for using Spark. Demo: Walk through ETL of the Reddit dataset. SparkSQL Analytics + Visualizations of the Dataset using MatplotLibSentiment Analysis on Reddit Comments
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The Nitty Gritty of Advanced Analytics Using Apache Spark in Python
- 1. The Nitty Gritty of Advanced Analytics Using Apache Spark in Python Miklos Christine Solutions Architect mwc@databricks.com, @Miklos_C
- 2. About Me Miklos Christine Solutions Architect @ Databricks - mwc@databricks.com - Miklos_C@twitter Systems Engineer @ Cloudera Supported a few of the largest clusters in the world Software Engineer @ Cisco UC Berkeley Graduate
- 3. We are Databricks, the company behind Spark Founded by the creators of Apache Spark in 2013 Share of Spark code contributed by Databricks in 2014 75% 3 Data Value Created Databricks on top of Spark to make big data simple.
- 4. … Apache Spark Engine Spark Core Spark Streaming Spark SQL MLlib GraphX Unified engine across diverse workloads & environments Scale out, fault tolerant Python, Java, Scala, and R APIs Standard libraries
- 6. 2012 started @ Berkeley 2010 research paper 2013 Databricks started & donated to ASF 2014 Spark 1.0 & libraries (SQL, ML, GraphX) 2015 DataFrames Tungsten ML Pipelines 2016 Spark 2.0
- 7. Spark Community Growth • Spark Survey 2015 Highlights • End of Year Spark Highlights
- 8. 2015: A Great Year for Spark Most active open source project in (big) data • 1000+ code contributors New language: R Widespread industry support & adoption
- 13. HOW RESPONDENTS ARE RUNNING SPARK 51% on a public cloud TOP ROLES USING SPARK of respondents identify themselves as Data Engineers 41% of respondents identify themselves as Data Scientists 22%
- 15. NOTABLE USERS THAT PRESENTED AT SPARK SUMMIT 2015 SAN FRANCISCO Source: Slide 5 of Spark Community Update
- 16. Large-Scale Usage Largest cluster: 8000 Nodes (Tencent) Largest single job: 1 PB (Alibaba, Databricks) Top Streaming Intake: 1 TB/hour (HHMI Janelia Farm) 2014 On-Disk Sort Record Fastest Open Source Engine for sorting a PB
- 18. History of Spark APIs RDD (2011) DataFrame (2013) Distribute collection of JVM objects Functional Operators (map, filter, etc.) Distribute collection of Row objects Expression-based operations and UDFs Logical plans and optimizer Fast/efficient internal representations DataSet (2015) Internally rows, externally JVM objects Almost the “Best of both worlds”: type safe + fast But slower than DF Not as good for interactive analysis, especially Python
- 19. Benefit of Logical Plan: Performance Parity Across Languages DataFrame RDD
- 20. ETL with Spark
- 21. ETL: Extract, Transform, Load ● Key factor for big data platforms ● Provides Speed Improvements in All Workloads ● Typically Executed by Data Engineers
- 22. File Formats ● Text File Formats ○ CSV ○ JSON ● Avro Row Format ● Parquet Columnar Format
- 23. File Formats + Compression ● File Formats ○ JSON ○ CSV ○ Avro ○ Parquet ● Compression Codecs ○ No compression ○ Snappy ○ Gzip ○ LZO
- 24. ● Industry Standard File Format: Parquet ○ Write to Parquet: df.write.format(“parquet”).save(“namesAndAges.parquet”) df.write.format(“parquet”).saveAsTable(“myTestTable”) ○ For compression: spark.sql.parquet.compression.codec = (gzip, snappy) Spark Parquet Properties
- 25. Small Files Problem ● Small files problem still exists ● Metadata loading ● APIs: df.coalesce(N) df.repartition(N) Ref: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame
- 26. ● RDD / DataFrame Partitions df.rdd.getNumPartitions() ● SparkSQL Shuffle Partitions spark.sql.shuffle.partitions ● Table Level Partitions df.write.partitionBy(“year”). save(“data.parquet”) All About Partitions
- 27. # CSV df = sqlContext.read. format('com.databricks.spark.csv'). options(header='true', inferSchema='true'). load('/path/to/data') # JSON df = sqlContext.read.json("/tmp/test.json") df.write.json("/tmp/test_output.json") PySpark ETL APIs - Text Formats
- 28. PySpark ETL APIs - Container Formats # Binary Container Formats # Avro df = sqlContext.read. format("com.databricks.spark.avro"). load("/path/to/files/") # Parquet df = sqlContext.read.parquet("/path/to/files/") df.write.parquet("/path/to/files/")
- 29. ● Manage Number of Files ○ APIs manage the number of files per directory df.repartition(80). write. parquet("/path/to/parquet/") df.repartition(80) partitionBy("year") write. parquet("/path/to/parquet/") PySpark ETL APIs
- 30. Common ETL Problems ● Malformed JSON Records sqlContext.sql("SELECT _corrupt_record FROM jsonTable WHERE _corrupt_record IS NOT NULL") ● Mismatched DataFrame Schema ○ Null Representation vs Schema DataType ● Many Small Files / No Partition Strategy ○ Parquet Files: ~128MB - 256MB Compressed Ref: https://databricks.gitbooks.io/databricks-spark-knowledge- base/content/best_practices/dealing_with_bad_data.html
- 31. Debugging Spark Spark Driver Error: Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 362.0 failed 4 times, most recent failure: Lost task 1.3 in stage 362.0 (TID 275202, ip-10-111-225-98.ec2.internal): java.nio.channels.ClosedChannelException Spark Executor Error: 16/04/13 20:02:16 ERROR DefaultWriterContainer: Aborting task. java.text.ParseException: Unparseable number: "N" at java.text.NumberFormat.parse(NumberFormat.java:385) at com.databricks.spark.csv.util.TypeCast$$anonfun$castTo$4.apply$mcD$sp(TypeCast.scala:58) at com.databricks.spark.csv.util.TypeCast$$anonfun$castTo$4.apply(TypeCast.scala:58) at com.databricks.spark.csv.util.TypeCast$$anonfun$castTo$4.apply(TypeCast.scala:58) at scala.util.Try.getOrElse(Try.scala:77) at com.databricks.spark.csv.util.TypeCast$.castTo(TypeCast.scala:58)
- 32. Debugging Spark
- 33. SQL with Spark
- 34. SparkSQL Best Practices ● DataFrames and SparkSQL are synonyms ● Use builtin functions instead of custom UDFs ○ import pyspark.sql.functions ● Examples: ○ to_date() ○ get_json_object() ○ regexp_extract() ○ hour() / minute() Ref: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#module-pyspark.sql.functions
- 35. SparkSQL Best Practices ● Large Table Joins ○ Largest Table on LHS ○ Increase Spark Shuffle Partitions ○ Leverage “cluster by” API included in Spark 1.6 sqlCtx.sql("select * from large_table_1 cluster by num1") .registerTempTable("sorted_large_table_1"); sqlCtx.sql(“cache table sorted_large_table_1”);
- 36. PySpark API Best Practices ● User Defined Functions (UDFs) from pyspark.sql import functions as F add_n = udf(lambda x, y: x + y, IntegerType()) # We register a UDF that adds a column to the DataFrame, and we cast the id column to an Integer type. df = df.withColumn('id_offset', add_n( F.lit(1000), df.id.cast(IntegerType())))
- 37. PySpark API Best Practices ● Built-in Functions corpus_df = df.select( F.lower( F.col('body')).alias('corpus'), F.monotonicallyIncreasingId().alias('id')) corpus_df = df.select( F.date_format( F.from_utc_timestamp( F.from_unixtime(F.col('created_utc'), "PST"), 'EEEE')).alias ('dayofweek')) Ref: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#module-pyspark.sql.functions
- 38. PySpark API Best Practices ● User Defined Functions (UDFs) def squared(s): return s * s sqlContext.udf.register("squaredWithPython", squared) display(df.select("id", squared_udf("id").alias("id_squared")))
- 39. ML with Spark
- 41. Why Spark ML Provide general purpose ML algorithms on top of Spark • Let Spark handle the distribution of data and queries; scalability • Leverage its improvements (e.g. DataFrames, Datasets, Tungsten) Advantages of MLlib’s Design: • Simplicity • Scalability • Streamlined end-to-end • Compatibility
- 42. High-level functionality in MLlib Learning tasks Classification Regression Recommendation Clustering Frequent itemsets 42 Workflow utilities • Model import/export • Pipelines • DataFrames • Cross validation Data utilities • Feature extraction & selection • Statistics • Linear algebra
- 43. Machine Learning: What and Why? ML uses data to identify patterns and make decisions. Core value of ML is automated decision making • Especially important when dealing with TB or PB of data Many Use Cases including: • Marketing and advertising optimization • Security monitoring / fraud detection • Operational optimizations
- 44. Algorithm coverage in MLlib Classification • Logistic regression w/ elastic net • Naive Bayes • Streaming logistic regression • Linear SVMs • Decision trees • Random forests • Gradient-boosted trees • Multilayer perceptron • One-vs-rest Regression • Least squares w/ elastic net • Isotonic regression • Decision trees • Random forests • Gradient-boosted trees • Streaming linear methods Recommendation • Alternating Least Squares Frequent itemsets • FP-growth • Prefix span Clustering • Gaussian mixture models • K-Means • Streaming K-Means • Latent Dirichlet Allocation • Power Iteration Clustering Statistics • Pearson correlation • Spearman correlation • Online summarization • Chi-squared test • Kernel density estimation Linear algebra • Local dense & sparse vectors & matrices • Distributed matrices • Block-partitioned matrix • Row matrix • Indexed row matrix • Coordinate matrix • Matrix decompositions Model import/export Pipelines Feature extraction & selection • Binarizer • Bucketizer • Chi-Squared selection • CountVectorizer • Discrete cosine transform • ElementwiseProduct • Hashing term frequency • Inverse document frequency • MinMaxScaler • Ngram • Normalizer • One-Hot Encoder • PCA • PolynomialExpansion • RFormula • SQLTransformer • Standard scaler • StopWordsRemover • StringIndexer • Tokenizer • StringIndexer • VectorAssembler • VectorIndexer • VectorSlicer • Word2Vec List based on Spark 1.5 44
- 45. Spark ML Best Practices ● Spark MLLib vs SparkML ○ Understand the differences ● Don’t Pipeline Too Many Stages ○ Check Results Between Stages
- 46. PySpark ML API Best Practices
- 47. PySpark ML API Best Practices
- 48. ● DataFrame to RDD Mapping def tokenize(text): tokens = word_tokenize(text) lowercased = [t.lower() for t in tokens] no_punctuation = [] for word in lowercased: punct_removed = ''.join([letter for letter in word if not letter in PUNCTUATION]) no_punctuation.append(punct_removed) no_stopwords = [w for w in no_punctuation if not w in STOPWORDS] stemmed = [STEMMER.stem(w) for w in no_stopwords] return [w for w in stemmed if w] rdd = wordsDataFrame.map(lambda x: (x.__getitem__('id'), tokenize(x.__getitem__('corpus')))) PySpark ML API Best Practices
- 49. Learning more about MLlib Guides & examples • Example workflow using ML Pipelines (Python) • The above 2 links are part of the Databricks Guide, which contains many more examples and references. References • Apache Spark MLlib User Guide • The MLlib User Guide contains code snippets for almost all algorithms, as well as links to API documentation. • Meng et al. “MLlib: Machine Learning in Apache Spark.” 2015. http://arxiv. org/abs/1505.06807 (academic paper) 49
- 50. Spark Demo
- 51. Thanks! Sign Up For Databricks Community Edition! http://go.databricks.com/databricks-community- edition-beta-waitlist