Application Architectures with Hadoop - UK Hadoop User Group

Application Architectures with Hadoop Hadoop Users Group UK – November, 2014 slideshare.com/hadooparchbook Mark Grover | @mark_grover Ted Malaska | @TedMalaska

3 About Us •  Mark –  Software Engineer –  Committer on Apache Bigtop, committer and PPMC member on Apache Sentry (incubating). –  Contributor to Hadoop, Hive, Spark, Sqoop, Flume. •  Ted –  Principal Solutions Architect –  Previously Lead Architect at FINRA –  Contributor to Apache Hadoop, HBase, Spark, Flume, Avro and Pig ©2014 Cloudera, Inc. All Rights Reserved.

4 Case Study Clickstream Analysis

7 Web Logs – Combined Log Format ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http://bestcyclingreviews.com/top_online_shops" "Mozilla/ 5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 244.157.45.12 - - [17/Oct/2014:21:59:59 ] "GET /Store/cart.jsp? productID=1023 HTTP/1.0" 200 3757 "http://www.casualcyclist.com" "Mozilla/5.0 (Linux; U; Android 2.3.5; en-us; HTC Vision Build/ GRI40) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1”

8 Clickstream Analytics ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/ 2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/ top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/ 537.36”

11 Hadoop Architectural Considerations •  Storage managers? –  HDFS? HBase? •  Data storage and modeling: –  File formats? Compression? Schema design? •  Data movement –  How do we actually get the data into Hadoop? How do we get it out? •  Metadata –  How do we manage data about the data? •  Data access and processing –  How will the data be accessed once in Hadoop? How can we transform it? How do we query it? •  Orchestration –  How do we manage the workflow for all of this? ©2014 Cloudera, Inc. All Rights Reserved.

12 Architectural Considerations Data Storage and Modeling

13 Data Modeling Considerations •  We need to consider the following in our architecture: –  Storage layer – HDFS? HBase? Etc. –  File system schemas – how will we lay out the data? –  File formats – what storage formats to use for our data, both raw and processed data? –  Data compression formats? ©2014 Cloudera, Inc. All Rights Reserved.

14 Architectural Considerations Data Modeling – Storage Layer

16 Data Storage Layer Choices •  Stores data directly as files •  Fast scans •  Poor random reads/writes •  Stores data as Hfiles on HDFS •  Slow scans •  Fast random reads/writes ©2014 Cloudera, Inc. All Rights Reserved.

17 Data Storage – Storage Manager Considerations •  Incoming raw data: –  Processing requirements call for batch transformations across multiple records – for example sessionization. •  Processed data: –  Access to processed data will be via things like analytical queries – again requiring access to multiple records. •  We choose HDFS –  Processing needs in this case served better by fast scans. ©2014 Cloudera, Inc. All Rights Reserved.

18 Architectural Considerations Data Modeling – Data Storage Format

20 Architectural Considerations Data Modeling – HDFS Schema Design

22 Recommended HDFS Schema Design /user/<username> - User specific data, jars, conf files /etl – Data in various stages of ETL workflow /tmp – temp data from tools or shared between users /data – shared data for the entire organization /app – Everything but data: UDF jars, HQL files, Oozie workflows ©2014 Cloudera, Inc. All Rights Reserved.

23 Architectural Considerations Data Modeling – Advanced HDFS Schema Design

24 Partitioning ©2014 Cloudera, Inc. All Rights Reserved. dataset col=val1/file.txt col=val2/file.txt … col=valn/file.txt dataset file1.txt file2.txt … filen.txt Un-partitioned HDFS directory structure Partitioned HDFS directory structure

25 Partitioning considerations •  What column to partition by? –  Don’t have too many partitions (<10,000) –  Don’t have too many small files in the partitions –  Good to have partition sizes at least ~1 GB •  We’ll partition by timestamp. This applies to both our raw and processed data. ©2014 Cloudera, Inc. All Rights Reserved.

26 Architectural Considerations Data Ingestion

28 Streaming Ingestion •  Flume –  Reliable, distributed, and available system for efficient collection, aggregation and movement of streaming data, e.g. logs. •  Kafka –  Reliable and distributed publish-subscribe messaging system. ©2014 Cloudera, Inc. All Rights Reserved.

29 Flume vs. Kafka •  Purpose built for Hadoop data ingest. •  Pre-built sinks for HDFS, HBase, etc. •  Supports transformation of data in-flight. •  General pub-sub messaging framework. •  Just a message transport. •  Have to use third party tool to ingest. ©2014 Cloudera, Inc. All Rights Reserved.

31 Sources Interceptors Selectors Channels Sinks Flume Agent Short Intro to Flume Twitter, logs, JMS, webserver Mask, re-format, validate… DR, critical Memory, file, Kafka HDFS, HBase, Solr

32 A Brief Discussion of Flume Patterns – Fan-in •  Flume agent runs on each of our servers. •  These agents send data to multiple agents to provide reliability. •  Flume provides support for load balancing. ©2014 Cloudera, Inc. All Rights Reserved.

33 Ingestion Decisions •  Historical Data –  File transfer •  Incoming Data –  Flume with the spooling directory source. •  Relational Data Sources – ODS, CRM, etc. –  Sqoop ©2014 Cloudera, Inc. All Rights Reserved.

34 Architectural Considerations Data Processing – Engines

35 Processing Engines •  MapReduce •  Abstractions – Pig, Hive, Cascading, Crunch •  Spark •  Impala Confidentiality Information Goes Here

36 MapReduce •  Oldie but goody •  Restrictive Framework / Innovated Work Around •  Extreme Batch Confidentiality Information Goes Here

37 MapReduce Basic High Level Confidentiality Information Goes Here Mapper HDFS (Replicated) Native File System Block of Data Temp Spill Data Partitioned Sorted Data Reducer Reducer Local Copy Output File

38 Abstractions •  SQL –  Hive •  Script/Code –  Pig: Pig Latin –  Crunch: Java/Scala –  Cascading: Java/Scala Confidentiality Information Goes Here

39 Spark •  The New Kid that isn’t that New Anymore •  Easily 10x less code •  Extremely Easy and Powerful API •  Very good for machine learning •  Scala, Java, and Python •  RDDs •  DAG Engine Confidentiality Information Goes Here

40 Impala • Real-time open source MPP style engine for Hadoop • Doesn’t build on MapReduce • Written in C++, uses LLVM for run-time code generation • Can create tables over HDFS or HBase data • Accesses Hive metastore for metadata • Access available via JDBC/ODBC ©2014 Cloudera, Inc. All Rights Reserved.

41 Architectural Considerations Data Processing – What processing needs to happen?

42 What processing needs to happen? Confidentiality Information Goes Here •  Sessionization •  Filtering •  Deduplication •  BI / Discovery

43 Sessionization Confidentiality Information Goes Here Website visit Visitor 1 Session 1 Visitor 1 Session 2 Visitor 2 Session 1 > 30 minutes

44 Why sessionize? Confidentiality Information Goes Here Helps answers questions like: •  What is my website’s bounce rate? –  i.e. how many % of visitors don’t go past the landing page? •  Which marketing channels (e.g. organic search, display ad, etc.) are leading to most sessions? –  Which ones of those lead to most conversions (e.g. people buying things, signing up, etc.) •  Do attribution analysis – which channels are responsible for most conversions?

45 How to Sessionize? Confidentiality Information Goes Here 1.  Given a list of clicks, determine which clicks came from the same user 2.  Given a particular user's clicks, determine if a given click is a part of a new session or a continuation of the previous session

46 #1 – Which clicks are from same user? •  We can use: –  IP address (244.157.45.12) –  Cookies (A9A3BECE0563982D) –  IP address (244.157.45.12)and user agent string ((KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36") ©2014 Cloudera, Inc. All Rights Reserved.

47 #1 – Which clicks are from same user? •  We can use: –  IP address (244.157.45.12) –  Cookies (A9A3BECE0563982D) –  IP address (244.157.45.12)and user agent string ((KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36") ©2014 Cloudera, Inc. All Rights Reserved.

48 #1 – Which clicks are from same user? ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 244.157.45.12 - - [17/Oct/2014:21:59:59 ] "GET /Store/cart.jsp?productID=1023 HTTP/1.0" 200 3757 "http://www.casualcyclist.com" "Mozilla/5.0 (Linux; U; Android 2.3.5; en-us; HTC Vision Build/GRI40) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1”

49 #2 – Which clicks part of the same session? ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 244.157.45.12 - - [17/Oct/2014:21:59:59 ] "GET /Store/cart.jsp?productID=1023 HTTP/1.0" 200 3757 "http://www.casualcyclist.com" "Mozilla/5.0 (Linux; U; Android 2.3.5; en-us; HTC Vision Build/GRI40) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1” > 30 mins apart = different sessions

50©2014 Cloudera, Inc. All rights reserved. Sessionization engine recommendation •  We have sessionization code in MR, Spark on github. The complexity of the code varies, depends on the expertise in the organization. •  We choose MR, since it’s fairly simple and maintainable code.

51 Filtering – filter out incomplete records ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 244.157.45.12 - - [17/Oct/2014:21:59:59 ] "GET /Store/cart.jsp?productID=1023 HTTP/1.0" 200 3757 "http://www.casualcyclist.com" "Mozilla/5.0 (Linux; U…

52 Filtering – filter out records from bots/spiders ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 209.85.238.11 - - [17/Oct/2014:21:59:59 ] "GET /Store/cart.jsp?productID=1023 HTTP/1.0" 200 3757 "http://www.casualcyclist.com" "Mozilla/5.0 (Linux; U; Android 2.3.5; en-us; HTC Vision Build/GRI40) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1” Google spider IP address

53©2014 Cloudera, Inc. All rights reserved. Filtering recommendation •  Bot/Spider filtering can be done easily in any of the engines •  Incomplete records are harder to filter in schema systems like Hive, Impala, Pig, etc. •  Pretty close choice between MR, Hive and Spark •  Can be done in Flume interceptors as well •  We can simply embed this in our sessionization job

54 Deduplication – remove duplicate records ©2014 Cloudera, Inc. All Rights Reserved. 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36” 244.157.45.12 - - [17/Oct/2014:21:08:30 ] "GET /seatposts HTTP/1.0" 200 4463 "http:// bestcyclingreviews.com/top_online_shops" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36”

55©2014 Cloudera, Inc. All rights reserved. Deduplication recommendation •  Can be done in all engines. •  We already have a Hive table with all the columns, a simple DISTINCT query will perform deduplication •  We use Pig

56©2014 Cloudera, Inc. All rights reserved. BI/Discovery engine recommendation •  Main requirements for this are: –  Low latency –  SQL interface (e.g. JDBC/ODBC) –  Users don’t know how to code •  We chose Impala –  It’s a SQL engine –  Much faster than other engines –  Provides standard JDBC/ODBC interfaces

57 Architectural Considerations Orchestration

58©2014 Cloudera, Inc. All rights reserved. •  Workflow is fairly simple •  Need to trigger workflow based on data •  Be able to recover from errors •  Perhaps notify on the status •  And collect metrics for reporting Choosing… Easier in Oozie

59©2014 Cloudera, Inc. All rights reserved. •  Workflow is fairly simple •  Need to trigger workflow based on data •  Be able to recover from errors •  Perhaps notify on the status •  And collect metrics for reporting Choosing the right Orchestration Tool Better in Azkaban

60©2014 Cloudera, Inc. All rights reserved. •  The best orchestration tool is the one you are an expert on – Oozie – Spark Streaming, etc. don’t require orchestration tool Important Decision Consideration!

61 Putting It All Together Final Architecture

62©2014 Cloudera, Inc. All rights reserved. Final architecture Hadoop Cluster BI/Visualization tool (e.g. microstrategy) BI Analysts Spark For machine learning and graph processing R/Python Statistical Analysis Custom Apps 3. Accessing 2. Processing 4. Orchestration 1. Ingestion Operational Data Store CRM System Via Sqoop Web servers Website users Web logsVia Flume

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Application Architectures with Hadoop - UK Hadoop User Group

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