hadoop2, spark - big data paris 2020 cedric carbone.pdf · hadoop2, spark big data, real time,...
TRANSCRIPT
Cédric Carbone Twitter : @carbone
Hadoop2, Spark Big Data, real time, machine learning & use cases
Agenda
• Map Reduce
• Hadoop v1 limits
• Hadoop v2 and YARN
• Apache Spark
• Streaming : Spark vs Storm
• Machine Learning : Recommender System
• Use Case : Next Product To Buy
• Q&A
What’s hadoop • The Apache™ Hadoop® project develops open-
source software for reliable, scalable, distributed computing.
• Java framework for storage and running data transformation on large cluster of commodity hardware
• Licensed under the Apache v2 license
• Created from Google's MapReduce, BigTable and Google File System (GFS) papers
HDFS : Distributed Storage
• Distributed, • Scalable, • Portable, • Reliable file system for the Hadoop framework. Metadata / data separation:
• Name Nodes • Data Nodes
Map Reduce • Map() : parse inputs and generate 0 to n <key,
value>
• Reduce() : sums all values of the same key and generate a <key, value>
WordCount Example
• Each map take a line as an input and break into words – It emits a key/value pair of the word and 1
• Each Reducer sums the counts for each word – It emits a key/value pair of the word and sum
Map Reduce
Data Node 1
Data Node 2
Map Reduce
Map Reduce
Map Reduce
Map Reduce
Hadoop MapReduce v1
Hadoop MapReduce v1
Hadoop MapReduce v1
Not good for low-latency jobs on smallest dataset
Hadoop MapReduce v1
Hadoop MapReduce v1
Good for off-line batch jobs on massive data
Hadoop 1
• Batch ONLY
– High latency jobs
HDFS (Redundant, Reliable Storage)
MapReduce1 Cluster Resource Management + Data Processing
BATCH
HIVE Query
Pig Scripting
Cascading Accelerate Dev.
Hadoop2 : Big Data Operating System
• Customers want to store ALL DATA in one place and interact with it in MULTIPLE WAYS
– Simultaneously & with predictable levels of service
– Data analysts and real-time applications
HDFS (Redundant, Reliable Storage)
MapReduce1 Data Processing
BATCH
YARN (Cluster Resource Management)
Other Data Processing
…
Hadoop2 : Big Data Operating System
• Customers want to store ALL DATA in one place and interact with it in MULTIPLE WAYS
– Simultaneously & with predictable levels of service
– Data analysts and real-time applications
HDFS (Redundant, Reliable Storage)
YARN (Cluster Resource Management)
BATCH (MapReduce)
INTERACTIVE (Tez)
STREAMING (Storm, Samza Spark Streaming)
GRAPH (Giraph, GraphX)
Machine Learning
(Spark MLLIb)
In-Memory (Spark)
ONLINE (Hbase HOYA)
OTHER (ElasticSearch)
Stinger.next
Stinger.next
https://spark.apache.org
Apache Spark™ is a fast and general engine for large-scale data processing.
The most active project
0
50
100
150
200
250
Patches
MapReduce Storm
Yarn Spark
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Lines Added
MapReduce Storm
Yarn Spark
Spark won the Daytona GraySort contest!
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Sort on disk 100TB of data 3x faster than Hadoop MapReduce using 10x fewer machines.
RDD & Operation
Resilient Distributed Datasets (RDDs)
Operations
➜ Transformations (e.g. map, filter, groupBy)
➜ Actions (e.g. count, collect, save)
Spark
scala> val textFile = sc.textFile("README.md")
➜ textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
scala> textFile.count()
➜ res0: Long = 126
scala> textFile.first()
➜ res1: String = # Apache Spark
scala> val linesWithSpark = textFile.filter(line =>
line.contains("Spark"))
➜ linesWithSpark: spark.RDD[String]=spark.FilteredRDD@7dd4
scala>
textFile.filter(line=>line.contains("Spark")).count()
➜ res3: Long = 15
Streaming
Streaming
Storm
Storm
Storm vs Spark
Spark Streaming Storm Storm Trident
Processing model Micro batches Record-at-a-time Micro batches
Thoughput ++++ ++ ++++
Latency Second Sub-second Second
Reliability Models Exactly once At least once Exactly once
Embedded Hadoop Distro HDP, CDH, MapR HDP HDP
Support Databricks N/A N/A
Community ++++ ++ ++
Spark Storm
Scope Batch, Streaming, Graph, ML, SQL Streaming only
Machine Learning Library (Mllib)
Collaborative Filtering
Collaborative Filtering (learning)
Collaborative Filtering (learning)
Collaborative Filtering (learning)
Collaborative Filtering : Let’s use the model
Collaborative Filtering : similar behaviors
Collaborative Filtering Prediction
Netflix Prize (2009) Netflix is a provider of on-demand Internet streaming media
Input Data
UserID::MovieID::Rating::Timestamp 1::1193::5::978300760 1::661::3::978302109 1::914::3::978301968 Etc… 2::1357::5::978298709 2::3068::4::978299000 2::1537::4::978299620
Matric Factorization
The result
1 ; Lyndon Wilson ; 4.608531808535918 ; 858 ; Godfather, The (1972) 1 ; Lyndon Wilson ; 4.596556961095434 ; 318 ; Shawshank Redemption, The (1994) 1 ; Lyndon Wilson ; 4.575789377957803 ; 527 ; Schindler's List (1993) 1 ; Lyndon Wilson ; 4.549694932928024 ; 593 ; Silence of the Lambs, The (1991) 1 ; Lyndon Wilson ; 4.46311974037361 ; 919 ; Wizard of Oz, The (1939) 2 ; Benjamin Harrison ; 4.99545499047152 ; 318 ; Shawshank Redemption, The (1994) 2 ; Benjamin Harrison ; 4.94255532354725 ; 356 ; Forrest Gump (1994) 2 ; Benjamin Harrison ; 4.80168679606128 ; 527 ; Schindler's List (1993) 2 ; Benjamin Harrison ; 4.7874247577586795 ; 1097 ; E.T. the Extra-Terrestrial (1982) 2 ; Benjamin Harrison ; 4.7635998147872325 ; 110 ; Braveheart (1995) 3 ; Richard Hoover ; 4.962687467351026 ; 110 ; Braveheart (1995) 3 ; Richard Hoover ; 4.8316542374095315 ; 318 ; Shawshank Redemption, The (1994) 3 ; Richard Hoover ; 4.7307103243995385 ; 356 ; Forrest Gump (19
Real Time Big Data Use Case Next Gen Data Marketing Platform
Next Product To Buy
“2013 Definitive Guide to Social Marketing” - Marketo.
Ready for Omni-channel? Traditional marketing
Current approach cannot keep up…
200m people on Do Not Call list
99.9%
of online banners are never clicked.
44%
of direct
marketing is never opened.
86% of TV viewers
skip commercials
Buyers complete
60%
of their research before reaching out
to vendors.
Statement
2000 2010 2013 2015
Multi Channel
Cross Channel
Omni Channel
Consumer Graph
Next Product to Buy in Action
Open data
Premium data
1
Next Product to Buy in Action
ERP
CRM Loyalty
Brand data
Open data
Premium data
1
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
2
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
3
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
4
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
4
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
4
Next Product to Buy in Action
CRM Loyalty
ERP Brand data
Open data
Premium data
5
Brand Premium Open Social Influans
Sales
Social Interactions
Graph
Fine Tune
Engage
OnBoard
Suggest
+
+
Real Time Big Data Use Case Next Gen Data Marketing Platform
Next Product To Buy
➜ Right Person
➜ Right Product
➜ Right Price
➜ Right Time
➜ Right Channel
Cédric Carbone
@carbone
www.hugfrance.fr
@hugfrance
Questions?
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