bw tech meetup: hadoop and the rise of big data
DESCRIPTION
Hadoop is an open source, distributed computation platform, that is very important in the worlds of search, analytics, and big data. Donald Miner, a Solutions Architect at Greenplum, will give an hour presentation that will focus on ways to get started with Hadoop and provide advice on how successfully utilize the platform Specific topics of discussion include how Hadoop works, what Hadoop should and should not be used for, MapReduce design patterns, and the upcoming synergy of SQL and NoSQL in Hadoop.TRANSCRIPT
About Don
Hadoop
• Distributed platform up to thousands of nodes• Data storage and application framework• Started at Yahoo!• Open source• Based on a few Google papers (2003, 2004)• Runs on commodity hardware
I’M HERE TO TELL YOU WHY HADOOP IS AWESOME
Hadoop users• Yahoo!• Facebook• eBay• AOL
• Riot Games• ComScore• Twitter• LinkedIn
Hadoop Companies• Cloudera, Hortonworks, EMC/Greenplum, IBM• Numerous startups
Buzzword glossary
• Unstructured & Structured Data• NoSQL• Big Data (volume, velocity, variety)• Data Science• Cloud computing
Hadoop component overview
• Core components:– HDFS (Hadoop Distributed File System)– MapReduce (Data analysis framework)
• Ecosystem– HBase (key-value store)– Pig (high-level data analysis language)– Hive (SQL-like data analysis language)– ZooKeeper (stores metadata)– Other stuff
Use cases
• Text processing– Indexing, counting, processing
• Large-scale reports• Data science• Mixing data sources (data lakes)• Ad targeting• Image/Video/Audio processing• Cybersecurity
HDFS
• Stores files in folders (that’s it)– Nobody cares what’s in your files
• Chunks large files into blocks (~64MB-1GB)• Blocks are scattered all over the place• 3 replicates of each block (better safe than sorry)• One NameNode (might be sorry)– Knows which computers blocks live on– Knows which blocks belong to which files
• One DataNode per computer (slaves!)– Hosts files
HDFS Demonstration
MapReduce• Analyzes data in HDFS where the data is• Jobs are split into Mappers and Reducers• JobTracker – keeps track of running jobs• TaskTracker – one per computer, executes tasks• Mappers (you code this)– Loads data from HDFS– Filter, transform, parse– Outputs (key, value) pairs
• Reducers (you code this, too)– Groups by the mapper’s output key– Aggregate, count, statistics– Outputs to HDFS
MapReduce Demonstration
Hadoop ecosystem
• HDFS and MapReduce don’t do everything• Pig – high-level language
• Hive – high-level SQL language
• HBase – key/value store
grpd = GROUP logs BY userAgent;counts = FOREACH grpd GENERATE group, AVG(logs.timeMicroSec)/1.0E+06 AS loadTimeSec;byCount = ORDER counts BY loadTimeSec DESC;top = limit byCount 15;
SELECT grp, SUM(col2), COUNT(*) FROM table1 GROUP BY grp;
Cool thing #1: Linear Scalability
• HDFS and MapReduce scale linearly• If you have twice as many computers, things run
twice as fast• If you have twice as much data, things run twice
as slow• If you have twice as many computers, you can
store twice as much data• This stays true (some minor caveats)• DATA LOCALITY!!
Cool thing #2: Schema on Read
LOAD DATA ???? PROFIT!!
Data is parsed/interpreted as it is loaded out of HDFS
What implications does this have?
Before:ETL, schema design, tossing out original data
Keep original data around!Have multiple views of the same data!Store first, figure out what to do with it later!
NOW:
Cool thing #3: Transparent Parallelism
Network programming?
Inter-process communication?
Threading?
Distributed stuff?
With MapReduce, I DON’T CARE
MapReduceSolution
… I just have to fit my solution into this tiny box
Fault tolerance?
Code deployment?RPC?
Message passing?
Locking?
Data center fires?
Cool thing #4: Cheap
• Commodity hardware (meh)• Open source (people cost more though)• Add more hardware later
How to get started
• Install Hadoop in a Linux VM– Wait how is this helpful?? Hadoop is distributed!
• Use Google (seriously)
• Some prerequisites: Java, Linux, Data, Time
Stuff Hadoop is good at
• Batch processing• Processing lots of data• Outputting lots of data• Storing lots of historical data• Flexible analysis of data• Dealing with unstructured or structured data
Stuff Hadoop is not good at
• Hadoop is a freight truck, not a sports car• Updating data (think “append-only”)• Being easy to use– Java– Administration
• Hadoop is not good storage (don’t throw away your EMC stuff!)
