Hadoop Fundamentals
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The document discusses Hadoop as a distributed data storage and processing infrastructure, capable of handling large-scale data from a variety of sources. It explains the Hadoop Distributed File System (HDFS) architecture, its fault tolerance, and how data is managed with blocks across multiple nodes. Additionally, it covers the MapReduce programming model that allows for efficient data processing across a cluster of machines.
![In our example
‣ Map: (doc-num, text) ➝ [(word, doc-num)]
‣ Reduce: (word, [doc1, doc3, ...]) ➝ [(word, “doc1, doc3, …”)]
General form:
‣ Two functions: Map and Reduce
‣ Operate on key and value pairs
‣ Map: (K1, V1) ➝ list(K2, V2)
‣ Reduce: (K2, list(V2)) ➝ (K3, V3)
‣ Primitives present in Lisp and other functional languages
Same principle extended to distributed computing
‣ Map and Reduce tasks run on distributed sets of machines
This is Map-Reduce](https://image.slidesharecdn.com/hadoop-fundamentals-140321091100-phpapp01/85/Hadoop-Fundamentals-21-320.jpg)
Hadoop Fundamentals
- 2. Why Hadoop?
- 3. Big Data • Sources: Server logs, clickstream, machine, sensor, social… • Use-cases: batch/interactive/real-time
- 4. Scalable o Petabytes of data Economical o Use commodity hardware o Share clusters among many applications Reliable o Failure is common when you run thousands of machines. Handle it well in the SW layer. Simple programming model o Applications must be simple to write and maintain What is needed from a Distributed Platform?
- 5. Hadoop is peta-byte scale distributed data storage and data processing infrastructure Based on Google GFS & MR paper Contributed mostly by Yahoo! in the initial years and now have a more widespread developer and user base 1000s of nodes, PBs of data in storage What is Hadoop?
- 6. • Cheap JBODs for storage • Move processing to where data is Location awareness (topology) • Assume hardware failures to be the norm • Map & Reduce primitives are fairly simple yet powerful Most set operations can be performed using these primitives • Isolation Hadoop Basics
- 7. Hadoop Distributed File System (HDFS)
- 8. Goals: Fault tolerant, scalable, distributed storage system Designed to reliably store very large files across machines in a large cluster Assumptions: Files are written once and read several times Applications perform large sequential streaming reads Not a Unix-like, POSIX file system Access via command line or Java API HDFS
- 9. • Data is organized into files and directories • Files are divided into uniform sized blocks and distributed across cluster nodes • Blocks are replicated to handle hardware failure • Filesystem keeps checksums of data for corruption detection and recovery • HDFS exposes block placement so that computes can be migrated to data HDFS – Data Model
- 11. • Namenode is SPOF (HA for NN is now available in 2.0 Alpha) • Responsible for managing a list of all active data nodes, FS name system (files, directories, blocks and their locations) • Block placement policy • Ensuring adequate replicas • Writing edit logs durably Namenode
- 12. • Service to allow data to be streamed in & out • Block is the unit of data that data node understands • Block reports to Namenode periodically • Checksum checks, disk usage stats are managed by datanode • Clients talk to datanode for actual data • As long as there is at least one data node available to service file blocks, failures in datanodes can be tolerated, albeit at lower performance. Datanode
- 13. HDFS – Write pipeline DFS Client Namenode Data node 1 Data node 2 Data node 3 Rack 2 Create file, get Block Loc (1) DN 1, 2 & 3 (2) Stream file (5) Ack (5a) Ack (4a) Ack (3a) Complete file (3b) Rack 1
- 14. • Default is 3 replicas, but configurable • Blocks are placed (writes are pipelined): On same node On different rack On the other rack • Clients read from closest replica • If the replication for a block drops below target, it is automatically re-replicated. HDFS – Block placement
- 15. • Data is checked with CRC32 • File Creation ‣ Client computes checksum per block ‣ DataNode stores the checksum • File access ‣ Client retrieves the data and checksum from DataNode ‣ If Validation fails, Client tries other replicas HDFS – Data correctness
- 16. Simple commands • hadoop fs -ls, -du, -rm, -rmr, -chown, -chmod Uploading files • hadoop fs -put foo mydata/foo • cat ReallyBigFile | hadoop fs -put - mydata/ReallyBigFile Downloading files • hadoop fs -get mydata/foo foo • hadoop fs -get - mydata/ReallyBigFile | grep “the answer is” • hadoop fs -cat mydata/foo Admin • hadoop dfsadmin –report • hadoop fsck Interacting with HDFS
- 17. Map-Reduce
- 18. Say we have 100s of machines available to us. How do we write applications on them? As an example, consider the problem of creating an index for search. ‣ Input: Hundreds of documents ‣ Output: A mapping of word to document IDs ‣ Resources: A few machines Map-Reduce Application
- 19. The problem : Inverted Index Farmer1 has the following animals: bees, cows, goats. Some other animals … Animals: 1, 2, 3, 4, 12 Bees: 1, 2, 23, 34 Dog: 3,9 Farmer1: 1, 7 …
- 20. Building an inverted index Machine1 Machine2 Machine3 Animals: 1,3 Dog: 3 Animals:2,12 Bees: 23 Dog:9 Farmer1: 7 Machine4 Animals: 1,3 Animals:2,12 Bees:23 Machine5 Dog: 3 Dog:9 Farmer1: 7 Machine4 Animals: 1,2,3,12 Bees:23 Machine5 Dog: 3,9 Farmer1: 7
- 21. In our example ‣ Map: (doc-num, text) ➝ [(word, doc-num)] ‣ Reduce: (word, [doc1, doc3, ...]) ➝ [(word, “doc1, doc3, …”)] General form: ‣ Two functions: Map and Reduce ‣ Operate on key and value pairs ‣ Map: (K1, V1) ➝ list(K2, V2) ‣ Reduce: (K2, list(V2)) ➝ (K3, V3) ‣ Primitives present in Lisp and other functional languages Same principle extended to distributed computing ‣ Map and Reduce tasks run on distributed sets of machines This is Map-Reduce
- 22. Abstracts functionality common to all Map/Reduce applications ‣ Distribute tasks to multiple machines ‣ Sorts, transfers and merges intermediate data from all machines from the Map phase to the Reduce phase ‣ Monitors task progress ‣ Handles faulty machines, faulty tasks transparently Provides pluggable APIs and configuration mechanisms for writing applications ‣ Map and Reduce functions ‣ Input formats and splits ‣ Number of tasks, data types, etc… Provides status about jobs to users Map-Reduce Framework
- 23. MR – Architecture Job Client Job Tracker DFS Client DFS Client DFS Client DFS Client DFS Client DFS Client Task Tracker Heartbeat Task Assignment Shuffle Submit Progress H D F S
- 24. • All user code runs in isolated JVM • Client computes splits • JT just schedules these splits (one mapper per split) • Mapper, Reducer, Partitioner and Combiner and any custom Input/OutputFormat runs in user JVM • Idempotence Map-Reduce
- 25. Hadoop HDFS + MR cluster Machines with Datanodes and Tasktrackers D D D DTT JobTracker Namenode T T TD Client Submit Job HTTP Monitoring UI Get Block Locations
- 26. • Input: A bunch of large text files • Desired Output: Frequencies of Words WordCount: Hello World of Hadoop
- 27. Hadoop – Two services in one
- 28. Mapper ‣ Input: value: lines of text of input ‣ Output: key: word, value: 1 Reducer ‣ Input: key: word, value: set of counts ‣ Output: key: word, value: sum Launching program ‣ Defines the job ‣ Submits job to cluster Word Count Example
- 29. Questions ?