A BigData Tour HDFS, Ceph and MapReduce

Transcription

1 A BigData Tour HDFS, Ceph and MapReduce These slides are possible thanks to these sources Jonathan Drusi - SCInet Toronto Hadoop Tutorial, Amir Payberah - Course in Data Intensive Computing SICS; Yahoo! Developer Network MapReduce Tutorial

2 Disk (MB/s), CPU (MIPS) Data Intensive Computing Data volumes increasing massively! Clusters, storage capacity increasing massively! Disk speeds are not keeping pace.! Seek speeds even worse than read/write Mahout! data mining 1000x!

3 Scale-Out Disk streaming speed ~ 50MB/s! 3TB =17.5 hrs! 1PB = 8 months! Scale-out (weak scaling) - filesystem distributes data on ingest Jonathan Dursi

4 Seeking too slow! ~10ms for a seek! Enough time to read half a megabyte! Batch processing! Go through entire data set in one (or small number) of passes Scale-Out Jonathan Dursi

5 Combining results Each node preprocesses its local data! Shuffles its data to a small number of other nodes! Final processing, output is done there Jonathan Dursi

6 Fault Tolerance Data also replicated upon ingest! Runtime watches for dead tasks, restarts them on live nodes! Re-replicates Jonathan Dursi

7 Data Distribution: Disk Hadoop and similar architectures handle the hardest part of parallelism for you - data distribution.! On disk: HDFS distributes, replicates data as it comes in! Keeps track; computations local to data Jonathan Dursi

8 Data Distribution: Network On network: Map Reduce (eg) works in terms of key-value pairs.! Preprocessing (map) phase ingests data, emits (k,v) pairs! Shuffle phase assigns reducers, gets all pairs with same key onto that reducer.! Programmer does not have to design communication patterns (key1,17) (key5, 23) (key1,99) (key2, 12) (key1,83) (key2, 9) (key1,[17,99]) (key5,[23,83]) (key2,[12,9]) Jonathan Dursi

9 Big Data Analytics Stack Amir Payberah

10 Big Data Storage (sans POSIX) I Traditional filesystems are not well-designed for large-scale data processing systems. I E ciency has a higher priority than other features, e.g., directory service. I Massive size of data tends to store it across multiple machines in a distributed way. I HDFS, Amazon S3,... Amir Payberah

11 Big Data - Databases I Relational Databases Management Systems (RDMS) were not designed to be distributed. I NoSQL databases relax one or more of the ACID properties: BASE I Di erent data models: key/value, column-family, graph, document. I Dynamo, Scalaris, BigTable, Hbase, Cassandra, MongoDB, Voldemort, Riak, Neo4J,... Amir Payberah

12 Big Data Resource Management I Di erent frameworks require di erent computing resources. I Large organizations need the ability to share data and resources between multiple frameworks. I Resource management share resources in a cluster between multiple frameworks while providing resource isolation. I Mesos, YARN, Quincy,... Amir Payberah

13 Big Data Execution Engine I Scalable and fault tolerance parallel data processing on clusters of unreliable machines. I Data-parallel programming model for clusters of commodity machines. I MapReduce, Spark, Stratosphere, Dryad, Hyracks,... Amir Payberah

14 Big Data Query/Scripting Languages I Low-level programming of execution engines, e.g., MapReduce, is not easy for end users. I Need high-level language to improve the query capabilities of execution engines. I It translates user-defined functions to low-level API of the execution engines. I Pig, Hive, Shark, Meteor, DryadLINQ, SCOPE,... Amir Payberah

15 Hadoop Ecosystem 2008 onwards usage exploded Creation of many tools on top of Hadoop infrastructure

16 The Need For Filesystems I Controls how data is stored in and retrieved from disk. Amir Payberah

17 Distributed Filesystems I When data outgrows the storage capacity of a single machine: partition it across a number of separate machines. I Distributed filesystems: manage the storage across a network of machines. Amir Payberah

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19 What HDFS is not good for I Low-latency reads High-throughput rather than low latency for small chunks of data. HBase addresses this issue. I Large amount of small files Better for millions of large files instead of billions of small files. I Multiple writers Single writer per file. Writes only at the end of file, no-support for arbitrary o set. Amir Payberah

20 HDFS Architecture The Hadoop Distributed File System (HDFS) Offers a way to store large files across multiple machines, rather than requiring a single machine to have disk capacity equal to/greater than the summed total size of the files HDFS is designed to be faulttolerant Using data replication and distribution of data When a file is loaded into HDFS, it is replicated and broken up into "blocks" of data These blocks are stored across the cluster nodes designated for storage, a.k.a. DataNodes.

21 Files and Blocks 1/3 I Files are split into blocks. I Blocks Single unit of storage: a contiguous piece of information on a disk. Transparent to user. Managed by Namenode, storedbydatanode. Blocks are traditionally either 64MB or 128MB: default is 64MB. Amir Payberah

22 Files and Blocks 2/3 I Why is a block in HDFS so large? To minimize the cost of seeks. I Time to read a block = seek time + transfer time seektime I Keeping the ratio transfertime small: we are reading data from the disk almost as fast as the physical limit imposed by the disk. I Example: if seek time is 10ms and the transfer rate is 100MB/s, to make the seek time 1% of the transfer time, we need to make the block size around 100MB. Amir Payberah

23 Files and Blocks 3/3 I Same block is replicated on multiple machines: default is 3 Replica placements are rack aware. 1st replica on the local rack. 2nd replica on the local rack but di erent machine. 3rd replica on the di erent rack. I Namenode determines replica placement. Amir Payberah

24 HDFS Daemons HDFS cluster is manager by three types of processes Namenode Manages the filesystem, e.g., namespace, meta-data, and file blocks Metadata is stored in memory Datanode Stores and retrieves data blocks Reports to Namenode Runs on many machines Secondary Namenode Only for checkpointing. Not a backup for Namenode Amir Payberah

25 Reading a file Client:! Read lines from bigdata.dat 1. Open Reading a file shorter! Get block locations! Read from a replica Namenode /user/ljdursi/diffuse bigdata.dat Jonathan Dursi datanode1 datanode2 datanode3

26 Reading a file Client:! Read lines from bigdata.dat 2. Get block locations Reading a file shorter! Get block locations! Read from a replica Namenode /user/ljdursi/diffuse bigdata.dat Jonathan Dursi datanode1 datanode2 datanode3

27 Reading a file Client:! Read lines from bigdata.dat 3. read blocks Reading a file shorter! Get block locations! Read from a replica Namenode /user/ljdursi/diffuse bigdata.dat datanode1 datanode2 datanode3 Jonathan Dursi

28 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back! Complete Client:! Write newdata.dat 1. create Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

29 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back! Complete Client:! Write newdata.dat 2. get nodes Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

30 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back! Complete 3. start writing Client:! Write newdata.dat Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

31 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back! Complete Client:! Write newdata.dat 4. repl Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

32 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back (while writing)! Complete Client:! Write newdata.dat 5. ack Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

33 Writing a file Writing a file multiple stage process! Create file! Get nodes for blocks! Start writing! Data nodes coordinate replication! Get ack back! Complete Client:! Write newdata.dat 6. complete Namenode /user/ljdursi/diffuse datanode1 datanode2 datanode3 bigdata.dat Jonathan Dursi

34 Communication Protocol All HDFS communication protocols are layered on top of the TCP/IP protocol A client establishes a connection to a configurable TCP port on the NameNode machine and uses ClientProtocol DataNodes talk to the NameNode using DataNode protocol A Remote Procedure Call (RPC) abstraction wraps both the ClientProtocol and DataNode protocol NameNode never initiates a RPC, instead it only responds to RPC requests issued by DataNodes or clients

35 Robustness Primary objective of HDFS is to store data reliably even during failures Three common types of failures: NameNode, DataNode and network partitions Data disk failure Heartbeat messages to track the health of DataNodes NameNodes performs necessary re-replication on DataNode unavailability, replica corruption or disk fault Cluster rebalancing Automatically move data between DataNodes, if the free space on a DataNode falls below a threshold or during sudden high demand Data integrity Checksum checking on HDFS files, during file creation and retrieval Metadata disk failure Manual intervention no auto recovery, restart or failover

36 MAP-REDUCE

37 What is it? I A programming model: to batch process large data sets (inspired by functional programming). I An execution framework: to run parallel algorithms on clusters of commodity hardware. I Don t worry about parallelization, fault tolerance, data distribution, and load balancing (MapReduce takes care of these). I Hide system-level details from programmers. Amir Payberah

38 MapReduce Simple Dataflow I map function: processes data and generates a set of intermediate key/value pairs. I reduce function: merges all intermediate values associated with the same intermediate key. Amir Payberah

39 Word Count Was used as an example in the original MapReduce paper! Now basically the hello world of map reduce! Do a count of words of some set of documents.! A simple model of many actual web analytics problem file01 Hello World! Bye World file02 output/part Hello 2! World 2! Bye 1! Hadoop 2! Goodbye 1 Hello Hadoop Goodbye Hadoop Jonathan Dursi

40 High-Level Structure of a MR Program 1/2 mapper (filename, file-contents): for each word in file-contents: emit (word, 1) reducer (word, values): sum = 0 for each value in values: sum = sum + value emit (word, sum)

41 High-Level Structure of a MR Program 2/2 Several instances of the mapper function are created on the different machines in a Hadoop cluster mapper (filename, file-contents): for each word in file-contents: emit (word, 1) reducer (word, values): sum = 0 for each value in values: sum = sum + value emit (word, sum) Each instance receives a different input file (it is assumed that there are many such files) The mappers output (word, 1) pairs which are then forwarded to the reducers Several instances of the reducer method are also instantiated on the different machines Each reducer is responsible for processing the list of values associated with a different word The list of values will be a list of 1's; the reducer sums up those ones into a final count associated with a single word. The reducer then emits the final (word, count) output which is written to an output file.

42 Word Count How would you do this with a huge document?! Each time you see a word, if it s a new word, add a tick mark beside it, otherwise add a new word with a tick!...but hard to parallelize (updating the list) file01 Hello World! Bye World file02 output/part Hello 2! World 2! Bye 1! Hadoop 2! Goodbye 1 Hello Hadoop Goodbye Hadoop Jonathan Dursi

43 Word Count MapReduce way - all hard work is done by the shuffle - eg, automatically.! Map: just emit a 1 for each word you see file01 Hello World! Bye World (Hello,1)! (World,1)! (Bye, 1)! (World,1) file02 Hello Hadoop Goodbye Hadoop (Hello, 1)! (Hadoop, 1)! (Goodbye,1)! (Hadoop, 1) Jonathan Dursi

44 Word Count Shuffle assigns keys (words) to each reducer, sends (k,v) pairs to appropriate reducer! Reducer just has to sum up the ones (Hello,1)! (World,1)! (Bye, 1)! (World,1) (Hello,[1,1])! (World,[1,1])! (Bye, 1) (Hello, 1)! (Hadoop, 1)! (Goodbye,1)! (Hadoop, 1) (Hadoop, [1,1])! (Goodbye,1) I The shu e phase between map and reduce phase creates a list of values associated with each key. Hello 2! World 1! Bye 1 Hadoop 2! Goodbye 1 Jonathan Dursi

45 MapReduce and HDFS Amir Payberah