google云计算课程Module 5 - Hadoop Technical Review(可编辑)

google云计算课程Module 5 - Hadoop Technical Review(可编辑) google云计算课程Module 5 - Hadoop Technical Review Google Cluster Computing Faculty Training Workshop Module V Hadoop Technical Review Overview Hadoop Technical Walkthrough HDFS Databases Using Hadoop in an Academic Environment Performance tips and other tools You Say tomato Some MapReduce Terminology Job – A full program - an execution of a Mapper and Reducer across a data set Task – An execution of a Mapper or a Reducer on a slice of data aka Task-In-Progress TIP Task Attempt – A particular instance of an attempt to execute a task on a machine Terminology Example Running Word Count across 20 files is one job 20 files to be mapped imply 20 map tasks some number of reduce tasks At least 20 map task attempts will be performed more if a machine crashes etc Task Attempts A particular task will be attempted at least once possibly more times if it crashes If the same input causes crashes over and over that input will eventually be abandoned Multiple attempts at one task may occur in parallel with speculative execution turned on Task ID from TaskInProgress is not a unique identifier dont use it that way MapReduce High Level Node-to-Node Communication Hadoop uses its own RPC protocol All communication begins in slave nodes Prevents circular-wait deadlock Slaves periodically poll for status message Classes must provide explicit serialization Nodes Trackers Tasks Master node runs JobTracker instance which accepts Job requests from clients TaskTracker instances run on slave nodes TaskTracker forks separate Java process for task instances Job Distribution MapReduce programs are contained in a Java jar file an XML file containing serialized program configuration options Running a MapReduce job places these files into the HDFS and notifies TaskTrackers where to retrieve the relevant program code Wheres the data distribution Data Distribution Implicit in design of MapReduce All mappers are equivalent so map whatever data is local to a particular node in HDFS If lots of data does happen to pile up on the same node nearby nodes will map instead Data transfer is handled implicitly by HDFS Configuring With JobConf MR Programs have many configurable options JobConf objects hold key value components mapping String a eg mapredmaptasks 20 JobConf is serialized and distributed before running the job Objects implementing JobConfigurable can retrieve elements from a JobConf What Happens In MapReduce Depth First Job Launch Process Client Client program creates a JobConf Identify classes implementing Mapper and Reducer interfaces JobConfsetMapperClass setReducerClass Specify inputs outputs JobConfsetInputPath setOutputPath Optionally other options too JobConfsetNumReduceTasks JobConfsetOutputFormat Job Launch Process JobClient Pass JobConf to JobClientrunJob or submitJob runJob blocks submitJob does not JobClient Determines proper division of input into InputSplits Sends job data to master JobTracker server Job Launch Process JobTracker JobTracker Inserts jar and JobConf serialized to XML in shared location Posts a JobInProgress to its run queue Job Launch Process TaskTracker TaskTrackers running on slave nodes periodically query JobTracker for work Retrieve job-specific jar and config Launch task in separate instance of Java main is provided by Hadoop Job Launch Process Task TaskTrackerChildmain Sets up the child TaskInProgress attempt Reads XML configuration Connects back to necessary MapReduce components via RPC Uses TaskRunner to launch user process Job Launch Process TaskRunner TaskRunner MapTaskRunner MapRunner work in a daisy-chain to launch your Mapper Task knows ahead of time which InputSplits it should be mapping Calls Mapper once for each record retrieved from the InputSplit Running the Reducer is much the same Creating the Mapper You provide the instance of Mapper Should extend MapReduceBase One instance of your Mapper is initialized by the MapTaskRunner for a TaskInProgress Exists in separate process from all other instances of Mapper – no data sharing Mapper void map WritableComparable key Writable value OutputCollector output Reporter reporter What is Writable Hadoop defines its own box classes for strings Text integers IntWritable etc All values are instances of Writable All keys are instances of WritableComparable Writing For Cache Coherency while more input exists myIntermediate new intermediate input myIntermediateprocess export outputs Writing For Cache Coherency myIntermediate new intermediate junk while more input exists myIntermediatesetupState input myIntermediateprocess export outputs Writing For Cache Coherency Running the GC takes time Reusing locations allows better cache usage Speedup can be as much as two-fold All serializable types must be Writable anyway so make use of the interface Getting Data To The Mapper Reading Data Data sets are specified by InputFormats Defines input data eg a directory Identifies partitions of the data that form an InputSplit Factory for RecordReader objects to extract k v records from the input source FileInputFormat and Friends TextInputFormat – Treats each \n-terminated line of a file as a value KeyValueTextInputFormat – Maps \n- terminated text lines of k SEP v SequenceFileInputFormat – Binary file of k v pairs with some addl metadata SequenceFileAsTextInputFormat – Same but maps ktoString vtoString Filtering File Inputs FileInputFormat will read all files out of a specified directory and send them to the mapper Delegates filtering this file list to a method subclasses may override eg Create your own xyzFileInputFormat to read xyz from directory list Record Readers Each InputFormat provides its own RecordReader implementation Provides unused capability multiplexing LineRecordReader – Reads a line from a text file KeyValueRecordReader – Used by KeyValueTextInputFormat Input Split Size FileInputFormat will divide large files into chunks Exact size controlled by mapredminsplitsize RecordReaders receive file offset and length of chunk Custom InputFormat implementations may override split size – eg NeverChunkFile Sending Data To Reducers Map function receives OutputCollector object OutputCollectorcollect takes k v elements Any WritableComparable Writable can be used WritableComparator Compares WritableComparable data Will call WritableComparablecompare Can provide fast path for serialized data JobConfsetOutputValueGroupingComparator Sending Data To The Client Reporter object sent to Mapper allows simple asynchronous feedback incrCounter Enum key long amount setStatus String msg Allows self-identification of input InputSplit getInputSplit Partition And Shuffle Partitioner int getPartition key val numPartitions Outputs the partition number for a given key One partition values sent to one Reduce task HashPartitioner used by default Uses keyhashCode to return partition num JobConf sets Partitioner implementation Reduction reduce WritableComparable key Iterator values OutputCollector output Reporter reporter Keys values sent to one partition all go to the same reduce task Calls are sorted by key – earlier keys are reduced and output before later keys Finally Writing The Output OutputFormat Analogous to InputFormat TextOutputFormat – Writes key val\n strings to output file SequenceFileOutputFormat – Uses a binary format to pack k v pairs NullOutputFormat – Discards output HDFS HDFS Limitations Almost GFS No file update options record append etc all files are write-once Does not implement demand replication Designed for streaming Random seeks devastate performance NameNode Head interface to HDFS cluster Records all global metadata Secondary NameNode Not a failover NameNode Records metadata snapshots from real NameNode Can merge update logs in flight Can upload snapshot back to primary NameNode Death No new requests can be served while NameNode is down Secondary will not fail over as new primary So why have a secondary at all NameNode Death contd If NameNode dies from software glitch just reboot But if machine is hosed metadata for cluster is irretrievable Bringing the Cluster Back If original NameNode can be restored secondary can re-establish the most current metadata snapshot If not create a new NameNode use secondary to copy metadata to new primary restart whole cluster Is there another way Keeping the Cluster Up Problem DataNodes fix the address of the NameNode in memory cant switch in flight Solution Bring new NameNode up but use DNS to make cluster believe its the original one Secondary can be the new one Further Reliability Measures Namenode can output multiple copies of metadata files to different directories Including an NFS mounted one May degrade performance watch for NFS locks Databases Life After GFS Straight GFS files are not the only storage option HBase on top of GFS provides column-oriented storage mySQL and other db engines still relevant HBase Can interface directly with Hadoop Provides its own Input- and OutputFormat classes sends rows directly to mapper receives new rows from reducer But might not be ready for classroom use least stable component MySQL Clustering MySQL database can be sharded on multiple servers For fast IO use same machines as Hadoop Tables can be split across machines by row key range Multiple replicas can serve same table Sharding Hadoop Partitioners For best performance Reducer should go straight to local mysql instance Get all data in the right machine in one copy Implement custom Partitioner to ensure particular key range goes to mysql-aware Reducer Academic Hadoop Requirements Server Profile UW cluster 40 nodes 80 processors total 2 GB ram processor 24 TB raw storage space 8 TB replicated One node reserved for JobTrackerNameNode Two more wouldnt cooperate But still vastly overpowered Setup Maintenance Took about two days to setup and configure Mostly hardware-related issues Hadoop setup was only a couple hours Maintenance only a few hoursweek Mostly rebooting the cluster when jobs got stuck Total Usage About 15000 CPU-hours consumed by 20 students Out of 130000 available over quarter Average load is about 12 Analyzing student usage patterns Not Quite the Whole Story Realistically students did most work very close to deadline Cluster sat unused for a few days followed by overloading for two days straight Analyzing student usage patterns Hadoop Job Scheduling FIFO queue matches incoming jobs to available nodes No notion of fairness Never switches out running job Run-away tasks could starve other student jobs Hadoop Security But on the bright side No security system for jobs Anyone can start a job but they can also cancel other jobs Realistically students did not cancel other student jobs even when they should Hadoop Security The Dark Side No permissions in HDFS either Just now added in 016 One student deleted the common data set for a project Email subject Oops No students could test their code until data set restored from backup Job Scheduling Lessons Getting students to play nice is hard No incentive Just plain badbuggy code Cluster contention caused problems at deadlines Work in groups Stagger deadlines Another Possibility Amazon EC2 provides on-demand servers May be able to have students use these for jobs Lab fee would be 150student Simple web-based interfaces exist Rightscalecom HadoopOnDemand HOD coming soon Injects new nodes into live clusters More Performance Scalability Number of Tasks Mappers 10 nodes or 32 cores Reducers 2 nodes or 105 cores Two degrees of freedom in mapper run time Number of tasksnode and size of InputSplits See More Performance Tweaks Hadoop defaults to heap cap of 200 MB Set mapredchildjavaopts -Xmx512m 1024 MB process may also be appropriate DFS block size is 64 MB For huge files set dfsblocksize 134217728 mapredreduceparallelcopies Set to 1550 more data more copies Dead Tasks Student jobs would run away admin restart needed Very often stuck in huge shuffle process Students did not know about Partitioner class may have had non-uniform distribution Did not use many Reducer tasks Lesson Design algorithms to use Combiners where possible Working With the Scheduler Remember Hadoop has a FIFO job scheduler No notion of fairness round-robin Design your tasks to play well with one another Decompose long tasks into several smaller ones which can be interleaved at Job level Additional Languages Components Hadoop and C Hadoop Pipes Library of bindings for native C code Operates over local socket connection Straight computation performance may be faster Downside Kernel involvement and context switches Hadoop and Python Option 1 Use Jython Caveat Jython is a subset of full Python Option 2 HadoopStreaming HadoopStreaming Allows shell pipe operator to be used with Hadoop You specify two programs for map and reduce stdin and stdout do the rest - Requires serialization to text context switches cat grep sort uniq is now a valid MR Eclipse Plugin Support for Hadoop in Eclipse IDE Allows MapReduce job dispatch Panel tracks live and recent jobs Included in Hadoop since 014 But works with older versions Contributed by IBM Conclusions Hadoop systems will put up with reasonable amounts of student abuse Biggest pitfall is deadlines HBase may not be ready for this quarters students next year almost certainly Other tools provide student design projects with additional options Lesson Resource demands are NOT constant Spinnaker Labs Inc nothing yet but planned Chubby HBase Bigtable HDFS GFS Hadoop MapReduce Hadoop equivalent Google calls it