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营业硬件?代写php尝试做业、代做留教死scoreinfo

作者:平凡 发布时间:2019-07-21 01:11 点击:

请减或邮箱@电话.com 微疑:codehelp

Project 4: Partitioning in Apache Hive This project will provide anintroduction to Hive, a big data tool that makes it *** to querystructured data. Hive is built on top of MapReduce, which is inturn built on top of HDFS. Hive accepts SQL queries and convertsthem into MapReduce jobs. Read through this page for an overview ofHive's architecture:hive/hive_introduction.h念晓得互联网疑息效劳问应证tm We willuse Purdue's OpenStack cluster for this assignment. The master nodefor this cluster is 172.18.11.17. You can SSH into this node withusername:[PurdueAlias]_ostack, password:[PurdueAlias]_ostackpwd. $ssh [PurdueAlias] To see a l营业架构设念ist of all the nodes in the cluster,run: $ cat /etc/hosts NOTE: Be sure to change your password afteryou have logged in: $ passwd NOTE: This cluster does not mount theCS department's NFS shared file syste进建试做m, so your CS home directoryis not available. CAUTION: This cluster is temporary. It will bewiped after the lab is graded. If you have any codes or resultsthat you wish to save, move them to permanent storage on anothersystem. To list the contents of your HDFS directory, use: $ hdfsdfs -ls /user/[PurdueAlias]_ostack To move files to HDFS and back:$ hdfs dfs -mkdir /user/[PurdueAlias]_ost看看代写php检验考试做业、代做留教死scoreinformationforarugbytack/new_dir $ hdfs dfs-put file.txt /user/[PurdueAlias]_ostack/new_dir $ hdfs dfs -get/user/[PurdueAlias]_ostack/new_dir/file.txt ./ $ hdfs dfs -getmerge/user/[PurdueAlias]_ostack/dir_with_multiple_files The Research andInnovative Technology Administration (RITA) has made available 22years worth of flight departure and arrival data. The totaldataset, when uncompressed, is approximately 10 GB.dataexpo/2009/the-data.html We wish toquery this data. An example query might be, "How many flightsdeparted on February 3, 1990" When we query this data, we wish todo so effici阿里巴巴营业架构图ently. If we can split the data into partitions basedon year, month, or day, then perhaps we will not have to read allof the data every time we run a query. Of course, we couldaccomplish these goals by distributing the data across our clusterwith HDFS and then writing MapReduce job听听scoreinformationforarugbyts to partition and queryour data. Instead, we will use Hive, which simplifies this processimmensely. The data was already downloaded and shared at /home/data(file name is 1996_noheader.csv) Load the shared data into yourpersonal HDFS directory: $ hdfs dfs -mkdir -p/user/[PurdueAlias]_ostack/rita/input $ hdfs dfs -put/home/data/1996_noheader.csv /user/[PurdueAlias]_ostack/rita/inputStart the Hive CLI, create a personal database, and use thatdatabase: $ hive hive> cr进建代写php检验考试做业、代做留教死scoreinformationforarugbyteate database [PurdueAlias]_ostack;hive> use [PurdueAlias]_ostack; NOTE: If you restart the HiveCLI, you will begin in the default database. In that case you mustagain switch to your database wi看着停业th "hive> use[PurdueAlias]_ostack". We need to declare some structure for ourdata. We will use a command from Hive's data definition language.Notice that the command specifies a comma as the field delimiter.hive> create table flights(Year int, Month int, dayOfMonth int,dayOfWeek int, depTime int, CRSDepTime int, arrTime int, CRSArrTimeint, uniqueCarrier string, flightNum int, tailNum int,actualElapsedTime int, CRSElapsedTime int, airTime int, arrDelayint, depDelay int, origin string, dest string, distance int, taxiInint, taxiOut int, cancelled int, cancellationCo看看停业硬件de string, divertedint, carrierDelay int, weatherDelay int, NASDelay int,securityDelay int, lateAircraftDelay int) row format delimitedfields termin物流疑息体系的观面ated by ','; Next we need to import the data into ourtable. Note that when we import an HDFS file into a Hive table,Hive does not copy the data. It simply changes the name of the fileand moves it to another HDFS directory (a Hive directory). hive>load da看着代写ta inpath'/user/[PurdueAlias]_ostack/rita/input/1996_noheader.csv' overwriteinto table flights; We are now ready to query our data. You canexperiment if you likphpe. The following queries might be interestingto you: hive> show tables; hive> describe flights; hive>select * from flights limit 3; hive> select count(*) fromflights where month=3; hive> select count(*) from flights wherecarrierdelay is null; Execute the queries below, and after eachquery completes, record the following performance metrics, whichappear under "MapReduce Jobs Launched": Cumulative CPU (for eachstage) HDFS Read (for each stage) HDFS Write (for each stage) TimeTaken (total) // Query 1 hive> select count(*) from flightswhere month = 4; // Query 2 hive> select count(*) from flightswhere month = 11 and dayofmonth = 6; // Quer看看京东营业架构y 3 hive> selectcount(*) from flights where month = 8 and dayofmonth > 9 anddayofmonth set hive.exec.dynamic.partition=true; hive> sethive.exec.dynamic.partition.mode=nonstrict; hive> sethive.exec.max.dynamic.partitions=900; hive> sethive.exec.max.dynamic.partitions.pernode=900; Next we declare a newtable with the same columns as "flights," but we indicate to Hivethat the data should be partitioned on the "Month" column: hive>create table flights_partitioned_month(Year int, dayOfMonth int,dayOfWeek int, depTime int, CRSDepTime int, arrTime int, CRSArrTi检验考试meint, uniqueCarrier string, flightNum int, tailNum int,actualElapsedTime int, CRSElapsedTime int, airTime int, arrDelayint, depDelay int, orig传闻营业硬件in string, dest string, distance int, taxiInint, taxiOut int, cancelled int, cancellationCode string, divertedint, carrierDelay int, weatherDelay int, NASDelay int,securityDelay int, lateAircraftDelay int) partitioned by (Monthint); Notice that we have omitted "Month" from the long list offields in our table. Instead, we have included it as a partitioncolumn at the end of our statement. After you have created themonth partition table, describe it with: hive> describeflights_partitioned_month; Notice that the "Month" field comeslast. This is how Hive chooses to order partition columns. Next wewill c硬件opy data from our "flights" table to our"flights_partitioned_month" table. Use this command: hive>insert into table flights_partitioned_month partition(month) selectyear, dayofmonth, dayofweek, deptime, crsdeptime, arrtime,crsarrtime, uniquecarrier, flightnum, tailnum, actualelapsedtime,crselapsedtime, airtime, arrdelay, depdelay, origin, dest,distance, taxiin, taxiout, cancelled, cancellationcode, diverted,carrierdelay, weatherdelay, nasdelay, securitydelay,lateaircraftdelay, month from flights; Here, the ordering ofcolumns in our insert statement matches the order of columns in"flights_partitioned_month", not "flights". Notice that when webegin our partition, Hive informs 简述疑息体系的观面us that "Number of reduce tasksis set to 0 since there's no reduce operator." Why do we not need areduce operator Run Task 1 queries 1⑶ on your partitioned tableand record their performance metrics. What do you observe forcumulative CPU time compared to our queries on the unpartitioneddata What do you observe for the wall clock time ("Time taken") Whydo you think this is We ask you to create two more partitionedtables: one partitioned on dayOfM停业硬件onth and one partitioned on twocolumns: month first, and dayOfMonth second. Re-run queries 1⑶ onthese partitioned tables and record their performance metrics. Nowis a good time for you to begin considering how to identify gooduse cases for big data tools like Hive and MapReduce. How big doesour data have to be before querying becomes faster on a clusterthan on a single machine Let's do a quick experiment. Run thefollowing command on the 1996 dataset that was downloaded to/home/data/ directory on the master node: $ date "T"; cat/home/data/1996_noheader.csv | awk -F',' '$2 == "8" {print $1}' |wc -l; date "T" This command will count how many flights occurredin August of 1996. Run a query on your Hive table that accomplishesthe same query. (For this comparison, don't use one of the sharedHive tables, and don't use a partitioned Hive table.) How does theruntime compare to our local job Please turn in the following: * Acommand.txt file containing the commands that you used to createand populate the partitioned tables in Task 3; * A metrics.txt filecontaining only the metrics which you recorded for Tasks 1⑶; * Aruntimes.txt file containing the row counts obtained in Task 4 andthe runtimes of the two approaches; To turn your work in, submitthe following files via Blackboard commands.txt metrics.txtruntimes.txt

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