Mibairo yeSpark inomira sei?

Iko⁢ kusanganiswa kweSpark zvawanikwa inzira yakakosha mukuongorora nekugadziriswa kwehuwandu hwakawanda hwe data. Spark, iyo yakakurumbira yakagoverwa yekugadzirisa chimiro, inopa akati wandei sarudzo kujoinha uye kusanganisa mhedzisiro yekushanda kunoitwa munharaunda yako. Muchinyorwa chino, isu tichaongorora akasiyana matekiniki uye nzira dzinopihwa naSpark kusanganisa mhedzisiro zvakanaka. Kubva pakusanganisa maRDD kusvika pakushandisa kuunganidza mashandiro, iwe uchaona maitiro ekuita zvakanyanya kugona kunopihwa neSpark nekukurumidza, kwakaringana mhinduro. mumapurojekiti ako yeData huru.

Iko kusanganiswa kwe RDDs Ndiyo imwe yedzakanyanya uye dzakajairika nzira dzekubatanidza mhedzisiro muSpark. RDDs (Resilients Distributed Datasets) ndiyo yakakosha data chimiro muSpark, uye inobvumira kugoverwa uye kunoenderana mashandiro. nenzira inoshanda. Nekubatanidza maviri kana anopfuura maRDD, mashandiro akadai semubatanidzwa, mharadzano, kana mutsauko unogona kuitwa pakati peseti yedata, nekudaro ichipa shanduko huru yekushandisa uye kusanganisa mhedzisiro yemashandiro akaitwa muSpark.

Imwe nzira yekubatanidza mhedzisiro muSpark iri kuburikidza neaggregation mashandiro. Mashandiro aya anobvumira mhinduro dzakawanda kuti dzibatanidzwe kuita mhedzisiro imwe chete, uchishandisa mabasa ekubatanidza akadai sehuwandu, maavhareji, maximum kana mashoma. Uchishandisa mashandiro aya, zvinokwanisika kuwana zvakabatanidzwa uye zvakapfupikiswa zvawanikwa kubva kuhuwandu hwe data mune imwe nhanho, iyo inogona kunyanya kubatsira mumamiriro ezvinhu apo inodiwa kuverenga metrics kana manhamba pane data seti.

Kuwedzera kune RDD kuunganidzwa uye kubatanidza mashandiro, Spark inopawo mamwe matekiniki ekubatanidza mhedzisiro, sekushandisa kuunganidza akasiyana uye kushandisa mabasa ekudzikisa. Accumulation variables inobvumira iwe kuunganidza mhedzisiro ye nzira inoshanda munzvimbo imwechete, kunyanya kana iwe uchida kugovera ruzivo pakati pemabasa akasiyana. Kune rumwe rutivi, mabasa ekuderedza anobvumira migumisiro yakawanda kuti ibatanidzwe kuva chigumisiro chimwe chete nekushandisa kushandiswa-kunotsanangurwa nemushandisi. Aya matekiniki anopa kushanduka kukuru uye kutonga pamusoro pekuti mhedzisiro inosanganiswa sei muSpark.

Muchidimbu, kusanganisa ⁢of⁤ zvinobuda muSpark inzira yakakosha pakugadzirisa nekuongorora mavhoriyamu akakura ⁤ data. nzira inoshanda. Spark inopa akasiyana matekiniki uye nzira dzekubatanidza mhedzisiro, sekubatanidza maRDD, kuunganidza mashandiro, kushandiswa kwekuunganidza zvakasiyana, uye kuderedza mabasa. Nekutora mukana wakazara wezvishandiso izvi, vanogadzira uye vanoongorora vanogona kuwana chaiyo uye nekukurumidza mhedzisiro mumapurojekiti avo ebudiriro. Big Data. Muzvikamu zvinotevera, isu tichaongorora imwe neimwe yeaya matekiniki zvakadzama uye nekupa mienzaniso inoshanda kuti tinzwisise zviri nani kuti mhedzisiro inosanganiswa sei muSpark.

1. Joinha maAlgorithms ⁢Inowanikwa muSpark

Spark ndeye yakagovaniswa computing framework inopa huwandu hwakasiyana hwekusanganisa algorithms kusanganisa mhedzisiro yekushanda kwakafanana. Aya maalgorithms akagadzirirwa kukwidziridza kunyatsoita uye scalability munzvimbo huru dzedata. Pazasi pane mamwe anonyanya kushandiswa kujoinha algorithms muSpark:

• Batanidza: Iyi algorithm inosanganisa maviri akarongwa data seti kuita imwechete yakarongwa seti. Inoshandisa kupatsanura uye kukunda nzira yekubatanidza data nemazvo uye kuve nechokwadi chekubatanidza kushanda zvakanaka.
• Join: Iyo yekujoinha algorithm inosanganisa maviri seti yedata zvichienderana nekiyi yakafanana. Inoshandisa matekiniki akadai sekugovanisa uye kugoverazve data kukwidziridza maitiro ekubatanidza. Iyi algorithm inobatsira kwazvo mutafura yekubatanidza mashandiro⁤in SQL mibvunzo.
• GroupByKey: Iyi algorithm mapoka eiyo kukosha kwakabatana nekiyi yega yega museti⁢ yedata. Inonyanya kukosha kana iwe uchida kuita maaggregation mashandiro, akadai sekuwedzera kana avhareji, zvichibva pane kiyi yakapihwa.

Aya ekujoinha algorithms ingori sampuli yesarudzo dziripo muSpark. Imwe neimwe inopa yakasarudzika mabhenefiti uye inogona kushandiswa mumamiriro akasiyana zvichienderana nezvinodiwa zvekushandisa. Izvo zvakakosha kuti unzwisise uye utore mukana wakazara weaya algorithms kuve nechokwadi chekuita kwakakwana uye scalability mumapurojekiti eSpark.

2. Musanganiswa wedata⁢ nzira muSpark

Variko⁢ zvakawanda izvo zvinobvumira kuti data seti dzakasiyana kuti dzibatanidzwe zvinobudirira. Imwe yenzira dzakajairika ndeye join method, iyo inobvumira maviri kana kupfuura seti yedata kuti abatanidzwe uchishandisa kiyi yakafanana. Iyi nzira inonyanya kubatsira kana iwe uchida kurondedzera data zvichibva pane chaiyo hunhu, senge yakasarudzika identifier. Spark inopa akasiyana marudzi ekujoinha, senge yemukati kujoinha, kuruboshwe kujoina, kurudyi kujoina uye yakazara yekunze kujoinha, kujairana neakasiyana mamiriro.

Imwe nzira yekubatanidza data muSpark⁤ ndiyo nzira yekuunganidza. Iyi nzira inobvumira data kusanganiswa nekuwedzera kukosha kwakavakirwa pane yakajairika kiyi. Zvinonyanya kubatsira kana iwe uchida kuwana aggregate mhedzisiro, sekuverenga huwandu, avhareji, hushoma kana huwandu hwehumwe hunhu. ⁤Spark inopa huwandu hwakawanda hwemabasa ekuunganidza,⁢ senge sum, count, avg, min uye max, izvo zvinoita kuti zvive nyore. Maitiro aya.

Pamusoro penzira dzakataurwa, Spark inopawo cross operations, iyo inobvumira seti mbiri dze data kuti dzibatanidzwe pasina kiyi yakafanana. Izvi zvinogadzira zvese zvinogoneka musanganiswa pakati pezvinhu zveese seti uye zvinogona kubatsira muzviitiko zvakaita sechizvarwa. yechigadzirwa Cartesian kana kugadzira data set yekuyedzwa kwakawanda. Nekudaro, nekuda kwesimba remakomputa rinodiwa, mabasa aya anogona kudhura maererano nenguva yekuuraya uye zviwanikwa.

3. Zvinhu zvekufunga⁤ kana uchibatanidza zvabuda muSpark

Spark yakagoverwa kugadzirisa

Imwe yeakanakisa mabhenefiti eSpark kugona kwayo kugadzirisa mavhoriyamu makuru e data nenzira yakagoverwa. Izvi zvinokonzerwa ne-mu-memory processing injini uye kugona kwayo kupatsanura uye kugovera mabasa pamapoka emanodhi Kana uchibatanidza mhedzisiro muSpark, zvakakosha kuti uchengete izvi mundangariro. ⁢Zvakakosha kugovera zvakanaka mabasa pakati pemanodhi uye kuita zvakanyanya zvezviwanikwa zviripo.

Data caching uye kutsungirira

Kushandiswa kwe caching uye data kuramba ⁢ ndechimwe chinhu chakakosha chekufunga nezvacho kana uchibatanidza mibairo ⁢muSpark.⁢ Kana ⁢kuvhiya kwaitwa, Spark⁢ inochengetedza mhedzisiro mundangariro kana kudhisiki, zvichienderana nemagadzirirwo ayo. Nekushandisa caching yakakodzera kana kushingirira, zvinokwanisika kuchengetedza data munzvimbo inosvikika yemibvunzo inotevera uye maverengero, nokudaro kudzivirira kuverengera zvakare zvawanikwa zvakare. Izvi zvinogona kuvandudza zvakanyanya kuita kana uchibatanidza akawanda mhedzisiro muSpark.

Kusarudza algorithm yakarurama

Kusarudza iyo algorithm yakanaka zvakare chinhu chakakosha kana uchibatanidza mhedzisiro muSpark Zvichienderana nerudzi rwe data uye mhedzisiro yaunoda, mamwe maalgorithms anogona kuve anoshanda kupfuura mamwe. Somuenzaniso, kana uchida kuita a grouping o kupatsanurwa yedata, unogona⁢kusarudza algorithms akakodzera,⁤ akadai seK-nzira kana Logistic Regression, zvichiteerana. Nekusarudza iyo algorithm chaiyo, zvinokwanisika kudzikisira nguva yekugadzirisa uye kuwana mhedzisiro chaiyo muSpark.

4. Maitiro ekubatanidza data anoshanda muSpark

Spark idhizaina rekugadzirisa dhata rinoshandiswa zvakanyanya pakugona kwayo kubata mavhoriyamu makuru edata zvakanaka. Chimwe chezvakakosha zveSpark kugona kwayo kusanganisa data nemazvo, izvo zvakakosha muzviitiko zvakawanda zvekushandisa. Kune akati wandei iyo inogona kushandiswa zvichienderana nezvinodiwa zveprojekiti.

Imwe yeakajairika nzira yekubatanidza data muSpark ndeye Join, iyo inokutendera kuti ubatanidze maviri kana anopfuura data seti zvichienderana nekoramu yakafanana. Kujoinwa kunogona kuva kwemhando dzakati wandei, kusanganisira kujoinwa kwemukati, kujoinwa kwekunze, uye kujoinwa kweruboshwe kana kurudyi rumwe nerumwe rune hunhu hwarwo uye rinoshandiswa zvichienderana nedata raunoda kusanganisa uye nemhedzisiro yaunoda kuwana.

Imwe nzira inoshanda yekubatanidza data muSpark ndiyo repartitioning. Repartitioning ndiyo maitiro ekugoverazve data kuyambuka Spark cluster zvichibva pane kiyi koramu kana seti yemakoramu. Izvi zvinogona kubatsira kana iwe uchida kusanganisa data zvakanyanya mushe uchishandisa yekubatanidza kushanda gare gare. Repartitioning inogona kuitwa uchishandisa basa distribution ⁢ muSpark.

5. Mafungiro ekuita kana uchibatanidza mhedzisiro muSpark

Kana uchibatanidza zvabuda mu⁤Spark, zvakakosha kuti urambe uchifunga nezvekuita. Izvi zvinova nechokwadi chekuti nzira yekubatanidza inoshanda ⁢uye haikanganisi ⁢nguva yekushandiswa kwechikumbiro. Heano mamwe kurudziro yekukwiridzira kuita kana uchibatanidza zvabuda muSpark:

1. Dzivisa shuffle mashandiro: Shuffle mashandiro, akadai groupByKey kana kuderedzaByKey, inogona kudhura maererano nekuita, sezvo ichisanganisira kuendesa data pakati pemasumbu masumbu. Kuti udzivise izvi, zvinokurudzirwa kushandisa aggregation mashandiro senge kuderedzaByKey o groupBy pachinzvimbo, sezvavanoderedza kufamba kwedata.

2. Shandisa iyo yepakati data cache⁤: Kana uchibatanidza zvabuda mu⁢Spark,⁤ data repakati rinogona kugadzirwa iro rinoshandiswa mukuita kwakawanda. Kuti uvandudze mashandiro, zvinokurudzirwa kushandisa⁢ the⁤ basa cache() o ramba () kuchengetedza iyi data yepakati mundangariro. Izvi zvinodzivirira kuaverengerazve pese paanoshandiswa mukuvhiya kunotevera.

3. Tora mukana wekufananidza: Spark inozivikanwa nekuenderana kwayo kugadzirisa kugona, iyo inobvumira mabasa kuti aitwe mukuwirirana pane dzakawanda node musumbu. Kana uchibatanidza mhedzisiro, zvakakosha kutora mukana weiyo parallelization kugona. Kuti uite izvi, zvinokurudzirwa kushandisa maitiro akadai mapPartitions o flatMap, iyo ⁢inobvumira data kuti igadziriswe mukufanana mune yega yega RDD chikamu.

6. Optimization yekubatanidza mhedzisiro mu⁢Spark

Ichi chinhu chakakosha chekuvandudza mashandiro uye kugona kwezvishandiso zvedu. MuSpark, kana tichiita mashandiro akadai semafirita, mepu, kana kuunganidzwa, mhedzisiro yepakati inochengetwa mundangariro kana padhisiki isati yabatanidzwa. Zvisinei, zvichienderana nekugadzirisa uye hukuru hwe data, kusanganiswa uku kunogona kudhura maererano nenguva uye zviwanikwa.

Kukwirisa iyi musanganiswa, Spark inoshandisa akasiyana matekiniki akadai sekugovanisa data uye parallel execution. Kugovanisa data kunosanganisira kupatsanura data rakaiswa kuita zvidimbu zvidiki uye nekuzvigovera pane dzakasiyana node kuti uwane zvakanyanya zviwanikwa zviripo. Izvi zvinobvumira imwe neimwe node kuti igadzirise chunk yayo yedata yakazvimiririra uye yakafanana, nokudaro ichideredza nguva yekuuraya.

Chimwe chinhu chakakosha ndeche parallel execution, uko Spark inokamura mashandiro mumabasa akasiyana uye oaita panguva imwe chete pane dzakasiyana node. Izvi⁤ zvinobvumira kushandiswa kwakanaka kwekugadzirisa zviwanikwa uye kunomhanyisa kusanganiswa kwemhedzisiro. Pamusoro pezvo, Spark ⁢ine kugona⁢ kugadzirisa otomatiki huwandu hwemabasa zvichienderana nesaizi yedata uye node huwandu, nekudaro kuve nechokwadi chekuenzanisa pakati pekuita nekuita. Aya maitiro ekugadzirisa anobatsira mukuvandudza zvakanyanya nguva yekupindura yemashandisirwo edu⁢ muSpark.

7. Kurudziro yekudzivirira kupokana paunenge uchibatanidza mhedzisiro muSpark

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1. Shandisa nzira dzakakodzera ⁢musanganiswa: ⁢Kana uchibatanidza mhedzisiro muSpark, zvakakosha kushandisa nzira dzakakodzera kudzivirira kupokana uye kuwana mhinduro chaidzo. Spark inopa nzira dzakasiyana dzekujoinha, senge kujoinha, mubatanidzwa, kubatanidza, pakati pevamwe. ⁢Zvinodikanwa kunzwisisa ⁢misiyano pakati penzira yega yega uye kusarudza yakanyatsokodzera yebasa riripo. Pamusoro pezvo, zvinokurudzirwa kuti ujairane nemaparamita uye sarudzo dziripo kune yega yega nzira, sezvo dzinogona kukanganisa kuita uye nekururama kwemhedzisiro.

2. Ita zvakadzama data yekuchenesa: Usati wabatanidza mhedzisiro muSpark, zvakakosha kuti uite kuchenesa kwe data. Izvi zvinosanganisira kubvisa null values, duplicates, and outliers, pamwe nekugadzirisa kusawirirana uye kusawirirana. Kuchenesa data kwakakodzera kunovimbisa kutendeseka uye kuenderana kwemhedzisiro yakabatanidzwa. Pamusoro pezvo, kuongororwa kwemhando yedata kunofanirwa kuitwa kuona zvikanganiso zvingangoitika kusanganisa kusati kwaitwa.

3. Sarudza chikamu chakakodzera: Kupatsanurwa kwedata muSpark kune chekuita kwakakura pakuita kwekujoinha mabasa. Zvinokurudzirwa kukwidziridza kugovaniswa kwedata usati wasanganisa mhedzisiro, kupatsanura data seti zvakaenzana uye zvine mwero kuti uwedzere kushanda zvakanaka. Spark inopa akasiyana siyana ekugovera sarudzo, senge repartition uye partitionBy, iyo inogona kushandiswa kugovera zvakakwana data. Nekusarudza chikamu chakakodzera, iwe unodzivirira mabhodhoro uye unovandudza kuita kwese kwekuita kwekubatanidza maitiro.