{"id":54918,"date":"2020-01-07T00:00:00","date_gmt":"2020-01-06T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/ispolzovanie-clickhouse-v-kachestve-zameny-elk-big-query-i-timescaledb"},"modified":"2020-02-18T14:02:58","modified_gmt":"2020-02-18T11:02:58","slug":"ispolzovanie-clickhouse-v-kachestve-zameny-elk-big-query-i-timescaledb","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/ispolzovanie-clickhouse-v-kachestve-zameny-elk-big-query-i-timescaledb","title":{"rendered":"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/clickhouse.yandex\/\">Clickhouse<\/a><\/noindex> \u2014 este un sistem de gestionare a bazelor de date coloane pentru procesarea analitic\u0103 online (OLAP) cu surs\u0103 deschis\u0103, creat de Yandex. Este folosit de Yandex, CloudFlare, VK.com, Badoo \u0219i alte servicii din \u00eentreaga lume pentru stocarea unor volume extrem de mari de date (inserarea a mii de r\u00e2nduri pe secund\u0103 sau petabytes de date stocate pe disc).<\/p>\n<p>\u00centr-o baz\u0103 de date \u201epreregistribu\u021bie\u201d, exemplele fiind MySQL, Postgres, MS SQL Server, datele sunt stocate \u00eentr-o ordine specific\u0103: <\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/398f260de0152baf3cc11d884fa0d52d.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n\u00cen acest caz, valorile apar\u021bin\u00e2nd unui singur r\u00e2nd sunt stocate fizic apropiate. \u00cen sistemele de baze de date coloane, valorile din coloane diferite sunt stocate separat, iar datele unei coloane sunt stocate \u00eempreun\u0103:<\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/938a2ee3d588ac866ec92bb3ea09c253.png\" style=\"display:block;margin: 0 auto;\" \/><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Exemple de sisteme de baze de date coloane sunt Vertica, Paraccel (Actian Matrix, Amazon Redshift), Sybase IQ, Exasol, Infobright, InfiniDB, MonetDB (VectorWise, Actian Vector), LucidDB, SAP HANA, Google Dremel, Google PowerDrill, Druid, kdb+.<\/p>\n<p>Compania \u2013 mailforwarder <noindex><a rel=\"nofollow\" href=\"https:\/\/qwintry.com\/\">Qwintry<\/a><\/noindex> a \u00eenceput s\u0103 utilizeze Clickhouse \u00een 2018 pentru generarea de rapoarte \u0219i a fost foarte impresionat\u0103 de simplitatea, scalabilitatea, suportul SQL \u0219i rapiditatea acesteia. Viteza de operare a acestui SGBD era aproape magic\u0103.<\/p>\n<h3>Simplitate<\/h3>\n<p>\nClickhouse se instaleaz\u0103 pe Ubuntu cu o singur\u0103 comand\u0103. Dac\u0103 \u0219ti\u021bi SQL, pute\u021bi \u00eencepe imediat s\u0103 utiliza\u021bi Clickhouse pentru nevoile dumneavoastr\u0103. Totu\u0219i, acest lucru nu \u00eenseamn\u0103 c\u0103 pute\u021bi efectua un 'show create table' \u00een MySQL \u0219i s\u0103 copia\u021bi \u0219i s\u0103 lipi\u021bi SQL \u00een Clickhouse. <\/p>\n<p>Comparativ cu MySQL, acest SGBD are diferen\u021be importante \u00eentre tipurile de date \u00een defini\u021biile schemei tabelului, a\u0219a c\u0103 pentru a lucra confortabil, va fi nevoie totu\u0219i de pu\u021bin timp pentru a modifica defini\u021biile schemei tabelului \u0219i a studia motoarele de tabel.<\/p>\n<p>Clickhouse func\u021bioneaz\u0103 excelent f\u0103r\u0103 software suplimentar, dar dac\u0103 dori\u021bi s\u0103 utiliza\u021bi replicarea, va trebui s\u0103 instala\u021bi ZooKeeper. Analiza performan\u021bei interog\u0103rilor arat\u0103 rezultate excelente \u2014 tabelele de sistem con\u021bin toate informa\u021biile, iar toate datele pot fi ob\u021binute cu ajutorul vechiului \u0219i plictisitorului SQL.<\/p>\n<h3>Performan\u021b\u0103<\/h3>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/clickhouse.yandex\/benchmark.html#%5B%2522100000000%2522,%5B%2522ClickHouse%2522,%2522Vertica%2522,%2522MySQL%2522%5D,%5B%25220%2522,%25221%2522%5D%5D\">Benchmark<\/a><\/noindex> compararea Clickhouse cu Vertica \u0219i MySQL pe un server cu configura\u021bia: dou\u0103 socket-uri Intel\u00ae Xeon\u00ae CPU E5-2650 v2 @ 2.60GHz; 128 GiB RAM; md RAID-5 pe 8 HDD SATA de 6TB, ext4.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.altinity.com\/blog\/2017\/6\/20\/clickhouse-vs-redshift\">Benchmark<\/a><\/noindex> compararea Clickhouse cu depozitul de date cloud Amazon RedShift.<\/li>\n<li>Extrase din blogul <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.cloudflare.com\/how-cloudflare-analyzes-1m-dns-queries-per-second\/\">Cloudflare despre performan\u021ba Clickhouse<\/a><\/noindex>:<\/li>\n<\/ul>\n<p>\n<img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/b6ef9fbad8e8c864a227bcffd97ec0fe.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nBaza de date ClickHouse are un design foarte simplu \u2014 toate nodurile din cluster au aceea\u0219i func\u021bionalitate \u0219i pentru coordonare folosesc doar ZooKeeper. Am construit un mic cluster din c\u00e2teva noduri \u0219i am efectuat teste, \u00een cadrul c\u0103rora am descoperit c\u0103 sistemul are o performan\u021b\u0103 destul de impresionant\u0103, care corespunde avantajelor declarate \u00een benchmark-urile bazelor de date analitice. Am decis s\u0103 examin\u0103m mai \u00een detaliu conceptul din spatele ClickHouse. Prima dificultate \u00een cercetare a fost lipsa instrumentelor \u0219i num\u0103rul redus al comunit\u0103\u021bii ClickHouse, a\u0219a c\u0103 am aprofundat designul acestei baze de date pentru a \u00een\u021belege cum func\u021bioneaz\u0103.<\/p>\n<p>ClickHouse nu accept\u0103 primirea datelor direct de la Kafka, deoarece este doar o baz\u0103 de date, a\u0219a c\u0103 am scris propriul serviciu de adaptoare \u00een limbajul Go. Acesta citea mesajele codificate Cap\u2019n Proto de la Kafka, le transforma \u00een TSV \u0219i le insera \u00een ClickHouse \u00een pachete prin intermediul interfe\u021bei HTTP. Mai t\u00e2rziu, am rescris acest serviciu pentru a folosi biblioteca Go \u00eempreun\u0103 cu propriul nostru interfe\u021bei ClickHouse pentru a \u00eembun\u0103t\u0103\u021bi performan\u021ba. La evaluarea performan\u021bei primirii pachetelor am descoperit un lucru important \u2014 s-a dovedit c\u0103 la ClickHouse, aceast\u0103 performan\u021b\u0103 depinde mult de dimensiunea pachetului, adic\u0103 de num\u0103rul de r\u00e2nduri inserate simultan. Pentru a \u00een\u021belege de ce se \u00eent\u00e2mpl\u0103 acest lucru, am studiat cum ClickHouse stocheaz\u0103 datele.<\/p>\n<p>Principalul motor, mai exact, familia de motoare pentru tabele, folosit\u0103 de ClickHouse pentru stocarea datelor, este MergeTree. Acest motor este conceptual similar cu algoritmul LSM utilizat \u00een Google BigTable sau Apache Cassandra, \u00eens\u0103 evit\u0103 construirea unei tabele intermediare \u00een memorie \u0219i scrie datele direct pe disc. Acest lucru \u00eei ofer\u0103 o capacitate excelent\u0103 de scriere, deoarece fiecare pachet inserat este sortat doar dup\u0103 \u201echeia principal\u0103\u201d primary key, comprimat \u0219i scris pe disc pentru a forma un segment. <\/p>\n<p>Lipsa unei tabele de memorie sau a unei no\u021biuni de \u201efresc\u0103\u201d a datelor \u00eenseamn\u0103 de asemenea c\u0103 acestea pot fi doar ad\u0103ugate, modificarea sau \u0219tergerea nefiind suportat\u0103 de sistem. \u00cen prezent, singura modalitate de a \u0219terge datele este prin eliminarea lor pe luni calendaristice, deoarece segmentele niciodat\u0103 nu trec grani\u021ba lunii. Echipa ClickHouse lucreaz\u0103 activ pentru a face aceast\u0103 func\u021bie configurabil\u0103. Pe de alt\u0103 parte, acest lucru face ca scrierea \u0219i \u00eembinarea segmentelor s\u0103 fie f\u0103r\u0103 conflicte, astfel c\u0103 l\u0103\u021bimea de band\u0103 a recep\u021biei se scaleaz\u0103 liniar cu num\u0103rul de inser\u021bii paralele, p\u00e2n\u0103 c\u00e2nd se atinge limita I\/O sau a nucleelor. <br \/>\nCu toate acestea, aceast\u0103 circumstan\u021b\u0103 \u00eenseamn\u0103 de asemenea c\u0103 sistemul nu este potrivit pentru pachete mici, motiv pentru care sunt folosite servicii Kafka \u0219i insertoare pentru tamponare. \u00cen continuare, ClickHouse continu\u0103 \u00een fundal s\u0103 efectueze constant \u00eembinarea segmentelor, astfel \u00eenc\u00e2t multe p\u0103r\u021bi mici de informa\u021bie vor fi unite \u0219i scrise de un num\u0103r mai mare de ori, cresc\u00e2nd astfel intensitatea scrierii. Totu\u0219i, prea multe p\u0103r\u021bi neconectate vor cauza un throttling agresiv al inser\u021biilor p\u00e2n\u0103 c\u00e2nd \u00eembinarea continu\u0103. Am descoperit c\u0103 cel mai bun compromis \u00eentre recep\u021bia datelor \u00een timp real \u0219i performan\u021ba recep\u021biei este primirea \u00eentr-o tabel\u0103 a unui num\u0103r limitat de inser\u021bii pe secund\u0103.<\/p>\n<p>Cheia performan\u021bei citirii tabelelor este indexarea \u0219i plasarea datelor pe disc. Indiferent c\u00e2t de rapid\u0103 este procesarea, atunci c\u00e2nd motorul trebuie s\u0103 scaneze terabytes de date de pe disc \u0219i s\u0103 utilizeze doar o parte din acestea, acest lucru va dura timp. ClickHouse este un depozit pe coloane, astfel \u00eenc\u00e2t fiecare segment con\u021bine un fi\u0219ier pentru fiecare coloan\u0103 cu valori sortate pentru fiecare r\u00e2nd. Astfel, coloanele \u00eentregi, care nu sunt incluse \u00een interogare, pot fi ocolite la \u00eenceput, iar apoi mai multe celule pot fi procesate \u00een paralel cu o execu\u021bie vectorizat\u0103. Pentru a evita scanarea complet\u0103, fiecare segment are un mic fi\u0219ier index. <\/p>\n<p>Av\u00e2nd \u00een vedere c\u0103 toate coloanele sunt sortate dup\u0103 \u201echeia primar\u0103\u201d, fi\u0219ierul index con\u021bine doar etichetele (r\u00e2ndurile capturate) ale fiec\u0103rui N-lea r\u00e2nd, pentru a putea s\u0103 le stocheze \u00een memorie chiar \u0219i pentru tabele foarte mari. De exemplu, se poate seta o valoare implicit\u0103 de \u201ea marca fiecare 8192-lea r\u00e2nd\u201d, astfel \u00eenc\u00e2t o indexare \u201es\u0103r\u0103c\u0103cioas\u0103\u201d a unei tabele cu 1 trilion de r\u00e2nduri, care se \u00eencadreaz\u0103 u\u0219or \u00een memorie, va ocupa doar 122.070 de caractere. <\/p>\n<h3>Dezvoltarea sistemului<\/h3>\n<p>\nDezvoltarea \u0219i \u00eembun\u0103t\u0103\u021birea Clickhouse poate fi urm\u0103rit\u0103 pe <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/yandex\/ClickHouse\/pulse\">repo-ul Github<\/a><\/noindex> \u0219i se poate verifica c\u0103 procesul de \u201ematurizare\u201d se desf\u0103\u0219oar\u0103 \u00eentr-un ritm impresionant. <\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/19211da28e42178d26dff7623689df71.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>Popularitate<\/h3>\n<p>\nSe pare c\u0103 popularitatea Clickhouse cre\u0219te exponen\u021bial, \u00een special \u00een comunitatea de limb\u0103 rus\u0103. Conferin\u021ba de anul trecut High load 2018 (Moscova, 8-9 noiembrie 2018) a ar\u0103tat c\u0103 mon\u0219tri precum vk.com \u0219i Badoo folosesc Clickhouse, cu ajutorul c\u0103ruia introduc date (de exemplu, jurnale) de la zeci de mii de servere simultan. \u00centr-un videoclip de 40 de minute <noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/watch?v=pbbcMcrQoXw\">Yuri Nasretdinov din echipa Vkontakte vorbe\u0219te despre cum se face acest lucru<\/a><\/noindex>. \u00cen cur\u00e2nd vom publica transcrierea pe Habr pentru a facilita lucrul cu materialul.<\/p>\n<h3>Domenii de aplicare<\/h3>\n<p>\nDup\u0103 ce am petrecut ceva timp cercet\u00e2nd, cred c\u0103 exist\u0103 domenii \u00een care ClickHouse poate fi util sau poate \u00eenlocui complet alte solu\u021bii mai tradi\u021bionale \u0219i populare, cum ar fi MySQL, PostgreSQL, ELK, Google Big Query, Amazon RedShift, TimescaleDB, Hadoop, MapReduce, Pinot \u0219i Druid. Detaliile privind utilizarea ClickHouse pentru modernizarea sau \u00eenlocuirea complet\u0103 a acestor SGBD-uri sunt prezentate mai jos. <\/p>\n<h3>Extinderea capacit\u0103\u021bilor MySQL \u0219i PostgreSQL<\/h3>\n<p>\nRecent, am \u00eenlocuit par\u021bial MySQL cu ClickHouse pentru platforma de buletine informative <noindex><a rel=\"nofollow\" href=\"https:\/\/www.mautic.org\/\">Mautic newsletter<\/a><\/noindex>. Problema a constat cu MySQL era c\u0103, din cauza unui design necorespunz\u0103tor, \u00eenregistra fiecare e-mail trimis \u0219i fiecare link din acest e-mail cu un hash base64, cre\u00e2nd o masiv\u0103 tabel\u0103 MySQL (email_stats). Dup\u0103 ce am trimis utilizatorilor serviciului doar 10 milioane de e-mailuri, aceast\u0103 tabel\u0103 ocupa 150 GB de spa\u021biu pe disc, iar MySQL \u00eencepea s\u0103 \u201e\u00eengreuneze\u201d la interog\u0103ri simple. Pentru a remedia problema spa\u021biului pe disc, am utilizat cu succes compresia tabelei InnoDB, care a redus dimensiunea acesteia de patru ori. Totu\u0219i, nu are sens s\u0103 p\u0103str\u0103m mai mult de 20-30 de milioane de e-mailuri \u00een MySQL doar pentru a citi istoricul, deoarece orice interogare simpl\u0103, care dintr-un anumit motiv trebuie s\u0103 efectueze o scanare complet\u0103, duce la swap \u0219i o mare \u00eenc\u0103rcare pe I\/O, despre care primeam regulat alerte Zabbix.<\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/11b3ee4b1a8b7c9457bd3871bd12049f.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nClickhouse folose\u0219te dou\u0103 algoritmi de compresie, care reduc volumul de date cu aproximativ <noindex><a rel=\"nofollow\" href=\"https:\/\/www.altinity.com\/blog\/2017\/11\/21\/compression-in-clickhouse\">3-4 ori<\/a><\/noindex>, dar \u00een acest caz particular, datele erau deosebit de \u201ecompresibile\u201d. <\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/d192502073d57967620aa07da0bbfa0c.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>\u00cenlocuirea ELK<\/h3>\n<p>\nDin experien\u021ba proprie, stiva ELK (ElasticSearch, Logstash \u0219i Kibana, \u00een acest caz particular ElasticSearch) necesit\u0103 mult mai multe resurse pentru a func\u021biona dec\u00e2t sunt necesare pentru stocarea jurnalelor. ElasticSearch este un motor excelent dac\u0103 ai nevoie de o c\u0103utare text complet bun\u0103 \u00een jurnale (iar eu nu cred c\u0103 ai nevoie de asta), dar m\u0103 \u00eentreb de ce a devenit de facto motorul standard pentru gestionarea jurnalele. Performan\u021ba sa \u00een primire, combinat\u0103 cu Logstash, ne-a creat probleme chiar \u0219i cu sarcini destul de mici \u0219i a necesitat ad\u0103ugarea unui volum tot mai mare de memorie RAM \u0219i spa\u021biu pe disc. Ca baz\u0103 de date, Clickhouse este mai bun dec\u00e2t ElasticSearch din urm\u0103toarele motive:<\/p>\n<ul>\n<li>Suport pentru dialectul SQL;<\/li>\n<li>O mai bun\u0103 rat\u0103 de compresie a datelor stocate;<\/li>\n<li>Suport pentru c\u0103utarea expresiilor regulate Regex \u00een loc de c\u0103utarea textului complet;<\/li>\n<li>Planificare \u00eembun\u0103t\u0103\u021bit\u0103 a interog\u0103rilor \u0219i o performan\u021b\u0103 general\u0103 mai bun\u0103.<\/li>\n<\/ul>\n<p>\n\u00cen prezent, cea mai mare problem\u0103 care apare \u00een compararea ClickHouse cu ELK este lipsa solu\u021biilor pentru livrarea jurnalelor, precum \u0219i lipsa documenta\u021biei \u0219i a ghidurilor \u00een acest domeniu. Totu\u0219i, orice utilizator poate configura ELK folosind ghidul Digital Ocean, ceea ce este foarte important pentru o adoptare rapid\u0103 a unor tehnologiilor similare. Aici exist\u0103 un motor de baz\u0103 de date, dar \u00eenc\u0103 nu exist\u0103 Filebeat pentru ClickHouse. Da, exist\u0103 <noindex><a rel=\"nofollow\" href=\"https:\/\/www.fluentd.org\/\">fluentd<\/a><\/noindex> \u0219i un sistem pentru lucrul cu jurnalele <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/flant\/loghouse\">loghouse<\/a><\/noindex>, exist\u0103 un instrument <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Altinity\/clicktail\">clicktail<\/a><\/noindex> pentru a introduce \u00een ClickHouse datele fi\u0219ierelor jurnal, dar toate acestea necesit\u0103 mai mult timp. Totu\u0219i, ClickHouse r\u0103m\u00e2ne lider datorit\u0103 simplit\u0103\u021bii sale, astfel \u00eenc\u00e2t chiar \u0219i \u00eencep\u0103torii o instaleaz\u0103 u\u0219or \u0219i \u00eencep s\u0103 o foloseasc\u0103 complet \u00een decurs de 10 minute. <\/p>\n<p>Prefer\u00e2nd solu\u021biile minimaliste, am \u00eencercat s\u0103 folosesc FluentBit, un instrument pentru exportarea jurnalele cu un consum foarte mic de memorie, \u00eempreun\u0103 cu ClickHouse, \u00eencerc\u00e2nd \u00een acela\u0219i timp s\u0103 evit utilizarea Kafka. Cu toate acestea, este necesar s\u0103 se rezolve mici incompatibilit\u0103\u021bi, cum ar fi <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/fluent\/fluent-bit\/issues\/848\">problemele cu formatul datei<\/a><\/noindex>, \u00eenainte de a putea fi f\u0103cut f\u0103r\u0103 un strat proxy care transform\u0103 datele din FluentBit \u00een ClickHouse.<\/p>\n<p>Ca alternativ\u0103 la Kibana, se poate folosi ClickHouse ca backend pentru <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Vertamedia\/clickhouse-grafana\">Grafana<\/a><\/noindex>. Din c\u00e2te am \u00een\u021beles, pot ap\u0103rea probleme de performan\u021b\u0103 atunci c\u00e2nd se renderizeaz\u0103 un num\u0103r foarte mare de puncte de date, \u00een special cu versiunile mai vechi de Grafana. La Qwintry nu am \u00eencercat \u00eenc\u0103 acest lucru, dar pl\u00e2ngerile despre astfel de probleme apar din c\u00e2nd \u00een c\u00e2nd pe canalul de suport ClickHouse de pe Telegram. <\/p>\n<h3>\u00cenlocuirea Google Big Query \u0219i Amazon RedShift (solu\u021bie pentru mari companii)<\/h3>\n<p>\nO utilizare ideal\u0103 a BigQuery este s\u0103 \u00eencarci 1 TB de date JSON \u0219i s\u0103 efectuezi interog\u0103ri analitice asupra acestora. Big Query este un produs excelent, a c\u0103rui scalabilitate este greu de supralicitat. Este un software mult mai complex dec\u00e2t ClickHouse, care func\u021bioneaz\u0103 pe un cluster intern, dar din perspectiva clientului are multe \u00een comun cu ClickHouse. BigQuery se poate \u201escumpi\u201d rapid, odat\u0103 ce \u00eencepi s\u0103 pl\u0103te\u0219ti pentru fiecare SELECT, astfel \u00eenc\u00e2t acesta este o adev\u0103rat\u0103 solu\u021bie SaaS cu toate avantajele \u0219i dezavantajele sale. <\/p>\n<p>ClickHouse este cea mai bun\u0103 alegere atunci c\u00e2nd efectuezi multe interog\u0103ri costisitoare din punct de vedere al resurselor de calcul. Cu c\u00e2t efectuezi mai multe interog\u0103ri SELECT \u00een fiecare zi \u2014 cu at\u00e2t mai mult are sens s\u0103 \u00eenlocuie\u0219ti Big Query cu ClickHouse, deoarece o astfel de \u00eenlocuire te poate ajuta s\u0103 economise\u0219ti mii de dolari, dac\u0103 este vorba despre multe terabytes de date procesate. Acest lucru nu se aplic\u0103 datelor stocate, al c\u0103ror procesare \u00een Big Query este destul de ieftin\u0103.<\/p>\n<p>\u00cen articolul co-fondatorului companiei Altinity, Alexandru Zaitsev <noindex><a rel=\"nofollow\" href=\"https:\/\/www.altinity.com\/blog\/2017\/10\/23\/migration-to-clickhouse\">\u201eMigr\u00e2nd la ClickHouse\u201d<\/a><\/noindex> se discut\u0103 despre avantajele migra\u021biei bazei de date.<\/p>\n<h3>\u00cenlocuirea TimescaleDB<\/h3>\n<p>\nTimescaleDB este o extensie PostgreSQL care optimizeaz\u0103 manipularea seriilor temporale \u00een baza de date obi\u0219nuit\u0103.<noindex><a rel=\"nofollow\" href=\"https:\/\/docs.timescale.com\/v1.0\/introduction\">https:\/\/docs.timescale.com\/v1.0\/introduction<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/zabbix\/blog\/458530\/\">https:\/\/habr.com\/ru\/company\/zabbix\/blog\/458530\/<\/a><\/noindex>). <\/p>\n<p>De\u0219i ClickHouse nu este un competitor serios \u00een ni\u0219a seriilor temporale, datorit\u0103 structurii sale pe coloane \u0219i execu\u021biei vectoriale a interog\u0103rilor, \u00een majoritatea cazurilor de procesare a interog\u0103rilor analitice, este mult mai rapid dec\u00e2t TimescaleDB. Performan\u021ba de preluare a datelor \u00een loturi a ClickHouse este de aproximativ 3 ori mai mare, folosind de asemenea de 20 de ori mai pu\u021bin spa\u021biu pe disc, ceea ce este cu adev\u0103rat important pentru procesarea unor volume mari de date istorice.<noindex><a rel=\"nofollow\" href=\"https:\/\/www.altinity.com\/blog\/ClickHouse-for-time-series\">https:\/\/www.altinity.com\/blog\/ClickHouse-for-time-series<\/a><\/noindex>.<\/p>\n<p>Spre deosebire de ClickHouse, singura modalitate de a economisi pu\u021bin spa\u021biu pe disc \u00een TimescaleDB este utilizarea ZFS sau a unor sisteme de fi\u0219iere similare.<\/p>\n<p>Actualiz\u0103rile viitoare ale ClickHouse vor introduce probabil compresia delta, ceea ce \u00eel va face \u0219i mai potrivit pentru procesarea \u0219i stocarea datelor de serii temporale. TimescaleDB ar putea deveni o alegere mai bun\u0103 dec\u00e2t ClickHouse \u201egol\u201d \u00een urm\u0103toarele cazuri:<\/p>\n<ul>\n<li>instala\u021bii mici cu foarte pu\u021bin RAM (&lt;3 GB);<\/li>\n<li>num\u0103r mare de INSERT-uri mici, pe care nu dori\u021bi s\u0103 le bufferiza\u021bi \u00een fragmente mari;<\/li>\n<li>consisten\u021b\u0103 mai bun\u0103, uniformitate \u0219i cerin\u021be ACID;<\/li>\n<li>suport pentru PostGIS;<\/li>\n<li>integrare cu tabelele existente PostgreSQL, deoarece TimescaleDB este, \u00een esen\u021b\u0103, PostgreSQL.<\/li>\n<\/ul>\n<p><\/p>\n<h3>Competitivitate cu sistemele Hadoop \u0219i MapReduce<\/h3>\n<p>\nHadoop \u0219i alte produse MapReduce pot efectua o mul\u021bime de calcule complexe, dar, \u00een general, func\u021bioneaz\u0103 cu \u00eent\u00e2rzieri mari. ClickHouse rezolv\u0103 aceast\u0103 problem\u0103, proces\u00e2nd terabytes de date \u0219i oferind rezultatul aproape instantaneu. Astfel, ClickHouse este mult mai eficient pentru realizarea de cercet\u0103ri analitice rapide \u0219i interactive, ceea ce ar trebui s\u0103 atrag\u0103 speciali\u0219ti \u00een domeniul prelucr\u0103rii datelor.<\/p>\n<h3>Competitivitate cu Pinot \u0219i Druid<\/h3>\n<p>\nCei mai apropia\u021bi concuren\u021bi ai ClickHouse sunt produsele open source scalabile pe coloane, Pinot \u0219i Druid. O compara\u021bie excelent\u0103 a acestor sisteme a fost publicat\u0103 \u00een articolul <noindex><a rel=\"nofollow\" href=\"https:\/\/medium.com\/@leventov\/comparison-of-the-open-source-olap-systems-for-big-data-ClickHouse-druid-and-pinot-8e042a5ed1c7\">Roman Leventov<\/a><\/noindex> de la 1 februarie 2018. <\/p>\n<p><img decoding=\"async\" alt=\"Utilizarea Clickhouse ca \u00eenlocuire pentru ELK, Big Query \u0219i TimescaleDB\" src=\"\/wp-content\/uploads\/2020\/01\/fe58814aa97b287b70c2f724b72b448a.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nAcest articol necesit\u0103 actualizare \u2013 afirm\u0103 c\u0103 ClickHouse nu suport\u0103 opera\u021bii UPDATE \u0219i DELETE, ceea ce nu este complet corect \u00een ceea ce prive\u0219te cele mai recente versiuni.<\/p>\n<p>Nu avem experien\u021ba necesar\u0103 cu aceste SGBD-uri, dar nu \u00eemi place deloc complexitatea infrastructurii utilizate pentru a rula Druid \u0219i Pinot \u2014 este o mul\u021bime de \u201ep\u0103r\u021bi mobile\u201d \u00eenv\u0103luite \u00een Java din toate p\u0103r\u021bile.<\/p>\n<p>Druid \u0219i Pinot sunt proiecte incubatoare Apache, iar evolu\u021bia lor este detaliat documentat\u0103 de Apache pe paginile proiectelor lor de pe GitHub. Pinot a ap\u0103rut \u00een incubator \u00een octombrie 2018, iar Druid s-a n\u0103scut cu 8 luni mai devreme - \u00een februarie.<\/p>\n<p>Lipsa informa\u021biilor despre modul \u00een care func\u021bioneaz\u0103 AFS \u00eemi ridic\u0103 c\u00e2teva, \u0219i poate chiar prostii, \u00eentreb\u0103ri. M\u0103 \u00eentreb dac\u0103 autorii Pinot au observat c\u0103 Funda\u021bia Apache este mai \u00eenclina\u021bi spre Druid \u0219i dac\u0103 aceast\u0103 atitudine fa\u021b\u0103 de competitor le-a provocat un sentiment de invidie? Se va \u00eencetini dezvoltarea Druid \u0219i se va accelera dezvoltarea Pinot, dac\u0103 sponsorii care sus\u021bin primul se vor interesa brusc de al doilea?<\/p>\n<h3>Dezavantajele ClickHouse<\/h3>\n<p>\nImaturitate: este evident c\u0103 este \u00eenc\u0103 o tehnologie incipient\u0103, dar oricum, nu existe nimic de acest gen \u00een alte SGBD-uri columnare. <\/p>\n<p>Inser\u021biile mici func\u021bioneaz\u0103 prost cu viteza mare: inser\u021biile trebuie s\u0103 fie \u00eemp\u0103r\u021bite \u00een buc\u0103\u021bi mari, deoarece performan\u021ba inser\u021biilor mici scade propor\u021bional cu num\u0103rul de coloane din fiecare r\u00e2nd. A\u0219a sunt stocate datele \u00een ClickHouse pe disc - fiecare coloan\u0103 reprezint\u0103 1 fi\u0219ier sau mai multe, a\u0219a c\u0103 pentru a insera 1 r\u00e2nd care con\u021bine 100 de coloane, trebuie s\u0103 deschide\u021bi \u0219i s\u0103 scrie\u021bi cel pu\u021bin 100 de fi\u0219iere. De aceea, pentru a bufferiza inser\u021biile este nevoie de un intermediar (cu excep\u021bia cazului \u00een care clientul \u00eensu\u0219i nu asigur\u0103 bufferizarea) - de obicei, acesta este Kafka sau un sistem de gestionare a co\u0219urilor. Se poate folosi, de asemenea, motorul Buffer table pentru a copia ulterior buc\u0103\u021bi mari de date \u00een tabelele MergeTree.<\/p>\n<p>\u00cembin\u0103rile de tabele sunt limitate de memoria RAM a serverului, dar, cel pu\u021bin, ele exist\u0103 acolo! De exemplu, Druid \u0219i Pinot nu au deloc astfel de \u00eembin\u0103ri, deoarece este dificil de implementat direct \u00een sistemele distribuite care nu suport\u0103 transferul de buc\u0103\u021bi mari de date \u00eentre noduri. <\/p>\n<h3>Conclusions<\/h3>\n<p>\n\u00cen urm\u0103torii ani, pl\u0103nuim s\u0103 folosim pe scar\u0103 larg\u0103 ClickHouse \u00een Qwintry, deoarece acest sistem de gestionare a bazelor de date ofer\u0103 un echilibru excelent \u00eentre performan\u021b\u0103, overhead redus, scalabilitate \u0219i simplitate. Sunt aproape sigur c\u0103 va \u00eencepe s\u0103 se r\u0103sp\u00e2ndeasc\u0103 rapid, odat\u0103 ce comunitatea ClickHouse va g\u0103si mai multe modalit\u0103\u021bi de a-l utiliza \u00een instala\u021bii mici \u0219i medii.<\/p>\n<h3>Pu\u021bin publicitate \ud83d\ude42<\/h3>\n<p>\nMul\u021bumim c\u0103 r\u0103m\u00e2ne\u021bi cu noi. V\u0103 plac articolele noastre? Dori\u021bi s\u0103 vede\u021bi mai multe materiale interesante? Sus\u021bine\u021bi-ne, efectu\u00e2nd o comand\u0103 sau recomand\u00e2ndu-ne prietenilor, <noindex><a rel=\"nofollow\" href=\"https:\/\/ua-hosting.company\/cloudvps\/nl\">VPS cloud pentru dezvoltatori de la 4,99 $<\/a><\/noindex>, <b>un echivalent unic pentru serverele entry-level, care a fost creat de noi pentru voi:<\/b> <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/company\/ua-hosting\/blog\/347386\/\">Toat\u0103 adev\u0103rul despre VPS (KVM) E5-2697 v3 (6 nuclee) 10GB DDR4 480GB SSD 1Gbps de la 19 $ sau cum s\u0103 \u00eemp\u0103r\u021bi\u021bi corect un server?<\/a><\/noindex> (sunt disponibile op\u021biuni cu RAID1 \u0219i RAID10, p\u00e2n\u0103 la 24 nuclee \u0219i p\u00e2n\u0103 la 40GB DDR4).<\/p>\n<p><b>Dell R730xd la jum\u0103tate de pre\u021b \u00een centrul de date Equinix Tier IV din Amsterdam?<\/b> Numai la noi <b><noindex><a rel=\"nofollow\" href=\"https:\/\/ua-hosting.company\/serversnl\">2 x Intel TetraDeca-Core Xeon 2x E5-2697v3 2.6GHz 14C 64GB DDR4 4x960GB SSD 1Gbps 100 TB de la 199 $<\/a><\/noindex> \u00een Olanda! <b>Dell R420 \u2014 2x E5-2430 2.2Ghz 6C 128GB DDR3 2x960GB SSD 1Gbps 100TB \u2014 de la 99 $!<\/b><\/b> Citi\u021bi despre <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/company\/ua-hosting\/blog\/329618\/\">Cum s\u0103 construi\u021bi o infrastructur\u0103 de clas\u0103 enterprise folosind servere Dell R730xd E5-2650 v4 la pre\u021buri foarte mici de 9000 \u20ac?<\/a><\/noindex><br \/>\n<br \/>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/ua-hosting\/blog\/483112\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>Clickhouse \u2014 \u044d\u0442\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432\u0430\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043e\u043d\u043b\u0430\u0439\u043d \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 (OLAP) \u0441 \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u043c \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u043c \u043a\u043e\u0434\u043e\u043c, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u0430\u044f \u042f\u043d\u0434\u0435\u043a\u0441\u043e\u043c. \u0415\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 \u042f\u043d\u0434\u0435\u043a\u0441, CloudFlare, VK.com, Badoo \u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0441\u0435\u0440\u0432\u0438\u0441\u044b \u043f\u043e \u0432\u0441\u0435\u043c\u0443 \u043c\u0438\u0440\u0443 \u0434\u043b\u044f \u0445\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u043e\u0431\u044a\u0435\u043c\u043e\u0432 \u0434\u0430\u043d\u043d\u044b\u0445 (\u0432\u0441\u0442\u0430\u0432\u043a\u0430 \u0442\u044b\u0441\u044f\u0447 \u0441\u0442\u0440\u043e\u043a \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0443 \u0438\u043b\u0438 \u043f\u0435\u0442\u0430\u0431\u0430\u0439\u0442\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u0445\u0440\u0430\u043d\u044f\u0449\u0438\u0445\u0441\u044f \u043d\u0430 \u0434\u0438\u0441\u043a\u0435). \u0412 \u043e\u0431\u044b\u0447\u043d\u043e\u0439, \u00ab\u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u0439\u00bb \u0421\u0423\u0411\u0414, \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-54918","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"Clickhouse \u2014 \u044d\u0442\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432\u0430\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043e\u043d\u043b\u0430\u0439\u043d \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 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