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2026","image":{"id":"image-f333184220","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--5317ad3a-be89-4b8c-89ee-05a1ad3c1f9c/29-general-.jpg?quality=85&preferwebp=true","isLcpImage":false,"height":"2366","width":"4206",":type":"snowflake-site/components/image"},"authors":[{"authorImage":{"id":"image-b319d0ac48","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--b217981d-40d1-4b4c-a0c3-cd85705ac331/screen-shot-2022-02-16-at-5.07.04-pm.png?quality=85&preferwebp=true","alt":"Brad Culberson","isLcpImage":false,"height":"340","width":"256",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-813df372c3","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/it/blog/authors/brad-culberson/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Brad Culberson"}}],"tag":{"tagText":"Data engineering","tagColor":"#29B5E8"},"title":{"lines":["Raggiungere 41 GB/sec su 1 tabella: benchmark di Elastic Channels per Snowpipe Streaming"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"],":type":"snowflake-site/components/container"},"responsivegrid_content":{"columnClassNames":{"blog_text_928085540":"aem-GridColumn aem-GridColumn--default--12","blog_text_727865939_":"aem-GridColumn aem-GridColumn--default--12","code_snippet_1251224445":"aem-GridColumn aem-GridColumn--default--12","blog_text_727865939":"aem-GridColumn aem-GridColumn--default--12","blog_text_727865939__1951811845":"aem-GridColumn aem-GridColumn--default--12","blog_text_1700383890_1671161186":"aem-GridColumn aem-GridColumn--default--12","blog_text_727865939__1731886985":"aem-GridColumn aem-GridColumn--default--12","blog_text_727865939__389005976":"aem-GridColumn aem-GridColumn--default--12","code_snippet_1309091417":"aem-GridColumn aem-GridColumn--default--12","blog_text_copy_1232224311":"aem-GridColumn 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aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-dcc9445635","text":"\u003Cp\u003ESi narra che Snowflake abbia preso il nome perché ai fondatori dell’azienda piaceva davvero tanto sciare, quindi ci è sembrato naturale testare i nuovi Elastic Channels facendo passare attraverso il sistema le vendite di un’intera stagione sciistica in un resort.\u003C/p\u003E\n\u003Cp\u003ECon la crescita delle applicazioni e la connessione di sempre più dispositivi, hai bisogno di un modo semplice per portare i dati in Snowflake per l’analisi dei dati e l’AI. Di recente abbiamo annunciato la general availability di \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/it/blog/snowpipe-streaming-elastic-channels-ga/\"\u003EElastic Channels\u003C/a\u003E per \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/snowpipe-streaming/\"\u003ESnowpipe Streaming\u003C/a\u003E, che consente di inviare eventi direttamente da applicazioni, dispositivi e servizi, mentre Snowflake gestisce la scalabilità dell’ingestion. Ma come si comporta questo percorso più semplice quando i volumi di dati crescono?\u003C/p\u003E\n\u003Cp\u003EPer rispondere a questa domanda, abbiamo creato un workload sintetico di vendite di un resort e aumentato il numero di producer indipendenti, raggiungendo un throughput totale di 41 GB/sec. Abbiamo eseguito questo benchmark utilizzando come destinazione una tabella Snowflake FDN (nativa) e lo abbiamo ripetuto per la tabella Apache Iceberg™ gestita da Snowflake. Questo articolo analizza come sono cambiati il throughput e l’aggiornamento dei dati con l’aumento del carico, e cosa significano questi risultati per i tuoi workload.\u003C/p\u003E\n\u003Ch2\u003EPremesse\u003C/h2\u003E\n\u003Cp\u003ESnowpipe Streaming offre una rigorosa semantica exactly-once e un ordinamento rigoroso dei messaggi. Per i workload mission-critical come il change data capture (CDC), le transazioni finanziarie e la gestione dell’inventario, questa meticolosa gestione contabile non è negoziabile, con gestione esplicita dei canali, tracciamento degli offset e coordinamento degli schema lato client.\u003C/p\u003E\n\u003Cp\u003EMa se stai creando pipeline di telemetria, reti di dispositivi Internet delle cose (IoT) o analisi dei log ad alto volume, dove la consegna at-least-once è del tutto sufficiente, tutto quell’overhead aggiuntivo può diventare un attrito superfluo. È esattamente per questo che abbiamo introdotto Elastic Channels: eliminare la complessità della gestione del ciclo di vita dei canali e della gestione contabile degli offset. Lasciando che Snowflake gestisca automaticamente lo scaling e la distribuzione dei canali, ottieni un’ingestion fulminea per i tuoi stream di dati più leggeri, senza l’overhead operativo di ingegneria.\u003C/p\u003E\n\u003Cp\u003ELa versione precedente di Snowpipe Streaming richiedeva che i client generassero dati e metadati in formato colonnare per ogni batch di file, li caricassero e li registrassero con Snowflake per l’elaborazione. Nel 2025, Snowflake ha rilasciato \u003Cb\u003El’architettura ad alte prestazioni\u003C/b\u003E: oggetti \u003Ccode\u003EPIPE\u003C/code\u003E lato server, core SDK basati su Rust e un percorso di ingestion riprogettato che ha eliminato gran parte dell’onere lato client. L’architettura ad alte prestazioni ha cambiato il client, che ora deve solo generare e inviare payload JSON a Snowflake. Snowflake accetta questi payload JSON ed elabora questi dati in modo asincrono con PIPE in esecuzione nella nostra infrastruttura Snowpipe altamente scalabile. Queste modifiche hanno eliminato una parte sostanziale dell’overhead di capacità di calcolo e memoria dai client, semplificando i deployment. Elastic Channels (\u003Ccode\u003Eclient.get_elastic_channel()\u003C/code\u003E) si basano sull’architettura ad alte prestazioni: un client SDK apre automaticamente un canale pooled, senza nomi di canale e senza gestione degli offset, scrivendo nella stessa pipe ad alte prestazioni.\u003C/p\u003E\n\u003Cp\u003ELa domanda a cui volevamo rispondere con questo test era semplice: \u003Ci\u003Equanto velocemente possono davvero andare gli Elastic Channels, e con quale efficienza?\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003ELa risposta ci ha sorpreso. Con un design lato client ottimale e una flotta Kubernetes scalata orizzontalmente, abbiamo misurato un throughput aggregato sostenuto \u003Cb\u003Esuperiore di oltre 3,5 volte rispetto alle versioni precedenti\u003C/b\u003E, utilizzando meno core totali, meno memoria e molti meno worker process rispetto a benchmark di ingestion su scala petabyte comparabili pubblicati altrove nel settore.\u003C/p\u003E\n\u003Ch2\u003EIn breve:\u003C/h2\u003E\n\u003Cp\u003ESnowpipe Streaming con Elastic Channels ha sostenuto 41,8 GB/sec (133,5 milioni di righe al secondo) verso una singola tabella Snowflake FDN, oltre 3,5 volte le versioni precedenti di Snowpipe Streaming, utilizzando 455 worker Kubernetes leggeri su 455 vCPU e 455 GiB di memoria, con zero errori e uno scaling lineare in ogni fase testata.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy":{"id":"blog-text-3bae200c47","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EThroughput massimo (non compresso)\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E41,8 GB/sec\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ETasso di righe massimo\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E133,5 milioni di righe al secondo\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EWorker\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E455 (1 canale elastico)\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EFootprint di calcolo\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E455 vCPU · 455 GiB di memoria\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ENodi Kubernetes\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E15 × m7g.8xlarge (Graviton)\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003Evs. versione precedente di Snowpipe Streaming\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003E3,5x throughput superiore\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_569306937":{"id":"blog-text-dbf4685e18","text":"\u003Ch2\u003ECosa abbiamo testato\u003C/h2\u003E\n\u003Cp\u003EAbbiamo creato un harness di benchmark open source (\u003Ca href=\"https://github.com/snowflake-labs/snowflake-elastic-channels\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Ccode\u003Esnowflake-elastic-channels\u003C/code\u003E\u003C/a\u003E) che testa direttamente l’SDK Python \u003Ca href=\"https://pypi.org/project/snowpipe-streaming/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Ccode\u003Esnowpipe-streaming\u003C/code\u003E\u003C/a\u003E:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EDeployment semplice:\u003C/b\u003E Un worker pod = un client SDK = un Elastic Channel su una RESORT_SALES_PIPE condivisa.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EConfigurazione di rate e throughput:\u003C/b\u003E Rate target per worker per una durata fissa; il throughput aggregato è la somma su tutti i pod.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EOsservabilità live:\u003C/b\u003E Un monitor residente nel cluster segue i log dei worker e scrive finestre di throughput/latenza in Snowflake (\u003Ccode\u003EELASTIC_K8S_METRICS\u003C/code\u003E) mentre l’esecuzione è ancora in corso.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ELanding tipizzato:\u003C/b\u003E \u003Ccode\u003EMATCH_BY_COLUMN_NAME = CASE_SENSITIVE\u003C/code\u003E mappa le chiavi JSON direttamente sulle colonne della tabella (non in un unico Blob \u003Ccode\u003EVARIANT\u003C/code\u003E).\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDestinazione configurabile:\u003C/b\u003E I dati vengono scritti in tabelle FDN o in tabelle Apache Iceberg.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EOgni misurazione riportata di seguito è un numero \u003Ci\u003Ereale\u003C/i\u003E ottenuto da uno scale-out orizzontale su Amazon EKS in \u003Ccode\u003Eus-west-2\u003C/code\u003E, nel settembre 2026. Riportiamo byte non compressi e conteggi di righe come osservati dall’SDK e dal monitor, senza estrapolazioni.\u003C/p\u003E\n\u003Ch2\u003EIl workload: vendite sintetiche di un resort su scala di produzione\u003C/h2\u003E\n\u003Cp\u003ELeggere da Kafka, S3 o da un data set pubblico introduce limiti del producer che oscurano ciò che Snowpipe Streaming è in grado di fare. Quindi abbiamo \u003Ci\u003Egenerato\u003C/i\u003E i dati in process per eliminare qualsiasi collo di bottiglia esterno.\u003C/p\u003E\n\u003Cp\u003EIl caso d’uso che abbiamo scelto includeva transazioni point-of-sale (POS) in un portfolio sintetico di resort sciistici: 42 resort, quattro categorie di prodotto (abbonamenti stagionali, biglietti giornalieri, cibo e bevande, merchandising), metodi di pagamento, tipologie di cliente e livelli di prezzo per resort. Ecco lo schema a tabella singola del modello:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-39aeff345f","codeSnippet":"TXID, RESORT, CATEGORY, ITEM, CUSTOMER_TYPE, PAYMENT_METHOD,\r\nQUANTITY, UNIT_PRICE, AMOUNT, SALE_TIME, RUN_ID, WORKER_ID","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_copy_1482587826":{"id":"blog-text-477bf8f3b0","text":"\u003Cp\u003EOgni resort ha un profilo di vendita deterministico (volume relativo, mix di categorie, livello di prezzo, quota di possessori di abbonamento), in modo che l’analisi dei dati aggregata risulti realistica, con ricavi per resort, cambiamenti nel mix e pattern geografici, piuttosto che somigliare a un rumore casuale uniforme. Una dashboard Streamlit nel repository visualizza il feed live come se stessi osservando una catena nazionale di resort durante un fine settimana festivo molto affollato.\u003C/p\u003E\n\u003Cp\u003EAbbiamo eseguito deliberatamente a un livello \u003Ci\u003Emolto superiore\u003C/i\u003E rispetto a quanto genererebbe qualsiasi singolo POS di resort. Questo era l’obiettivo: Isolare la capacità di ingestion di Snowflake, non il realismo del POS. Le righe avevano una media di \u003Cb\u003E~277 byte\u003C/b\u003E di NDJSON non compresso. La generazione era \u003Cb\u003Epriva di RNG\u003C/b\u003E sul percorso critico (una tabella pesata precompilata è indicizzata in modalità round-robin), quindi il client non è mai diventato il collo di bottiglia al throughput target.\u003C/p\u003E\n\u003Cp\u003EUna dashboard Streamlit era disponibile anche per la direzione del resort, per monitorare vendite e ricavi tra i vari resort e per monitorare il comportamento dei clienti in tempo reale. Le transazioni point-of-sale venivano incluse nella dashboard in ben meno di un minuto.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-c4ebc1af76","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--3acfc54f-c76c-4d96-bb7f-90c002f175b2/snowpipebenchfig1.png?quality=85&preferwebp=true","alt":"Figure 1: Real-time sales report in Streamlit.","isLcpImage":true,"height":"248","width":"1271","title":"Figure 1: Real-time sales report in Streamlit.",":type":"snowflake-site/components/image"},"image_copy":{"id":"image-cd8681f3ef","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--4a9bf8b0-7094-4167-be3e-91c4e1562875/snowpipebenchfig2.png?quality=85&preferwebp=true","alt":"Figure 2: Live transaction feed in Streamlit.","isLcpImage":false,"height":"692","width":"1401","title":"Figure 2: Live transaction feed in Streamlit.",":type":"snowflake-site/components/image"},"blog_text_copy_1232224311":{"id":"blog-text-b442637a09","text":"\u003Ch2\u003EDeployment Kubernetes\u003C/h2\u003E\n\u003Cp\u003EIl benchmark è stato eseguito su un \u003Cb\u003Ecluster EKS persistente\u003C/b\u003E (\u003Ccode\u003Esnowflake-elastic-channels\u003C/code\u003E). I nodi worker venivano scalati verso l’alto per ogni esecuzione e riportati a zero in seguito; solo un piccolo nodo di sistema e il deployment del monitor rimanevano sempre attivi.\u003C/p\u003E\n\u003Ch3\u003EArchitettura\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy_copy":{"id":"image-59cdb0cb8b","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--5c0e7a22-b849-46c9-be02-e14dc5c4c2c9/snowpipebenchfig3.png?quality=85&preferwebp=true","alt":"Figure 3: Overview of the components in this benchmark. EKS was used to load data into Snowflake using Snowpipe Streaming Elastic Channels.","isLcpImage":false,"height":"909","width":"1095","title":"Figure 3: Overview of the components in this benchmark. EKS was used to load data into Snowflake using Snowpipe Streaming Elastic Channels.",":type":"snowflake-site/components/image"},"blog_text_copy_12322":{"id":"blog-text-1ac0bf7f51","text":"\u003Ch3\u003EParametri del benchmark\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_12322_729252006":{"id":"blog-text-ea4f8147c7","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EParametro\u003C/th\u003E\n\u003Cth\u003EValore\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker pod\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E455\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ECanali\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E1 (condiviso, elastico)\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERichiesta/limite CPU worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1 vCPU\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERichiesta/limite memoria worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker per nodo\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E31 (\u003Ccode\u003Em7g.8xlarge\u003C/code\u003E, Graviton arm64)\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ENodi worker al picco\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E15\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EvCPU worker totali\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemoria worker totale\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFormato messaggio\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003ENDJSON (mappatura delle colonne tipizzata)\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EDimensione media della riga (non compressa)\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~277 byte\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EPercorso di codifica rapido\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003EELASTIC_FAST_JSON_ENCODE=true\u003C/code\u003E (di default)\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESDK\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EPackage Python \u003Ccode\u003Esnowpipe-streaming\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EPipe di ingest\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003ERESORT_SALES_PIPE\u003C/code\u003E → \u003Ccode\u003ERESORT_SALES\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_928085540":{"id":"blog-text-c2127cc416","text":"\u003Cp\u003EI pod worker erano \u003Cb\u003Edimensionati correttamente in base all’utilizzo in stato stazionario misurato.\u003C/b\u003E La CPU rappresenta il vincolo limitante nel packing dei nodi, che richiede circa 240m-540m di CPU, dove 1000m corrisponde a 1 vCPU completa, che è stata allocata. L’utilizzo della memoria osservato era compreso tra 68 e 183 MiB RSS, a fronte di 1Gi allocato. Un’istanza \u003Ccode\u003Em7g.8xlarge\u003C/code\u003E pianifica \u003Cb\u003E31 worker per nodo\u003C/b\u003E, con margine di memoria disponibile.\u003C/p\u003E\n\u003Ch2\u003ERisultati delle prestazioni\u003C/h2\u003E\n\u003Ch5\u003EPicco di throughput sostenuto su FDN\u003C/h5\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1867199046":{"id":"blog-text-33d4487624","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EMetrica\u003C/th\u003E\n\u003Cth\u003EValore\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThroughput sostenuto (non compresso)\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E41,8 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFrequenza di righe sostenuta\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E133,5M righe al secondo\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker al picco\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E455\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThroughput per worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~92 MB/sec (~293K righe al secondo), praticamente costante da 15 a 455 worker\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThrottling\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003ENessuno osservato\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939_":{"id":"blog-text-dee85a0db3","text":"\u003Ch5\u003EConfronto con Snowpipe Streaming\u003C/h5\u003E\n\u003Cp\u003EAl Summit 2024, Snowflake ha dimostrato come la versione precedente di Snowpipe Streaming fosse in grado di eseguire l’ingestion a circa 10 GB/sec aggregati su una singola tabella, un risultato di riferimento per l’epoca. I nostri benchmark, eseguiti su Snowpipe Streaming ad alte prestazioni con la funzionalità Elastic Channels di recente rilascio, hanno prodotto livelli di throughput superiori di oltre 3,5 volte rispetto ai benchmark precedenti, sia su tabelle FDN che su tabelle Apache Iceberg gestite da Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1951811845":{"id":"blog-text-911677b128","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E \u003C/th\u003E\n\u003Cth\u003EDemo originale Summit 2024 (approssimativo)\u003C/th\u003E\n\u003Cth\u003EHP + Elastic Channels su tabelle FDN\u003C/th\u003E\n\u003Cth\u003EHP + Elastic Channels su tabelle Apache Iceberg gestite da Snowflake\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThroughput (non compresso)\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~10 GB/sec\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E41,8 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E39,0 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFrequenza di righe\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~39M righe al secondo¹\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E133,5M righe al secondo\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E121,6M righe al secondo\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAccelerazione\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E–\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E\u003E 3,5x\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E \u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1731886985":{"id":"blog-text-1e24aad6af","text":"\u003Cp\u003E¹ Stima di ~277 byte/riga per adattarsi alla struttura delle righe di questo workload.\u003C/p\u003E\n\u003Ch5\u003EScalabilità orizzontale (EKS scale-out)\u003C/h5\u003E\n\u003Cp\u003EIl throughput ha seguito quasi esattamente il numero di worker a ogni passaggio, con una scalabilità lineare netta e nessun plateau. La latenza è rimasta in un intervallo limitato anche con l’aumento del throughput: \u003Cb\u003EBatch ack\u003C/b\u003E (completamento future dell’SDK) si è mantenuto in media vicino a 600 ms in tutte le fasi. Il tempo di batch ack è la durata totale che intercorre tra l’invio dei dati dal client all’SDK e la ricezione del callback (i dati sono durevoli). La latenza \u003Cb\u003Eingest-to-visible\u003C/b\u003E (\u003Ccode\u003ESALE_TIME\u003C/code\u003E → \u003Ccode\u003EMETADATA$ROW_LAST_COMMIT_TIME\u003C/code\u003E) è rimasta nella fascia di \u003Cb\u003E~12-29 secondi\u003C/b\u003E: i dati sono interrogabili entro pochi secondi dall’arrivo, anche a 133,5 milioni di righe al secondo. Questa è la durata necessaria perché i dati siano inclusi nelle query.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__991293704":{"id":"blog-text-44015773e2","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EWorker\u003C/th\u003E\n\u003Cth\u003ENodi\u003C/th\u003E\n\u003Cth\u003ERighe al secondo\u003C/th\u003E\n\u003Cth\u003EThroughput\u003C/th\u003E\n\u003Cth\u003EBatch ack medio\u003C/th\u003E\n\u003Cth\u003EIngest-to-visible medio²\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E15\u003C/td\u003E\n\u003Ctd\u003E1\u003C/td\u003E\n\u003Ctd\u003E4,4M\u003C/td\u003E\n\u003Ctd\u003E1,38 GB/sec\u003C/td\u003E\n\u003Ctd\u003E582 ms\u003C/td\u003E\n\u003Ctd\u003E11,7 s\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E90\u003C/td\u003E\n\u003Ctd\u003E3\u003C/td\u003E\n\u003Ctd\u003E26,6M\u003C/td\u003E\n\u003Ctd\u003E8,27 GB/sec\u003C/td\u003E\n\u003Ctd\u003E595 ms\u003C/td\u003E\n\u003Ctd\u003E14,0 s\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E180\u003C/td\u003E\n\u003Ctd\u003E6\u003C/td\u003E\n\u003Ctd\u003E52,8M\u003C/td\u003E\n\u003Ctd\u003E16,52 GB/sec\u003C/td\u003E\n\u003Ctd\u003E611 ms\u003C/td\u003E\n\u003Ctd\u003E16,7 s\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E300\u003C/td\u003E\n\u003Ctd\u003E10\u003C/td\u003E\n\u003Ctd\u003E87,8M\u003C/td\u003E\n\u003Ctd\u003E27,50 GB/sec\u003C/td\u003E\n\u003Ctd\u003E572 ms\u003C/td\u003E\n\u003Ctd\u003E17,7 s\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E420\u003C/td\u003E\n\u003Ctd\u003E14\u003C/td\u003E\n\u003Ctd\u003E123,2M\u003C/td\u003E\n\u003Ctd\u003E38,6 GB/sec\u003C/td\u003E\n\u003Ctd\u003E587 ms\u003C/td\u003E\n\u003Ctd\u003E28,9 s\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E15\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E133,5M\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E41,84 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E570 ms\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E19,9 s\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__38648953":{"id":"blog-text-4be145bbbd","text":"\u003Cp\u003E² La latenza ingest-to-visible è misurata come \u003Ccode\u003ETIMESTAMPDIFF(SALE_TIME, METADATA$ROW_LAST_COMMIT_TIME)\u003C/code\u003E da \u003Ccode\u003EELASTIC_K8S_METRICS\u003C/code\u003E tramite il monitor del cluster.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy_copy_copy":{"id":"image-c696cbc17d","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--a363ecbb-7a87-4415-8e74-d387c72995ee/snowpipebenchfig4.png?quality=85&preferwebp=true","alt":"Figure 4: Four-panel view of the scale-out campaign. Top: Uncompressed throughput (GB/sec) and row rate (millions per second) grow linearly with worker count. Bottom: Batch ack latency (SDK, milliseconds) stays flat near ~600 ms; ingest-to-visible latency (seconds) does not track throughput — it stays in a bounded band even as aggregate rate climbs past 38 GB/sec.","isLcpImage":false,"height":"1317","width":"1996","title":"Figure 4: Four-panel view of the scale-out campaign. Top: Uncompressed throughput (GB/sec) and row rate (millions per second) grow linearly with worker count. Bottom: Batch ack latency (SDK, milliseconds) stays flat near ~600 ms; ingest-to-visible latency (seconds) does not track throughput — it stays in a bounded band even as aggregate rate climbs past 38 GB/sec.",":type":"snowflake-site/components/image"},"blog_text_727865939__389005976":{"id":"blog-text-37b286a7dc","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDue metriche di latenza rispondono a due domande diverse:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ELatenza di batch ack\u003C/b\u003E (in media ~570-611 ms) risponde alla domanda “Quanto velocemente l’SDK ha accettato il mio batch?” Viene misurata sul lato client, da \u003Ccode\u003Eappend_rows()\u003C/code\u003E fino al completamento del future dell’SDK. È rimasta quasi invariata nell’intero intervallo da 15 a 455 worker; il client non ha mai dovuto attendere che Snowflake assorbisse il batch successivo.\u003C/li\u003E\n\u003Cli\u003ELa \u003Cb\u003Elatenza ingest-to-visible\u003C/b\u003E (in media ~12-29 s) risponde alla domanda “Quando posso eseguire query su questa riga?” Viene misurata sul lato server come \u003Ccode\u003ESALE_TIME\u003C/code\u003E → \u003Ccode\u003EMETADATA$ROW_LAST_COMMIT_TIME\u003C/code\u003E. È il numero che conta per le dashboard e l’analisi dei dati operativa. Non è cresciuta in proporzione al throughput (un aumento di 30 volte nella frequenza delle righe non ha prodotto un aumento di 30 volte nel tempo di disponibilità per le query) e si è mantenuta in un intervallo pratico per le dashboard quasi in tempo reale delle vendite dei resort durante tutta la campagna.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELa leva di scalabilità è \u003Ci\u003Eorizzontale\u003C/i\u003E: Aggiungere pod worker leggeri, non pod più grandi. Assegnare più CPU a un singolo worker non ha aumentato il suo throughput nei nostri test: ogni pod da 1 vCPU/1 GiB ha contribuito in modo indipendente con lo stesso ~0,086 GiB/sec.\u003C/p\u003E\n\u003Ch5\u003EEfficienza delle risorse al picco\u003C/h5\u003E\n\u003Cp\u003EConfrontando i numeri principali con un tipico layout di benchmark di streaming su scala di petabyte del settore, l’efficienza risulta evidente.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939":{"id":"blog-text-196cd1136f","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E \u003C/th\u003E\n\u003Cth\u003ETipico benchmark di streaming su larga scala³\u003C/th\u003E\n\u003Cth\u003ESnowpipe Streaming Elastic Channels verso tabella FDN\u003C/th\u003E\n\u003Cth\u003ESnowpipe Streaming Elastic Channels verso Apache Iceberg table gestita da Snowflake\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker concorrenti\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E2048\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ECore per worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E1,5\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1,0\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1,0\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemoria per worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E2 GiB\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ECore totali dei worker\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E3072\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemoria worker totale\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E4096 GiB\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThroughput sostenuto\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~12 GB/sec\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E~41,8 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E~39,0 GB/sec\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERighe al secondo sostenute\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E~12M\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E133,5M\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E121,6M\u003C/b\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1882309474":{"id":"blog-text-67cfe92264","text":"\u003Cp\u003E³ Parametri pubblicati rappresentativi per un benchmark di ingestione di una singola tabella su scala di petabyte, della durata di 24 ore, che utilizza migliaia di pod producer Kubernetes e un encoder binario nativo per le righe.\u003C/p\u003E\r\n\u003Cp\u003EAbbiamo utilizzato \u003Cb\u003Ecirca un settimo dei core\u003C/b\u003E, \u003Cb\u003Eun nono della memoria\u003C/b\u003E e \u003Cb\u003Emeno di un quarto dei processi worker\u003C/b\u003E, pur ottenendo \u003Cb\u003Eoltre 3 volte i byte al secondo\u003C/b\u003E e \u003Cb\u003Eoltre 10 volte le righe al secondo, sia con FDN che con tabelle Apache Iceberg come destinazione\u003C/b\u003E.\u003C/p\u003E\r\n\u003Ch2\u003ECome siamo arrivati qui: evoluzione di Snowpipe Streaming\u003C/h2\u003E\r\n\u003Cp\u003EQuesto progetto è partito da dove iniziano molte valutazioni di streaming Snowflake: su istanze EC2 di grandi dimensioni con decine di worker per host, canali espliciti e row dicts Python costruiti a ogni batch. Quelle esecuzioni sono state preziose: Hanno evidenziato i limiti lato client molto prima che lo facesse Snowflake.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1700383890_811970419":{"id":"blog-text-b33337c72c","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EEra\u003C/th\u003E\n\u003Cth\u003ECosa abbiamo misurato\u003C/th\u003E\n\u003Cth\u003ECosa ci ha limitato\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EVersione precedente di Snowpipe Streaming\u003C/b\u003E (demo al Summit 2024)\u003C/td\u003E\n\u003Ctd\u003E~10 GB/sec sul palco; meno di 1 GB/sec per host nelle nostre prime fasi di scalata\u003C/td\u003E\n\u003Ctd\u003ECosti operativi generali di canale/offset, columnarization lato client e metadati, architettura SDK classica\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EArchitettura ad alte prestazioni\u003C/b\u003E (rilasciata nel 2025)\u003C/td\u003E\n\u003Ctd\u003E~2-8 GB/sec per istanza di grandi dimensioni; ~16 GB/sec sostenuti multi-nodo su EC2\u003C/td\u003E\n\u003Ctd\u003ERete per host, gestione worker/canale, percorso di encoding Python\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EElastic Channels + EKS + encoding Rust\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003E\u003Cb\u003E41,8 GB/sec\u003C/b\u003E con 455 worker\u003C/td\u003E\n\u003Ctd\u003ENiente, in questo test: scalabilità lineare in tutto l’intervallo supportato\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1700383890_262624709":{"id":"blog-text-1e44769db6","text":"\u003Cp\u003ETre modifiche si sono combinate:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EElastic Channels:\u003C/b\u003E \u003Ccode\u003Eget_elastic_channel()\u003C/code\u003E raggruppa tutti i canali sotto un unico client. Il codice dell’applicazione non deve più gestire nomi e offset.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EKubernetes con la granularità corretta:\u003C/b\u003E Un pod per worker, 1 vCPU ciascuno, 31 pod per nodo Graviton. Scalare la flotta, non il processo.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUn percorso rapido Rust per la generazione e l’encoding:\u003C/b\u003E Python si occupa dell’orchestrazione, non del ciclo critico, come descritto di seguito.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EGli aggiornamenti a Snowpipe Streaming, incluso l’aggiunta di Elastic Channels, rendono l’invio di dati a Snowflake migliore che mai. L’architettura ad alte prestazioni di Snowpipe Streaming consente di acquisire enormi volumi di dati in modo rapido e semplice su un’infrastruttura serverless e scalabile, ed eseguire query sui dati in pochi secondi dalla fonte. La fatturazione è semplice, basata sui gigabyte acquisiti. Oggi non esiste sul mercato un modo più semplice e scalabile per eseguire query sui dati streaming.\u003C/p\u003E\n\u003Ch2\u003EPerché Rust per l’encoding dei dati\u003C/h2\u003E\n\u003Cp\u003EA centinaia di migliaia di righe al secondo per worker, l’allocazione dei dict Python e la serializzazione JSON dominano la CPU. Il percorso di default dell’SDK accetta dict di righe e li ricodifica internamente: corretto, ma ridondante quando lo schema è già noto.\u003C/p\u003E\n\u003Cp\u003EPer utilizzare la CPU e la memoria del worker nel modo più efficiente possibile, i dati sono stati generati e serializzati in Rust. Questo utilizza metodi interni dell’SDK. Questi metodi potrebbero cambiare in futuro, complicando gli aggiornamenti.\u003C/p\u003E\n\u003Cp\u003EAbbiamo aggiunto un’estensione nativa opzionale (\u003Ccode\u003Enative/\u003C/code\u003E, creata con PyO3 + maturin nell’immagine Docker del worker) che si occupa \u003Ci\u003Esia\u003C/i\u003E della generazione delle righe sia della serializzazione NDJSON in Rust:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESaltare gli oggetti Python per riga:\u003C/b\u003E Nessun \u003Ccode\u003Edict\u003C/code\u003E per riga, nessun round-trip di struct \u003Ccode\u003Emsgspec\u003C/code\u003E nel percorso critico.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESaltare la seconda codifica dell’SDK:\u003C/b\u003E \u003Ccode\u003Efast_append.py\u003C/code\u003E passa i byte preserializzati direttamente all’append FFI di Elastic Channel, bypassando il passaggio interno \u003Ccode\u003Eencode_lines\u003C/code\u003E di \u003Ccode\u003Eappend_rows_with_wait\u003C/code\u003E.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ERimanere identico byte per byte a Python:\u003C/b\u003E \u003Ccode\u003Enative/generate_tables.py\u003C/code\u003E deriva tutte le tabelle pesate e le stringhe di prezzo preformattate direttamente da \u003Ccode\u003Eresort_sales.py\u003C/code\u003E. Rust riproduce i dati già risolti; non reimplementa la logica di business. I test unitari verificano che i percorsi nativo e Python producano NDJSON identici.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Ccode\u003EUNIT_PRICE\u003C/code\u003E e \u003Ccode\u003EAMOUNT\u003C/code\u003E vengono incorporati come stringhe decimali al momento della generazione del codice (tramite la formattazione \u003Ccode\u003Ejson.dumps\u003C/code\u003E di Python), in modo che Rust non sia mai in disaccordo con la formattazione float di Python sull’insieme finito di combinazioni \u003Ccode\u003E(item, tier, quantity)\u003C/code\u003E che si verificano.\u003C/p\u003E\n\u003Cp\u003EQuando la wheel non è installata, l’harness passa automaticamente all’encoding msgspec in Python. Nelle esecuzioni di benchmark in produzione, \u003Cb\u003ERust è attivo di default\u003C/b\u003E (\u003Ccode\u003EELASTIC_FAST_JSON_ENCODE=true\u003C/code\u003E).\u003C/p\u003E\n\u003Cp\u003EIl risultato: Ogni worker da 1 vCPU sostiene ~0,086 GiB/sec con CPU di riserva per l’I/O di rete dell’SDK: il throughput per worker che rende possibile la scalabilità lineare a 455 worker.\u003C/p\u003E\n\u003Ch2\u003ECosa abbiamo imparato\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EElastic Channels è semplice da integrare:\u003C/b\u003E Chiamare \u003Ccode\u003Eget_elastic_channel()\u003C/code\u003E, aggiungere righe (o byte precodificati) e il lavoro è fatto.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl throughput scala in modo lineare con il numero di worker:\u003C/b\u003E Con la possibilità di scalare da 15 a 455 worker, non si osserva alcuna flessione nella curva né alcun throttling.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPod dimensionati correttamente battono i pod sovradimensionati:\u003C/b\u003E 1 vCPU / 1 GiB è sufficiente quando l’encoding non è il tuo collo di bottiglia.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EI carichi di lavoro sintetici sono una funzionalità, non un trucco:\u003C/b\u003E La generazione deterministica in-process elimina le distorsioni di I/O e rende le esecuzioni riproducibili: fondamentale quando si confrontano architetture, non fonti di dati.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EL’ottimizzazione lato client resta importante:\u003C/b\u003E Anche con un core server Rust, dimezzare la CPU del client raddoppia circa il numero di stream che puoi permetterti per host.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003EGuarda una demo di Elastic Channels\u003C/h2\u003E\n\u003Cp\u003EGuarda un \u003Ca href=\"https://www.youtube.com/watch?v=tMDjw8uYt50\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Evideo\u003C/a\u003E che mostra Elastic Channels in Snowflake per un caso d’uso IoT.\u003C/p\u003E\n\u003Ch2\u003EProva subito\u003C/h2\u003E\n\u003Cp\u003ETutto il necessario per riprodurre un’esecuzione end‑to‑end \u003Ci\u003Eminima\u003C/i\u003E si trova nel repository. Non sono necessari 455 worker per validare la pipeline: un singolo Elastic Channel per un minuto è sufficiente per dimostrare la connettività, la configurazione del pipe e le metriche live.\u003C/p\u003E\n\u003Ch3\u003E1. Clonare e configurare\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1374505483":{"id":"code-snippet-eafc92da9d","language":"git","codeSnippet":"git clone https://github.com/snowflake-labs/snowflake-elastic-channels.git\r\ncd snowflake-elastic-channels\r\npip install -r requirements.txt\r\ncp .env.example .env   # Snowflake account, user, key-pair auth\r\npython scripts/setup_elastic_ramp.py --ingest-ip \u003CYOUR_INGEST_IP\u003E","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1700383890_1671161186":{"id":"blog-text-e3dfa3125a","text":"\u003Ch3\u003E2. Eseguire la configurazione EKS una tantum (oppure saltare questo passaggio se si desiderano solo micro-benchmark locali)\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1374505":{"id":"code-snippet-22c633392b","language":"git","codeSnippet":"./infra/eks/create_cluster.sh\r\n./infra/eks/build_and_push.sh\r\n./infra/eks/create_secret.sh\r\n./infra/eks/allowlist_eks.sh\r\n./infra/eks/deploy_monitor.sh","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1700383890_992775869":{"id":"blog-text-f5a115ee1e","text":"\u003Ch3\u003E3. Eseguire un test di fumo con un singolo worker/canale\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1251224445":{"id":"code-snippet-6c56404cb6","language":"git","codeSnippet":"./infra/eks/run_job.sh quicktest-$(date +%Y%m%d%H%M%S) \\\r\n    --target-bps 100000000 \\\r\n    --duration-seconds 60 \\\r\n    --workers 1","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1700383890":{"id":"blog-text-a8b84bf545","text":"\u003Cp\u003EOsservare il throughput live in Snowflake:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1309091417":{"id":"code-snippet-9b1b7900e8","language":"sql","codeSnippet":"SELECT *\r\nFROM SC_DB.SC_SCHEMA.ELASTIC_K8S_METRICS\r\nWHERE RUN_ID = '\u003Cyour-run-id\u003E'\r\nORDER BY WINDOW_START;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1700383890_952738100":{"id":"blog-text-520aac32c0","text":"\u003Cp\u003EPer esplorare la scalabilità, \u003Ccode\u003E--workers\u003C/code\u003E accetta qualsiasi valore da 1 a 455; il node group si dimensiona automaticamente.\u003C/p\u003E\n\u003Ch3\u003E4. Eseguire un benchmark locale solo client (senza bisogno di Snowflake)\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1374505_121434193":{"id":"code-snippet-248d0a2c5b","language":"python","codeSnippet":"python scripts/benchmark_client_cpu.py --workers 1 --aggregate-mbps 100","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1625648899":{"id":"blog-text-9a2f53d5c7","text":"\u003Ch2\u003ERepository\u003C/h2\u003E\n\u003Cp\u003EDisponibile su \u003Ca href=\"https://github.com/snowflake-labs/snowflake-elastic-channels\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EGitHub\u003C/a\u003E.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_722815504":{"id":"blog-text-4cc964c057","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPercorso\u003C/th\u003E\n\u003Cth\u003EScopo\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Esrc/elastic_channels/k8s_worker.py\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EEntry point del pod worker\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Esrc/elastic_channels/resort_sales.py\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EGeneratore di righe POS sintetiche\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Enative/\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EEncoder NDJSON rapido in Rust\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Einfra/eks/run_job.sh\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EOrchestratore di benchmark scale-out\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Estreamlit/Monitor_App.py\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EDashboard di throughput/latenza live\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ccode\u003Edocs/architecture.md\u003C/code\u003E\u003C/td\u003E\n\u003Ctd\u003EDesign completo del sistema\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_821923898":{"id":"blog-text-bf5119d246","text":"\u003Ch2\u003EAvvertenze\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003EI risultati si riferiscono a un account Snowflake, una regione (\u003Ccode\u003Eus-west-2\u003C/code\u003E) e una forma di riga sintetica (righe NDJSON di circa 277 byte) su una tabella non clusterizzata. La larghezza delle righe, la definizione della pipe e i limiti dell’account possono variare.\u003C/li\u003E\n\u003Cli\u003EIl confronto \u003E 3,5x è rispetto a Snowpipe Streaming, come dimostrato al Summit 2024 (~10 GB/sec); questo benchmark utilizza l’architettura ad alte prestazioni rilasciata nel 2025 con Elastic Channels. 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