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We recently announced the general availability of \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/snowpipe-streaming-elastic-channels-ga/\"\u003EElastic Channels\u003C/a\u003E for \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/snowpipe-streaming/\"\u003ESnowpipe Streaming\u003C/a\u003E, which let you send events directly from applications, devices and services while Snowflake manages ingestion scale. But how does that simpler path perform as data volumes grow?\u003C/p\u003E\r\n\u003Cp\u003ETo explore that question, we created a synthetic resort-sales workload and increased the number of independent producers achieving a total throughput of 41 GB/sec. We ran this benchmark with the destination as a Snowflake FDN (native) table and repeated it for the Snowflake managed Apache Iceberg™ table. This post examines how throughput and data freshness changed as the load increased and what the results mean for your own workloads.\u003C/p\u003E\r\n\u003Ch2\u003EBackground\u003C/h2\u003E\r\n\u003Cp\u003ESnowpipe Streaming delivers rigorous exactly-once semantics and strict message ordering. For mission-critical workloads such as change data capture (CDC), financial transactions and inventory management, that meticulous bookkeeping is nonnegotiable — complete with explicit channel management, offset tracking and client-side schema coordination.\u003C/p\u003E\r\n\u003Cp\u003EBut if you’re building telemetry pipelines, Internet of Things (IoT) device networks or high-volume log analytics where at-least-once delivery is entirely sufficient, all that extra overhead can become unnecessary friction. That’s precisely why we introduced Elastic Channels: to strip away the complexity of channel lifecycle management and offset bookkeeping. By letting Snowflake handle the scaling and channel distribution automatically, you get lightning-fast ingestion for your lighter-weight data streams without the engineering overhead.\u003C/p\u003E\r\n\u003Cp\u003EThe previous version of Snowpipe Streaming had clients generate columnar data and metadata for every batch of files, upload it and register it with Snowflake for processing. In 2025, Snowflake released the \u003Cb\u003Ehigh-performance architecture\u003C/b\u003E: server-side \u003Ccode\u003EPIPE\u003C/code\u003E objects, Rust-based SDK cores and a redesigned ingest path that removed much of the client-side burden. The high-performance architecture changed the client so now it only needs to generate and send JSON payloads to Snowflake. Snowflake accepts those JSON payloads and asynchronously processes this data with PIPEs running in our highly scalable Snowpipe infrastructure. These changes removed a substantial amount of the compute and memory overhead from the clients, simplifying deployments. Elastic Channels (\u003Ccode\u003Eclient.get_elastic_channel()\u003C/code\u003E) build on the high-performance architecture — one SDK client opens a pooled channel automatically, with no channel names and no offset management — while writing into the same high-performance pipe.\u003C/p\u003E\r\n\u003Cp\u003EThe question we set out to answer in this test was straightforward: \u003Ci\u003EHow fast can Elastic Channels actually go, and how efficiently?\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003EThe answer surprised us. With an optimal client-side design and a horizontally scaled Kubernetes fleet, we measured sustained aggregate throughput \u003Cb\u003Emore than 3.5x previous versions\u003C/b\u003E, on fewer total cores, less memory and far fewer worker processes than comparable petabyte-scale ingest benchmarks published elsewhere in the industry.\u003C/p\u003E\r\n\u003Ch2\u003EAt a glance\u003C/h2\u003E\r\n\u003Cp\u003ESnowpipe Streaming with Elastic Channels sustained 41.8 GB/sec (133.5 million rows per second) to a single FDN Snowflake table — more than 3.5x previous versions of Snowpipe Streaming — using 455 lightweight Kubernetes workers on 455 vCPU and 455 GiB of memory, with zero errors and linear scaling across every step tested.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy":{"id":"blog-text-e2c2b175d5","text":"\u003Ctable\u003E\r\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EPeak throughput (uncompressed)\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E41.8 GB/sec\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EPeak row rate\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E133.5 million rows per second\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EWorkers\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E455 (1 elastic channel)\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ECompute footprint\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E455 vCPU · 455 GiB memory\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EKubernetes nodes\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E15 × m7g.8xlarge (Graviton)\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003Evs. previous Snowpipe Streaming\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E>3.5x higher throughput\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_569306937":{"id":"blog-text-199902fa21","text":"\u003Ch2\u003EWhat we tested\u003C/h2\u003E\r\n\u003Cp\u003EWe built an open source benchmark harness (\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) that exercises the \u003Ca href=\"https://pypi.org/project/snowpipe-streaming/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Ccode\u003Esnowpipe-streaming\u003C/code\u003E\u003C/a\u003E Python SDK directly:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ESimple deployment:\u003C/b\u003E One worker pod = one SDK client = one Elastic Channel against a shared RESORT_SALES_PIPE.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERate and throughput configuration:\u003C/b\u003E Target rate per worker for a fixed duration; aggregate throughput is the sum across pods.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELive observability:\u003C/b\u003E A cluster-resident monitor tails worker logs and writes throughput/latency windows to Snowflake (\u003Ccode\u003EELASTIC_K8S_METRICS\u003C/code\u003E) while the run is still in progress.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETyped landing:\u003C/b\u003E \u003Ccode\u003EMATCH_BY_COLUMN_NAME = CASE_SENSITIVE\u003C/code\u003E maps JSON keys straight to table columns (not a single \u003Ccode\u003EVARIANT\u003C/code\u003E blob).\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EConfigurable destination:\u003C/b\u003E Data lands in FDN tables or Apache Iceberg tables.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EEvery measurement below is an \u003Ci\u003Eactual\u003C/i\u003E number from a horizontal scale-out on Amazon EKS in \u003Ccode\u003Eus-west-2\u003C/code\u003E, in September 2026. We report uncompressed bytes and row counts as observed by the SDK and monitor — no extrapolation.\u003C/p\u003E\r\n\u003Ch2\u003EThe workload: Synthetic resort sales at production scale\u003C/h2\u003E\r\n\u003Cp\u003EReading from Kafka, S3 or a public data set introduces producer limits that obscure what Snowpipe Streaming itself can do. So we \u003Ci\u003Efabricated\u003C/i\u003E data in process to remove any external bottlenecks.\u003C/p\u003E\r\n\u003Cp\u003EThe use case we chose included point-of-sale (POS) transactions across a synthetic ski resort portfolio: 42 resorts, four product categories (season tickets, day tickets, food and drink, merchandise), payment methods, customer types and per-resort pricing tiers. Here is the model’s single-table schema:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-222063f5c3","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-dc3ede2aee","text":"\u003Cp\u003EEach resort has a deterministic sales profile (relative volume, category mix, price tier, passholder share) so aggregate analytics look realistic — revenue by resort, mix shifts, geographic patterns — rather than like uniform random noise. A Streamlit dashboard in the repo visualizes the live feed as if you were watching a national resort chain on a busy holiday weekend.\u003C/p\u003E\r\n\u003Cp\u003EWe deliberately ran \u003Ci\u003Efar above\u003C/i\u003E what any single resort POS would generate. That was the point: Isolate Snowflake ingest capacity, not POS realism. Rows averaged \u003Cb\u003E~277 bytes\u003C/b\u003E uncompressed NDJSON. Generation was \u003Cb\u003ERNG-free\u003C/b\u003E on the hot path — a prebuilt weighted table is round-robin indexed — so the client never became the bottleneck at target throughput.\u003C/p\u003E\r\n\u003Cp\u003EA Streamlit dashboard was also available for resort leadership to monitor sales and revenue across their resorts and to monitor customer behavior in real time. Point-of-sale transactions were included in the dashboard in well under a minute.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-fd59386e7c","height":"248","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c0b6ad68-01be-4cff-bcec-ccf0b68a5e12/snowpipebenchfig1.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Figure 1: Real-time sales report in Streamlit.","isLcpImage":true,"width":"1271","title":"Figure 1: Real-time sales report in Streamlit.",":type":"snowflake-site/components/image"},"image_copy":{"id":"image-d30c5508e6","height":"692","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--4c4a01f2-fbe8-4ea0-aee4-5372e2df0609/snowpipebenchfig2.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Figure 2: Live transaction feed in Streamlit.","isLcpImage":false,"width":"1401","title":"Figure 2: Live transaction feed in Streamlit.",":type":"snowflake-site/components/image"},"blog_text_copy_1232224311":{"id":"blog-text-e009f5df15","text":"\u003Ch2\u003EKubernetes deployment\u003C/h2\u003E\r\n\u003Cp\u003EThe benchmark ran on a \u003Cb\u003Epersistent EKS cluster\u003C/b\u003E (\u003Ccode\u003Esnowflake-elastic-channels\u003C/code\u003E). Worker nodes scaled up for each run and back to zero afterward; only a small system node and monitor deployment stayed always on.\u003C/p\u003E\r\n\u003Ch3\u003EArchitecture\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy_copy":{"id":"image-362245d366","height":"909","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--227ee184-b169-498e-bfc3-25afddfb70b5/snowpipebenchfig3.png?quality=85&preferwebp=true","lazyEnabled":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,"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-e4b2c9df1c","text":"\u003Ch3\u003EBenchmark parameters\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_12322_729252006":{"id":"blog-text-507921bc4c","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EParameter\u003C/th\u003E\r\n\u003Cth\u003EValue\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker pods\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E455\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChannels\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E1 (shared, elastic)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker CPU request/limit\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1 vCPU\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker memory request/limit\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorkers per node\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E31 (\u003Ccode\u003Em7g.8xlarge\u003C/code\u003E, Graviton arm64)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorker nodes at peak\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E15\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETotal worker vCPU\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETotal worker memory\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMessage format\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ENDJSON (typed column mapping)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAverage row size (uncompressed)\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~277 bytes\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFast encode path\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Ccode\u003EELASTIC_FAST_JSON_ENCODE=true\u003C/code\u003E (default)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESDK\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Ccode\u003Esnowpipe-streaming\u003C/code\u003E Python package\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EIngest pipe\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Ccode\u003ERESORT_SALES_PIPE\u003C/code\u003E → \u003Ccode\u003ERESORT_SALES\u003C/code\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_928085540":{"id":"blog-text-aa3689eeb2","text":"\u003Cp\u003EWorker pods were \u003Cb\u003Eright-sized from measured steady-state usage.\u003C/b\u003E CPU is the binding constraint of node packing needing ~240m-540m CPU where 1000m is 1 full vCPU which was allocated. Memory utilization was between 68-183 MiB RSS observed where 1Gi was allocated. An \u003Ccode\u003Em7g.8xlarge\u003C/code\u003E schedules \u003Cb\u003E31 workers per node\u003C/b\u003E with headroom to spare on memory.\u003C/p\u003E\r\n\u003Ch2\u003EPerformance results\u003C/h2\u003E\r\n\u003Ch5\u003EPeak sustained throughput on FDN\u003C/h5\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1867199046":{"id":"blog-text-e9a4a30821","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EMetric\u003C/th\u003E\r\n\u003Cth\u003EValue\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESustained throughput (uncompressed)\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E41.8 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESustained row rate\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E133.5M rows per second\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EWorkers at peak\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E455\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EPer-worker throughput\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~92 MB/sec (~293K rows per second), essentially flat from 15 to 455 workers\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThrottling\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ENone observed\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939_":{"id":"blog-text-895c7a2c6d","text":"\u003Ch5\u003EComparison to Snowpipe Streaming\u003C/h5\u003E\r\n\u003Cp\u003EAt Summit 2024, Snowflake demonstrated Snowpipe Streaming’s previous version ingesting at roughly 10 GB/sec aggregate to a single table — a landmark result at the time. Our benchmark runs on the high-performance Snowpipe Streaming using the newly released Elastic Channels feature, which produced throughput levels greater than 3.5x the previous benchmarks on both FDN and Snowflake managed Apache Iceberg tables.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1951811845":{"id":"blog-text-d932dd3e63","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E \u003C/th\u003E\r\n\u003Cth\u003ESummit 2024 original demo (approx.)\u003C/th\u003E\r\n\u003Cth\u003EHP + Elastic Channels to FDN tables\u003C/th\u003E\r\n\u003Cth\u003EHP + Elastic Channels to Snowflake managed Apache Iceberg tables\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EThroughput (uncompressed)\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~10 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E41.8 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E39.0 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERow rate\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~39M rows per second¹\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E133.5M rows per second\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E121.6M rows per second\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESpeedup\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E—\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E> 3.5x\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E \u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__1731886985":{"id":"blog-text-765bf47c6a","text":"\u003Cp\u003E¹ Estimated at ~277 bytes/row to match this workload's row shape.\u003C/p\u003E\r\n\u003Ch5\u003EHorizontal scaling (EKS scale-out)\u003C/h5\u003E\r\n\u003Cp\u003EThroughput tracked worker count almost exactly at every step — clean linear scaling with no plateau. Latency stayed in a bounded range even as throughput climbed: \u003Cb\u003EBatch ack\u003C/b\u003E (SDK future completion) held near 600 ms on average across all steps. The batch ack time is the total duration from the client sending the data into the SDK until the callback is received (data is durable). \u003Cb\u003EIngest-to-visible\u003C/b\u003E latency (\u003Ccode\u003ESALE_TIME\u003C/code\u003E → \u003Ccode\u003EMETADATA$ROW_LAST_COMMIT_TIME\u003C/code\u003E) remained in the \u003Cb\u003E~12-29-second\u003C/b\u003E band — data queryable within seconds of landing, even at 133.5 million rows per second. This is the duration until data is included in queries.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__991293704":{"id":"blog-text-3a356f8d33","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EWorkers\u003C/th\u003E\r\n\u003Cth\u003ENodes\u003C/th\u003E\r\n\u003Cth\u003ERows per second\u003C/th\u003E\r\n\u003Cth\u003EThroughput\u003C/th\u003E\r\n\u003Cth\u003EAverage batch ack\u003C/th\u003E\r\n\u003Cth\u003EAverage ingest-to-visible²\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E15\u003C/td\u003E\r\n\u003Ctd\u003E1\u003C/td\u003E\r\n\u003Ctd\u003E4.4M\u003C/td\u003E\r\n\u003Ctd\u003E1.38 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E582 ms\u003C/td\u003E\r\n\u003Ctd\u003E11.7 s\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E90\u003C/td\u003E\r\n\u003Ctd\u003E3\u003C/td\u003E\r\n\u003Ctd\u003E26.6M\u003C/td\u003E\r\n\u003Ctd\u003E8.27 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E595 ms\u003C/td\u003E\r\n\u003Ctd\u003E14.0 s\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E180\u003C/td\u003E\r\n\u003Ctd\u003E6\u003C/td\u003E\r\n\u003Ctd\u003E52.8M\u003C/td\u003E\r\n\u003Ctd\u003E16.52 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E611 ms\u003C/td\u003E\r\n\u003Ctd\u003E16.7 s\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E300\u003C/td\u003E\r\n\u003Ctd\u003E10\u003C/td\u003E\r\n\u003Ctd\u003E87.8M\u003C/td\u003E\r\n\u003Ctd\u003E27.50 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E572 ms\u003C/td\u003E\r\n\u003Ctd\u003E17.7 s\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E420\u003C/td\u003E\r\n\u003Ctd\u003E14\u003C/td\u003E\r\n\u003Ctd\u003E123.2M\u003C/td\u003E\r\n\u003Ctd\u003E38.6 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E587 ms\u003C/td\u003E\r\n\u003Ctd\u003E28.9 s\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E15\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E133.5M\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E41.84 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E570 ms\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E19.9 s\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939__38648953":{"id":"blog-text-0af2b04887","text":"\u003Cp\u003E² Ingest-to-visible latency measured as \u003Ccode\u003ETIMESTAMPDIFF(SALE_TIME, METADATA$ROW_LAST_COMMIT_TIME)\u003C/code\u003E from \u003Ccode\u003EELASTIC_K8S_METRICS\u003C/code\u003E by the cluster monitor.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy_copy_copy":{"id":"image-153fa13279","height":"1317","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e5299f62-80d8-408d-8600-5953b090f503/snowpipebenchfig4.png?quality=85&preferwebp=true","lazyEnabled":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,"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-96d3a06bb5","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003ETwo latency metrics answer two different questions:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EBatch ack latency\u003C/b\u003E (~570-611 ms on average) answers the question “How fast did the SDK accept my batch?” It’s measured client-side from \u003Ccode\u003Eappend_rows()\u003C/code\u003E until the SDK future completes. It barely moved across the full 15 → 455 worker range; the client was never waiting on Snowflake to absorb the next batch.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIngest-to-visible latency\u003C/b\u003E (~12-29 s on average) answers the question “When can I query this row?” It’s measured server-side as \u003Ccode\u003ESALE_TIME\u003C/code\u003E → \u003Ccode\u003EMETADATA$ROW_LAST_COMMIT_TIME\u003C/code\u003E. This is the number that matters for dashboards and operational analytics. It did not grow in proportion to throughput — a 30x increase in row rate did not produce a 30x increase in time-to-queryable — and remained in a practical range for near-real-time resort-sales dashboards throughout the campaign.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThe scaling lever is \u003Ci\u003Ehorizontal\u003C/i\u003E: Add lightweight worker pods, not bigger pods. Giving an individual worker more CPU did not raise its throughput in our tests; each 1 vCPU / 1 GiB pod contributed the same ~0.086 GiB/sec independently.\u003C/p\u003E\r\n\u003Ch5\u003EResource efficiency at peak\u003C/h5\u003E\r\n\u003Cp\u003EPutting the headline numbers next to a typical industry petabyte-scale streaming benchmark layout makes the efficiency clear.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_727865939":{"id":"blog-text-86b79a8867","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E \u003C/th\u003E\r\n\u003Cth\u003ETypical large-scale streaming benchmark³\u003C/th\u003E\r\n\u003Cth\u003ESnowpipe Streaming Elastic Channels to FDN table\u003C/th\u003E\r\n\u003Cth\u003ESnowpipe Streaming Elastic Channels to Snowflake managed Apache Iceberg Table\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EConcurrent workers\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E2,048\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ECores per worker\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E1.5\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1.0\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1.0\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemory per worker\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E2 GiB\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E1 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETotal worker cores\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E3,072\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETotal worker memory\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E4,096 GiB\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E455 GiB\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESustained throughput\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~12 GB/sec\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E~41.8 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E~39.0 GB/sec\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESustained rows per second\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E~12M\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E133.5M\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E121.6M\u003C/b\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1882309474":{"id":"blog-text-cf68832765","text":"\u003Cp\u003E³ Representative published parameters for a 24-hour, petabyte-scale single-table ingest benchmark using thousands of Kubernetes producer pods and a native binary row encoder.\u003C/p\u003E\r\n\u003Cp\u003EWe used \u003Cb\u003Eroughly one-seventh the cores\u003C/b\u003E, \u003Cb\u003Eone-ninth the memory\u003C/b\u003E and \u003Cb\u003Efewer than one-quarter the worker processes\u003C/b\u003E — and still delivered \u003Cb\u003Emore than 3x the bytes per second\u003C/b\u003E and \u003Cb\u003Emore than 10x the rows per second on both FDN and Apache Iceberg tables as the destination\u003C/b\u003E.\u003C/p\u003E\r\n\u003Ch2\u003EHow we got here: Snowpipe Streaming evolution\u003C/h2\u003E\r\n\u003Cp\u003EThis project started where many Snowflake streaming evaluations do — on large EC2 instances with dozens of workers per host, explicit channels and Python row dicts built on every batch. Those runs were valuable: They exposed client-side ceilings long before Snowflake did.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1700383890_811970419":{"id":"blog-text-cf706f4bdd","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EEra\u003C/th\u003E\r\n\u003Cth\u003EWhat we measured\u003C/th\u003E\r\n\u003Cth\u003EWhat limited us\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESnowpipe Streaming previous version\u003C/b\u003E (Summit 2024 demo)\u003C/td\u003E\r\n\u003Ctd\u003E~10 GB/sec on-stage; sub-GB/sec per host in our early ramps\u003C/td\u003E\r\n\u003Ctd\u003EChannel/offset overhead, client-side columnarization and metadata, classic SDK architecture\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EHigh-performance architecture\u003C/b\u003E (released 2025)\u003C/td\u003E\r\n\u003Ctd\u003E~2-8 GB/sec per large instance; ~16 GB/sec sustained multi-node on EC2\u003C/td\u003E\r\n\u003Ctd\u003ENetwork per host, worker/channel management, Python encode path\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EElastic Channels + EKS + Rust encode\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cb\u003E41.8 GB/sec\u003C/b\u003E at 455 workers\u003C/td\u003E\r\n\u003Ctd\u003ENothing in this test — linear scaling through the supported range\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1700383890_262624709":{"id":"blog-text-6e561c396f","text":"\u003Cp\u003EThree changes compounded:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EElastic Channels:\u003C/b\u003E \u003Ccode\u003Eget_elastic_channel()\u003C/code\u003E pools all channels under one client. Application code stops managing names and offsets.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EKubernetes at the right granularity:\u003C/b\u003E One pod per worker, 1 vCPU each, 31 pods per Graviton node. Scale the fleet, not the process.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EA Rust fast path for generation and encoding:\u003C/b\u003E Python is orchestration, not the hot loop, as described below.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThe updates to Snowpipe Streaming, including adding Elastic Channels, make sending data into Snowflake better than ever. Snowpipe Streaming high-performance architecture allows for huge volumes of data to be ingested quickly and easily in scalable, serverless infrastructure and queried in seconds from the source. Billing is simple, based on gigabytes ingested. There isn’t an easier and more scalable way to query streaming data in the market today.\u003C/p\u003E\r\n\u003Ch2\u003EWhy Rust for data encoding\u003C/h2\u003E\r\n\u003Cp\u003EAt hundreds of thousands of rows per second per worker, Python dict allocation and JSON serialization dominate the CPU. The SDK's default path accepts row dicts and re-encodes them internally — correct, but redundant when you already know the schema.\u003C/p\u003E\r\n\u003Cp\u003EIn order to most efficiently use the worker’s CPU and memory, data was generated and serialized in Rust. This uses internal methods of the SDK. These methods may change in the future, complicating upgrades.\u003C/p\u003E\r\n\u003Cp\u003EWe added an optional native extension (\u003Ccode\u003Enative/\u003C/code\u003E, built with PyO3 + maturin into the worker Docker image) that does \u003Ci\u003Eboth\u003C/i\u003E row generation and NDJSON serialization in Rust:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ESkip per-row Python objects:\u003C/b\u003E No \u003Ccode\u003Edict\u003C/code\u003E per row, no \u003Ccode\u003Emsgspec\u003C/code\u003E struct round-trip on the hot path.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESkip the SDK's second encode:\u003C/b\u003E \u003Ccode\u003Efast_append.py\u003C/code\u003E hands preserialized bytes directly to the Elastic Channel's FFI append, bypassing \u003Ccode\u003Eappend_rows_with_wait\u003C/code\u003E's internal \u003Ccode\u003Eencode_lines\u003C/code\u003E pass.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStay byte-identical to Python:\u003C/b\u003E \u003Ccode\u003Enative/generate_tables.py\u003C/code\u003E derives all weighted tables and preformatted price strings from \u003Ccode\u003Eresort_sales.py\u003C/code\u003E itself. Rust replays resolved data; it does not reimplement business logic. Unit tests assert the native and Python paths produce identical NDJSON.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ccode\u003EUNIT_PRICE\u003C/code\u003E and \u003Ccode\u003EAMOUNT\u003C/code\u003E are baked as decimal strings at codegen time (via Python's \u003Ccode\u003Ejson.dumps\u003C/code\u003E formatting) so Rust never disagrees with Python float formatting on the finite set of \u003Ccode\u003E(item, tier, quantity)\u003C/code\u003E combinations that occur.\u003C/p\u003E\r\n\u003Cp\u003EWhen the wheel is not installed, the harness falls back to msgspec encoding in Python automatically. 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Your row width, pipe definition and account limits may differ.\u003C/li\u003E\r\n\u003Cli\u003EThe > 3.5x comparison is against Snowpipe Streaming as demonstrated at Summit 2024 (~10 GB/sec); this benchmark uses the high-performance architecture released in 2025 with Elastic Channels. 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