[{"data":1,"prerenderedAt":1757},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh":1086,"blog-authors-\u002Fblog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh":1721,"related-\u002Fblog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh":1736},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"category":289,"createdAt":290,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":290,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":290,"seo":299,"stem":300,"tags":301,"__hash__":305},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[48,125,126],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[48,128,129,130,133],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[36,131,132],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[48,144,145],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[48,158,159,160],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[48,173,174],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[48,176,177],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[48,195,196],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[48,198,199,200,203],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[44,201,202],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[48,205,206],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[36,257,258],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[36,274,275],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings","Company",null,"2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":307,"title":308,"authors":309,"body":314,"category":1073,"createdAt":290,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":290,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":290,"seo":1080,"stem":1081,"tags":1082,"__hash__":1085},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[310,311,312,313],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":315,"toc":1043},[316,319,322,325,328,331,335,338,348,354,357,365,370,374,381,384,387,395,399,402,407,411,414,417,420,423,432,436,439,450,453,457,460,463,474,477,481,485,493,496,500,508,537,541,544,549,553,556,560,563,566,571,580,585,588,591,602,606,609,620,624,627,630,635,638,667,671,673,679,682,687,692,695,699,713,717,728,732,747,756,767,770,773,777,780,783,794,797,800,803,808,813,817,821,838,842,856,861,865,876,879,895,899,910,915,920,928,932,935,939,946,950,953,962,967,976,982,991,1000,1009,1018,1027,1035],[48,317,318],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[48,320,321],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[48,323,324],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[48,326,327],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[48,329,330],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[40,332,334],{"id":333},"key-benchmark-findings","Key Benchmark Findings",[48,336,337],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[339,340,341,345],"ul",{},[342,343,344],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[342,346,347],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,349,350],{},[351,352],"img",{"alt":18,"src":353},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,355,356],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[339,358,359,362],{},[342,360,361],{},"50% cheaper than Confluent WarpStream",[342,363,364],{},"85% cheaper than MSK and Redpanda",[48,366,367],{},[351,368],{"alt":18,"src":369},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,371,373],{"id":372},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,375,376,380],{},[55,377,379],{"href":378},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[48,382,383],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[48,385,386],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[339,388,389,392],{},[342,390,391],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[342,393,394],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,396,398],{"id":397},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,400,401],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[48,403,404],{},[351,405],{"alt":18,"src":406},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,408,410],{"id":409},"leaderless-architecture","Leaderless architecture",[48,412,413],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[48,415,416],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,418,419],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[48,421,422],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[48,424,425,426,431],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[55,427,430],{"href":428,"rel":429},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,433,435],{"id":434},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,437,438],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[339,440,441,444,447],{},[342,442,443],{},"Handles millions of metadata\u002Findex operations per second",[342,445,446],{},"Generates sequential IDs to maintain strict message ordering",[342,448,449],{},"Optimized for Kubernetes with horizontal scalability",[48,451,452],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,454,456],{"id":455},"zero-interzone-data-replication","Zero interzone data replication",[48,458,459],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[48,461,462],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[339,464,465,468,471],{},[342,466,467],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[342,469,470],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[342,472,473],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[48,475,476],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[40,478,480],{"id":479},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,482,484],{"id":483},"ursa-cluster-deployment","Ursa Cluster Deployment",[339,486,487,490],{},[342,488,489],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[342,491,492],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,494,495],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,497,499],{"id":498},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,501,502,503,507],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,504,505],{"href":505,"rel":506},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[339,509,510,525,534],{},[342,511,512,513,518,519,524],{},"12 OMB workers: 6 for ",[55,514,517],{"href":515,"rel":516},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,520,523],{"href":521,"rel":522},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[264],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[342,526,527,528,533],{},"Sample YAML ",[55,529,532],{"href":530,"rel":531},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[342,535,536],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,538,540],{"id":539},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,542,543],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,545,546],{},[351,547],{"alt":18,"src":548},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,550,552],{"id":551},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,554,555],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[32,557,559],{"id":558},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,561,562],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[48,564,565],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[339,567,568],{},[342,569,570],{},"9 × m6i.8xlarge instances",[48,572,573,574,579],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[55,575,578],{"href":576,"rel":577},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[264]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[339,581,582],{},[342,583,584],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,586,587],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,589,590],{},"Additional key assumptions include:",[339,592,593,596,599],{},[342,594,595],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[342,597,598],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[342,600,601],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,603,605],{"id":604},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,607,608],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[339,610,611,614,617],{},[342,612,613],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[342,615,616],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[342,618,619],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,621,623],{"id":622},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,625,626],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,628,629],{},"Consumers:",[339,631,632],{},[342,633,634],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[48,636,637],{},"Producers:",[339,639,640,649,658],{},[342,641,642,643,648],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,644,647],{"href":645,"rel":646},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[264],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[342,650,651,652,657],{},"Redpanda recently introduced ",[55,653,656],{"href":654,"rel":655},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[264],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[342,659,660,661,666],{},"Ursa is the only system in this test with ",[55,662,665],{"href":663,"rel":664},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[264],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[32,668,670],{"id":669},"cost-comparison-results","Cost Comparison Results",[48,672,337],{},[339,674,675,677],{},[342,676,344],{},[342,678,347],{},[48,680,681],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[48,683,684],{},[351,685],{"alt":18,"src":686},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,688,689],{},[351,690],{"alt":18,"src":691},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,693,694],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,696,698],{"id":697},"streamnative-ursa","StreamNative - Ursa",[339,700,701,704,707,710],{},[342,702,703],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[342,705,706],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[342,708,709],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[342,711,712],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,714,716],{"id":715},"aws-msk","AWS MSK",[339,718,719,722,725],{},[342,720,721],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[342,723,724],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[342,726,727],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,729,731],{"id":730},"redpanda","RedPanda",[339,733,734,736,738,741,744],{},[342,735,703],{},[342,737,706],{},[342,739,740],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[342,742,743],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[342,745,746],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,748,749,750,755],{},"Please note that we were unable to test ",[55,751,754],{"href":752,"rel":753},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[264],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[339,757,758,764],{},[342,759,760,763],{},[55,761,647],{"href":645,"rel":762},[264]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[342,765,766],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[48,768,769],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[48,771,772],{},"We may revisit this comparison as more details become available.",[40,774,776],{"id":775},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,778,779],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[48,781,782],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[339,784,785,788,791],{},[342,786,787],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[342,789,790],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[342,792,793],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,795,796],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[48,798,799],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[48,801,802],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[48,804,805],{},[351,806],{"alt":18,"src":807},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,809,810],{},[351,811],{"alt":18,"src":812},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,814,816],{"id":815},"cost-breakdown","Cost Breakdown",[818,819,820],"h4",{"id":697},"StreamNative – Ursa",[339,822,823,826,829,832,835],{},[342,824,825],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[342,827,828],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[342,830,831],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[342,833,834],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[342,836,837],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[818,839,841],{"id":840},"warpstream","WarpStream",[339,843,844,847],{},[342,845,846],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[342,848,849,850,855],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[55,851,854],{"href":852,"rel":853},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[264],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[48,857,858],{},[351,859],{"alt":18,"src":860},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[818,862,864],{"id":863},"msk","MSK",[339,866,867,870,873],{},[342,868,869],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[342,871,872],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[342,874,875],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[818,877,731],{"id":878},"redpanda-1",[339,880,881,884,886,889,892],{},[342,882,883],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[342,885,872],{},[342,887,888],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[342,890,891],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[342,893,894],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[818,896,898],{"id":897},"additional-notes","Additional Notes",[339,900,901],{},[342,902,903,904,909],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[264],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[48,911,912],{},[351,913],{"alt":18,"src":914},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,916,917],{},[351,918],{"alt":18,"src":919},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[339,921,922,925],{},[342,923,924],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[342,926,927],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[40,929,931],{"id":930},"conclusion","Conclusion",[48,933,934],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[32,936,938],{"id":937},"balancing-latency-and-cost","Balancing Latency and Cost",[48,940,941,945],{},[55,942,944],{"href":943},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[32,947,949],{"id":948},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,951,952],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[48,954,955,956,961],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[55,957,960],{"href":958,"rel":959},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[963,964,966],"h1",{"id":965},"references","References",[48,968,969,972,973],{},[970,971,430],"span",{}," ",[55,974,975],{"href":975},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,977,978,972,980],{},[970,979,379],{},[55,981,378],{"href":378},[48,983,984,972,987],{},[970,985,986],{},"StreamNative pricing",[55,988,989],{"href":989,"rel":990},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,992,993,972,996],{},[970,994,995],{},"WarpStream pricing",[55,997,998],{"href":998,"rel":999},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1001,1002,972,1005],{},[970,1003,1004],{},"AWS S3 pricing",[55,1006,1007],{"href":1007,"rel":1008},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1010,1011,972,1014],{},[970,1012,1013],{},"AWS EBS pricing",[55,1015,1016],{"href":1016,"rel":1017},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1019,1020,972,1023],{},[970,1021,1022],{},"AWS MSK pricing",[55,1024,1025],{"href":1025,"rel":1026},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1028,1029,972,1032],{},[970,1030,1031],{},"The Brutal Truth about Kafka Cost Calculators",[55,1033,852],{"href":852,"rel":1034},[264],[48,1036,1037,972,1040],{},[970,1038,1039],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1041,906],{"href":906,"rel":1042},[264],{"title":18,"searchDepth":19,"depth":19,"links":1044},[1045,1046,1047,1052,1056,1057,1066,1069],{"id":333,"depth":19,"text":334},{"id":372,"depth":19,"text":373},{"id":397,"depth":19,"text":398,"children":1048},[1049,1050,1051],{"id":409,"depth":279,"text":410},{"id":434,"depth":279,"text":435},{"id":455,"depth":279,"text":456},{"id":479,"depth":19,"text":480,"children":1053},[1054,1055],{"id":483,"depth":279,"text":484},{"id":498,"depth":279,"text":499},{"id":539,"depth":19,"text":540},{"id":551,"depth":19,"text":552,"children":1058},[1059,1060,1061,1062,1063,1064,1065],{"id":558,"depth":279,"text":559},{"id":604,"depth":279,"text":605},{"id":622,"depth":279,"text":623},{"id":669,"depth":279,"text":670},{"id":697,"depth":279,"text":698},{"id":715,"depth":279,"text":716},{"id":730,"depth":279,"text":731},{"id":775,"depth":19,"text":776,"children":1067},[1068],{"id":815,"depth":279,"text":816},{"id":930,"depth":19,"text":931,"children":1070},[1071,1072],{"id":937,"depth":279,"text":938},{"id":948,"depth":279,"text":949},"StreamNative Cloud","2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":308,"description":1075},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1083,1084,303],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1087,"title":1088,"authors":1089,"body":1091,"category":1707,"createdAt":290,"date":1708,"description":1709,"extension":8,"featured":294,"image":1710,"isDraft":294,"link":290,"meta":1711,"navigation":7,"order":296,"path":1712,"readingTime":1713,"relatedResources":290,"seo":1714,"stem":1715,"tags":1716,"__hash__":1720},"blogs\u002Fblog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh.md","Using Pulsar Functions in a Cloud-native Way with Function Mesh",[1090],"Rui Fu",{"type":15,"value":1092,"toc":1686},[1093,1096,1113,1117,1120,1150,1153,1158,1161,1165,1168,1180,1183,1194,1197,1202,1205,1209,1218,1221,1224,1229,1235,1238,1241,1246,1250,1253,1256,1259,1262,1273,1276,1279,1283,1286,1295,1298,1309,1312,1316,1319,1323,1332,1342,1351,1357,1360,1365,1369,1372,1375,1380,1384,1387,1396,1399,1413,1417,1424,1427,1432,1435,1438,1443,1447,1484,1492,1497,1501,1504,1508,1517,1520,1525,1529,1538,1544,1553,1556,1570,1573,1577,1592,1596,1599,1602,1616,1620,1629,1677,1681],[48,1094,1095],{},"Apache Pulsar, a distributed streaming and messaging platform, is inherently designed to excel in cloud-native environments. It offers Pulsar Functions, a serverless computing framework that enables users to create functions that utilize one topic as an input and another topic as an output. However, leveraging Pulsar Functions in a cloud-native setting may present challenges for users. In this blog post, I will discuss the following topics:",[339,1097,1098,1101,1104,1107,1110],{},[342,1099,1100],{},"Why individuals and organizations use Pulsar;",[342,1102,1103],{},"The challenges of running Pulsar Functions;",[342,1105,1106],{},"What is Function Mesh and how does it deal with the challenges;",[342,1108,1109],{},"New capabilities and extensions brought to Pulsar Functions on Kubernetes with Function Mesh;",[342,1111,1112],{},"Future plans for Function Mesh.",[40,1114,1116],{"id":1115},"pulsar-overview","Pulsar overview",[48,1118,1119],{},"In recent years, an increasing number of individuals and organizations have chosen to use Pulsar for various reasons, such as:",[339,1121,1122,1135,1138,1141],{},[342,1123,1124,1125,1129,1130,1134],{},"High throughput and low latency: According to the ",[55,1126,1128],{"href":1127},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","Apache Pulsar vs. Apache Kafka 2022 Benchmark"," report and the ",[55,1131,1133],{"href":1132},"\u002Fblog\u002Fcomparison-of-messaging-platforms-apache-pulsar-vs-rabbitmq-vs-nats-jetstream","2023 Messaging Benchmark Report: Apache Pulsar vs. RabbitMQ vs. NATS JetStream",", Pulsar can achieve high throughput and low latency even with tens of thousands of topics or partitions in a cluster, while ensuring message persistence. It outperformed other messaging systems in the reports.",[342,1136,1137],{},"Excellent scalability: Pulsar’s exceptional scalability is another attractive feature. When users scale a cluster by adding new nodes, both Pulsar brokers and BookKeeper can immediately allocate new workloads to them without waiting for existing data to be redistributed. This operator-friendly feature significantly reduces the complexity and risks of scaling.",[342,1139,1140],{},"High availability for large-scale distributed data storage: Pulsar natively supports features like multi-tenancy, asynchronous geo-replication, and tiered storage. It is suitable for long-term persistent storage of large-scale streaming messages.",[342,1142,1143,1144,1149],{},"A thriving ecosystem: The ",[55,1145,1148],{"href":1146,"rel":1147},"https:\u002F\u002Fhub.streamnative.io\u002F",[264],"StreamNative Hub"," lists a variety of tools integrated into Pulsar’s ecosystem, such as IO connectors, protocol handlers, and offloaders. They allow for easy integration of Pulsar with other systems for data migration and processing.",[48,1151,1152],{},"Although Pulsar offers these open-source features natively, deploying a production-grade Pulsar cluster in a private environment and fully utilizing its capabilities is still a challenging task. As shown in Figure 1, a minimal Pulsar cluster includes a ZooKeeper cluster for metadata storage, a BookKeeper cluster as a distributed storage system, and a broker cluster for messaging and streaming capabilities. If you want to expose Pulsar externally, you need an additional proxy layer to route traffic.",[48,1154,1155],{},[351,1156],{"alt":18,"src":1157},"\u002Fimgs\u002Fblogs\u002F6447bfd55239d1749d93cfc2_image10.webp",[48,1159,1160],{},"For easier deployment and operation, more users may opt to use Pulsar in cloud-native environments such as Kubernetes. In this connection, the ability to efficiently utilize Pulsar's native features on Kubernetes is a crucial factor when making containerization decisions, with Pulsar Functions being a prominent example.",[40,1162,1164],{"id":1163},"understanding-pulsar-functions","Understanding Pulsar Functions",[48,1166,1167],{},"Before I talk about using Pulsar Functions on Kubernetes, let me briefly explain its concept. We know that big data computing typically falls into three categories:",[1169,1170,1171,1174,1177],"ol",{},[342,1172,1173],{},"Interactive queries: Common computing scenarios are based on Presto.",[342,1175,1176],{},"Batch\u002Fstream processing: Frequently used systems include Apache Flink and Apache Spark.",[342,1178,1179],{},"IO connectors: Pulsar provides sink and source connectors, allowing different engines to understand Pulsar schemas and treat Pulsar topics as tables to read data.",[48,1181,1182],{},"Different from the above-mentioned tools, which are used for complex computing scenarios, Pulsar Functions are lightweight computing processes that",[339,1184,1185,1188,1191],{},[342,1186,1187],{},"Consume messages from Pulsar topics;",[342,1189,1190],{},"Apply a user-supplied processing approach to each message;",[342,1192,1193],{},"Publish results to another Pulsar topic.",[48,1195,1196],{},"Figure 2 illustrates this process. Internally, Pulsar Functions offer simplified message processing with a function abstraction, which allows users to use basic features like creation, management, and replica scheduling.",[48,1198,1199],{},[351,1200],{"alt":18,"src":1201},"\u002Fimgs\u002Fblogs\u002F6447bff992f03d019741fa23_image6.webp",[48,1203,1204],{},"Pulsar Functions are not designed to provide a complex computing engine but to integrate serverless technologies with Pulsar. Common use cases such as ETL and real-time aggregation account for approximately 60%-70% of overall scenarios and about 80%-90% of IoT scenarios. With Pulsar Functions, users can perform basic data processing at Pulsar’s messaging end without building complex clusters, saving on data transmission and computing resources.",[32,1206,1208],{"id":1207},"function-workers","Function workers",[48,1210,1211,1212,1217],{},"We know that Pulsar brokers provide messaging and streaming services, but how do they schedule and manage functions and offer the corresponding APIs? Pulsar relies on function workers to monitor, orchestrate, and execute individual functions in the ",[55,1213,1216],{"href":1214,"rel":1215},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Ffunctions-deploy-cluster\u002F",[264],"cluster-mode"," deployment. Function workers provide a complete set of RESTful APIs for full lifecycle management of functions, which are integrated into tools like pulsar-admin.",[48,1219,1220],{},"When using Pulsar Functions, function workers run together with brokers, which is the default behavior set in the Helm chart provided by the Pulsar community. This is easy for deployment and management, suitable for scenarios with limited resources and non-intensive function usage.",[48,1222,1223],{},"If you require higher isolation and want to prevent function workers from impacting your cluster (intensive function usage), you can choose to run function workers as a separate cluster for functions. That said, this approach still needs more best practices for cloud-native deployment and management, so you may need to invest more time and effort in configuration and maintenance.",[48,1225,1226],{},[351,1227],{"alt":18,"src":1228},"\u002Fimgs\u002Fblogs\u002F6447c0266ca2bec699077837_image1.webp",[1230,1231,1232],"blockquote",{},[48,1233,1234],{},"Note: The StreamNative team has validated this mode, and we will share our experience later in the article.",[48,1236,1237],{},"Function workers support running functions in different ways. Generally, they can run functions on their own or together with brokers; in other words, you can invoke function threads in function workers or in processes forked by function workers. As function workers include a Kubernetes runtime implementation, you can package functions as StatefulSets and deploy them on Kubernetes.",[48,1239,1240],{},"In Figure 4, functions are not running within broker or function worker Pods. They are deployed in a separate StatefulSet to avoid the security risks of running together with brokers or function workers.",[48,1242,1243],{},[351,1244],{"alt":18,"src":1245},"\u002Fimgs\u002Fblogs\u002F6447c049641c0c81b4d12aa9_image8.webp",[40,1247,1249],{"id":1248},"challenges-of-running-pulsar-functions-on-kubernetes","Challenges of running Pulsar Functions on Kubernetes",[48,1251,1252],{},"As the number of Pulsar Functions users increases, we have started to see the limitations of running Pulsar Functions on Kubernetes.",[48,1254,1255],{},"One major issue is the potential crash loop when launching functions on Kubernetes. As each broker has a function worker, all management and maintenance interfaces are aggregated for the corresponding function. When you submit a function to a function worker, its metadata information and related resources are stored in a topic. During scheduling, Kubernetes must access the topic to retrieve the function’s metadata (for example, replica count) before deploying it as a StatefulSet. If the broker is not started or is unavailable, a crash loop may occur. The function will not begin to run until the broker is back online.",[48,1257,1258],{},"Another challenge in the process is metadata management. This process contains metadata in two separate places: the function’s metadata stored in a Pulsar topic and the StatefulSet submitted to Kubernetes. This complicates metadata management. For example, when you use kubectl to manage a function StatefulSet, there is no mechanism to synchronize the data stored in the Pulsar topic, leaving the change unknown to the function worker.",[48,1260,1261],{},"In addition to the two major issues, Pulsar Functions have the following problems when running on Kubernetes:",[339,1263,1264,1267,1270],{},[342,1265,1266],{},"Non-cloud-native: Kubernetes provides powerful capabilities like dynamic scaling and management. However, it is very difficult to leverage these cloud-native features for Pulsar Functions.",[342,1268,1269],{},"Token expiration: Due to the limitations of the current Kubernetes runtime implementation, tokens are the only available method for authentication and authorization with Pulsar brokers when submitting functions. As a result, function instances may fail to start once the token expires. To address this issue, the Pulsar community added the --update-auth-data option for pulsar-admin to help update tokens. However, it requires you to manually run the command to maintain token validity.",[342,1271,1272],{},"Complex task handling: In many scenarios, you may need to use multiple functions for a single task, or even combine functions with source and sink tools as a whole. Additionally, you need to use multiple commands to operate each function with different topics. All of these contribute to higher management and operation pressure.",[48,1274,1275],{},"In light of these challenges, the community was looking for a more efficient and compatible way to bring Pulsar Functions to cloud-native environments, enabling users to better leverage Kubernetes capabilities to manage and use Pulsar Functions for complex use cases.",[48,1277,1278],{},"This is where Function Mesh comes to play.",[40,1280,1282],{"id":1281},"function-mesh-rising-to-the-challenges","Function Mesh: Rising to the challenges",[48,1284,1285],{},"The primary goal of Function Mesh is not to support more complex, universally applicable computing frameworks, but to help users manage and use Pulsar Functions in a cloud-native way.",[48,1287,1288,1289,1294],{},"In 2020, the StreamNative team ",[55,1290,1293],{"href":1291,"rel":1292},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fwiki\u002FPIP-66%3A-Pulsar-Function-Mesh",[264],"submitted a PIP"," in the Pulsar community, as we looked to provide a unified component allowing users to easily describe the relations between functions (like which function serves as the input\u002Foutput of another function). By combining this mindset with features such as scheduling and scaling in Kubernetes, we might be able to provide a better user experience of Pulsar Functions. As such, StreamNative proposed the open-source Function Mesh Operator built with the Kubernetes operator framework.",[48,1296,1297],{},"Function Mesh is an open-source Kubernetes operator for:",[339,1299,1300,1303,1306],{},[342,1301,1302],{},"Running Pulsar Functions natively on Kubernetes;",[342,1304,1305],{},"Utilizing Kubernetes native resources and scheduling capabilities;",[342,1307,1308],{},"Integrating separate functions together to process data.",[48,1310,1311],{},"Let’s look at some core concepts of Function Mesh.",[32,1313,1315],{"id":1314},"kubernetes-operator","Kubernetes operator",[48,1317,1318],{},"Generally, deploying a Kubernetes operator involves creating the associated custom resource definition (CRD) and the custom controller. I will explain these two concepts in more detail in the context of Function Mesh.",[818,1320,1322],{"id":1321},"custom-resource-definitions","Custom resource definitions",[48,1324,1325,1326,1331],{},"With CRDs, the Kubernetes operator can solve two major problems when using Pulsar Functions: describing and submitting functions, and scheduling functions. All function, sink, and source configurations can be described using CRDs, such as parallelism, input and output topics, autoscaling, and resource quotas. The following code snippet displays some ",[55,1327,1330],{"href":1328,"rel":1329},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Ffunction-mesh\u002Fblob\u002Fmaster\u002Fconfig\u002Fcrd\u002Fbases\u002Fcompute.functionmesh.io_functions.yaml",[264],"function CRD"," specifications.",[1333,1334,1339],"pre",{"className":1335,"code":1337,"language":1338},[1336],"language-text","type FunctionSpec struct {\n  \u002F\u002F INSERT ADDITIONAL SPEC FIELDS - desired state of cluster\n  \u002F\u002F Important: Run \"make\" to regenerate code after modifying this file\n\n  Name         string                      `json:\"name,omitempty\"`\n  ClassName    string                      `json:\"className,omitempty\"`\n  Tenant       string                      `json:\"tenant,omitempty\"`\n  ClusterName  string                      `json:\"clusterName,omitempty\"`\n  Replicas     *int32                      `json:\"replicas,omitempty\"`\n  MaxReplicas  *int32                      `json:\"maxReplicas,omitempty\"`\n  Input        InputConf                   `json:\"input,omitempty\"`\n  Output       OutputConf                  `json:\"output,omitempty\"`\n  LogTopic     string                      `json:\"logTopic,omitempty\"`\n  FuncConfig   map[string]string           `json:\"funcConfig,omitempty\"`\n  Resources    corev1.ResourceRequirements `json:\"resources,omitempty\"`\n  SecretsMap   map[string]SecretRef        `json:\"secretsMap,omitempty\"`\n  VolumeMounts []corev1.VolumeMount        `json:\"volumeMounts,omitempty\"`\n}\n","text",[1340,1341,1337],"code",{"__ignoreMap":18},[48,1343,1344,1345,1350],{},"Additionally, we provide the ",[55,1346,1349],{"href":1347,"rel":1348},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Ffunction-mesh\u002Fblob\u002Fmaster\u002Fconfig\u002Fcrd\u002Fbases\u002Fcompute.functionmesh.io_functionmeshes.yaml",[264],"FunctionMesh CRD"," that allows users to configure functions and sources\u002Fsinks in complex computing scenarios. See the Function Mesh specifications below.",[1333,1352,1355],{"className":1353,"code":1354,"language":1338},[1336],"type FunctionMeshSpec struct {\n  \u002F\u002F INSERT ADDITIONAL SPEC FIELDS - desired state of cluster\n  \u002F\u002F Important: Run \"make\" to regenerate code after modifying this file\n\n  Sources    []SourceSpec    `json:\"sources,omitempty\"`\n  Sinks      []SinkSpec      `json:\"sinks,omitempty\"`\n  Functions  []FunctionSpec  `json:\"functions,omitempty\"`\n}\n",[1340,1356,1354],{"__ignoreMap":18},[48,1358,1359],{},"Figure 5 depicts a typical use case of Function Mesh and let’s assume this is a CDC scenario. You may want to use a source connector to ingest data from MongoDB, configure ETL, filtering, and routing, and then deliver messages to MySQL through a sink connector. With the Function Mesh CRD, you can describe the entire process in YAML and run the corresponding custom resource (CR) on Kubernetes.",[48,1361,1362],{},[351,1363],{"alt":18,"src":1364},"\u002Fimgs\u002Fblogs\u002F6447c08a8ec6ec368ede52cf_image3.webp",[818,1366,1368],{"id":1367},"custom-controller","Custom controller",[48,1370,1371],{},"After you create the CR on Kubernetes, the custom controller enables and manages functions. The controller is an extension of the Kubernetes control plane and interacts directly with the Kubernetes API. It maps CRD configurations to corresponding Kubernetes resources and manages them throughout their lifecycle. The controller converts operational knowledge into a program that performs certain operations on the Kubernetes cluster when needed (making sure resources are in their desired state). It acts like an engineer but with greater efficiency and speed.",[48,1373,1374],{},"As shown in Figure 6, you can create CRs using kubectl based on their associated CRDs. With the help of custom controllers, the Kubernetes API schedules internal resources and monitors the status of the CRs. If CRDs are updated, CRs will be changed accordingly. Note that the Pulsar cluster only provides data pipeline services and that it does not store any function metadata.",[48,1376,1377],{},[351,1378],{"alt":18,"src":1379},"\u002Fimgs\u002Fblogs\u002F6447c0bbb1c6b7616e6bdbd6_image9.webp",[32,1381,1383],{"id":1382},"function-runner","Function Runner",[48,1385,1386],{},"The second concept you need to know is the runtime, or the containers that run functions submitted by users (also known as the Function Runner). Pulsar Functions support multiple programming languages for runtime, including Java, Python, and Go. Generally, they are packaged together with Pulsar images. However, it is not practical to use Pulsar’s image for each function container in Function Mesh. Additionally, as functions may come from third-party programs, there are security risks in Pulsar images as root privileges are used by default prior to version 2.10.",[48,1388,1389,1390,1395],{},"For a more secure experience of using Pulsar Functions, the StreamNative team provides separate ",[55,1391,1394],{"href":1392,"rel":1393},"https:\u002F\u002Ffunctionmesh.io\u002Fdocs\u002Ffunctions\u002Ffunction-crd#runner-images",[264],"runner images for different languages",", including Java, Python, and Go. The Java runner image is integrated with StreamNative Sink and Source Connectors, which can be used directly in Function Mesh.",[48,1397,1398],{},"With runner images, you can choose either of the following ways to submit functions.",[1169,1400,1401,1404],{},[342,1402,1403],{},"Use a runner image to package the function and dependencies into a new image and submit it to Function Mesh;",[342,1405,1406,1407,1412],{},"Interact with Pulsar’s ",[55,1408,1411],{"href":1409,"rel":1410},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fnext\u002Fadmin-api-packages\u002F",[264],"package management service"," by uploading the package to Pulsar. Function Mesh will schedule and download functions, and then run them in runner Pods.",[32,1414,1416],{"id":1415},"function-mesh-worker-service","Function Mesh Worker Service",[48,1418,1419,1420,1423],{},"The Function Mesh Worker Service",[970,1421,1422],{},"1"," is similar to the Pulsar Function Worker Service, while it uses the Function Mesh Operator to schedule and run functions. When using Pulsar Functions, you use Function Worker REST APIs to access data. In the case of Function Mesh, the Function Mesh Worker Service also allows you to manage functions on Kubernetes with CLI tools like pulsar-admin and pulsarctl, providing a consistent user experience. See Figure 7 for details.",[48,1425,1426],{},"‍",[48,1428,1429],{},[351,1430],{"alt":18,"src":1431},"\u002Fimgs\u002Fblogs\u002F6447c0e55e16201ad48c407e_image5.webp",[48,1433,1434],{},"The StreamNative team proposed a plan to abstract the Function Mesh Worker Service as an interface in Pulsar 2.8. Based on the interface, the Kubernetes API can be used as an independent Worker Service implementation. This way, users only need to deploy the new Worker Service to the cluster in the same way as the Function Worker, and they can continue using pulsar-admin and pulsarctl to manage the functions in Function Mesh. To allow users to better utilize Kubernetes’ native capabilities, we added some customizable configurations in the Mesh Worker Service.",[48,1436,1437],{},"The following table lists the existing differences between the Pulsar Functions and Function Mesh Worker Service interfaces. The Function Mesh Worker Service has implemented most of the basic management interfaces, such as Create, Delete, and Update.",[48,1439,1440],{},[351,1441],{"alt":18,"src":1442},"\u002Fimgs\u002Fblogs\u002F6447c10b0c4f5ccc806b4490_image4.webp",[32,1444,1446],{"id":1445},"getting-started-with-function-mesh","Getting started with Function Mesh",[48,1448,1449,1450,1455,1456,1461,1462,1466,1467,1472,1473,1478,1479,1483],{},"To install the Function Mesh Operator, you can use ",[55,1451,1454],{"href":1452,"rel":1453},"https:\u002F\u002Foperatorhub.io\u002Foperator\u002Ffunction-mesh",[264],"Operator Lifecycle Manager (OLM)"," or ",[55,1457,1460],{"href":1458,"rel":1459},"https:\u002F\u002Fartifacthub.io\u002Fpackages\u002Fhelm\u002Ffunction-mesh\u002Ffunction-mesh-operator",[264],"the Helm chart",". As the Function Mesh Operator has been ",[55,1463,1465],{"href":1464},"\u002Fblog\u002Fstreamnatives-function-mesh-operator-certified-red-hat-openshift-operator","certified as a Red Hat OpenShift Operator",", you can also deploy it on OpenShift. I will not demonstrate the installation steps in this post, as the deployment deserves a separate article to explain the details. For more information, see ",[55,1468,1471],{"href":1469,"rel":1470},"https:\u002F\u002Ffunctionmesh.io\u002Fdocs\u002F",[264],"the Function Mesh documentation",", and ",[55,1474,1477],{"href":1475,"rel":1476},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Ffunction-mesh",[264],"Function Mesh"," and ",[55,1480,1416],{"href":1481,"rel":1482},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Ffunction-mesh-worker-service",[264]," GitHub repositories.",[48,1485,1486,1487,507],{},"Note that all the key features in Pulsar Functions are now supported by Function Mesh as shown in Table 2, including end-to-end encryption, secret management, and stateful functions. You can see ",[55,1488,1491],{"href":1489,"rel":1490},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fwiki\u002FPIP-108:-Pulsar-Feature-Matrix-(Client-and-Function)",[264],"PIP 108",[48,1493,1494],{},[351,1495],{"alt":18,"src":1496},"\u002Fimgs\u002Fblogs\u002F6447c14230a3e7527293e8ce_image2.webp",[40,1498,1500],{"id":1499},"using-pulsar-functions-in-cloud-native-environments","Using Pulsar Functions in cloud-native environments",[48,1502,1503],{},"Now that we have a basic understanding of Function Mesh, let’s explore what we can do with it for Pulsar Functions in cloud-native environments.",[32,1505,1507],{"id":1506},"automatic-scaling","Automatic scaling",[48,1509,1510,1511,1516],{},"In Kubernetes, a ",[55,1512,1515],{"href":1513,"rel":1514},"https:\u002F\u002Fkubernetes.io\u002Fdocs\u002Ftasks\u002Frun-application\u002Fhorizontal-pod-autoscale\u002F",[264],"HorizontalPodAutoscaler (HPA)"," supports scaling based on CPU, memory, or custom metrics. Function Mesh allows users to define CRD-level autoscaling policies. Using tools like Prometheus and Prometheus Metrics Adapter, we can use Pulsar topic metrics or function metrics for HPA references in response to varying workloads.",[48,1518,1519],{},"As shown in Figure 7, a single-copy function saw increasing workloads, and the HPA immediately scaled the number of replicas to 10 according to the corresponding metric. After the load decreased, the HPA instructed the resource to scale back down. Previously, implementing load-based autoscaling was challenging in Pulsar Functions, but it becomes much easier with Function Mesh.",[48,1521,1522],{},[351,1523],{"alt":18,"src":1524},"\u002Fimgs\u002Fblogs\u002F6447c16230a3e7eac194186f_image7.webp",[32,1526,1528],{"id":1527},"security","Security",[48,1530,1531,1532,1537],{},"In a Kubernetes cluster, a Pod often needs to communicate with another Pod or even an external entity. When using Pulsar Functions, you need to run external code in many cases. If you can impose some network restrictions, you can greatly enhance cluster security. Therefore, we integrated Function Mesh with Istio, allowing users to leverage Istio’s capabilities to define Pod network rules through ",[55,1533,1536],{"href":1534,"rel":1535},"https:\u002F\u002Fistio.io\u002Flatest\u002Fdocs\u002Freference\u002Fconfig\u002Fsecurity\u002Fauthorization-policy\u002F",[264],"Istio Authorization Policy",". As shown below, you can allow the function to only talk to the broker Pods, preventing it from accessing other services like BookKeeper.",[1333,1539,1542],{"className":1540,"code":1541,"language":1338},[1336],"apiVersion: security.istio.io\u002Fv1beta1\nkind: AuthorizationPolicy\n...\nspec:\n  rules:\n    - from:\n        - source:\n            principals:\n              - demo\u002Fns\u002Fdemo\u002Fsa\u002Fcluster-broker\n  selector:\n    matchLabels:\n      cloud.streamnative.io\u002Fpulsar-cluster: cluster\n      cloud.streamnative.io\u002Frole: pulsar-function\n",[1340,1543,1541],{"__ignoreMap":18},[48,1545,1546,1547,1552],{},"To further enhance security for functions, you can also use built-in security policies in Kubernetes. For example, you can define privileges and access control settings for files and users using a ",[55,1548,1551],{"href":1549,"rel":1550},"https:\u002F\u002Fkubernetes.io\u002Fdocs\u002Ftasks\u002Fconfigure-pod-container\u002Fsecurity-context\u002F",[264],"Security Context",", or create a Secret a configure a Service Account for each function to prevent unauthorized access to the Kubernetes API.",[48,1554,1555],{},"Security has always been a top priority in our work and we have put great efforts into ensuring the security of each and every component. For example:",[339,1557,1558,1561,1564,1567],{},[342,1559,1560],{},"Non-root images are introduced for runner images;",[342,1562,1563],{},"The controller ensures that functions run with non-root privileges;",[342,1565,1566],{},"Separate Service Accounts are used for functions;",[342,1568,1569],{},"Users are able to configure authorization with the broker for each function.",[48,1571,1572],{},"Function Mesh allows users to run Pulsar Functions in a more cloud-native way, and more importantly, it is more secure, manageable, and controllable. This is another vision of the StreamNative team when developing Function Mesh.",[32,1574,1576],{"id":1575},"ecosystem-integration","Ecosystem integration",[48,1578,1579,1580,1585,1586,1591],{},"Function Mesh integrates Pulsar Functions into the Kubernetes ecosystem, which means users can take advantage of Kubernetes’ powerful capabilities and use Pulsar Functions with more ecosystem tools. For example, ",[55,1581,1584],{"href":1582,"rel":1583},"https:\u002F\u002Fkeda.sh\u002F",[264],"KEDA"," can help Pulsar Functions scale more efficiently; ",[55,1587,1590],{"href":1588,"rel":1589},"https:\u002F\u002Fbuildpacks.io\u002F",[264],"Buildpacks"," allow Function Mesh to build function images at runtime, letting users upload code directly or submit Pulsar Functions through GitHub repositories; it is even possible to integrate WebAssembly and Rust into Pulsar Functions using Krustlet. The Kubernetes ecosystem offers more possibilities, enabling Pulsar users to leverage functions in a wider range of use cases.",[40,1593,1595],{"id":1594},"conclusion-and-future-plans","Conclusion and future plans",[48,1597,1598],{},"Function Mesh simplifies the management of Pulsar Functions and enables users to leverage more powerful features in Kubernetes like autoscaling. By bringing Pulsar Functions into the cloud-native world, functions can run as first-class citizens and benefit from the Kubernetes ecosystem. With Function Mesh, Pulsar Functions can run in a separate cluster (not in a Pulsar cluster, but in a compute-intensive cluster), which greatly improves resource scheduling and utilization.",[48,1600,1601],{},"Here are StreamNative’s future plans for Function Mesh:",[339,1603,1604,1607,1610,1613],{},[342,1605,1606],{},"Improve the Function Mesh Operator, especially in terms of observability and autoscaling;",[342,1608,1609],{},"Feature parity with Pulsar Functions to ensure a consistent user experience;",[342,1611,1612],{},"Provide better tools to help users orchestrate Function Mesh resources and easily build complete workflows;",[342,1614,1615],{},"Support package integration with cloud storage providers.",[40,1617,1619],{"id":1618},"more-on-apache-pulsar","More on Apache Pulsar",[48,1621,1622,1623,1628],{},"Pulsar has become ",[55,1624,1627],{"href":1625,"rel":1626},"https:\u002F\u002Fblogs.apache.org\u002Ffoundation\u002Fentry\u002Fapache-in-2021-by-the",[264],"one of the most active Apache projects"," over the past few years, with a vibrant community driving innovation and improvements to the project. Check out the following resources to learn more about Pulsar, Pulsar Functions, and Function Mesh.",[339,1630,1631,1644,1652,1661,1668],{},[342,1632,1633,1634,1478,1638,1643],{},"Pulsar Virtual Summit Europe 2023 will take place on Tuesday, May 23rd, 2023! See the ",[55,1635,1637],{"href":1636},"\u002Fblog\u002Fspeakers-and-agenda-announced-for-pulsar-virtual-summit-europe-2023","schedule",[55,1639,1642],{"href":1640,"rel":1641},"https:\u002F\u002Fevents.zoom.us\u002Fev\u002FAp6rsDg9LeVfmdajJ_eB13HH026J1d_o8OoTKkQnl_jzVl-srhwB~AggLXsr32QYFjq8BlYLZ5I06Dg",[264],"register now for free","!",[342,1645,1646,1647,190],{},"Start your on-demand Pulsar training today with ",[55,1648,1651],{"href":1649,"rel":1650},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[264],"StreamNative Academy",[342,1653,1654,972,1657],{},[970,1655,1656],{},"Blog",[55,1658,1660],{"href":1659},"\u002Fblog\u002Fusing-cloud-native-buildpacks-improve-function-image-building-capability-function-mesh","Using Cloud Native Buildpacks to Improve the Function Image Building Capability of Function Mesh",[342,1662,1663,972,1665],{},[970,1664,1656],{},[55,1666,1667],{"href":1464},"StreamNative’s Function Mesh Operator Certified as a Red Hat OpenShift Operator",[342,1669,1670,972,1673],{},[970,1671,1672],{},"Doc",[55,1674,1676],{"href":1469,"rel":1675},[264],"What is Function Mesh?",[40,1678,1680],{"id":1679},"notes","Notes",[48,1682,1683,1685],{},[970,1684,1422],{}," The Function Mesh Worker Service is part of the StreamNative Cloud offering. It provides compatibility with the Pulsar Functions admin API, allowing you to submit functions using Pulsar's admin tools without altering your existing function deployment workflow.",{"title":18,"searchDepth":19,"depth":19,"links":1687},[1688,1689,1692,1693,1699,1704,1705,1706],{"id":1115,"depth":19,"text":1116},{"id":1163,"depth":19,"text":1164,"children":1690},[1691],{"id":1207,"depth":279,"text":1208},{"id":1248,"depth":19,"text":1249},{"id":1281,"depth":19,"text":1282,"children":1694},[1695,1696,1697,1698],{"id":1314,"depth":279,"text":1315},{"id":1382,"depth":279,"text":1383},{"id":1415,"depth":279,"text":1416},{"id":1445,"depth":279,"text":1446},{"id":1499,"depth":19,"text":1500,"children":1700},[1701,1702,1703],{"id":1506,"depth":279,"text":1507},{"id":1527,"depth":279,"text":1528},{"id":1575,"depth":279,"text":1576},{"id":1594,"depth":19,"text":1595},{"id":1618,"depth":19,"text":1619},{"id":1679,"depth":19,"text":1680},"Apache Pulsar","2023-04-25","Understand the limitations of using Pulsar Functions on Kubernetes and explore a more cloud-native way with Function Mesh.","\u002Fimgs\u002Fblogs\u002F6447bd1fd3c0ee995ef99e07_using-pulsar-functions-in-a-cloud-native-way-with-function-mesh.png",{},"\u002Fblog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh","9 min read",{"title":1088,"description":1709},"blog\u002Fusing-pulsar-functions-in-a-cloud-native-way-with-function-mesh",[1717,1707,1718,1719],"Functions","Serverless","Kubernetes","9ELjlGNTdGvWVOoKuuhhMfZqBj0kvukkrqo4RR6x6jw",[1722],{"id":1723,"title":1090,"bioSummary":1724,"email":290,"extension":8,"image":1725,"linkedinUrl":290,"meta":1726,"position":1733,"stem":1734,"twitterUrl":290,"__hash__":1735},"authors\u002Fauthors\u002Frui-fu.md","Rui Fu is a software engineer at StreamNative. Before joining StreamNative, he was a platform engineer at the Energy Internet Research Institute of Tsinghua University. He was leading and focused on stream data processing and IoT platform development at Energy Internet Research Institute. Rui received his postgraduate degree from HKUST and an undergraduate degree from The University of Sheffield.","\u002Fimgs\u002Fauthors\u002Frui-fu.webp",{"body":1727},{"type":15,"value":1728,"toc":1731},[1729],[48,1730,1724],{},{"title":18,"searchDepth":19,"depth":19,"links":1732},[],"Staff Software Engineer, StreamNative","authors\u002Frui-fu","XDbfy8w4q98ff7uwio7dlL5QGPbUKOPnUjFe_NtuJWA",[1737,1744,1749],{"path":1738,"title":1739,"date":1740,"image":1741,"link":-1,"collection":1742,"resourceType":1656,"score":1743,"id":1738},"\u002Fblog\u002Fauto-scaling-pulsar-functions-kubernetes-using-custom-metrics","Auto-Scaling Pulsar Functions in Kubernetes Using Custom Metrics","2022-01-19","\u002Fimgs\u002Fblogs\u002F63c7fb09bc45dd26c48c6156_63bf321e8e20fda65e2b99dc_top.png","blogs",0.8,{"path":1745,"title":1746,"date":1747,"image":1748,"link":-1,"collection":1742,"resourceType":1656,"score":1743,"id":1745},"\u002Fblog\u002Ffunction-mesh-simplify-complex-streaming-jobs-in-cloud","Function Mesh - Simplify Complex Streaming Jobs in Cloud","2021-05-03","\u002Fimgs\u002Fblogs\u002F63c7fd3febac459bdc2f7ff9_63a39dab7bdd430d7c1c1d75_mesh-top.jpeg",{"path":1750,"title":1751,"date":1752,"image":1753,"link":-1,"collection":1754,"resourceType":1755,"score":1756,"id":1750},"\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-1","Developing Event-driven Microservices using Apache Pulsar - Part 1","2022-12-27","\u002Fimgs\u002Fwebinars\u002F63aac2b2e74980ea9421c613_OG_webinar-Developing%20Event-driven%20Microservices%20using%20Apache%20Pulsar.webp","webinars","Webinar",0.66,1775615059408]