[{"data":1,"prerenderedAt":1596},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Foffset-implementation-kafka-pulsar":1086,"blog-authors-\u002Fblog\u002Foffset-implementation-kafka-pulsar":1558,"related-\u002Fblog\u002Foffset-implementation-kafka-pulsar":1573},{"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":1545,"createdAt":290,"date":1546,"description":1547,"extension":8,"featured":294,"image":1548,"isDraft":294,"link":290,"meta":1549,"navigation":7,"order":296,"path":1550,"readingTime":1551,"relatedResources":290,"seo":1552,"stem":1553,"tags":1554,"__hash__":1557},"blogs\u002Fblog\u002Foffset-implementation-kafka-pulsar.md","Offset Implementation in Kafka-on-Pulsar",[1090],"Yunze Xu",{"type":15,"value":1092,"toc":1526},[1093,1101,1109,1112,1115,1118,1121,1125,1129,1132,1135,1144,1147,1158,1161,1165,1168,1179,1182,1185,1189,1192,1203,1206,1210,1214,1217,1227,1230,1234,1237,1240,1243,1246,1249,1252,1256,1259,1270,1273,1277,1285,1288,1294,1297,1303,1306,1312,1315,1319,1322,1328,1331,1334,1342,1345,1348,1351,1354,1362,1365,1369,1372,1377,1380,1386,1412,1415,1435,1438,1442,1451,1454,1457,1460,1464,1467,1470,1473,1477],[48,1094,1095,1100],{},[55,1096,1099],{"href":1097,"rel":1098},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fwiki\u002FPIP-41%3A-Pluggable-Protocol-Handler",[264],"Protocol handlers"," were introduced in Pulsar 2.5.0 (released in January 2020) to extend Pulsar’s capabilities to other messaging domains. By default, Pulsar brokers only support Pulsar protocol. With protocol handlers, Pulsar brokers can support other messaging protocols, including Kafka, AMQP, and MQTT. This allows Pulsar to interact with applications built on other messaging technologies, expanding the Pulsar ecosystem.",[48,1102,1103,1108],{},[55,1104,1107],{"href":1105,"rel":1106},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fkop",[264],"Kafka-on-Pulsar (KoP)"," is a protocol handler that brings native Kafka protocol into Pulsar. It enables developers to publish data into or fetch data from Pulsar using existing Kafka applications without code change. KoP significantly lowers the barrier to Pulsar adoption for Kafka users, making it one of the most popular protocol handlers.",[48,1110,1111],{},"KoP works by parsing Kafka protocol and accessing BookKeeper storage directly via streaming storage abstraction provided by Pulsar. While Kafka and Pulsar share many common concepts, such as topic and partition, there is no corresponding concept of Kafka’s offset in Pulsar. Early versions of KoP tackled this problem with a simple conversion method, which did not allow continuous offset and was prone to problems.",[48,1113,1114],{},"To solve this pain point, broker entry metadata was introduced in KoP 2.8.0 to enable continuous offset. With this update, KoP is available and production-ready. It is important to note that with this update backward compatibility is broken. In this blog, we dive into how KoP implemented offset before and after 2.8.0. and explain the rationale behind the breaking change.",[48,1116,1117],{},"Note on Version Compatibility",[48,1119,1120],{},"Since Pulsar 2.6.2, KoP version has been updated with Pulsar version accordingly. The version of KoP x.y.z.m conforms to Pulsar x.y.z, while m is the patch version number. For instance, the latest KoP release 2.8.1.22 is compatible with Pulsar 2.8.1. In this blog, 2.8.0 refers to both Pulsar 2.8.0 and KoP 2.8.0.",[40,1122,1124],{"id":1123},"message-identifier-in-kafka-and-pulsar","Message Identifier in Kafka and Pulsar",[32,1126,1128],{"id":1127},"offset-in-kafka","Offset in Kafka",[48,1130,1131],{},"In Kafka, offset is a 64-bit integer that represents the position of a message in a specific partition. Kafka consumers can commit an offset to a partition. If the offset is committed successfully, after the consumer restarts, it can continue consuming from the committed offset.",[48,1133,1134],{},"Kafka's offset is continuous as it follows the following constraints:",[1136,1137,1138,1141],"ol",{},[342,1139,1140],{},"The first message's offset is 0.",[342,1142,1143],{},"If the latest message's offset is N, then the next message's offset will be N+1.",[48,1145,1146],{},"Kafka stores messages in each broker's file system:",[339,1148,1149,1152,1155],{},[342,1150,1151],{},"Each partition is divided into segments",[342,1153,1154],{},"Each segment is a file that stores messages of an offset range",[342,1156,1157],{},"Each offset has a position, which is the message's start file offset (A file offset is the character's location within a file, while a Kafka offset is the index of a message within a partition.)",[48,1159,1160],{},"Since each message records the message size in the header, for a given offset, Kafka can easily find its segment file and position.",[32,1162,1164],{"id":1163},"message-id-in-pulsar","Message ID in Pulsar",[48,1166,1167],{},"Unlike Kafka, which stores messages in each broker's file system, Pulsar uses BookKeeper as its storage system. In BookKeeper:",[339,1169,1170,1173,1176],{},[342,1171,1172],{},"Each log unit is an entry",[342,1174,1175],{},"Streams of log entries are ledgers",[342,1177,1178],{},"Individual servers storing ledgers of entries are called bookies",[48,1180,1181],{},"A bookie can find any entry via a 64-bit ledger ID and a 64-bit entry ID. Pulsar can store a message or a batch (one or more messages) in an entry. Therefore, Pulsar finds a message via its message ID that consists of a ledger ID, an entry ID, and a batch index (-1 if it’s not batched). In addition, the message ID also contains the partition number.",[48,1183,1184],{},"Just like a Kafka consumer can commit an offset to record the consumer position, a Pulsar consumer can acknowledge a message ID to record the consumer position.",[32,1186,1188],{"id":1187},"how-does-kop-deal-with-a-kafka-offset","How Does KoP Deal with a Kafka Offset",[48,1190,1191],{},"KoP needs the following Kafka requests to deal with a Kafka offset:",[339,1193,1194,1197,1200],{},[342,1195,1196],{},"PRODUCE: After messages from a Kafka producer are persisted, KoP needs to tell the Kafka producer the offset of the first message. However, the BookKeeper client only returns a message ID.",[342,1198,1199],{},"FETCH : When a Kafka consumer wants to fetch some messages from a given offset, KoP needs to find the corresponding message ID to read entries from the ledger.",[342,1201,1202],{},"LIST_OFFSET: Find the earliest or latest available message, or find a message by timestamp.",[48,1204,1205],{},"We must support computing a specific message offset or locating a message by a given offset.",[40,1207,1209],{"id":1208},"how-kop-implements-offset-before-280","How KoP Implements Offset before 2.8.0",[32,1211,1213],{"id":1212},"the-implementation","The Implementation",[48,1215,1216],{},"As explained earlier, Kafka locates a message via a partition number and an offset, while Pulsar locates a message via a message ID. Before Pulsar 2.8.0, KoP simply performed conversions between Kafka offsets and Pulsar message IDs. A 64-bit offset is mapped into a 20-bit ledger ID, a 32-bit entry id, and a 12-bit batch index. Here is a simple Java implementation.",[1218,1219,1224],"pre",{"className":1220,"code":1222,"language":1223},[1221],"language-text","public static long getOffset(long ledgerId, long entryId, int batchIndex) {\n        return (ledgerId >> (32 + 12);\n        long entryId = (offset & 0x0F_FF_FF_FF_FF_FFL) >>> BATCH_BITS;\n        \u002F\u002F BookKeeper only needs a ledger id and an entry id to locate an entry\n        return new PositionImpl(ledgerId, entryId);\n    }\n","text",[1225,1226,1222],"code",{"__ignoreMap":18},[48,1228,1229],{},"In this blog, we use (ledger id, entry id, batch index) to represent a message ID. For example, assuming a message's message ID is (10, 0, 0), the converted offset will be 175921860444160. This works in some cases because the offset is monotonically increasing. But it’s problematic when a ledger rollover happens or the application wants to manage offsets manually. The section below goes into details about the problems of this simple conversion implementation.",[32,1231,1233],{"id":1232},"the-problems-of-the-simple-conversion","The Problems of the Simple Conversion",[48,1235,1236],{},"The converted offset is not continuous, which causes many serious problems.",[48,1238,1239],{},"For example, let’s assume the current message's ID is (10, 5, 100). The next message's ID could be (11, 0, 0) if a ledger rollover happens. In this case, the offsets of these two messages are 175921860464740 and 193514046488576. The delta value between the two is 17,592,186,023,836.",[48,1241,1242],{},"KoP leverages Kafka's MemoryRecordBuilder to merge multiple messages into a batch. The MemoryRecordBuilder must ensure the batch size is less than the maximum value of a 32-bit integer (4,294,967,296). In our example, the delta value of the two continuous offsets is much greater than 4,294,967,296. This will result in an exception that says Maximum offset delta exceeded.",[48,1244,1245],{},"To avoid the exception, before KoP 2.8.0, we must configure maxReadEntriesNum (this config limits the maximum number of entries read by the BookKeeper client) to 1. Naturally, reading only one entry for each FETCH request worsens the performance significantly.",[48,1247,1248],{},"However, even with the workaround of maxReadEntriesNum=1, this conversion implementation doesn’t work in some scenarios. For example, the Kafka integration with Spark relies on the continuance of Kafka offsets. After it consumes a message with an offset of N, it will seek the next offset (N+1). However, the offset N+1 might not be able to convert to a valid message ID.",[48,1250,1251],{},"There are other problems caused by the conversion method. And before 2.8.0, there is no good way to implement the continuous offset.",[40,1253,1255],{"id":1254},"the-continuous-offset-implementation-since-280","The Continuous Offset Implementation since 2.8.0",[48,1257,1258],{},"The solution to implement continuous offset is to record the offset into the metadata of a message. However, an offset is determined at the broker side before publishing messages to bookies, while the metadata of a message is constructed at the client side. To solve this problem, we need to do some extra jobs at the broker side:",[1136,1260,1261,1264,1267],{},[342,1262,1263],{},"Deserialize the metadata",[342,1265,1266],{},"Set the \"offset\" property of metadata",[342,1268,1269],{},"Serialize the metadata again, including re-computing the checksum value",[48,1271,1272],{},"This results in a significant increase in CPU overhead on the broker side.",[32,1274,1276],{"id":1275},"broker-entry-metadata","Broker Entry Metadata",[48,1278,1279,1284],{},[55,1280,1283],{"href":1281,"rel":1282},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fwiki\u002FPIP-70:-Introduce-lightweight-broker-entry-metadata",[264],"PIP 70"," introduced lightweight broker entry metadata. It's a metadata of a BookKeeper entry and should only be visible inside the broker.",[48,1286,1287],{},"The default message flow is:",[48,1289,1290],{},[351,1291],{"alt":1292,"src":1293},"graph of broker entry metadata","\u002Fimgs\u002Fblogs\u002F63b35801a3553e27d2a30b07_screen-shot-2021-11-30-at-4.45.32-pm.png",[48,1295,1296],{},"If you configured brokerEntryMetadataInterceptors, which represents a list of broker entry metadata interceptors, then the message flow would be:",[48,1298,1299],{},[351,1300],{"alt":1301,"src":1302},"graph pf broker entry metadata interceptors","\u002Fimgs\u002Fblogs\u002F63b3580180a8309857ce13bd_screen-shot-2021-11-30-at-4.51.22-pm.png",[48,1304,1305],{},"We can see the broker entry metadata is stored in bookies, but is not visible to a Pulsar consumer.",[1307,1308,1309],"blockquote",{},[48,1310,1311],{},"From 2.9.0, a Pulsar consumer can be configured to read the broker entry metadata.",[48,1313,1314],{},"Each broker entry metadata interceptor adds the specific metadata (called \"broker entry metadata\") before the message metadata. Since the broker entry metadata is independent of the message metadata, the broker does not need to deserialize the message metadata. In addition, the BookKeeper client supports sending a Netty CompositeByteBuf that is a list of ByteBuf without any copy operation. From the perspective of a BookKeeper client, only some extra bytes are sent into the socket buffer. Therefore, the extra overhead is low and acceptable.",[32,1316,1318],{"id":1317},"the-index-metadata","The Index Metadata",[48,1320,1321],{},"We need to configure the AppendIndexMetadataInterceptor (we say index metadata interceptor) to support the Kafka offset.",[1218,1323,1326],{"className":1324,"code":1325,"language":1223},[1221],"brokerEntryMetadataInterceptors=org.apache.pulsar.common.intercept.AppendIndexMetadataInterceptor\n",[1225,1327,1325],{"__ignoreMap":18},[48,1329,1330],{},"In Pulsar brokers, there is a component named \"managed ledger\" that manages all ledgers of a partition. The index metadata interceptor maintains an index that starts from 0. The \"index\" term is used instead of \"offset\".",[48,1332,1333],{},"Each time before an entry is written to bookies, the following two things happen:",[1136,1335,1336,1339],{},[342,1337,1338],{},"The index is serialized into the broker entry metadata.",[342,1340,1341],{},"The index increases by the number of messages in the entry.",[48,1343,1344],{},"After that, each entry records the first message's index, which is equivalent to the \"base offset\" concept in Kafka.",[48,1346,1347],{},"Now, we must make sure even if the partition's owner broker was down, the index metadata interceptor would recover the index from somewhere.",[48,1349,1350],{},"There are some cases where the managed ledger needs to store its metadata (usually in ZooKeeper). For example, when a ledger is rolled over, the managed ledger must archive all ledger IDs in a z-node. Here we don't look deeper into the metadata format. We only need to know there is a property map in the managed ledger's metadata.",[48,1352,1353],{},"Before metadata is stored in ZooKeeper (or another metadata store):",[1136,1355,1356,1359],{},[342,1357,1358],{},"Retrieve the index from the index metadata interceptor, which represents the latest message's index.",[342,1360,1361],{},"Add the property whose key is \"index\" and value is the index to the property map.",[48,1363,1364],{},"Each time a managed ledger is initialized, it will restore the metadata from the metadata store. At that time, we can set the index metadata intercerptor's index to the value associated with the \"index\" key.",[32,1366,1368],{"id":1367},"how-kop-implements-the-continuous-offsets","How KoP Implements the Continuous Offsets",[48,1370,1371],{},"Let's look back to the How does KoP deal with a Kafka offset section and review how we deal with the offset in following Kafka requests.",[339,1373,1374],{},[342,1375,1376],{},"PRODUCE",[48,1378,1379],{},"When KoP handles PRODUCE requests, it leverages the managed ledger to write messages to bookies. The API has a callback that can access the entry's data.",[1218,1381,1384],{"className":1382,"code":1383,"language":1223},[1221],"@Override\n    public void addComplete(Position pos, ByteBuf entryData, Object ctx) {\n",[1225,1385,1383],{"__ignoreMap":18},[339,1387,1388,1391,1394,1397,1400,1403,1406,1409],{},[342,1389,1390],{},"We only need to parse the broker entry metadata from entryData and then retrieve the index. The index is just the base offset returned to the Kafka producer.",[342,1392,1393],{},"FETCH",[342,1395,1396],{},"The task is to find the position (ledger id and entry id) for a given offset. KoP implements a callback that reads the index from the entry and compares it with the given offset. It then passes the callback to a class named OpFindNewest, which uses binary search to find an entry.",[342,1398,1399],{},"The binary search could take some time. But it only happens on the initial search unless the Kafka consumer disconnects. After the position is found, a non-durable cursor will be created to record the position. The cursor will move to a newer position as the fetch offset increases.",[342,1401,1402],{},"LIST_OFFSET",[342,1404,1405],{},"Earliest: Get the first valid position in a managed ledger. Then read the entry of the position, and parse the index.",[342,1407,1408],{},"Latest: Retrieve the index from the index metadata interceptor and increase by one. It should be noted that the latest offset (or called LEO) in Kafka is the next offset to be assigned to a message, while the index metadata interceptor's index is the offset assigned to the latest message.",[342,1410,1411],{},"By timestamp: First leverage broker's timestamp based binary search to find the target entry. Then parse the index from the entry.",[48,1413,1414],{},"You can find more details about the offset implementation in the following PRs:",[339,1416,1417,1423,1429],{},[342,1418,1419],{},[55,1420,1421],{"href":1421,"rel":1422},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fpull\u002F8618",[264],[342,1424,1425],{},[55,1426,1427],{"href":1427,"rel":1428},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fpull\u002F9039",[264],[342,1430,1431],{},[55,1432,1433],{"href":1433,"rel":1434},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fkop\u002Fpull\u002F296",[264],[48,1436,1437],{},"‍",[40,1439,1441],{"id":1440},"upgrade-from-kop-version-before-280-to-280-or-higher","Upgrade from KoP Version before 2.8.0 to 2.8.0 or Higher",[48,1443,1444,1445,1450],{},"KoP 2.8.0 implements continuous offset with a tradeoff – ",[55,1446,1449],{"href":1447,"rel":1448},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fkop\u002Fblob\u002Fmaster\u002Fdocs\u002Fupgrade.md",[264],"the backward compatibility is broken",". The offset stored by KoP versions before 2.8.0 cannot be recognized by KoP 2.8.0 or higher.",[48,1452,1453],{},"If you have not tried KoP, please upgrade your Pulsar to 2.8.0 or higher and then use the corresponding KoP. As of this writing, the latest KoP release for Pulsar 2.8.1 is 2.8.1.22.",[48,1455,1456],{},"If you have already tried KoP before 2.8.0, you need to know that there's a breaking change from version less than 2.8.0 to version 2.8.0 or higher. You must delete the __consumer_offsets topic and all existing topics previously used by KoP.",[48,1458,1459],{},"There is a latest feature in KoP that can skip these old messages by enabling a config. It would be included in 2.8.1.23 or later. Note that the old messages still won’t be accessible. It just saves the work of deleting old topics.",[40,1461,1463],{"id":1462},"summary","Summary",[48,1465,1466],{},"In this blog, we first explained the concept of Kafka offset and the similar concept of message ID in Pulsar. Then we talked about how KoP implemented Kafka offset before 2.8.0 and the related problems.",[48,1468,1469],{},"To solve these problems, the broker entry metadata was introduced from Pulsar 2.8.0. Based on this feature, index metadata is implemented via a corresponding interceptor. After that, KoP can leverage the index metadata interceptor to implement the continuous offset.",[48,1471,1472],{},"Finally, since it's a breaking change, we talked about the upgrade from KoP version less than 2.8.0 to 2.8.0 or higher. It's highly recommended to try KoP 2.8.0 or higher directly.",[40,1474,1476],{"id":1475},"more-resources","More Resources",[339,1478,1479,1486,1494,1502,1511],{},[342,1480,1481,1482],{},"To learn more about KoP, read ",[55,1483,1485],{"href":1484},"\u002Fblog\u002Ftech\u002F2020-03-24-bring-native-kafka-protocol-support-to-apache-pulsar\u002F","Announcing Kafka-on-Pulsar: Bringing native Kafka protocol support to Apache Pulsar.",[342,1487,1488,1489,1493],{},"Read the ",[55,1490,1492],{"href":1491},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","2022 Pulsar vs. Kafka Benchmark Report"," for the latest performance comparison on maximum throughput, publish latency, and historical read rate.",[342,1495,1496,1497,1501],{},"Make an inquiry: Interested in a fully-managed Pulsar offering built by the original creators of Pulsar? ",[55,1498,1500],{"href":1499},"\u002Fcontact\u002F","Contact us"," now.",[342,1503,1504,1505,1510],{},"Learn the Pulsar Fundamentals: Sign up for ",[55,1506,1509],{"href":1507,"rel":1508},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[264],"StreamNative Academy",", developed by the original creators of Pulsar, and learn at your own pace with on-demand courses and hands-on labs.",[342,1512,1513,1514,1519,1520,1525],{},"Pulsar Summit Europe 2023 is taking place virtually on May 23rd. Engage with the community by ",[55,1515,1518],{"href":1516,"rel":1517},"https:\u002F\u002Fsessionize.com\u002Fpulsar-virtual-summit-europe-2023\u002F",[264],"submitting a CFP"," or ",[55,1521,1524],{"href":1522,"rel":1523},"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FSponsorship%20Prospectus%20Pulsar%20Virtual%20Summit%20Europe%202023.pdf",[264],"becoming a community sponsor"," (no fee required).",{"title":18,"searchDepth":19,"depth":19,"links":1527},[1528,1533,1537,1542,1543,1544],{"id":1123,"depth":19,"text":1124,"children":1529},[1530,1531,1532],{"id":1127,"depth":279,"text":1128},{"id":1163,"depth":279,"text":1164},{"id":1187,"depth":279,"text":1188},{"id":1208,"depth":19,"text":1209,"children":1534},[1535,1536],{"id":1212,"depth":279,"text":1213},{"id":1232,"depth":279,"text":1233},{"id":1254,"depth":19,"text":1255,"children":1538},[1539,1540,1541],{"id":1275,"depth":279,"text":1276},{"id":1317,"depth":279,"text":1318},{"id":1367,"depth":279,"text":1368},{"id":1440,"depth":19,"text":1441},{"id":1462,"depth":19,"text":1463},{"id":1475,"depth":19,"text":1476},"Apache Pulsar","2021-11-01","Learn how a Kafka offset is implemented in KoP – a protocol handler that brings native Kafka protocol into Pulsar.","\u002Fimgs\u002Fblogs\u002F63c7fb709476d0eb89637391_63b357c334dbb642be219684_screen-shot-2021-12-01-at-9.15.10-am.png",{},"\u002Fblog\u002Foffset-implementation-kafka-pulsar","10 min read",{"title":1088,"description":1547},"blog\u002Foffset-implementation-kafka-pulsar",[1084,1545,1555,1556],"RabbitMQ","MQTT","xs9cDkbttOI8lxbzBasCuRc1lPzWaUzVdYsPQZGwcI4",[1559],{"id":1560,"title":1090,"bioSummary":1561,"email":290,"extension":8,"image":1562,"linkedinUrl":290,"meta":1563,"position":1570,"stem":1571,"twitterUrl":290,"__hash__":1572},"authors\u002Fauthors\u002Fyunze-xu.md","Yunze Xu is a software engineer at StreamNative and an Apache Pulsar PMC member. He is also a maintainer of the Kafka-on-Pulsar (KoP) project.","\u002Fimgs\u002Fauthors\u002Fyunze-xu.webp",{"body":1564},{"type":15,"value":1565,"toc":1568},[1566],[48,1567,1561],{},{"title":18,"searchDepth":19,"depth":19,"links":1569},[],"Software Engineer, StreamNative & Apache Pulsar PMC member","authors\u002Fyunze-xu","3T3zxbPoUKLtHmaJ9acNXsG_b5FDKEUm9aA8XkJZxMw",[1574,1582,1590],{"path":1575,"title":1576,"date":1577,"image":1578,"link":-1,"collection":1579,"resourceType":1580,"score":1581,"id":1575},"\u002Fsuccess-stories\u002Fhow-apache-pulsar-helping-iterable-scale-its-customer-engagement-platform","How Apache Pulsar is Helping Iterable Scale its Customer Engagement Platform","2022-12-22","\u002Fimgs\u002Fsuccess-stories\u002F67942deea5d4e9499e9436b2_SN-SuccessStories-iterable.webp","successStories","Case Study",0.825,{"path":1583,"title":1584,"date":1585,"image":1586,"link":-1,"collection":1587,"resourceType":1588,"score":1589,"id":1583},"\u002Fblog\u002Fspring-into-pulsar-part-2-spring-based-microservices-multiple-protocols-apache-pulsar","Spring into Pulsar Part 2: Spring-based Microservices for Multiple Protocols with Apache Pulsar AMQP","2022-11-29","\u002Fimgs\u002Fblogs\u002F63c7bc41ff0c0c10aa45f2d3_63bf3102dba42638e2a29535_spring-into-pulsar-part-2-top.jpeg","blogs","Blog",0.667,{"path":1591,"title":1592,"date":1593,"image":1594,"link":-1,"collection":1587,"resourceType":1588,"score":1595,"id":1591},"\u002Fblog\u002Fcomparison-of-messaging-platforms-apache-pulsar-vs-rabbitmq-vs-nats-jetstream","A Comparison of Messaging Platforms: Apache Pulsar vs. RabbitMQ vs. NATS JetStream","2023-03-01","\u002Fimgs\u002Fblogs\u002F63ff931387c5e89f84e91fb0_Pulsar-Rabbitmq-benchmark.png",0.6,1775564865547]