[{"data":1,"prerenderedAt":1472},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication":1086,"blog-authors-\u002Fblog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication":1434,"related-\u002Fblog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication":1449},{"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":1421,"createdAt":290,"date":1422,"description":1423,"extension":8,"featured":294,"image":1424,"isDraft":294,"link":290,"meta":1425,"navigation":7,"order":296,"path":1426,"readingTime":1427,"relatedResources":290,"seo":1428,"stem":1429,"tags":1430,"__hash__":1433},"blogs\u002Fblog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication.md","Migrating Tenants across Clusters with Pulsar’s Geo-replication",[1090],"Mingze Han",{"type":15,"value":1092,"toc":1403},[1093,1096,1110,1114,1121,1125,1132,1136,1139,1142,1149,1153,1156,1159,1166,1176,1179,1183,1190,1194,1197,1204,1210,1213,1219,1222,1228,1239,1243,1246,1250,1259,1263,1266,1270,1273,1277,1280,1283,1294,1297,1301,1304,1311,1317,1324,1342,1344,1347,1351,1360],[48,1094,1095],{},"Apache Pulsar is a distributed messaging system that offers robust features such as geo-replication, which allows for the replication of data across multiple data centers or geographical regions. In this blog, I will discuss the following topics:",[339,1097,1098,1101,1104,1107],{},[342,1099,1100],{},"How geo-replication works in Pulsar;",[342,1102,1103],{},"How Pulsar synchronizes consumption progress across clusters;",[342,1105,1106],{},"The problems during consumption progress synchronization in Pulsar and how we optimized the existing logic for our use case at Tencent Cloud;",[342,1108,1109],{},"How to migrate Pulsar tenants across clusters using geo-replication.",[40,1111,1113],{"id":1112},"understanding-geo-replication-in-pulsar","Understanding geo-replication in Pulsar",[48,1115,1116,1117,1120],{},"Geo-replication in Pulsar enables the replication of messages across multiple data centers or geographical regions, providing data redundancy and disaster recovery for Pulsar topics. This ensures that your entire system remains available and resilient to failures or regional outages, maintaining data consistency and enabling low-latency access to data for consumers in different locations.\n",[351,1118],{"alt":18,"src":1119},"\u002Fimgs\u002Fblogs\u002F643ca460774f257b85b2f556_image3.webp","Figure 1. Geo-replication in Pulsar\nA typical use case of geo-replication is that producers and consumers can be located in separate regions. For example, producers can be located in San Francisco while consumers may be in Houston. This can happen in cases where latency requirements between message production and consumption are low. The benefit is that it ensures all writes occur in the same place with a low write latency. After data is replicated to different locations, consumers can all read messages no matter where they are.",[40,1122,1124],{"id":1123},"how-does-geo-replication-work","How does geo-replication work?",[48,1126,1127,1128,1131],{},"The logic behind Pulsar's geo-replication is quite straightforward. Typically, if you want to replicate data across regions (without using Pulsar’s geo-replication),  you may want to create a service that includes both a consumer and a producer. The consumer retrieves data from the source cluster, and the producer sends the data to the target cluster. Pulsar’s geo-replication feature follows a similar pattern as depicted in Figure 2.\n",[351,1129],{"alt":18,"src":1130},"\u002Fimgs\u002Fblogs\u002F643ca48387ca32e4c05dfe9b_image11.webp","Figure 2. How geo-replication works in Pulsar\nIf you enable geo-replication, Pulsar creates a Replication Cursor and a Replication Producer for each topic. The Replication Producer retrieves messages from the local cluster and dispatches them to the target cluster. The Replication Cursor is used to track the data replication process using an internal subscription. Similarly, if a producer sends messages to the target cluster, it can also create its own Replication Cursor and Producer to dispatch the messages back to the source cluster. The replication process does not impact message reads and writes in the local cluster.",[40,1133,1135],{"id":1134},"understanding-consumption-progress-synchronization","Understanding consumption progress synchronization",[48,1137,1138],{},"In some use cases, it is necessary to synchronize the consumption progress of subscriptions between clusters located in different regions. In a disaster recovery scenario, for example, if the primary data center in San Francisco experiences an outage, you must switch to a backup cluster in Houston to continue your service. In this case, clients should be able to continue consuming messages from the Houston cluster from where they left off in the primary cluster.",[48,1140,1141],{},"If the consumption progress is not synchronized, it would be difficult to know which messages have already been consumed in the primary data center. If a client starts consuming messages from the latest position, it might lead to message loss; if it starts from the earliest message, it could result in duplicate consumption. Both ways are usually unacceptable to the client. A possible compromise is to rewind topics to a specific message and begin reading from there. However, this approach still can’t guarantee messages are not lost or repeatedly consumed.",[48,1143,1144,1145,1148],{},"To solve this issue, Pulsar supports consumption progress synchronization for subscriptions so that users can smoothly transition to a backup cluster during disaster recovery without worrying about message duplication or loss. Figure 3 shows an example where both messages and consumption progress are synchronized between Cluster-A and Cluster-B.\n",[351,1146],{"alt":18,"src":1147},"\u002Fimgs\u002Fblogs\u002F643ca4a8eab04a00762959b0_image8.webp","Figure 3. Consumption progress synchronization between Cluster-A and Cluster-B",[32,1150,1152],{"id":1151},"how-does-pulsar-track-consumption-progress","How does Pulsar track consumption progress?",[48,1154,1155],{},"Before I explain how consumption progress is synchronized between clusters, let’s first understand the consumption tracking mechanism in Pulsar, which leverages two important attributes - markDeletePosition and individuallyDeletedMessages.",[48,1157,1158],{},"markDeletePosition is similar to the consumer offset in Kafka. The message marked by markDeletePosition and all the preceding messages have been acknowledged, which means they are ready for deletion.",[48,1160,1161,1162,1165],{},"individuallyDeletedMessages is what sets Pulsar apart from most streaming and messaging systems. Unlike them, Pulsar supports both selective and cumulative acknowledgments. The former allows consumers to individually acknowledge entries, the information of which is stored in individuallyDeletedMessages.\n",[351,1163],{"alt":18,"src":1164},"\u002Fimgs\u002Fblogs\u002F643ca501511bd424727b0bae_image4.webp","Figure 4. markDeletePosition and individuallyDeletedMessages\nAs illustrated in Figure 4, let’s consider a shared subscription with multiple consumer instances. Messages from 0 to 9 are distributed to all of them. Each consumer may consume messages at different speeds, so the order of message delivery and acknowledgment may vary. Suppose messages 0, 1, 2, 3, 4, 6, and 9 have been acknowledged, while messages 5, 7, and 8 have not. The markDeletePosition marker, which represents the consumption progress, points to message 4, indicating that all messages before 4 (inclusive) have been successfully consumed. If you check the statistics of the topic (pulsar-admin topics stats), you can see that markDeletePosition and individuallyDeletedMessages have the following values:",[1167,1168,1173],"pre",{"className":1169,"code":1171,"language":1172},[1170],"language-text","\"markDeletePosition\": \"1:4\",\n\"individuallyDeletedMessages\": \"[(1:5‥1:6], (1:8‥1:9]]\",\n","text",[1174,1175,1171],"code",{"__ignoreMap":18},[48,1177,1178],{},"These values are essentially message IDs and intervals. A message ID consists of a ledger ID and an entry ID. A left-open and right-closed interval means the message at the beginning of this interval has not been acknowledged while the message at the end has.",[32,1180,1182],{"id":1181},"message-id-inconsistency-across-clusters","Message ID inconsistency across clusters",[48,1184,1185,1186,1189],{},"The complexity of consumption progress synchronization lies in the ID inconsistency of the same message across different clusters. It’s impossible to ensure that the ledger ID and the entry ID of the same message are consistent. In Figure 5, for example, the ID of message A is 1:0 in cluster A while it is 3:0 in cluster B.\n",[351,1187],{"alt":18,"src":1188},"\u002Fimgs\u002Fblogs\u002F643ca592511bd44e7b7b62d4_image10.webp","Figure 5. Message ID inconsistency across clusters\nIf the message IDs for the same message were consistent across both clusters, synchronizing consumption progress would be very simple. For instance, if a message with ID 1:2 is consumed in cluster A, cluster B could simply acknowledge message 1:2. However, messages IDs can hardly be the same across clusters and without knowing the relation of different message IDs across clusters, how can we synchronize the consumption progress?",[32,1191,1193],{"id":1192},"cursor-snapshots","Cursor snapshots",[48,1195,1196],{},"Pulsar uses cursor snapshots to let clusters know how different message IDs are related to each other.",[48,1198,1199,1200,1203],{},"As shown in Figure 6 and the code snippet below, when acknowledging a message, Cluster A immediately creates a snapshot and sends a ReplicatedSubscriptionsSnapshotRequest to both Cluster B and Cluster C. It requires them to tell it the respective IDs of this message in Cluster B and Cluster C.\n",[351,1201],{"alt":18,"src":1202},"\u002Fimgs\u002Fblogs\u002F643ca5bd511bd4f5647b7f31_image9.webp","Figure 6. Cluster A sends a ReplicatedSubscriptionsSnapshotRequest to both clusters",[1167,1205,1208],{"className":1206,"code":1207,"language":1172},[1170],"\"ReplicatedSubscriptionsSnapshotRequest\" : {\n    \"snapshot_id\" : \"444D3632-F96C-48D7-83DB-041C32164EC1\",\n    \"source_cluster\" : \"a\"\n}\n",[1174,1209,1207],{"__ignoreMap":18},[48,1211,1212],{},"Upon receiving the request from Cluster A, Cluster B (and Cluster C) responds with the ID of this message in its cluster. See the code snippet below for details.",[1167,1214,1217],{"className":1215,"code":1216,"language":1172},[1170],"\"ReplicatedSubscriptionSnapshotResponse\" : {\n    \"snapshotid\" : \"444D3632-F96C-48D7-83DB-041C32164EC1\",\n    \"cluster\" : {\n        \"cluster\" : \"b\",\n        \"message_id\" : {\n            \"ledger_id\" : 1234,\n            \"entry_id\" : 45678\n            }\n    }\n}\n",[1174,1218,1216],{"__ignoreMap":18},[48,1220,1221],{},"After receiving the message IDs from Cluster B and Cluster C, Cluster A stores them in the cursor snapshot as below. This allows Pulsar to know which messages should be acknowledged in Cluster B and Cluster C when the same message is acknowledged in Cluster A.",[1167,1223,1226],{"className":1224,"code":1225,"language":1172},[1170],"{\n    \"snapshot_id\" : \"444D3632-F96C-48D7-83DB-041C32164EC1\",\n    \"local_message_id\" : {\n         \"ledger_id\" : 192,\n         \"entry_id\" : 123123\n    },\n    \"clusters\" : [\n        {\n            \"cluster\" : \"b\",\n            \"message_id\" : {\n                \"ledger_id\" : 1234, \n                \"entry_id\" : 45678\n            }\n        },\n        {\n            \"cluster\" : \"c\",\n            \"message_id\" : {\n                \"ledger_id\" : 7655,\n                \"entry_id\" : 13421\n            }\n        }\n    ],\n}\n",[1174,1227,1225],{"__ignoreMap":18},[48,1229,1230,1231,1234,1235,1238],{},"Let’s look at the implementation in more detail. Based on cursor snapshots, Pulsar creates the corresponding snapshot markers and puts them between messages within the original topic. When the consumer reaches the snapshot marker, it will be loaded into memory. With message 3 acknowledged in Cluster A (i.e. markDeletePosition moves to message 3), the markDeletePosition of the same messages in Cluster B and Cluster C will also be updated.\n",[351,1232],{"alt":18,"src":1233},"\u002Fimgs\u002Fblogs\u002F643ca6a402179a228df19428_image1.webp","Figure 7. Snapshot marker\nIn the example in Figure 8, Cluster A has two snapshots on message 1:2 and message 1:6 respectively. When the markDeletePosition of Cluster A points to message 1:4, the markDeletePosition of Cluster B can move to message 3:4 as it knows the same message has already been acknowledged according to the snapshot.\n",[351,1236],{"alt":18,"src":1237},"\u002Fimgs\u002Fblogs\u002F643ca6e40e200542af29ce6d_image7.webp","Figure 8. How cursor snapshots work in Pulsar\nNote that Figure 8 is a very simple illustration of how Pulsar synchronizes consumption progress across clusters. This process includes many details and explaining all of them requires a separate blog post. If you are interested in this topic, I am willing to share more in the Pulsar community.",[40,1240,1242],{"id":1241},"problems-in-consumption-progress-synchronization","Problems in consumption progress synchronization",[48,1244,1245],{},"Before diving into tenant migration across clusters, I would like to analyze three major problems during consumption progress synchronization. These issues are the primary obstacles in tenant migration.",[32,1247,1249],{"id":1248},"no-synchronization-for-individuallydeletedmessages","No synchronization for individuallyDeletedMessages",[48,1251,1252,1253,1258],{},"The current implementation ensures that markDeletePosition is synchronized across different clusters but individuallyDeletedMessages is not. This can lead to a large number of unacknowledged messages (namely acknowledgment holes), particularly impacting scenarios with ",[55,1254,1257],{"href":1255,"rel":1256},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Fconcepts-messaging\u002F#delayed-message-delivery",[264],"delayed messages",". If a topic contains a delayed message set to be delivered one day later, the acknowledgment of it will be postponed by a day. In this case, markDeletePosition can only point to the latest acknowledged message before the delayed message; if you switch to a new cluster, it will result in duplicate message consumption. This is because the new cluster does not know which individual messages after markDeletePosition have already been acknowledged in the primary cluster (in other words, individuallyDeletedMessages is not synchronized).",[32,1260,1262],{"id":1261},"synchronization-blocked-by-message-backlogs","Synchronization blocked by message backlogs",[48,1264,1265],{},"In the previous examples (Figure 6 and Figure 7), Cluster A doesn’t send requests through an RPC interface. Instead, snapshot markers are written into the topic alongside other messages. If the target cluster (Cluster B) has a large message backlog, requests sent by the primary cluster (Cluster A) may remain unprocessed for a long time (there is an internal timeout mechanism waiting for the target cluster's response for 30 seconds). As a result, the snapshot cannot be successfully created, preventing synchronization of consumption progress and markDeletePosition.",[32,1267,1269],{"id":1268},"periodic-creation-of-cursor-snapshots","Periodic creation of cursor snapshots",[48,1271,1272],{},"Pulsar does not create a cursor snapshot for every message. Instead, snapshots are created periodically. In Figure 8, only message 1:2 and message 1:6 have snapshots; it is impossible for Cluster B to know markDeletePosition points to 1:4 in Cluster A, so it cannot acknowledge the same message in its own cluster.",[40,1274,1276],{"id":1275},"optimizing-the-consumption-progress-synchronization-logic","Optimizing the consumption progress synchronization logic",[48,1278,1279],{},"The issues mentioned above can cause duplicate consumption of messages. For our online business, a small amount of short-term and controllable duplicate consumption may be acceptable, but it makes no sense if clients need to consume an excessive number of duplicate messages.",[48,1281,1282],{},"As such, we optimized the existing logic by synchronizing both markDeletePosition and individuallyDeletedMessages before migration. However, establishing the connections of message IDs for the same messages between different clusters still remained the most challenging part.",[48,1284,1285,1286,1289,1290,1293],{},"To solve this issue, we added the originalClusterPosition and the entry position to the message’s metadata when sending a message from the original cluster to the target cluster. originalClusterPosition is used to store the message ID in the original cluster. See Figure 9 for details.\n",[351,1287],{"alt":18,"src":1288},"\u002Fimgs\u002Fblogs\u002F643ca753774f250cedb44d07_image2.webp","Figure 9. Introducing originalClusterPosition in the message metadata\nThe updated logic allows us to easily retrieve the ID of a message in the primary cluster according to originalClusterPosition and compare it with the information of individuallyDeletedMessages synchronized to the target cluster. This way, messages that have already been acknowledged in the primary cluster will not be sent to the consumers of the target cluster.\n",[351,1291],{"alt":18,"src":1292},"\u002Fimgs\u002Fblogs\u002F643ca77d02179a02e6f21786_image6.webp","Figure 10. How acknowledged messages are filtered out with the updated logic\nFigure 10 shows the implementation logic in more detail. Before migration, we need to synchronize individuallyDeletedMessages from the primary cluster (cluster-1) to the target cluster (cluster-2). Before sending messages to consumers, we use the filterEntriesForConsumer method to filter out messages already consumed in cluster-1 and only push unacknowledged messages to the consumers of cluster-2.",[48,1295,1296],{},"The updated logic above represents “a shift in thinking”. In the original implementation, the primary cluster periodically creates snapshots to figure out the relations of messages between clusters. After messages are acknowledged in the primary cluster, they can be acknowledged in target clusters based on the snapshots. By contrast, our implementation puts message position information directly into the metadata instead of using a separate entity to synchronize the consumption progress. This approach keeps duplicate consumption within an acceptable range.",[40,1298,1300],{"id":1299},"migrating-tenants-across-pulsar-clusters","Migrating tenants across Pulsar clusters",[48,1302,1303],{},"Previously, we were using shared physical clusters at Tencent Cloud to support different business scenarios. However, this could lead to mutual interference between users. Additionally, different users may have different service SLA requirements. For those who demand higher service quality, we may need to set up a dedicated cluster to physically isolate resources to reduce the impact on other users. In such cases, we need a smooth migration plan.",[48,1305,1306,1307,1310],{},"Figure 11 shows the diagram of our internal implementation for tenant migration across Pulsar clusters. The core module, LookupService, handles clients’ lookup requests. It stores the map of each tenant to the corresponding physical cluster. When a client’s lookup request arrives, we forward it to the associated physical cluster, allowing the client to establish connections with the broker. Note that LookupService also acts as the proxy for getPartitionState, getPartitionMetadata, and getSchema requests. However, it does not proxy data stream requests, which are sent directly to the cluster via CLB or VIP without going through LookupService.\n",[351,1308],{"alt":18,"src":1309},"\u002Fimgs\u002Fblogs\u002F643ca7c60d0f4475895db2c7_image12.webp","Figure 11. Tenant migration",[1312,1313,1314],"blockquote",{},[48,1315,1316],{},"Note: LookupService is not designed specifically for cross-cluster migration. Its primary purpose is to provide centralized processing of different network service routes for cloud clusters. During cross-cluster migration, we used LookupService to ensure a smooth cluster switch while utilizing Pulsar’s geo-replication feature to synchronize data.",[48,1318,1319,1320,1323],{},"Now, let’s look at the five steps during migration:\n",[351,1321],{"alt":18,"src":1322},"\u002Fimgs\u002Fblogs\u002F643ca7e3e4b0fa2ceb6756b3_image5.webp","Figure 12. Data migration process",[1325,1326,1327,1330,1333,1336,1339],"ol",{},[342,1328,1329],{},"Synchronize metadata: Create the corresponding resources on the target cluster, such as tenants, namespaces, topics, subscriptions, and roles.",[342,1331,1332],{},"Synchronize topic data: Enable geo-replication to migrate the topic data of each tenant.",[342,1334,1335],{},"Synchronize consumption progress: Enable consumption progress synchronization to synchronize each subscription’s individuallyDeletedMessages and markDeleteMessages to the target cluster.",[342,1337,1338],{},"Switch to the new cluster: Modify the tenant-to-physical cluster map in LookupService and trigger topic unload so that clients can renew the server’s IP address. LookupService will return the address of the new cluster based on the new map.",[342,1340,1341],{},"Clean up resources: Delete unnecessary resources in the original cluster after the migration is complete.",[40,1343,931],{"id":930},[48,1345,1346],{},"There are many ways to migrate your data across clusters. In this article, I shared a method with low implementation costs, less complexity, and high reliability on the public cloud. This approach allows for a smooth migration without modifying Pulsar’s protocol on the client and server sides.",[40,1348,1350],{"id":1349},"more-on-apache-pulsar","More on Apache Pulsar",[48,1352,1353,1354,1359],{},"Pulsar has become ",[55,1355,1358],{"href":1356,"rel":1357},"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.",[339,1361,1362,1370,1376,1386,1395],{},[342,1363,1364,1365],{},"Start your on-demand Pulsar training today with ",[55,1366,1369],{"href":1367,"rel":1368},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[264],"StreamNative Academy",[342,1371,1372],{},[55,1373,1375],{"href":1374},"\u002Fuse-cases\u002Fgeo-replication","Geo-replication use cases",[342,1377,1378,972,1381],{},[970,1379,1380],{},"Doc",[55,1382,1385],{"href":1383,"rel":1384},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Fconcepts-replication\u002F",[264],"Geo-replication",[342,1387,1388,972,1391],{},[970,1389,1390],{},"Blog",[55,1392,1394],{"href":1393},"\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily","Client Optimization: How Tencent Maintains Apache Pulsar Clusters with over 100 Billion Messages Daily",[342,1396,1397,972,1399],{},[970,1398,1390],{},[55,1400,1402],{"href":1401},"\u002Fblog\u002F600k-topics-per-cluster-stability-optimization-apache-pulsar-tencent-cloud","600K Topics Per Cluster: Stability Optimization of Apache Pulsar at Tencent Cloud",{"title":18,"searchDepth":19,"depth":19,"links":1404},[1405,1406,1407,1412,1417,1418,1419,1420],{"id":1112,"depth":19,"text":1113},{"id":1123,"depth":19,"text":1124},{"id":1134,"depth":19,"text":1135,"children":1408},[1409,1410,1411],{"id":1151,"depth":279,"text":1152},{"id":1181,"depth":279,"text":1182},{"id":1192,"depth":279,"text":1193},{"id":1241,"depth":19,"text":1242,"children":1413},[1414,1415,1416],{"id":1248,"depth":279,"text":1249},{"id":1261,"depth":279,"text":1262},{"id":1268,"depth":279,"text":1269},{"id":1275,"depth":19,"text":1276},{"id":1299,"depth":19,"text":1300},{"id":930,"depth":19,"text":931},{"id":1349,"depth":19,"text":1350},"Apache Pulsar","2023-04-17","Learn how geo-replication works in Apache Pulsar and how Tencent Cloud migrated tenants across clusters using the feature.","\u002Fimgs\u002Fblogs\u002F644009e24b93f00ba2e70cfe_Blog-Migrating-Tenants-across-Clusters-with-Pulsar's-Geo-replication.png",{},"\u002Fblog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication","10 min read",{"title":1088,"description":1423},"blog\u002Fmigrating-tenants-across-clusters-with-pulsars-geo-replication",[1431,1432],"Geo-Replication","Tutorials","tDGZ3qlCcIOe7cHplvnj-v-SEkk60ANl79WvDfap8DQ",[1435],{"id":1436,"title":1090,"bioSummary":1437,"email":290,"extension":8,"image":1438,"linkedinUrl":290,"meta":1439,"position":1446,"stem":1447,"twitterUrl":290,"__hash__":1448},"authors\u002Fauthors\u002Fmingze-han.md","Mingze Han is a Senior Software Engineer at Tencent Cloud with multiple years of experience in the development and maintenance of messaging systems. He is a maintainer of the RoP (RocketMQ-on-Pulsar) project.","\u002Fimgs\u002Fauthors\u002Fmingze-han.jpeg",{"body":1440},{"type":15,"value":1441,"toc":1444},[1442],[48,1443,1437],{},{"title":18,"searchDepth":19,"depth":19,"links":1445},[],"Senior Software Engineer, Tencent Cloud","authors\u002Fmingze-han","aFi08MLQhXUIHPv3fC7pspui1tvszNIEJ6GtdYQjuPw",[1450,1457,1466],{"path":1451,"title":1452,"date":1453,"image":1454,"link":-1,"collection":1455,"resourceType":1390,"score":1456,"id":1451},"\u002Fblog\u002Fan-operators-guide-configuring-geo-replication-with-the-pulsar-resources-operator","An Operator’s Guide: Configuring Geo-Replication with the Pulsar Resources Operator","2023-04-20","\u002Fimgs\u002Fblogs\u002F6441b53c7518a6ab97ce970d_Geo-replication-Resources-Operator.png","blogs",1,{"path":1458,"title":1459,"date":1460,"image":1461,"link":1462,"collection":1463,"resourceType":1464,"score":1465,"id":1458},"\u002Fsuccess-stories\u002Fcisco","Scaling IoT Control Center with Pulsar Integration at Cisco","2023-12-18","\u002Fimgs\u002Fsuccess-stories\u002F67942f278a347375dcaac014_SN-SuccessStories-cisco.webp","https:\u002F\u002Fyoutu.be\u002FobWe-bjEk_w","successStories","Case Study",0.367,{"path":1467,"title":1468,"date":1469,"image":1470,"link":-1,"collection":1455,"resourceType":1390,"score":1471,"id":1467},"\u002Fblog\u002Fdeep-dive-into-data-placement-policies","Data Placement Policy Best Practices for Apache Pulsar","2023-07-16","\u002Fimgs\u002Fblogs\u002F63c7f9f43d691125f4ff6429_63b3ff32877620a3217db989_deep-dive-of-data-placement-policies-top-.jpeg",0.333,1775716431274]