[{"data":1,"prerenderedAt":1537},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"blog-\u002Fblog\u002Fachieving-broker-load-balancing-apache-pulsar":306,"navbar-pricing-featured":706,"blog-authors-\u002Fblog\u002Fachieving-broker-load-balancing-apache-pulsar":1481,"related-\u002Fblog\u002Fachieving-broker-load-balancing-apache-pulsar":1517},{"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,"canonicalUrl":289,"category":290,"createdAt":289,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":289,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":289,"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",null,"Company","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","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":307,"title":308,"authors":309,"body":312,"canonicalUrl":289,"category":697,"createdAt":289,"date":698,"description":308,"extension":8,"featured":294,"image":699,"isDraft":294,"link":289,"meta":700,"navigation":7,"order":296,"path":701,"readingTime":289,"relatedResources":289,"seo":702,"stem":703,"tags":704,"__hash__":705},"blogs\u002Fblog\u002Fachieving-broker-load-balancing-apache-pulsar.md","Achieving Broker Load Balancing with Apache Pulsar",[310,311],"Heesung Sohn","Kai Wang",{"type":15,"value":313,"toc":674},[314,316,319,322,326,329,332,335,339,342,350,354,357,360,363,366,369,379,382,386,389,392,402,405,409,412,420,424,427,453,457,464,467,471,474,482,486,492,496,499,505,508,512,515,518,525,528,534,540,544,547,556,562,571,575,581,584,587,591,594,600,603,618,622,625,642,646,649],[40,315,46],{"id":42},[48,317,318],{},"In this blog, we talk about the importance of load balancing in distributed computing systems and provide a deep dive on how Pulsar handles broker load balancing. First, we’ll cover Pulsar’s topic-bundle grouping, bundle-broker ownership, and load data models. Then, we'll walk through Pulsar’s load balancing logic with sequence diagrams that demonstrate bundle assignment, split, and shedding. By the end of this blog, you’ll understand how Pulsar dynamically balances brokers.",[48,320,321],{},"Before we dive into the details of Pulsar’s broker load balancing, we'll briefly discuss the challenges of distributed computing, and specifically, systems with monolithic architectures.",[32,323,325],{"id":324},"the-challenges-of-load-balancing-in-distributed-streaming","The challenges of load balancing in distributed streaming",[48,327,328],{},"A key challenge of distributed computing is load balancing. Distributed systems need to evenly distribute message loads among servers to avoid overloaded servers that can malfunction and harm the performance of the cluster. Topics are naturally a good choice to partition messages because messages under the same topic (or topic partition) can be grouped and served by a single logical server. In most distributed streaming systems, including Pulsar, topics or groups of topics are considered a load-balance entity, where the systems need to evenly distribute the message load among the servers.",[48,330,331],{},"Topic load balancing can be challenging when topic loads are unpredictable. When there is a load increase in certain topics, these topics must offload directly or repartition to redistribute the load to other machines. Alternatively, when machines receive low traffic or become idle, the cluster needs to rebalance to avoid wasting server resources.",[48,333,334],{},"Dynamic rebalancing can be difficult in monolithic architectures, where messages are both served and persisted in the same stateful server. In monolithic streaming systems, rebalancing often involves copying messages from one server to another. Admins must carefully compute the initial topic distribution to avoid future rebalancing as much as possible. In many cases, they need careful orchestration to execute topic rebalancing.",[32,336,338],{"id":337},"an-overview-of-load-balancing-in-pulsar","An overview of load balancing in Pulsar",[48,340,341],{},"By contrast, Apache Pulsar is equipped with automatic broker load balancing that requires no admin intervention. Pulsar’s architecture separates storage and compute, making the broker-topic assignment more flexible. Pulsar brokers persist messages in the storage servers, which removes the need for Pulsar to copy messages from one broker to another when rebalancing topics among brokers. In this scenario, the new broker simply looks up the metadata store to point to the correct storage servers where the topic messages are located.",[48,343,344,345,190],{},"Let's briefly talk about the Pulsar storage architecture to have the complete Pulsar's scaling context here. On the storage side, topic messages are segmented into Ledgers, and these Ledgers are distributed to multiple BookKeeper servers, known as bookies. Pulsar horizontally scales its bookies to distribute as many Ledger (Segment) entities as possible. For a high write load, if all bookies are full, you could add more bookies, and the new message entries (new ledgers) will be placed on the new bookies. With this segmentation, during the storage scaling, Pulsar does not involve recopying old messages from bookies. For a high read load, because Pulsar caches messages in the brokers' memory, the read load on the bookies significantly offloads to the brokers, which are load-balanced. You can read more about Pulsar Storage architecture and scaling information in the blog post ",[55,346,349],{"href":347,"rel":348},"https:\u002F\u002Fwww.splunk.com\u002Fen_us\u002Fblog\u002Fit\u002Fcomparing-pulsar-and-kafka-how-a-segment-based-architecture-delivers-better-performance-scalability-and-resilience.html",[264],"Comparing Pulsar and Kafka",[40,351,353],{"id":352},"topics-are-assigned-to-brokers-at-the-bundle-level","Topics are assigned to brokers at the bundle level",[48,355,356],{},"From the client perspective, Pulsar topics are the basic units in which clients publish and consume messages. On the broker side, a single broker will serve all the messages for a topic from all clients. A topic can be partitioned, and partitions will be distributed to multiple brokers. You could regard a topic partition as a topic and a partitioned topic as a group of topics.",[48,358,359],{},"Because it would be inefficient for each broker to serve only one topic, brokers need to serve multiple topics simultaneously. For this multi-topic ownership, the concept of a bundle was introduced in Pulsar to represent a middle-layer group.",[48,361,362],{},"Related topics are logically grouped into a namespace, which is the administrative unit. For instance, you can set configuration policies that apply to all the topics in a namespace. Internally, a namespace is divided into shards, aka the bundles. Each of these bundles becomes an assignment unit.",[48,364,365],{},"Pulsar uses bundles to shard topics, which will help reduce the amount of information to track. For example, Pulsar LoadManger aggregates topic load statistics, such as message rates at the bundle layer, which helps reduce the number of load samples to monitor. Also, Pulsar needs to track which broker currently serves a particular topic. With bundles, Pulsar can reduce the space needed for this ownership mapping.",[48,367,368],{},"Pulsar uses a hash to map topics to bundles. Here’s an example of two bundles in a namespace.",[370,371,376],"pre",{"className":372,"code":374,"language":375},[373],"language-text","\nBundle_Key_Partitions : [0x00000000, 0x80000000, 0xFFFFFFFF]\nBundle1_Key_Range: [0x00000000, 0x80000000)\nBundle2_Key_Range: [0x80000000, 0xFFFFFFFF]\n\n","text",[377,378,374],"code",{"__ignoreMap":18},[48,380,381],{},"Pulsar computes the hashcode given topic name by Long hashcode = hash(topicName). Let’s say hash(“my-topic”) = 0x0000000F. Then Pulsar could do a binary search by NamespaceBundle getBundle(hashCode) to which bundle the topic belongs given the bundle key ranges. In this example, “Bundle1” is the one to which “my-topic” belongs.",[40,383,385],{"id":384},"brokers-dynamically-own-bundles-on-demand","Brokers dynamically own bundles on demand",[48,387,388],{},"One of the advantages of Pulsar’s compute (brokers) and storage (bookies) separation is that Pulsar brokers can be stateless and horizontally scalable with dynamic bundle ownership. When brokers are overloaded, more brokers can be easily added to a cluster and redistribute bundle ownerships.",[48,390,391],{},"To discover the current bundle-broker ownership in a given topic, Pulsar uses a server-side discovery mechanism that redirects clients to the owner brokers’ URLs. This discovery logic requires:",[393,394,395,399],"ul",{},[396,397,398],"li",{},"Bundle key ranges for a given namespace, in order to map a topic to a bundle.",[396,400,401],{},"Bundle-Broker ownership mapping to direct the client to the current owner or to trigger a new ownership acquisition in case there is no broker assigned.",[48,403,404],{},"Pulsar stores bundle ranges and ownership mapping in the metadata store, such as ZooKeeper or etcd, and the information is also cached by each broker.",[40,406,408],{"id":407},"load-data-model","Load data model",[48,410,411],{},"Collecting up-to-date load information from brokers is crucial to load balancing decisions. Pulsar constantly updates the following load data in the memory cache and metadata store and replicates it to the leader broker. Based on this load data, the leader broker runs topic-broker assignment, bundle split, and unload logic:",[393,413,414,417],{},[396,415,416],{},"Bundle Load Data contains bundle-specific load information, such as bundle-specific msg in\u002Fout rates.",[396,418,419],{},"Broker Load Data contains broker-specific load information, such as CPU, memory, and network throughput in\u002Fout rates.",[40,421,423],{"id":422},"load-balance-sequence","Load balance sequence",[48,425,426],{},"In this section, we’ll walk through load balancing logic with sequence diagrams:",[428,429,430,439,446],"ol",{},[396,431,432,433,438],{},"Assigning topics to brokers dynamically (",[55,434,437],{"href":435,"rel":436},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fadministration-load-balance\u002F#assign-topics-to-brokers-dynamically",[264],"Read the complete documentation",".)",[396,440,441,442,438],{},"Splitting overloaded bundles (",[55,443,437],{"href":444,"rel":445},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fadministration-load-balance\u002F#split-namespace-bundles",[264],[396,447,448,449,438],{},"Shedding bundles from overloaded brokers (",[55,450,437],{"href":451,"rel":452},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fadministration-load-balance\u002F#shed-load-automatically",[264],[32,454,456],{"id":455},"assigning-topics-to-brokers-dynamically","Assigning topics to brokers dynamically",[48,458,459],{},[460,461],"img",{"alt":462,"src":463},"brokers illustration","\u002Fimgs\u002Fblogs\u002F63b4023480291650b0b03b6e_lb1.png",[48,465,466],{},"Imagine a client trying to connect to a broker for a topic. The client connects to a random broker, and the broker first searches the matching bundle by the hash of the topic and its namespace bundle ranges. Then the broker checks if any broker already owns the bundle in the metadata store. If already owned, the broker redirects the client to the owner URL. Otherwise, the broker redirects the client to the leader for a broker assignment. For the assignment, the leader first filters out available brokers by the configured rules and then randomly selects one of the least loaded brokers to the bundle, as shown in Section 1 below, and returns its URL. The leader redirects the client to the returned URL, and the client connects to the assigned broker. This new broker-bundle ownership creates an ephemeral lock in the metadata store, and the lock is automatically released if the owner becomes unavailable.",[32,468,470],{"id":469},"section-1-selecting-a-broker","Section 1: Selecting a broker",[48,472,473],{},"This step selects a broker from the filtered broker list. As a tie-breaker strategy, it uses ModularLoadManagerStrategy (LeastLongTermMessageRate by default). LeastLongTermMessageRate computes brokers’ load scores and randomly selects one among the minimal scores by the following logic:",[393,475,476,479],{},[396,477,478],{},"If the maximum local usage of CPU, memory, and network is bigger than the LoadBalancerBrokerOverloadedThresholdPercentage (default 85%), then score=INF.",[396,480,481],{},"Otherwise, score = longTermMsgIn rate and longTermMsgOut rate.",[40,483,485],{"id":484},"splitting-overloaded-bundles","Splitting overloaded bundles",[48,487,488,491],{},[460,489],{"alt":18,"src":490},"\u002Fimgs\u002Fblogs\u002F63b402344106f46a97e93e31_lb2.png","\nWith the bundle load data, the leader broker identifies which bundles are overloaded beyond the threshold as shown in Section 2 below and asks the owner broker to split them. For the split, the owner broker first computes split positions, as shown in Section 3 below, and repartition the target bundles at them, as shown in Section 4 below. After the split, the owner broker updates the bundle ownerships and ranges in the metadata store. The newly split bundles can be automatically unloaded from the owner broker, configurable by the LoadBalancerAutoUnloadSplitBundlesEnabled flag.",[32,493,495],{"id":494},"section-2-finding-target-bundles","Section 2: Finding target bundles",[48,497,498],{},"If the auto bundle split is enabled by loadBalancerAutoBundleSplitEnabled (default true) configuration, the leader broker checks if any bundle’s load is beyond LoadBalancerNamespaceBundle* thresholds.",[370,500,503],{"className":501,"code":502,"language":375},[373],"\nDefaults\nLoadBalancerNamespaceBundleMaxTopics = 1000\nLoadBalancerNamespaceBundleMaxSessions = 1000\nLoadBalancerNamespaceBundleMaxMsgRate = 30000\nLoadBalancerNamespaceBundleMaxBandwidthMbytes = 100\nLoadBalancerNamespaceMaximumBundles = 128\n\n",[377,504,502],{"__ignoreMap":18},[48,506,507],{},"If the number of bundles in the namespace is already larger than or equal to MaximumBundles, it skips the split logic.",[32,509,511],{"id":510},"section-3-computing-bundle-split-boundaries","Section 3: Computing bundle split boundaries",[48,513,514],{},"Split operations compute the target bundle’s range boundaries to split. The bundle split boundary algorithm is configurable by supportedNamespaceBundleSplitAlgorithms.",[48,516,517],{},"If we have two bundle ranges in a namespace with range partitions (0x0000, 0X8000, 0xFFFF), and we are currently targeting the first bundle range (0x0000, 0x8000) to split:",[48,519,520,521,190],{},"RANGE_EQUALLY_DIVIDE_NAME (default): This algorithm divides the bundle into two parts with the same hash range size, for example ttarget bundle to split=(0x0000, 0x8000) => bundle split boundary=",[522,523,524],"span",{},"0x4000",[48,526,527],{},"TOPIC_COUNT_EQUALLY_DIVIDE: It divides the bundle into two parts with the same topic count. Let’s say there are 6 topics in the target bundle [0x0000, 0x8000):",[370,529,532],{"className":530,"code":531,"language":375},[373],"\nhash(topic1) = 0x0000\nhash(topic2) = 0x0005\nhash(topic3) = 0x0010\nhash(topic4) = 0x0015\nhash(topic5) = 0x0020\nhash(topic6) = 0x0025\n\n",[377,533,531],{"__ignoreMap":18},[48,535,536,537,190],{},"Here we want to split at 0x0012 to make the left and right sides of the number of topics the same. E.g. target bundle to split [0x0000, 0x8000) => bundle split boundary=",[522,538,539],{},"0x0012",[32,541,543],{"id":542},"section-4-splitting-bundles-by-boundaries","Section 4: Splitting bundles by boundaries",[48,545,546],{},"Example:",[48,548,549,550,553,554],{},"Given bundle partitions ",[522,551,552],{},"0x0000, 0x8000, 0xFFFF",", splitBoundaries: ",[522,555,524],{},[48,557,558,559],{},"Bundle partitions after split = ",[522,560,561],{},"0x0000, 0x4000, 0x8000, 0xFFFF",[48,563,564,565],{},"Bundles ranges after split = [[0x0000, 0x4000),",[522,566,567,568],{},"0x4000, 0x8000), ",[522,569,570],{},"0x8000, 0xFFFF",[40,572,574],{"id":573},"shedding-unloading-bundles-from-overloaded-brokers","Shedding (unloading) bundles from overloaded brokers",[48,576,577,580],{},[460,578],{"alt":18,"src":579},"\u002Fimgs\u002Fblogs\u002F63b4028d494f09abfefbc61e_lb3.png","\nWith the broker load information collected from all brokers, the leader broker identifies which brokers are overloaded and triggers bundle unload operations, with the objective of rebalancing the traffic throughout the cluster.",[48,582,583],{},"Using the default ThresholdShedder strategy, the leader broker computes the average of the maximal resource usage among CPU, memory, and network IO. After that, the leader finds brokers whose load is higher than the average-based threshold, as shown in Section 5 below. If identified, the leader asks the overloaded brokers to unload some bundles of topics, starting from the high throughput ones, enough to bring the broker load to below the critical threshold.",[48,585,586],{},"For the unloading request, the owner broker removes the target bundles’ ownerships in the metadata store and closes the client topic connections. Then the clients reinitiate the broker discovery mechanism. Eventually, the leader assigns less-loaded brokers to the unloaded bundles and the clients connect to them.",[32,588,590],{"id":589},"section-5-thresholdshedder-finding-overloaded-brokers","Section 5: ThresholdShedder: finding overloaded brokers",[48,592,593],{},"It first computes the average resource usage of all brokers using the following formula.",[370,595,598],{"className":596,"code":597,"language":375},[373],"\nFor each broker: \n    usage =  \n    max (\n    %cpu * cpuWeight\n    %memory * memoryWeight,\n    %bandwidthIn * bandwidthInWeight,\n    %bandwidthOut * bandwidthOutWeight) \u002F 100;\n\n    usage = x * prevUsage + (1 - x) * usage\n\n    avgUsage = sum(usage) \u002F numBrokers \n\n",[377,599,597],{"__ignoreMap":18},[48,601,602],{},"If any broker’s usage is bigger than avgUsage + y, it is considered an overloaded broker.",[393,604,605,612,615],{},[396,606,607,608,611],{},"The resource usage “Weight” is by default 1.0 and configurable by ",[377,609,610],{},"loadBalancerResourceWeight"," configurations.",[396,613,614],{},"The historical usage multiplier x is configurable by loadBalancerHistoryResourcePercentage. By default, it is 0.9, which weighs the previous usage more than the latest.",[396,616,617],{},"The avgUsage buffer y is configurable by ​​loadBalancerBrokerThresholdShedderPercentage, which is 10% by default.",[40,619,621],{"id":620},"takeaways","Takeaways",[48,623,624],{},"In this blog, we reviewed the Pulsar broker load balance logic focusing on its sequence. Here are the broker load balance behaviors that I found important in this review.",[393,626,627,630,633,636,639],{},[396,628,629],{},"Pulsar groups topics into bundles for easier tracking, and it dynamically assigns and balances the bundles among brokers. If specific bundles are overloaded, they get automatically split to maintain the assignment units to a reasonable level of traffic.",[396,631,632],{},"Pulsar collects the global broker (cpu, memory, network usage) and bundle load data (msg in\u002Fout rate) to the leader broker in order to run the algorithmic load balance logic: bundle-broker assignment, bundle splitting, and unloading (shedding).",[396,634,635],{},"The bundle-broker assignment logic randomly selects the least loaded brokers and redirects clients to the assigned brokers’ URLs. The broker-bundle ownerships create ephemeral locks in the metadata store, which are automatically released if the owners become unavailable (lose ownership).",[396,637,638],{},"The bundle-split logic finds target bundles based on the LoadBalancerNamespaceBundle* configuration thresholds, and by default, the bundle ranges are split evenly. After splits, by default, the owner automatically unloads the newly split bundles.",[396,640,641],{},"The auto bundle-unload logic uses the default LoadSheddingStrategy, which finds overloaded brokers based on the average of the max resource usage among CPU, Memory, and Network IO. Then, the leader asks the overloaded brokers to unload some high loaded bundles of topics. Clients’ topic connections under the unloading bundles experience connection close and re-initiate the bundle-broker assignment.",[40,643,645],{"id":644},"more-resources","More Resources",[48,647,648],{},"Stay tuned for more operational content around Pulsar load balance, such as Admin APIs, metrics, logs, and troubleshooting tips. Meanwhile, check out more Pulsar resources:",[393,650,651,665],{},[396,652,653,654,659,660,664],{},"Take Apache Pulsar Training: Take the ",[55,655,658],{"href":656,"rel":657},"https:\u002F\u002Fwww.academy.streamnative.io\u002Ftracks",[264],"self-paced Pulsar courses"," or ",[55,661,663],{"href":662},"\u002Ftraining\u002F","instructor-led Pulsar training"," developed by the original creators of Pulsar. This will get you started with Pulsar and help accelerate your learning.",[396,666,667,668,673],{},"Spin up a Pulsar Cluster in Minutes: If you want to try building microservices without having to set up a Pulsar cluster yourself, ",[55,669,672],{"href":670,"rel":671},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"sign up for StreamNative Cloud today",". StreamNative Cloud is the simple, fast, and cost-effective way to run Pulsar in the public cloud.",{"title":18,"searchDepth":19,"depth":19,"links":675},[676,680,681,682,683,687,692,695,696],{"id":42,"depth":19,"text":46,"children":677},[678,679],{"id":324,"depth":279,"text":325},{"id":337,"depth":279,"text":338},{"id":352,"depth":19,"text":353},{"id":384,"depth":19,"text":385},{"id":407,"depth":19,"text":408},{"id":422,"depth":19,"text":423,"children":684},[685,686],{"id":455,"depth":279,"text":456},{"id":469,"depth":279,"text":470},{"id":484,"depth":19,"text":485,"children":688},[689,690,691],{"id":494,"depth":279,"text":495},{"id":510,"depth":279,"text":511},{"id":542,"depth":279,"text":543},{"id":573,"depth":19,"text":574,"children":693},[694],{"id":589,"depth":279,"text":590},{"id":620,"depth":19,"text":621},{"id":644,"depth":19,"text":645},"Apache Pulsar","2023-01-03","\u002Fimgs\u002Fblogs\u002F63c7f9d38f903d860a03ec52_63b40202fefdbc403c9fb3af_blb2.png",{},"\u002Fblog\u002Fachieving-broker-load-balancing-apache-pulsar",{"title":308,"description":308},"blog\u002Fachieving-broker-load-balancing-apache-pulsar",[697],"4-qpxjXmqYYl5uLqBb3orKi7zP557HbLlSGORMBNE4g",{"id":707,"title":708,"authors":709,"body":714,"canonicalUrl":289,"category":1468,"createdAt":289,"date":1469,"description":1470,"extension":8,"featured":7,"image":1471,"isDraft":294,"link":289,"meta":1472,"navigation":7,"order":296,"path":1473,"readingTime":1474,"relatedResources":289,"seo":1475,"stem":1476,"tags":1477,"__hash__":1480},"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",[710,711,712,713],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":715,"toc":1438},[716,719,722,725,728,731,735,738,746,751,754,762,767,771,778,781,784,792,796,799,804,808,811,814,817,820,829,833,836,847,850,854,857,860,871,874,878,882,890,893,897,905,934,938,941,946,950,953,957,960,963,968,977,982,985,988,999,1003,1006,1017,1021,1024,1027,1032,1035,1064,1068,1070,1076,1079,1084,1089,1092,1096,1110,1114,1125,1129,1144,1153,1164,1167,1170,1174,1177,1180,1191,1194,1197,1200,1205,1210,1214,1218,1235,1239,1253,1258,1262,1273,1276,1292,1296,1307,1312,1317,1325,1329,1332,1336,1343,1347,1350,1358,1363,1371,1377,1386,1395,1404,1413,1422,1430],[48,717,718],{},"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.",[48,720,721],{},"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.",[48,723,724],{},"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.",[48,726,727],{},"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.",[48,729,730],{},"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.",[40,732,734],{"id":733},"key-benchmark-findings","Key Benchmark Findings",[48,736,737],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[393,739,740,743],{},[396,741,742],{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[396,744,745],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,747,748],{},[460,749],{"alt":18,"src":750},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,752,753],{},"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:",[393,755,756,759],{},[396,757,758],{},"50% cheaper than Confluent WarpStream",[396,760,761],{},"85% cheaper than MSK and Redpanda",[48,763,764],{},[460,765],{"alt":18,"src":766},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,768,770],{"id":769},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,772,773,777],{},[55,774,776],{"href":775},"\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.",[48,779,780],{},"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.",[48,782,783],{},"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:",[393,785,786,789],{},[396,787,788],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[396,790,791],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,793,795],{"id":794},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,797,798],{},"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.",[48,800,801],{},[460,802],{"alt":18,"src":803},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,805,807],{"id":806},"leaderless-architecture","Leaderless architecture",[48,809,810],{},"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.",[48,812,813],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,815,816],{},"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",[48,818,819],{},"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.",[48,821,822,823,828],{},"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 ",[55,824,827],{"href":825,"rel":826},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,830,832],{"id":831},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,834,835],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[393,837,838,841,844],{},[396,839,840],{},"Handles millions of metadata\u002Findex operations per second",[396,842,843],{},"Generates sequential IDs to maintain strict message ordering",[396,845,846],{},"Optimized for Kubernetes with horizontal scalability",[48,848,849],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,851,853],{"id":852},"zero-interzone-data-replication","Zero interzone data replication",[48,855,856],{},"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.",[48,858,859],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[393,861,862,865,868],{},[396,863,864],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[396,866,867],{},"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).",[396,869,870],{},"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.",[48,872,873],{},"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.",[40,875,877],{"id":876},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,879,881],{"id":880},"ursa-cluster-deployment","Ursa Cluster Deployment",[393,883,884,887],{},[396,885,886],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[396,888,889],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,891,892],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,894,896],{"id":895},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,898,899,900,904],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,901,902],{"href":902,"rel":903},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[393,906,907,922,931],{},[396,908,909,910,915,916,921],{},"12 OMB workers: 6 for ",[55,911,914],{"href":912,"rel":913},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,917,920],{"href":918,"rel":919},"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.",[396,923,924,925,930],{},"Sample YAML ",[55,926,929],{"href":927,"rel":928},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[396,932,933],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,935,937],{"id":936},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,939,940],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,942,943],{},[460,944],{"alt":18,"src":945},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,947,949],{"id":948},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,951,952],{},"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.",[32,954,956],{"id":955},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,958,959],{},"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.",[48,961,962],{},"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:",[393,964,965],{},[396,966,967],{},"9 × m6i.8xlarge instances",[48,969,970,971,976],{},"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",[55,972,975],{"href":973,"rel":974},"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:",[393,978,979],{},[396,980,981],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,983,984],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,986,987],{},"Additional key assumptions include:",[393,989,990,993,996],{},[396,991,992],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[396,994,995],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[396,997,998],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,1000,1002],{"id":1001},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,1004,1005],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[393,1007,1008,1011,1014],{},[396,1009,1010],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[396,1012,1013],{},"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.",[396,1015,1016],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,1018,1020],{"id":1019},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,1022,1023],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,1025,1026],{},"Consumers:",[393,1028,1029],{},[396,1030,1031],{},"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",[48,1033,1034],{},"Producers:",[393,1036,1037,1046,1055],{},[396,1038,1039,1040,1045],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,1041,1044],{"href":1042,"rel":1043},"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).",[396,1047,1048,1049,1054],{},"Redpanda recently introduced ",[55,1050,1053],{"href":1051,"rel":1052},"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.",[396,1056,1057,1058,1063],{},"Ursa is the only system in this test with ",[55,1059,1062],{"href":1060,"rel":1061},"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.",[32,1065,1067],{"id":1066},"cost-comparison-results","Cost Comparison Results",[48,1069,737],{},[393,1071,1072,1074],{},[396,1073,742],{},[396,1075,745],{},[48,1077,1078],{},"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.",[48,1080,1081],{},[460,1082],{"alt":18,"src":1083},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,1085,1086],{},[460,1087],{"alt":18,"src":1088},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,1090,1091],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,1093,1095],{"id":1094},"streamnative-ursa","StreamNative - Ursa",[393,1097,1098,1101,1104,1107],{},[396,1099,1100],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[396,1102,1103],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[396,1105,1106],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[396,1108,1109],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,1111,1113],{"id":1112},"aws-msk","AWS MSK",[393,1115,1116,1119,1122],{},[396,1117,1118],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[396,1120,1121],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[396,1123,1124],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,1126,1128],{"id":1127},"redpanda","RedPanda",[393,1130,1131,1133,1135,1138,1141],{},[396,1132,1100],{},[396,1134,1103],{},[396,1136,1137],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[396,1139,1140],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[396,1142,1143],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,1145,1146,1147,1152],{},"Please note that we were unable to test ",[55,1148,1151],{"href":1149,"rel":1150},"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.",[393,1154,1155,1161],{},[396,1156,1157,1160],{},[55,1158,1044],{"href":1042,"rel":1159},[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).",[396,1162,1163],{},"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.",[48,1165,1166],{},"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.",[48,1168,1169],{},"We may revisit this comparison as more details become available.",[40,1171,1173],{"id":1172},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,1175,1176],{},"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.",[48,1178,1179],{},"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:",[393,1181,1182,1185,1188],{},[396,1183,1184],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[396,1186,1187],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[396,1189,1190],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,1192,1193],{},"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.",[48,1195,1196],{},"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.",[48,1198,1199],{},"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.",[48,1201,1202],{},[460,1203],{"alt":18,"src":1204},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,1206,1207],{},[460,1208],{"alt":18,"src":1209},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,1211,1213],{"id":1212},"cost-breakdown","Cost Breakdown",[1215,1216,1217],"h4",{"id":1094},"StreamNative – Ursa",[393,1219,1220,1223,1226,1229,1232],{},[396,1221,1222],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[396,1224,1225],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[396,1227,1228],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[396,1230,1231],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[396,1233,1234],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[1215,1236,1238],{"id":1237},"warpstream","WarpStream",[393,1240,1241,1244],{},[396,1242,1243],{},"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.",[396,1245,1246,1247,1252],{},"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 “",[55,1248,1251],{"href":1249,"rel":1250},"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.",[48,1254,1255],{},[460,1256],{"alt":18,"src":1257},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[1215,1259,1261],{"id":1260},"msk","MSK",[393,1263,1264,1267,1270],{},[396,1265,1266],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[396,1268,1269],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[396,1271,1272],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[1215,1274,1128],{"id":1275},"redpanda-1",[393,1277,1278,1281,1283,1286,1289],{},[396,1279,1280],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[396,1282,1269],{},[396,1284,1285],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[396,1287,1288],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[396,1290,1291],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[1215,1293,1295],{"id":1294},"additional-notes","Additional Notes",[393,1297,1298],{},[396,1299,1300,1301,1306],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,1302,1305],{"href":1303,"rel":1304},"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.",[48,1308,1309],{},[460,1310],{"alt":18,"src":1311},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,1313,1314],{},[460,1315],{"alt":18,"src":1316},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[393,1318,1319,1322],{},[396,1320,1321],{},"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.",[396,1323,1324],{},"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.",[40,1326,1328],{"id":1327},"conclusion","Conclusion",[48,1330,1331],{},"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.",[32,1333,1335],{"id":1334},"balancing-latency-and-cost","Balancing Latency and Cost",[48,1337,1338,1342],{},[55,1339,1341],{"href":1340},"\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.",[32,1344,1346],{"id":1345},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,1348,1349],{},"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.",[48,1351,1352,1353,1357],{},"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. ",[55,1354,1356],{"href":670,"rel":1355},[264],"Get started"," with StreamNative Ursa today!",[1359,1360,1362],"h1",{"id":1361},"references","References",[48,1364,1365,1367,1368],{},[522,1366,827],{}," ",[55,1369,1370],{"href":1370},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,1372,1373,1367,1375],{},[522,1374,776],{},[55,1376,775],{"href":775},[48,1378,1379,1367,1382],{},[522,1380,1381],{},"StreamNative pricing",[55,1383,1384],{"href":1384,"rel":1385},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,1387,1388,1367,1391],{},[522,1389,1390],{},"WarpStream pricing",[55,1392,1393],{"href":1393,"rel":1394},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1396,1397,1367,1400],{},[522,1398,1399],{},"AWS S3 pricing",[55,1401,1402],{"href":1402,"rel":1403},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1405,1406,1367,1409],{},[522,1407,1408],{},"AWS EBS pricing",[55,1410,1411],{"href":1411,"rel":1412},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1414,1415,1367,1418],{},[522,1416,1417],{},"AWS MSK pricing",[55,1419,1420],{"href":1420,"rel":1421},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1423,1424,1367,1427],{},[522,1425,1426],{},"The Brutal Truth about Kafka Cost Calculators",[55,1428,1249],{"href":1249,"rel":1429},[264],[48,1431,1432,1367,1435],{},[522,1433,1434],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1436,1303],{"href":1303,"rel":1437},[264],{"title":18,"searchDepth":19,"depth":19,"links":1439},[1440,1441,1442,1447,1451,1452,1461,1464],{"id":733,"depth":19,"text":734},{"id":769,"depth":19,"text":770},{"id":794,"depth":19,"text":795,"children":1443},[1444,1445,1446],{"id":806,"depth":279,"text":807},{"id":831,"depth":279,"text":832},{"id":852,"depth":279,"text":853},{"id":876,"depth":19,"text":877,"children":1448},[1449,1450],{"id":880,"depth":279,"text":881},{"id":895,"depth":279,"text":896},{"id":936,"depth":19,"text":937},{"id":948,"depth":19,"text":949,"children":1453},[1454,1455,1456,1457,1458,1459,1460],{"id":955,"depth":279,"text":956},{"id":1001,"depth":279,"text":1002},{"id":1019,"depth":279,"text":1020},{"id":1066,"depth":279,"text":1067},{"id":1094,"depth":279,"text":1095},{"id":1112,"depth":279,"text":1113},{"id":1127,"depth":279,"text":1128},{"id":1172,"depth":19,"text":1173,"children":1462},[1463],{"id":1212,"depth":279,"text":1213},{"id":1327,"depth":19,"text":1328,"children":1465},[1466,1467],{"id":1334,"depth":279,"text":1335},{"id":1345,"depth":279,"text":1346},"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. 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