[{"data":1,"prerenderedAt":1556},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"navbar-pricing-featured":304,"blog-\u002Fblog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar":1084,"blog-authors-\u002Fblog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar":1519,"related-\u002Fblog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar":1534},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":10,"meta":12,"stem":20,"variant":21,"__hash__":22},"banners\u002Fbanners\u002Fkafka-company-2025.md","Native Apache Kafka Service Is Coming Soon to StreamNative Cloud. Join the waitlist and get $1,000 in credits.","2026-04-01",true,"md","\u002Fnative-kafka-service-waitlist",null,"Join Waitlist",{"body":13},{"type":14,"value":15,"toc":16},"minimark",[],{"title":17,"searchDepth":18,"depth":18,"links":19},"",2,[],"banners\u002Fkafka-company-2025","default","IMIJszQOOWTfA_DV33eYUA5jqV7DrX1FWbBTBZfNvWc",{"id":24,"title":25,"authors":26,"body":28,"category":288,"createdAt":10,"date":289,"description":290,"extension":8,"featured":7,"image":291,"isDraft":292,"link":10,"meta":293,"navigation":7,"order":294,"path":295,"readingTime":296,"relatedResources":10,"seo":297,"stem":298,"tags":299,"__hash__":303},"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",[27],"David Kjerrumgaard",{"type":14,"value":29,"toc":275},[30,38,46,50,66,72,77,80,86,101,108,114,117,123,126,133,139,142,145,156,162,168,171,174,177,183,190,193,196,203,206,209,223,228,232,236,240,244,248,250,267,269],[31,32,34],"h3",{"id":33},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[35,36,37],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[39,40,42],"h2",{"id":41},"introduction",[43,44,45],"strong",{},"Introduction",[47,48,49],"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.",[47,51,52,53,62,63],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[54,55,57],"a",{"href":56},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[35,58,59],{},[43,60,61],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[43,64,65],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[47,67,68,69],{},"***Forrester's Take: ***",[35,70,71],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[47,73,74],{},[35,75,76],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[47,78,79],{},"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.",[39,81,83],{"id":82},"trusted-by-industry-leaders",[43,84,85],{},"Trusted by Industry Leaders",[47,87,88,89,92,93,96,97,100],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[43,90,91],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[43,94,95],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[43,98,99],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[47,102,103,104,107],{},"The Forrester report notes that, “",[35,105,106],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[39,109,111],{"id":110},"modern-cloud-native-architecture-built-for-scale",[43,112,113],{},"Modern, Cloud-Native Architecture Built for Scale",[47,115,116],{},"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.",[47,118,119,120,107],{},"Forrester's evaluation described that “",[35,121,122],{},"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.",[47,124,125],{},"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.",[47,127,128,129,132],{},"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 ",[35,130,131],{},"\"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.",[39,134,136],{"id":135},"open-source-foundation-and-pulsar-expertise",[43,137,138],{},"Open Source Foundation and Pulsar Expertise",[47,140,141],{},"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.",[47,143,144],{},"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.",[47,146,147,148,151,152,155],{},"Forrester's assessment noted that StreamNative’s “",[35,149,150],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[43,153,154],{},"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.",[47,157,158,159],{},"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 ",[35,160,161],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[39,163,165],{"id":164},"powering-real-time-use-cases-across-industries",[43,166,167],{},"Powering Real-Time Use Cases Across Industries",[47,169,170],{},"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.",[47,172,173],{},"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.",[47,175,176],{},"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.",[39,178,180],{"id":179},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[43,181,182],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[47,184,185,186,189],{},"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 ",[43,187,188],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[47,191,192],{},"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.",[47,194,195],{},"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.",[47,197,198,199,202],{},"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. ",[43,200,201],{},"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.",[47,204,205],{},"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!",[207,208],"hr",{},[31,210,212],{"id":211},"streamnative-in-the-forrester-wave-evaluation-findings",[43,213,214,215,222],{},"StreamNative in ",[43,216,217],{},[54,218,219],{"href":56},[43,220,221],{},"The Forrester Wave™",": Evaluation Findings",[224,225,227],"h5",{"id":226},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[224,229,231],{"id":230},"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",[224,233,235],{"id":234},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[224,237,239],{"id":238},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[224,241,243],{"id":242},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[224,245,247],{"id":246},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[207,249],{},[251,252,254,255,258,259,189],"h6",{"id":253},"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: **",[35,256,257],{},"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 *",[54,260,264],{"href":261,"rel":262},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[263],"nofollow",[35,265,266],{},"here",[207,268],{},[251,270,272],{"id":271},"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",[35,273,274],{},"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":17,"searchDepth":18,"depth":18,"links":276},[277,279,280,281,282,283,284],{"id":33,"depth":278,"text":37},3,{"id":41,"depth":18,"text":45},{"id":82,"depth":18,"text":85},{"id":110,"depth":18,"text":113},{"id":135,"depth":18,"text":138},{"id":164,"depth":18,"text":167},{"id":179,"depth":18,"text":182,"children":285},[286],{"id":211,"depth":278,"text":287},"StreamNative in The Forrester Wave™: Evaluation Findings","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":25,"description":290},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[300,301,302],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":305,"title":306,"authors":307,"body":312,"category":1071,"createdAt":10,"date":1072,"description":1073,"extension":8,"featured":7,"image":1074,"isDraft":292,"link":10,"meta":1075,"navigation":7,"order":294,"path":1076,"readingTime":1077,"relatedResources":10,"seo":1078,"stem":1079,"tags":1080,"__hash__":1083},"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",[308,309,310,311],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":14,"value":313,"toc":1041},[314,317,320,323,326,329,333,336,346,352,355,363,368,372,379,382,385,393,397,400,405,409,412,415,418,421,430,434,437,448,451,455,458,461,472,475,479,483,491,494,498,506,535,539,542,547,551,554,558,561,564,569,578,583,586,589,600,604,607,618,622,625,628,633,636,665,669,671,677,680,685,690,693,697,711,715,726,730,745,754,765,768,771,775,778,781,792,795,798,801,806,811,815,819,836,840,854,859,863,874,877,893,897,908,913,918,926,930,933,937,944,948,951,960,965,974,980,989,998,1007,1016,1025,1033],[47,315,316],{},"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.",[47,318,319],{},"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.",[47,321,322],{},"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.",[47,324,325],{},"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.",[47,327,328],{},"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.",[39,330,332],{"id":331},"key-benchmark-findings","Key Benchmark Findings",[47,334,335],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[337,338,339,343],"ul",{},[340,341,342],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[340,344,345],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[47,347,348],{},[349,350],"img",{"alt":17,"src":351},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[47,353,354],{},"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:",[337,356,357,360],{},[340,358,359],{},"50% cheaper than Confluent WarpStream",[340,361,362],{},"85% cheaper than MSK and Redpanda",[47,364,365],{},[349,366],{"alt":17,"src":367},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[39,369,371],{"id":370},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[47,373,374,378],{},[54,375,377],{"href":376},"\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.",[47,380,381],{},"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.",[47,383,384],{},"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:",[337,386,387,390],{},[340,388,389],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[340,391,392],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[39,394,396],{"id":395},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[47,398,399],{},"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.",[47,401,402],{},[349,403],{"alt":17,"src":404},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[31,406,408],{"id":407},"leaderless-architecture","Leaderless architecture",[47,410,411],{},"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.",[47,413,414],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[47,416,417],{},"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",[47,419,420],{},"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.",[47,422,423,424,429],{},"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 ",[54,425,428],{"href":426,"rel":427},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[263],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[31,431,433],{"id":432},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[47,435,436],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[337,438,439,442,445],{},[340,440,441],{},"Handles millions of metadata\u002Findex operations per second",[340,443,444],{},"Generates sequential IDs to maintain strict message ordering",[340,446,447],{},"Optimized for Kubernetes with horizontal scalability",[47,449,450],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[31,452,454],{"id":453},"zero-interzone-data-replication","Zero interzone data replication",[47,456,457],{},"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.",[47,459,460],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[337,462,463,466,469],{},[340,464,465],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[340,467,468],{},"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).",[340,470,471],{},"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.",[47,473,474],{},"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.",[39,476,478],{"id":477},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[31,480,482],{"id":481},"ursa-cluster-deployment","Ursa Cluster Deployment",[337,484,485,488],{},[340,486,487],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[340,489,490],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[47,492,493],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[31,495,497],{"id":496},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[47,499,500,501,505],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[54,502,503],{"href":503,"rel":504},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[263]," for details.",[337,507,508,523,532],{},[340,509,510,511,516,517,522],{},"12 OMB workers: 6 for ",[54,512,515],{"href":513,"rel":514},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[263],"producers",", 6 for ",[54,518,521],{"href":519,"rel":520},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[263],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[340,524,525,526,531],{},"Sample YAML ",[54,527,530],{"href":528,"rel":529},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[263],"scripts"," provided for Kafka-compatible configuration and rate limits.",[340,533,534],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[39,536,538],{"id":537},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[47,540,541],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[47,543,544],{},[349,545],{"alt":17,"src":546},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[39,548,550],{"id":549},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[47,552,553],{},"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.",[31,555,557],{"id":556},"test-setup-key-assumptions","Test Setup & Key Assumptions",[47,559,560],{},"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.",[47,562,563],{},"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:",[337,565,566],{},[340,567,568],{},"9 × m6i.8xlarge instances",[47,570,571,572,577],{},"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",[54,573,576],{"href":574,"rel":575},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[263]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[337,579,580],{},[340,581,582],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[47,584,585],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[47,587,588],{},"Additional key assumptions include:",[337,590,591,594,597],{},[340,592,593],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[340,595,596],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[340,598,599],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[31,601,603],{"id":602},"inter-broker-replication-costs","Inter-Broker Replication Costs",[47,605,606],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[337,608,609,612,615],{},[340,610,611],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[340,613,614],{},"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.",[340,616,617],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[31,619,621],{"id":620},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[47,623,624],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[47,626,627],{},"Consumers:",[337,629,630],{},[340,631,632],{},"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",[47,634,635],{},"Producers:",[337,637,638,647,656],{},[340,639,640,641,646],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[54,642,645],{"href":643,"rel":644},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[263],"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).",[340,648,649,650,655],{},"Redpanda recently introduced ",[54,651,654],{"href":652,"rel":653},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[263],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[340,657,658,659,664],{},"Ursa is the only system in this test with ",[54,660,663],{"href":661,"rel":662},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[263],"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.",[31,666,668],{"id":667},"cost-comparison-results","Cost Comparison Results",[47,670,335],{},[337,672,673,675],{},[340,674,342],{},[340,676,345],{},[47,678,679],{},"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.",[47,681,682],{},[349,683],{"alt":17,"src":684},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[47,686,687],{},[349,688],{"alt":17,"src":689},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[47,691,692],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[31,694,696],{"id":695},"streamnative-ursa","StreamNative - Ursa",[337,698,699,702,705,708],{},[340,700,701],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[340,703,704],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[340,706,707],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[340,709,710],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[31,712,714],{"id":713},"aws-msk","AWS MSK",[337,716,717,720,723],{},[340,718,719],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[340,721,722],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[340,724,725],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[31,727,729],{"id":728},"redpanda","RedPanda",[337,731,732,734,736,739,742],{},[340,733,701],{},[340,735,704],{},[340,737,738],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[340,740,741],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[340,743,744],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[47,746,747,748,753],{},"Please note that we were unable to test ",[54,749,752],{"href":750,"rel":751},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[263],"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.",[337,755,756,762],{},[340,757,758,761],{},[54,759,645],{"href":643,"rel":760},[263]," (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).",[340,763,764],{},"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.",[47,766,767],{},"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.",[47,769,770],{},"We may revisit this comparison as more details become available.",[39,772,774],{"id":773},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[47,776,777],{},"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.",[47,779,780],{},"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:",[337,782,783,786,789],{},[340,784,785],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[340,787,788],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[340,790,791],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[47,793,794],{},"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.",[47,796,797],{},"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.",[47,799,800],{},"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.",[47,802,803],{},[349,804],{"alt":17,"src":805},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[47,807,808],{},[349,809],{"alt":17,"src":810},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[31,812,814],{"id":813},"cost-breakdown","Cost Breakdown",[816,817,818],"h4",{"id":695},"StreamNative – Ursa",[337,820,821,824,827,830,833],{},[340,822,823],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[340,825,826],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[340,828,829],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[340,831,832],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[340,834,835],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[816,837,839],{"id":838},"warpstream","WarpStream",[337,841,842,845],{},[340,843,844],{},"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.",[340,846,847,848,853],{},"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 “",[54,849,852],{"href":850,"rel":851},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[263],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[47,855,856],{},[349,857],{"alt":17,"src":858},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[816,860,862],{"id":861},"msk","MSK",[337,864,865,868,871],{},[340,866,867],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[340,869,870],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[340,872,873],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[816,875,729],{"id":876},"redpanda-1",[337,878,879,882,884,887,890],{},[340,880,881],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[340,883,870],{},[340,885,886],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[340,888,889],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[340,891,892],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[816,894,896],{"id":895},"additional-notes","Additional Notes",[337,898,899],{},[340,900,901,902,907],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[54,903,906],{"href":904,"rel":905},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[263],"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.",[47,909,910],{},[349,911],{"alt":17,"src":912},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[47,914,915],{},[349,916],{"alt":17,"src":917},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[337,919,920,923],{},[340,921,922],{},"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.",[340,924,925],{},"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.",[39,927,929],{"id":928},"conclusion","Conclusion",[47,931,932],{},"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.",[31,934,936],{"id":935},"balancing-latency-and-cost","Balancing Latency and Cost",[47,938,939,943],{},[54,940,942],{"href":941},"\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.",[31,945,947],{"id":946},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[47,949,950],{},"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.",[47,952,953,954,959],{},"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. ",[54,955,958],{"href":956,"rel":957},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[263],"Get started"," with StreamNative Ursa today!",[961,962,964],"h1",{"id":963},"references","References",[47,966,967,970,971],{},[968,969,428],"span",{}," ",[54,972,973],{"href":973},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[47,975,976,970,978],{},[968,977,377],{},[54,979,376],{"href":376},[47,981,982,970,985],{},[968,983,984],{},"StreamNative pricing",[54,986,987],{"href":987,"rel":988},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[263],[47,990,991,970,994],{},[968,992,993],{},"WarpStream pricing",[54,995,996],{"href":996,"rel":997},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[263],[47,999,1000,970,1003],{},[968,1001,1002],{},"AWS S3 pricing",[54,1004,1005],{"href":1005,"rel":1006},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[263],[47,1008,1009,970,1012],{},[968,1010,1011],{},"AWS EBS pricing",[54,1013,1014],{"href":1014,"rel":1015},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[263],[47,1017,1018,970,1021],{},[968,1019,1020],{},"AWS MSK pricing",[54,1022,1023],{"href":1023,"rel":1024},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[263],[47,1026,1027,970,1030],{},[968,1028,1029],{},"The Brutal Truth about Kafka Cost Calculators",[54,1031,850],{"href":850,"rel":1032},[263],[47,1034,1035,970,1038],{},[968,1036,1037],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[54,1039,904],{"href":904,"rel":1040},[263],{"title":17,"searchDepth":18,"depth":18,"links":1042},[1043,1044,1045,1050,1054,1055,1064,1067],{"id":331,"depth":18,"text":332},{"id":370,"depth":18,"text":371},{"id":395,"depth":18,"text":396,"children":1046},[1047,1048,1049],{"id":407,"depth":278,"text":408},{"id":432,"depth":278,"text":433},{"id":453,"depth":278,"text":454},{"id":477,"depth":18,"text":478,"children":1051},[1052,1053],{"id":481,"depth":278,"text":482},{"id":496,"depth":278,"text":497},{"id":537,"depth":18,"text":538},{"id":549,"depth":18,"text":550,"children":1056},[1057,1058,1059,1060,1061,1062,1063],{"id":556,"depth":278,"text":557},{"id":602,"depth":278,"text":603},{"id":620,"depth":278,"text":621},{"id":667,"depth":278,"text":668},{"id":695,"depth":278,"text":696},{"id":713,"depth":278,"text":714},{"id":728,"depth":278,"text":729},{"id":773,"depth":18,"text":774,"children":1065},[1066],{"id":813,"depth":278,"text":814},{"id":928,"depth":18,"text":929,"children":1068},[1069,1070],{"id":935,"depth":278,"text":936},{"id":946,"depth":278,"text":947},"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":306,"description":1073},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1081,1082,301],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1085,"title":1086,"authors":1087,"body":1088,"category":1507,"createdAt":10,"date":1508,"description":1509,"extension":8,"featured":292,"image":1510,"isDraft":292,"link":10,"meta":1511,"navigation":7,"order":294,"path":1512,"readingTime":1513,"relatedResources":10,"seo":1514,"stem":1515,"tags":1516,"__hash__":1518},"blogs\u002Fblog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar.md","A Deep Dive into the Topic Data Lifecycle in Apache Pulsar",[310],{"type":14,"value":1089,"toc":1491},[1090,1093,1096,1113,1117,1120,1123,1129,1148,1151,1155,1158,1162,1168,1172,1175,1183,1186,1189,1192,1199,1203,1206,1210,1213,1220,1223,1227,1230,1233,1236,1247,1250,1253,1257,1260,1263,1279,1283,1286,1292,1299,1303,1306,1310,1313,1323,1326,1330,1339,1347,1351,1358,1369,1372,1387,1396,1400,1403,1414,1417,1429,1432,1435,1439,1442,1446,1455],[47,1091,1092],{},"The lifecycle of topic data in Apache Pulsar is managed by two key retention policies: the topic retention policy on the broker side, and the bookie data retention policy on the bookie side. All the data deletion operations can only be triggered by the broker. We shouldn’t delete ledger files from bookies directly. Otherwise, the data will be lost.",[47,1094,1095],{},"This blog focuses on the following three topics in Pulsar:",[1097,1098,1099,1107,1110],"ol",{},[340,1100,1101,1102,189],{},"Topic retention policy. We will mainly discuss the cases where the retention policy doesn’t work. For more information about retention and expiry strategies, see ",[54,1103,1106],{"href":1104,"rel":1105},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fcookbooks-retention-expiry\u002F",[263],"Message retention and expiry",[340,1108,1109],{},"Bookie data compaction policy.",[340,1111,1112],{},"How to detect and deal with orphan ledgers to fix bookie ledger files that can’t be deleted.",[39,1114,1116],{"id":1115},"overview-topic-data-lifecycle","Overview: Topic data lifecycle",[47,1118,1119],{},"In Pulsar, when a producer publishes messages on a topic, these data are written to specific ledgers managed by ManagedLedger, which is owned by the Pulsar broker. The metadata is stored in Pulsar’s meta store, such as Apache ZooKeeper. Ledgers are written to specific bookies according to the replica policies configured (i.e. the values of E, WQ, and AQ). For each ledger, its metadata is stored in BookKeeper’s meta store, such as Apache ZooKeeper.",[47,1121,1122],{},"When a ledger (for example, Ledger 3 in Figure 1) needs to be deleted according to the configured retention policy, it goes through the following steps.",[47,1124,1125],{},[349,1126],{"alt":1127,"src":1128},"illustration","\u002Fimgs\u002Fblogs\u002F63b53c677851da757d11535c_compaction-checker-flow.png",[1097,1130,1131,1134,1142,1145],{},[340,1132,1133],{},"Delete Ledger 3 from the ManagedLedger’s ledger list in the meta store.",[340,1135,1136,1137,189],{},"If the first step succeeds, the broker will send the deletion request of Ledger 3 to the BookKeeper cluster asynchronously. However, this does not ensure the ledger can be deleted successfully. The risk of leaving Ledger 3 an orphan ledger in the BookKeeper cluster still exists. For more information about how to solve this problem, see this ",[54,1138,1141],{"href":1139,"rel":1140},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fissues\u002F16569",[263],"proposal",[340,1143,1144],{},"Each bookie performs a regular compaction check, which is configured through minorCompactionInterval and majorCompactionInterval.",[340,1146,1147],{},"In the compaction check, the bookie checks whether the metadata of each ledger exists in the meta store. If not, the bookie will delete the data of this ledger from the log file.",[47,1149,1150],{},"From the last two steps above, we can see that the deletion of topic data, which are stored in BookKeeper, is triggered by the compaction checker, not by the Pulsar broker. This means that the ledger data are not deleted immediately when the ledger is removed from the ManagedLedger’s ledger list.",[39,1152,1154],{"id":1153},"topic-retention-policy","Topic retention policy",[47,1156,1157],{},"In this section, we will briefly talk about topic retention policies in Pulsar, and focus on the drawbacks of current ledger deletion logic as well as the cases where retention policies don't work.",[31,1159,1161],{"id":1160},"topic-retention-and-ttl","Topic retention and TTL",[47,1163,1164,1165,189],{},"When messages arrive at a broker, they are stored until they have been acknowledged on all subscriptions, at which point it is marked for deletion. You can control this behavior and retain messages that have already been acknowledged on all subscriptions by setting a retention policy for all topics in a given namespace. If messages are not acknowledged, Pulsar stores them forever by default, which can lead to heavy disk space usage. This is where TTL (Time to live) can be useful as it determines how long unacknowledged messages will be retained. For more information, see ",[54,1166,1106],{"href":1104,"rel":1167},[263],[31,1169,1171],{"id":1170},"drawbacks-of-the-current-ledger-deletion-logic","Drawbacks of the current ledger deletion logic",[47,1173,1174],{},"The current logic entails two separate steps to delete a ledger.",[1097,1176,1177,1180],{},[340,1178,1179],{},"Remove all the ledgers to be deleted from the ledger list and update the newest ledger list in the meta store.",[340,1181,1182],{},"In the meta store update callback operation, remove the ledgers to be deleted from storage asynchronously, such as BookKeeper or Tiered storage. Note that this does not ensure the deletion is successful.",[47,1184,1185],{},"As these two steps are separated, we can't ensure the ledger deletion transaction. If the first step succeeds and the second step fails, then the ledgers can no longer be deleted from the storage system. The second step may fail in some cases (for example, the broker restarts), resulting in orphan ledgers in the storage system.",[47,1187,1188],{},"Can we swap step 1 with step 2 to fix this problem? No, If we delete the ledger from the storage system first, and then remove the ledger from ManagedLedger’s ledger list, we still can’t ensure the ledger deletion transaction. If we delete the ledger from the storage system successfully while the ledger fails to be removed from the ManageLedger’s ledger list, consumers will fail to read data from the topic. This is because the topic still sees the deleted ledger as readable. This issue is even more serious than orphan ledgers.",[47,1190,1191],{},"Another risk in topic deletion is that when a ledger is deleted on the broker side (i.e. removed from the ManagedLedger’s ledger list), the topic metadata may remain if the ledger fails to be deleted in BookKeeper. Therefore, when a consumer fetches data according to the topic metadata, it will fail since the actual data does not exist on bookies.",[47,1193,1194,1195,1198],{},"In order to resolve the above issues, we are working on a ",[54,1196,1141],{"href":1139,"rel":1197},[263]," to introduce a two-phase deletion protocol to make sure the ledger deletion from the storage system is retriable.",[31,1200,1202],{"id":1201},"why-doesnt-the-retention-policy-work","Why doesn’t the retention policy work?",[47,1204,1205],{},"Topic retention policies may not take effect in the following two cases.",[816,1207,1209],{"id":1208},"the-topic-is-not-loaded-into-brokers","The topic is not loaded into brokers",[47,1211,1212],{},"Each topic’s retention policy checker belongs to its own ManagedLedger. If the ManagedLedger is not loaded into the broker, the retention policy checker won’t work. Let’s see the following example.",[47,1214,1215,1216,1219],{},"We produce 100GB of data on topic-a at timestamp t0, and finish producing messages at t0 + 3 hours. The retention policy of topic-a is configured for 6 hours, which means the data won’t be expired until t0 + 6 hours later. However, topic-a may be unloaded between ",[968,1217,1218],{},"t0 + 3, t0 +6"," due to broker restarts, its bundle unloaded by the load balancer, or related operations triggered by the pulsar-admin command. If there is no producer, consumer, or other load topic operations for it, it remains in the unloaded status. When the time reaches t0 + 6 hours, the 100GB of data on topic-a should be expired according to the retention policy. However, as topic-a is not loaded into any broker, the broker's retention policy checker cannot find topic-a. Therefore, the retention policy does not work. In this case, the 100GB of data won’t be expired until topic-a is loaded into the broker again.",[47,1221,1222],{},"We are developing a tool to fix this issue. In this tool, we will check all the long-term ledgers stored in the BookKeeper cluster, and parse out the topic names that the ledgers belong to. After that, these topics can be loaded into Pulsar brokers so that the retention policy can be applied to them.",[816,1224,1226],{"id":1225},"the-ledger-is-in-the-open-state","The ledger is in the OPEN state",[47,1228,1229],{},"The retention policy checker applies each topic’s retention policy, while it only checks the ledgers in the CLOSED state. If a ledger is OPEN, the retention policy won’t take effect even though the ledger should be expired. See the following example for details.",[47,1231,1232],{},"We produce messages to a topic at the rate of 100MB\u002Fs with the minimum rollover time of the ledger set to 10 minutes. The minimum rollover time is used to prevent ledger rollovers from happening frequently, and it must be reached before a ledger rollover.",[47,1234,1235],{},"This means the ledger will remain in the OPEN state until 10 minutes are reached. 10 minutes later, the ledger size is about 60GB. If we set the retention time to 5 minutes, these data cannot be expired since the ledger is in the OPEN state. Note that a ledger rollover can be triggered after the minimum rollover time (managedLedgerMinLedgerRolloverTimeMinutes) is reached and one of the following conditions is met:",[337,1237,1238,1241,1244],{},[340,1239,1240],{},"The maximum rollover time has been reached (managedLedgerMaxLedgerRolloverTimeMinutes).",[340,1242,1243],{},"The number of entries written to the ledger has reached the maximum value (managedLedgerMaxEntriesPerLedger)",[340,1245,1246],{},"The entries written to the ledger have reached the maximum size value (managedLedgerMaxSizePerLedgerMbytes).",[47,1248,1249],{},"These parameters can be configured in broker.conf.",[47,1251,1252],{},"In this example, if the topic is unloaded at timestamp t0 + 9 minutes and remains unloaded, there will be at least about 54GB of data that cannot be expired no matter what the configured retention policy is.",[39,1254,1256],{"id":1255},"bookie-data-compaction-policy","Bookie data compaction policy",[47,1258,1259],{},"Whether a ledger still exists or not in the BookKeeper cluster is tracked based on the ledger metadata stored in the meta store, such as Zookeeper. If Pulsar has deleted the metadata from the meta store, it means the ledger data needs to be removed from all the bookies that store the ledger’s replicas. When a ledger needs to be deleted based on the topic retention policy, Pulsar only deletes the ledger’s metadata instead of the actual replica data stored on bookies. Whether the actual data is deleted depends on each bookie’s garbage collection thread.",[47,1261,1262],{},"Each bookie’s garbage collection can be triggered in the following three cases.",[1097,1264,1265,1268,1271],{},[340,1266,1267],{},"Minor compaction. You can configure it through minorCompactionThreshold=0.2 and minorCompactionInterval=3600. By default, minor compaction is triggered every hour. If an entryLogFile’s remaining data size is less than 20% of the total size, the entryLogFile will be compacted.",[340,1269,1270],{},"Major compaction. You can configure it through majorCompactionThreshold=0.5 and majorCompactionInterval=86400. By default, major compaction is triggered every day. If the remaining data size of an entry log file is less than 50% of the total size, the entry log file will be compacted.",[340,1272,1273,1274,1278],{},"Compaction triggered by REST API. curl -XPUT ",[54,1275,1276],{"href":1276,"rel":1277},"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fv1\u002Fbookie\u002Fgc",[263],". For the rest api, we should turn it on first by setting httpServerEnabled=true.",[31,1280,1282],{"id":1281},"how-bookie-gc-works","How bookie GC works",[47,1284,1285],{},"When a bookie triggers compaction, the compaction checker checks each ledger’s metadata to get the ledger list. For each ledger in the ledger list, it checks whether the ledger’s metadata still exists in the meta store, such as Ledger 2 in Figure 2.",[47,1287,1288],{},[349,1289],{"alt":1290,"src":1291},"illustration to explain How bookie GC works","\u002Fimgs\u002Fblogs\u002F63b53c67a48e6a060ddc5d96_ledger-deletion-process.png",[47,1293,1294,1295,1298],{},"After that, the compaction checker filters out the ledgers that still exist, and calculates the remaining data size percentage of the entry log file. If the percentage is lower than the threshold (by default, minorCompactionThreshold=0.2 and majorCompactionThreshold=0.5), it starts the compaction for the entry log file. Specifically, it reads the remaining ledger’s data from the old entry log file and writes them into the current entry log file. After all the remaining ledgers are compacted successfully, it deletes old entry log files. This frees up storage space.\n",[349,1296],{"alt":17,"src":1297},"\u002Fimgs\u002Fblogs\u002F63b53c670e058c15e846aca9_compaction-checker.png","Figure 3",[31,1300,1302],{"id":1301},"how-to-reduce-gc-io-impacts","How to reduce GC IO impacts",[47,1304,1305],{},"As the compaction checker reads data from old entry log files and writes them into current ones, it may cause mixed read and write IOs for disks. If we do not introduce a throttling policy, it will affect the performance of the ledger disk.",[816,1307,1309],{"id":1308},"compaction-throttling","Compaction throttling",[47,1311,1312],{},"In bookies, there are two kinds of compaction throttle policies, namely by bytes or by entries. The related configurations are listed as follows.",[1314,1315,1320],"pre",{"className":1316,"code":1318,"language":1319},[1317],"language-text","# Throttle compaction by bytes or by entries.\nisThrottleByBytes=false\n\n# Set the rate at which compaction will re-add entries. The unit is adds per second.\ncompactionRateByEntries=1000\n\n# Set the rate at which compaction will re-add entries. The unit is bytes added per second.\ncompactionRateByBytes=1000000\n","text",[1321,1322,1318],"code",{"__ignoreMap":17},[47,1324,1325],{},"By default, the bookie uses the throttle-by-entries policy. However, as the data size of each entry is not the same, we cannot control the compaction read and write throughput, and it will have a great impact on the ledger disk performance. Therefore, we recommend using the throttle-by-bytes policy.",[816,1327,1329],{"id":1328},"pagecache-pre-reads","PageCache pre-reads",[47,1331,1332,1333,1338],{},"For an entry log file, if more than 90% of the entries have been deleted, the compactor will scan the entries' header metadata one by one. When reading one entry's metadata, it will miss the BufferedChannel read buffer cache, and it will trigger prefetch from the disk. For the following entries, the header metadata reading will also miss the BufferedChannel read buffer cache, and will continue to prefetch from the disk without throttling. This will lead to high ledger disk IO utilization. For more information, see this pull request ",[54,1334,1337],{"href":1335,"rel":1336},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fbookkeeper\u002Fpull\u002F3192",[263],"#3192"," to fix this bug.",[47,1340,1341,1342,189],{},"Moreover, each prefetch operation from the disk will also trigger OS PageCache prefetch. For the compaction model, the OS PageCache prefetch will lead to PageCache pollution, and may also affect the journal sync latency. To solve this problem, we can use the Direct IO to reduce the PageCache effect. For more information, see this issue ",[54,1343,1346],{"href":1344,"rel":1345},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fbookkeeper\u002Fissues\u002F2943",[263],"#2943",[31,1348,1350],{"id":1349},"why-doesnt-the-bookie-gc-work","Why doesn’t the bookie GC work",[47,1352,1353,1354,1357],{},"When one ledger disk reaches the maximum usage threshold, it suspends minor and major compaction. When we use the curl -XPUT ",[54,1355,1276],{"href":1276,"rel":1356},[263]," command to trigger compaction, it will be filtered by the suspendMajor and suspendMinor flags. Consequently:",[1097,1359,1360,1363,1366],{},[340,1361,1362],{},"The bookie doesn’t clean up deleted ledgers.",[340,1364,1365],{},"The disk space can't be freed up.",[340,1367,1368],{},"The bookie can't recover from the readOnly state to the Writable state.",[47,1370,1371],{},"In this case, we can only trigger compaction through the following steps.",[1097,1373,1374,1377,1380],{},[340,1375,1376],{},"Increase the maximum disk usage threshold.",[340,1378,1379],{},"Restart the bookie.",[340,1381,1382,1383,1386],{},"Use the command curl -XPUT ",[54,1384,1276],{"href":1276,"rel":1385},[263]," to trigger compaction.",[47,1388,1389,1390,1395],{},"There is a pull request ",[54,1391,1394],{"href":1392,"rel":1393},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fbookkeeper\u002Fpull\u002F3205",[263],"#3205"," to add a flag forceAllowCompaction=true for REST API to ignore the suspendMajor and suspendMinor flags to force trigger compaction.",[39,1397,1399],{"id":1398},"remove-entry-log-files-that-cannot-be-deleted","Remove entry log files that cannot be deleted",[47,1401,1402],{},"When a Pulsar cluster keeps running for a few months, some old entry log files on bookies may fail to be deleted. The main reasons are listed as follows.",[1097,1404,1405,1408,1411],{},[340,1406,1407],{},"Ledger deletion logic’s bug, which leads to orphan ledgers.",[340,1409,1410],{},"Inactive topics are not loaded into the broker. As a result, the topic retention policy can’t take effect on them.",[340,1412,1413],{},"Inactive cursors still exist in the cluster, and their corresponding cursor ledgers can’t be deleted.",[47,1415,1416],{},"We need a tool to detect and repair these ledgers in the above cases.",[47,1418,1419,1420,1423,1424,189],{},"For case 1, after this ",[54,1421,1141],{"href":1139,"rel":1422},[263]," is applied, it can be resolved. However, the existing orphan ledgers still can’t be deleted. We need to scan the whole BookKeeper cluster’s metadata, and check each ledger’s metadata. If the ledger related topic’s ledger list doesn’t contain the ledger, it means the ledger has been deleted. We can directly delete these ledgers safely using the bookkeeper command. For more information, see the ",[54,1425,1428],{"href":1426,"rel":1427},"https:\u002F\u002Fdocs.streamnative.io\u002Fplatform\u002Flatest\u002Foperator-guides\u002Fconfigure\u002Fsn-pulsar-tool\u002Fpck-tutorial",[263],"tool here",[47,1430,1431],{},"For case 2, we will develop a checker to detect inactive topics, which still hold ledger data. After these topics are detected, an operation will be triggered to load them into brokers, and apply a retention policy for them. This feature is still in development.",[47,1433,1434],{},"For case 3, we are considering directly deleting the cursors which have been inactive for a long time, such as 7 days.",[39,1436,1438],{"id":1437},"summary","Summary",[47,1440,1441],{},"This blog explains the topic data lifecycle in Apache Pulsar, including the topic retention policy and BookKeeper garbage collection logic. At the same time, it also discusses cases where topic data can’t be deleted, and gives some solutions.",[39,1443,1445],{"id":1444},"more-on-apache-pulsar","More on Apache Pulsar",[47,1447,1448,1449,1454],{},"Pulsar has become ",[54,1450,1453],{"href":1451,"rel":1452},"https:\u002F\u002Fblogs.apache.org\u002Ffoundation\u002Fentry\u002Fapache-in-2021-by-the",[263],"one of the most active Apache projects"," over the past few years, with a vibrant community that continues to drive innovation and improvements to the project.",[337,1456,1457,1465,1472],{},[340,1458,1459,1460,189],{},"Start your on-demand Pulsar training today with ",[54,1461,1464],{"href":1462,"rel":1463},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[263],"StreamNative Academy",[340,1466,1467,1468,1471],{},"Spin up a Pulsar cluster in minutes with ",[54,1469,1071],{"href":1470},"\u002Fstreamnativecloud\u002F",". StreamNative Cloud provides a simple, fast, and cost-effective way to run Pulsar in the public cloud.",[340,1473,1474,1479,1480,1484,1485,1490],{},[54,1475,1478],{"href":1476,"rel":1477},"https:\u002F\u002Fpulsar-summit.org\u002F",[263],"Pulsar Summit Asia 2022"," will take place on November 19th and 20th, 2022. ",[54,1481,1483],{"href":1482},"\u002Fblog\u002Fcommunity\u002F2022-08-22-pulsar-summit-asia-2022-cfp-is-open-now\u002F","The CFP is open now","! ",[54,1486,1489],{"href":1487,"rel":1488},"https:\u002F\u002Fsessionize.com\u002Fpulsar-summit-asia-2022\u002F",[263],"Submit a proposal"," to share your Pulsar story!",{"title":17,"searchDepth":18,"depth":18,"links":1492},[1493,1494,1499,1504,1505,1506],{"id":1115,"depth":18,"text":1116},{"id":1153,"depth":18,"text":1154,"children":1495},[1496,1497,1498],{"id":1160,"depth":278,"text":1161},{"id":1170,"depth":278,"text":1171},{"id":1201,"depth":278,"text":1202},{"id":1255,"depth":18,"text":1256,"children":1500},[1501,1502,1503],{"id":1281,"depth":278,"text":1282},{"id":1301,"depth":278,"text":1302},{"id":1349,"depth":278,"text":1350},{"id":1398,"depth":18,"text":1399},{"id":1437,"depth":18,"text":1438},{"id":1444,"depth":18,"text":1445},"Apache Pulsar","2022-09-27","Understand how ledger data are deleted in Pulsar and some problems you may have during the process.","\u002Fimgs\u002Fblogs\u002F63c7c1b47534714d7a35604d_63b53c679b07673934732295_a-deep-dive-into-the-topic-data-lifecycle-in-apache-pulsar-top.jpeg",{},"\u002Fblog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar","10 min read",{"title":1086,"description":1509},"blog\u002Fdeep-dive-into-topic-data-lifecycle-apache-pulsar",[1517,1507],"BookKeeper","93giXYBx-9XuDdAdZkSt-x4A3Z--442WLi5SxtwjuZY",[1520],{"id":1521,"title":310,"bioSummary":1522,"email":10,"extension":8,"image":1523,"linkedinUrl":10,"meta":1524,"position":1531,"stem":1532,"twitterUrl":10,"__hash__":1533},"authors\u002Fauthors\u002Fhang.md","Hang Chen, an Apache Pulsar and BookKeeper PMC member, is Director of Storage at StreamNative, where he leads the design of next-generation storage architectures and Lakehouse integrations. His work delivers scalable, high-performance infrastructure powering modern cloud-native event streaming platforms.","\u002Fimgs\u002Fauthors\u002Fhang.webp",{"body":1525},{"type":14,"value":1526,"toc":1529},[1527],[47,1528,1522],{},{"title":17,"searchDepth":18,"depth":18,"links":1530},[],"Director of Storage, StreamNative & Apache Pulsar PMC Member","authors\u002Fhang","titaSDxZRJWAW0SkpJSq43NuDvps9XQ6gZIMSPCtUwo",[1535,1543,1548],{"path":1536,"title":1537,"date":1538,"image":1539,"link":-1,"collection":1540,"resourceType":1541,"score":1542,"id":1536},"\u002Fblog\u002Fpulsar-newbie-guide-for-kafka-engineers-part-3-ledgers-bookies","Pulsar Newbie Guide for Kafka Engineers (Part 3): Ledgers & Bookies","2025-08-12","\u002Fimgs\u002Fblogs\u002F689b52280db497fdd1646215_03.-Ledgers-&-Bookies.png","blogs","Blog",0.667,{"path":1544,"title":1545,"date":1546,"image":1547,"link":-1,"collection":1540,"resourceType":1541,"score":1542,"id":1544},"\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning","Taking a Deep-Dive into Apache Pulsar Architecture for Performance Tuning","2021-01-14","\u002Fimgs\u002Fblogs\u002F63be72252cb463483869a062_top.jpg",{"path":1549,"title":1550,"date":1551,"image":1552,"link":-1,"collection":1553,"resourceType":1554,"score":1555,"id":1549},"\u002Fsuccess-stories\u002Fgetui","Build a Priority-based Push Notification System Using Apache Pulsar at GeTui","2022-12-27","\u002Fimgs\u002Fsuccess-stories\u002F67942ee3c017499ff6794b64_SN-SuccessStories-GeTui.webp","successStories","Case Study",0.55,1775235687228]