[{"data":1,"prerenderedAt":1605},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"navbar-pricing-featured":304,"blog-\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily":1084,"blog-authors-\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily":1572,"related-\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily":1587},{"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":1089,"category":1560,"createdAt":10,"date":1561,"description":1562,"extension":8,"featured":292,"image":1563,"isDraft":292,"link":10,"meta":1564,"navigation":7,"order":294,"path":1565,"readingTime":1566,"relatedResources":10,"seo":1567,"stem":1568,"tags":1569,"__hash__":1571},"blogs\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily.md","Client Optimization: How Tencent Maintains Apache Pulsar Clusters with over 100 Billion Messages Daily",[1088],"Sherlock Xu",{"type":14,"value":1090,"toc":1540},[1091,1095,1098,1101,1105,1108,1111,1114,1123,1127,1130,1138,1141,1144,1148,1151,1154,1158,1164,1168,1171,1177,1180,1186,1189,1192,1195,1199,1202,1212,1215,1218,1222,1225,1228,1234,1237,1240,1244,1247,1250,1254,1257,1260,1266,1269,1273,1276,1282,1285,1302,1305,1308,1312,1315,1329,1332,1336,1339,1342,1346,1349,1352,1356,1359,1362,1366,1369,1372,1376,1389,1393,1396,1413,1416,1419,1422,1425,1428,1437,1451,1454,1457,1460,1463,1466,1469,1472,1475,1478,1482,1485,1488,1491,1495,1537],[39,1092,1094],{"id":1093},"authors","Authors",[47,1096,1097],{},"Mingyu Bao, Senior Software Engineer at the Tencent TEG Data Platform Department. He is responsible for the development of projects like Apache Pulsar, Apache Inlong, and DB data collection. He is focused on big data and message middleware, with over 10 years of experience in Java development.",[47,1099,1100],{},"Dawei Zhang, Apache Pulsar Committer, Senior Software Engineer at the Tencent TEG Data Platform Department. He is responsible for the development of the Apache Pulsar project. He is focused on MQ and real-time data processing, with over 6 years of experience in big data platform development.",[39,1102,1104],{"id":1103},"background","Background",[47,1106,1107],{},"Tencent is a world-leading internet and technology company that develops innovative products and services for users across the globe. It provides communication and social services that connect more than one billion people around the world, helping them to keep in touch with friends and family members, pay for daily necessities, and even be entertained.",[47,1109,1110],{},"To offer a diverse product portfolio, Tencent needs to stay at the forefront of technological innovations. At Tencent, the Technology Engineering Group (TEG) is responsible for supporting the company and its business groups in technology and operational platforms, as well as the construction and operation of R&D management and data centers.",[47,1112,1113],{},"Recently, an internal team working on messaging queuing (MQ) solutions at TEG developed a performance analysis system (referred to as the “Data Project” in this blog) for maintenance metrics. This system provides general infrastructure services to the entire Tencent group. The Data Project collects performance metrics and reports data for business operations and monitoring. It may also be adopted for real-time analysis at both the front end and back end in the future.",[47,1115,1116,1117,1122],{},"The Data Project uses Apache Pulsar as its message system with servers deployed on ",[54,1118,1121],{"href":1119,"rel":1120},"https:\u002F\u002Fintl.cloud.tencent.com\u002Fproducts\u002Fcvm",[263],"Cloud Virtual Machines"," (CVM), and producers and consumers deployed on Kubernetes. The Pulsar clusters managed by the Data Project produce a greater number of messages than any other cluster managed by the MQ team. Due to the large cluster size, we have faced specific challenges and hope to share our learnings and best practices in this post.",[39,1124,1126],{"id":1125},"high-system-reliability-and-low-latency-why-apache-pulsar","High system reliability and low latency: Why Apache Pulsar",[47,1128,1129],{},"Our use case has the following two important characteristics:",[337,1131,1132,1135],{},[340,1133,1134],{},"Large message volumes. The Data Project is running with a large number of nodes to handle numerous messages every day with up to thousands of consumers bound to a single subscription. While we don’t have too many topics in total, each topic is connected to multiple clients. Each partition is attached to over 150 producers and more than 8000 consumers.",[340,1136,1137],{},"Strict requirements for high system reliability and low latency. With such large clusters to maintain, we must stick to higher standards of deployment, operations, and stability.",[47,1139,1140],{},"Therefore, when selecting potential message solutions, we put low latency and high throughput as key metrics for analysis. Compared with some of the common message systems in the market, ultimately Apache Pulsar stood out with its outstanding performance and capabilities.",[47,1142,1143],{},"Pulsar provides different subscription types, namely exclusive, failover, shared, and key_shared. Shared and key_shared subscriptions are able to support use cases where a large number of consumers are working at the same time. This is where other message systems like Kafka fall short. They function with rather low performance in a multi-partition scenario as consumers are restricted by the number of partitions.",[39,1145,1147],{"id":1146},"configurations-at-a-glance-large-clusters-with-over-100-billion-messages-per-day","Configurations at a glance: Large clusters with over 100 billion messages per day",[47,1149,1150],{},"Now that we know the reason behind our preference for Pulsar, let’s look at how we configured our cluster to take full advantage of it.",[47,1152,1153],{},"The business data in the Data Project are handled by two Pulsar clusters, which we will refer to as T-1 and T-2 in this blog. The client Pods (producers and consumers are deployed in different Kubernetes Pods) that connect to cluster T-1 are placed in the same server room as the Pulsar cluster. The client Pods that interact with cluster T-2 are deployed in different server rooms from the Pulsar cluster. Note that the latency of data transmission across different server rooms is a little bit higher than that in the same server room.",[31,1155,1157],{"id":1156},"server-side-configurations","Server-side configurations",[47,1159,1160],{},[349,1161],{"alt":1162,"src":1163},"table Server-side configurations","\u002Fimgs\u002Fblogs\u002F63b530703450ab1cb4248103_server-side-config.png",[31,1165,1167],{"id":1166},"client-side-configurations","Client-side configurations",[47,1169,1170],{},"Developed in Go, the business system of the Data Project is built on the master branch (latest version) of the Pulsar Go Client, deployed on STKE (Tencent’s internal container platform).",[47,1172,1173],{},[349,1174],{"alt":1175,"src":1176},"illustration of  Massive amounts of acknowledgment information ","\u002Fimgs\u002Fblogs\u002F63b53070789b6a3150344a30_message-acks.png",[47,1178,1179],{},"When pushing messages to the client with a shared type subscription, brokers only send a subset of the messages to each consumer in a round-robin distribution. After each consumer acknowledges their messages, you can see that there is plenty of range information as shown below stored on the broker.",[47,1181,1182],{},[349,1183],{"alt":1184,"src":1185},"figure 2 with some numbers","\u002Fimgs\u002Fblogs\u002F63b5307047daaa62c0729147_individualldeletedmessages.png",[47,1187,1188],{},"An acknowledgment hole refers to the gap between two consecutive ranges. Its information is stored through the individualDeletedMessages attribute. The number of consumers attached to the same subscription and their consumption speed can all impact acknowledgment holes. A larger number of acknowledgment holes could mean that you have plenty of acknowledgment information.",[47,1190,1191],{},"Pulsar periodically aggregates all acknowledgment information of all consumers associated with the same subscription as an entry and writes it to bookies. The process is the same as writing ordinary messages. Therefore, when you have too many acknowledgment holes and large amounts of acknowledgment information, your system might be overwhelmed. For example, you might notice a longer production time, latency spikes, or even timeouts on the client side.",[47,1193,1194],{},"In these cases, you can reduce the number of consumers, increase the consumption speed, adjust the frequency of storing acknowledgment information, and change the number of saved ranges.",[816,1196,1198],{"id":1197},"analysis-2-the-pulsar-io-thread-gets-stuck","Analysis 2: The pulsar-io thread gets stuck",[47,1200,1201],{},"The pulsar-io thread pool is used to process client requests on Pulsar brokers. When threads are too slow or get stuck, there might be timeouts or disconnections on the client side. We can identify and analyze these problems through jstack information, which displays that there can be many connections in the CLOSE_WAIT state on brokers as shown below:",[1203,1204,1209],"pre",{"className":1205,"code":1207,"language":1208},[1206],"language-text","\n36          0 :6650           :57180         CLOSE_WAIT    20714\u002Fjava\n36          0 :6650           :48858         CLOSE_WAIT    20714\u002Fjava\n36          0 :6650           :49846         CLOSE_WAIT    20714\u002Fjava\n36          0 :6650           :55342         CLOSE_WAIT    20714\u002Fjava\n \n","text",[1210,1211,1207],"code",{"__ignoreMap":17},[47,1213,1214],{},"Usually, these can be caused by server code bugs (such as deadlocks in some concurrent scenarios), while configurations could also be a reason. If the pulsar-io thread pool has been running for a long time, you can modify the numioThreads parameter in broker.conf to change the number of working threads in the pool on the premise that you have sufficient CPU resources. This can help improve performance in concurrent tasks.",[47,1216,1217],{},"A busy pulsar-io thread pool is essentially not going to cause problems. Nevertheless, on the broker side, there is a background thread periodically checking if each channel is receiving requests from the client within the expected threshold. If not, the broker will close the channel (similar logic also exists in the client SDK). This is why a client is disconnected when the pulsar-io thread pool gets stuck or slows down.",[816,1219,1221],{"id":1220},"analysis-3-excessive-time-consumption-in-ledger-switching","Analysis 3: Excessive time consumption in ledger switching",[47,1223,1224],{},"As a logic storage unit in Apache BookKeeper, each ledger stores a certain number of log entries, and each entry contains one or multiple messages (if message batching is enabled). When certain conditions are met (for example, the entry number, total message size, or lifespan reaches the preset threshold), a ledger is switched (ledger rollover).",[47,1226,1227],{},"Here’s what it looks like when ledger switching takes too much time:",[1203,1229,1232],{"className":1230,"code":1231,"language":1208},[1206],"\n14:40:44.528 [bookkeeper-ml-workers-OrderedExecutor-16-0] INFO org.apache.pulsar.broker.service.Producer - Disconnecting producer: Producer{topic=PersistentTopic{topic=persistent:\u002F\u002F}, client=\u002F:51550, producerName=}\n14:59:00.398 [bookkeeper-ml-workers-OrderedExecutor-16-0] INFO org.apache.bookkeeper.mledger.impl.OpAddEntry - Closing ledger 7383265 for being full\n$ cat pulsar-broker-11-135-219-214.log-11-05-2021-2.log | grep ‘bookkeeper-ml-workers-OrderedExecutor-16-0’ | grep ‘15:0’ | head -n 10\n15:01:01:256 [bookkeeper-ml-workers-OrderedExecutor-16-0] INFO org.apache.bookkeeper.mledger.impl.ManagedLedgerImpl - [\n] Ledger creation was initiated 120005 ms ago but it never completed and creation timeout task didn’t kick in as well. Force to fail the create ledger operation.\n15:01:01:256 [bookkeeper-ml-workers-OrderedExecutor-16-0] ERROR org.apache.bookkeeper.mledger.impl.ManagedLedgerImpl - [] Error creating ledger rc=-23 Bookie operation timeout\n \n",[1210,1233,1231],{"__ignoreMap":17},[47,1235,1236],{},"When ledger switching happens, new messages or existing ones that are yet to be processed will go to appendingQueue. After the new ledger is created, the system can continue to process data in the queue, thus making sure no messages are lost.",[47,1238,1239],{},"When ledger switching takes a longer time, it means that messages are produced slowly or that there could be timeouts. In this case, you need to check whether this is a ZooKeeper issue (pay more attention to the performance of machines running ZooKeeper and garbage collection).",[816,1241,1243],{"id":1242},"analysis-4-busy-bookkeeper-io-thread","Analysis 4: Busy bookkeeper-io thread",[47,1245,1246],{},"In our current Pulsar clusters, we are using a relatively stable version of BookKeeper. To optimize performance, you can modify the number of client threads and key configurations (for example, ensemble size, write quorum, and ack quorum).",[47,1248,1249],{},"If you notice that the bookkeeper-io thread pool of the BookKeeper client is busy or a single thread in the pool is busy, you need to first check ZooKeeper, bookie processes, and Full GC. If there is no problem, it may be a good idea to change the number of threads in the bookkeeper-io thread pool and the number of partitions.",[816,1251,1253],{"id":1252},"analysis-5-debug-logging-impact","Analysis 5: DEBUG logging impact",[47,1255,1256],{},"If producers spend too much time producing messages, the Java client is usually where the issue exists in terms of logging levels. If Log4j is adopted in your business system with debug logging enabled, the performance of Pulsar Client SDK might be impacted. Therefore, we suggest that you use the Pulsar Java app coupled with Log4j or Log4j plus SLF4J for logging. At the same time, change your logging level to at least INFO or ERROR for your Pulsar package.",[47,1258,1259],{},"In extreme cases, DEBUG logs may affect thread performance, which could mean a longer time (second-level) in producing messages. After optimization, it can fall back to the normal level (millisecond-level). See Figure 3 below for details:",[47,1261,1262],{},[349,1263],{"alt":1264,"src":1265},"example of DEBUG logging impact","\u002Fimgs\u002Fblogs\u002F63b530e1fdbbb30d59503cb4_debug-logs.png",[47,1267,1268],{},"When you have a large number of messages, you may want to disable DEBUG level log printing on brokers and bookies for your Pulsar cluster. It is also recommended to change the logging level to INFO or ERROR.",[816,1270,1272],{"id":1271},"analysis-6-uneven-partition-distribution","Analysis 6: Uneven partition distribution",[47,1274,1275],{},"In Pulsar, we can configure bundles in each namespace (4 bundles by default) as shown below. Topics are assigned to a particular bundle by taking the hash of the topic name and checking which bundle the hash falls into. Each bundle is independent of the others and thus is independently assigned to different brokers. When too many partitions fall into the same broker, it becomes overloaded, which impairs the efficiency of producing and consuming messages.",[47,1277,1278],{},[349,1279],{"alt":1280,"src":1281},"illustration of Uneven partition distribution","\u002Fimgs\u002Fblogs\u002F63b530e2dc98716d76fee334_bundles.png",[47,1283,1284],{},"Note that:",[337,1286,1287,1290,1293,1296,1299],{},[340,1288,1289],{},"Each namespace has a bundle list.",[340,1291,1292],{},"Partitions fall into different bundles based on the hash value.",[340,1294,1295],{},"Each bundle is bound to a single broker.",[340,1297,1298],{},"Bundles can be dynamically split, which is configurable.",[340,1300,1301],{},"Bundles and brokers are bound based on the brokers' load.",[47,1303,1304],{},"When the Data Project started, there were only a few partitions in each topic and a few bundles in each namespace. As we modified the number of partitions and bundles, we gradually achieved load balancing across brokers.",[47,1306,1307],{},"Pulsar still has room for improvement in dynamic bundle splitting and partition distribution, especially the splitting algorithm. It currently supports range_equally_divide (default) and topic_count_equally_divide (We suggest using the latter). That said, the improvement needs to be carried out without compromising system stability and load balancing.",[31,1309,1311],{"id":1310},"optimization-2-frequent-client-disconnections-and-reconnections","Optimization 2: Frequent client disconnections and reconnections",[47,1313,1314],{},"There are various reasons for disconnections and reconnections. Based on our own use case, we have summarized the following major causes.",[337,1316,1317,1320,1323,1326],{},[340,1318,1319],{},"Client disconnection and reconnection mechanism",[340,1321,1322],{},"Go SDK exception handling",[340,1324,1325],{},"Go SDK producer sequence id inconsistency",[340,1327,1328],{},"Consumers were frequently created and deleted at scale",[47,1330,1331],{},"Now, let’s analyze each of these causes and examine some solutions.",[816,1333,1335],{"id":1334},"analysis-1-client-disconnection-and-reconnection-mechanism","Analysis 1: Client disconnection and reconnection mechanism",[47,1337,1338],{},"The Pulsar client SDK has similar logic (see Analysis 2 in the previous section above) that periodically checks if requests from the server within the expected threshold are received. If not, the client will be disconnected from the server.",[47,1340,1341],{},"Usually, the problem could be a lack of resources on client machines that already have a high utilization rate (and let’s assume there is nothing wrong with the server). This means your application is not able to handle the data from the server. To solve this, change the business logic or the deployment method of your client.",[816,1343,1345],{"id":1344},"analysis-2-go-sdk-exception-handling","Analysis 2: Go SDK exception handling",[47,1347,1348],{},"The Pulsar community provides integration support for clients in different languages, such as Java, Go, C++, and Python. However, besides Java and Go, the implementation of other languages still needs to be improved. Compared with the SDK for Java, the SDK for Go needs to be more detail-oriented.",[47,1350,1351],{},"When receiving an exception from the server, the Java SDK is able to identify whether the channel should be deleted or not for the exception (for example, ServerError_TooManyRequests) and recreate it if necessary. By contrast, the Go client deletes the channel directly and recreates it.",[816,1353,1355],{"id":1354},"analysis-3-go-sdk-producer-sequence-id-inconsistency","Analysis 3: Go SDK producer sequence id inconsistency",[47,1357,1358],{},"After sending messages, a Go SDK producer (written with a relatively low version) will receive broker responses. If the sequenceID in the responses is not consistent with the sequenceID at the front of the queue on the client, it will result in a disconnect.",[47,1360,1361],{},"In higher Go SDK versions, this issue and the one mentioned in Analysis 1 have been properly handled. Therefore, it is suggested that you choose the latest version of Go SDK. If you are interested, you are welcome to make contributions to the development of Pulsar Go SDK.",[816,1363,1365],{"id":1364},"analysis-4-consumers-were-frequently-created-and-deleted-at-scale","Analysis 4: Consumers were frequently created and deleted at scale",[47,1367,1368],{},"In cluster maintenance, we adjusted the number of partitions to meet the growing business demand. The client, which did not restart, noticed the change on the server, thus creating new consumers for new partitions. We found that this was caused by an SDK bug in Java 2.6.2. Because of the bug, the client could repeatedly create a great number of consumers and delete them. To solve this, we suggest you upgrade your Java client.",[47,1370,1371],{},"In addition, our client Pods once had a similar issue of frequent restarts. After troubleshooting, we found that this was a panic error. As such, we advise you to take fault tolerance into consideration for your logic implementation to avoid potential problems.",[31,1373,1375],{"id":1374},"optimization-3-upgrade-zookeeper","Optimization 3: Upgrade ZooKeeper",[47,1377,1378,1379,1382,1383,1388],{},"Initially, we were using ZooKeeper 3.4.6 while a bug as shown in the figure below continuously occurred.\n",[349,1380],{"alt":17,"src":1381},"\u002Fimgs\u002Fblogs\u002F63b530e20312746a2ed104ca_zookeeper-bug346.png","Figure 5\nThe bug was later fixed. For more information, see the Apache Zookeeper ",[54,1384,1387],{"href":1385,"rel":1386},"https:\u002F\u002Fissues.apache.org\u002Fjira\u002Fbrowse\u002FZOOKEEPER-2044.",[263],"issue",". Therefore, we suggest that you apply a patch to fix it or upgrade ZooKeeper. In the Data Project, we upgraded to 3.6.3 and the issue was resolved.",[39,1390,1392],{"id":1391},"pulsar-cluster-maintenance-guidance","Pulsar cluster maintenance guidance",[47,1394,1395],{},"In cluster maintenance, there might be issues of timeouts, slow message production and consumption, and large message backlogs. To improve troubleshooting efficiency, you can get started with the following perspectives:",[337,1397,1398,1401,1404,1407,1410],{},[340,1399,1400],{},"Cluster resource configurations",[340,1402,1403],{},"Client message consumption",[340,1405,1406],{},"Message acknowledgment information",[340,1408,1409],{},"Thread status",[340,1411,1412],{},"Log analysis",[47,1414,1415],{},"Let’s take a closer look at each of them.",[31,1417,1400],{"id":1418},"cluster-resource-configurations",[47,1420,1421],{},"First, check whether the current resource configurations are sufficient to help your cluster handle the workload. This can be analyzed by checking CPU, memory, and disk IO information of brokers, bookies, and ZooKeeper on the Pulsar cluster dashboard. Second, check the GC state of Java processes, especially those with frequent Full GC. Make timely decisions to put more resources into your cluster if necessary.",[31,1423,1403],{"id":1424},"client-message-consumption",[47,1426,1427],{},"The client may have a backpressure issue resulting from a lack of consumption activities. In this scenario, message reception is rather slow or no messages can be received even though there are consumer processes.",[47,1429,1430,1431,1436],{},"You can use the pulsar-admin CLI tool to ",[54,1432,1435],{"href":1433,"rel":1434},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fadmin-api-topics\u002F#get-stats",[263],"see detailed statistics"," of the impacted topic (pulsar-admin topics stats). In the output, pay special attention to the following fields:",[337,1438,1439,1442,1445,1448],{},[340,1440,1441],{},"unackedMessages: The number of delivered messages that are yet to be acknowledged.",[340,1443,1444],{},"msgBacklog: The number of messages in the subscription backlog that are yet to be delivered.",[340,1446,1447],{},"type: The subscription type.",[340,1449,1450],{},"blockedConsumerOnUnackedMsgs: Whether or not the consumer is blocked because of too many unacknowledged messages.",[47,1452,1453],{},"If there are too many unacknowledged messages, it will affect message distribution from brokers to the client. This is usually caused by code in the business application, resulting in messages not getting acknowledged.",[31,1455,1406],{"id":1456},"message-acknowledgment-information",[47,1458,1459],{},"If producers spend too much time producing messages, check your system configurations as well as acknowledgment hole information. For the latter, use pulsar-admin topics stats-internal to see the status of a topic and check the value of the field individuallyDeletedMessages in the subscription.",[47,1461,1462],{},"Pulsar uses a ledger inside BookKeeper (also known as the “cursor ledger”) for each subscriber to track message acknowledgments. After a consumer has processed a message, it sends an acknowledgment to the broker, which then updates the cursor ledger for the consumer’s subscription. If there is too much acknowledgment information sent for storage, it will put greater pressure on bookies, increasing the time spent producing messages.",[31,1464,1409],{"id":1465},"thread-status",[47,1467,1468],{},"You can check the status of threads running on brokers, especially the pulsar-io, bookkeeper-io, and bookkeeper-ml-workers-OrderedExecutor thread pool. It is possible that some thread pools do not have sufficient resources or a thread in a certain pool has been occupied for a long time.",[47,1470,1471],{},"To do so, use the top -p PID H command to list the threads with high CPU usage and then locate the specific thread based on the jstack information.",[31,1473,1412],{"id":1474},"log-analysis",[47,1476,1477],{},"If you still cannot find the reason for your problem, check logs (for example, those in clients, brokers, and bookies) in detail. Try to find any valuable information and analyze it while also taking into account the features of your business, scenarios when the problem occurs, and recent events.",[39,1479,1481],{"id":1480},"looking-ahead","Looking ahead",[47,1483,1484],{},"In this post, we have analyzed some common issues we faced in our deployment regarding cluster maintenance and detailed solutions that worked for us. As we continue to work on cluster optimization and make further contributions to the community, we hope these insights provide a solid foundation for future work.",[47,1486,1487],{},"In our use case, for example, we have a considerable number of clients interacting with a single topic, which means there are numerous producers and consumers. This has further raised the bar for the Pulsar SDK, which needs to be more detail-oriented. Even a trivial issue that may lead to disconnections or reconnections could affect the entire system. This is also where the Pulsar Go SDK needs to be continuously upgraded. In this connection, the Tencent TEG MQ team has been an active driving force to work with the community in improving the Pulsar Go SDK.",[47,1489,1490],{},"Additionally, our Data Project contains a large amount of metadata information that needs to be processed on brokers. This means that we have to keep improving broker configurations and making adjustments to further enhance reliability and stability. In addition to scaling machines with more resources, we also plan to optimize configurations in Pulsar read\u002Fwrite threads, entry caches, bookie write\u002Fwrite caches, and bookie read\u002Fwrite threads.",[39,1492,1494],{"id":1493},"more-resources","More resources",[337,1496,1497,1505,1520,1529],{},[340,1498,1499,1500,1504],{},"Make an inquiry: Interested in a fully-managed Pulsar offering built by the original creators of Pulsar? ",[54,1501,1503],{"href":1502},"\u002Fcontact\u002F","Contact us"," now.",[340,1506,1507,1508,1513,1514,1519],{},"Pulsar Summit Europe 2023 is taking place virtually on May 23rd. Engage with the community by ",[54,1509,1512],{"href":1510,"rel":1511},"https:\u002F\u002Fsessionize.com\u002Fpulsar-virtual-summit-europe-2023\u002F",[263],"submitting a CFP"," or ",[54,1515,1518],{"href":1516,"rel":1517},"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FSponsorship%20Prospectus%20Pulsar%20Virtual%20Summit%20Europe%202023.pdf",[263],"becoming a community sponsor"," (no fee required).",[340,1521,1522,1523,1528],{},"Learn the Pulsar Fundamentals: Sign up for ",[54,1524,1527],{"href":1525,"rel":1526},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[263],"StreamNative Academy",", developed by the original creators of Pulsar, and learn at your own pace with on-demand courses and hands-on labs.",[340,1530,1531,1532,1536],{},"Read the ",[54,1533,1535],{"href":1534},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","2022 Pulsar vs. Kafka Benchmark Report"," for the latest performance comparison on maximum throughput, publish latency, and historical read rate.",[47,1538,1539],{},"‍",{"title":17,"searchDepth":18,"depth":18,"links":1541},[1542,1543,1544,1545,1551,1558,1559],{"id":1093,"depth":18,"text":1094},{"id":1103,"depth":18,"text":1104},{"id":1125,"depth":18,"text":1126},{"id":1146,"depth":18,"text":1147,"children":1546},[1547,1548,1549,1550],{"id":1156,"depth":278,"text":1157},{"id":1166,"depth":278,"text":1167},{"id":1310,"depth":278,"text":1311},{"id":1374,"depth":278,"text":1375},{"id":1391,"depth":18,"text":1392,"children":1552},[1553,1554,1555,1556,1557],{"id":1418,"depth":278,"text":1400},{"id":1424,"depth":278,"text":1403},{"id":1456,"depth":278,"text":1406},{"id":1465,"depth":278,"text":1409},{"id":1474,"depth":278,"text":1412},{"id":1480,"depth":18,"text":1481},{"id":1493,"depth":18,"text":1494},"Apache Pulsar","2022-08-18","Recently, an internal team working on messaging queuing (MQ) solutions at Tencent developed a performance analysis system (“Data Project”) for maintenance metrics. The Data Project uses Apache Pulsar as its message system. The Pulsar clusters managed by the Data Project produce a greater number of messages than any other cluster managed by the MQ team.","\u002Fimgs\u002Fblogs\u002F63c7c234f86b5b755c37354d_63b53070e64eae9f14d4b108_client-optimization-top-.jpeg",{},"\u002Fblog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily","8 min read",{"title":1086,"description":1562},"blog\u002Fclient-optimization-how-tencent-maintains-apache-pulsar-clusters-100-billion-messages-daily",[1570,1560],"Success Stories","Dj87kiwafySZyRzxOWxfbqekGlAX6wJst_0Dc1tX-cQ",[1573],{"id":1574,"title":1088,"bioSummary":1575,"email":10,"extension":8,"image":1576,"linkedinUrl":10,"meta":1577,"position":1584,"stem":1585,"twitterUrl":10,"__hash__":1586},"authors\u002Fauthors\u002Fsherlock-xu.md","As a content strategist, Sherlock is keen on open-source technologies such as Kubernetes and Apache Pulsar. His areas of expertise include documentation and localisation.","\u002Fimgs\u002Fauthors\u002Fsherlock-xu.webp",{"body":1578},{"type":14,"value":1579,"toc":1582},[1580],[47,1581,1575],{},{"title":17,"searchDepth":18,"depth":18,"links":1583},[],"Content Strategist, StreamNative","authors\u002Fsherlock-xu","3Dx8TtPCUV6cDz7Uys4D3TrG25NNb6nVkG1MCsZ5zmc",[1588,1596,1600],{"path":1589,"title":1590,"date":1591,"image":1592,"link":-1,"collection":1593,"resourceType":1594,"score":1595,"id":1589},"\u002Fblog\u002Fpulsar-user-survey-2021-highlights","Pulsar User Survey 2021 Highlights","2021-06-11","\u002Fimgs\u002Fblogs\u002F63c7fcc9f98d44856d02eb16_63b2f2b167ae66abcb8b12a2_top.png","blogs","Blog",1,{"path":1597,"title":1598,"date":1599,"image":-1,"link":-1,"collection":1593,"resourceType":1594,"score":1595,"id":1597},"\u002Fblog\u002Fhow-apache-pulsar-is-helping-iterable-scale-its-customer-engagement-platform","How Apache Pulsar is Helping Iterable Scale its Customer Engagement Platform","2021-01-05",{"path":1601,"title":1602,"date":1603,"image":1604,"link":-1,"collection":1593,"resourceType":1594,"score":1595,"id":1601},"\u002Fblog\u002Fpowering-federated-learning-tencent-with-apache-pulsar","Powering Federated Learning at Tencent with Apache Pulsar","2020-11-26","\u002Fimgs\u002Fblogs\u002F63d7964c4a22f648f7f1e8b6_63a3919fa74e893f0e4026cf_tencent-angel-top.webp",1775235684347]