[{"data":1,"prerenderedAt":1532},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fdeep-dive-transactions-apache-pulsar":1086,"blog-authors-\u002Fblog\u002Fdeep-dive-transactions-apache-pulsar":1495,"related-\u002Fblog\u002Fdeep-dive-transactions-apache-pulsar":1512},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"category":289,"createdAt":290,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":290,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":290,"seo":299,"stem":300,"tags":301,"__hash__":305},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[48,125,126],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[48,128,129,130,133],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[36,131,132],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[48,144,145],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[48,158,159,160],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[48,173,174],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[48,176,177],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[48,195,196],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[48,198,199,200,203],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[44,201,202],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[48,205,206],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[36,257,258],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[36,274,275],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings","Company",null,"2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":307,"title":308,"authors":309,"body":314,"category":1073,"createdAt":290,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":290,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":290,"seo":1080,"stem":1081,"tags":1082,"__hash__":1085},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[310,311,312,313],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":315,"toc":1043},[316,319,322,325,328,331,335,338,348,354,357,365,370,374,381,384,387,395,399,402,407,411,414,417,420,423,432,436,439,450,453,457,460,463,474,477,481,485,493,496,500,508,537,541,544,549,553,556,560,563,566,571,580,585,588,591,602,606,609,620,624,627,630,635,638,667,671,673,679,682,687,692,695,699,713,717,728,732,747,756,767,770,773,777,780,783,794,797,800,803,808,813,817,821,838,842,856,861,865,876,879,895,899,910,915,920,928,932,935,939,946,950,953,962,967,976,982,991,1000,1009,1018,1027,1035],[48,317,318],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[48,320,321],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[48,323,324],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[48,326,327],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[48,329,330],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[40,332,334],{"id":333},"key-benchmark-findings","Key Benchmark Findings",[48,336,337],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[339,340,341,345],"ul",{},[342,343,344],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[342,346,347],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,349,350],{},[351,352],"img",{"alt":18,"src":353},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,355,356],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[339,358,359,362],{},[342,360,361],{},"50% cheaper than Confluent WarpStream",[342,363,364],{},"85% cheaper than MSK and Redpanda",[48,366,367],{},[351,368],{"alt":18,"src":369},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,371,373],{"id":372},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,375,376,380],{},[55,377,379],{"href":378},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[48,382,383],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[48,385,386],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[339,388,389,392],{},[342,390,391],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[342,393,394],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,396,398],{"id":397},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,400,401],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[48,403,404],{},[351,405],{"alt":18,"src":406},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,408,410],{"id":409},"leaderless-architecture","Leaderless architecture",[48,412,413],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[48,415,416],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,418,419],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[48,421,422],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[48,424,425,426,431],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[55,427,430],{"href":428,"rel":429},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,433,435],{"id":434},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,437,438],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[339,440,441,444,447],{},[342,442,443],{},"Handles millions of metadata\u002Findex operations per second",[342,445,446],{},"Generates sequential IDs to maintain strict message ordering",[342,448,449],{},"Optimized for Kubernetes with horizontal scalability",[48,451,452],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,454,456],{"id":455},"zero-interzone-data-replication","Zero interzone data replication",[48,458,459],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[48,461,462],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[339,464,465,468,471],{},[342,466,467],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[342,469,470],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[342,472,473],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[48,475,476],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[40,478,480],{"id":479},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,482,484],{"id":483},"ursa-cluster-deployment","Ursa Cluster Deployment",[339,486,487,490],{},[342,488,489],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[342,491,492],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,494,495],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,497,499],{"id":498},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,501,502,503,507],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,504,505],{"href":505,"rel":506},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[339,509,510,525,534],{},[342,511,512,513,518,519,524],{},"12 OMB workers: 6 for ",[55,514,517],{"href":515,"rel":516},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,520,523],{"href":521,"rel":522},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[264],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[342,526,527,528,533],{},"Sample YAML ",[55,529,532],{"href":530,"rel":531},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[342,535,536],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,538,540],{"id":539},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,542,543],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,545,546],{},[351,547],{"alt":18,"src":548},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,550,552],{"id":551},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,554,555],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[32,557,559],{"id":558},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,561,562],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[48,564,565],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[339,567,568],{},[342,569,570],{},"9 × m6i.8xlarge instances",[48,572,573,574,579],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[55,575,578],{"href":576,"rel":577},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[264]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[339,581,582],{},[342,583,584],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,586,587],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,589,590],{},"Additional key assumptions include:",[339,592,593,596,599],{},[342,594,595],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[342,597,598],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[342,600,601],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,603,605],{"id":604},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,607,608],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[339,610,611,614,617],{},[342,612,613],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[342,615,616],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[342,618,619],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,621,623],{"id":622},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,625,626],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,628,629],{},"Consumers:",[339,631,632],{},[342,633,634],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[48,636,637],{},"Producers:",[339,639,640,649,658],{},[342,641,642,643,648],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,644,647],{"href":645,"rel":646},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[264],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[342,650,651,652,657],{},"Redpanda recently introduced ",[55,653,656],{"href":654,"rel":655},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[264],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[342,659,660,661,666],{},"Ursa is the only system in this test with ",[55,662,665],{"href":663,"rel":664},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[264],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[32,668,670],{"id":669},"cost-comparison-results","Cost Comparison Results",[48,672,337],{},[339,674,675,677],{},[342,676,344],{},[342,678,347],{},[48,680,681],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[48,683,684],{},[351,685],{"alt":18,"src":686},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,688,689],{},[351,690],{"alt":18,"src":691},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,693,694],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,696,698],{"id":697},"streamnative-ursa","StreamNative - Ursa",[339,700,701,704,707,710],{},[342,702,703],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[342,705,706],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[342,708,709],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[342,711,712],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,714,716],{"id":715},"aws-msk","AWS MSK",[339,718,719,722,725],{},[342,720,721],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[342,723,724],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[342,726,727],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,729,731],{"id":730},"redpanda","RedPanda",[339,733,734,736,738,741,744],{},[342,735,703],{},[342,737,706],{},[342,739,740],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[342,742,743],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[342,745,746],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,748,749,750,755],{},"Please note that we were unable to test ",[55,751,754],{"href":752,"rel":753},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[264],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[339,757,758,764],{},[342,759,760,763],{},[55,761,647],{"href":645,"rel":762},[264]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[342,765,766],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[48,768,769],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[48,771,772],{},"We may revisit this comparison as more details become available.",[40,774,776],{"id":775},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,778,779],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[48,781,782],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[339,784,785,788,791],{},[342,786,787],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[342,789,790],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[342,792,793],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,795,796],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[48,798,799],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[48,801,802],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[48,804,805],{},[351,806],{"alt":18,"src":807},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,809,810],{},[351,811],{"alt":18,"src":812},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,814,816],{"id":815},"cost-breakdown","Cost Breakdown",[818,819,820],"h4",{"id":697},"StreamNative – Ursa",[339,822,823,826,829,832,835],{},[342,824,825],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[342,827,828],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[342,830,831],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[342,833,834],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[342,836,837],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[818,839,841],{"id":840},"warpstream","WarpStream",[339,843,844,847],{},[342,845,846],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[342,848,849,850,855],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[55,851,854],{"href":852,"rel":853},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[264],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[48,857,858],{},[351,859],{"alt":18,"src":860},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[818,862,864],{"id":863},"msk","MSK",[339,866,867,870,873],{},[342,868,869],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[342,871,872],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[342,874,875],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[818,877,731],{"id":878},"redpanda-1",[339,880,881,884,886,889,892],{},[342,882,883],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[342,885,872],{},[342,887,888],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[342,890,891],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[342,893,894],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[818,896,898],{"id":897},"additional-notes","Additional Notes",[339,900,901],{},[342,902,903,904,909],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[264],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[48,911,912],{},[351,913],{"alt":18,"src":914},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,916,917],{},[351,918],{"alt":18,"src":919},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[339,921,922,925],{},[342,923,924],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[342,926,927],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[40,929,931],{"id":930},"conclusion","Conclusion",[48,933,934],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[32,936,938],{"id":937},"balancing-latency-and-cost","Balancing Latency and Cost",[48,940,941,945],{},[55,942,944],{"href":943},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[32,947,949],{"id":948},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,951,952],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[48,954,955,956,961],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[55,957,960],{"href":958,"rel":959},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[963,964,966],"h1",{"id":965},"references","References",[48,968,969,972,973],{},[970,971,430],"span",{}," ",[55,974,975],{"href":975},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,977,978,972,980],{},[970,979,379],{},[55,981,378],{"href":378},[48,983,984,972,987],{},[970,985,986],{},"StreamNative pricing",[55,988,989],{"href":989,"rel":990},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,992,993,972,996],{},[970,994,995],{},"WarpStream pricing",[55,997,998],{"href":998,"rel":999},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1001,1002,972,1005],{},[970,1003,1004],{},"AWS S3 pricing",[55,1006,1007],{"href":1007,"rel":1008},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1010,1011,972,1014],{},[970,1012,1013],{},"AWS EBS pricing",[55,1015,1016],{"href":1016,"rel":1017},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1019,1020,972,1023],{},[970,1021,1022],{},"AWS MSK pricing",[55,1024,1025],{"href":1025,"rel":1026},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1028,1029,972,1032],{},[970,1030,1031],{},"The Brutal Truth about Kafka Cost Calculators",[55,1033,852],{"href":852,"rel":1034},[264],[48,1036,1037,972,1040],{},[970,1038,1039],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1041,906],{"href":906,"rel":1042},[264],{"title":18,"searchDepth":19,"depth":19,"links":1044},[1045,1046,1047,1052,1056,1057,1066,1069],{"id":333,"depth":19,"text":334},{"id":372,"depth":19,"text":373},{"id":397,"depth":19,"text":398,"children":1048},[1049,1050,1051],{"id":409,"depth":279,"text":410},{"id":434,"depth":279,"text":435},{"id":455,"depth":279,"text":456},{"id":479,"depth":19,"text":480,"children":1053},[1054,1055],{"id":483,"depth":279,"text":484},{"id":498,"depth":279,"text":499},{"id":539,"depth":19,"text":540},{"id":551,"depth":19,"text":552,"children":1058},[1059,1060,1061,1062,1063,1064,1065],{"id":558,"depth":279,"text":559},{"id":604,"depth":279,"text":605},{"id":622,"depth":279,"text":623},{"id":669,"depth":279,"text":670},{"id":697,"depth":279,"text":698},{"id":715,"depth":279,"text":716},{"id":730,"depth":279,"text":731},{"id":775,"depth":19,"text":776,"children":1067},[1068],{"id":815,"depth":279,"text":816},{"id":930,"depth":19,"text":931,"children":1070},[1071,1072],{"id":937,"depth":279,"text":938},{"id":948,"depth":279,"text":949},"StreamNative Cloud","2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":308,"description":1075},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1083,1084,303],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1087,"title":1088,"authors":1089,"body":1090,"category":1483,"createdAt":290,"date":1484,"description":1485,"extension":8,"featured":294,"image":1486,"isDraft":294,"link":290,"meta":1487,"navigation":7,"order":296,"path":1488,"readingTime":1489,"relatedResources":290,"seo":1490,"stem":1491,"tags":1492,"__hash__":1494},"blogs\u002Fblog\u002Fdeep-dive-transactions-apache-pulsar.md","A Deep-dive of Transactions in Apache Pulsar",[313],{"type":15,"value":1091,"toc":1464},[1092,1100,1111,1114,1118,1121,1124,1127,1133,1136,1139,1151,1154,1158,1161,1164,1168,1171,1174,1177,1183,1186,1190,1193,1197,1200,1203,1206,1214,1218,1221,1227,1230,1236,1240,1249,1252,1258,1262,1266,1269,1272,1275,1278,1281,1284,1288,1291,1295,1298,1301,1305,1308,1312,1315,1319,1322,1325,1329,1332,1335,1338,1342,1345,1348,1351,1354,1358,1361,1365,1368,1379,1382,1385,1389,1392,1396,1399,1423,1437,1439,1442,1445,1448,1451],[48,1093,1094,1095,1099],{},"In a previous blog post, ",[55,1096,1098],{"href":1097},"\u002Fen\u002Fblog\u002Frelease\u002F2021-06-14-exactly-once-semantics-with-transactions-in-pulsar","Exactly-Once Semantics with Transactions in Pulsar",", we introduced the exactly-once semantics enabled by Transaction API for Apache Pulsar. That blog post covered the various message delivery semantics, including:",[339,1101,1102,1105,1108],{},[342,1103,1104],{},"The single-topic exactly-once semantics enabled by idempotent producer",[342,1106,1107],{},"The Transaction API",[342,1109,1110],{},"The end-to-end exactly-once processing semantics for the Pulsar and Flink integration",[48,1112,1113],{},"In this blog post, we will dive deeper into the transactions in Apache Pulsar. The goal here is to familiarize you with the main concepts needed to use the Pulsar Transaction API effectively.",[40,1115,1117],{"id":1116},"why-transactions","Why Transactions?",[48,1119,1120],{},"Transactions strengthen the message delivery semantics and the processing guarantees for stream processing (i.e using Pulsar Functions or integrating with other stream processing engines). These stream processing applications usually exhibit a “consume-process-produce” pattern when consuming and producing from and to data streams such as Pulsar topics.",[48,1122,1123],{},"The demand for stream processing applications with stronger processing guarantees has grown along with the rise of stream processing. For example, in the financial industry, financial institutions use stream processing engines to process debits and credits for users. This type of use case requires that every message is processed exactly once, without exception.",[48,1125,1126],{},"In other words, if a stream processing application consumes message A and produces the result as a message B (B = f(A)), then exactly-once processing guarantee means that A can only be marked as consumed if and only if B is successfully produced, and vice versa.",[48,1128,1129],{},[351,1130],{"alt":1131,"src":1132},"illusatrion of transaction","\u002Fimgs\u002Fblogs\u002F63b2f9cb3ddd88f0dbf748a4_1.png",[48,1134,1135],{},"Prior to Pulsar 2.8.0, there was no easy way to build stream processing applications with Apache Pulsar to achieve exactly-once processing guarantees. If you integrate a stream processing engine, like Flink, you might be able to achieve exactly-once processing guarantees. For example, using Flink you can achieve exactly-once processing reading from Pulsar topics, but it is not possible to achieve exactly-once processing writing to Pulsar topics.",[48,1137,1138],{},"When you configure Pulsar producers and consumers for at-least-once delivery semantics, a stream processing application cannot achieve exactly-once processing semantics in the following scenarios:",[1140,1141,1142,1145,1148],"ol",{},[342,1143,1144],{},"Duplicate writes: A producer can potentially write a message multiple times due to the internal retry logic. The idempotent producer addresses this via guaranteed message deduplication.",[342,1146,1147],{},"Application crashes: The stream processing application can crash at any time. If the application crashes after writing the result message B but before making the source message A as consumed. The application can reprocess the source message A after it restarts, resulting in a duplicated result message B being written again to the output topic, violating the exactly-once processing guarantees.",[342,1149,1150],{},"Zombie application: The stream processing application can potentially be partitioned from the network in a distributed environment. Typically, new instances of the same stream processing application will be automatically started to replace the ones which were deemed lost. In such a situation, multiple instances of the same processing application may be running. They will process the same input topics and write the results to the same output topics, causing duplicate output messages and violating the exactly-once processing semantics.",[48,1152,1153],{},"The new Transaction API introduced in Pulsar 2.8.0 release is designed to solve the second and third problems.",[40,1155,1157],{"id":1156},"transactional-semantics","Transactional Semantics",[48,1159,1160],{},"The Transaction API enables stream processing applications to consume, process, and produce messages in one atomic operation. That means, a batch of messages in a transaction can be received from, produced to and acknowledged to many topic partitions. All the operations involved in a transaction succeed or fail as one single until.",[48,1162,1163],{},"But how does the Transaction API resolve the three problems above?",[32,1165,1167],{"id":1166},"atomic-writes-and-acknowledgements-across-multiple-topics","Atomic writes and acknowledgements across multiple topics",[48,1169,1170],{},"First, the Transaction API enables atomic writes and atomic acknowledgments to multiple Pulsar topics together as one single unit. All the messages produced or consumed in one transaction are successfully written or acknowledged together, or none of them are. For example, an error during processing can cause a transaction to be aborted, in which case none of the messages produced by the transaction will be consumable by any consumers.",[48,1172,1173],{},"What does this mean to an atomic “consume-process-produce” operation?",[48,1175,1176],{},"Let’s assume that if an application consumes message A from topic T0 and produces a result message B to topic T1 after applying some transforming logic on message A (B = f(A)), then the consume-process-produce operation is atomic only if message A and B are considered successfully consumed and published together, or not at all. The message A is ONLY considered consumed from topic T0 only when it is successfully acknowledged.",[48,1178,1179],{},[351,1180],{"alt":1181,"src":1182},"illusatrion of transaction semantics","\u002Fimgs\u002Fblogs\u002F63b2f9cb304affbe8af329e6_2.png",[48,1184,1185],{},"Transaction API ensures the acknowledgement of message A and the write of message B to happen as atomic, hence the “consume-process-produce” operation is atomic.",[32,1187,1189],{"id":1188},"fence-zombie-instances-via-conditional-acknowledgement","Fence zombie instances via conditional acknowledgement",[48,1191,1192],{},"We solve the problem of zombie instances by conditional acknowledgement. Conditional acknowledgement means if there are two transactions attempting to acknowledge on the same message, Pulsar guarantees that there is ONLY one transaction that can succeed and the other transaction is aborted.",[32,1194,1196],{"id":1195},"read-transactional-messages","Read transactional messages",[48,1198,1199],{},"What is the guarantee for reading messages written as part of a transaction?",[48,1201,1202],{},"The Pulsar broker only dispatches transactional messages to a consumer if the transaction was actually committed. In other words, the broker will not deliver transactional messages which are part of an open transaction, nor will it deliver messages which are part of an aborted transaction.",[48,1204,1205],{},"However, Pulsar doesn’t guarantee that the messages produced within one committed transaction will be consumed all together. There are several reasons for this:",[1140,1207,1208,1211],{},[342,1209,1210],{},"Consumers may not consume from all the topic partitions that participated in the committed transaction. Hence they will never be able to read all the messages that are produced in that transaction.",[342,1212,1213],{},"Consumers may have a different receiver queue size or buffering window size, allowing only a certain amount of messages. That amount can be any arbitrary number.",[40,1215,1217],{"id":1216},"transactions-api","Transactions API",[48,1219,1220],{},"The transaction feature is primarily a server-side and protocol-level feature. Currently it is only available for Java clients. (Support for other language clients will be added in the future releases.) An example “consume-process-produce” application written in Java and using Pulsar’s transaction API would look something like:",[48,1222,1223],{},[351,1224],{"alt":1225,"src":1226},"image of transaction API","\u002Fimgs\u002Fblogs\u002F63b2f9cb89919e09213209b9_3.png",[48,1228,1229],{},"Let’s walk through this example step by step.",[48,1231,1232],{},[351,1233],{"alt":1234,"src":1235},"table with steps and description of transaction API","\u002Fimgs\u002Fblogs\u002F63b2fa6103dcd190e3c092d6_table.webp",[40,1237,1239],{"id":1238},"how-transactions-work","How transactions work",[48,1241,1242,1243,1248],{},"In this section, we present a brief overview of the new components and new request flows introduced by the Transaction APIs. For a more exhaustive treatment of this subject, you may checkout the original design document, or watch ",[55,1244,1247],{"href":1245,"rel":1246},"https:\u002F\u002Fwww.na2021.pulsar-summit.org\u002Fexactly-once-made-easy-transactional-messaging-in-apache-pulsar",[264],"the upcoming Pulsar Summit talk"," where transactions were introduced.",[48,1250,1251],{},"The content below provides an overview to help with debugging or tuning transactions for better performance.",[48,1253,1254],{},[351,1255],{"alt":1256,"src":1257},"illustration to explain how transactions work","\u002Fimgs\u002Fblogs\u002F63b2fa82ccfce60d1605adad_4.png",[32,1259,1261],{"id":1260},"components","Components",[818,1263,1265],{"id":1264},"transaction-coordinator-and-transaction-log","Transaction coordinator and Transaction log",[48,1267,1268],{},"The transaction coordinator (TC) maintains the topics and subscriptions that interact in a transaction. When a transaction is committed, the transaction coordinator interacts with the topic owner broker to complete the transaction.",[48,1270,1271],{},"The transaction coordinator is a module running inside a Pulsar broker. It maintains the entire life cycle of transactions and prevents a transaction from getting into an incorrect status. The transaction coordinator also handles transaction timeout, and ensures that the transaction is aborted after a transaction timeout.",[48,1273,1274],{},"All the transaction metadata persists in the transaction log. The transaction log is backed by a Pulsar topic. After the transaction coordinator crashes, it can restore the transaction metadata from the transaction log.",[48,1276,1277],{},"Each coordinator owns some subset of the partitions of the transaction log topics, i.e. the partitions for which its broker is the owner.",[48,1279,1280],{},"Each transaction is identified with a transaction id (TxnID). The transaction id is 128-bits long. The highest 16 bits are reserved for the partition of the transaction log topic and the remaining bits are used for generating monotonically increasing numbers by the TC who owns that transaction log topic partition.",[48,1282,1283],{},"It is worth noting that the transaction log topic just stores the state of a transaction and not the actual messages in the transaction. The messages are stored in the actual topic partitions. The transaction can be in various states like “Open”, “Prepare commit”, and “committed”. It is this state and associated metadata that is stored in the transaction log.",[818,1285,1287],{"id":1286},"transaction-buffer","Transaction buffer",[48,1289,1290],{},"Messages produced to a topic partition within a transaction are stored in the transaction buffer of that topic partition. The messages in the transaction buffer are not visible to consumers until the transactions are committed. The messages in the transaction buffer are discarded when the transactions are aborted.",[818,1292,1294],{"id":1293},"pending-acknowledge-state","Pending acknowledge state",[48,1296,1297],{},"Message acknowledgments within a transaction are maintained by the pending acknowledge state before the transaction is committed. If a message is in the pending acknowledge state, the message cannot be acknowledged by other transactions until the message is removed from the pending acknowledge state when a transaction is aborted.",[48,1299,1300],{},"The pending acknowledge state is persisted to the pending acknowledge log. The pending acknowledge log is backed by a cursor log. A new broker can restore the state from the pending acknowledge log to ensure the acknowledgement is not lost.",[32,1302,1304],{"id":1303},"data-flow","Data flow",[48,1306,1307],{},"At a high level, the data flow can be broken into multiple steps. 1. Start a transaction. 2. Publish messages with a transaction. 3. Acknowledge messages with a transaction. 4. Complete a transaction.",[818,1309,1311],{"id":1310},"begin-transaction","Begin transaction",[48,1313,1314],{},"At the beginning of a transaction, the Pulsar client will locate a Transaction Coordinator to request a new transaction ID. The Transaction Coordinator will allocate a transaction ID for the transaction. The transaction will be logged with its transaction id and status of OPEN in the transaction log (as shown in step 1a). This ensures the transaction status is persisted regardless of whether the Transaction Coordinator crashes. After a transaction status entry is logged, TC returns the transaction ID back to the Pulsar client.",[818,1316,1318],{"id":1317},"publish-messages-with-a-transaction","Publish messages with a transaction",[48,1320,1321],{},"Before the pulsar client produces messages to a new topic partition, the client sends a request to TC to add the partition to the transaction. TC logs the partition changes into its transaction log for durability (as shown in 2.1a). This step ensures TC knows all the partitions that a transaction is handling, so TC can commit or abort changes on each partition at the end-partition phase.",[48,1323,1324],{},"The Pulsar client starts producing messages to partitions. This producing flow is the same as the normal message producing flow. The only difference is the batch of messages produced by a transaction will contain the transaction id. The broker that receives the batch of messages checks if the batch of messages belongs to a transaction. If it doesn’t belong to a transaction, the broker handles the writes as it normally would. If it belongs to a transaction, the broker writes the batch into the partition’s transaction buffer.",[818,1326,1328],{"id":1327},"acknowledge-messages-with-a-transaction","Acknowledge messages with a transaction",[48,1330,1331],{},"The Pulsar client sends a request to TC the first time a new subscription is acknowledged as part of a transaction. The addition of the subscription to the transaction is logged by TC in step 2.3a. This step ensures TC knows all the subscriptions that a transaction is handling, so TC can commit or abort changes on each subscription at the EndTxn phase.",[48,1333,1334],{},"The Pulsar client starts acknowledging messages on subscriptions. This transactional acknowledgement flow is the same as the normal acknowledgement flow. However the ack request carries a transaction id. The broker receiving the acknowledgement request checks if the acknowledgment belongs to a transaction or not. If it belongs to a transaction, the broker will mark the message as:PENDING_ACK state. PENDING_ACK state means the message can not be acknowledged or negative-acknowledged by other consumers until the ack is committed or aborted. This ensures if there are two transactions attempting to acknowledge one message, only one will succeed and the other one will be aborted.",[48,1336,1337],{},"The Pulsar client will abort the whole transaction when it tries to acknowledge but the conflict is detected on both individual and cumulative acknowledgements.",[818,1339,1341],{"id":1340},"complete-a-transaction","Complete a transaction",[48,1343,1344],{},"At the end of a transaction, the application will decide to commit or abort the transaction. The transaction can also be aborted if a conflict is detected when acknowledging messages.",[48,1346,1347],{},"When a pulsar client is finished with a transaction, it can issue an end transaction request to TC, with a field indicating whether the transaction is committed or aborted.",[48,1349,1350],{},"TC writes a COMMITTING or ABORTING message to its transaction log (as shown in 3.1a) and begins the process of committing or aborting messages or acknowledgments to all the partitions involved in this transaction. It is shown in 3.2.",[48,1352,1353],{},"After all the partitions involved in this transaction are successfully committed or aborted, TC writes COMMITTED or ABORTED messages to its transaction log. It is shown in 3.3 in the diagram.",[40,1355,1357],{"id":1356},"how-transactions-perform","How transactions perform",[48,1359,1360],{},"So far, this document covered the semantics of transactions and how they work, next let’s turn our attention to how transactions perform.",[32,1362,1364],{"id":1363},"performance-for-transactional-producers","Performance for transactional producers",[48,1366,1367],{},"Transactions cause only moderate write amplification. The additional writes are due to:",[339,1369,1370,1373,1376],{},[342,1371,1372],{},"For each transaction, the producers receive additional requests to register the topic partitions with the coordinator.",[342,1374,1375],{},"When completing a transaction, one transaction marker is written to each partition participating in the transaction.",[342,1377,1378],{},"Finally, the TC writes transaction status changes to the transaction log. This includes a write for each batch of topic partitions added to the transaction ( “prepare commit” and the “committed” status).",[48,1380,1381],{},"The overhead is independent of the number of messages written as part of a transaction. So the key to having higher throughput is to include a large number of messages per transaction. Smaller messages or shorter transaction commit intervals result in more amplification.",[48,1383,1384],{},"The main tradeoff when increasing the transaction duration is that it increases end-to-end latency. Recall that a consumer reading transactional messages will not deliver messages which are part of open transactions. So the longer the interval between commits, the longer consumers will have to wait, increasing the end-to-end latency.",[32,1386,1388],{"id":1387},"performance-for-transactional-consumers","Performance for transactional consumers",[48,1390,1391],{},"The transactional consumer is much simpler than the producer. All the logic is done by the Pulsar broker at the server side. The broker only dispatches the messages that are in completed transactions.",[40,1393,1395],{"id":1394},"further-reading","Further reading",[48,1397,1398],{},"In the blog post, we only scratched the surface of transactions in Apache Pulsar. All the details of the design are documented online. You can find those references listed below:",[1140,1400,1401,1409,1417],{},[342,1402,1403,1408],{},[55,1404,1407],{"href":1405,"rel":1406},"https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F145VYp09JKTw9jAT-7yNyFU255FptB2_B2Fye100ZXDI\u002Fedit#heading=h.bm5ainqxosrx",[264],"The design document",": This is the definitive place to learn about the public interfaces, the data flow, the components. You will also learn about how each transaction component is implemented, how each transactional request is processed, how the transactional data is purged, etc.",[342,1410,1411,1416],{},[55,1412,1415],{"href":1413,"rel":1414},"http:\u002F\u002Fpulsar.apache.org\u002Fapi\u002Fclient\u002F2.8.0-SNAPSHOT\u002Forg\u002Fapache\u002Fpulsar\u002Fclient\u002Fapi\u002Ftransaction\u002Fpackage-frame.html",[264],"The Pulsar Client javadocs",": The Javadocs is a great place to learn about how to use the new APIs.",[342,1418,1419,1422],{},[55,1420,1421],{"href":1097},"Exactly-Once Semantics with Transaction Support in Pulsar",": This is the first part of this blog series.",[48,1424,1425,1426,1430,1431,1436],{},"My fellow colleagues Sijie Guo and Addison Higham are going to give a presentation “",[55,1427,1429],{"href":1245,"rel":1428},[264],"Exactly-Once Made Easy: Transactional Messaging in Apache Pulsar","” at the upcoming Pulsar Summit North America 2021 on June 16-17th. If you are interested in this topic, ",[55,1432,1435],{"href":1433,"rel":1434},"https:\u002F\u002Fhopin.com\u002Fevents\u002Fpulsar-summit-north-america-2021",[264],"reserve your spot today"," and listen to them diving into every detail of Pulsar Transaction.",[40,1438,931],{"id":930},[48,1440,1441],{},"In the first blog post of this series, Exactly-Once Semantics Made Simple with Transaction Support in Pulsar, we introduced the exactly-once semantics enabled by Transaction API for Apache Pulsar. In this post, we talked about the key design goals for the Transaction API in Apache Pulsar, the semantics of the transaction API, and a high-level idea of how the APIs actually work.",[48,1443,1444],{},"If we consider stream processing as a read-process-write processor, this blog post focuses on the read and write paths with the processing itself being a black box. However, in the real world, a lot happens in the processing stage, which makes exactly-once processing impossible to guarantee using the Transaction API alone. For example, if the processing logic modifies external storage systems, the Transaction API covered here is not sufficient to guarantee exactly-once processing.",[48,1446,1447],{},"The Pulsar and Flink integration uses the Transaction API described here to provide end-to-end exactly-once processing for a wide variety of stream processing applications, even those which update additional state stores during processing.",[48,1449,1450],{},"In the next few weeks we will share the third blog in this series to provide the details on how the Pulsar and Flink integration provides end-to-end exactly-once processing semantics based on the new Pulsar transactions, as well as how to easily write streaming applications with Pulsar and Flink.",[48,1452,1453,1454,1457,1458,1463],{},"If you want to try out the new exactly-once functionality, check out ",[55,1455,1073],{"href":958,"rel":1456},[264]," or install the ",[55,1459,1462],{"href":1460,"rel":1461},"https:\u002F\u002Fdocs.streamnative.io\u002Fplatform\u002Fv1.0.0\u002Fquickstart",[264],"StreamNative Platform"," today, to create your own applications to process streams of events using the Transaction API.",{"title":18,"searchDepth":19,"depth":19,"links":1465},[1466,1467,1472,1473,1477,1481,1482],{"id":1116,"depth":19,"text":1117},{"id":1156,"depth":19,"text":1157,"children":1468},[1469,1470,1471],{"id":1166,"depth":279,"text":1167},{"id":1188,"depth":279,"text":1189},{"id":1195,"depth":279,"text":1196},{"id":1216,"depth":19,"text":1217},{"id":1238,"depth":19,"text":1239,"children":1474},[1475,1476],{"id":1260,"depth":279,"text":1261},{"id":1303,"depth":279,"text":1304},{"id":1356,"depth":19,"text":1357,"children":1478},[1479,1480],{"id":1363,"depth":279,"text":1364},{"id":1387,"depth":279,"text":1388},{"id":1394,"depth":19,"text":1395},{"id":930,"depth":19,"text":931},"Apache Pulsar","2021-06-16","Previously, we introduced the exactly-once semantics enabled by Transaction API for Pulsar. In this blog, we dive deeper into the transactions in Pulsar and familiarize you with the main concepts needed to use the Pulsar Transaction API effectively.","\u002Fimgs\u002Fblogs\u002F63c7fc8bf8ae5a428cd165ce_63b2f9cbccfce613d70568ab_top.png",{},"\u002Fblog\u002Fdeep-dive-transactions-apache-pulsar","12 min read",{"title":1088,"description":1485},"blog\u002Fdeep-dive-transactions-apache-pulsar",[1483,1493],"Transactions","zpc8_GPhWvlmASo_hSoK0vnNaG0xFJa6VxPLy5GT3YQ",[1496],{"id":1497,"title":313,"bioSummary":1498,"email":290,"extension":8,"image":1499,"linkedinUrl":1500,"meta":1501,"position":1508,"stem":1509,"twitterUrl":1510,"__hash__":1511},"authors\u002Fauthors\u002Fpenghui-li.md","Penghui Li is passionate about helping organizations to architect and implement messaging services. Prior to StreamNative, Penghui was a Software Engineer at Zhaopin.com, where he was the leading Pulsar advocate and helped the company adopt and implement the technology. He is an Apache Pulsar Committer and PMC member.","\u002Fimgs\u002Fauthors\u002Fpenghui-li.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpenghui-li-244173184\u002F",{"body":1502},{"type":15,"value":1503,"toc":1506},[1504],[48,1505,1498],{},{"title":18,"searchDepth":19,"depth":19,"links":1507},[],"Director of Streaming, StreamNative & Apache Pulsar PMC Member","authors\u002Fpenghui-li","https:\u002F\u002Ftwitter.com\u002Flipenghui6","WDjET7GfxqVQJ8mTEMaRhgpxRdDy18qZkgQDJlwjvbI",[1513,1521,1525],{"path":1514,"title":1515,"date":1516,"image":1517,"link":-1,"collection":1518,"resourceType":1519,"score":1520,"id":1514},"\u002Fsuccess-stories\u002Fbestpay","How Orange Financial combats financial fraud in over 50M transactions a day using Apache Pulsar","2022-12-27","\u002Fimgs\u002Fsuccess-stories\u002F67956b0055b8586d148f8b68_SN-SuccessStories-bestpay.webp","successStories","Case Study",1.1,{"path":1522,"title":1523,"date":1516,"image":1524,"link":-1,"collection":1518,"resourceType":1519,"score":1520,"id":1522},"\u002Fsuccess-stories\u002Ftencent","Powering Tencent Billing Platform with Apache Pulsar","\u002Fimgs\u002Fsuccess-stories\u002F67942e3b5f9411fb93dcb9f9_SN-SuccessStories-tencent.webp",{"path":1526,"title":1527,"date":1528,"image":1529,"link":-1,"collection":1530,"resourceType":1531,"score":1520,"id":1526},"\u002Fwhitepapers\u002Fapache-pulsar-helps-tencent-process-financial-transactions","Apache Pulsar Helps Tencent Process Tens of Billions of Financial Transactions Efficiently with Virtually No Data Loss","2022-12-23","\u002Fimgs\u002Fwhitepapers\u002F63aecf471d9c96089c87bff2_open-graph-wp-pulsar-tencent-billing.jpg","whitepapers","Whitepaper",1775716421476]