[{"data":1,"prerenderedAt":1603},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions":1086,"blog-authors-\u002Fblog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions":1565,"related-\u002Fblog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions":1584},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"canonicalUrl":289,"category":290,"createdAt":289,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":289,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":289,"seo":299,"stem":300,"tags":301,"__hash__":305},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[48,125,126],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[48,128,129,130,133],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[36,131,132],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[48,144,145],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[48,158,159,160],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[48,173,174],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[48,176,177],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[48,195,196],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[48,198,199,200,203],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[44,201,202],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[48,205,206],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[36,257,258],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[36,274,275],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings",null,"Company","2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":307,"title":308,"authors":309,"body":314,"canonicalUrl":289,"category":1073,"createdAt":289,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":289,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":289,"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","CDUawvFKTs_AD8usvmIcTleU3mbfA0QAoPZM6xfVuo8",{"id":1087,"title":1088,"authors":1089,"body":1091,"canonicalUrl":289,"category":1552,"createdAt":289,"date":1553,"description":1554,"extension":8,"featured":294,"image":1555,"isDraft":294,"link":289,"meta":1556,"navigation":7,"order":296,"path":1557,"readingTime":1558,"relatedResources":289,"seo":1559,"stem":1560,"tags":1561,"__hash__":1564},"blogs\u002Fblog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions.md","Apache Pulsar® Helps Tencent Process Tens of Billions of Financial Transactions Efficiently with Virtually No Data Loss",[1090],"Dezhi Liu",{"type":15,"value":1092,"toc":1528},[1093,1097,1100,1103,1107,1116,1125,1131,1135,1139,1142,1145,1148,1152,1155,1161,1165,1168,1171,1185,1189,1192,1195,1198,1201,1204,1207,1210,1216,1220,1223,1229,1233,1236,1239,1242,1246,1249,1252,1255,1259,1262,1265,1269,1272,1278,1281,1298,1302,1305,1316,1319,1323,1326,1329,1335,1338,1341,1345,1348,1351,1354,1357,1362,1366,1369,1380,1383,1389,1393,1396,1402,1405,1416,1420,1423,1429,1432,1446,1449,1460,1464,1467,1487,1493,1496,1507,1510,1514,1517],[40,1094,1096],{"id":1095},"executive-summary","Executive summary",[48,1098,1099],{},"As the largest provider of Internet products and services in China, Tencent serves billions of users and over a million merchants—and these numbers are growing fast! Tencent's enterprises generate a huge volume of financial transactions, placing a tremendous load on their billing service, which processes hundreds of millions of dollars in revenue each day.",[48,1101,1102],{},"Because Tencent had been unable to scale its current billing service to handle their rapidly growing business, the possibility of data loss had become an escalating concern. To ensure data consistency, the company decided to redesign their system's transaction processing pipeline. After evaluating the pros and cons of several messaging systems, Tencent chose to implement Apache Pulsar. As a result, Tencent can now run their billing service on a very large scale with virtually no data loss.",[40,1104,1106],{"id":1105},"customer-background","Customer background",[48,1108,1109,1110,1115],{},"Tencent Holdings Limited is a multinational conglomerate holding company based in",[55,1111,1114],{"href":1112,"rel":1113},"https:\u002F\u002Fwww.google.com\u002Fsearch?rlz=1C1CHBF_enUS813US813&sxsrf=ACYBGNQ4k93MEkBznOhAuMNdkKN2gNfgEQ:1577016259811&q=Shenzhen&stick=H4sIAAAAAAAAAOPgE-LSz9U3MDEwLivJU-IAsXOScsu0tLKTrfTzi9IT8zKrEksy8_NQOFYZqYkphaWJRSWpRcWLWDmCM1LzqoAYALKaliFPAAAA&sa=X&ved=2ahUKEwjG_eGvm8nmAhWWvp4KHcYnB-oQmxMoATAeegQIExAL",[264]," Shenzhen",", China. It has hundreds of subsidiaries located in China and elsewhere around the globe. Tencent is considered to be one of the most innovative technology companies in the world specializing in internet-related products and services such as entertainment (gaming), financial services (e-commerce, payment systems), business services, a social networking platform (WeChat), and more.",[48,1117,1118,1119,1124],{},"Tencent uses an Internet billing platform internally known as ",[55,1120,1123],{"href":1121,"rel":1122},"https:\u002F\u002Fcloud.tencent.com\u002Fproduct\u002Fmidas",[264],"Midas"," to handle the enormous volume of transactions that flow through all of its businesses. Midas integrates both domestic and international payment channels and provides various services such as account management, precision marketing, security risk control, auditing and accounting, billing analysis, and more. On a typical day, Midas processes hundreds of millions of dollars in revenue which amount to hundreds of billions of dollars per year. Midas handles more than 30 billion escrow accounts and provides comprehensive billing services for more than 180 countries (regions), 10,000+ companies, and over 1 million merchants doing business in a variety of industries (see Figure 1).",[48,1126,1127],{},[351,1128],{"alt":1129,"src":1130}," illustration of Midas environment","\u002Fimgs\u002Fblogs\u002F63a2cc38a1ed687ae658a676_image10.png",[40,1132,1134],{"id":1133},"figure-1-industries-and-business-platforms-supported-by-midas","Figure 1 Industries and business platforms supported by Midas",[32,1136,1138],{"id":1137},"challenge","Challenge",[48,1140,1141],{},"Tencent's enterprises continuously generate massive transaction volumes and their numbers are steadily growing. To handle this increased activity, the company needed a robust billing platform that could be scaled as their business grows.",[48,1143,1144],{},"Because Midas supports mission-critical services like billing and payments, the most essential challenges were to ensure data consistency and prevent data loss in transactions.",[48,1146,1147],{},"In addition, it was also very important to develop a solution that could handle high throughput with minimal delays in processing.",[32,1149,1151],{"id":1150},"a-closer-look-at-midas","A closer look at Midas",[48,1153,1154],{},"Figure 2 provides a high-level overview of Midas. This diagram illustrates the technical design of the entire platform and shows how the underlying layers work together to support the merchant side, the user side, and the various payment channels.",[48,1156,1157],{},[351,1158],{"alt":1159,"src":1160},"Technical overview of the Midas platform ","\u002Fimgs\u002Fblogs\u002F63a2cc37b8ed98554a05b6a3_image1.png",[40,1162,1164],{"id":1163},"requirement","Requirement",[48,1166,1167],{},"To meet its need for a more elastic and scalable billing platform, Tencent decided to redesign Midas's transaction processing pipeline. The company believed the problem could be solved by implementing a new messaging system, but which one?",[48,1169,1170],{},"Before evaluating the various available options, Tencent defined a set of requirements. To be a viable solution, the new messaging system would need to score high in all of the following areas:",[339,1172,1173,1176,1179,1182],{},[342,1174,1175],{},"Consistency: A billing service cannot tolerate data loss. This is a basic requirement—and the most essential one.",[342,1177,1178],{},"Availability: It must have failover capability. And, it must be able to recover from a failure automatically.",[342,1180,1181],{},"Massive storage: Mobile applications generate copious amounts of transaction data, so massive storage capacity is also a must.",[342,1183,1184],{},"Low latency: A payment service that handles billions of transactions per day must be able to process them with minimal delay (typically, less than 10 milliseconds per transaction).",[40,1186,1188],{"id":1187},"evaluation-phase","Evaluation phase",[48,1190,1191],{},"With the above requirements in mind, Tencent evaluated several Apache open-source, streaming platforms for Midas—specifically, Kafka®, RocketMQ™, and Pulsar. Here's what they found.",[48,1193,1194],{},"Apache Kafka aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. It is a popular choice for log collection and processing. However, Kafka can be unreliable when it comes to data consistency and durability (data loss). Therefore, Tencent deemed it unsuitable for mission-critical financial applications like Midas.",[48,1196,1197],{},"Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity, and flexible scalability. Unfortunately, its application program interface (API) is limited in that there is no user-friendly way to delete invalid messages by topic. Moreover, RocketMQ's open-source version does not provide the needed failover capability, making it a poor choice for Midas.",[48,1199,1200],{},"Apache Pulsar is an enterprise-grade publish\u002Fsubscribe (aka pub\u002Fsub) messaging system. Pulsar provides highly available storage through its Apache Bookkeeper service. Because Pulsar uses a decoupled architecture, its storage and processing layers can be scaled independently.",[48,1202,1203],{},"Message streaming and queuing are necessary for an event-driven system and Pulsar supports both of these consumption modes. Streaming is strictly ordered (that is, exclusive to one consumer) whereas queueing is unordered (shared by many).",[48,1205,1206],{},"Another key Pulsar feature, geo replication, helps improve application response time by adjusting the distribution of data across geographically distributed data networks.",[48,1208,1209],{},"Tencent ultimately chose Apache Pulsar as a service for its native high consistency, durability, low latency, scalability, and general flexibility.",[48,1211,1212],{},[351,1213],{"alt":1214,"src":1215},"Table summarizes Tencent's comparison of Kafka, RocketMQ, and Pulsar","\u002Fimgs\u002Fblogs\u002F63a3181323d4115d2167d342_Tencent's-comparison-of-Kafka,-RocketMQ,-and-Pulsar..webp",[40,1217,1219],{"id":1218},"solution","Solution",[48,1221,1222],{},"Tencent solved their scalability problem by integrating Pulsar into a distributed transaction framework called TDXA. TDXA leverages a message queue in both online transaction processing (OLTP) and real-time data processing to ensure consistency and prevent data loss. The message queue also handles, in a highly reliable way, any failures that might occur during transaction processing. Thus, the new solution is able to manage very high throughput with minimal delays.",[48,1224,1225],{},[351,1226],{"alt":1227,"src":1228},"examples of some of Tencent’s most common online transaction processing and real-time data processing activities ","\u002Fimgs\u002Fblogs\u002F63a2cc375623a8863ed010d9_image3.png",[32,1230,1232],{"id":1231},"online-transaction-processing","Online transaction processing",[48,1234,1235],{},"In online transaction processing, the workflow associated with any given payment often involves multiple internal and external systems. This can lead to longer RPC chains (that is, communications) and more numerous failures—in particular, network timeouts (for example, when interacting with overseas payment services).",[48,1237,1238],{},"By integrating with a local transaction state, TDXA is able to recover automatically in the event of a failure. It then systematically resumes processing, thus ensuring the consistency of billions of transactions daily.",[48,1240,1241],{},"An automated teller machine (ATM) for a bank is an example of a commercial OLTP application. OLTP applications have high throughput and are insert- or update- intensive in database management. These applications are used concurrently by hundreds of users. The key goals of OLTP applications are availability, speed, concurrency, and recoverability. OLTP applications help simplify business in various ways—for example, by reducing paper trails and providing faster, more accurate forecasts for revenues and expenses.",[32,1243,1245],{"id":1244},"real-time-data-processing","Real-time data processing",[48,1247,1248],{},"To overcome the challenge of validating data consistency in Midas, Tencent implemented a reconciliation system to authenticate data. This enabled the company to shorten reconciliation time and detect problems much sooner",[48,1250,1251],{},"For mobile payments, real-time user experience is critical. For example, if a player purchases a hero in a mobile game like \"King of Glory\" and the hero is not delivered in a timely manner, it will inevitably affect the user's experience negatively and result in complaints.",[48,1253,1254],{},"With TDXA, Tencent can reconcile billing transactions in real time using a stream computing framework to process the transactions produced in the message queue.",[818,1256,1258],{"id":1257},"other-significant-benefits-in-real-time","Other significant benefits in real-time",[48,1260,1261],{},"During peak times (for example, a King of Glory anniversary celebration event), the transaction traffic in Midas can surge to more than ten times the average rate. The Pulsar message queue can buffer waves of high traffic to reduce the demand on the core transaction system for requests such as transaction inquiries, delivery notifications, and tips notifications.",[48,1263,1264],{},"Also, with the ability to process messages in a message queue in real-time, Tencent can offer real-time data analysis and provide precise marketing services to its customers and subsidiaries. Examples of typical services include transaction and balance reconciliation, fraud detection, and real-time risk control.",[32,1266,1268],{"id":1267},"a-deeper-dive-into-tdxa","A deeper dive into TDXA",[48,1270,1271],{},"TDXA is a distributed transaction framework designed to solve the data consistency and durability problems associated with processing huge transaction volumes in the application layer. Figure 4 provides a technical diagram of Midas.",[48,1273,1274],{},[351,1275],{"alt":1276,"src":1277}," Figure of technical diagram of Midas","\u002Fimgs\u002Fblogs\u002F63a2cc37c98edb2fe5b202e6_image2.png",[48,1279,1280],{},"The TDF network manages the flow of traffic through the billing transaction system. These are the main components of the TDF network:",[339,1282,1283,1286,1289,1292,1295],{},[342,1284,1285],{},"Distributed transaction manager(TM): The distributed transaction manager serves as the control center for TDXA. It uses a decentralized approach that will allow Tencent to scale the system as their business grows, offers necessary services, and ensures that systems are running and available 99.999% of the time. TM supports both the REST API-based Try-Confirm\u002FCancel (TCC) approach and hybrid DB transactions.",[342,1287,1288],{},"With TDF (which is an asynchronous coroutine framework and asynchronous transaction processing in TDSQL), TM is able to support the entire company's billing business in a highly efficient manner.",[342,1290,1291],{},"Configuration manager (CM): TDXA's configuration manager provides a flexible mechanism for registering, managing, and updating transaction processing flow at runtime. CM automatically checks the accuracy and completeness of the transaction flow. It also displays the transaction flow in a GUI console where users have the ability to manage it.",[342,1293,1294],{},"Distributed transactional database (TDSQL): A distributed transactional database which features high consistency, high availability, global deployment architecture, distributed horizontal scalability, high performance, enterprise-grade security support, and more. TDSQL provides a comprehensive distributed database solution.",[342,1296,1297],{},"Message queue (MQ): A highly consistent and available message queue that enables TDXA to handle various failure scenarios during transaction processing. A robust message queue plays a vital role in processing transactions for Midas.",[40,1299,1301],{"id":1300},"implementation","Implementation",[48,1303,1304],{},"In the process of adopting Pulsar, Tencent needed to make certain changes to Pulsar in order to meet their own unique requirements. In general, these changes provided support for the following:",[339,1306,1307,1310,1313],{},[342,1308,1309],{},"Delayed messaging and delayed retries (supported in v2.4.0)",[342,1311,1312],{},"An improved management console",[342,1314,1315],{},"An improved monitoring and alert system",[48,1317,1318],{},"Each of these system enhancements is described in greater detail below.",[40,1320,1322],{"id":1321},"delayed-message-delivery","Delayed message delivery",[48,1324,1325],{},"Delayed message delivery is a common requirement in a billing service. This feature is used for handling timeouts in transaction processing. In the event of a service failure or timeout, it makes little sense to retry a transaction many times within a short period of time because it is likely to fail again. Instead, it is better to retry by leveraging Pulsar's delayed message delivery feature.",[48,1327,1328],{},"Delayed message delivery can be implemented in two different ways. One is by organizing messages by different topics based on the time delay interval (see Figure 5). Pulsar's internal broker checks those delay topics periodically and delivers the delayed messages accordingly.",[48,1330,1331],{},[351,1332],{"alt":1333,"src":1334},"Figure of a messages organized by different topics based on the time delay interval ","\u002Fimgs\u002Fblogs\u002F63a2cc375623a84f39d010e7_image4.png",[48,1336,1337],{},"The above approach satisfies most requirements, except when you want to specify an arbitrary time delay. An arbitrary time delay can be implemented using a time wheel, which can support a finer level of granularity. However, for this approach, the system needs to maintain an index for the time wheel, thus rendering this method unsuitable when there is a large volume of delayed messages.",[48,1339,1340],{},"While keeping Pulsar's internal storage unchanged, Tencent implemented both of the above approaches to support bargaining activities in the King of Glory game.",[32,1342,1344],{"id":1343},"secondary-tag","Secondary tag",[48,1346,1347],{},"To ensure security across the tens of thousands of businesses it supports, Midas must synchronize transaction flow for each business.",[48,1349,1350],{},"Suppose you were to create a unique topic for each business. You would need to create tens of thousands of topics. This would greatly increase the burden of topic management. For example, if a consumer needed to consume messages from all the businesses involved in a given transaction flow, Midas would have to maintain tens of thousands of subscriptions.",[48,1352,1353],{},"To solve this problem, Tencent introduced an attribute called \"Tag\" to the metadata associated with a Pulsar message. Users can set multiple tags while producing a Pulsar message queue. When messages are consumed, the broker filters out the desired tags.",[48,1355,1356],{},"The example below illustrates how the tags \"King of Glory,\" \"Wechat Payment,\" and \"Successful Payment\" could be used in a payment message. Here, the tags indicate where the transaction originated from (King of Glory game vs. Wechat Payment) and what the status of the transaction is (success vs. failure).",[48,1358,1359],{},[351,1360],{"alt":972,"src":1361},"\u002Fimgs\u002Fblogs\u002F63a2cc38c88838ec7d61fb3c_image5.png",[32,1363,1365],{"id":1364},"management-console","Management console",[48,1367,1368],{},"You need to have a robust management console if you plan to use message queues on a large scale. Tencent needed the Midas management console to be able to handle the following requests from its users.",[339,1370,1371,1374,1377],{},[342,1372,1373],{},"What is the content of this message?",[342,1375,1376],{},"Who produced this message?",[342,1378,1379],{},"Will this message be consumed? If so, by whom?",[48,1381,1382],{},"To service these types of requests, Tencent added life-cycle-related information to Pulsar's message metadata. Doing so enabled Midas to track messages throughout their entire life cycle (from production to consumption). The numbered arrows in Figure 7 show the various stages in the life cycle of a message.",[48,1384,1385],{},[351,1386],{"alt":1387,"src":1388}," illustration of The life cycle of a message","\u002Fimgs\u002Fblogs\u002F63a2cc38c7e2b101da1cf7f9_image6.png",[32,1390,1392],{"id":1391},"monitor-and-alert","Monitor and alert",[48,1394,1395],{},"Figure 8 shows how Tencent uses Pulsar to monitor and alert on various metrics. Monitoring is accomplished using a series of user-defined alert rules. The metrics are collected and stored in Midas's Eagle-Eye monitoring platform.",[48,1397,1398],{},[351,1399],{"alt":1400,"src":1401}," illiustration of how Midas uses Pulsar to alert on various metrics","\u002Fimgs\u002Fblogs\u002F63a2cc382c0c6bb8d61a4d9a_image7.png",[48,1403,1404],{},"Tencent monitors and alerts on the following key metrics:",[339,1406,1407,1410,1413],{},[342,1408,1409],{},"Backlog: If a massive amount of information accumulates for online services, it means that consumption has become a bottleneck. When this happens, the system provides a timely alert so the appropriate personnel can deal with the problem.",[342,1411,1412],{},"Delay: The system should be able to search a purchase record within one second. By matching the production flow and consumption flow collected by the monitoring component, Tencent can calculate the end-to-end latency of each message.",[342,1414,1415],{},"Failure: The Midas Eagle-Eye platform maintains statistics of errors in the pipeline, monitoring and alerting from various dimensions such as business, IP, and others.",[32,1417,1419],{"id":1418},"tencents-new-midas-implementation-with-pulsar","Tencent's new Midas implementation with Pulsar",[48,1421,1422],{},"After making the enhancements described above, Tencent deployed Apache Pulsar with the architecture shown in Figure 9.",[48,1424,1425],{},[351,1426],{"alt":1427,"src":1428},"Illustration of the new architecture of midas with Pulsar ","\u002Fimgs\u002Fblogs\u002F63a2cc38d1dcd680a97ae68f_image8.png",[48,1430,1431],{},"Pulsar has greatly enhanced Midas by providing the following components and capabilities:",[339,1433,1434,1437,1440,1443],{},[342,1435,1436],{},"Broker, the message queue proxy layer, is responsible for message production and consumption requests. Broker supports horizontal scalability and rebalances partitions automatically by topic based on the throughput.",[342,1438,1439],{},"BookKeeper serves as the distributed storage for message queues. You can configure multiple replicas of messages in BookKeeper. BookKeeper is enabled with automatic failover capability under exceptional circumstances—for example, when a storage node has broken disks.",[342,1441,1442],{},"ZooKeeper serves as the metadata and cluster configuration for message queues.",[342,1444,1445],{},"Some Midas businesses are written in JavaScript while others are in PHP. The HTTP proxy provides a unified access endpoint and failure retry capability for clients that use other languages. When the production cluster fails, the proxy will downgrade processing and route messages to other clusters for disaster recovery.",[48,1447,1448],{},"In addition, Pulsar lets Tencent designate how each subscription is to consume messages. A subscription is a consumer group associated with a topic. Three types of subscriptions can be used in streaming:",[339,1450,1451,1454,1457],{},[342,1452,1453],{},"A shared subscription allows you to scale consumption beyond the number of partitions.",[342,1455,1456],{},"A failover subscription works well for stream processing in transaction cleanup workflow.",[342,1458,1459],{},"An exclusive subscription is used when only one consumer in a subscription is allowed to consume a topic partition at any given time.",[40,1461,1463],{"id":1462},"result","Result",[48,1465,1466],{},"After successfully adopting Pulsar, Tencent can now run their billing and transaction framework on a very large scale. With Pulsar's help, Midas now efficiently supports the following:",[339,1468,1469,1472,1475,1478,1481,1484],{},[342,1470,1471],{},"More than 80 payment channels with various characteristics",[342,1473,1474],{},"More than 300 different business processing units",[342,1476,1477],{},"Up to 8 clusters",[342,1479,1480],{},"More than 600 topics",[342,1482,1483],{},"Throughput rates of 50w+ queries per second",[342,1485,1486],{},"Data consumption rates averaging 10T+ per day",[48,1488,1489],{},[351,1490],{"alt":1491,"src":1492},"illustration of the combined power of Midas and Pulsar ","\u002Fimgs\u002Fblogs\u002F63a2cc38f41b5147a2370638_image9.png",[48,1494,1495],{},"As a result of implementing Pulsar, Tencent can now:",[339,1497,1498,1501,1504],{},[342,1499,1500],{},"Handle tens of billions of transactions during peak time.",[342,1502,1503],{},"Guarantee data consistency in processing transactions.",[342,1505,1506],{},"Provide 99.999% availability for the services it supports.",[48,1508,1509],{},"In summary, Pulsar's high consistency, availability, stability, and flexible framework have solved Tencent's biggest transaction processing challenges. By redesigning their transaction processing pipeline, Tencent can now scale Midas to handle the increased billing volume demands associated with their growing business.",[40,1511,1513],{"id":1512},"about-apache-pulsar","About Apache Pulsar",[48,1515,1516],{},"Apache Pulsar is a young open-source project with attractive features. The Apache Pulsar community is growing rapidly with new adoptions in a variety of industries. We look forward to further collaborations with the Apache Pulsar community. We like to share advances with the greater community, and work with other users on making continuous improvements to Pulsar.",[339,1518,1519,1522,1525],{},[342,1520,1521],{},"Tencent is a trademark of Tencent Holdings Limited.",[342,1523,1524],{},"Apache and Kafka are registered trademarks of The Apache Software Foundation.",[342,1526,1527],{},"Pulsar and RocketMQ are trademarks of The Apache Software Foundation.",{"title":18,"searchDepth":19,"depth":19,"links":1529},[1530,1531,1532,1536,1537,1538,1543,1544,1550,1551],{"id":1095,"depth":19,"text":1096},{"id":1105,"depth":19,"text":1106},{"id":1133,"depth":19,"text":1134,"children":1533},[1534,1535],{"id":1137,"depth":279,"text":1138},{"id":1150,"depth":279,"text":1151},{"id":1163,"depth":19,"text":1164},{"id":1187,"depth":19,"text":1188},{"id":1218,"depth":19,"text":1219,"children":1539},[1540,1541,1542],{"id":1231,"depth":279,"text":1232},{"id":1244,"depth":279,"text":1245},{"id":1267,"depth":279,"text":1268},{"id":1300,"depth":19,"text":1301},{"id":1321,"depth":19,"text":1322,"children":1545},[1546,1547,1548,1549],{"id":1343,"depth":279,"text":1344},{"id":1364,"depth":279,"text":1365},{"id":1391,"depth":279,"text":1392},{"id":1418,"depth":279,"text":1419},{"id":1462,"depth":19,"text":1463},{"id":1512,"depth":19,"text":1513},"Apache Pulsar","2020-02-18","An inside look at why and how Tencent uses Apache Pulsar messaging to power its billing platform for processing tens of billions of transactions every day.","\u002Fimgs\u002Fblogs\u002F63d79974d2a5679026c47152_63a317e28f20527970e09257_tencent-top-background-1.webp",{},"\u002Fblog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions","14 min",{"title":1088,"description":1554},"blog\u002Fapache-pulsar-helps-tencent-process-tens-of-billions-of-financial-transactions",[1562,1552,1563],"Success Stories","Transactions","cJFYJTWAJJByuQB28fkb1oZAcAhviPFewNXFoeDmPQQ",[1566],{"id":1567,"title":1090,"bioSummary":1568,"email":289,"extension":8,"image":1569,"linkedinUrl":289,"meta":1570,"position":289,"stem":1582,"twitterUrl":289,"__hash__":1583},"authors\u002Fauthors\u002Fdezhi-liu.md","Dezhi Liu is a senior software developer in the billing department at Tencent, where he specializes in the development of middleware and software components related to finance.","\u002Fimgs\u002Fauthors\u002Fdezhi-liu.webp",{"body":1571},{"type":15,"value":1572,"toc":1580},[1573,1575],[48,1574,1568],{},[48,1576,1577],{},[55,1578],{"href":1579},"\u002F",{"title":18,"searchDepth":19,"depth":19,"links":1581},[],"authors\u002Fdezhi-liu","7Vvo2o6QMtM0S_PyXlsR3mSUq6cO1tmPkIaT6EqMC8U",[1585,1592,1599],{"path":1586,"title":1587,"date":1588,"image":-1,"link":-1,"collection":1589,"resourceType":1590,"score":1591,"id":1586},"\u002Fblog\u002Fhow-orange-financial-combats-financial-fraud-using-apache-pulsar","How Orange Financial combats financial fraud in over 50M transactions a day using Apache Pulsar","2019-11-11","blogs","Blog",1,{"path":1593,"title":1587,"date":1594,"image":1595,"link":-1,"collection":1596,"resourceType":1597,"score":1598,"id":1593},"\u002Fsuccess-stories\u002Fbestpay","2022-12-27","\u002Fimgs\u002Fsuccess-stories\u002F67956b0055b8586d148f8b68_SN-SuccessStories-bestpay.webp","successStories","Case Study",0.733,{"path":1600,"title":1601,"date":1594,"image":1602,"link":-1,"collection":1596,"resourceType":1597,"score":1598,"id":1600},"\u002Fsuccess-stories\u002Ftencent","Powering Tencent Billing Platform with Apache Pulsar","\u002Fimgs\u002Fsuccess-stories\u002F67942e3b5f9411fb93dcb9f9_SN-SuccessStories-tencent.webp",1776409741228]