[{"data":1,"prerenderedAt":1652},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning":1086,"blog-authors-\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning":1600,"related-\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning":1632},{"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":1091,"category":1587,"createdAt":290,"date":1588,"description":1589,"extension":8,"featured":294,"image":1590,"isDraft":294,"link":290,"meta":1591,"navigation":7,"order":296,"path":1592,"readingTime":1593,"relatedResources":290,"seo":1594,"stem":1595,"tags":1596,"__hash__":1599},"blogs\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning.md","Taking a Deep-Dive into Apache Pulsar Architecture for Performance Tuning",[313,1090],"Devin Bost",{"type":15,"value":1092,"toc":1568},[1093,1096,1099,1103,1106,1110,1117,1120,1123,1127,1130,1136,1139,1143,1146,1150,1153,1157,1160,1164,1167,1170,1174,1182,1185,1191,1194,1197,1203,1207,1210,1214,1222,1226,1229,1232,1242,1245,1248,1252,1255,1258,1264,1268,1271,1274,1280,1283,1287,1290,1293,1299,1303,1306,1310,1313,1316,1327,1333,1337,1340,1343,1347,1350,1353,1357,1360,1366,1369,1375,1378,1381,1387,1390,1393,1399,1402,1405,1408,1414,1418,1421,1425,1428,1434,1438,1441,1447,1449,1452,1457,1460,1463,1466,1471,1477,1481,1484,1492,1496,1499,1513,1517,1564,1566],[48,1094,1095],{},"When we talk about Apache Pulsar’s performance, we are usually referring to the throughput and latency associated with message writes and reads. Pulsar has certain configuration parameters that allow you to control how the system handles message writes or reads. To effectively tune Pulsar clusters for optimal performance, you need to understand Pulsar’s architecture and its storage layer, Apache BookKeeper.",[48,1097,1098],{},"In this blog, we explain some basic concepts and how messages are sent (produced) and received (consumed) in Apache Pulsar. You will learn key components, data flow and key metrics to monitor for Pulsar performance tuning.",[40,1100,1102],{"id":1101},"_1-apache-pulsar-basic-concepts","1. Apache Pulsar Basic Concepts",[48,1104,1105],{},"The basic concepts and terminology explained in this section are key to understanding how Apache Pulsar works.",[32,1107,1109],{"id":1108},"_11-message","1.1 Message",[48,1111,1112,1113,190],{},"The basic unit of data in Pulsar is called a message. Producers send messages to brokers, and brokers send messages to consumers using flow control. For an in-depth discussion of Pulsar’s flow command, click ",[55,1114,267],{"href":1115,"rel":1116},"http:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fen\u002Fdevelop-binary-protocol\u002F#flow-control",[264],[48,1118,1119],{},"Messages contain the data written to the topic by the producer, along with some important metadata.",[48,1121,1122],{},"In Pulsar, a message can be either of two types: a batch message or a single message. A batch message is a sequence of single messages. (See Section 1.5.1 below for more detailed information about batch messages.)",[32,1124,1126],{"id":1125},"_12-topic","1.2 Topic",[48,1128,1129],{},"A topic is a category or feed name to which messages are published (produced). Topics in Pulsar can have multiple producers and\u002For consumers. Producers write messages to the topic, and consumers consume messages from the topic. Figure 1 shows how they work together.",[48,1131,1132],{},[351,1133],{"alt":1134,"src":1135},"Figure 1. How Producers and Consumers Work on Topics","\u002Fimgs\u002Fblogs\u002F63be7241482366a21e2f5e03_1.png",[48,1137,1138],{},"‍",[32,1140,1142],{"id":1141},"_13-bookie","1.3 Bookie",[48,1144,1145],{},"Apache Pulsar uses Apache BookKeeper as its storage layer. Apache BookKeeper is a scalable, fault-tolerant, and low-latency storage service optimized for real-time workloads. Messages published by clients are stored in a server instance of Bookkeeper, which is called a bookie.",[818,1147,1149],{"id":1148},"_131-entry-and-ledger","1.3.1 Entry and Ledger",[48,1151,1152],{},"Entry and ledger are basic terms used within BookKeeper. An entry contains the data written to the ledger, along with some important metadata. A ledger is the basic unit of storage in BookKeeper. A ledger is a sequence of entries. Entries are written to a ledger sequentially.",[818,1154,1156],{"id":1155},"_132-journal","1.3.2 Journal",[48,1158,1159],{},"A journal file contains BookKeeper transaction logs. Before a ledger update takes place, the bookie ensures that a transaction describing the update, called a transaction log entry, is written to non-volatile storage. A new journal file is created when a bookie is first started, or when the older journal file reaches the specified journal file size threshold.",[818,1161,1163],{"id":1162},"_133-entry-log","1.3.3 Entry Log",[48,1165,1166],{},"An entry log file manages the written entries received from BookKeeper clients. Entries from different ledgers are aggregated and written sequentially, while their offsets are kept as pointers in a ledger cache for fast lookup.",[48,1168,1169],{},"A new entry log file is created when the bookie is started,​ o​ r when the older entry log file reaches the specified entry log size threshold. The Garbage Collector Thread removes old entry log files when they are no longer associated with any active ledgers.",[818,1171,1173],{"id":1172},"_134-index-db","1.3.4 Index DB",[48,1175,1176,1177,1181],{},"A bookie uses RocksDB as the entry index DB. RocksDB is a high-performance, embeddable, persistent, key-value store based on log-structured merge (LSM) trees. Understanding the mechanics of an LSM tree will provide additional insights into the mechanics of Bookkeeper. More information about the design of the LSM tree is available in its original paper which can be found at ",[55,1178,267],{"href":1179,"rel":1180},"https:\u002F\u002Fwww.cs.umb.edu\u002F~poneil\u002Flsmtree.pdf",[264],"。",[48,1183,1184],{},"When a BookKeeper client writes an entry to a ledger, the bookie writes the entry to the journal and sends a response to a client after the journal is written. A background thread writes the entry to an entry log. When the bookie’s background thread flushes data to the entry log, the index is simultaneously updated. This process is illustrated in Figure 2.",[48,1186,1187],{},[351,1188],{"alt":1189,"src":1190},"Figure 3. Ledgers and Cursors Within a Managed Ledger Associated with a Topic","\u002Fimgs\u002Fblogs\u002F63be7241d7bf17ba3226490a_3.png",[48,1192,1193],{},"The cursor uses the ledger to store the mark delete position of a subscription. The mark delete position is similar to an offset in Apache Kafka®, but it is more than a simple offset because Pulsar supports multiple subscription modes.",[48,1195,1196],{},"A managed ledger has many ledgers, so how does the managed ledger decide whether to start a new ledger? If a ledger is too large, data recovery time increases. If a ledger is too small, the ledger must switch more frequently, and the managed ledger calls upon Meta Store more often to update the metadata in the managed ledger. The ledger rollover policy for the managed ledger determines how frequently a new ledger is created. You use the following Pulsar parameters to control ledger behavior in broker.conf:",[48,1198,1199],{},[351,1200],{"alt":1201,"src":1202},"Pulsar configuration parameters to control ledger behavior in broker. ","\u002Fimgs\u002Fblogs\u002F63be72f0cace4e29996f4386_Screenshot-2023-01-11-at-09.27.18.png",[818,1204,1206],{"id":1205},"_142-managed-ledger-cache","1.4.2 Managed Ledger Cache",[48,1208,1209],{},"Managed ledger cache is a type of cache memory used for storing tailing messages across topics.​ ​For tailing reads, consumers read the data from the serving broker. Because the broker already has the data cached in memory, there’s no need to read from disk or compete for resources with writes.",[32,1211,1213],{"id":1212},"_15-client","1.5 Client",[48,1215,1216,1217,1221],{},"Users utilize Pulsar clients to create producers (which publish messages to topics) or consumers (which consume messages from topics). There are many Pulsar client libraries available. For more details, visit ",[55,1218,267],{"href":1219,"rel":1220},"http:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fen\u002Fclient-libraries\u002F",[264],".​",[818,1223,1225],{"id":1224},"_151-batch-message","1.5.1 Batch Message",[48,1227,1228],{},"A batch message consists of a set of single messages that are assumed to represent a single contiguous sequence. Using a batch can reduce the overhead on both the client and server sides. Messages are grouped into small batches to achieve some of the performance advantages of batch processing without increasing the latency for each task too much.",[48,1230,1231],{},"In Pulsar, when using batch processing, producers send the batch to the broker. After the batch reaches the broker, the broker coordinates with the bookie,​ ​which then stores the batch in BookKeeper. When the consumer reads messages from the broker, the broker also dispatches the batch to the consumer. So, both combining batches and splitting batches occurs in the client. The sample code below shows how to enable and configure message batching for a producer:",[1233,1234,1239],"pre",{"className":1235,"code":1237,"language":1238},[1236],"language-text","client.newProducer()\n    .topic(“topic-name”)\n    .enableBatching(true)\n    .batchingMaxPublishDelay(2, TimeUnit.MILLISECONDS) .batchingMaxMessages(100)\n    .batchingMaxBytes(1024 * 1024) \n    .create();\n","text",[1240,1241,1237],"code",{"__ignoreMap":18},[48,1243,1244],{},"In this example, the producer flushes the batch when the size of the batch exceeds 100 messages or 1MB of data.​ If these parameters are not met within two milliseconds, the producer will trigger batch flushing.",[48,1246,1247],{},"Therefore, your parameter settings will depend on message throughput and whatever publish latency you deem acceptable when publishing messages.",[818,1249,1251],{"id":1250},"_152-message-compression","1.5.2 Message Compression",[48,1253,1254],{},"Message compression can reduce message size by paying some CPU overhead. The Pulsar client supports multiple compression types, such as lz4, zlib, zstd, and snappy. Compression types are stored in the message metadata, so consumers can adopt different compression types automatically, as needed.",[48,1256,1257],{},"When you enable message batching, the Pulsar client provides improved compression by reducing the size of the batch. The sample code below shows how to enable compression type for a producer:",[1233,1259,1262],{"className":1260,"code":1261,"language":1238},[1236],"client.newProducer()\n    .topic(“topic-name”) \n    .compressionType(CompressionType.LZ4) \n    .create();\n",[1240,1263,1261],{"__ignoreMap":18},[818,1265,1267],{"id":1266},"_153-setting-the-maximum-number-of-pending-messages-for-a-producer","1.5.3 Setting the Maximum Number of Pending Messages for a Producer",[48,1269,1270],{},"Each producer uses a queue to hold the messages that are waiting to receive acknowledgments from the broker. Increasing the size of this queue can improve the throughput of published messages. However, doing so can cause unwanted memory overhead.",[48,1272,1273],{},"The sample code below shows how to configure the size of the pending messages queue for a producer:",[1233,1275,1278],{"className":1276,"code":1277,"language":1238},[1236],"client.newProducer() \n    .topic(“topic-name”) \n    .maxPendingMessages(2000) \n    .create();\n",[1240,1279,1277],{"__ignoreMap":18},[48,1281,1282],{},"When setting the value of maxPendingMessages, it is important to consider the memory impact on the client application. To estimate the memory impact, multiply the number of bytes per message by the number of maxPendingMessages. For example, if each message is 100 KB, setting 2000 maxPendingMessages may add 200 MB (2000 * 100 KB = 200,000 KB = 200 MB) of additional required memory.",[818,1284,1286],{"id":1285},"_154-configuring-the-size-of-the-receiver-queue-for-a-consumer","1.5.4 Configuring the Size of the Receiver Queue for a Consumer",[48,1288,1289],{},"The consumer’s receiver queue controls how many messages the consumer is allowed to accumulate before the messages are taken away by the user’s application. Making the receiver queue size larger could potentially increase consumption throughput at the expense of higher memory utilization.",[48,1291,1292],{},"The sample code below shows how to configure the size of the receiver queue for a consumer:",[1233,1294,1297],{"className":1295,"code":1296,"language":1238},[1236],"client.newConsumer() \n    .topic(“topic-name”) \n    .subscriptionName(“sub-name”) \n    .receiverQueueSize(2000) \n    .subscribe();\n",[1240,1298,1296],{"__ignoreMap":18},[40,1300,1302],{"id":1301},"_2-how-message-writing-works-on-the-server-side","2. How Message Writing Works on the Server Side",[48,1304,1305],{},"To be able to tune message writing performance effectively, you first need to understand how message writing works.",[32,1307,1309],{"id":1308},"_21-interactions-between-brokers-and-bookies","2.1. Interactions Between Brokers and Bookies",[48,1311,1312],{},"When a client publishes a message to a topic, the message is sent to the broker that is serving the topic, and the broker writes data in parallel to the storage layer.",[48,1314,1315],{},"As shown in Figure 4, having more data replicas makes the broker pay more network bandwidth overhead. You can mitigate the impact on network bandwidth by configuring persistence parameters at the following levels:",[339,1317,1318,1321,1324],{},[342,1319,1320],{},"In Pulsar",[342,1322,1323],{},"At the broker level",[342,1325,1326],{},"At namespace level",[48,1328,1329],{},[351,1330],{"alt":1331,"src":1332},"Configuring Persistence Parameters at the Broker Level","\u002Fimgs\u002Fblogs\u002F63be73a511e947552c943434_Screenshot-2023-01-11-at-09.30.12.png",[818,1334,1336],{"id":1335},"_213-configuring-persistence-parameters-at-the-namespace-level","2.1.3 Configuring Persistence Parameters at the Namespace Level",[48,1338,1339],{},"Optionally, you can overwrite the persistence parameters at namespace level policy. In the example shown below, all three persistence parameters have been set to a value of \"3\".",[48,1341,1342],{},"$ bin\u002Fpulsar-admin namespaces set-persistence --bookkeeper-ack-quorum 3 --bookkeeper-ensemble 3 --bookkeeper-write-quorum 3 my-tenant\u002Fmy-namespace",[818,1344,1346],{"id":1345},"_214-configuring-the-size-of-the-worker-thread-pool","2.1.4 Configuring the Size of the Worker Thread Pool",[48,1348,1349],{},"To guarantee that the messages within a topic are stored in the order in which they are written, the broker uses a single thread for writing the managed ledger entries associated with a topic. The broker takes a thread from the managed ledger worker thread pool that bears the same name. You use the following parameter to configure the size of the worker thread pool in broker.conf.",[48,1351,1352],{},"Parameter managedLedgerNumWorkerThreads is used to specify the number of threads to be used for dispatching managed ledger tasks. Negative numbers are not allowed. If no value has been specified, the system will use the number of processors available to the Java virtual machine by default.8",[32,1354,1356],{"id":1355},"_22-understanding-how-bookies-handle-entry-requests","2.2 Understanding How Bookies Handle Entry Requests",[48,1358,1359],{},"This section provides a more detailed, step-by-step explanation of how a bookie handles the addition of entry requests. The diagram in Figure 5 gives you an overview of the process.",[48,1361,1362],{},[351,1363],{"alt":1364,"src":1365},"Configuration parameters that control the journal directories and ledger directories in bookkeeper.conf","\u002Fimgs\u002Fblogs\u002F63be73d92f025bf27960b345_Screenshot-2023-01-11-at-09.31.01.png",[48,1367,1368],{},"When the request processor appends the new entries to the journal log, which is a type of write-ahead log (WAL), a bookie asks the processor to provide a thread from the write thread pool associated with the ledger ID. You can configure the size of the thread pool and the maximum number of pending requests in each thread for handling entry write requests.",[48,1370,1371],{},[351,1372],{"alt":1373,"src":1374},"Configuration parameters that control the size of the thread pool and the maximum number of pending requests in each thread for handling entry write requests.","\u002Fimgs\u002Fblogs\u002F63be73f88141108f9ad0b2a3_Screenshot-2023-01-11-at-09.31.39.png",[48,1376,1377],{},"If the number of pending requests of adding entry exceeds the maximum number of pending requests of adding entry specified in bookkeeper.conf, the bookie will reject new requests of adding entry.",[48,1379,1380],{},"By default, all journal log entries are synchronized to disk to avoid data loss in the event that a machine loses power. So, the latency of data synchronization has the most important influence on write throughput and latency. If you use the HDD as journal disks, be sure to disable the journal sync mechanism so the bookie client gets responses after the entry writes to the OS page cache successfully. Use the following parameter to enable or disable journal data synchronization in bookkeeper.conf:",[48,1382,1383],{},[351,1384],{"alt":1385,"src":1386},"Parameter to enable or disable journal data synchronization in bookkeeper.conf","\u002Fimgs\u002Fblogs\u002F63be741363863b256204020f_Screenshot-2023-01-11-at-09.32.07.png",[48,1388,1389],{},"The group commit mechanism allows any tasks that are waiting to be executed to be grouped into small batches. This technique achieves better performance for batch processing without a sharp increase in latency for each task. Bookies can also use the same method to improve the throughput for journal data writes. Enabling group committing for journal data can reduce disk operations and avoid excessive small file writes. However, to avoid an increase in latency, you can disable group commit.",[48,1391,1392],{},"Use the following parameters to enable or disable the​ g​roup commit mechanism in bookkeeper.conf:",[48,1394,1395],{},[351,1396],{"alt":1397,"src":1398},"Parameters to enable or disable the​ g​roup commit mechanism in bookkeeper.conf","\u002Fimgs\u002Fblogs\u002F63be742ac9d13eee89bce3ee_Screenshot-2023-01-11-at-09.32.30.png",[48,1400,1401],{},"After the entry is written to the journal, the entry is also added to the ledger storage. By default, the bookie uses the value you specify in DbLedgerStorage as the ledger storage. DbLedgerStorage is an implementation of ledger storage that uses RocksDB to keep the indices for entries stored in entry logs. Requests of adding entry in ledger storage are completed after the entry is successfully written to the memory table, and then the requests on the bookie’s client-side are completed. The memory table will periodically flush to the entry logs and build the indices for entries stored in entry logs, also called the checkpoint.",[48,1403,1404],{},"The checkpoint introduces much random disk I\u002FO. If journal directories and ledger directories are located on separate devices, then flushing will not affect performance. But, if journal directories and ledger directories are located on the same device, then performance degrades significantly due to frequent flushing. You can consider increasing a bookie’s flush interval to improve performance. However, if you increase the flush interval, recovery will take longer when the bookie restarts (for example, after a failure).",[48,1406,1407],{},"For optimal performance, the memory table should be big enough to hold a substantial number of entries during the flush interval. Use the following parameters to set up the write cache size and the flush interval in bookkeeper.conf:",[48,1409,1410],{},[351,1411],{"alt":1412,"src":1413},"Parameters to set up the write cache size and the flush interval in bookkeeper.conf","\u002Fimgs\u002Fblogs\u002F63be7442814110006dd0b5ae_Screenshot-2023-01-11-at-09.32.55.png",[40,1415,1417],{"id":1416},"_3-how-message-reading-works-on-the-server-side","3. How Message Reading Works on the Server Side",[48,1419,1420],{},"Apache Pulsar is a multi-layer system that allows message reading to be split into tailing reads and catch-up reads. Tailing reads refers to reading the most recently written data. Catch-up reads read historical data. In Pulsar, there are different approaches for tailing reads and catch-up reads.",[32,1422,1424],{"id":1423},"_31-tailing-reads","3.1 Tailing Reads",[48,1426,1427],{},"For tailing reads, consumers read the data from the serving broker, which already has that data stored in managed ledger cache. This process is illustrated in Figure 6.",[48,1429,1430],{},[351,1431],{"alt":1432,"src":1433},"table","\u002Fimgs\u002Fblogs\u002F63be7464f1dcf760c562766d_Screenshot-2023-01-11-at-09.33.28.png",[32,1435,1437],{"id":1436},"_32-catch-up-reads","3.2 Catch-up reads",[48,1439,1440],{},"Catch-up reads go to the storage layer to read data. This process is illustrated in Figure 7.",[48,1442,1443],{},[351,1444],{"alt":1445,"src":1446},"Figure 7. How Catch-up Reads Are Read from the Storage Layer","\u002Fimgs\u002Fblogs\u002F63be7243cace4e852b6f3bb4_7.png",[48,1448,1138],{},[48,1450,1451],{},"The bookie server uses a single thread to handle entries that read requests from a ledger. The bookie server takes a thread from the read worker thread pool associated with the ledger ID. You use the following parameters in bookkeeper.conf to set up the size of the read worker thread pool and the maximum number of pending read requests for each thread:",[48,1453,1454],{},[351,1455],{"alt":1432,"src":1456},"\u002Fimgs\u002Fblogs\u002F63be7480814110d67dd0b6ec_Screenshot-2023-01-11-at-09.33.59.png",[48,1458,1459],{},"When reading entries from ledger storage, the bookie will first find an entry's position in the entry logs through the index file. DbLedgerStorage uses RocksDB to store the index for ledger entries. So, be sure to allocate enough memory to hold a significant portion of the index database to avoid swap-in and swap-out index entries.",[48,1461,1462],{},"For optimum performance, the size of the RocksDB block-cache needs to be big enough to hold a significant portion of the index database, which has been known to reach ~2GB in some cases.",[48,1464,1465],{},"You use the following parameter in bookkeeper.conf to control the size of the RocksDB block cache:",[48,1467,1468],{},[351,1469],{"alt":1432,"src":1470},"\u002Fimgs\u002Fblogs\u002F63be749fbd2de3a3057fce12_Screenshot-2023-01-11-at-09.34.29.png",[48,1472,1473,1474],{},"Enabling the entry read-ahead cache can reduce the operation of the disk for sequential reading. You use the following parameters to configure the entry read-ahead cache size in bookkeeper.conf:\n",[351,1475],{"alt":18,"src":1476},"\u002Fimgs\u002Fblogs\u002F63be74b3482366a4482f8d0b_Screenshot-2023-01-11-at-09.34.50.png",[40,1478,1480],{"id":1479},"_4-metadata-storage-optimization","4. Metadata Storage Optimization",[48,1482,1483],{},"Pulsar uses Apache® ZookeeperTM as its default metadata storage area. ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.",[48,1485,1486,1487,1491],{},"Zookeeper performance tuning is not discussed in this post. For excellent guidance on how to tune Zookeeper, visit ",[55,1488,267],{"href":1489,"rel":1490},"https:\u002F\u002Fzookeeper.apache.org\u002Fdoc\u002Fr3.4.13\u002FzookeeperAdmin.pdf",[264],".​ Of the recommendations mentioned in that document, pay special attention to those pertaining to disk I\u002FO.",[40,1493,1495],{"id":1494},"_5-conclusion","5. Conclusion",[48,1497,1498],{},"Hopefully, this introduction has given you a better understanding of some Pulsar basic concepts and, in particular, some insights into how pulsar handles message writing and reading. To review, we addressed the following concepts:",[339,1500,1501,1504,1507,1510],{},[342,1502,1503],{},"Improving read and write I\u002FO isolation gives bookies higher throughput and lower latency.",[342,1505,1506],{},"Taking advantage of I\u002FO parallelism between multiple disks allows us to optimize the performance of the journal and ledger.",[342,1508,1509],{},"For tailing reads, the entry cache in the broker can reduce resource overhead and avoid competing for resources with writes.",[342,1511,1512],{},"Improving Zookeeper performance maximizes system stability.",[40,1514,1516],{"id":1515},"_6-more-pulsar-resources","6. More Pulsar Resources",[339,1518,1519,1532,1540,1548,1556],{},[342,1520,1521,1522,1526,1527,1531],{},"Interested in ",[55,1523,1525],{"href":1524},"\u002Fproduct","fully-managed Apache Pulsar"," with enhanced reliability, tools, and features? ",[55,1528,1530],{"href":1529},"\u002Fthank\u002Fcontact-us","Contact us"," now!",[342,1533,1534,1539],{},[55,1535,1538],{"href":1536,"rel":1537},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[264],"Learn Pulsar"," from the original creators of Pulsar. Watch on-demand videos, enroll in self-paced courses, and complete our certification program to demonstrate your Pulsar knowledge.",[342,1541,1542,1543,1547],{},"Read the ",[55,1544,1546],{"href":1545},"\u002Fwhitepapers\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","2022 Pulsar vs. Kafka Benchmark Report"," for a side-by-side comparison of Pulsar and Kafka performance, including tests on throughput, latency, and more.",[342,1549,1550,1551,1555],{},"Watch sessions from ",[55,1552,1554],{"href":1553},"\u002Fpulsar-summit","Pulsar Summit San Francisco 2022"," for best practices and the future of messaging and event streaming technologies.",[342,1557,1558,1563],{},[55,1559,1562],{"href":1560,"rel":1561},"https:\u002F\u002Fhubs.ly\u002FQ016_Wgd0",[264],"Sign up"," for the monthly StreamNative Newsletter for Apache Pulsar.",[48,1565,1138],{},[48,1567,1138],{},{"title":18,"searchDepth":19,"depth":19,"links":1569},[1570,1576,1580,1584,1585,1586],{"id":1101,"depth":19,"text":1102,"children":1571},[1572,1573,1574,1575],{"id":1108,"depth":279,"text":1109},{"id":1125,"depth":279,"text":1126},{"id":1141,"depth":279,"text":1142},{"id":1212,"depth":279,"text":1213},{"id":1301,"depth":19,"text":1302,"children":1577},[1578,1579],{"id":1308,"depth":279,"text":1309},{"id":1355,"depth":279,"text":1356},{"id":1416,"depth":19,"text":1417,"children":1581},[1582,1583],{"id":1423,"depth":279,"text":1424},{"id":1436,"depth":279,"text":1437},{"id":1479,"depth":19,"text":1480},{"id":1494,"depth":19,"text":1495},{"id":1515,"depth":19,"text":1516},"Apache Pulsar","2021-01-14","Learn how to control Pulsar message writes and reads using certain configuration parameters to achieve optimal throughput and latency.","\u002Fimgs\u002Fblogs\u002F63be72252cb463483869a062_top.jpg",{},"\u002Fblog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning","12 min read",{"title":1088,"description":1589},"blog\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning",[1597,1598,1587],"Intro","BookKeeper","VFTb_rdoCLOwBB20-d6LF7nnxguy-0UKX4vNOum7qgA",[1601,1617],{"id":1602,"title":313,"bioSummary":1603,"email":290,"extension":8,"image":1604,"linkedinUrl":1605,"meta":1606,"position":1613,"stem":1614,"twitterUrl":1615,"__hash__":1616},"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":1607},{"type":15,"value":1608,"toc":1611},[1609],[48,1610,1603],{},{"title":18,"searchDepth":19,"depth":19,"links":1612},[],"Director of Streaming, StreamNative & Apache Pulsar PMC Member","authors\u002Fpenghui-li","https:\u002F\u002Ftwitter.com\u002Flipenghui6","WDjET7GfxqVQJ8mTEMaRhgpxRdDy18qZkgQDJlwjvbI",{"id":1618,"title":1090,"bioSummary":1619,"email":290,"extension":8,"image":1620,"linkedinUrl":290,"meta":1621,"position":290,"stem":1630,"twitterUrl":290,"__hash__":1631},"authors\u002Fauthors\u002Fdevin-bost.md","Senior Data Engineer @Overstock","\u002Fimgs\u002Fauthors\u002Fdevin-bost.webp",{"body":1622},{"type":15,"value":1623,"toc":1628},[1624,1626],[48,1625,1619],{},[48,1627,1138],{},{"title":18,"searchDepth":19,"depth":19,"links":1629},[],"authors\u002Fdevin-bost","I5dG6kUXozyix9i2XRLuiUB7CqKIaAJ2VZa96EDNc6E",[1633,1641,1647],{"path":1634,"title":1635,"date":1636,"image":1637,"link":-1,"collection":1638,"resourceType":1639,"score":1640,"id":1634},"\u002Fblog\u002Fpulsar-newbie-guide-for-kafka-engineers-part-3-ledgers-bookies","Pulsar Newbie Guide for Kafka Engineers (Part 3): Ledgers & Bookies","2025-08-12","\u002Fimgs\u002Fblogs\u002F689b52280db497fdd1646215_03.-Ledgers-&-Bookies.png","blogs","Blog",1,{"path":1642,"title":1643,"date":1644,"image":1645,"link":-1,"collection":1638,"resourceType":1639,"score":1646,"id":1642},"\u002Fblog\u002Fapache-pulsar-seven-years-on-what-we-built-what-we-learned-whats-next","Apache Pulsar, Seven Years On: What We Built, What We Learned, What’s Next","2025-09-25","\u002Fimgs\u002Fblogs\u002F68d4ddd72eeca005c8fc8334_Pulsar-7-years.png",0.667,{"path":1648,"title":1649,"date":1650,"image":1651,"link":-1,"collection":1638,"resourceType":1639,"score":1646,"id":1648},"\u002Fblog\u002Finside-apache-pulsars-millisecond-write-path-a-deep-performance-analysis","Inside Apache Pulsar’s Millisecond Write Path: A Deep Performance Analysis","2025-09-11","\u002Fimgs\u002Fblogs\u002F68c2e16ccf31a040e2f87e3e_Inside-Apache-Pulsar’s-Millisecond-Write-Path-no-logo.png",1776228082866]