[{"data":1,"prerenderedAt":1435},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fopentelemetry-metrics-primer-for-java-developers":1086,"blog-authors-\u002Fblog\u002Fopentelemetry-metrics-primer-for-java-developers":1397,"related-\u002Fblog\u002Fopentelemetry-metrics-primer-for-java-developers":1412},{"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":290,"createdAt":290,"date":1386,"description":1387,"extension":8,"featured":294,"image":1388,"isDraft":294,"link":290,"meta":1389,"navigation":7,"order":296,"path":1390,"readingTime":1391,"relatedResources":290,"seo":1392,"stem":1393,"tags":1394,"__hash__":1396},"blogs\u002Fblog\u002Fopentelemetry-metrics-primer-for-java-developers.md","OpenTelemetry Metrics Primer for Java Developers",[1090],"Asaf Mesika",{"type":15,"value":1092,"toc":1373},[1093,1113,1116,1120,1123,1126,1140,1143,1146,1155,1158,1162,1165,1169,1172,1175,1185,1188,1191,1195,1198,1230,1233,1239,1243,1246,1249,1253,1256,1262,1271,1274,1277,1281,1284,1287,1295,1298,1301,1315,1318,1321,1327,1330,1333,1336,1342,1345,1351,1354,1358,1361,1364,1367,1370],[48,1094,1095,1096,1101,1102,1107,1108,1112],{},"I spent the last months learning about ",[55,1097,1100],{"href":1098,"rel":1099},"https:\u002F\u002Fopentelemetry.io\u002F",[264],"OpenTelemetry"," and its Java SDK while researching how to integrate it into ",[55,1103,1106],{"href":1104,"rel":1105},"https:\u002F\u002Fpulsar.apache.org\u002F",[264],"Apache Pulsar"," at my work at ",[55,1109,1111],{"href":1110},"\u002F","StreamNative",", which provides flexible Pulsar-as-a-Service that can run in the cloud. If you don’t know Pulsar, you should — it’s a game-changer technology.",[48,1114,1115],{},"OpenTelemetry is a project that is gaining traction these days. Understanding what it is, its features, and how it works requires quite a substantial amount of time (days), even if you try getting some help from Google by using the articles or videos that appear there. In this blog post, I’ll try to summarize the key information to save you a lot of time.",[40,1117,1119],{"id":1118},"super-short-intro-to-opentelemetry","Super short intro to OpenTelemetry",[48,1121,1122],{},"Before diving into the Metrics part of OpenTelemetry, we need a basic understanding of the project.",[48,1124,1125],{},"OpenTelemetry’s goal is to provide a complete solution for telemetry applications. Telemetry means Metrics, Traces, and Logs. Complete means:",[339,1127,1128,1131,1134,1137],{},[342,1129,1130],{},"Defining an API, meaning a library containing interfaces for you to use to define metrics, report their values, define loggers, report logs, and define traces and report spans for them.",[342,1132,1133],{},"Creating implementation for those APIs — called the SDK — which also contains additional functionality for manipulating the telemetry and exporting it in various formats.",[342,1135,1136],{},"Creating an efficient protocol for relaying this telemetry data. The protocol here mainly means schema for the data (i.e., Protobuf schema), its encoding (Protobuf), and the protocol to use to carry it on the wire (gRPC or HTTP).",[342,1138,1139],{},"A Telemetry Collector, a lightweight process written in Go, which allows you to configure multiple ways to receive the data (protocols, push\u002Fpull), transform it, and then send it to various destinations. The latter includes some open-source formats and databases and some proprietary vendors. You can extend it easily by writing a plugin to either of the 3: source, transform, or sink. Most chances, you won’t need to since there are so many community contributions already. You can bundle all the plugins you need yourself or just use a binary distribution (Docker image primarily) by a specific vendor containing their specific plugins.",[48,1141,1142],{},"The novelty of OpenTelemetry (a.k.a., OTel for short) is that they wanted it to look the same way in every language, so they created specifications for both the API and the SDK. If you understand the basic entities of the SDK and the API in one language, switching between different languages using its respective SDK should feel almost the same.",[48,1144,1145],{},"Their end goal is that every library will use OTel API. Today, library owners have two ways to expose their metrics to your application:",[1147,1148,1149,1152],"ol",{},[342,1150,1151],{},"Write an extension for each metric framework (Dropwizard, Prometheus Client, Micrometer, etc.) to expose the metrics to it. Application developers using your library will also use the extension, matching their metrics framework.",[342,1153,1154],{},"Not everybody uses the popular metrics frameworks, so library developers are forced to create a bespoke interface (since there aren’t standards yet for this) for supplying the metrics, and you implement this interface to connect it to your custom metrics framework.",[48,1156,1157],{},"OTel aims to be the interface through which the library reports logs and traces as well. In Java, the logging bit feels like that today due to SLF4J, as most libraries are using it and most logging frameworks support a bridge from SLF4J to them. The key difference in OpenTelemetry is that they don’t want to rely on static variables, so they encourage library maintainers to receive the OpenTelemetry interface via a parameter at the library initialization and use that to report metrics, logs, and traces.",[40,1159,1161],{"id":1160},"the-api","The API",[48,1163,1164],{},"Before I explain what the API is used for and what it offers, let’s see a few concepts used in OTel.",[32,1166,1168],{"id":1167},"concepts","Concepts",[48,1170,1171],{},"In OTel Instruments are the entities through which you report measurements. An instrument is very much like in real life, a device, but since this is a programming language, it’s in the form of an object you use through its methods. The instrument methods allow you to report Measurements. For example, add 5 to http.request.body.lines, add -1 to processing.jobs.executing, and report 32 (milliseconds) to http.server.response.latency. The numbers are the measurements.",[48,1173,1174],{},"When you report a measurement to an instrument, you are most likely doing it for specific Attributes. For example, if you have an instrument named http.server.response.latency, you would report a specific response latency together with several attributes of the request, such as response status code and request method:",[1176,1177,1182],"pre",{"className":1178,"code":1180,"language":1181},[1179],"language-text","httpResponseLatency.record(32,\n Attributes.of(\n   AttributeKey.longKey(\"statusCode\"), 404L,\n   AttributeKey.stringKey(\"method\"), \"GET\"));\n","text",[1183,1184,1180],"code",{"__ignoreMap":18},[48,1186,1187],{},"Attributes are key-value pairs of attribute name and attribute value.",[48,1189,1190],{},"Instruments are grouped into Meters, each having a name and a version. All instrument creation is done through a Meter. In your microservice, you will use a meter for its metrics, while your connection pool library will have its Meter and its instruments defined using it.",[32,1192,1194],{"id":1193},"instruments","Instruments",[48,1196,1197],{},"Instruments have a name, like http.request.count, a description (will show up in UIs like Grafana), and a unit. The instruments offered by the API are:",[339,1199,1200,1203,1206,1209],{},[342,1201,1202],{},"Counter — An instrument that only increases and never decreases: DoubleCounter, LongCounter. Examples: HTTP request count, number of logins, etc.",[342,1204,1205],{},"UpDownCounter — An instrument that can increase or decrease: DoubleUpDownCounter, LongUpDownCounter. Examples: Number of concurrently running background jobs, number of active connections, etc. It’s a number that you can aggregate across attributes. This is very different from a Gauge.",[342,1207,1208],{},"Gauge — An instrument only registered via a callback - a function returning the gauge value. A gauge value cannot be aggregated across attributes. Gauge examples are temperature and CPU usage.",[342,1210,1211,1212,1217,1218,1223,1224,1229],{},"Histogram — used to collect measurements that are aggregated to statistically meaningful numbers. OTel supports Explicit Bucket Histograms and Exponential Bucket Histograms, while Summary is not supported (There is an ",[55,1213,1216],{"href":1214,"rel":1215},"https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fopentelemetry-specification\u002Fissues\u002F2704",[264],"issue"," addressing that). As opposed to the known metric libraries, in OTel, there isn’t a specific interface for an explicit bucket or exponential histogram (In the Prometheus client, you have ",[55,1219,1222],{"href":1220,"rel":1221},"https:\u002F\u002Fgithub.com\u002Fprometheus\u002Fclient_java#summary",[264],"Summary"," for summary and ",[55,1225,1228],{"href":1226,"rel":1227},"https:\u002F\u002Fgithub.com\u002Fprometheus\u002Fclient_java#histogram",[264],"Histogram"," for explicit bucket histogram). There is a way to configure OpenTelemetry (the SDK — implementation), upon initialization, instructing what histograms would be by default and deciding that also for specific histograms — i.e., decide whether it will be an Explicit Bucket or Exponential Bucket and specify the bucket list. I’ll describe that in the SDK section. The interfaces are DoubleHistogram and LongHistogram.",[48,1231,1232],{},"Here is a code example for defining instruments using the API only.",[1176,1234,1237],{"className":1235,"code":1236,"language":1181},[1179],"LongCounter bytesOutCounter = meter.counterBuilder(\"pulsar_bytes_out\")\n       .setDescription(\"Size of messages dispatched from this broker to consumers\")\n       .setUnit(\"bytes\")\n       .build();\n    \nmeter.gaugeBuilder(\"room_temperature\")\n       .setUnit(\"celsius\")\n       .buildWithCallback(observableDoubleMeasurement ->\n               observableDoubleMeasurement.record(\n                     RoomManager.currentRoom().getTemperature(),\n                     Attributes.of(\n                             AttributeKey.stringKey(\"room\"),\n                             RoomManager.currentRoom().getName())));\n\nmeter.histogramBuilder(\"http.response.latency\")\n       .setUnit(\"seconds\")\n       .setDescription(\"HTTP Response Latency\")\n       .build();\n",[1183,1238,1236],{"__ignoreMap":18},[40,1240,1242],{"id":1241},"the-sdk","The SDK",[48,1244,1245],{},"As we explained before, the SDK is the implementation of the interfaces contained within the API: MeterProvider, Meter, and all the instruments described above. It also contains several other entities used for reading and exporting the metrics and configuring instruments further (override).",[48,1247,1248],{},"Before we explain Metric Reader, Metric Exporter, and Views, we first need to learn an important concept in OTel called Aggregations.",[32,1250,1252],{"id":1251},"aggregations","Aggregations",[48,1254,1255],{},"When you learn OTel for the first time by reading its API or just trying out its API, you stumble across the following scenario ending up with a question: “I just defined a histogram, but I can’t find a way to define its buckets — how can it be?!”",[1176,1257,1260],{"className":1258,"code":1259,"language":1181},[1179],"meter.histogramBuilder(\"http.response.latency\")\n       .setUnit(\"seconds\")\n       .setDescription(\"HTTP Response Latency\")\n       .build();\n",[1183,1261,1259],{"__ignoreMap":18},[48,1263,1264,1265,1270],{},"You expected to have setBuckets(10, 100, 1000, 5000), but this method doesn’t exist. There is a logic behind it which is actually pretty amazing, yet there is also ",[55,1266,1269],{"href":1267,"rel":1268},"https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fopentelemetry-specification\u002Fissues\u002F2229",[264],"ongoing work"," to add such a method.",[48,1272,1273],{},"The basic idea in the SDK is that an instrument has an associated aggregation, which is an object through which you feed the measurements, and it’s the one deciding how it aggregates those measurements and what it outputs. For example, when you define a Counter, you normally have a Sum aggregation associated with it, adding the measurements you report (those +1, +3) into a sum counter variable. Upon collection, it emits the counter sum so far. Another example is Explicit Bucket aggregation: When you report the measurement, it finds the matching bucket counter, increases it by 1, and increases a sum counter by the measurement. It emits a sum of the values, a count of the values, and a bucket counter counting each value reported matching the bucket boundaries.",[48,1275,1276],{},"There are sensible default aggregations per instrument, like Sum for Counter or Explicit Buckets for a histogram. The latter also comes with a default bucket boundaries list. OTel allows you to override the default aggregation and configure it per instrument using another concept called Views which you configure upon initialization. The last part is exactly why people created the GitHub issue above since, in some cases, it doesn’t make sense to split the definition of a histogram into two separate places in your code.",[32,1278,1280],{"id":1279},"views","Views",[48,1282,1283],{},"Views are the most powerful tool OTel SDK offers, and it is a unique feature compared to all other metric libraries.",[48,1285,1286],{},"You can configure multiple views for an instrument. A view allows you to define an aggregation, configure it, and override the name, description, and units. In essence, you create multiple instruments from the same original instrument. Think of it as such: When you defined an instrument with a name, you defined a way to report many numbers (measurements). A view takes all those measurements as input and uses the aggregation defined to create a metric, using the name, units, and description defined in the view (if not defined, take the defaults from the instrument definition). So you can decide, for example, to take http.response.latency which was defined as a histogram, and create 2 views for it:",[1147,1288,1289,1292],{},[342,1290,1291],{},"An explicit bucket histogram, using buckets (1, 10, 1000) named http.response.latency.",[342,1293,1294],{},"A metric showing the last latency collected named http.response.latency.last where you defined a Last aggregation (which only keeps the last measurement reported and emits it as gauge)",[48,1296,1297],{},"If you only define a single view for an instrument, you just override the original definition and perhaps override the default aggregation and its default configuration.",[48,1299,1300],{},"The second strong part about views is that you can also define them to be applied to multiple instruments. For example, you can say that all instruments with histogram type named “*latency” should have their aggregation set to Explicit Histogram and have their buckets be 10, 200, 3000. It is done by something called an Instrument Selector, allowing you to choose multiple instruments based on the following:",[339,1302,1303,1306,1309,1312],{},[342,1304,1305],{},"name wildcard",[342,1307,1308],{},"instrument type",[342,1310,1311],{},"instrumentation scope (I will explain it later)",[342,1313,1314],{},"…",[48,1316,1317],{},"For each instrument selected, the view defined will be added.",[48,1319,1320],{},"Here’s a code example:",[1176,1322,1325],{"className":1323,"code":1324,"language":1181},[1179],"SdkMeterProvider meterProvider = SdkMeterProvider.builder()\n       .registerView(\n               InstrumentSelector.builder()\n                       .setName(\"*latency\")\n                       .build(),\n               View.builder()\n                       .setAggregation(Aggregation.explicitBucketHistogram(List.of(10.0, 20.0, 100.0)))\n                       .build())\n       .registerView(\n               InstrumentSelector.builder()\n                       .setMeterName(\"hikari\")\n                       .setType(InstrumentType.HISTOGRAM)\n                       .build(),\n               View.builder()\n                       .setAggregation(Aggregation.explicitBucketHistogram(List.of(2.0, 10.0, 50.0, 200.0)))\n                       .build())\n       .build();\n",[1183,1326,1324],{"__ignoreMap":18},[48,1328,1329],{},"Views provide a brilliant way to manipulate metrics you didn’t code yourself — coming from the libraries you use. You can decide whether a latency reported in the Hikari Connection Pool library will have buckets as you wish it to be (something you can’t do in other metric frameworks) or even drop it by setting the Drop aggregation for certain instruments of that library.",[48,1331,1332],{},"Finally, views also allow you to select only a subset of the reported attributes, thus achieving less cardinality without losing data since the measurements will be rolled up to your defined attributes.",[48,1334,1335],{},"Your HTTP client may have the following in its code:",[1176,1337,1340],{"className":1338,"code":1339,"language":1181},[1179],"var attr = Attributes.of(AttributeKey.longKey(\"statusCode\"), requestStatusCode,\n             AttributeKey.stringKey(\"method\"), requestMethod);\nhttpRequestLatency.record(requestLatency, attr)\n",[1183,1341,1339],{"__ignoreMap":18},[48,1343,1344],{},"You can decide to modify it only to include the attribute statusCode:",[1176,1346,1349],{"className":1347,"code":1348,"language":1181},[1179],".registerView(\n       InstrumentSelector.builder()\n               .setMeterName(\"http-commons\")\n               .setName(\"http.request.latency\")\n               .build(),\n       View.builder()\n               .setAttributeFilter(attrName -> attrName.equals(\"statusCode\"))\n               .build())\n",[1183,1350,1348],{"__ignoreMap":18},[48,1352,1353],{},"In the implementation, when you report the value 30 associated with the attributes (statusCode=500, method=GET), it will modify the attributes to be (statusCode=500) and report the value 30 for it; thus, you achieve a roll-up of the (statusCode, method) to statusCode for the instrument the view is configured for. It means that the roll-up is only in the scope of a single instrument, not multiple.",[32,1355,1357],{"id":1356},"metric-reader-and-exporter","Metric Reader and Exporter",[48,1359,1360],{},"When you initialize the SDK, you can (should) provide a Metric Reader. It’s the component that reads the metrics from the SDK and uses a Metric Exporter to expose them out — either via a pull mechanism (like exposing a REST endpoint that responds with the metrics in a certain format) or a push mechanism which periodically pushes the metrics to the exporter (writing it in OTLP protocol to Open Telemetry Collector).",[48,1362,1363],{},"Some Metric Readers have a bundled exporter like Prometheus Metric Exporter. Others, like the Periodic Metric Reader, require you to pass an exporter when creating them. Exporters can be OTLP gRPC exporters or HTTP OTLP Exporters.",[40,1365,1222],{"id":1366},"summary",[48,1368,1369],{},"OTel is, in my opinion, the best metric library created for the JVM. They literally thought of everything and managed to design it with elegance. Using specifications to make all SDKs look the same is brilliant, as it makes moving between languages a breeze, and packing it with an external collector capable of modifying, keeping state, and exporting to all the destinations needed. The only downside OTel has is the documentation, as it requires you to take a few days at the very least to understand how it works and how to use it, and I hope in time, it will improve. This blog post's goal was to try to explain it “shortly,” so in 10–20 minutes of reading, you’ll understand the basic workings of it.",[48,1371,1372],{},"I haven’t touched all the aspects of OTel Metrics — I will leave them to future blog posts. I believe OTel will revolutionize the Metrics JVM frameworks, just like Docker and Maven were in their respective terms.",{"title":18,"searchDepth":19,"depth":19,"links":1374},[1375,1376,1380,1385],{"id":1118,"depth":19,"text":1119},{"id":1160,"depth":19,"text":1161,"children":1377},[1378,1379],{"id":1167,"depth":279,"text":1168},{"id":1193,"depth":279,"text":1194},{"id":1241,"depth":19,"text":1242,"children":1381},[1382,1383,1384],{"id":1251,"depth":279,"text":1252},{"id":1279,"depth":279,"text":1280},{"id":1356,"depth":279,"text":1357},{"id":1366,"depth":19,"text":1222},"2023-03-13","Learn the basics of OpenTelemetry.","\u002Fimgs\u002Fblogs\u002F640e7f87b006224ae0cc2adb_OpenTelemetry-Metrics-Primer-for-Java-Developers.png",{},"\u002Fblog\u002Fopentelemetry-metrics-primer-for-java-developers","12 min read",{"title":1088,"description":1387},"blog\u002Fopentelemetry-metrics-primer-for-java-developers",[1106,1395],"Observability","4HKQtKzke5FRJfSKhzRF7k0gujJ6LxufOfJJvmpYxys",[1398],{"id":1399,"title":1090,"bioSummary":1400,"email":290,"extension":8,"image":1401,"linkedinUrl":290,"meta":1402,"position":1409,"stem":1410,"twitterUrl":290,"__hash__":1411},"authors\u002Fauthors\u002Fasaf-mesika.md","Asaf Mesika is a Principal Engineer at StreamNative. He combines his passion for clean code, 22 years of experience and appreciation for great team work to build a truly outstanding open-source based event streaming platform. Asaf previously worked at Logz.io, building the core foundations for Logz.io platform. Asaf is also the co-founder of Java.IL, the Israeli Java User Group, fostering a thriving community since 2010, and co-founder of Tech Leads IL - the leading community for Tech Leads in Israel.","\u002Fimgs\u002Fauthors\u002Fasaf-mesika.webp",{"body":1403},{"type":15,"value":1404,"toc":1407},[1405],[48,1406,1400],{},{"title":18,"searchDepth":19,"depth":19,"links":1408},[],"Principal Engineer, StreamNative","authors\u002Fasaf-mesika","lPYt7ILrO1a3vErDuTNjj8BTVLiTSTYw0ux0yHv7qcg",[1413,1422,1428],{"path":1414,"title":1415,"date":1416,"image":1417,"link":1418,"collection":1419,"resourceType":1420,"score":1421,"id":1414},"\u002Fsuccess-stories\u002Fnetdata","Pulsar Journey At Netdata","2023-01-08","\u002Fimgs\u002Fsuccess-stories\u002F67942f01771e4d3778d0d0c1_SN-SuccessStories-netdata.webp","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=h2-cjNJjzzE","successStories","Case Study",0.733,{"path":1423,"title":1424,"date":1425,"image":1426,"link":1427,"collection":1419,"resourceType":1420,"score":1421,"id":1423},"\u002Fsuccess-stories\u002Fsplunk","Scaling Apache Pulsar to 10 Petabytes\u002FDay - Pulsar Summit NA 2021 Keynote","2022-12-27","\u002Fimgs\u002Fsuccess-stories\u002F67942eb613d2f02fdbdc91f5_SN-SuccessStories-splunk.webp","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1uG3mdfh0nk",{"path":1429,"title":1430,"date":1431,"image":-1,"link":-1,"collection":1432,"resourceType":1433,"score":1434,"id":1429},"\u002Fblog\u002Fhow-to-trace-pulsar-messages-with-opentracing-jaeger","How to trace Pulsar messages with OpenTracing and Jaeger","2020-06-11","blogs","Blog",0.667,1775716433187]