Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
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Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. For mission-critical applications, how do you ensure that the performance delivered is the performance required? This is especially important as Kafka is written in Java and Scala and runs on the JVM. The JVM is a fantastic platform that delivers on an internet scale. In this session, we'll explore how making changes to the JVM design can eliminate the problems of garbage collection pauses and raise the throughput of applications. For cloud-based Kafka applications, this can deliver both lower latency and reduced infrastructure costs. All without changing a line of code!
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Confluent Cloud runs a modified version of Apache Kafka - redesigned to be cloud-native and deliver a serverless user experience. In this talk, we will discuss key improvements we've made to Kafka and how they contribute to Confluent Cloud availability, elasticity, and multi-tenancy. You'll learn about innovations that you can use on-prem, and everything you need to make the most of Confluent Cloud.
Securing the Message Bus with Kafka Streams | Paul Otto and Ryan Salcido, Raf...
Organizations have a need to protect Personally Identifiable Information (PII). As Event Streaming Architecture (ESA) becomes ubiquitous in the enterprise, the prevalence of PII within data streams will only increase. Data architects must be cognizant of how their data pipelines can allow for potential leaks. In highly distributed systems, zero-trust networking has become an industry best practice. We can do the same with Kafka by introducing message-level security.
A DevSecOps Engineer with some Kafka experience can leverage Kafka Streams to protect PII by enforcing role-based access control using Open Policy Agent. Rather than implementing a REST API to handle message-level security, Kafka Streams can filter, or even transform outgoing messages in order to redact PII data while leveraging the native capabilities of Kafka.
In our proposed presentation, we will provide a live demonstration that consists of two consumers subscribing to the same Kafka topic, but receiving different messages based on the rules specified in Open Policy Agent. At the conclusion of the presentation, we will provide attendees with a GitHub repository, so that they can enjoy a sandbox environment for hands-on experimentation with message-level security.
Serverless Workflow: New approach to Kubernetes service orchestration | DevNa...
With the rise of Serverless Architectures, Workflows have gained a renewed interest and usefulness. Typically thought of as centralized and monolithic, they now play a key role in service orchestration and coordination as well as modular processing. With many different architecture approaches already in place, the Cloud Native Computing Foundation (CNCF) has started an initiative to specify serverless workflows to ensure portability and vendor neutrality. In this talk, we introduce the CNCF Serverless Workflow specification and provide examples and demos on top of Kogito, Red Hat's business automation toolkit. You will learn: 1- The what, why, and how of the CNCF Serverless Workflow specification 2- Why using the Serverless Workflow specification and orchestration can improve your serverless architecture 3- When to use CNCF Serverless Workflow and Kogito together and the benefits derived.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
Everything you ever needed to know about Kafka on Kubernetes but were afraid ...
Kubernetes became the de-facto standard for running cloud-native applications. And many users turn to it also to run stateful applications such as Apache Kafka. You can use different tools to deploy Kafka on Kubernetes - write your own YAML files, use Helm Charts, or go for one of the available operators. But there is one thing all of these have in common. You still need very good knowledge of Kubernetes to make sure your Kafka cluster works properly in all situations. This talk will cover different Kubernetes features such as resources, affinity, tolerations, pod disruption budgets, topology spread constraints and more. And it will explain why they are important for Apache Kafka and how to use them. If you are interested in running Kafka on Kubernetes and do not know all of these, this is a talk for you.
Deploying Confluent Platform for Production
Presented by Gwen Shapira as part 1 of 5 in the Best Practices for Apache Kafka in Production, Confluent Online Talk Series
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Event streaming allows companies to build more scalable and loosely coupled real-time applications supporting massive concurrency demands and simplifying the construction of services.
At the same time, API management provides capabilities to securely control the upstream services consumption, including the event processing infrastructure.
This session shows how Kong Konnect Enterprise can complement Kafka Event Streaming, exposing it to new and external consumers while applying specific and critical policies to control its consumption, including API key, OAuth/OIDC and others for authentication, rate limiting, caching, log processing, etc.
Monitoring and Resiliency Testing our Apache Kafka Clusters at Goldman Sachs ...
In our payments platform at Goldman Sachs Transaction Banking, Apache Kafka plays a critical role as the messaging bus in our micro-services architecture. Being a part of the financial service industry we need to ensure high-availability of our platform and quick response time during failures.
In this talk we will explore how we monitor and alert on the health of our Kafka clusters using our heartbeat application and clients using DataDog dashboards. We will see how we consolidate JMX metrics such as error-rates, connection-rates, latencies and consumer lag from all producers and consumers using JMX agent sidecar to provide a live view of the health of our entire infrastructure. We will also discuss our culture of game days where we regularly test the resiliency of all the clients in our infrastructure by simulating various failure scenarios to improve the overall availability of our infrastructure.
Utilizing Kafka Connect to Integrate Classic Monoliths into Modern Microservi...
Having started with classic monolith applications in the late 90s and adopting a new microservice architecture in 2015, our organization needed a convenient, reliable, and low-cost way to push changes back and forth between them. One that preferably utilized technology already on hand and could exchange information between multiple data stores.
In this session we will explore how Kafka Connect and its various connectors satisfied this need. We will review the two disparate tech stacks we needed to integrate, and the strategies and connectors we used to exchange information. Finally, we will cover some enhancements we made to our own processes including integrating Kafka Connect and its connectors into our CI/CD pipeline and writing tools to monitor connectors in our production environment.
Asynchronous Transaction Processing With Kafka as a Single Source of Truth - ...
Our backend system (ERP, CRM, Billing) is completely cloud, asynchronous Microservices and Kafka based. No databases at all. In this environment most challenging part is transaction management with error handling and compensation. Especially for bus tickets mobile application backend that has to respond fast. This short talk explains how we overcome this challenges and what are some of the solutions we implemented.
How Apache Kafka® Works
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
Friends don't let friends do dual writes: Outbox pattern with OpenShift Strea...
Dual writes are a common source of issues in distributed event-driven applications. A dual write occurs when an application has to change data in two different systems - for instance, when an application needs to persist data in the database and send a Kafka message to notify other systems. If one of these two operations fail, you might end up with inconsistent data which can be hard to detect and fix.
OpenShift Streams for Apache Kafka is Red Hat's fully hosted and managed Apache Kafka service targeting development teams that want to incorporate streaming data and scalable messaging in their applications, without the burden of setting up and maintaining a Kafka cluster infrastructure. Debezium is an open source distributed platform for change data capture. Built on top of Apache Kafka, it allows applications to react to inserts, updates, and deletes in your databases.
In this session you will learn how you can leverage OpenShift Streams for Apache Kafka and Debezium to avoid the dual write issue in an event-driven application using the outbox pattern. More specifically, we will show you how to:
Provision a Kafka cluster on OpenShift Streams for Apache Kafka.
Deploy and configure Debezium to use OpenShift Streams for Apache Kafka.
Refactor an application to leverage Debezium and OpenShift Streams for Apache Kafka to avoid the dual write problem.
Taming a massive fleet of Python-based Kafka apps at Robinhood | Chandra Kuch...
Robinhood uses Kafka in every line of its business, from stock and crypto trading to clearing and data analytics. One interesting aspect of our architecture is that many of our microservices leveraging Kafka are written in Python. When you combine Python's relatively slow performance coupled, its reliance on process-based parallelism and Robinhood’s scale, the result is a massive fleet of application processes producing to and consuming from our Kafka clusters. This fleet generates an atypical workload on Kafka that warrants a deeper investment in scalability and reliability.
This talk discusses our investments in Kafka infrastructure for a large-scale Python-based environment:
kafkahood: our librdkafka-based client library wrapper that codifies best practices, sane defaults and deep client-side observability.
kafkaproxy: a Rust-based sidecar proxy that reduces connection fan-in from Python gunicorn worker pools to our Kafka clusters.
We'll also present challenges we encountered along the way and share our learnings with the audience.
Look how easy it is to go from events to blazing-fast analytics! | Neha Pawar...
Apache Kafka is the de facto standard for real-time event streaming, but what do you do if you want to perform user-facing, ad-hoc, real-time analytics too? That's a hard problem. Apache Pinot solves it, and the two together are like chocolate and peanut butter, peaches and cream, and Steve Rogers and Peggy Carter.
Come to this talk for an introduction to Pinot and an overview of how the Pinot Kafka Connector works. Hear the challenges unique to a user-facing realtime analytics system, and how Pinot and Kafka work harmoniously to solve them. Witness an action-packed demo, showing just how easy it is to go from events to blazing-fast analytics, and how to use powerful features of both systems that help you do this at scale.
Kafka Summit SF 2017 - Kafka Stream Processing for Everyone with KSQL
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Managing a distributed system like Apache Kafka can be extremely challenging, especially when you try to approach monitoring and managing from a single centralized GUI approach. In this talk come here and see a demo of a more decoupled approach to Kafka management and Kafka Monitoring where data is centralized but access is is distributed to scale to enterprise deployments, CICD pipelines and much much more.
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Not Your Mother's Kafka - Deep Dive into Confluent Cloud Infrastructure | Gwe...
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Everything you ever needed to know about Kafka on Kubernetes but were afraid ...
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Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
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Similar to Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
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Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. For mission-critical applications, how do you ensure that the performance delivered is the performance required? This is especially important as Kafka is written in Java and Scala and runs on the JVM. The JVM is a fantastic platform that delivers on an internet scale. In this session, we'll explore how making changes to the JVM design can eliminate the problems of garbage collection pauses and raise the throughput of applications. For cloud-based Kafka applications, this can deliver both lower latency and reduced infrastructure costs. All without changing a line of code!
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Let’s dig into those differences with the pros & cons
Værktøjer udviklet på AAU til analyse af SCJ programmer
Oplæg på InfinIT-seminar den 30.3.2016 -
Udviklingsplatform og programmeringssprog til indlejrede systemer
Capital One Delivers Risk Insights in Real Time with Stream Processing
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
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-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
Cloud Native Compiler
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Monoliths, macroservices, microservices, cloud-native and serverless... Where do we even start? If you are a Java developer, you will likely have to work with one, some, or even all of these deployment approaches. Does this mean learning multiple frameworks, tools and methods? It certainly looks that way, based on the many deployment-specific solutions being proposed to the Java development community.
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”Bare-Metal Container" presented at HPCC2016
”Bare-Metal Container" presented at HPCC2016
http://www.swinflow.org/confs/2016/hpcc/
The official HP is http://www.itri.aist.go.jp/cpc/research/bmc/
Transaction Management on Cassandra
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Workday's Next Generation Private Cloud
Workday has built one of the largest OpenStack-based private clouds in the world, hosting a workload of over a million physical cores on over 16,000 compute nodes in 5 data centers for over ten years. However, there was a growing need for a newer, more maintainable deployment model that would closely follow the upstream community. We would like to share our new architecture and deployment approach as well as lessons learned from our experience.
We’ve converted many of our technologies in the process, from…
Migrating from Mitaka, to Victoria
Converting from OpenContrail, to pure L3 Calico with BGP on the host
Deploying with Chef, to deploying with Ansible
Building home-grown container images, to Kolla
Monitoring with Sensu and Wavefront, to Prometheus and Grafana
CI/CD in Jenkins, to Zuul
CentOS 7, to CentOS 8 Stream
We'll also talk about some internal tools we wrote that, while Workday-specific, may inspire you to see what value-add you can make for your customers.
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anynines ran a public PaaS located in a German datacenter based on Cloud Foundry. In more than 12 months of running a Cloud Foundry PaaS man lessons about security, high availability, open stack and many other exciting topics have been learned. See how Bosh can be used and how it shouldn't be used. Learn how to perform Cloud Foundry upgrades and read how to harden Cloud Foundry by adding more fault tolerance with pacemaker.
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Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
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Get Lower Latency and Higher Throughput for Java Applications
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Værktøjer udviklet på AAU til analyse af SCJ programmer
Værktøjer udviklet på AAU til analyse af SCJ programmer
Capital One Delivers Risk Insights in Real Time with Stream Processing
Capital One Delivers Risk Insights in Real Time with Stream Processing
BMC: Bare Metal Container ＠Open Source Summit Japan 2017
BMC: Bare Metal Container ＠Open Source Summit Japan 2017
Running Cloud Foundry for 12 months - An experience report | anynines
Running Cloud Foundry for 12 months - An experience report | anynines
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Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
"In this talk, attendees will be provided with an introduction to Kafka Connect and the basics of Single Message Transforms (SMTs) and how they can be used to transform data streams in a simple and efficient way. SMTs are a powerful feature of Kafka Connect that allow custom logic to be applied to individual messages as they pass through the data pipeline. The session will explain how SMTs work, the types of transformations they can be used for, and how they can be applied in a modular and composable way.
Further, the session will discuss where SMTs fit in with Kafka Connect and when they should be used. Examples will be provided of how SMTs can be used to solve common data integration challenges, such as data enrichment, filtering, and restructuring. Attendees will also learn about the limitations of SMTs and when it might be more appropriate to use other tools or frameworks.
Additionally, an overview of the alternatives to SMTs, such as Kafka Streams and KSQL, will be provided. This will help attendees make an informed decision about which approach is best for their specific use case.
Whether attendees are developers, data engineers, or data scientists, this talk will provide valuable insights into how Kafka Connect and SMTs can help streamline data processing workflows. Attendees will come away with a better understanding of how these tools work and how they can be used to solve common data integration challenges."
Renaming a Kafka Topic | Kafka Summit London
"While Apache Kafka lacks native support for topic renaming, there are scenarios where renaming topics becomes necessary. This presentation will delve into the utilization of MirrorMaker 2.0 as a solution for renaming Kafka topics. It will illustrate how MirrorMaker 2.0 can efficiently facilitate the migration of messages from the old topic to the new one and how Kafka Connect Metrics can be employed to monitor the mirroring progress. The discussion will encompass the complexity of renaming Kafka topics, addressing certain limitations, and exploring potential workarounds when using MirrorMaker 2.0 for this purpose. Despite not being originally designed for topic renaming, MirrorMaker 2.0 has a suitable solution for renaming Kafka topics.
Blog Post : https://engineering.hellofresh.com/renaming-a-kafka-topic-d6ff3aaf3f03"
Evolution of NRT Data Ingestion Pipeline at Trendyol
"Trendyol, Turkey's leading e-commerce company, is committed to positively impacting the lives of millions of customers. Our decision-making processes are entirely driven by data. As a data warehouse team, our primary goal is to provide accurate and up-to-date data, enabling the extraction of valuable business insights.
We utilize the benefits provided by Kafka and Kafka Connect to facilitate the transfer of data from the source to our analytical environment. We recently transitioned our Kafka Connect clusters from on-premise VMs to Kubernetes. This shift was driven by our desire to effectively manage rapid growth(marked by a growing number of producers, consumers, and daily messages), ensuring proper monitoring and consistency. Consistency is crucial, especially in instances where we employ Single Message Transforms to manipulate records like filtering based on their keys or converting a JSON Object into a JSON string.
Monitoring our cluster's health is key and we achieve this through Grafana dashboards and alerts generated through kube-state-metrics. Additionally, Kafka Connect's JMX metrics, coupled with NewRelic, are employed for comprehensive monitoring.
The session will aim to explain our approach to NRT data ingestion, outlining the role of Kafka and Kafka Connect, our transition journey to K8s, and methods employed to monitor the health of our clusters."
Ensuring Kafka Service Resilience: A Dive into Health-Checking Techniques
"Join our lightning talk to delve into the strategies vital for maintaining a resilient Kafka service.
While proactive monitoring is key for issue prevention, failures will still occur. Rapid detection tools will enable you to identify and resolve problems before they impact end-users. This session explores the techniques employed by Kafka cloud providers for this detection, many of which are also applicable if you are managing independent Kafka clusters or applications.
The talk focuses on health-checking, a powerful tool that encompasses an application and its monitoring to validate Kafka environment availability. The session navigates through Kafka health-check methods, sharing best practices, identifying common pitfalls, and highlighting the monitoring of critical performance metrics like throughput and latency for early issue detection.
Attendees will gain valuable insights into the art of health-checking their Kafka environment, equipping them with the tools to identify and address issues before they escalate into critical problems. We invite all Kafka enthusiasts to join us in this talk to foster a deeper understanding of Kafka health-checking and ensure the continued smooth operation of your Kafka environment."
Exactly-once Stream Processing with Arroyo and Kafka
"Stream processing systems traditionally gave their users the choice between at least once processing and at most once processing: accepting duplicate data or missing data. But ideally we would provide exactly-once processing, where every event in the input data is represented exactly once in the output.
Kafka provides a transaction API that enables exactly-once when using Kafka as your source and sink. But this API has turned out to not be well suited for use by high level streaming systems, requiring various work arounds to still provide transactional processing.
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"In this talk, we will explore the exciting world of IoT and computer vision by presenting a unique project: Fish Plays Pokemon. Using an ESP Eye camera connected to an ESP32 and other IoT devices, to monitor fish's movements in an aquarium.
This project showcases the power of IoT and computer vision, demonstrating how even a fish can play a popular video game. We will discuss the challenges we faced during development, including real-time processing, IoT device integration, and Kafka message consumption.
By the end of the talk, attendees will have a better understanding of how to combine IoT, computer vision, and the usage of a serverless cloud to create innovative projects. They will also learn how to integrate IoT devices with Kafka to simulate keyboard behavior, opening up endless possibilities for real-time interactions between the physical and digital worlds."
Tiered Storage 101 | Kafla Summit London
What is tiered storage and what is it good for? After this session you will know how to leverage the tiered storage feature to enable longer retention than the storage attached to brokers allows. You will get acquainted with the different configuration options and know what to expect when you enable the feature, like for example when will the first upload to the remote object storage take place.
Building a Self-Service Stream Processing Portal: How And Why
"Real-time 24/7 monitoring and verification of massive data is challenging – even more so for the world’s second largest manufacturer of memory chips and semiconductors. Tolerance levels are incredibly small, any small defect needs to be identified and dealt with immediately. The goal of semiconductor manufacturing is to improve yield and minimize unnecessary work.
However, even with real-time data collection, the data was not easy to manipulate by users and it took many days to enable stream processing requests – limiting its usefulness and value to the business.
You’ll hear why SK hynix switched to Confluent and how we developed a self-service stream process portal on top of it. Now users have an easy-to-use service to manipulate the data they want.
Results have been impressive, stream processing requests are available the same day – previously taking 5 days! We were also able to drive down costs by 10% as stream processing requests no longer require additional hardware.
What you’ll take away from our talk:
- What were the pain points in the previous environment
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"Discover how default configurations might impact ingestion times, especially when dealing with large files. We'll explore a real-world scenario with a 20,000,000+ line file, assessing metrics and exploring the bottleneck in the default setup. Understand the intricacies of batch size calculations and how to optimize them based on your unique data characteristics.
Walk away with actionable insights as we showcase a practical example, turning a 7-hour ingestion process into a mere 30 minutes for over 30,000,000 records in a Kafka topic. Uncover metrics, configurations, and best practices to elevate the performance of your Kafka Connect CSV source connectors. Don't miss this opportunity to optimize your data pipeline and ensure smooth, efficient data flow."
Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...
"In order to meet the current and ever-increasing demand for near-zero RPO/RTO systems, a focus on resiliency is critical. While Kafka offers built-in resiliency features, a perfect blend of client and cluster resiliency is necessary in order to achieve a highly resilient Kafka client application.
At Fidelity Investments, Kafka is used for a variety of event streaming needs such as core brokerage trading platforms, log aggregation, communication platforms, and data migrations. In this lightening talk, we will discuss the governance framework that has enabled producers and consumers to achieve their SLAs during unprecedented failure scenarios. We will highlight how we automated resiliency tests through chaos engineering and tightly integrated observability dashboards for Kafka clients to analyze and optimize client configurations. And finally, we will summarize the chaos test suite and the ""test, test and test"" mantra that are helping Fidelity Investments reach its goal of a future with zero down-time."
Navigating Private Network Connectivity Options for Kafka Clusters
"There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options ... might not be an option!
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Data Contracts Management: Schema Registry and Beyond
"Data contracts are one of the hottest topics in the data management community. A data contract is a formal agreement between a data producer and its consumers, aimed at reducing data downtime and improving data quality. Schemas are an important part of data contracts, but they are not the only relevant element.
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[1] https://debezium.io/"
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Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
- 1. Better Kafka Performance Without Changing Any Code Presentedby SimonRitter, Deputy CTO Azul Systems,Inc.
- 2. Kafka Architecture 2 Producer 1 Producer 2 Producer n . . . Consumer 1 Consumer 2 Consumer n . . . Broker 1 Broker 2 Broker n . . . ZooKeeper Kafka JVM
- 3. JVM Performance Challenges 3 • Latency ‒ Biggest issue is Garbage Collection ‒ Stop-the-world pauses for almost all collectors ‒ Pauses are typically proportional to heap size, not live data • Throughput ‒ Adaptive JIT compilation: Interpreted, C1 compiled, C2 compiled ‒ Deoptimisations ‒ Level of optimisation is key ‒ Significant impact on number of brokers required
- 4. • Based on OpenJDK source code • Passes all Java SE TCK/JCK tests ‒ Drop-in replacement for other JVMs No code changes, no recompilation • Hotspot collectors replaced with C4 • Falcon JIT compiler ‒ C2 replacement • ReadyNow! warm up elimination technology • Only supported on Linux ‒ OS memory management interface Zing: A Better JVM 4
- 5. Azul Continuous Concurrent Compacting Collector (C4)
- 6. • Generational (young and old) ‒ Uses the same GC collector for both ‒ For efficiency rather than pause containment • All phases are parallel • No STW compacting fallback • Algorithm is mark, relocate, remap C4 Basics 6
- 7. • Read barrier ‒ Tests all object references as they are loaded • Enforces two invariants ‒ Reference is marked through ‒ Reference points to correct object position • Minimal performance overhead ‒ Test and jump (2 instructions) ‒ x86 architecture reduces this to one micro-op Loaded Value Barrier 7
- 8. Concurrent Mark Phase 8 Root Set GC Threads App Threads X X X X X
- 9. Relocation Phase 9 Compaction A B C D E A’ B’ C’ D’ E’ A -> A’ B -> B’ C -> C’ D -> D’ E -> E’
- 10. Remapping Phase App Threads GC Threads A -> A’ B -> B’ C -> C’ D -> D’ E -> E’ X X X
- 11. • jHiccup • Spends most of its time asleep ‒ Minimal effect on perfomance ‒ Wakes every 1 ms ‒ Records delta of time it expects to wake up ‒ Measured effect is what would be experienced by your application • Generates histogram log files ‒ These can be graphed for easy evaluation Measuring Platform Performance 11
- 12. Eliminating Kafka Latency 12 HotSpot Zing
- 13. Azul Falcon JIT Compiler
- 14. Advancing Adaptive Compilation • Replacement for C2 compiler • Azul Falcon JIT compiler ‒ Based on latest compiler research ‒ LLVM project • Better performance ‒ Better intrinsics ‒ More inlining ‒ Fewer compiler excludes
- 15. More Complex Code Example • Conditional array cell addition loop ‒ Hard for compiler to identify for vector instruction use 15
- 16. Traditional JVM JIT 16 Per element jumps 2 elements per iteration
- 17. Falcon JIT Using AVX2 vector instructions 32 elements per iteration Broadwell E5-2690-v4
- 18. Recent Kafka Customer Story • Leading cloud-based IT security company ‒ Cloud security, compliance and other services • Big Kafka user ‒ 2.5 billion messages across Kafka clusters daily ‒ Initially approached us about their Cassandra clusters and eliminating latency • Kafka improvements ‒ 20% performance gain, out-of-the-box, with no tuning ‒ Falcon improved code generation ‒ Resulted in a 15% saving in cloud hardware costs ‒ Zing was effectively cheaper than free!
- 19. Summary
- 20. • Kafka starts faster • Kafka goes faster • Kafka stays fast • Eliminate GC latency effects and reduce cluster and node sizes ‒ Reduced cloud deployment costs • Simple replacement for other JVMs ‒ No recoding necessary The Zing JVM 20 Try Zing free for 30 days: azul.com/ZingTrial
- 21. Questions?