Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Today, most any application can be “Dockerized.” However, there are special challenges when deploying a distributed application such as Spark on containers. This session will describe how to overcome these challenges in deploying Spark on Docker containers, with many practical tips and techniques for running Spark in a container environment.
Containers are typically used to run stateless applications on a single host. There are significant real-world enterprise requirements that need to be addressed when running a stateful, distributed application in a secure multi-host container environment.
There are decisions that need to be made concerning which tools and infrastructure to use. There are many choices with respect to container managers, orchestration frameworks, and resource schedulers that are readily available today and some that may be available tomorrow including:]
• Mesos
• Kubernetes
• Docker Swarm
Each has its own strengths and weaknesses; each has unique characteristics that may make it suitable, or unsuitable, for Spark. Understanding these differences is critical to the successful deployment of Spark on Docker containers.
This session will describe the work done by the BlueData engineering team to run Spark inside containers, on a distributed platform, including the evaluation of various orchestration frameworks and lessons learned. You will learn how to apply practical networking and storage techniques to achieve high performance and agility in a distributed, container environment.
Speaker
Thomas Phelan, Chief Architect, Blue Data, Inc
Data Highway Rainbow - Petabyte Scale Event Collection, Transport & Delivery ...DataWorks Summit
This paper will present the architecture and features of Data Highway Rainbow, Yahoo’s hosted multi-tenant infrastructure which offers event collection, transport and aggregated delivery as a service. Data Highway supports collection from multiple data centers & aggregated delivery in primary Yahoo data centers which provide a big data computing cluster. From a delivery perspective, Data Highway supports endpoints/sinks such as HDFS, Storm and Kafka; with Storm & Kafka endpoints tailored towards latency latency consumers.
We will also look into the evolution of the service in terms of prominent features added, the motivation behind these features starting from it’s initial launch, some of which were customer asks, while others were driven from optimizing the efficiency and footprint of the deployed infrastructure. Some of the features we will touch upon are
* Delivery Completeness Audit WebService
* Publisher Daemon & Client API Robustness
* Aggregated HDFS File Delivery
* Filters for Low Latency Delivery.
* Schema Registry
* Adaptive Rate Limiting
* Various Load Balancing techniques.
* Event Deduplication
Aggregated Daily Metrics
* Events Ingested: 250 Billion
* Bytes Ingested (Uncompressed) : 700 Tera Bytes
* Bytes Delivered (Batch + Near Real Time) : 1.5 Peta Bytes
* Near Real Time Delivery (Storm & Kafka) Latency : 95th percentile 500ms - 1 second
* Batch Delivery Latency (Aggregated into 1 minute files) : 95th percentile within 3 minutes
* Production H/W Footprint : 651
* Total Active Event Schema Types: ~200
Underlying Technology Stack : ZeroMQ, Apache Avro, libevent, Apache HttpComponents
The paper will conclude with the next steps we’re considering as a logical evolution for Data Highway in light of considerable developments in similar open source projects such as Apache Kafka.
Bringing Real-Time to the Enterprise with Hortonworks DataFlowDataWorks Summit
TELUS is Canada’s fastest-growing national telecommunications company, with $12.9B of annual revenue and 12.7M customer connections. TELUS provides TELUS TV to more than 1.1 million customers in western Canada, with the goal of maximizing clients’ service experience. Every television set-top box (STB) in the TELUS network regularly logs its diagnostic state and those logs serve as an important signal of the state of the customers equipment. TELUS’ goal was to move from analysis of these STB logs from a daily, batch process towards streaming analytics that would allow the company to respond within minutes to changes in a device or service health. TELUS increased logging frequency by a factor of 50 and adapted the overall architecture to keep pace with that transformation. Members of the TELUS team will walk through the implementation and workflow challenges they overcame, working with Hortonworks to connect Apache Hadoop, Apache NiFi and Apache Spark, within a secure enterprise environment. They will also discuss the impact to their business and their customers’ satisfaction.
Microsoft has embraced OSS by placing a big bet on Apache YARN to govern the resources of our computing clusters, and we did so by working with the community and adding many new capabilities in YARN. We now look to undertake a similar journey and build the next generation of our job execution engine on top of Apache Tez. We will be building a common platform for executing batch, interactive, ML, and streaming queries at exabyte scale for Microsoft's BigData system called Cosmos. This requires us to push the limits of Tez API to support new graph models, change the executing DAG by dynamically adding new vertices, scheduling for interactive and streaming workloads, squeeze out all the computing power in the cluster by integrating Tez with opportunistic containers in YARN, and scaling a DAG across tens of thousands of machines. We have started out on this journey and want to share our progress, lessons learned, seek help from the community to add these new capabilities, and push Apache Tez to new levels.
SPEAKERS
Hitesh Sharma, Principal Software Engineering Manager, Microsoft Engineering manager in the Big Data team at Microsoft.
Anupam, Senior Software Engineer, Microsoft
Practice of large Hadoop cluster in China MobileDataWorks Summit
China Mobile Limited is the leading telecommunications services provider in China, with more than 800 million active users. In China Mobile, distributed big data clusters are built by branch companies in each province for their unique requirements. Meanwhile, we have built a centralized Hadoop cluster with scale more than 1600 nodes, on which we collect data from dozens of distributed clusters and make analysis for our business.
In this session, we will introduce the architecture of the centralized Hadoop cluster and experience of constructing and tuning this large scale Hadoop cluster. Key points are as follows:
1. About Ambari: we improve Ambari with features like supporting HDFS Federation and Ambari HA , improving its performance and enabling it to support up to 1600 nodes.
2. About HDFS: we build a large HDFS cluster with data up to 60PB, using federation, ViewFS, FairCallQueue. Our best practice of cluster operation and management will also be included.
3. About Flume: We use the reformed Flume to collect data as much as 200TB per day.
Speakers
Yuxuan Pan, Software Engineer, China Mobile Software Technology
Duan Yunfeng, Chief Designer of China Mobile's big data system, China Mobile Communications Corporation
Scaling HDFS to Manage Billions of Files with Distributed Storage SchemesDataWorks Summit
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
Today, most any application can be “Dockerized.” However, there are special challenges when deploying a distributed application such as Spark on containers. This session will describe how to overcome these challenges in deploying Spark on Docker containers, with many practical tips and techniques for running Spark in a container environment.
Containers are typically used to run stateless applications on a single host. There are significant real-world enterprise requirements that need to be addressed when running a stateful, distributed application in a secure multi-host container environment.
There are decisions that need to be made concerning which tools and infrastructure to use. There are many choices with respect to container managers, orchestration frameworks, and resource schedulers that are readily available today and some that may be available tomorrow including:]
• Mesos
• Kubernetes
• Docker Swarm
Each has its own strengths and weaknesses; each has unique characteristics that may make it suitable, or unsuitable, for Spark. Understanding these differences is critical to the successful deployment of Spark on Docker containers.
This session will describe the work done by the BlueData engineering team to run Spark inside containers, on a distributed platform, including the evaluation of various orchestration frameworks and lessons learned. You will learn how to apply practical networking and storage techniques to achieve high performance and agility in a distributed, container environment.
Speaker
Thomas Phelan, Chief Architect, Blue Data, Inc
Data Highway Rainbow - Petabyte Scale Event Collection, Transport & Delivery ...DataWorks Summit
This paper will present the architecture and features of Data Highway Rainbow, Yahoo’s hosted multi-tenant infrastructure which offers event collection, transport and aggregated delivery as a service. Data Highway supports collection from multiple data centers & aggregated delivery in primary Yahoo data centers which provide a big data computing cluster. From a delivery perspective, Data Highway supports endpoints/sinks such as HDFS, Storm and Kafka; with Storm & Kafka endpoints tailored towards latency latency consumers.
We will also look into the evolution of the service in terms of prominent features added, the motivation behind these features starting from it’s initial launch, some of which were customer asks, while others were driven from optimizing the efficiency and footprint of the deployed infrastructure. Some of the features we will touch upon are
* Delivery Completeness Audit WebService
* Publisher Daemon & Client API Robustness
* Aggregated HDFS File Delivery
* Filters for Low Latency Delivery.
* Schema Registry
* Adaptive Rate Limiting
* Various Load Balancing techniques.
* Event Deduplication
Aggregated Daily Metrics
* Events Ingested: 250 Billion
* Bytes Ingested (Uncompressed) : 700 Tera Bytes
* Bytes Delivered (Batch + Near Real Time) : 1.5 Peta Bytes
* Near Real Time Delivery (Storm & Kafka) Latency : 95th percentile 500ms - 1 second
* Batch Delivery Latency (Aggregated into 1 minute files) : 95th percentile within 3 minutes
* Production H/W Footprint : 651
* Total Active Event Schema Types: ~200
Underlying Technology Stack : ZeroMQ, Apache Avro, libevent, Apache HttpComponents
The paper will conclude with the next steps we’re considering as a logical evolution for Data Highway in light of considerable developments in similar open source projects such as Apache Kafka.
Bringing Real-Time to the Enterprise with Hortonworks DataFlowDataWorks Summit
TELUS is Canada’s fastest-growing national telecommunications company, with $12.9B of annual revenue and 12.7M customer connections. TELUS provides TELUS TV to more than 1.1 million customers in western Canada, with the goal of maximizing clients’ service experience. Every television set-top box (STB) in the TELUS network regularly logs its diagnostic state and those logs serve as an important signal of the state of the customers equipment. TELUS’ goal was to move from analysis of these STB logs from a daily, batch process towards streaming analytics that would allow the company to respond within minutes to changes in a device or service health. TELUS increased logging frequency by a factor of 50 and adapted the overall architecture to keep pace with that transformation. Members of the TELUS team will walk through the implementation and workflow challenges they overcame, working with Hortonworks to connect Apache Hadoop, Apache NiFi and Apache Spark, within a secure enterprise environment. They will also discuss the impact to their business and their customers’ satisfaction.
Microsoft has embraced OSS by placing a big bet on Apache YARN to govern the resources of our computing clusters, and we did so by working with the community and adding many new capabilities in YARN. We now look to undertake a similar journey and build the next generation of our job execution engine on top of Apache Tez. We will be building a common platform for executing batch, interactive, ML, and streaming queries at exabyte scale for Microsoft's BigData system called Cosmos. This requires us to push the limits of Tez API to support new graph models, change the executing DAG by dynamically adding new vertices, scheduling for interactive and streaming workloads, squeeze out all the computing power in the cluster by integrating Tez with opportunistic containers in YARN, and scaling a DAG across tens of thousands of machines. We have started out on this journey and want to share our progress, lessons learned, seek help from the community to add these new capabilities, and push Apache Tez to new levels.
SPEAKERS
Hitesh Sharma, Principal Software Engineering Manager, Microsoft Engineering manager in the Big Data team at Microsoft.
Anupam, Senior Software Engineer, Microsoft
Practice of large Hadoop cluster in China MobileDataWorks Summit
China Mobile Limited is the leading telecommunications services provider in China, with more than 800 million active users. In China Mobile, distributed big data clusters are built by branch companies in each province for their unique requirements. Meanwhile, we have built a centralized Hadoop cluster with scale more than 1600 nodes, on which we collect data from dozens of distributed clusters and make analysis for our business.
In this session, we will introduce the architecture of the centralized Hadoop cluster and experience of constructing and tuning this large scale Hadoop cluster. Key points are as follows:
1. About Ambari: we improve Ambari with features like supporting HDFS Federation and Ambari HA , improving its performance and enabling it to support up to 1600 nodes.
2. About HDFS: we build a large HDFS cluster with data up to 60PB, using federation, ViewFS, FairCallQueue. Our best practice of cluster operation and management will also be included.
3. About Flume: We use the reformed Flume to collect data as much as 200TB per day.
Speakers
Yuxuan Pan, Software Engineer, China Mobile Software Technology
Duan Yunfeng, Chief Designer of China Mobile's big data system, China Mobile Communications Corporation
Scaling HDFS to Manage Billions of Files with Distributed Storage SchemesDataWorks Summit
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
Present and future of unified, portable, and efficient data processing with A...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, Apache Software Foundation; Simbly, V.P. of Apache Beam; Founder/CEO at Operiant
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
Present and future of unified, portable and efficient data processing with Ap...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, V.P. of Apache Beam; Founder/CEO at Operiant
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats.
SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc.
In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache Nifi, Apache Kafka, Apache Storm.
Data is the fuel for the idea economy, and being data-driven is essential for businesses to be competitive. HPE works with all the Hadoop partners to deliver packaged solutions to become data driven. Join us in this session and you’ll hear about HPE’s Enterprise-grade Hadoop solution which encompasses the following
-Infrastructure – Two industrialized solutions optimized for Hadoop; a standard solution with co-located storage and compute and an elastic solution which lets you scale storage and compute independently to enable data sharing and prevent Hadoop cluster sprawl.
-Software – A choice of all popular Hadoop distributions, and Hadoop ecosystem components like Spark and more. And a comprehensive utility to manage your Hadoop cluster infrastructure.
-Services – HPE’s data center experts have designed some of the largest Hadoop clusters in the world and can help you design the right Hadoop infrastructure to avoid performance issues and future proof you against Hadoop cluster sprawl.
-Add-on solutions – Hadoop needs more to fill in the gaps. HPE partners with the right ecosystem partners to bring you solutions such an industrial grade SQL on Hadoop with Vertica, data encryption with SecureData, SAP ecosystem with SAP HANA VORA, Multitenancy with Blue Data, Object storage with Scality and more.
Real-time Freight Visibility: How TMW Systems uses NiFi and SAM to create sub...DataWorks Summit
TMW Systems, A TRIMBLE Company, is the industry-leading transportation management software. 3PLs, brokers, distribution and supply operations, dedicated and private fleets, commercial carriers, and energy service providers rely on our transportation management systems, our fleet maintenance management software, or our routing and scheduling software to make them more efficient and profitable. Billions of data points exist in the trucking industry, and we at TMW Systems are pioneers of tracking millions of trucks, freights, and assets.
The architecture team at TMW leverages Nifi and SAM to deliver the immense volume of data in real-time. In this session, you will get a thorough understanding of all the streaming components. We have utilized Apache Kafka, Apache Nifi, and Streaming Analytics Manager to build our real-time data pipeline. We will also discuss the real-time event processing using SAM and Schema Registry. Lastly, we will show custom processors in Nifi and SAM that helped us with complex event processing.
Speaker
Krishna Potluri, TMW Systems, A Trimble Company, Big Data Architect
Donnie Wheat, Trimble, Senior Big Data Architect
Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and “plug-in” the new services into the stack.
However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture.
In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldn’t have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF.
Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari.
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Microsoft Azure or Amazon S3, and on-premises object stores, such as Western Digital’s ActiveScale. In these settings, applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems for business continuity planning (BCP) and/or supporting hybrid cloud architectures to achieve the required business goals for durability, performance, and coordination.
To resolve this complexity, HDFS-9806 has added a PROVIDED storage tier to mount external storage systems in the HDFS NameNode. Building on this functionality, we can now allow remote namespaces to be synchronized with HDFS, enabling asynchronous writes to the remote storage and the possibility to synchronously and transparently read data back to a local application wanting to access file data which is stored remotely. In this talk, which corresponds to the work in progress under HDFS-12090, we will present how the Hadoop admin can manage storage tiering between clusters and how that is then handled inside HDFS through the snapshotting mechanism and asynchronously satisfying the storage policy.
Speakers
Chris Douglas, Microsoft, Principal Research Software Engineer
Thomas Denmoor, Western Digital, Object Storage Architect
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
Providing true interactive and scalable BI on Hadoop is proven to be one of the biggest challenges that is preventing completion of legacy EDW OLAP system transit to Hadoop. While we have all seen many benchmarks running consecutive queries claiming success, having thousands of concurrent business users sending complicated generated queries from their dashboards over billions of records while delivering interactive speed is yet to be seen.
In this session we will discuss how an architecture that replaces full-scan brute-force approach with adaptive indexing and auto-generated cubes can dramatically reduce the resources and effort per query, resulting in interactive performance for high concurrency workloads and explain how this is achieved with minimum data engineering efforts. We will also discuss how this architecture can be seamlessly integrated with Hive to provide a complete OLAP-on-Hadoop solution.
Session will include live demo of complex business dashboards connected to Hive and accessing billions of rows at interactive speed.
Speaker
Boaz Raufman, CTO and Co-Founder, JethroData
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
Present and future of unified, portable, and efficient data processing with A...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, Apache Software Foundation; Simbly, V.P. of Apache Beam; Founder/CEO at Operiant
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
Present and future of unified, portable and efficient data processing with Ap...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, V.P. of Apache Beam; Founder/CEO at Operiant
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats.
SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc.
In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache Nifi, Apache Kafka, Apache Storm.
Data is the fuel for the idea economy, and being data-driven is essential for businesses to be competitive. HPE works with all the Hadoop partners to deliver packaged solutions to become data driven. Join us in this session and you’ll hear about HPE’s Enterprise-grade Hadoop solution which encompasses the following
-Infrastructure – Two industrialized solutions optimized for Hadoop; a standard solution with co-located storage and compute and an elastic solution which lets you scale storage and compute independently to enable data sharing and prevent Hadoop cluster sprawl.
-Software – A choice of all popular Hadoop distributions, and Hadoop ecosystem components like Spark and more. And a comprehensive utility to manage your Hadoop cluster infrastructure.
-Services – HPE’s data center experts have designed some of the largest Hadoop clusters in the world and can help you design the right Hadoop infrastructure to avoid performance issues and future proof you against Hadoop cluster sprawl.
-Add-on solutions – Hadoop needs more to fill in the gaps. HPE partners with the right ecosystem partners to bring you solutions such an industrial grade SQL on Hadoop with Vertica, data encryption with SecureData, SAP ecosystem with SAP HANA VORA, Multitenancy with Blue Data, Object storage with Scality and more.
Real-time Freight Visibility: How TMW Systems uses NiFi and SAM to create sub...DataWorks Summit
TMW Systems, A TRIMBLE Company, is the industry-leading transportation management software. 3PLs, brokers, distribution and supply operations, dedicated and private fleets, commercial carriers, and energy service providers rely on our transportation management systems, our fleet maintenance management software, or our routing and scheduling software to make them more efficient and profitable. Billions of data points exist in the trucking industry, and we at TMW Systems are pioneers of tracking millions of trucks, freights, and assets.
The architecture team at TMW leverages Nifi and SAM to deliver the immense volume of data in real-time. In this session, you will get a thorough understanding of all the streaming components. We have utilized Apache Kafka, Apache Nifi, and Streaming Analytics Manager to build our real-time data pipeline. We will also discuss the real-time event processing using SAM and Schema Registry. Lastly, we will show custom processors in Nifi and SAM that helped us with complex event processing.
Speaker
Krishna Potluri, TMW Systems, A Trimble Company, Big Data Architect
Donnie Wheat, Trimble, Senior Big Data Architect
Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and “plug-in” the new services into the stack.
However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture.
In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldn’t have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF.
Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari.
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Microsoft Azure or Amazon S3, and on-premises object stores, such as Western Digital’s ActiveScale. In these settings, applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems for business continuity planning (BCP) and/or supporting hybrid cloud architectures to achieve the required business goals for durability, performance, and coordination.
To resolve this complexity, HDFS-9806 has added a PROVIDED storage tier to mount external storage systems in the HDFS NameNode. Building on this functionality, we can now allow remote namespaces to be synchronized with HDFS, enabling asynchronous writes to the remote storage and the possibility to synchronously and transparently read data back to a local application wanting to access file data which is stored remotely. In this talk, which corresponds to the work in progress under HDFS-12090, we will present how the Hadoop admin can manage storage tiering between clusters and how that is then handled inside HDFS through the snapshotting mechanism and asynchronously satisfying the storage policy.
Speakers
Chris Douglas, Microsoft, Principal Research Software Engineer
Thomas Denmoor, Western Digital, Object Storage Architect
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
Providing true interactive and scalable BI on Hadoop is proven to be one of the biggest challenges that is preventing completion of legacy EDW OLAP system transit to Hadoop. While we have all seen many benchmarks running consecutive queries claiming success, having thousands of concurrent business users sending complicated generated queries from their dashboards over billions of records while delivering interactive speed is yet to be seen.
In this session we will discuss how an architecture that replaces full-scan brute-force approach with adaptive indexing and auto-generated cubes can dramatically reduce the resources and effort per query, resulting in interactive performance for high concurrency workloads and explain how this is achieved with minimum data engineering efforts. We will also discuss how this architecture can be seamlessly integrated with Hive to provide a complete OLAP-on-Hadoop solution.
Session will include live demo of complex business dashboards connected to Hive and accessing billions of rows at interactive speed.
Speaker
Boaz Raufman, CTO and Co-Founder, JethroData
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
Hadoop Meetup Jan 2019 - Overview of OzoneErik Krogen
A presentation by Anu Engineer of Cloudera regarding the state of the Ozone subproject. He covers a brief introduction of what Ozone is, and where it's headed.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
Hadoop Meetup Jan 2019 - Router-Based Federation and Storage TieringErik Krogen
A presentation by CR Hota and Ekanth Sethuramalingam of Uber regarding their storage infrastructure, leveraging HDFS features like Router-Based Federation and Storage Tiering.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
With Hadoop-3.0.0-alpha2 being released in January 2017, it's time to have a closer look at the features and fixes of Hadoop 3.0.
We will have a look at Core Hadoop, HDFS and YARN, and answer the emerging question whether Hadoop 3.0 will be an architectural revolution like Hadoop 2 was with YARN & Co. or will it be more of an evolution adapting to new use cases like IoT, Machine Learning and Deep Learning (TensorFlow)?
The current major release, Hadoop 2.0 offers several significant HDFS improvements including new append-pipeline, federation, wire compatibility, NameNode HA, Snapshots, and performance improvements. We describe how to take advantages of these new features and their benefits. We cover some architectural improvements in detail such as HA, Federation and Snapshots. The second half of the talk describes the current features that are under development for the next HDFS release. This includes much needed data management features such as backup and Disaster Recovery. We add support for different classes of storage devices such as SSDs and open interfaces such as NFS; together these extend HDFS as a more general storage system. Hadoop has recently been extended to run first-class on Windows which expands its enterprise reach and allows integration with the rich tool-set available on Windows. As with every release we will continue improvements to performance, diagnosability and manageability of HDFS. To conclude, we discuss the reliability, the state of HDFS adoption, and some of the misconceptions and myths about HDFS.
Gestione gerarchica dei dati con SUSE Enterprise Storage e HPE DMFSUSE Italy
In questa sessione HPE e SUSE illustrano con casi reali come HPE Data Management Framework e SUSE Enterprise Storage permettano di risolvere i problemi di gestione della crescita esponenziale dei dati realizzando un’architettura software-defined flessibile, scalabile ed economica. (Alberto Galli, HPE Italia e SUSE)
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam...HostedbyConfluent
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam K Dey | Current 2022
Robinhood’s mission is to democratize finance for all. Data driven decision making is key to achieving this goal. Data needed are hosted in various OLTP databases. Replicating this data near real time in a reliable fashion to data lakehouse powers many critical use cases for the company. In Robinhood, CDC is not only used for ingestion to data-lake but is also being adopted for inter-system message exchanges between different online micro services. .
In this talk, we will describe the evolution of change data capture based ingestion in Robinhood not only in terms of the scale of data stored and queries made, but also the use cases that it supports. We will go in-depth into the CDC architecture built around our Kafka ecosystem using open source system Debezium and Apache Hudi. We will cover online inter-system message exchange use-cases along with our experience running this service at scale in Robinhood along with lessons learned.
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storageMayaData Inc
Webinar Session - https://youtu.be/_5MfGMf8PG4
In this webinar, we share how the Container Attached Storage pattern makes performance tuning more tractable, by giving each workload its own storage system, thereby decreasing the variables needed to understand and tune performance.
We then introduce MayaStor, a breakthrough in the use of containers and Kubernetes as a data plane. MayaStor is the first containerized data engine available that delivers near the theoretical maximum performance of underlying systems. MayaStor performance scales with the underlying hardware and has been shown, for example, to deliver in excess of 10 million IOPS in a particular environment.
Save 60% of Kubernetes storage costs on AWS & others with OpenEBSMayaData Inc
With features like thin provisioning, per workload replication and snapshots, using OpenEBS can lower your storage TCO on any Kubernetes cloud by up to 60%. In this webinar you will see with in depth examples of the method a MayaData OpenEBS Enterprise customer used to save $ 75,000 a month.
Similar to Apache Hadoop 3.0 Community Update (20)
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. Andrew Wang
● HDFS @ Cloudera
● Hadoop PMC Member
● Release Manager for Hadoop 3.0
Junping Du
● YARN @ Hortonworks
● Hadoop PMC Member
● Release Manager for Hadoop 2.8
Who We Are
3. An Abbreviated History of Hadoop Releases
Date Release Major Notes
2007-11-04 0.14.1 First release at the ASF
2011-12-27 1.0.0 Security, HBase support
2012-05-23 2.0.0 YARN, NameNode HA, wire compat
2014-11-18 2.6.0 HDFS encryption, rolling upgrade, node labels
2015-04-21 2.7.0 Most recent production-quality release line
4. Motivation for Hadoop 3
● Upgrade minimum Java version to Java 8
○ Java 7 end-of-life in April 2015
○ Many Java libraries now only support Java 8
● HDFS erasure coding
○ Major feature that refactored core pieces of HDFS
○ Too big to backport to 2.x
● YARN as Data/container cloud
○ Significant change to support Docker and native service in YARN
● Other miscellaneous incompatible bugfixes and improvements
○ Hadoop 2.x was branched in 2011
○ 6 years of changes waiting for 3.0
5. Hadoop 3 status and release plan
● A series of alphas and betas leading up to GA
● Alpha4 feature complete
● Beta1 compatibility freeze
● GA by the end of the year
Release Date
3.0.0-alpha1 2016-09-03 ✔
3.0.0-alpha2 2017-01-25 ✔
3.0.0-alpha3 2017-05-16 ✔
3.0.0-alpha4 2017 Q2
3.0.0-beta1 2017 Q3
3.0.0 GA 2017 Q4
https://cwiki.apache.org/confluence/display/HADOOP/Hadoop+3.0.0+release
17. EC Reconstruction
b1 b2 b3
/foo.csv - 3 block file
p1 p2
X
Read 3 remaining blocks
Reed-Solomon (3,2)
b3
Run RS to recover b3
New copy of b3 recovered
18. EC implications
● File data is striped across multiple nodes and racks
● Reads and writes are remote and cross-rack
● Reconstruction is network-intensive, reads m blocks cross-rack
● Important to use Intel’s optimized ISA-L for performance
○ 1+ GB/s encode/decode speed, much faster than Java implementation
○ CPU is no longer a bottleneck
● Need to combine data into larger files to avoid an explosion in replica count
○ Bad: 1x1GB file -> RS(10,4) -> 14x100MB EC blocks (4.6x # replicas)
○ Good: 10x1GB file -> RS(10,4) -> 14x1GB EC blocks (0.46x # replicas)
● Works best for archival / cold data usecases
22. Erasure coding status
● Massive development effort by the Hadoop community
○ 20+ contributors from many companies (Cloudera, Intel, Hortonworks, Huawei, Y! JP, …)
○ 100s of commits over three years (started in 2014)
● Erasure coding is feature complete!
● Solidifying some user APIs in preparation for beta1
● Current focus is on testing and integration efforts
○ Want the complete Hadoop stack to work with HDFS erasure coding enabled
○ Stress / endurance testing to ensure stability
23. Classpath isolation (HADOOP-11656)
● Hadoop leaks lots of
dependencies onto the
application’s classpath
○ Known offenders: Guava, Protobuf,
Jackson, Jetty, …
● No separate HDFS client jar
means server jars are leaked
● YARN / MR clients not shaded
● HDFS-6200: Split HDFS client
into separate JAR
● HADOOP-11804: Shaded
hadoop-client dependency
● YARN-6466: Shade the task
umbilical for a clean YARN
container environment (ongoing)
24. Miscellaneous
● Shell script rewrite
● Support for multiple Standby NameNodes
● Intra-DataNode balancer
● Support for Microsoft Azure Data Lake and Aliyun OSS
● Move default ports out of the ephemeral range
● S3 consistency and performance improvements (ongoing)
● Tightening the Hadoop compatibility policy (ongoing)
26. Apache Hadoop 3.0 - YARN Enhancements
● Built-in support for Long Running Services
● Better resource isolation and Docker!!
● YARN Scheduling Enhancements
● Re-architecture for YARN Timeline Service - ATS v2
● Better User Experiences
● Other Enhancements
27. YARN Native Service - Key Drivers
● Consolidation of Infrastructure
○ Hadoop clusters have a lot of compute and storage resources (some unused)
○ Can’t I use Hadoop’s resources for non-Hadoop load?
○ Other open source infra/cloud is hard to run, can I use YARN?
○ But does it support Docker? – yes, we heard you
○ Can we run hadoop (Hive, HBase, etc.) or related services on YARN?
■ Benefit from YARN’s Elasticity and resource management
28. Built-in support for long running service in YARN
● A native YARN framework - YARN-4692
○ Abstract common Framework for long running service
■ Similar to Slider
○ More simplified API
● Recognition of long running service
○ Affect the policy of preemption, container reservation, etc.
○ Auto-restart of containers
○ Long running containers are retried to same node in case of local state
● Service/application upgrade support - YARN-4726
○ Services are expected to run long enough to cross versions
● Dynamic container configuration
● Service Discovery
○ Expose existing service information in YARN registry via DNS (YARN-4757)
29. Docker on YARN
● Why Docker?
○ Lightweight mechanism for packaging, distributing,
and isolating processes
○ Most popular containerization framework
○ Packaging new apps for YARN easier
■ TensorFlow, etc.
○ Focus on integration instead of container primitives
○ Mostly fits into the YARN Container model
30. Docker on YARN (Contd.)
● Docker support in LinuxContainerExecutor
○ YARN-3611 (Umbrella)
○ Multiple container types are supported in the same executor.
○ A new docker container runtime is introduced that manages docker containers
○ LinuxContainerExecutor can delegate to either runtime on a per application basis
○ Clients specify which container type they want to use
■ currently via environment variables but eventually through well-defined client
APIs.
32. Scheduling Enhancements
● Generic Resource Types
○ Abstract ResourceTypes to allow new resources, like: GPU, Network, etc.
○ Resource profiles for containers
■ small, medium, large etc. similar to EC2 instance types
● Global Scheduling: YARN-5139
○ Replace trigger scheduling only on heartbeat with global scheduler that has parallel threads
○ Globally optimal placement
■ Critical for long running services – they stick to the allocation – better be a good one
■ Enhanced container scheduling throughput (6 - 8x)
33. Scheduling Enhancements (Contd.)
● Other CapacityScheduler improvements
○ Queue Management Improvements
■ More Dynamic Queue reconfiguration
■ REST API support for queue management
○ Absolute resource configuration support
○ Priority Support in Application and Queue
○ Preemption improvements
■ Inter-Queue preemption support
● FairScheduler improvements
○ Preemption improvements
■ Preemption considers resource requirements of starved applications
○ Better defaults:
■ Assign multiple containers in a heartbeat based on resource availability
34. Application Timeline Service v2
● ATS: Captures system/application
events/metrics
● v2 improvements:
○ Enhanced Data Model: first-class citizen for Flows,
Config, etc.
○ Scalable backend: HBase
○ Distributed Reader/Writer
○ Others
■ Captures system metrics. E.g. memory/cpu
usage per container over time
■ Efficient updates: just write a new version to
the appropriate HBase cell
36. YARN New WebUI
● Improved visibility into cluster usage
○ Memory, CPU
○ By queues and applications
○ Sunburst graphs for hierarchical queues
○ NodeManager heatmap
● ATSv2 integration
○ Plot container start/stop events
○ Easy to capture delays in app execution
37. Misc. YARN/MR improvements
● Opportunistic containers (YARN-2877 & YARN-5542)
○ Motivation: Resource utilization is typically low in most clusters
○ Solution: Run some containers at a lower priority, and preempted as and when needed for
Guaranteed containers
● YARN Federation (YARN-2915 & YARN-5597)
○ Allows YARN to scale to 100k nodes and beyond
● HA improvements
○ Better handling of transient network issues
○ ZK-store scalability: Limit number of children under a znode
● MapReduce Native Collector (MAPREDUCE-2841)
○ Native implementation of the map output collector
○ Upto 30% faster for shuffle-intensive jobs
38. Summary: What’s new in Hadoop 3.0?
● Storage Optimization
○ HDFS: Erasure codes
● Improved Utilization
○ YARN: Long Running Services
○ YARN: Schedule Enhancements
● Additional Workloads
○ YARN: Docker & Isolation
● Easier to Use
○ New User Interface
● Refactor Base
○ Lots of Trunk content
○ JDK8 and newer dependent libraries
3.0
40. Compatibility
● Strong feedback from large users on the need for compatibility
● Preserves wire-compatibility with Hadoop 2 clients
○ Impossible to coordinate upgrading off-cluster Hadoop clients
● Will support rolling upgrade from Hadoop 2 to Hadoop 3
○ Can’t take downtime to upgrade a business-critical cluster
● Not fully preserving API compatibility!
○ Dependency version bumps
○ Removal of deprecated APIs and tools
○ Shell script rewrite, rework of Hadoop tools scripts
○ Incompatible bug fixes
41. Testing and validation
● Extended alpha → beta → GA plan designed for stabilization
● EC already has some users in production (700 nodes at Y! JP)
● Cloudera is rebasing CDH against upstream and running full test suite
○ Integration of Hadoop 3 with all components in CDH stack
○ Same integration tests used to validate CDH5
● Hortonworks is also integrating and testing Hadoop 3
● Plans for extensive HDFS EC testing by Cloudera and Intel
● Happy synergy between 2.8.x and 3.0.x lines
○ Shares much of the same code, fixes flow into both
○ Yahoo! doing scale testing of 2.8.0
42. Conclusion
● Expect Hadoop 3.0.0 GA by the end of the year
● Shiny new features
○ HDFS Erasure Coding
○ YARN Docker and Native Service Support
○ YARN ATSv2
○ Client classpath isolation
● Great time to get involved in testing and validation
● Come to the BoFs on Thursday at 5PM
○ YARN BoF: Room 211
○ HDFS BoF: Room 212