Overcome the limitations of distributed computing with real-time intelligence
Most Hadoop admins have learned a set of “best practices” to address and mitigate this performance dilemma, such as manual tuning, cluster isolation, or adding new hardware, but most of these remedies are not sustainable long-term solutions. Join us for this webcast as we survey some of these “best practices” and offer up some new ways to address the performance gap. We’ll also tell you the warning signs to look out for, so you can assess the health and production readiness of your cluster.
In this webcast, we’ll examine:
the reality of what the current tools in the ecosystem do before and after a job has run,
the most common “best practice” approaches to improve performance and the positive and negative outcomes of each, and
a new approach to performance gains-how to use software to fill the gap of human capability.
RecordedMar 2 201649 mins
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Big Data has increased the demand for big data management solutions that operate at scale and meet business requirements. Big Data organizations realize quickly that scaling from small, pilot projects to large-scale production clusters involves a steep learning curve. Despite tremendous progress, critically important areas including multi-tenancy, performance optimization, and workflow monitoring remain areas where the operations team still needs management help.
Intended for enterprises who already have a data lake or are setting up their first data lake, this presentation will discuss how to implement data lakes with operations tools that automatically optimize clusters with solutions for monitoring, performance tuning, and troubleshooting in production environments.
Sean is the co-founder and CTO of Pepperdata. Previously, Sean was the founding GM of Microsoft’s Silicon Valley Search Technology Center, where he led the integration of Facebook and Twitter content into Bing search. Prior to Microsoft, Sean managed the Yahoo Search Technology team, the first production user of Hadoop. Sean joined Yahoo through the acquisition of Inktomi, and holds a B.S. in Engineering and Applied Science from Caltech.
Intended for software engineers, developers, architects and technical leads who develop Spark applications, Vinod Nair will discuss how Pepperdata the product suite helps developers in Big Data Environments.
The growing adoption of Hadoop and Spark has increased the demand for Big Data management solutions that operate at scale and meet business requirements. However, Big Data organizations realize quickly that scaling from small, pilot projects to large-scale production clusters involves a steep learning curve. Despite tremendous progress, there remain critically important area, including multi-tenancy, performance optimization, and workflow monitoing where the DevOps team still needs management help. In this webinar, field engineer Kirk Lewis discusses the top considerations when choosing a big data management and performance solution.
HDFS on Kubernetes: Lessons Learned is a webinar presentation intended for software engineers, developers, and technical leads who develop Spark applications and are interested in running Spark on Kubernetes. Pepperdata has been exploring Kubernetes as potential Big Data platform with several other companies as part of a joint open source project.
In this webinar, Kimoon Kim will show you how to:
–Run Spark application natively on Kubernetes
–Enable Spark on Kubernetes read and write data securely on HDFS protected by Kerberos
OpenTSDB is a open-source time series database built on top of HBase. Thanks to HBase, OpenTSDB scales very nicely to accommodate large amounts of data in terms of bytes or data points -- at Pepperdata we ingest hundreds of billions of data points per day. Where OpenTSDB struggles to scale is in the number of distinct time series. Pepperdata stores time series data on all the hardware and processes across many Hadoop clusters: billions of discrete series per day. Speaker, Simon King, will discuss some of OpenTSDB's strengths and weaknesses, and some of the techniques Pepperdata uses to work around its limitations. Originally presented at Galvanize in San Francisco on 8/21/17.
Vinod Nair, Director of Product Management at Pepperdata
Overcome Performance Challenges in Building Spark Applications for AWS is a webinar presentation intended for software engineers, developers, and technical leads who develop Spark applications for EMR or EC2 clusters.
In this webinar, Vinod Nair will show you how to:
Identify which portion of your application consumes the most resources
Identify the bottlenecks slowing down your applications
Test your applications against development or production workloads
Significantly reduce troubleshooting issues due to ambient cluster conditions
This webinar is followed by a live Q & A. A replay of this webinar will be available within 24 hours at https://www.pepperdata.com/resources/webinars/.
Charles Boicey, Chief Innovation Officer, Clearsense
Clearsense is a pioneer in healthcare data science solutions using Spark Streaming to provide real time updates to health care providers for critical health care needs. Clinicians are enabled to make timely decisions from the assessment of a patient's risk for Code Blue, Sepsis and other conditions based on the analysis of information gathered from streaming physiological monitoring along with streaming diagnostic data and the patient historical record. Additionally this technology is used to monitor operational and financial process for efficiency and cost savings. This talk discusses the architecture needed and the challenges associated with providing real time SLAs along with 100% uptime expectations in a multi-tenant Hadoop cluster.
Apache Spark is a dynamic execution engine that can take relatively simple Scala code and create complex and optimized execution plans. In this talk, we will describe how user code translates into Spark drivers, executors, stages, tasks, transformations, and shuffles. We will also discuss various sources of information on how Spark applications use hardware resources, and show how application developers can use this information to write more efficient code. We will show how Pepperdata’s products can clearly identify such usages and tie them to specific lines of code. We will show how Spark application owners can quickly identify the root causes of such common problems as job slowdowns, inadequate memory configuration, and Java garbage collection issues.
Bay Area Apache Spark Meetup at the 10th Spark Summit featuring tech-talks about using Apache Spark at scale from Pepperdata’s CTO Sean Suchter, RISELab’s Dan Crankshaw, and Databricks’ Spark committers and contributors.
There is growing interest in running Spark natively on Kubernetes (see https://github.com/apache-spark-on-k8s/spark). Spark applications often access data in HDFS, and Spark supports HDFS locality by scheduling tasks on nodes that have the task input data on their local disks. When running Spark on Kubernetes, if the HDFS daemons run outside Kubernetes, applications will slow down while accessing the data remotely.
In this webinar, we will demonstrate how to run HDFS inside Kubernetes to speed up Spark. In particular, we will show:
- Spark scheduler can still provide HDFS data locality on Kubernetes by discovering the mapping of Kubernetes containers to physical nodes to HDFS datanode daemons.
A Spark Application that worked well in a development environment or with sample data may not behave as expected when run against a much larger dataset in a production environment. Pepperdata Application Profiler, based on open source Dr Elephant, can help you tune you Spark Application based on current dataset characteristics and cluster execution environment. Application Profiler uses a set of heuristics to provide actionable recommendations to help you quickly tune your applications.
Occasionally an application will fail (or execute too slowly) due to circumstances outside your control: a busy cluster, another misbehaving YARN application, bad luck, or bad "cluster weather". We'll discuss ways to use Pepperdata's Cluster Analyzer to quickly determine when an application failure may not be your fault and how to diagnose and fix symptoms that you can affect.
Spark is a dynamic execution engine that can take relatively simple Scala code and create complex and optimized execution plans. In this talk, we will describe how user code translates into Spark drivers, executors, stages, tasks, transformations, and shuffles. We will describe how this is critical to the design of Spark and how this tight interplay allows very efficient execution. Users and operators who are aware of the concepts will become more effective at their interactions with Spark.
Sean Suchter, CTO/Co-Founder, Pepperdata and Shekhar Gupta, Software Engineer, Pepperdata
Learn how to use machine learning to improve cluster performance.
This talk describes the use of very fine-grained performance data from many Hadoop clusters to build a model predicting excessive swapping events.
Performance of batch processing systems such as YARN is generally determined by the throughput, which measures the amount of workload (tasks) completed in a given time window. For a given cluster size, the throughput can be increased by running as much workload as possible on each host, to utilize all the free resources available on host. Because each node is running a complex combination of different tasks/containers, the performance characteristics of the cluster are dynamically changing. As a result, there is always a danger of overutilizing host memory, which can result into extreme swapping or thrashing. The impacts of thrashing can be very severe; it can actually reduce the throughput instead of increasing it.
By using very fine-grained (5 second) data from many production clusters running very different workloads, we have trained a generalized model that very rapidly detects the onset of thrashing, within seconds from the first symptom. This detection has proven fast enough to enable effective mitigation of the negative symptom of thrashing, allowing the hosts to continuously provide high throughput.
To build this system we used hand-labeling of bad events combined with large scale data processing using Hadoop, HBase, Spark, and iPython for experimentation. We will discuss the methods used as well as the novel findings about Big Data cluster performance.
Chad Carson, Co-Founder and Ed Colonna, VP of Marketing
Join us for our Part 1 of our Production Spark Webinar Series. This first installment gathers Spark experts and practitioners from varying backgrounds to discuss the top trends, challenges and use cases for production Spark applications. Our expert panel will discuss several key considerations when running Spark in production and take questions directly from the audience.
Our distinguished panel of industry experts is as follows:
Dr. Babak Behzad, Senior Software Engineer, SAP/Altiscale
Charles Boicey, Chief Innovation Officer, Clearsense
Richard Williamson, Principal Engineer, Silicon Valley Data Science
Andrew Ray, Principal Data Engineer, Silicon Valley Data Science
Sean Suchter, CTO and Co-Founder, Pepperdata
Geovanie Marquez, Hadoop Architect at Philips Wellcentive
Philips Wellcentive, a SaaS health management and data analytics company, relies on a nightly Mapreduce job to process and analyze data for their entire patient population; from birth to current day. It looks at their entire patient population to assess a number of different characteristics and powers the analytics that physician organizations need to deliver better services. When this job began to fail repeatedly, the Hadoop team spent months trying to identify the root cause using existing monitoring tools, but were unable to come up with an explanation for the job failures and slowdowns.
Join our webinar to hear more about why existing Hadoop monitoring tools were insufficient to diagnose the root cause of Philips Wellcentive’s problems and how Pepperdata helped them to significantly improve their Big Data operations. The webinar will cover the different approaches that Philips Wellcentive took to rectify their missed SLAs, and how Pepperdata ultimately helped them quickly troubleshoot their performance problems and ensure their jobs complete on time.
As the Hadoop market matures and new applications and use cases for Big Data emerge, organizations are dealing with more complex environments than ever before. In days past, deployments often focused on single, batch-oriented workloads, and if you wanted to run multiple workloads at the same time, you needed to split your clusters. With Hadoop 2 and YARN, organizations are able to run multiple workloads on the same cluster. But, in multi-tenant environments, resource contention can become a daily problem and low-priority, ad-hoc jobs can sometimes monopolize hardware resource that is needed for high-priority workloads.
Pepperdata is the first and only software that guarantees service levels in multi-tenant Hadoop environments. We have helped dozens of companies of all industries and all sizes to effectively manage and scale their multi-tenant environments, guaranteeing service levels and improving overall cluster performance.
Join us for this webcast to hear best practices for running multi-tenant environments and how you can improve visibility, performance, and overall management of your big data environment.
Are you currently running Amazon EMR but lacking the visibility and measurement of how your cluster is performing? Pepperdata for Amazon EMR enables users of Amazon Elastic MapReduce to run jobs up to four times faster and simultaneously reduce costs. Users can see over 300 metrics even after the cluster has been terminated, so users have a historical view of performance.
Register for our webinar to learn how Amazon EMR can help streamline your big data projects, and how Pepperdata can help you get the most value from your investment.
Sean Suchter of Pepperdata and Andy Oram of O'Reilly Media
This webcast will show you how Pepperdata can help your organization guarantee quality of service in multi-tenant Hadoop environments by eliminating resource contention and guaranteeing service levels for high-priority jobs. Run HBase, MapReduce, Spark, Hive, and more all on a single cluster without worrying about jobs stomping on each other. We'll show you how Pepperdata automates cluster optimization to reduce time and cost, and keep your Hadoop humming happily.
Pepperdata is the DevOps for Big Data company. Leading Enterprise companies depend on Pepperdata to manage and improve the performance of Hadoop and Spark. Developers and operators use Pepperdata products and services to diagnose and solve performance problems in production and increase cluster utilization. The Pepperdata product suite improves communication of performance issues between Dev and Ops, shortens time to production, and increases cluster ROI. Pepperdata products and services work with customer Big Data systems both on-premise and in the cloud