Platform Spotlight

Watch this video to see how Pepperdata performance management solutions enable you to manage and achieve optimal hardware performance in your multi-tenant distributed computing environment.

Platform Spotlight enables operators to quickly understand performance impacts and receive recommendations on how to better optimize the big data infrastructure. Platform Spotlight enables you to quickly diagnose Hadoop performance issues up to 90% faster while making resource decisions based on user priorities and needs.

360° Platform View

Pepperdata cluster performance monitoring provides relevant real-time and historical information about your clusters including system demand, abusive users, and wasteful applications, as well as queue and container sizes. Drill down to analyze any big data application to understand its performance in the context of the entire multi-tenant cluster.

• Complete instrumentation of your big data ecosystem – Monitor any process including Kafka, Impala, HBase, Hive, MapReduce, Tez, Spark, IBM BigSQL, LLAP and more.

• Quantify the impact that big data applications have on the cluster – Identify the impact that applications have on cluster-wide resources such as CPU, disk, memory, network, NameNode, and HDFS.

• Quantify the impact that the Hadoop cluster has on big data Hadoop and Spark applications  – Identify application slowdowns caused by disk, network or node failures/congestion.

• Attribute accurate costs to cluster users and business units – Pepperdata chargeback provides you detailed utilization of every cluster resource, which when combined with your cost model, generates the most accurate reports of the most costly users and business units.

Real-Time Platform Tuning

Platform Spotlight real-time platform tuning allows you to automatically improve the performance and efficiency of your Hadoop and Spark infrastructure, automatically recover wasted cluster resources, maximize existing infrastructure investment, and autoscale big data cluster resources for peak efficiency.

• Maximize existing big data infrastructure investment – Increase hardware utilization and eliminate or reduce millions of dollars in hardware costs.

• Autoscale Hadoop cluster resources for peak efficiency – Rightsize resource allocation based on actual real-time capacity

• Works with existing YARN scheduler – Be confident that all YARN capabilities will remain intact.

• Identify applications reading and writing small files and improve HDFS performance – Improve system scalability by determining which applications and users are reading and writing small files, impacting the overall performance of your HDFS environment.

Peppedata Platform Spotlight real-time tuning automatically improves the performance and efficiency of your infrastructure to recover wasted cluster resources, maximize your existing infrastructure investment, and autoscale big data cluster resources for peak efficiency. With Capacity Optimizer enabled, some customers see up to a 50% throughput improvement.

Platform Spotlight recommendations enable operators to achieve optimal application and cluster performance on big data, multi-tenant platforms.

Platform Recommendations

Big Data operators are challenged to configure and size critical resources that are running on multi-tenant clusters with mixed workloads. Pepperdata provides actionable reporting and recommendations to rightsize containers, queues and other resources.

• Size big data queues appropriately based on criticality – get recommendations for queue sizing

• Size containers for optimal resource consumption – Get recommendations for optimal container sizes for workloads

• Accurately forecast resource needs – Identify growth trends across resource groups

Alerting

Platform Spotlight enables you to create and receives alerts about events that interfere with performance.

• Identify cluster bottlenecks

• Use Pepperdata’s rich real-time data to identify resource bottlenecks, including CPU, memory, and IO

• Identify application bottlenecks

Platform Spotlight enables operators to set and receive notifications on metrics of interest, and alerts about events that interfere with infrastructure performance.

Benefits

Operators

• Understand how jobs use resources

• Understand why jobs are running slowly

• Understand performance of different job phases

• Compare job runs over time

Developers

• Generate application-specific recommendations to improve application performance

• Highlight applications that need attention

• Automatically identify bottlenecks, and alert on duration, failure conditions, and resource usage

• Search all applications running on the cluster, compare current and previous runs, and visualize Spark applications and its stages for easy root cause failure analysis and performance tuning.

Enterprise Organizations

• Report on capacity trends

• Get accurate chargeback reporting

• Increase productivity