The Experts below are selected from a list of 126 Experts worldwide ranked by ideXlab platform
Dave Jaimin Mukesh - One of the best experts on this subject based on the ideXlab platform.
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SIGMOD Conference - Eon Mode: Bringing the Vertica Columnar Database to the Cloud
Proceedings of the 2018 International Conference on Management of Data, 2018Co-Authors: Ben Vandiver, Amin Saeidi, Styliani Pantela, Prasad Shreya, Rana Pratibha, Zik Eden, Parimal Pratyush, Dave Jaimin MukeshAbstract:The Vertica Analytic Database is a powerful tool for high performance, large scale SQL analytics. Historically, Vertica has managed direct-attached disk for performance and reliability, at a cost of product complexity and scalability. Eon mode is a new architecture for Vertica that places the data on a reliable shared storage, matching the original architecture's performance on existing workloads and supporting new workloads. While the design reuses Vertica's optimizer and execution engine, the metadata, storage, and fault tolerance mechanisms are re-architected to enable and take advantage of shared storage. A sharding mechanism distributes load over the nodes while retaining the capability of running node-local table joins. Running on Amazon EC2 compute and S3 storage, Eon mode demonstrates good performance, superior scalability, and robust operational behavior. With these improvements, Vertica delivers on the promise of cloud economics, consuming only the compute and storage resources needed, while supporting efficient elasticity.
Ben Vandiver - One of the best experts on this subject based on the ideXlab platform.
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eon mode bringing the vertica Columnar Database to the cloud
International Conference on Management of Data, 2018Co-Authors: Ben Vandiver, Shreya Prasad, Pratibha Rana, Eden Zik, Amin Saeidi, Pratyush Parimal, Styliani Pantela, Jaimin Mukesh DaveAbstract:The Vertica Analytic Database is a powerful tool for high performance, large scale SQL analytics. Historically, Vertica has managed direct-attached disk for performance and reliability, at a cost of product complexity and scalability. Eon mode is a new architecture for Vertica that places the data on a reliable shared storage, matching the original architecture's performance on existing workloads and supporting new workloads. While the design reuses Vertica's optimizer and execution engine, the metadata, storage, and fault tolerance mechanisms are re-architected to enable and take advantage of shared storage. A sharding mechanism distributes load over the nodes while retaining the capability of running node-local table joins. Running on Amazon EC2 compute and S3 storage, Eon mode demonstrates good performance, superior scalability, and robust operational behavior. With these improvements, Vertica delivers on the promise of cloud economics, consuming only the compute and storage resources needed, while supporting efficient elasticity.
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SIGMOD Conference - Eon Mode: Bringing the Vertica Columnar Database to the Cloud
Proceedings of the 2018 International Conference on Management of Data, 2018Co-Authors: Ben Vandiver, Amin Saeidi, Styliani Pantela, Prasad Shreya, Rana Pratibha, Zik Eden, Parimal Pratyush, Dave Jaimin MukeshAbstract:The Vertica Analytic Database is a powerful tool for high performance, large scale SQL analytics. Historically, Vertica has managed direct-attached disk for performance and reliability, at a cost of product complexity and scalability. Eon mode is a new architecture for Vertica that places the data on a reliable shared storage, matching the original architecture's performance on existing workloads and supporting new workloads. While the design reuses Vertica's optimizer and execution engine, the metadata, storage, and fault tolerance mechanisms are re-architected to enable and take advantage of shared storage. A sharding mechanism distributes load over the nodes while retaining the capability of running node-local table joins. Running on Amazon EC2 compute and S3 storage, Eon mode demonstrates good performance, superior scalability, and robust operational behavior. With these improvements, Vertica delivers on the promise of cloud economics, consuming only the compute and storage resources needed, while supporting efficient elasticity.
Gustavo Alonso - One of the best experts on this subject based on the ideXlab platform.
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High Bandwidth Memory on FPGAs: A Data Analytics Perspective
arXiv: Distributed Parallel and Cluster Computing, 2020Co-Authors: Kaan Kara, Christoph Hagleitner, Dionysios Diamantopoulos, Dimitris Syrivelis, Gustavo AlonsoAbstract:FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the ensuing necessity for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit data and compute intensive workloads. Inclusion of High Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises overcoming the bandwidth bottleneck, faced often by FPGA-based accelerators due to their throughput oriented design. In this paper, we study the usage and benefits of HBM on FPGAs from a data analytics perspective. We consider three workloads that are often performed in analytics oriented Databases and implement them on FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a Columnar Database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. In certain cases, FPGA+HBM based solutions are able to surpass the highest performance provided by either a 2-socket POWER9 system or a 14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
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FPL - High Bandwidth Memory on FPGAs: A Data Analytics Perspective
2020 30th International Conference on Field-Programmable Logic and Applications (FPL), 2020Co-Authors: Kaan Kara, Christoph Hagleitner, Dionysios Diamantopoulos, Dimitris Syrivelis, Gustavo AlonsoAbstract:FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the resulting need for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit data and compute intensive workloads. Inclusion of High Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises overcoming the bandwidth bottleneck, often faced by FPGA-based accelerators due to their throughput oriented design. In this paper, we study the usage and benefits of HBM on FPGAs from a data analytics perspective. We consider three workloads that are often performed in analytics oriented Databases and implement them on FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a Columnar Database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. In certain cases, FPGA+HBM based solutions are able to surpass the highest performance provided by either a 2-socket POWER9 system or a 14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
Luís Veiga - One of the best experts on this subject based on the ideXlab platform.
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WaaS: Workflow-as-a-Service for the Cloud with Scheduling of Continuous and Data-Intensive Workflows
Computer Journal, 2014Co-Authors: Sergio Esteves, Luís VeigaAbstract:Data-intensive and long-lasting applications running in the form of workflows are being increasingly dispatched to cloud computing systems. Current scheduling approaches for graphs of dependencies fail to deliver high resource efficiency while keeping computation costs low, especially for continuous data processing workflows, where the scheduler does not perform any reasoning about the impact new input data may have in the workflow final output. To face such a challenge, we introduce a new scheduling criterion, Quality-of-Data (QoD), which describes the requirements about the data that are worthy of the triggering of tasks in workflows. Based on the QoD notion, we propose a novel service-oriented scheduler planner, for continuous data processing workflows, that is capable of enforcing QoD constraints and guide the scheduling to attain resource efficiency, overall controlled performance and task prioritization. To contrast the advantages of our scheduling model against others, we developed WaaS (Workflow-as-a-Service), a workflow coordinator system for the Cloud where data is shared among tasks via cloud Columnar Database.
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ICSOC Workshops - Planning and Scheduling Data Processing Workflows in the Cloud with Quality-of-Data Constraints
Lecture Notes in Computer Science, 2014Co-Authors: Sergio Esteves, Luís VeigaAbstract:Data-intensive and long-lasting applications running in the form of workflows are being increasingly more dispatched to cloud computing systems. Current scheduling approaches for graphs of dependencies fail to deliver high resource efficiency while keeping computation costs low, especially for continuous data processing workflows, where the scheduler does not perform any reasoning about the impact new input data may have in the workflow final output. To face such stark challenge, we introduce a new scheduling criterion, Quality-of-Data (QoD), which describes the requirements about the data that worth the triggering of tasks in workflows. Based on the QoD notion, we propose a novel service-oriented scheduler planner, for continuous data processing workflows, that is capable of enforcing QoD constraints and guide the scheduling to attain resource efficiency, overall controlled performance, and task prioritization. To contrast the advantages of our scheduling model against others, we developed WaaS (Workflow-as-a-Service), a workflow coordinator system for the Cloud where data is shared among tasks via cloud Columnar Database.
Jaimin Mukesh Dave - One of the best experts on this subject based on the ideXlab platform.
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eon mode bringing the vertica Columnar Database to the cloud
International Conference on Management of Data, 2018Co-Authors: Ben Vandiver, Shreya Prasad, Pratibha Rana, Eden Zik, Amin Saeidi, Pratyush Parimal, Styliani Pantela, Jaimin Mukesh DaveAbstract:The Vertica Analytic Database is a powerful tool for high performance, large scale SQL analytics. Historically, Vertica has managed direct-attached disk for performance and reliability, at a cost of product complexity and scalability. Eon mode is a new architecture for Vertica that places the data on a reliable shared storage, matching the original architecture's performance on existing workloads and supporting new workloads. While the design reuses Vertica's optimizer and execution engine, the metadata, storage, and fault tolerance mechanisms are re-architected to enable and take advantage of shared storage. A sharding mechanism distributes load over the nodes while retaining the capability of running node-local table joins. Running on Amazon EC2 compute and S3 storage, Eon mode demonstrates good performance, superior scalability, and robust operational behavior. With these improvements, Vertica delivers on the promise of cloud economics, consuming only the compute and storage resources needed, while supporting efficient elasticity.
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ICDE - DBDesigner: A customizable physical design tool for Vertica Analytic Database
2014 IEEE 30th International Conference on Data Engineering, 2014Co-Authors: Ramakrishna Varadarajan, Jaimin Mukesh Dave, Vivek Bharathan, Ariel Cary, Sreenath BodagalaAbstract:In this paper, we present Vertica's customizable physical design tool, called the DBDesigner (DBD), that produces designs optimized for various scenarios and applications. For a given workload and space budget, DBD automatically recommends a physical design that optimizes query performance, storage footprint, fault tolerance and recovery to meet different customer requirements. Vertica is a distributed, massively parallel Columnar Database that physically organizes data into projections. Projections are attribute subsets from one or more tables with tuples sorted by one or more attributes, that are replicated or segmented (distributed) on cluster nodes. The key challenges involved in projection design are picking appropriate column sets, sort orders, cluster data distributions and column encodings. To achieve the desired trade-off between query performance and storage footprint, DBD operates under three different design policies: (a) load-optimized, (b) query-optimized or (c) balanced. These policies indirectly control the number of projections proposed and queries optimized to achieve the desired balance. To cater to query workloads that evolve over time, DBD also operates in a comprehensive and incremental design mode. In addition, DBD lets users override specific features of projection design based on their intimate knowledge about the data and query workloads. We present the complete physical design algorithm, describing in detail how projection candidates are efficiently explored and evaluated using optimizer's cost and benefit model. Our experimental results show that DBD produces good physical designs that satisfy a variety of customer use cases.