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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. layout: publication title: Publication subtitle: > RHEEM: Enabling Cross-Platform Data Processing link-name: Paper at PVLDB 11(11) 2018 img-thumb: assets/img/screenshot/rheem.png authors: Divy Agrawal, Sanjay Chawla, Zoi Kaoudi, Sebastian Kruse, Jorge-Arnulfo Quiané-Ruiz, Bertty Contreras-Rojas, Ahmed Elmagarmid, Yasser Idris, Ji Lucas, Essam Mansour, Mourad Ouzzani, Paolo Papotti, Nan Tang, Saravanan Thirumuruganathan and Anis Troudi year: 2018 month: 08 day: 27 link-paper: assets/pdf/paper/rheem.pdf link-external: false

Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present Rheem, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) an interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with Rheem, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.