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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: > TagSniff: Simplified Big Data Debugging for Dataflow Jobs link-name: SoCC 2019 img-thumb: assets/img/screenshot/rheem.png authors: Bertty Contreras-Rojas, Jorge-Arnulfo Quiané-Ruiz, Zoi Kaoudi and Saravanan Thirumuruganathan year: 2019 month: 11 day: 20 link-paper: assets/pdf/paper/socc19.pdf link-external: false

Although big data processing has become dramatically easier over the last decade, there has not been matching progress over bigdata debugging. It is estimated that users spend more than 50% of their time debugging their big data applications, wasting machine resources and taking longer to reach valuable insights. One cannot simply transplant traditional debugging techniques to big data. In this paper, we propose the TagSniff model, which can dramatically simplify data debugging for dataflows (the de-facto programming model for big data). It is based on two primitives –tag and sniff– that are flexible and expressive enough to model all common bigdata debugging scenarios. We then presentSnoopy– a general purpose monitoring and debugging system based on the TagSniff model. It supports both online and post-hoc debugging modes.Our experimental evaluation shows that Snoopy incurs a very low over head on the main dataflow, 6%on average, as well as it is highly responsive to system events and users instructions.