Bertty Contreras created COMDEV-475:
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Summary: Apache Wayang(Incubating): Efficiently Dealing with
Iterative Jobs
Key: COMDEV-475
URL: https://issues.apache.org/jira/browse/COMDEV-475
Project: Community Development
Issue Type: New Feature
Components: GSoC/Mentoring ideas
Reporter: Bertty Contreras
*Synopsis*
The current Apache Wayang (Incubating) uses a cost model to compute the right
platforms and optimize the plans. The Apache Wayang(Incubating) Query Optimizer
currently supports the loop operations used in the machine learning context.
Nevertheless, the current loop optimization is primitive and does not support
many aspects that are required to get the correct prediction, e.g., support
changes in the cardinality of the data inside a loop, expansion of the loop,
support for loop with condition dynamics.
*Benefits to Community*
The benefits for the community will be getting a better approach to deal with
iterative jobs in Apache Wayang (Incubating). Particularly, the community will
benefit of a more efficient approach to run their ML workloads over big data.
*Deliverables*
The delivery expected is to add a robust solution to optimize the loops inside
of the query optimizer of Apache Wayang(Incubating).
The step expected are the following:
* Understand the paper [1]
* Get into the internal Apache Wayang(Incubating) cost model
* Discuss and design the solution for the loop optimizations
* Implement and integrate the loop optimization inside of the current Query
Optimizer
*Related Work*
[1] [RHEEMix in the data jungle: a cost-based optimizer for cross-platform
systems]([https://wayang.apache.org/assets/pdf/paper/journal_vldb.pdf])
*Biographical Information of possible Mentors*
Bertty Contreras-Rojas is a Senior Software Engineer at Databloom Inc. He is
one of the PPMC of Apache Wayang(Incubating). He has many years of experience
developing intensive processing data systems for several industries, such as
banking systems. He was a research engineer at the Qatar Computing Research
Institute, where he was responsible for developing the declarative query engine
for Rheem and adding new underlying platforms to Rheem.
Rodrigo Pardo-Meza is a Senior Software Engineer at Databloom Inc. He is one of
the PPMC of Apache Wayang(Incubating). He has many years of experience
developing applications that support Big Data processing, with experience
implementing ETL processes over distributed systems to optimize inventories in
supply chains. He was a research engineer at the Qatar Computing Research
Institute, where he specialized in human interface interaction with big data
analytics. During this time, he co-develop an ML-based cross-platform query
optimizer.
Jorge Quiané is the head of the Big Data Systems research group at the Berlin
Institute for the Foundations of Learning and Data (BIFOLD) and a Principal
Researcher at DIMA (TU Berlin). He also acts as the Scientific Coordinator of
the IAM group at the German Research Center for ArtificialIntelligence (DFKI).
His current research is in the broad area of big data: mainly in federated data
analytics, scalable data infrastructures, and distributed query processing. He
has published numerous research papers on data management and novel system
architectures. He has recently been honoured with the 2022 ACM SIGMOD Research
Highlight Award and the Best Paper Award at ICDE 2021 for his work on
“EfficientControl Flow in Dataflow Systems”. He holds five patents in core
database areas and on machine learning. Earlier in his career, he was a Senior
Scientist at the Qatar Computing Research Institute (QCRI) and a Postdoctoral
Researcher at Saarland University. He obtained his PhD in computer science from
INRIA (Nantes University).
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